def createMarkers(map,df,x,y,markers_name,comment_cols,comment_names,color_var,radius,colors,popup =True,indivMarkers = True):
feature_group = FeatureGroup(name=markers_name)
marker_cluster = MarkerCluster(name = 'Point clusters').add_to(map)
for i in comment_cols:
if df[i].dtype == object:
df[i] = df[i].str.replace("'","'")
for index, row in df.iterrows():
if index % 1000 == 0:
print(index,' markers added to the map!')
x1,y1 = row[x],row[y]
# x1_r,y1_r = "{0:.2f}".format(x1),"{0:.2f}".format(y1)
popup_text = createPopupText(comment_names)
comment_list = [row[i] for i in comment_cols]
popup_text = popup_text.format(*comment_list)
if "'" in popup_text:
popup_text = popup_text.replace("'"," ")
color = colors[list(df[color_var].unique()).index(row[color_var])]
if popup == True:
marker = folium.Circle(location = ([y1,x1]), fill=True,fill_opacity = 1,fill_color = color, color = color,radius = radius,popup = popup_text)
folium.Circle(location = ([y1,x1]), fill=True,fill_opacity = 1,fill_color = color, color = color,radius = radius,popup = popup_text).add_to(marker_cluster)
elif popup == False:
marker = folium.Circle(location = ([y1,x1]), fill=True,fill_opacity = 1,fill_color = color, color = color,radius = radius)
folium.Circle(location = ([y1,x1]), fill=True,fill_opacity = 1,fill_color = color, color = color,radius = radius).add_to(marker_cluster)
feature_group.add_child(marker)
if indivMarkers == True:
map.add_child(feature_group)
def _add_layer(cls, map_object: Map,
layer_document: MapLayerDocument) -> None:
"""
Create layer on a Folium Map. Method creates layer and adjusts it based on configuration. The layer is
appended to the provided Folium Map.
:param map_object: (Map) map object to create layer on
:param layer_document: (MapLayerDocument) layer document configuration object.
:return: None
"""
model = layer_document.model
latitude_column = layer_document.latitude
longtitude_column = layer_document.longtitude
axis = model.axis
axis_label = axis.name or axis.data_field
feature_group = FeatureGroup(name=axis_label)
data_source = layer_document.data_source
for row in data_source.itertuples():
latitude = getattr(row, latitude_column.data_field)
longtitude = getattr(row, longtitude_column.data_field)
value = getattr(row, axis.data_field)
coordinates = [latitude, longtitude]
# model.figure.size *= 5
marker = cls._create_marker(layer_value=str(value),
coordinates=coordinates,
layer_figure=model.figure)
feature_group.add_child(marker)
map_object.add_child(feature_group)
def add_random_markers(feature_group: FeatureGroup) -> FeatureGroup:
for i in range(random.randint(5, 10)):
feature_group.add_child(child=Marker(location=[
51.7522202 + random.uniform(-0.1, 0.1), -1.25596 +
random.uniform(-0.1, 0.1)
],
popup="Marker",
icon=Icon()))
return feature_group
def get_population_feature_group() -> FeatureGroup:
population_feature_group = FeatureGroup('Population')
population_feature_group.add_child(
GeoJson(
data=open('world.json', mode='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'
}))
return population_feature_group
def add_volcanoes(map):
map = map
volcanoes = read_excel('volcanoes.xlsx').iloc[1:, [1, 2, 4, 5, 6]]
latitudes, longitudes = volcanoes['Latitude'], volcanoes['Longitude']
name = volcanoes['Volcano Name']
country = volcanoes['Country']
elevation = volcanoes['Elev']
fg_volcanoes = FeatureGroup(name='Volcanoes', show=False)
def color_by_elevation(elev):
if type(elev) == str:
return 'white'
if elev <= -4000:
return 'darkblue'
if elev <= -2000:
return 'cadetblue'
if elev <= 0:
return 'lightblue'
if elev <= 2000:
return 'orange'
if elev <= 4000:
return 'red'
if elev <= 6000:
return 'darkred'
else:
return 'black'
for i in range(len(volcanoes)):
i += 1
if isnan(elevation[i]):
elevation[i] = 'Unknown'
if name[i] == 'Unnamed':
html = f'Volcano name:{name[i]}<br>Height: {elevation[i]} m<br> {country[i]}'
else:
html = f'Volcano name:<b4> <a href="https://www.google.com/search?q={name[i]} volcano" target="_blank">{name[i]}</a><br> \
