def get_map_list_after_segmentation(section_map, outlier_algo = None, filter_algo = None):
mapList = []
for trip, section_list in section_map:
logging.debug("%s %s -> %s %s" % ("=" * 20, trip.start_time, trip.end_time, "=" * 20))
trip_df = lq.get_points_for_section(trip)
curr_map = folium.Map([trip_df.mLatitude.mean(), trip_df.mLongitude.mean()])
last_section_end = None
for (i, section) in enumerate(section_list):
logging.debug("%s %s: %s -> %s %s" %
("-" * 20, i, section.start_time, section.end_time, "-" * 20))
raw_section_df = trip_df[np.logical_and(trip_df.mTime >= section.start_ts,
trip_df.mTime <= section.end_ts)]
section_df = ls.filter_points(raw_section_df, outlier_algo, filter_algo)
if section_df.shape[0] == 0:
logging.info("Found empty df! skipping...")
continue
logging.debug("for section %s, section_df.shape = %s, formatted_time.head() = %s" %
(section, section_df.shape, section_df["formatted_time"].head()))
update_map(curr_map, section_df, line_color = sel_color_list[section.activity.value],
popup = "%s" % (section.activity))
if section_df.shape[0] > 0:
curr_section_start = section_df.iloc[0]
if i != 0 and last_section_end is not None:
# We want to join this to the previous section.
curr_map.line([[last_section_end.mLatitude, last_section_end.mLongitude],
[curr_section_start.mLatitude, curr_section_start.mLongitude]],
line_color = sel_color_list[-1],
popup = "%s -> %s" % (section_list[i-1].activity, section.activity))
last_section_end = section_df.iloc[-1]
mapList.append(curr_map)
return mapList
def get_maps_for_range_old(user_id, start_ts, end_ts):
# First, get the timeline for that range.
ts = esta.TimeSeries.get_time_series(user_id)
trip_list = esdt.get_trips(user_id, estt.TimeQuery("data.start_ts", start_ts, end_ts))
# TODO: Should the timeline support random access as well?
# If it did, we wouldn't need this additional map
# I think that it would be good to support a doubly linked list, i.e. prev and next in addition
# to the iteration interface
place_list = esdp.get_places(user_id, estt.TimeQuery("data.exit_ts", start_ts, end_ts))
place_list = place_list + (esdp.get_places(user_id, estt.TimeQuery("data.enter_ts", start_ts, end_ts)))
place_map = dict([(p.get_id(), p) for p in place_list])
map_list = []
flipped_midpoint = lambda p1_p22: [old_div((p1_p22[0].coordinates[1] + p1_p22[1].coordinates[1]),2),
old_div((p1_p22[0].coordinates[0] + p1_p22[1].coordinates[0]),2)]
for i, trip in enumerate(trip_list):
logging.debug("-" * 20 + trip.start_fmt_time + "=>" + trip.end_fmt_time
+ "(" + str(trip.end_ts - trip.start_ts) + ")")
if (len(esdt.get_raw_sections_for_trip(user_id, trip.get_id())) == 0 and
len(esdt.get_raw_stops_for_trip(user_id, trip.get_id())) == 0):
logging.debug("Skipping trip because it has no stops and no sections")
continue
start_point = gj.GeoJSON.to_instance(trip.start_loc)
end_point = gj.GeoJSON.to_instance(trip.end_loc)
curr_map = folium.Map(flipped_midpoint((start_point, end_point)))
map_list.append(curr_map)
logging.debug("About to display places %s and %s" % (trip.start_place, trip.end_place))
update_place(curr_map, trip.start_place, place_map, marker_color='green')
update_place(curr_map, trip.end_place, place_map, marker_color='red')
# TODO: Should get_timeline_for_trip work on a trip_id or on a trip object
# it seems stupid to convert trip object -> id -> trip object
curr_trip_timeline = esdt.get_raw_timeline_for_trip(user_id, trip.get_id())
for i, trip_element in enumerate(curr_trip_timeline):
# logging.debug("Examining element %s of type %s" % (trip_element, type(trip_element)))
if type(trip_element) == ecws.Stop:
time_query = esds.get_time_query_for_stop(trip_element.get_id())
logging.debug("time_query for stop %s = %s" % (trip_element, time_query))
