def star_select() -> 'html':
the_region = request.form["the_region_selected"]
print(the_region) # 检查用户输入
dfs = disdata4.query("region=='{}'".format(the_region))
df_summary = dfs.groupby("行业").agg(
{"数量": "number"}).sort_values(by="数量", ascending=False)
print(df_summary.head(5)) # 在后台检查描述性统计
# 显示前100名富豪所在的国籍的地图
latitude = 39.92
longitude = 116.46
from folium import plugins
# let's start again with a clean copy of the map of San Francisco
san_map = folium.Map(location=[latitude, longitude], zoom_start=4)
# instantiate a mark cluster object for the incidents in the dataframe
incidents = plugins.MarkerCluster().add_to(san_map)
# loop through the dataframe and add each data point to the mark cluster
for lat, lng, label, in zip(d2.y, d2.x, N.中文姓名):
folium.Marker(
location=[lat, lng],
icon=None,
popup=label,
).add_to(incidents)
# add incidents to map
san_map.add_child(incidents)
def cafeinfo(request):
popup=[]
data_lat_log=[]
a_path='C:/JMS/data'
df=pd.read_csv(os.path.join(a_path,'cafe_info_1.csv'),encoding='utf-8')
print(df)
for data in df.values:
popup.append(data[0])
data_lat_log.append([data[8],data[9]])
m=folium.Map([35.16370389566739, 129.15879330965538],zoom_start=11)
plugins.MarkerCluster(data_lat_log, popups=popup).add_to(m)
#folium.Marker([35.1803305,129.0516257],popups=popup).add_to(m)
#m.save(os.path.join(TEMPLATE_DIR,'board/kotags/kocafe/cafe_map.html'))
conn=MongoClient('127.0.0.1')
db=conn.test1
makeup=db.cafe_info_1
finds=makeup.find({},{'_id':0})
cafetags=db.cafetags
cafe15=cafetags.find({},{'_id':0})
return render(request,'board/kotags/kocafe/cafeinfo.html',{'finds':finds,'cafe15':cafe15})
def plot_point_clusters(points, filename="ODs_clusters.html"):
lats = points.apply(lambda x: x[0])
lons = points.apply(lambda x: x[1])
lon_avg = np.average(lons)
lat_avg = np.average(lats) + 0.03
#map_tiles='OpenStreetMap'
map_tiles = 'Stamen Terrain'
#map_tiles='cartodbpositron'
my_map = folium.Map(location=[lat_avg, lon_avg],
zoom_start=12,
tiles=map_tiles)
#folium.LayerControl().add_to(my_map)
# Build list of (lat, lon) values, ex: [('a', 'b'), ('a1', 'b1')]
locations = list(zip(lats.values, lons.values))
# Create a folium marker cluster
custom_options = {
'maxClusterRadius': 80 * 2,
'chunkedLoading': 1,
'spiderfyDistanceMultiplier': 1
}
#custom_options = {'maxClusterRadius':int(80*1.5)}
custom_options = {'maxClusterRadius': int(80 * 1)}
marker_cluster = plugins.MarkerCluster(locations=locations,
options=custom_options)
# Add marker cluster to map
marker_cluster.add_to(my_map)
my_map.save(filename)
return my_map, marker_cluster
def plot_coordinates(dataframe, save_path=None):
"""
Dynamic plot of 'lat' and 'lon' coordinates.
