# def generateBaseMap(default_location=[45.7372756, 4.8175837], default_zoom_start=12):
# base_map = folium.Map(location=default_location, control_scale=True, zoom_start=default_zoom_start)
# return base_map
# base_map = generateBaseMap()
# HeatMapWithTime(locationlist).add_to(base_map)
# base_map.save('L.html')
#HeatMap
def generateBaseMap(default_location=[45.7372756, 4.8175837], default_zoom_start=12):
base_map = folium.Map(location=default_location, control_scale=True, zoom_start=default_zoom_start)
return base_map
base_map = generateBaseMap()
HeatMap(data=data[['latitude', 'longitude']], gradient={0.2: 'blue', 0.4: 'lime', 0.6: 'orange', 0.8: 'yellow', 1: 'red'}, radius=15, max_zoom=18).add_to(base_map)
base_map.save('M.html')
###Marker Cluster
# map2 = folium.Map(location=[45.7372756, 4.8175837], tiles='CartoDB dark_matter', zoom_start=11)
# marker_cluster = folium.plugins.MarkerCluster().add_to(map2)
# for point in range(0, len(locationlist)):
# folium.Marker(locationlist[point], popup=price[point]).add_to(marker_cluster)
# map2.save('carte4.html')
# # A good blog post on Kaggle about Folium can also be found here: # https://www.kaggle.com/daveianhickey/how-to-folium-for-maps-heatmaps-time-data m = folium.Map() # m ## Adding markers to map # Convert to list of lat,long, name tuples markers = list(zip(latitudes, longitudes, names, WebPageAddress)) markers = np.array(markers) markers # `i[:2]` slices first two elements of list which for `markers` # is latitude and longitude for i in markers: coords = i[:2] title = i[2] webaddress = i[3] #folium.Marker(location=coords, popup=title).add_to(m) folium.Marker(location=coords, popup=(title,webaddress)).add_to(m) # m ## Adding heatmap layer HeatMap(markers[:,:2]).add_to(m) m ## Ports Heatmap
import folium
from folium.plugins import HeatMap
import pandas as pd
##Dados da Plataforma Brasil.io
dados_covid19 = pd.read_csv(
'https://raw.githubusercontent.com/wcota/covid19br/master/cases-gps.csv')
mapa = folium.Map(location=[-15.77972, -47.92972],
zoom_start=4,
min_zoom=4,
control_scale=True)
locais = dados_covid19[["lat", "lon"]].values.tolist()
HeatMap(locais, radius=13).add_to(mapa)
mapa.save('mapa.html')
x = np.array(df[['longitude','latitude', 'mag']], dtype='float64')
plt.scatter(x[:,0], x[:,1], alpha=0.2, s= 50)
plt.show()
m = folium.Map(location=[df.latitude.mean(), df.longitude.mean()], zoom_start=5)
for _, row in df.iterrows():
if row.mag >= 5:
folium.CircleMarker(
location=[row.latitude, row.longitude],
radius=5,
popup=re.sub(r'[^a-zA-Z]+', '', row.time),
color='#FE1A16',
fill=True,
fill_colour='#FE1A16'
).add_to(m)
else:
folium.CircleMarker(
location=[row.latitude, row.longitude],
radius=5,
popup=re.sub(r'[^a-zA-Z]+', '', row.time),
color='#ffbd87',
fill=True,
fill_colour='#ffbd87'
).add_to(m)
m.save("scattered_plot.png")
im = Image.open("scattered_plot.png")
im.show()
m1=folium.Map(location=[df.latitude.mean(), df.longitude.mean()], zoom_start=5)
HeatMap(data=df[['latitude', 'longitude', 'mag']].groupby(['latitude', 'longitude']).sum().reset_index().values.tolist(), radius=8, max_zoom=13).add_to(m1)