Height: {elevation[i]} m<br> {country[i]}'
iframe = IFrame(html=html, width=200, height=100)
coords = (latitudes[i], longitudes[i])
fg_volcanoes.add_child(
CircleMarker(location=coords,
popup=Popup(iframe),
color="gry",
fill_color=color_by_elevation(elevation[i]),
fill_opacity=0.9,
radius=6))
i -= 1
map.add_child(fg_volcanoes)
def generate_map(name_and_coordinates:dict):
"""
generates map
"""
mp = Map( zoom_start=15)
markers = FeatureGroup()
for screen_name in name_and_coordinates:
markers.add_child(Marker(location=[name_and_coordinates[screen_name][0], name_and_coordinates[screen_name][1]],
popup=screen_name,
icon=Icon()))
mp.add_child(markers)
mp.add_child(LayerControl())
return mp._repr_html_()
def get_volcanoes_feature_group() -> FeatureGroup:
feature_group = FeatureGroup(name="Volcanoes")
data = pandas.read_csv("./Volcanoes_USA.txt")
latitudes = list(data['LAT'])
longitudes = list(data['LON'])
elevation = list(data['ELEV'])
for lat, lon, elev in zip(latitudes, longitudes, elevation):
feature_group.add_child(
CircleMarker(location=(lat, lon),
color='grey',
weight=1,
radius=6,
popup=Popup(str(elev) + "m", parse_html=True),
fill_color=get_elevation_color(elev)))
return feature_group
def add_cities(map):
cities = read_csv('worldcities.csv').iloc[:, [1, 2, 3, 4, 7, 8, 9]]
latitudes, longitiudes = cities['lat'], cities['lng']
name = cities['city_ascii']
country = cities['country']
admin_name = cities['admin_name']
population = cities['population']
fg_05to1m_cities = FeatureGroup(name='500k-1m', show=False)
fg_1to2m_cities = FeatureGroup(name='1m-2m', show=False)
fg_2to5m_cities = FeatureGroup(name='2m-5m', show=False)
fg_5to10m_cities = FeatureGroup(name='5m-10m', show=False)
fg_more_than_10m_cities = FeatureGroup(name='>10m', show=False)
for i in range(len(cities)):
if nan_to_num(population[i]) < 500000:
continue
html = f'City: <a href="https://www.google.com/search?q={name[i]} city" target="_blank">{name[i]}</a><br> \
Admin: {admin_name[i]}<br> \
Country: {country[i]}<br>\
Population: {population[i]}'
iframe = IFrame(html=html, width=200, height=200)
coords = (latitudes[i], longitiudes[i])
if population[i] < 1000000:
fg_05to1m_cities.add_child(
Marker(location=coords, popup=Popup(iframe)))
elif population[i] < 2000000:
fg_1to2m_cities.add_child(
Marker(location=coords, popup=Popup(iframe)))
elif population[i] < 5000000:
fg_2to5m_cities.add_child(
Marker(location=coords, popup=Popup(iframe)))
elif population[i] < 10000000:
fg_5to10m_cities.add_child(
Marker(location=coords, popup=Popup(iframe)))
elif population[i] >= 1000000:
fg_more_than_10m_cities.add_child(
Marker(location=coords, popup=Popup(iframe)))
map.add_child(fg_05to1m_cities)
map.add_child(fg_1to2m_cities)
map.add_child(fg_2to5m_cities)
map.add_child(fg_5to10m_cities)
map.add_child(fg_more_than_10m_cities)
def compute_map(df):
lat = list(df['lat'])
lng = list(df['lng'])
company_name = list(df['company_name'])
map = Map(location=[15.2993, 74.1240], zoom_start=10)
fgv = FeatureGroup()
for lt, ln, name in zip(lat, lng, company_name):
if not math.isnan(lt):
fgv.add_child(
CircleMarker(location=[lt, ln],
radius=6,
popup=name,
fill_color="green",
fill=True,
color='grey',
fill_opacity=0.7))
pass
map.add_child(fgv)
map.save(map_name)
def create_map(user_coords: list, closest_points: list) -> None:
"""
creates map and saves it
"""
mp = Map(location=[user_coords[0], user_coords[1]], zoom_start=15)
markers = FeatureGroup()
closest_dict = create_dictionary(closest_points)
for key in closest_dict:
markers.add_child(
Marker(location=[key[0], key[1]],
popup=closest_dict[key],
icon=Icon()))
your_location = FeatureGroup()
your_location.add_child(
Marker(location=[user_coords[0], user_coords[1]],
popup='You are here',
icon=Icon()))
mp.add_child(markers)
mp.add_child(your_location)
mp.add_child(LayerControl())
mp.save('map1.html')
def init_map(path, data):
"""
str, list -> folium map
Creates a folium map .html file in the direction path.
