def show_acce(map_data_p, lat0_p, lng0_p, vehicleplate_number_p, num1_p,
acceleration_data_p, lat_p, lng_p, location_time_p,
file_out_path_p):
m = folium.Map([lat0_p, lng0_p], zoom_start=8)
m.add_child(folium.LatLngPopup()) #在地图上显示经纬度；
route = folium.PolyLine( #polyline方法为将坐标用线段形式连接起来
map_data_p, #将坐标点连接起来
weight=3, #线的大小为3
color='blue', #线的颜色为橙色
opacity=0.8 #线的透明度
).add_to(m) #将这条线添加到刚才的区域m内
for i in range(num1_p):
if i != 0 and i % 59 == 0:
y_data = [acceleration_data_p[j] for j in range(i - 59, i + 1, 1)]
vis = vincent.Line(y_data, width=320, height=150)
vis.axis_titles(x=location_time_p[i - 59] + '至' +
location_time_p[i] + '的加速度变化',
y='单位：m/s^2')
vis_json = vis.to_json()
tooltip = location_time_p[i - 59] + '至' + location_time_p[i]
status = 0
for k in range(i - 59, i + 1):
if acceleration_data_p[k] > 3 or acceleration_data_p[
k] < -3: #判断加速度
status = 1
if status == 1:
folium.Marker(
location=[lat_p[i], lng_p[i]],
popup=folium.Popup(max_width=3250).add_child(
folium.Vega(vis_json, width=380, height=200)),
icon=folium.Icon(color='red', icon='info-sign'),
tooltip=tooltip).add_to(
m
) #在每60条记录处显示一个标记点,且点击标记点可以看到过去60条记录内车辆的加速度变化折线图速度变化折线图
else:
folium.Marker(
location=[lat_p[i], lng_p[i]],
popup=folium.Popup(max_width=3250).add_child(
folium.Vega(vis_json, width=380, height=200)),
tooltip=tooltip).add_to(
m
) #在每60条记录处显示一个标记点,且点击标记点可以看到过去的60条记录内车辆的加速度变化折线图速度变化折线图
html_path = os.path.join('r', file_out_path_p,
vehicleplate_number_p + '_acceleration.html')
m.save(html_path) #将结果以HTML形式保存
webbrowser.open(html_path, new=1)
def make_line_chart_popup(data_row:pd.Series, title:str) -> folium.Popup:
'''Create a line chart popup from temporal Series for departements
Index of the Series have to be in {year}_median, {year}_decile1, {year}_decile9, {year+1}_median, {year+1}_decile1... format
this popup can be added in map layers'''
# filter index names and build 3 columns from one(series)
data = {
'decile_1': data_row.filter(regex=".*decile_1$").values,
'decile_9': data_row.filter(regex=".*decile_9$").values,
'median': data_row.filter(like="median").values,
}
df_to_display = pd.DataFrame.from_dict(data)
data_row = data_row.drop("color")
# create index of the dataframe from the inital data_row Series.index
df_to_display.index = pd.to_datetime(list(dict.fromkeys([int(annee_c[:4]) for annee_c in data_row.index.tolist()])), format="%Y")
line_chart = vincent.Line(df_to_display,
width=300,
height=200)
line_chart.axis_titles(x='Année', y='prix m2')
line_chart.legend(title=title)
popup = folium.Popup()
folium.Vega(line_chart, width = 400, height=250).add_to(popup)
return popup
def map_plot(file):
# generate a new map
folium_map = folium.Map(location=[43.6532, -79.3832],
zoom_start=13,
tiles="Stamen Terrain")
for index, row in file.iterrows():
#Defining marker colours.
if int(row['actual_height']/3) <= int(row['ht_height']/3):
color="#4daf4a"
else:
color="#fc8d62"