m1.save("HeatMap.png")
im1 = Image.open("scattered_plot.png")
im1.show()
YY=np.array(Y)
z = list(zip(YY,XX))
zz=np.array(z)
print(zz)from sklearn.cluster import KMeans
y_pred = KMeans(n_clusters=3, random_state=9).fit_predict(zz)
plt.scatter(zz[:, 0], zz[:, 1], c=y_pred)
plt.show()schools_map = folium.Map(location=[gdf['latitude'].mean(), gdf['longitude'].mean()], zoom_start=10)
marker_cluster = plugins.MarkerCluster().add_to(schools_map)
tupxy = [tuple(x) for x in gdf[["latitude", "longitude"]].values]
for coord in tupxy:
folium.CircleMarker(location=[coord[0], coord[1]],radius=5,color='#3186cc',fill_color='#3186cc',).add_to(schools_map)
file_path = r"test2.html"
schools_map.save(file_path)# from folium.plugins import HeatMap
data = gdf[["latitude", "longitude"]].values.tolist()
hmap = folium.Map(location=[gdf['latitude'].mean(), gdf['longitude'].mean()],control_scale=True, zoom_start=12)
hmap.add_child(HeatMap(data, radius=5, gradient={.2: 'blue', .35: 'lime', .6: 'yellow'}))
file_path = r"test_heatmap.html"
hmap.save(file_path)gdf_chi = gdf[gdf['venueCategory'] == 'Chinese Restaurant']
gdf_chiChinese_map = folium.Map(location=[gdf_chi['latitude'].mean(), gdf_chi['longitude'].mean()], zoom_start=12)
tupxy = [tuple(x) for x in gdf_chi[["latitude", "longitude"]].values]
for coord in tupxy:
folium.CircleMarker(location=[coord[0], coord[1]],radius=5,color='#3186cc',fill= True, fill_color='#3186cc',).add_to(Chinese_map)
file_path = r"test_chi.html"
Chinese_mapdata = gdf_chi[["latitude", "longitude"]].values.tolist()
hmap = folium.Map(location=[gdf_chi['latitude'].mean(), gdf_chi['longitude'].mean()],control_scale=True, zoom_start=13)
hmap.add_child(HeatMap(data, radius=5, gradient={.1: 'blue', .2: 'lime', .3: 'yellow'}))
file_path = r"test_chi_heatmap.html"
hmapcluster_map = folium.Map(location=[gdf['latitude'].mean(), gdf['longitude'].mean()], zoom_start=10)
# create a mark cluster object
marker_cluster = plugins.MarkerCluster().add_to(cluster_map)
hints = sum(moments[6:12])
elif PERIOD == 'afternoon':
hints = sum(moments[12:18])
elif PERIOD == 'evening':
hints = sum(moments[18:22])
else:
hints = moments[23] + sum(moments[0:6])
return ceil(hints / 40)
def place_hints_per_hour(place, hour):
moments = json.loads(place[WEEK_DAY])
hints = moments[hour]
return ceil(hints / 40)
natal = folium.Map(location=[-5.8313086, -35.2047059], zoom_start=13)
places = pd.read_csv('data/places_cleaned.csv')
heat_dots = []
for index, row in places.iterrows():
place_dots = place_hints_per_hour(row, 7) * [[row.lat, row.lng]]
heat_dots += place_dots
# folium.CircleMarker([row.lat, row.lng], popup=row.place_name, radius=2).add_to(natal)
HeatMap(heat_dots).add_to(natal)
natal.save('natal.html')
def reponseQuestion2():
# Create connexion
cluster = Cluster(["localhost"])
session = cluster.connect(KEYSPACE)
# User input
instant = input("\nDonnez un instant (par example 2014-6-15 12:55) : ")
qury = f'''SELECT station, lon, lat, tmp, relh, drct, sknt, vsby FROM asos2 WHERE time='{instant}';'''
result = session.execute(qury)