"""
map = Map()
data_group = FeatureGroup(name='Data Layer')
geolocator = Nominatim(user_agent='shumakov')
geocode = RateLimiter(geolocator.geocode, min_delay_seconds=0.01)
for row in data:
if not row[0] or not row[1]:
continue
pos = geolocator.geocode(row[1])
data_group.add_child(
CircleMarker(location=[pos.latitude, pos.longitude],
radius=7,
popup=row[0],
fill_color='red',
color='white',
fill_opacity=0.5))
map.add_child(data_group)
map.save(path)
return map
from datawrangling.geomunging import depthm3half
from pprint import pprint
from folium import LayerControl, Map, FeatureGroup
from folium.plugins import HeatMap
# create map object
m = Map(tiles='openstreetmap',min_zoom=7, max_zoom=14, zoom_start=7,\
min_lat=40, max_lat=48,min_lon=-114, max_lon=-128, \
max_bounds=True)
# create heat map object
fg = FeatureGroup('Heat Map')
# add list of coordinates to map object
fg.add_child(HeatMap(list(zip(depthm3half.lat.values,
depthm3half.lon.values))))
m.add_child(fg)
LayerControl().add_to(m)
m.save(r'templates\depthm3halfeventheat.html')
from datawrangling.geomunging import oqh
from pprint import pprint
from folium import LayerControl, Map, FeatureGroup
from folium.plugins import HeatMap
# create map object
m = Map(tiles='openstreetmap',min_zoom=7, max_zoom=14, zoom_start=7,\
min_lat=40, max_lat=48,min_lon=-114, max_lon=-128, \
max_bounds=True)
fg = FeatureGroup('Heat Map')
# add a list of coordinates to the map object
fg.add_child(HeatMap(list(zip(oqh.lat.values, oqh.lon.values))))
m.add_child(fg)
LayerControl().add_to(m)
m.save(r'templates\alleventheat.html')
""".format
iframe = IFrame(html_popup(row['Articulo'], row['Autor'], row['Fecha'],
row['Secuenciación'], row['Trypanosoma']),
width=500,
height=300,
ratio='1%')
popup = Popup(iframe, max_width=2650)
html_tooltip = """
<h4><i>{}</i></h4>
<p>Click para más información</p>
""".format
feature_articulos.add_child(
Marker([row['Latitud'], row['Longitud ']],
popup=popup,
tooltip=html_tooltip(row['Cita']),
icon=Icon(color='red', icon='info-sign')))
feature_articulos.add_to(hospedero_map)
for especie in df_resumen.Especie.unique():
temp_df = df_resumen[df_resumen['Especie'] == especie]
# show_especie = True if especie == 'Carollia perspicillata' else False
feature_group = FeatureGroup(especie, show=False)
html_tooltip = """
<h4><i>{}</i></h4>
<h5><b>Cantidad:</b> {}</h5>
""".format
from datawrangling.geomunging import g3half
from pprint import pprint
from folium import LayerControl, Map, FeatureGroup
from folium.plugins import HeatMap
# create map object
m = Map(tiles='openstreetmap',min_zoom=7, max_zoom=14, zoom_start=7,\
min_lat=40, max_lat=48,min_lon=-114, max_lon=-128, \
max_bounds=True)
fg = FeatureGroup('Heat Map')
# add list of coordinates to map object
fg.add_child(HeatMap(list(zip(g3half.lat.values, g3half.lon.values))))
m.add_child(fg)
LayerControl().add_to(m)
m.save(r'templates\g3halfeventheat.html')