stop_points_df = ts.get_data_df("background/filtered_location", time_query)
# logging.debug("stop_points_df.head() = %s" % stop_points_df.head())
if len(stop_points_df) > 0:
update_line(curr_map, stop_points_df, line_color = sel_color_list[-1],
popup="%s -> %s" % (trip_element.enter_fmt_time, trip_element.exit_fmt_time))
else:
assert(type(trip_element) == ecwsc.Section)
time_query = esdsc.get_time_query_for_section(trip_element.get_id())
logging.debug("time_query for section %s = %s" %
(trip_element, "[%s,%s,%s]" % (time_query.timeType, time_query.startTs, time_query.endTs)))
section_points_df = ts.get_data_df("background/filtered_location", time_query)
logging.debug("section_points_df.tail() = %s" % section_points_df.tail())
if len(section_points_df) > 0:
update_line(curr_map, section_points_df, line_color = sel_color_list[trip_element.sensed_mode.value],
popup="%s (%s -> %s)" % (trip_element.sensed_mode, trip_element.start_fmt_time,
trip_element.end_fmt_time))
else:
logging.warning("found no points for section %s" % trip_element)
return map_list
def get_maps_for_geojson_unsectioned(feature_list):
map_list = []
for feature in feature_list:
# logging.debug("Getting map for feature %s" % bju.dumps(feature))
feature_coords = list(get_coords(feature))
# feature_coords = list(gj.utils.coords(feature))
curr_map = folium.Map(get_center_for_map(feature_coords))
curr_plugin = fgjp.FoliumGeojsonPlugin(dict(feature))
curr_map.add_plugin(curr_plugin)
map_list.append(curr_map)
return map_list
def draw_stations(all_stations_in_one_line):
map = folium.Map(location=[43.25654, 76.92848], zoom_start=12)
for j in all_stations_in_one_line:
folium.CircleMarker(location=[j.get_lat(), j.get_lng()],
radius=9,
popup=j.get_name(),
fill_color=j.get_color(),
color="black",
fill_opacity=0.9).add_to(map)
return map
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 get_maps_for_geojson_list(trip_geojson_list):
map_list = []
for trip_doc in trip_geojson_list:
# logging.debug(trip_doc)
trip_geojson = ad.AttrDict(trip_doc)
logging.debug("centering based on start = %s, end = %s " % (trip_geojson.features[0], trip_geojson.features[1]))
flipped_midpoint = lambda(p1, p2): [(p1.coordinates[1] + p2.coordinates[1])/2,
(p1.coordinates[0] + p2.coordinates[0])/2]
curr_map = folium.Map(flipped_midpoint((trip_geojson.features[0].geometry,
trip_geojson.features[1].geometry)))
curr_plugin = fgjp.FoliumGeojsonPlugin(dict(trip_geojson))
curr_map.add_plugin(curr_plugin)
map_list.append(curr_map)
return map_list
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 map_view(request):
"""
Main page - map of all the sensors
"""
gmaps = googlemaps.Client(key='AIzaSyCZO9lx7k1gFhU-Fv9hSZ9PUynZb0EjeBg')
sensors = Sensor.objects.all()
sensor_map = folium.Map(location=[50.064316, 19.985638],
zoom_start=12,
tiles='Stamen Terrain')
for sensor in sensors:
localisation = sensor.localisation
sensor_id = sensor.sensor_id
coordinates = (gmaps.geocode(localisation))[0]['geometry']['location']
popup = '%s; sensor_id = %i' % (localisation, sensor_id)
folium.Marker([coordinates['lat'], coordinates['lng']],
popup=popup).add_to(sensor_map)
sensor_map.save('templates/map.html')
return render_to_response('sensors_map.html')
def map_01_details():
incidents = folium.map.FeatureGroup()
# 将每个枪击案件添加到事件特性组
for lat, lng, in zip(cdata1.latitude, data.longitude):
incidents.add_child(
folium.CircleMarker(
[lat, lng],
radius=7, # define how big you want the circle markers to be
color='yellow',
fill=True,
fill_color='red',
fill_opacity=0.4))
# Add incidents to map
san_map = folium.Map(location=[latitude, longitude], zoom_start=4)
incidents_map = san_map.add_child(incidents)
display(incidents_map)
incidents_map.save('templates/map_01_details.html')
return render_template("map_01_details.html")
def layer() -> None:
"""Create map with layer.