map_data = pd.DataFrame()
for row in result:
map_data = map_data.append([[element for element in row]], ignore_index=True)
map_data.columns=['station','lon','lat','temperature','relative_humidity','wind_direction','wind_speed','visibility']
# Create point map
weather_map = folium.Map(location=[42, 13], zoom_start=6)
for i in range(len(map_data)):
# Define popup
popup_text = folium.Html(
'<b>Temps d\'observation : {}</b></br> <b>Station : {}</b></br> <b>lon : {}</b></br> <b>lat : {}</b></br> <b>Température (fahrenheit) : {}</b></br> \
<b>Humidité relative (%) : {}</b></br> <b>Direction du vent : {}</b></br> <b>Vitesse du vent (mille marin) : {}</b></br> <b>Visibilité (mile) : {}</b></br>'\
.format(instant,
map_data.iloc[i]['station'],
round(map_data.iloc[i]['lon'],2),
round(map_data.iloc[i]['lat'],2),
round(map_data.iloc[i]['temperature'],2),
round(map_data.iloc[i]['relative_humidity'],2),
map_data.iloc[i]['wind_direction'],
map_data.iloc[i]['wind_speed'],
round(map_data.iloc[i]['visibility'],2)),
script=True)
map_popup = folium.Popup(popup_text, max_width=2650)
# Add points to the map
folium.Marker([map_data.iloc[i]['lat'], map_data.iloc[i]['lon']], popup=map_popup).add_to(weather_map)
# Save map
weather_map.save(f'''images/Meteo_carte_{instant}.html''')
# Create heat map
lats = np.array(map_data['lat'][0:len(map_data)])
lons = np.array(map_data['lon'][0:len(map_data)])
tmps = np.array(map_data['temperature'][0:len(map_data)])
heat_data = [[lats[i],lons[i],tmps[i]] for i in range(len(map_data))]
heat_map = folium.Map(location=[42, 13], zoom_start=6)
HeatMap(heat_data).add_to(heat_map)
for i in range(len(map_data)):
# Define popup
popup_text = folium.Html(
'<b>Station : {}</b></br> <b>lon : {}</b></br> <b>lat : {}</b></br> <b>Température (fahrenheit) : {}</b>'\
.format(map_data.iloc[i]['station'],
round(map_data.iloc[i]['lon'],2),
round(map_data.iloc[i]['lat'],2),
round(map_data.iloc[i]['temperature'],2)),
script=True)
map_popup = folium.Popup(popup_text, max_width=2650)
# Add points to the map
folium.Marker([map_data.iloc[i]['lat'], map_data.iloc[i]['lon']], popup=map_popup).add_to(heat_map)
heat_map.save(f'''images/Carte_de_chaleur_{instant}.html''')
print("""
*** Cartes creees avec succes ! ***\n
==================\n""")
def heat_map(df):
locs = zip(df.lon, df.lat)
m = folium.Map([22.3511148, 78.6677428], tiles='stamentoner', zoom_start=5)
HeatMap(locs).add_to(m)
return st.markdown(m._repr_html_(), unsafe_allow_html=True)
# print(MAES)
print(sum([float(i) for i in MAES]))
print('RSME Average : ')
print(sum([float(i) for i in RSMES]))
# part 3
df = pd.read_csv('boliga_zealand.csv').drop(['Index', '_1', 'Unnamed: 0'], axis=1)
df = df[['lat', 'lon', 'price']].dropna()
norreport = (55.6836329, 12.5709413)
my_map = folium.Map(location=[55.6836329, 12.5709413], zoom_start=10)
folium.Marker(location=norreport, icon=folium.Icon(color='red', icon='home')).add_to(my_map)
data = []
for row in df.itertuples():
data.append([row.lat, row.lon, row.price])
HeatMap(data, radius=7).add_to(my_map)
my_map.save('heatmap.html')