Parameters
----------
dataframe: pd.DataFrame
Source dataframe with 'lat' and 'lon' columns
save_path: str or None
Where the map will be saved
"""
# Folium's Map object
m = folium.Map(location=[dataframe.lat.mean(),
dataframe.lon.mean()],
zoom_start=12)
# Markers
marker_cluster = plugins.MarkerCluster().add_to(m)
# Iterate over df
for i, obs in dataframe.iterrows():
folium.Marker(location=[obs['lat'], obs['lon']]).add_to(marker_cluster)
# Save map
if save_path is not None:
m.save(save_path)
return m
def Map_plot(data_, address_): #地圖詳細資訊
d = data_.loc[all_data['縣市'] == address_]
if d.shape[0] > 10000: #超過一萬筆就隨機抽一萬筆資料
d = d.sample(n=10000, random_state=1)
m = folium.Map(d[['LAT', 'LONG']].mean().values.tolist())
mc = plugins.MarkerCluster().add_to(m)
for _, row in d.iterrows():
trade_date = f"民國{str(row['交易年月日'])[:(len(str(row['交易年月日']))-4)].lstrip('0')}年" \
f"{str(row['交易年月日'])[-4:-2].lstrip('0')}月" \
f"{str(row['交易年月日'])[-2:].lstrip('0')}日"
pop_info = f"<font size=\"3\"><u>{row['土地區段位置建物區段門牌']}</u></font><br>" \
f"<table>" \
f"<tr><th>交易日期</th><td>{trade_date}</td>" \
f"<tr><th>種類</th><td>{row['交易標的']}</td>" \
f"<tr><th>建物型態</th><td>{row['建物型態']}</td>" \
f"<tr><th>使用分區</th><td>{row['都市土地使用分區']}</td>" \
f"<tr><th>土地面積</th><td>{round(row['土地移轉總面積平方公尺'],2)}</td>" \
f"<tr><th>建物面積</th><td>{round(row['建物移轉總面積平方公尺'],2)}</td>" \
f"<tr><th>車位面積</th><td>{round(row['車位移轉總面積平方公尺'],2)}</td>" \
f"<tr><th>樓層</th><td>{row['總樓層數']}</td>" \
f"<tr><th>建物現況格局</th><td><b>房:</b>{row['建物現況格局-房']} <b>廳:</b>{row['建物現況格局-廳']} <b>衛:</b>{row['建物現況格局-衛']} <b>隔間:</b>{row['建物現況格局-隔間']}</td>" \
f"<tr><th>管理組織</th><td>{row['有無管理組織']}</td>" \
f"<tr><th>屋齡</th><td>{row['屋齡']}</td>" \
f"<tr><th>總價格</th><td>{row['總價元']}</td>" \
f"<tr><th>每平方公尺單價：</th><td>{int(row['單價元平方公尺'])}</td>" \
f"</table>"
folium.Marker(row[['LAT', 'LONG']],
popup=folium.Popup(utf2asc(pop_info),
max_width=800,
min_width=200)).add_to(mc)
sw = d[['LAT', 'LONG']].min().values.tolist()
ne = d[['LAT', 'LONG']].max().values.tolist()
m.fit_bounds([sw, ne])
return m.get_root().render()
def Cluster_map_generation(Data_to_save, geoJsonData):
m = folium.Map(
location=[float(Data_to_save[100, 9]),
float(Data_to_save[100, 10])],
zoom_start=15)
folium.GeoJson(geoJsonData,
style_function=lambda x: {
'color': x['properties']['stroke'],
'weight': x['properties']['stroke-width'],
'opacity': 0.6,
}).add_to(m)
# instantiate a mark cluster object for the incidents in the dataframe
incidents = plugins.MarkerCluster().add_to(m)
# loop through the dataframe and add each data point to the mark cluster
for i in range(0, len(Data_to_save)):
folium.Marker(
location=[float(Data_to_save[i, 9]),
float(Data_to_save[i, 10])],
icon=None,
).add_to(incidents)
# display map
m.save('Result/Map_Cluster.html')
def cctv_map():
popup=[]
data_lat_log=[]
df=pd.read_csv("e:/data/input/map/cctv.csv",encoding="utf-8")
for data in df.values:
if data[4]>0:
popup.append(data[2])
data_lat_log.append([data[3],data[4]])
m=folium.Map([35.1803305,129.0516257], zoop_start=11)
plugins.MarkerCluster(data_lat_log,popups=popup).add_to(m)
m.save(os.path.join(TEMPLATE_DIR,'map/map01.html'))
def make_map(features):
#print('> Making map...')