Creates map with given data, where f.geo_lat and f.get_long are latitude and longitude respectively, avg(f.ppm) is
weight for such point on map.
"""
m = folium.Map(location=[59.93399475, 30.367615217112643])
query = db_query_hm()
g = {
0: "black",
0.2: "blue",
0.3: "teal",
0.4: "green",
0.5: "yellow",
0.6: "orange",
0.7: "red",
}
heatmap = HeatMap(query, radius=25, blur=45, gradient=g)
m.add_child(heatmap)
m.save("./templates/heatmap.html")
def draw_choropleth_map(dataset):
# Draw the choropleth map according to the average price of AV_TOTAL based on zip code
m = folium.Map(location=[42.37909, -71.03215], zoom_start=12)
bins = list(dataset['average_price'].quantile([0, 0.25, 0.5, 0.75, 1]))
print(dataset.info())
dataset = dataset.dropna()
folium.Choropleth(
geo_data=bzp,
data=dataset,
columns=['zipcode', 'average_price'],
key_on='feature.properties.ZCTA5CE10',
# fill_color='red',
fill_color='YlOrRd',
fill_opacity=0.7,
line_opacity=0.2,
highlight=True,
bins = bins,
legend_name='Boston Average House Price'
).add_to(m)
m.save('choropleth.html')
def get_map_for_geojson_unsectioned(geojson):
div_icon = folium.DivIcon()
all_div_markers = [folium.CircleMarker(p["geometry"]["coordinates"][::-1],
popup=bju.dumps(p["properties"]),
radius=5)
for p in geojson["features"][0]["features"]]
# all_div_markers = [folium.Marker(p["geometry"]["coordinates"][::-1],
# popup=json.dumps(p["properties"]),
# icon=div_icon)
# for p in all_geojson["features"][0]["features"]]
print("Points are ", [m.location for m in all_div_markers[:5]], "...")
geojson_line_string = geojson["features"][1]["geometry"]
polyline_coords = [c[::-1] for c in geojson_line_string["coordinates"]]
print("Linestring is", polyline_coords[:5], "...")
polyline = folium.PolyLine(polyline_coords)
bounds = polyline.get_bounds()
m = folium.Map(tiles='Stamen Terrain')
m.fit_bounds(bounds)
for marker in all_div_markers:
marker.add_to(m)
polyline.add_to(m)
return m
from folium import folium import markers import methods import trasyPlugin mapka = folium.Map(location=[42.77, -7.41], zoom_start=8, control_scale=True) trasyPlugin.trasy_camino2019(mapka) markers.camino_marker(mapka) tooltip = 'RAPORT Camino Santiago de Compostela 2019' methods.marker_raport(mapka, 42.98, -9.36, methods.popup_raport(), tooltip) methods.open_webb_and_save(mapka, 'C:/Users/zs/Downloads/Camino.html')
def generateBaseMap(default_location=[42.37909, -71.03215]):
base_map = folium.Map(location=default_location )
return base_map
from folium import folium
map = folium.Map(location=[19.0760, 72.8777])
map.save("mylocation.html")
from folium import folium import methods import trasy import markers import private lat = 52.22534 lng = 21.02793 mapka = folium.Map(location=[lat, lng], zoom_start=6, control_scale=True) trasy.trasy_beskidy2016(mapka) trasy.trasy_beskidy2017(mapka) trasy.trasy_beskidy2018(mapka) trasy.trasy_beskidy2019(mapka) trasy.trasy_beskidy2020(mapka) trasy.trasy_tatry2015(mapka) trasy.trasy_tatry2016(mapka) trasy.trasy_tatry2018(mapka) trasy.trasy_tatry2019(mapka) trasy.trasy_tatry2020(mapka) trasy.trasy_sudety2016(mapka) trasy.trasy_sudety2018(mapka) trasy.trasy_sudety2019(mapka) trasy.trasy_slovakia2016(mapka) trasy.trasy_slovakia2017(mapka) trasy.trasy_slovakia2018(mapka)