# According to the map data we see as the best the triangle area between Bronshøj, Islev and Vanløse. Not only that
# the HeatMap shows that as a reasonably priced area (however we do not consider the size of the apartments, so it
# could be that the apartments are just smaller in the area) but also the distance and connection to the city center
# is pretty good, especially around Vanløse where is also metro.
# Part 4: Housing price model
def haversine_distance(lat_dest, lon_dest):
lat_orig, lon_orig = (55.6836722, 12.5693963)
# lat_dest, lon_dest = destination
radius = 6371
from datawrangling.geomunging import g2
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=17, 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(g2.lat.values, g2.lon.values))))
m.add_child(fg)
LayerControl().add_to(m)
m.save(r'templates\g2eventheat.html')
us_map = folium.Map(location=[40, -99], zoom_start=4)
# Ensure you're handing it floats
ufo['latitude'] = ufo['latitude'].astype(float)
ufo['longitude'] = ufo['longitude'].astype(float)
heat_df = ufo[['latitude', 'longitude']]
heat_df = heat_df.dropna(axis=0, subset=['latitude', 'longitude'])
# List comprehension to make out list of lists
heat_data = [[row['latitude'], row['longitude']]
for index, row in heat_df.iterrows()]
# Plot it on the map
HeatMap(heat_data).add_to(us_map)
# Display the map
us_map
#us_map.save('ufoheatmap.html')
sns.catplot(y=ufo["shape"],
kind="count",
palette="viridis",
edgecolor=".6",
data=ufo.sort_values("shape"))
ufo.loc[ufo['shape'] == 'rectangle', 'shape'] = 'geometric'
ufo.loc[ufo['shape'] == 'chevron', 'shape'] = 'geometric'
ufo.loc[ufo['shape'] == 'triangle', 'shape'] = 'geometric'
ufo.loc[ufo['shape'] == 'diamond', 'shape'] = 'geometric'
print(accuracy)
# plotting the results
import pandas as pd
import folium
from folium.plugins import HeatMap
for_map = pd.read_csv('/home/cmlare/Data/RF Data/Processed/2018-05-08 to 2018-05-15_integrated_classified.csv')
for_map
max_amount = float(for_map['TSL_MIN'].max())
max_amount
hmap = folium.Map(location=[6.917200, 79.913300], zoom_start=7, )
hm_wide = HeatMap( list(zip(for_map.XEnd.values, for_map.YEnd.values, for_map.TSL_MIN.values)),
min_opacity=0.8,
max_val=max_amount,
radius=17, blur=15,
max_zoom=1,
)
# folium.GeoJson(district23).add_to(hmap)
hmap.add_child(hm_wide)
# subset = data[(data.GETON_DATE >= begin) & (data.GETON_DATE <= end)]
# subset = subset[(subset.GETON_LONGITUDE >= 118.05) & (subset.GETON_LATITUDE >= 24.44)]
# subset = subset.sample(frac=0.25, random_state=99)
# x = list(subset.GETON_LONGITUDE)
# y = list(subset.GETON_LATITUDE)
od = pd.read_csv('data/od_temp.csv')
x = list(od.DEP_LONGITUDE)
y = list(od.DEP_LATITUDE)
x = np.asarray(x).reshape((len(x), 1))
y = np.asarray(y).reshape((len(y), 1))
X = np.hstack((x, y))
for i in range(len(X)):
X[i][0], X[i][1] = wgs2gcj(X[i][0], X[i][1])
X1 = np.hstack((y, x))
for i in range(len(X1)):
X1[i][1], X1[i][0] = wgs2gcj(X1[i][1], X1[i][0])
band = getBandWidth(X)
print('Band Width:' + str(band))
kde = KernelDensity(bandwidth=band, kernel='epanechnikov').fit(X)
res = kde.score_samples(X)
res = np.asarray(res).reshape((len(res), 1))
kde_y = np.hstack((X1, res))