m = f.Map()
a = {}
z = 0.3 * len(d)
y = int(z)
for i in range(0, y):
z1 = d.iloc[i]['latitude']
z2 = d.iloc[i]['longitude']
try:
loc.append((float(z1), float(z2)))
#a={i:{d.iloc[i]['tweetCreated']:{loc}}}
except:
pass
plugins.MarkerCluster(loc).add_to(m)
TimestampedGeoJson({
'type': 'FeatureCollection',
'features': features
},
period='P1M',
add_last_point=True,
auto_play=False,
loop=False,
max_speed=1,
loop_button=True,
date_options='YYYY/MM',
time_slider_drag_update=True).add_to(m)
#print('> Done.')
return pollution_map
def map_display(data):
'''create and fill the maps with the filtered dataset infos.'''
st.markdown('## :earth_americas: **Rental Houses Map**')
c1, c2 = st.beta_columns((1, 1))
# 1st map - houses for rent
lat = 51.1642292
long = 10.4541194
map_b = folium.Map(location=[lat, long], zoom_start=5.5)
prices = plugins.MarkerCluster().add_to(map_b)
for lat, lng, i in zip(data['lat'],
data['long'],
list(range(data.shape[0]))):
label = f'{data["street"].values[i]} - Price: €{data["montly_rent"].values[i]} - {data["pets"].values[i]}'
label = folium.Popup(label, parse_html=True)
folium.Marker(
location=[lat, lng],
icon=None,
popup=label).add_to(prices)
folium.GeoJson(geo_json_data, style_function=lambda feature: {'color': 'darkred', 'weight': 0.5, }).add_to(map_b)
folium_static(map_b, width=1200, height=550)
return None
def createMap(self):
mc = plugins.MarkerCluster()
for index, location in self.data.iterrows():
mc.add_child(folium.Marker([location['latitude'], location['longitude']])).add_to(self.map)
self.map.save('test.html')
return self.map
def index():
requested_city = request.args.get('city')
if requested_city is None:
requested_city = DEFAULT_CITY
stumps = Stump.query.join(City).filter(
City.name == requested_city).join(Stump_type).all()
stumps_count = len(stumps)
city_list = City.query.order_by(City.name).all()
if stumps_count == 0:
geolocator = Nominatim(user_agent=USER_AGENT)
location = geolocator.geocode(requested_city)
if location is None:
flash("Unknown city {}!".format(requested_city))
requested_city = DEFAULT_CITY
location = geolocator.geocode(requested_city)
stumps = Stump.query.join(City).filter(
City.name == requested_city).join(Stump_type).all()
stumps_count = len(stumps)
base_latitude = location.latitude
base_longitude = location.longitude
else:
base_latitude = stumps[0].latitude
base_longitude = stumps[0].longitude
map = folium.Map(location=[base_latitude, base_longitude],
zoom_start=13,
tiles='OpenStreetMap',
scrollWheelZoom=False)
marker_cluster = plugins.MarkerCluster().add_to(map)
for stump in stumps:
latitude = stump.latitude
longitude = stump.longitude
stump_description = "<a href='/stumps/details/{}' target='_PARENT'>{}</a> ".format(
stump.id, stump.stump_type.name)
stump_icon = folium.Icon(color=stump.stump_type.color,
icon='envira',
angle=180,
prefix='fa')
folium.Marker([latitude, longitude],
popup=stump_description,
icon=stump_icon).add_to(marker_cluster)
return render_template('index.html',
map=map._repr_html_(),
city_list=city_list,
city=requested_city,
stumps_count=stumps_count)
def top_rated_by_country_map():
url = 'data/custom.geo.json'
world_geo = f'{url}'
world_map = folium.Map(min_zoom=1,
max_zoom=6,
zoom_start=2,
location=(40, 15))
conn = sqlite3.connect("data/movies.db")
df = pd.read_sql_query(
"Select *, Count(m.country) as count from Country c Join Location l on (c.country_id = l.id) Join Movies m on (c.country_id = m.country) where m.rank>=1 and m.rank<=250 group by m.country",
conn)
# df.dropna(axis='rows',inplace=True)
df['e'] = df.iloc[:, 2:31].sum(axis=1)
df = df[['country_name', 'iso_2', 'iso_3', 'long', 'lat', 'e', 'count']]
# df.dropna(axis='columns',inplace=True)
df = df[df['e'] != 0]
conn.close()
choropleth = folium.Choropleth(
geo_data=world_geo,
name='Number of Produced Movies',