def create_map(data, file_name="index.html"):
map = Map(
location=[data[col].mean() for col in ["latitude", "longitude"]],
tiles="Stamen Toner",
zoom_start=2,
)
fg_nodes_locations = FeatureGroup(name="Locations")
fg_nodes_counts = FeatureGroup(name="Counts")
circles = [
{
"radius": 5,
"stroke": False,
"fill": True,
"fill_color": "crimson",
"fill_opacity": opacity,
"location": [latitude, longitude],
"popup": Popup(
html=f"""
<style>
tbody {{
text-align: center;
}}
table,
tr,
td {{
border-collapse: collapse;
border: 1px solid black;
}}
</style>
<h3>{ip}</h3>
<table>
<tbody>
<tr>
<td><strong>ISP</strong></td>
<td>{org}</td>
</tr>
<tr>
<td><strong>Completed requests</strong></td>
<td>{completed_requests}</td>
</tr>
</tbody>
</table>
""",
max_width=300,
),
}
for ip, latitude, longitude, org, completed_requests, opacity in [
named_tuple[2:] for named_tuple in data.itertuples(index=False)
]
]
q1, q2, q3, q4 = nodes_counts_quantiles()
def opacity(scaled_nodes_count):
# print(scaled_nodes_count)
if scaled_nodes_count < q1:
return 0
elif q1 <= scaled_nodes_count < q2:
return 0.1
elif q2 <= scaled_nodes_count < q3:
return 0.25
elif q3 <= scaled_nodes_count < q4:
return 0.5
else:
return 0.75
def style_function(feature):
fillOpacity = opacity(feature["properties"]["scaled_nodes_count"])
return {"fillColor": "blue", "weight": 0, "fillOpacity": fillOpacity}
fg_nodes_counts.add_child(
GeoJson(
data=open("data/enriched_world.json", encoding="utf-8-sig").read(),
style_function=style_function,
)
)
for circle in circles:
fg_nodes_locations.add_child(CircleMarker(**circle))
map.add_child(fg_nodes_counts)
map.add_child(fg_nodes_locations)
map.add_child(LayerControl())
map.save(file_name)
def mapper(self):
m = folium.Map(location=[39.5, -98.4],
zoom_start=5,
tiles='stamentoner',
prefer_canvas=True,
world_copy_jump=True,
no_wrap=True)
folium.TileLayer('openstreetmap').add_to(m)
med_cities_fg = FeatureGroup(name='Cities Between 175,000 and 500,000',
show=False)
large_cities_fg = FeatureGroup(name='Cities Larger than 500,000',
show=True)
res_ind_fg = FeatureGroup(name='Resilience Indicator', show=True)
lmi_burd_fg = FeatureGroup(name='LMI Energy Buden', show=False)
rev_fg = FeatureGroup(name='County Revenue', show=False)
FEMA_spend_fg = FeatureGroup(name='Total FEMA Spending', show=False)
ports_fg = FeatureGroup(name='Air/Sea Ports', show=False)
xw_fg = FeatureGroup(name='Extreme Weather Susceptibility', show=False)
urban_fg = FeatureGroup(name='Urban Areas', show=False)
NLC_fg = FeatureGroup(name='NLC Participants', show=False)
airporticon = folium.features.CustomIcon(
'https://image.flaticon.com/icons/svg/579/579268.svg',
icon_size=(20, 20))
seaporticon = folium.features.CustomIcon(
'https://image.flaticon.com/icons/svg/1198/1198475.svg',
icon_size=(20, 20))
staricon = folium.features.CustomIcon(
'https://image.flaticon.com/icons/svg/148/148839.svg',
icon_size=(15, 15))
for index, row in self.airports.iterrows():