xmmap = folium.Map(
location=(24.45, 118.08),
zoom_start=13,
max_zoom=14,
tiles=
'http://wprd02.is.autonavi.com/appmaptile?x={x}&y={y}&z={z}&lang=zh_cn&size=1&scl=1&style=7',
attr='Powered by Amap ©️2020 高德软件 GS(2019)6379号 - 甲测资字11002004')
HeatMap(kde_y).add_to(xmmap)
xmmap.save('out/passengerkde.html')
distances = stations.geometry.distance(recent_release.geometry)
distances
print('Mean distance to monitoring stations: {} feet'.format(distances.mean()))
print('Closest monitoring station ({} feet):'.format(distances.min()))
print(stations.iloc[distances.idxmin()][["ADDRESS", "LATITUDE", "LONGITUDE"]])
# Buffer - to understand all points on a map are within what radius away from a point
two_mile_buffer = stations.geometry.buffer(2 * 5280)
two_mile_buffer.head()
# Create map with release incidents and monitoring stations
m = folium.Map(location=[39.9526, -75.1652], zoom_start=11)
HeatMap(data=releases[['LATITUDE', 'LONGITUDE']], radius=15).add_to(m)
for idx, row in stations.iterrows():
Marker([row['LATITUDE'], row['LONGITUDE']]).add_to(m)
# Plot each polygon on the map
GeoJson(two_mile_buffer.to_crs(epsg=4326)).add_to(m)
# Show the map
m
# Turn group of polygons into single multipolygon
my_union = two_mile_buffer.geometry.unary_union
print('Type:', type(my_union))
# Show the MultiPolygon object
my_union
(0.0021852 * (abs(data.iloc[i]['lon']))) + 1) * 50000,
color=colorgradient[counter],
fill=True,
fill_color=colorgradient[counter],
blur=15,
# max_zoom=1,
min_opacity=0.8).add_to(m)
counter += 1
data_list = list(zip(data.lon.values, data.lat.values, data.value.values))
max_amount = float(data['value'].max())
print(max_amount)
hm_wide = HeatMap(
data_list,
min_opacity=0.2,
max_val=max_amount,
radius=10,
blur=15,
max_zoom=1,
)
h.add_child(hm_wide)
h.save('heatmap.html')
# for i in range(0, len(data)):
# colors = colorgradient
# folium.Circle(
# location=[data.iloc[i]['lon'], data.iloc[i]['lat']],
# popup=(data.iloc[i]['name']),
# #radius=data.iloc[i]['value'] * 100000,
# radius= (2)*
# ((-(0.0000992*(abs(data.iloc[i]['lon']**2))))-(0.0021852*(abs(data.iloc[i]['lon'])))+1)
# * 50000,
import seaborn as sns
import folium
#import webbrowser
from folium.plugins import HeatMap
#posi=pd.read_excel("2015Cities-CHINA.xlsx")
posi = pd.read_excel("2015Cities-CHINA_4.xlsx")
num = 130
lat = np.array(posi["lat"][0:num]) # 获取维度之纬度值
lon = np.array(posi["lon"][0:num]) # 获取经度值
pop = np.array(posi["pop"][0:num], dtype=float) # 获取人口数，转化为numpy浮点型
gdp = np.array(posi["GDP"][0:num], dtype=float) # 获取数，gdp转化为numpy浮点型
gdp_ave = np.array(posi["GDP_Average"][0:num] / 10,
dtype=float) # 获取数，平均gdp转化为numpy浮点型
print(posi["GDP_Average"][0:num])
#exit(0)
#data1 = [[lat[i],lon[i],pop[i]] for i in range(num)] #将数据制作成[lats,lons,population]的形式
#data1 = [[lat[i],lon[i],gdp[i]] for i in range(num)] #将数据制作成[lats,lons,gdp]的形式
data1 = [[lat[i], lon[i], gdp_ave[i]]
for i in range(num)] #将数据制作成[lats,lons,gdp_ave]的形式
map_generate = folium.Map(location=[35, 110], zoom_start=5) #绘制Map，开始缩放程度是5倍
HeatMap(data1).add_to(map_generate) # 将热力图添加到前面建立的map里
#file_path = r"/Users/fuchuang/Downloads/polulation.html"
file_path = r"/Users/fuchuang/Downloads/gdp_average.html"