data=df,
columns=['iso_3', 'e'],
key_on='feature.properties.iso_a3',
fill_color='YlGn',
fill_opacity=0.7,
line_opacity=0.2,
bins=8,
legend_name='Number of Produced Movies').add_to(world_map)
choropleth.geojson.add_child(
folium.features.GeoJsonTooltip(fields=['sovereignt'], labels=False))
incidents = plugins.MarkerCluster(
name='Distribution of 250 Top Rated Movies').add_to(world_map)
for lat, lng, label, in zip(df.lat, df.long, df['count']):
for i in range(label):
folium.Marker(
location=[lat, lng],
icon=plugins.BeautifyIcon(icon="leaf",
iconSize=[36, 36],
background_color="#87CEFA",
border_color="#B0E0E6",
borderWidth=5,
inner_icon_style='size : 18px',
number=label),
popup=label,
).add_to(incidents)
i += 1
world_map.add_child(incidents)
folium.LayerControl().add_to(world_map)
return world_map
def test_marker_cluster(self):
N = 100
data = np.array([
np.random.uniform(low=35, high=60, size=N), # Random latitudes.
np.random.uniform(low=-12, high=30, size=N), # Random longitudes.
range(N), # Popups.
]).T
mapa = folium.Map([45., 3.], zoom_start=4)
mapa.add_children(plugins.MarkerCluster(data))
mapa._repr_html_()
def point_adder(df, info):
latitude = 41.8781
longitude = -87.6298
chi_map1 = folium.Map(location=[latitude, longitude], zoom_start=12, tiles='OpenStreetMap')
r_la = list(df["LATITUDE"])
r_lo = list(df["LONGITUDE"])
labels = list(info)
incidents = plugins.MarkerCluster().add_to(chi_map1)
for la, lo, label in zip(r_la, r_lo, labels):
folium.Marker([la, lo], popup=label, icon=None, tooltip="Show me").add_to(incidents)
chi_map1.add_child(incidents)
return folium_static(chi_map1)
def test_marker_cluster(self):
N = 100
data = np.array([
np.random.uniform(low=35, high=60,
size=N), # random latitudes in Europe
np.random.uniform(low=-12, high=30,
size=N), # random longitudes in Europe
range(N), # popups are simple numbers
]).T
mapa = folium.Map([45., 3.], zoom_start=4)
mapa.add_plugin(plugins.MarkerCluster(data))
mapa._build_map()
def draw_map_mon(ds):
"""
Esta función recibe un conjunto de incidentes y los dibuja en un mapa usando el paquete folium,
el mapa se guarda en un fichero.
:param ds: dataset de incidentes en formato de DataFrame
"""
incid_map = folium.Map(location=[37.7617007179518, -122.42158168136999], zoom_start=11, tiles='Stamen Terrain')
marker_cluster = plugins.MarkerCluster().add_to(incid_map)
for name, row in ds.iterrows():
folium.Marker([row["Y"], row["X"]], popup=row["Descript"]).add_to(marker_cluster)
incid_map.save('incidents.html')
return incid_map
def numper(result):
icd = plugins.MarkerCluster()
# result=[[ -0.152680,51.514570]]
# lix=[]
# for i in result:
# lix.append(i)
# print(len(lix))
print(result)
for lat, lng, lable in result:
folium.Marker(location=[lat, lng], icon=None,
popup=str(lable) + ',').add_to(icd)
return icd
def make_markersmap(map_obj):
curs.execute("SELECT ip from log_mapper.markers ORDER BY INET_ATON(ip);")# ip ASC;")
list_ips = curs.fetchall()
mc = plugins.MarkerCluster()
for ip_tup in list_ips:
if ip_tup[0] is None:
continue
try:
make_marker(mc, str(ip_tup[0]))
except:
print("[*] Error with IP: {}".format(ip_tup[0]))
map_obj.add_child(mc)
return map_obj
def getMap_with_statistic_area(self, input_data): # 區域統計地圖
new_map = self.get_clean_map()
features = plugins.MarkerCluster().add_to(new_map)
for lat, lng, label, in zip(input_data.lat, input_data.lng,
input_data.address):
folium.Marker(
location=[lat, lng],
icon=None,
popup=label,
).add_to(features)
new_map.add_child(features)
return new_map
def cluster_map(self):
'''
Creates and saves a map that shows a cluster of stop lat/lng points that have over 50 data points and
assigns them an icon based on their mode of transportation. The icon is also shaded to represent the
on-time departure distribution with red being low and green being high.