ports_fg.add_child(
folium.map.Marker(
location=row['lat_long'],
icon=airporticon,
popup=(f"<b>Airport Code:</b> {row['iata_code']}<br>"
f"<b>Type:</b> {row['type']}")))
for index, row in self.seaports.iterrows():
ports_fg.add_child(
folium.map.Marker(location=row['lat_long'],
icon=seaporticon,
popup=(f"<b>Port</b><br>"
f"<b>Name:</b> {row['name']}")))
for index, row in self.NLC.iterrows():
NLC_fg.add_child(
folium.map.Marker(location=row['lat_long'],
icon=staricon,
popup=(f"<b>NLC Participant</b><br>"
f"<b>Name:</b> {row['City']}<br>"
f"<b>Type:</b> {row['Type']}")))
folium.GeoJson(self.urbanshp,
style_function=lambda feature: {
'fillColor': '#F37748',
'fillOpacity': 0.5,
'color': '#F37748',
'opacity': 0.5,
'weight': 0.1
}).add_to(urban_fg)
for index, row in self.md_Citiesshp.iterrows():
med_cities_fg.add_child(
folium.Circle(
location=row['lat_long'],
radius=(row['size'] + row['pop'] / 200),
color=row['color'],
fill_color=row['color'],
fill=True,
fill_opacity=0.7,
popup=
(f"<b><i>{row['name']}, {row['state_abbr']}</b></i><br>"
f"<b>Population:</b> {row['pop_class_desc']}<br>"
f"<b>Climate Zone:</b> {row['ashrae_climate_zone_desc']}<br>"
f"<b>Residential Consumption:</b> {row['res_elec_mwh']} MWh<br>"
f"<b>Commercial Consumption:</b> {row['comm_elec_mwh']} MWh<br>"
f"<b>Industrial Consumption:</b> {row['ind_elec_mwh']} MWh<br>"
)))
for index, row in self.lg_Citiesshp.iterrows():
large_cities_fg.add_child(
folium.Circle(
location=row['lat_long'],
radius=(row['size'] + row['pop'] / 200),
color=row['color'],
fill_color=row['color'],
fill=True,
fill_opacity=0.7,
popup=
(f"<b><i>{row['name']}, {row['state_abbr']}</b></i><br>"
f"<b>Population:</b> {row['pop_class_desc']}<br>"
f"<b>Climate Zone:</b> {row['ashrae_climate_zone_desc']}<br>"
f"<b>Residential Consumption:</b> {row['res_elec_mwh']} MWh<br>"
f"<b>Commercial Consumption:</b> {row['comm_elec_mwh']} MWh<br>"
f"<b>Industrial Consumption:</b> {row['ind_elec_mwh']} MWh<br>"
)))
for index, row in self.Cshp.loc[self.Cshp['resil_ind'] > 0].iterrows():
geojson_ = folium.GeoJson(
self.Cshp.loc[index:index + 1],
style_function=lambda feature: {
'fillColor': feature['properties']['resil_ind_color'],
'fillOpacity': 0.5,
'color': feature['properties']['resil_ind_color'],
'opacity': 0.6,
'weight': 0.5
})
popup_ = folium.Popup(
f"<b>County FIP:</b> {row['GEOID']}<br>"
f"<b>Resilience Indicator:</b> {row['resil_ind']}<br>"
f"<b>LMI Burden:</b> {row['lmi_burd']*100}%<br>"
f"<b>County Revenue:</b> ${format(row['rev'], ',d')}<br>"
f"<b>FEMA Spending:</b> ${format(row['total_FEMA_spend'], ',d')}<br>"
)
popup_.add_to(geojson_)
geojson_.add_to(res_ind_fg)
for index, row in self.Cshp.loc[self.Cshp['lmi_burd'] > 0].iterrows():
geojson_ = folium.GeoJson(
self.Cshp.loc[index:index + 1],
style_function=lambda feature: {
'fillColor': feature['properties']['lmi_burd_color'],
'fillOpacity': 0.5,
'color': feature['properties']['lmi_burd_color'],