map_generate.save(file_path) # 保存为html文件
rest_loc.columns = ['Name', 'count']
restaurant_locations = rest_loc.merge(locations, on='Name').dropna()
restaurant_locations
# In[46]:
import folium
from folium.plugins import HeatMap
# In[47]:
basemap = folium.Map(location=[12.97, 77.59])
# In[48]:
HeatMap(data=restaurant_locations[['latitude', 'longitude', 'count']]).add_to(
basemap)
# In[49]:
basemap
# In[50]:
import wordcloud
# In[51]:
from wordcloud import WordCloud, STOPWORDS
# In[52]:
import numpy as np
import pandas as pd
import seaborn as sns
import folium
import webbrowser
from folium.plugins import HeatMap
posi = pd.read_excel("2015Cities-CHINA.xlsx")
num = 100
lat = np.array(posi["lat"][0:num]) # 获取维度之维度值
lon = np.array(posi["lon"][0:num]) # 获取经度值
pop = np.array(posi["pop"][0:num], dtype=float) # 获取人口数，转化为numpy浮点型
gdp = np.array(posi["GDP"][0:num], dtype=float) # 获取人口数，转化为numpy浮点型
data1 = [[lat[i], lon[i], pop[i]]
for i in range(num)] #将数据制作成[lats,lons,weights]的形式
map_osm = folium.Map(location=[35, 110], zoom_start=4) #绘制Map，开始缩放程度是5倍
HeatMap(data1).add_to(map_osm) # 将热力图添加到前面建立的map里
file_path = "map5.html"
map_osm.save(file_path) # 保存为html文件
webbrowser.open(file_path) # 默认浏览器打开
import numpy as np
import pandas as pd
import folium
from folium import plugins
from folium.plugins import HeatMap
# read a csv file into a dataframe
df_acc = pd.read_csv('Chicago_Crimes_2012_to_2017.csv', dtype=object)
df_acc['Latitude'] = df_acc['Latitude'].astype(float)
df_acc['Longitude'] = df_acc['Longitude'].astype(float)
my_map = folium.Map(location=[41.881832, -87.623177], zoom_start=10)
heat_df = df_acc[['Latitude', 'Longitude']]
heat_df = heat_df.dropna(axis=0, subset=['Latitude', 'Longitude'])
heat_data = [[row['Latitude'], row['Longitude']]
for index, row in heat_df.iterrows()]
# create heatmap based on the coordiates in the data frame
HeatMap(heat_data, radius=5, blur=10, max_val=1,
min_opacity=0.5).add_to(my_map)
my_map.save('chicagoMKcluster.html')
from sklearn.metrics import r2_score
# ## 因子分析
# In[54]:
# 经纬度
import folium
from folium.plugins import HeatMap
geo_center = [24.5580803, 118.0747703]
geo_map = folium.Map(location=geo_center, zoom_start=13.5)
heatdata = ori_df[['纬度', '经度', '单价']].values.tolist()
HeatMap(heatdata,
radius=7).add_to(geo_map) # gradient={.4:'blue',.65:'yellow',1:'red'}
geo_map
# ### 从重要性分析可看出，经纬度分布是影响房价的最主要因素，即“地段”为影响房价的强因子
# ### 通过房价地理热图，可发现：高房价房源主要分布在思明区及湖里区，并且处在厦门市中心区域，其中房价最高的区域落在湖里区“厦门市软件园”地带
# ### 另外，集美区及海沧区高房价区有着沿海、靠近市中心等特点
# In[76]:
# 年份与房价分布关系
ori_df.sort_values(by='年份', ascending=True, inplace=True)
plt.figure(figsize=(18, 9))
cmap = sns.cubehelix_palette(rot=-.2, as_cmap=True)
fig = sns.scatterplot(x="年份",
y="单价",
! pip install folium -t "/tmp" > /dev/null 2>&1
import pandas as pd
import folium
from folium.plugins import HeatMap
ds = datasets[0]
ds = ds.fillna(0)
#Assign Map Attributes
m = folium.Map(location=[37.700, -122.39],
title="Geographic Sales",
height=500,
center_lat=37.700,
center_lng=-122.39,
dot_size=.001,
dot_opacity=.9,)
# Conver lat and lon to numpy array (old method: .as_matrix())
points_array = ds[['lat', 'lng']].values