Args:
None
'''
# Cluster of Stops with On-Time Departure %
on_time = self.data.groupby(['stop_id', 'stop_name', 'stop_lat', 'stop_lng', 'route_type']).departure_status.apply(lambda x: (x == 'on_time').sum()).reset_index(name='on_time_stops')
total = self.data.groupby(['stop_id', 'stop_name', 'stop_lat', 'stop_lng', 'route_type']).size().reset_index(name='total_stops')
map_data = pd.merge(on_time, total, on=['stop_id', 'stop_name', 'stop_lat', 'stop_lng', 'route_type'])
map_data['on_time_percent'] = map_data.on_time_stops / map_data.total_stops
map_data['on_time_str'] = map_data.on_time_percent.apply(lambda x: f"{x:.1%}")
map_data['map_icon'] = map_data.route_type.apply(lambda x: x if x=='bus' else 'train')
map_data = map_data[map_data.total_stops >= 50]
step = cmp.LinearColormap(
['red', 'yellow', 'green']
,vmin=round(min(map_data.on_time_percent),2)
,vmax=round(max(map_data.on_time_percent),2)
,caption='On-Time Departure Percentage'
)
stops = map_data[['stop_lat', 'stop_lng']]
stop_list = stops.values.tolist()
icon_list = map_data.map_icon.tolist()
on_time_list = map_data.on_time_percent.tolist()
name_list = map_data.stop_name.tolist()
stop_map = folium.Map(location=[39.7426534, -104.9904138]
,tiles='Stamen Terrain')
marker_cluster = plugins.MarkerCluster().add_to(stop_map)
for stop in range(0, len(stop_list)):
folium.Marker(location=stop_list[stop]
,icon=folium.Icon(
color='white'
,icon_color=step(on_time_list[stop])
,icon=icon_list[stop]
,prefix='fa')
,popup=f"{name_list[stop]}: {on_time_list[stop]:.1%}"
).add_to(marker_cluster)
if self.route_label == 'All':
stop_map.save(f"html/{self.route_type}_cluster_map.html")
else:
stop_map.save(f"html/{self.route_label}_cluster_map.html")
def cctv_map():
popup=[]
data_lat_log=[]
a_path='c:/hj/data/'
df=pd.read_csv(os.path.join(a_path,'cctv.csv'),encoding='utf-8')
print(df)
for data in df.values: #필드빼고 값만 가져온다.