'opacity': 0.6,
'weight': 0.5
})
popup_ = folium.Popup(
f"<b>County FIP:</b> {row['GEOID']}<br>"
f"<b>Resilience Indicator:</b> {row['resil_ind']}<br>"
f"<b>LMI Burden:</b> {row['lmi_burd']*100}%<br>"
f"<b>County Revenue:</b> ${format(row['rev'], ',d')}<br>"
f"<b>FEMA Spending:</b> ${format(row['total_FEMA_spend'], ',d')}<br>"
)
popup_.add_to(geojson_)
geojson_.add_to(lmi_burd_fg)
for index, row in self.Cshp.loc[self.Cshp['rev'] > 0].iterrows():
geojson_ = folium.GeoJson(
self.Cshp.loc[index:index + 1],
style_function=lambda feature: {
'fillColor': feature['properties']['rev_color'],
'fillOpacity': 0.5,
'color': feature['properties']['rev_color'],
'opacity': 0.6,
'weight': 0.5
})
popup_ = folium.Popup(
f"<b>County FIP:</b> {row['GEOID']}<br>"
f"<b>Resilience Indicator:</b> {row['resil_ind']}<br>"
f"<b>LMI Burden:</b> {row['lmi_burd']*100}%<br>"
f"<b>County Revenue:</b> ${format(row['rev'], ',d')}<br>"
f"<b>FEMA Spending:</b> ${format(row['total_FEMA_spend'], ',d')}<br>"
)
popup_.add_to(geojson_)
geojson_.add_to(rev_fg)
for index, row in self.Cshp.loc[
self.Cshp['total_FEMA_spend'] > 0].iterrows():
geojson_ = folium.GeoJson(
self.Cshp.loc[index:index + 1],
style_function=lambda feature: {
'fillColor': feature['properties']['total_FEMA_spend_color'
],
'fillOpacity': 0.5,
'color': feature['properties']['total_FEMA_spend_color'],
'opacity': 0.6,
'weight': 0.5
})
popup_ = folium.Popup(
f"<b>County FIP:</b> {row['GEOID']}<br>"
f"<b>Resilience Indicator:</b> {row['resil_ind']}<br>"
f"<b>LMI Burden:</b> {row['lmi_burd']*100}%<br>"
f"<b>County Revenue:</b> ${format(row['rev'], ',d')}<br>"
f"<b>FEMA Spending:</b> ${format(row['total_FEMA_spend'], ',d')}<br>"
)
popup_.add_to(geojson_)
geojson_.add_to(FEMA_spend_fg)
for index, row in self.Cshp.loc[self.Cshp['risk'] > 0].iterrows():
geojson_ = folium.GeoJson(
self.Cshp.loc[index:index + 1],
style_function=lambda feature: {
'fillColor': feature['properties']['risk_color'],
'fillOpacity': 0.5,
'color': feature['properties']['risk_color'],
'opacity': 0.6,
'weight': 0.5
})
popup_ = folium.Popup(
f"<b>County FIP:</b> {row['GEOID']}<br>"
f"<b>Resilience Indicator:</b> {row['resil_ind']}<br>"
f"<b>LMI Burden:</b> {row['lmi_burd']*100}%<br>"
f"<b>County Revenue:</b> ${format(row['rev'], ',d')}<br>"
f"<b>FEMA Spending:</b> ${format(row['total_FEMA_spend'], ',d')}<br>"
)
popup_.add_to(geojson_)
geojson_.add_to(xw_fg)
m.add_child(res_ind_fg)
m.add_child(lmi_burd_fg)
m.add_child(rev_fg)
m.add_child(FEMA_spend_fg)
m.add_child(xw_fg)
m.add_child(ports_fg)
m.add_child(urban_fg)
m.add_child(med_cities_fg)
m.add_child(large_cities_fg)
m.add_child(NLC_fg)
m.keep_in_front(large_cities_fg)
m.add_child(folium.map.LayerControl(collapsed=False, autoZIndex=True))
self.html = m
return self
def mapper(self):
m = folium.Map(
location=[39.068305, -105.612593],
zoom_start=7,
tiles='stamentoner',
attr=
'Map created by NREL using 2017 Polk Vehicle Registration Data.')