HeatMap(points_array, min_opacity=0.3, radius=20).add_to(m)
m
# heat_df = df_acc[['Latitude', 'Longitude']]
# heat_df = heat_df.dropna(axis=0, subset=['Latitude','Longitude'])
# heat_data = [[row['Latitude'],row['Longitude']] for index, row in heat_df.iterrows()]
# #print(heat_data)
# read a csv file of chicago crimes
fd = open('Chicago_Crimes_2012_to_2017.csv', 'r')
csvReader = csv.reader(fd, delimiter=',')
next(csvReader)
# create two lists recording all the coordinates of day and night
heat_data_day = []
heat_data_night = []
for row in csvReader:
if(row[20] != "" and row[21] != ""):
lat = float(row[20])
lon = float(row[21])
if(selectDay(row[3])):
heat_data_day.append([lat,lon])
else:
heat_data_night.append([lat,lon])
fd.close()
HeatMap(heat_data_day, radius = 12, blur = 18, max_val = 20, min_opacity = 0.5).add_to(my_map_day)
HeatMap(heat_data_night, radius = 12, blur = 18, max_val = 20, min_opacity = 0.5).add_to(my_map_night)
# save the files as html
my_map_day.save('visualization/heatmapDaytime.html')
my_map_night.save('visualization/heatmapNight.html')
def test_heat_map_exception():
with pytest.raises(ValueError):
HeatMap(np.array([[4, 5, 1], [3, 6, np.nan]]))
with pytest.raises(Exception):
HeatMap(np.array([3, 4, 5]))
# # Viewing the Districts that have the Highest Crime Rates
# In[22]:
crime = df.groupby(['DISTRICT', 'STREET', 'REPORTING_AREA', 'Lat',
'Long']).sum().reset_index()
# In[24]:
crime.update(crime['DISTRICT'].map('District:{}'.format))
crime.update(crime['REPORTING_AREA'].map('Reports:{}'.format))
# In[25]:
m2 = folium.Map(location=boston, tiles='stamentoner', zoom_start=13)
HeatMap(data=crime[['Lat', 'Long']], radius=15).add_to(m2)
# marking crime scenes
def plotDot(point):
folium.CircleMarker(location=[point.Lat, point.Long],
radius=5,
weight=2,
popup=[point.DISTRICT, point.REPORTING_AREA],
fill_color='#000000').add_to(m2)
crime.apply(plotDot, axis=1)
m2.fit_bounds(m2.get_bounds())
m2
import pandas as pd
import matplotlib.pyplot as plt
#%matplotlib inline
df1=pd.read_csv("school.csv")
def generateBaseMap(default_location=[28.4595, 77.0266], default_zoom_start=12):
base_map = folium.Map(location=default_location, control_scale=True, zoom_start=default_zoom_start)
return base_map
from folium.plugins import HeatMap
df1 = df1[df1['Latitude']>=1].copy()
df1['count'] = 1
base_map3 = generateBaseMap()
HeatMap(data=df1[['Latitude', 'Longitude', 'count']].groupby(['Latitude', 'Longitude']).sum().reset_index().values.tolist(), radius=8, max_zoom=13).add_to(base_map3)
log = file['log']
# n: entries people number for this stop (All 7 months' summary)
n = file['n']
# For loop to merge
for j in range(len(lat)):
# Child list to store object[lat,log,count]
child = []
# Add object into child list
child.append(lat[j])
child.append(log[j])
child.append(n[j] / 2100)
# Add child list into total data list
data.append(child)
# Folium itself function to define a map with a view lat/log and map zoom level
m = folium.Map([42.3, -71.1], tiles='stamentoner', zoom_start=11)
# Add all the point data into map function
HeatMap(data).add_to(m)
mkdir('/Users/Eddy/Desktop/Python_MBTA/Step4_heatmap/')