if data[4]>0:
popup.append(data[1])
data_lat_log.append([data[10],data[11]])
m=folium.Map([35.1803305,129.0516257],zoom_start=11)
plugins.MarkerCluster(data_lat_log, popups=popup).add_to(m)
m.save(os.path.join(TEMPLATE_DIR, 'map/map01.html'))
def cctv_map():
popup = []
data_lat_log = []
a_path = 'E:/pythonProject/data/'
df = pd.read_csv(os.path.join(a_path, "cctv/CCTV_20190917.csv"),
encoding="utf-8")
for data in df.values:
if data[4] > 0:
popup.append(data[1])
data_lat_log.append([data[10], data[11]])
m = folium.Map([35.1803305, 129.0516257], zoop_start=11)
plugins.MarkerCluster(data_lat_log, popups=popup).add_to(m)
m.save(os.path.join(TEMPLATE_DIR, "map/map01.html"))
def map_hydrant_data(relpath):
# Load data
df = pd.read_csv(relpath, sep=';')
# Custom function Get Type, Latitude, Longitude
def return_coordinates(x):
d = json.loads(x)
lst = [d['type']]+ d['coordinates']
return pd.Series(lst, index=['type','lon','lat'])
# Add processed data
df = pd.concat([df, df['Geom'].apply(return_coordinates)], axis=1)
df['color'] = df['COLOR'].fillna('NA')
# Color map for hydrants
colors= {'Blue':'blue',
'Green':'green',
'Yellow':'#ffff00',
'Red':'red',
'NA':'gray',
'White':'white'}
# Use folium to display hydrants on the map
van_cluster = folium.Map(location=[49.28721476882593,-123.11269938372442],zoom_start=16)
from folium import plugins
cluster = plugins.MarkerCluster().add_to(van_cluster)
for idx, location in enumerate(zip(df.lat, df.lon)):
popup_html = '<p>ID: {ID}<br>STATUS: {STATUS}</p>'.format(**dict(df.loc[idx, ['ID','STATUS']]))
popup_iframe = folium.IFrame(html= popup_html, width=200, height=50)
if (idx < 50):
popup = folium.Popup(popup_iframe, parse_html=True, show=True)
else:
popup = folium.Popup(popup_iframe, parse_html=True)
folium.CircleMarker(
location,
radius=6,
color=colors[df['color'][idx] ],
fill=True,
fill_color=colors[df['color'][idx]],
fill_opacity=0.4,
popup=popup
).add_to(cluster)
return van_cluster
def gethos():
let = df.iloc[0, 2]
let2 = df.iloc[0, 3]
m = folium.Map(location=[let, let2], zoom_start=12)
N = 100
data = df[["latitude", "longitude"]].values
# popups = [str(i) for i in range(N)] # Popups texts are simple numbers.
m = folium.Map([let, let2], zoom_start=7)
plugins.MarkerCluster(data).add_to(m)
return m._repr_html_()
def build_map(data,data_1):
###Initialize a map centered [avg_lat, avg_lng]
m = folium.Map([avg_lat, avg_lng], zoom_start=10)
### add markers to the map which shows the information of ['Name','famous_for','webpage','address','rating_in_blog','rating_in_yelp','sentiment_score_review','sentiment_score_comment','weather','temperature'].
folium.GeoJson(data, name='Restaurants', tooltip=folium.features.GeoJsonTooltip(fields=['Name','famous_for','webpage','address','rating_in_blog','rating_in_yelp','sentiment_score_review','sentiment_score_comment','weather','temperature'], localize=True)).add_to(m)
### add additional layer which shows the open hour
folium.GeoJson(data, name='Open Hours', tooltip=folium.features.GeoJsonTooltip(fields=['Name','Monday','Tuesday','Wednesday','Thursday','Friday','Saturday','Sunday'], localize=True)).add_to(m)
### add additional layer which shows the concentration of the restaurants based on cluster
plugins.MarkerCluster(data_1, popups=list(df['name']), name='Concentration').add_to(m)###this uses data_1 as lat and lng are the only information needed to make this layer
folium.LayerControl(collapsed=False).add_to(m)
m.save('../Result/map_LA')
###Save as an html file
m.save(outfile='../Result/map_LA.html')
return m
def map_02_details():
# let's start again with a clean copy of the map of United State
san_map = folium.Map(location=[latitude, longitude], zoom_start=4)
# instantiate a mark cluster object for the incidents in the dataframe
incidents = plugins.MarkerCluster().add_to(san_map)
# loop through the dataframe and add each data point to the mark cluster
for lat, lng, label, in zip(data.latitude, data.longitude, cdata1.Title):
folium.Marker(
location=[lat, lng],
icon=None,
popup=label,
).add_to(incidents)
# add incidents to map
incident_map2 = san_map.add_child(incidents)
display(incident_map2)
incident_map2.save('templates/map_02_details.html')
return render_template("map_02_details.html")
def folium_map(map_object,
data,
zoom_start=11,
popup=False,
icon=False,
color=False):
"""
map_object: The variable name of the map object that you have instantiated.
data: List of lists, or array, of latitude and longitude data
popup = String information that pops up when clicking on a map marker.