folium.TileLayer('openstreetmap').add_to(m)
level1_fg = FeatureGroup(name='Level 1 Chargers', show=False)
level2_fg = FeatureGroup(name='Level 2 Chargers', show=False)
dcfc_fg = FeatureGroup(name='DC Fast Chargers', show=True)
hev_fg = FeatureGroup(name='Registered Hybrids', show=False)
phev_fg = FeatureGroup(name='Registered PHEVs', show=False)
ev_fg = FeatureGroup(name='Registered EVs', show=False)
hevnorm_fg = FeatureGroup(name='Percent LDVs Hybrids', show=False)
phevnorm_fg = FeatureGroup(name='Percent LDVs PHEVs', show=False)
evnorm_fg = FeatureGroup(name='Percent LDVs EVs', show=True)
transmission_fg = FeatureGroup(name='Transmission Lines', show=False)
PSCo_fg = FeatureGroup(name='PSCo Territory', show=False)
folium.GeoJson(self.xcelterritorygpd,
style_function=lambda feature: {
'fillColor': '#B6174B',
'fillOpacity': 0.3,
'color': '#B6174B',
'weight': 0.5
}).add_to(PSCo_fg)
for index, row in self.transmissiongpd.iterrows():
geojson_ = folium.GeoJson(self.transmissiongpd.loc[index:index +
1],
style_function=lambda feature: {
'fillColor': '#4863CE',
'fillOpacity': 0.3,
'color': '#4863CE',
'weight': 2
})
popup_ = folium.Popup(
f"<b>Owner:</b> {row['OWNER']}<br>"
f"<b>Volt Class:</b> {row['VOLT_CLASS']}<br>"
f"<b>Substations:</b> {row['SUB_1']} to {row['SUB_2']}<br>")
popup_.add_to(geojson_)
geojson_.add_to(transmission_fg)
for index, row in self.zips.loc[self.zips['HEV'] > 0].iterrows():
geojson_ = folium.GeoJson(
self.zips.loc[index:index + 1],
style_function=lambda feature: {
'fillColor': feature['properties']['HEV_color'],
'fillOpacity': 0.5,
'color': feature['properties']['HEV_color'],
'opacity': 0.7,
'weight': 0.5
})
popup_ = folium.Popup(f"<b>ZIP Code:</b> {int(row['ZIP'])}<br>"
f"<b>Num Hybrids:</b> {int(row['HEV'])}<br>"
f"<b>Num PHEVs:</b> {int(row['PHEV'])}<br>"
f"<b>Num EVs:</b> {int(row['EV'])}<br>")
popup_.add_to(geojson_)
geojson_.add_to(hev_fg)
for index, row in self.zips.loc[self.zips['PHEV'] > 0].iterrows():
geojson_ = folium.GeoJson(
self.zips.loc[index:index + 1],
style_function=lambda feature: {
'fillColor': feature['properties']['PHEV_color'],
'fillOpacity': 0.3,
'color': feature['properties']['PHEV_color'],
'opacity': 0.5,
'weight': 0.5
})
popup_ = folium.Popup(f"<b>ZIP Code:</b> {int(row['ZIP'])}<br>"
f"<b>Num Hybrids:</b> {int(row['HEV'])}<br>"
f"<b>Num PHEVs:</b> {int(row['PHEV'])}<br>"
f"<b>Num EVs:</b> {int(row['EV'])}<br>")
popup_.add_to(geojson_)
geojson_.add_to(phev_fg)
for index, row in self.zips.loc[self.zips['EV'] > 0].iterrows():
geojson_ = folium.GeoJson(
self.zips.loc[index:index + 1],
style_function=lambda feature: {
'fillColor': feature['properties']['EV_color'],
'fillOpacity': 0.5,
'color': feature['properties']['EV_color'],
'opacity': 0.7,
'weight': 0.5
})
popup_ = folium.Popup(f"<b>ZIP Code:</b> {int(row['ZIP'])}<br>"
f"<b>Num Hybrids:</b> {int(row['HEV'])}<br>"
f"<b>Num PHEVs:</b> {int(row['PHEV'])}<br>"
f"<b>Num EVs:</b> {int(row['EV'])}<br>")
popup_.add_to(geojson_)
geojson_.add_to(ev_fg)
for index, row in self.zips.loc[self.zips['HEVnorm'] > 0].iterrows():
geojson_ = folium.GeoJson(
self.zips.loc[index:index + 1],
style_function=lambda feature: {
'fillColor': feature['properties']['HEVnorm_color'],
'fillOpacity': 0.5,
'color': feature['properties']['HEVnorm_color'],
'opacity': 0.7,
'weight': 0.5
})
popup_ = folium.Popup(
f"<b>ZIP Code:</b> {int(row['ZIP'])}<br>"
f"<b>Pct of LDVs Hybrids:</b> {row['HEVnorm']}%<br>"