which_result=2) #返回结果为graph 对象
street_gdfs = ox.save_load.graph_to_gdfs(
street_graph,
nodes=False,
edges=True,
node_geometry=True,
fill_edge_geometry=True) #将graph_转化为_gdf 对象
ox.save_load.save_graph_shapefile(street_graph,
filename='Baoji_jintai',
folder=None,
encoding='utf-8') #保存graph 对象为shape文件
ox.save_gdf_shapefile(street_gdfs, 'Baoji_jintai', r'./data/') # 保存gdf类型的对象
ox.plot_graph(street_graph) #graph 对象绘图
foliumHtml = ox.plot.plot_graph_folium(street_graph,
graph_map=None,
popup_attribute=None,
tiles='cartodbpositron',
zoom=1,
fit_bounds=True,
edge_color='#333333',
edge_width=5,
edge_opacity=1)
foliumHtml.save('./Features_0.html') #保存为网页
print('ppp')
map_osm = folium.Map(location=[45.5236, -122.6750])
#此处 map_osm.create_map() 方法 貌似失效了 替换为 map_osm.save() 方法可用
map_osm.save('./Features_0.html')
#address_rect
city
def get_map(section_points, line_color=None, popup=None):
currMap = folium.Map(
[section_points.mLatitude.mean(),
section_points.mLongitude.mean()])
update_map(currMap, section_points, line_color, popup)
return currMap
from folium import folium import methods import trasyPlugin import markers mapka = folium.Map(location=[46.47, 11.77], zoom_start=11, control_scale=True) trasyPlugin.trasy_camino2016(mapka) markers.dolomity_marker(mapka) tooltip = 'RAPORT z wszystkich tras w Alpach (Dolomity)' methods.marker_raport(mapka, 46.47, 11.77, methods.popup_raport(), tooltip) methods.open_webb_and_save(mapka, 'C:/Users/zs/Downloads/Dolomity.html')
def cluster(locationdata):
print "generating a clustered map"
map_osm.add_children(plugins.MarkerCluster(locationdata))
if __name__ == "__main__":
input_files = []
for filename in args.filename:
input_files.append(filename.name)
parsedlocations, frequentLocation = parser(input_files, args.accuracy)
if args.heatmap:
map_osm = folium.Map(
location=[frequentLocation[0], frequentLocation[1]])
heatmap(parsedlocations)
if args.night:
map_osm.add_children(plugins.Terminator())
map_osm.save('./heat_map.html')
map_osm._repr_html_()
if args.cluster:
map_osm = folium.Map(
location=[frequentLocation[0], frequentLocation[1]])
cluster(parsedlocations)
if args.night:
map_osm.add_children(plugins.Terminator())
map_osm.save('./cluster_map.html')
map_osm._repr_html_()
# -*- coding: utf-8 -*-
import os
import pandas as pd
from folium import folium
from pandas import compat
from folium.plugins import MarkerCluster
os.chdir('../VKHCG/01-Vermeulen/00-RawData')
compat.PY3 = True
universities = pd.read_csv("uk_universities_locations2.csv")
uk = folium.Map(
location=[universities.lat.mean(axis=0),
universities.lon.mean(axis=0)],
zoom_start=5)
marker_cluster = MarkerCluster("Universities").add_to(uk)
#add a marker for each university
for each in universities.iterrows():
folium.Marker(list([each[1]['lat'], each[1]['lon']]),
popup=each[1]['Name']).add_to(marker_cluster)
uk.save("./universities.html")