# Save the created map into a html file
m.save('/Users/Eddy/Desktop/Python_MBTA/Step4_heatmap/heatmap' +
str(i % 24) + '_' + str((i + 2) % 24) + '.html')
map[d] = map[d] + 1
else:
map[d] = 1
print(len(map.keys()))
loc_count = list()
def parse_key(map):
for key in map.keys():
temp = list()
temp_key = key.split(sep=" ")
lon = float(temp_key[1])
lat = float(temp_key[-1].split("]")[0])
temp.append(lon)
temp.append(lat)
temp.append(map[key])
loc_count.append(temp)
return loc_count
loc_count = parse_key(map)
loc_count = np.array(loc_count)
print(np.mean(loc_count, axis=0))
import pandas as pd
import folium
from folium.plugins import HeatMap
map_osm = folium.Map(location=[31.14, 121.5], zoom_start=5) #绘制Map，开始缩放程度是5倍
HeatMap(loc_count).add_to(map_osm) # 将热力图添加到前面建立的map里
file_path = r"./people.html"
map_osm.save(file_path) # 保存为html文件
import pandas as pd
import folium
import os
import sys
from folium.plugins import HeatMap
# Read map data from CSV
map_data = pd.read_csv("world.csv")
# Find highest purchase amount
max_amount = float(map_data['s'].max())
# Makes a new map centered on the given location and zoom
startingLocation = [30, 35] # EDIT THIS WITH YOUR CITIES GPS COORDINATES!
hmap = folium.Map(location=startingLocation, zoom_start=7)
# Creates a heatmap element
hm_wide = HeatMap( list(zip(map_data.lat.values, map_data.lon.values, map_data.s.values)),
min_opacity=0.3,
max_val=max_amount,
radius=20, blur=15,
max_zoom=1)
# Adds the heatmap element to the map
hmap.add_child(hm_wide)
# Saves the map to heatmap.hmtl
hmap.save(os.path.join('.', 'heatmap.html'))
folium.GeoJson(data=gj8,
style_function=lambda feature:{
'fillColor': '#D2DD30',
'color': '#5F6500',
'weight': 0.4,
}
).add_to(opsp)
tree = folium.map.FeatureGroup(name='Trees')
dTree=[]
for feature in dgj12['features']:
if feature['geometry']['type'] == 'Point':
loc = feature['geometry']['coordinates']
dTree.append(loc)
#npTree=np.array(dTree)
HeatMap(data=dTree,radius=15).add_to(tree)
'''comers.add_to(folium_map)
rdl.add_to(folium_map)
plaza.add_to(folium_map)
plt.add_to(folium_map)
uni.add_to(folium_map)
#bft.add_to(folium_map)
#sw.add_to(folium_map)
mts.add_to(folium_map)
bus.add_to(folium_map)
bike.add_to(folium_map)'''
opsp.add_to(folium_map)
tree.add_to(folium_map)
folium.LayerControl().add_to(folium_map)
import folium
import webbrowser
from folium.plugins import HeatMap
posi = pd.read_csv(
'DistrictMap.csv') # Get seismic data stored on your computer
#print (posi)
num = 7 ## assuming to start from 0 so total num+1 data points here
lat = np.array(posi['lat'][0:num + 1]) # Get latitude value
lon = np.array(posi['lon'][0:num + 1]) # Get longitude value
js = np.array(posi['Confirmed'][0:num + 1],
dtype=float) # Convert to numpy floating point data
#print(js)
data1 = [[lat[i], lon[i], js[i]]
for i in range(num + 1)] # Specification data format
map_osm = folium.Map(location=[23.8103, 90.412],
zoom_start=7,
tiles='Stamen Terrain',
control_scale=True)
HeatMap(data1).add_to(map_osm) # Add heat map to the previously created map
#print(data1)
#map_osm
file_path = 'E:\Corona\test2.html'
print(data1)
map_osm.save(file_path) # Save as html file
webbrowser.open(file_path) # Default browser open
map_osm