f"<b>Pct of LDVs PHEVs:</b> {row['PHEVnorm']}%<br>"
f"<b>Pct of LDVs EVs:</b> {row['EVnorm']}%<br>")
popup_.add_to(geojson_)
geojson_.add_to(hevnorm_fg)
for index, row in self.zips.loc[self.zips['PHEVnorm'] > 0].iterrows():
geojson_ = folium.GeoJson(
self.zips.loc[index:index + 1],
style_function=lambda feature: {
'fillColor': feature['properties']['PHEVnorm_color'],
'fillOpacity': 0.3,
'color': feature['properties']['PHEVnorm_color'],
'opacity': 0.5,
'weight': 0.5
})
popup_ = folium.Popup(
f"<b>ZIP Code:</b> {int(row['ZIP'])}<br>"
f"<b>Pct of LDVs Hybrids:</b> {row['HEVnorm']}%<br>"
f"<b>Pct of LDVs PHEVs:</b> {row['PHEVnorm']}%<br>"
f"<b>Pct of LDVs EVs:</b> {row['EVnorm']}%<br>")
popup_.add_to(geojson_)
geojson_.add_to(phevnorm_fg)
for index, row in self.zips.loc[self.zips['EVnorm'] > 0].iterrows():
geojson_ = folium.GeoJson(
self.zips.loc[index:index + 1],
style_function=lambda feature: {
'fillColor': feature['properties']['EVnorm_color'],
'fillOpacity': 0.5,
'color': feature['properties']['EVnorm_color'],
'opacity': 0.7,
'weight': 0.5
})
popup_ = folium.Popup(
f"<b>ZIP Code:</b> {int(row['ZIP'])}<br>"
f"<b>Pct of LDVs Hybrids:</b> {row['HEVnorm']}%<br>"
f"<b>Pct of LDVs PHEVs:</b> {row['PHEVnorm']}%<br>"
f"<b>Pct of LDVs EVs:</b> {row['EVnorm']}%<br>")
popup_.add_to(geojson_)
geojson_.add_to(evnorm_fg)
for index, row in self.level1chrg.iterrows():
level1_fg.add_child(
folium.CircleMarker(
location=row['lat_long'],
radius=4,
color='#76B041',
fill=True,
fill_opacity=0.8,
popup=
(f"<b>Num Level 1 Chargers:</b> {int(row['EV Level1 EVSE Num'])}<br>"
)))
for index, row in self.level2chrg.iterrows():
level2_fg.add_child(
folium.CircleMarker(
location=row['lat_long'],
radius=4,
color='#17BEBB',
fill=True,
fill_opacity=0.8,
popup=
(f"<b>Num Level 2 Chargers:</b> {int(row['EV Level2 EVSE Num'])}<br>"
)))
for index, row in self.dcfchrg.iterrows():
dcfc_fg.add_child(
folium.CircleMarker(
location=row['lat_long'],
radius=4,
color='#F9A03F',
fill=True,
fill_opacity=0.8,
popup=
(f"<b>Num DC Fast Chargers:</b> {int(row['EV DC Fast Count'])}<br>"
)))
m.add_child(hev_fg)
m.add_child(phev_fg)
m.add_child(ev_fg)
m.add_child(hevnorm_fg)
m.add_child(phevnorm_fg)
m.add_child(evnorm_fg)
m.add_child(dcfc_fg)
m.add_child(level1_fg)
m.add_child(level2_fg)
m.add_child(PSCo_fg)
m.add_child(transmission_fg)
m.add_child(folium.map.LayerControl(collapsed=False, autoZIndex=True))
self.html = m
return m
icon_HL = folium.features.CustomIcon(logo_HL, icon_size=(25, 25))
if place.entry_type == data.TYPES['HiLife']:
folium.Marker(location=[place.lat, place.lon],
icon=icon_HL).add_to(group_HL)
xinyi_map.add_child(group_HL)
icon_OK = folium.features.CustomIcon(logo_OK, icon_size=(25, 25))
if place.entry_type == data.TYPES['OKmart']:
folium.Marker(location=[place.lat, place.lon],
icon=icon_OK).add_to(group_OK)
xinyi_map.add_child(group_OK)
for lat, lon in xinyi_exits.itervalues():
icon_mrt = folium.features.CustomIcon(logo_mrt, icon_size=(25, 25))
folium.Marker(location=[lat, lon], icon=icon_mrt).add_to(group_mrt)
xinyi_map.add_child(group_mrt)
# heatmap
for place in data.all:
folium.CircleMarker(location=[place.lat, place.lon], radius=1,
opacity=0).add_to(group_heat)
xinyi_map.add_child(group_heat)
geocode = pd.DataFrame([(place.lat, place.lon)
for place in data.all]).values.tolist()
group_heat.add_child(plugins.HeatMap(geocode, radius=40))
# add layer control
folium.LayerControl().add_to(xinyi_map)
xinyi_map.save("xinyi_map.html")