"""
marker_cluster = fp.MarkerCluster().add_to(map_object)
folium.Marker(data).add_to(marker_cluster)
name = str(map_object)
map_object.save(name + '.html')
return map_object
def createLayer(df, layerName, header_value, header_base, tooltip_func):
layer = fl.FeatureGroup(name=layerName, overlay=False, control=True)
df_ratio = df[header_value] / df[header_base]
maxCases = df_ratio.max()
# vmax = maxCase*100 lenne ideális esetben, de egy színkód lesz az összes adathoz
colormap = cm.LinearColormap(
colors=['yellow', 'green', 'blue', 'purple', 'red'], vmin=0, vmax=100)
# különböző koordináták
unique_locs = df[['lats', 'longs']].drop_duplicates()
marker_cluster = flp.MarkerCluster(name='SzocInt',
overlay=True,
control=False,
icon_create_function=None)
# koordináták szerint rakjuk ki a markereket, mert az egy helyre esoket
# így lehet majd láthatóvá tenni (mint a google térképnél)
for idx, loc in enumerate(zip(unique_locs['lats'], unique_locs['longs'])):
if loc[0] < 0.001:
continue
# az azonos térképi koordinátára eső sorok
mask = (df['lats'] == loc[0]) & (df['longs'] == loc[1])
df_part = df[mask]
df_ratio_part = df_ratio[mask]
for i in range(df_part.shape[0]):
row = df_part.iloc[i]
# 0 férőhelyszámnál/dolgozószámnál szürkével jelöli meg
color = "#808080ff" if pandas.isnull(df_ratio_part.iloc[i]) \
else colormap(df_ratio_part.iloc[i]*100)
fl.Marker(location=[row['lats'], row['longs']],
tooltip=tooltip_func(row),
icon=fl.Icon(color='black', icon_color=color)).add_to(
layer if df_part.shape[0] == 1 else marker_cluster)
marker_cluster.add_to(layer)
return layer
def test_marker_cluster():
N = 100
np.random.seed(seed=26082009)
data = np.array([
np.random.uniform(low=35, high=60, size=N), # Random latitudes.
np.random.uniform(low=-12, high=30, size=N), # Random longitudes.
range(N), # Popups.
]).T
m = folium.Map([45., 3.], zoom_start=4)
mc = plugins.MarkerCluster(data)
m.add_child(mc)
m._repr_html_()
out = m._parent.render()
# We verify that imports
assert ('<script src="https://cdnjs.cloudflare.com/ajax/libs/leaflet.'
'markercluster/1.0.0/leaflet.markercluster.js"></script>') in out
assert ('<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/'
'libs/leaflet.markercluster/1.0.0/MarkerCluster.css" />') in out
assert ('<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/'
'libs/leaflet.markercluster/1.0.0/MarkerCluster.Default.css" />'
) in out
# Verify the script part is okay.
tmpl = Template("""
var {{this.get_name()}} = L.markerClusterGroup();
{{this._parent.get_name()}}.addLayer({{this.get_name()}});
{% for marker in this._children.values() %}
var {{marker.get_name()}} = L.marker(
[{{marker.location[0]}},{{marker.location[1]}}],
{
icon: new L.Icon.Default()
}
)
.addTo({{this.get_name()}});
{% endfor %}
""")
assert ''.join(tmpl.render(this=mc).split()) in ''.join(out.split())
bounds = m.get_bounds()
assert bounds == [[35.147332572663785, -11.520684337300109],
[59.839718052359274, 29.94931046497927]], bounds
def add_clusters(dictionary):
"""
(dict) -> class 'folium.map.FeatureGroup'
Returns clusters to add it to the map
"""
clusters = folium.FeatureGroup(name="Clusters by the area")
first_loc = []
second_loc = []
for key in dictionary:
points = dictionary[key]
first_loc.append(points[0])
second_loc.append(points[1])
data = np.array(
[np.array(first_loc),
np.array(second_loc),
range(len(dictionary))]).T
plugins.MarkerCluster(data).add_to(clusters)
return clusters