# Create vincent chart and popup.
data = [int(row["ht_height"]/3), int(row["actual_height"]/3),int(row["nearest_neigh"]/3),int(row["rand_forest"]/3)]
ind = ['By-law Limit', 'Actual', 'Nearest Buildings', 'Random Forest']
df = pd.DataFrame (data, index = ind)
bar_chart = vincent.Bar(df,
width=350,
height=300)
bar_chart.axis_titles (x='', y='Number of Floors')
bar_chart.colors(brew='Set3')
bar_chart.scales['x'].padding = 0.2
bar_json = bar_chart.to_json()
bar_dict = json.loads(bar_json)
popup = folium.Popup(max_width=400)
folium.Vega(bar_dict, height=350, width=400).add_to(popup)
folium.CircleMarker(location=(row["lat"],row["long"]),popup=popup, color=color, radius=5, fill=True ).add_to(folium_map)
return folium_map
def visualMap(self):
mdCoords = [39.38, -77.36] # Frederick County MD GPS coordinates
allScores = self.score2Json()
map = folium.Map(location=[mdCoords[0], mdCoords[1]],
zoom_start=10,
control_scale=True,
prefer_canvas=True,
disable_3d=True)
meanScores = allScores[0]
folium.Choropleth(geo_data=self.gdf,
name='choropleth',
data=meanScores,
columns=['School', 'Mean Score'],
key_on='feature.properties.School',
fill_color='YlGnBu',
legend_name='Mean sentiment score',
na_fill_color='white',
na_fill_opacity=0.2,
fill_opacity=0.7,
line_weight=0.6,
line_opacity=0.2).add_to(map)
scores = allScores[1]
schools = list(scores.keys())
coords = allScores[2]
for school, coord in zip(schools, coords):
chart = self.json2PieChart(scores[school], school)
vega = folium.Vega(chart, width=200, height=100)
pop_up = folium.Popup(max_width=400).add_child(vega)
icon = folium.Icon(color='blue', icon='info-sign')
folium.Marker(location=[coord[0], coord[1]],
popup=pop_up,
icon=icon).add_to(map)
return map
def create_map(df, col1):
regions_geo = 'regions.geojson'
df1 = df.groupby('Region').size()
map_1 = folium.Map(location=[52.958, 0.7], zoom_start=7)
map_1.choropleth(geo_path=regions_geo,
data=df1,
columns=['region_code', 'Size'],
key_on='properties.region_code',
fill_color='BuPu',
fill_opacity=0.7,
line_opacity=0.2,
legend_name='No. of people from region in census')
for region in df['Region'].unique().tolist():
df1 = df[df['Region'].str.contains(region)]
bar = vincent.Bar(df1[col1].value_counts(), width=350, height=250)
xtitle = col1 + "in: " + regions.get(region)
bar.axis_titles(x=xtitle, y='')
loc = coordinates.get(region)
popup1 = folium.Popup(max_width=800, ).add_child(
folium.Vega(bar, width=400, height=300))
folium.RegularPolygonMarker(loc,
fill_color='#43d9de',
radius=12,
popup=popup1).add_to(map_1)
return map_1
def popups(id):
labels=["Total Confirmed cases","Cured/Discharged/Migrated","Death"]
sizes=[df_list[0]["Total Confirmed cases (Including 111 foreign Nationals)"][id],df_list[0]["Cured/Discharged/Migrated"][id],df_list[0]["Death"][id]]
data={labels[0]:sizes[0],labels[1]:sizes[1],labels[2]:sizes[2]}
a=vincent.Pie(data,width=500,height=300)
a.legend(state["Name"][i])
popup=folium.Popup(width=550,height=100)
folium.Vega(a).add_to(popup)
return popup
def add_air_station_marker_with_graph(self, data, *args, **kwargs):
"""Esta función se encarga de agregar graficos al los marcadores existentes"""
locations = data.iloc[:, [1, 2]]
station_names = data.iloc[:, [3]]
station_ids = list(pd.unique(data.id))
station_groups = data.groupby('id')
for station_id in station_ids:
filtered_data = station_groups.get_group(station_id)
print('***** data get group')
print(type(filtered_data))
print(filtered_data.head())
plot_data = filtered_data.groupby(
['magnitude', 'year', 'month', 'day']).agg({
'value': 'mean'
}).reset_index()
print('***** data plot data')
print(type(plot_data))
print(plot_data.head())
x = [int(hour) for hour in list(plot_data['day'])]
y = [int(value) for value in list(plot_data['value'])]
print(x)
print(y)
xy_values = {
'x': x,
'y': y,
}
scatter_chart = vincent.Scatter(xy_values,
iter_idx='x',
width=600,
height=300)
scatter_chart.axis_titles(x='Día',
y='Promedio Dióxido de Nitrogeno día')
popup_scatter_plot = folium.Popup(max_width=900).add_child(
folium.Vega(scatter_chart, height=350, width=700))
air_quality_station = [
filtered_data.iloc[0, 1], filtered_data.iloc[0, 2]
]
print(air_quality_station)
station_name = [filtered_data.iloc[0, 3]]
print(station_name)
folium.Marker(
location=air_quality_station,
tooltip=folium.Tooltip(
f'Estación: {station_name[0]}<br>Latitud: {round(air_quality_station[0], 4)}<br>Longitud: {round(air_quality_station[1], 4)}'
),
popup=popup_scatter_plot,
icon=folium.CustomIcon(icon_image='icons/forecast.png',
icon_size=(40, 40))).add_to(self._map)
def createTSMap(pos, timeSeries, zoom_start=4):
map = folium.Map(location=pos.items(), zoom_start=4,crs='EPSG4326')
df = timeSeries;
df.index = df.index.values.astype('M8[D]')
chart = vincent.Line(df[['evi','ndvi']],width=300,height=150)
chart.legend(title='')
chart.axis_titles(x='dates', y='')
popup = folium.Popup(max_width=400)
folium.Vega(chart.to_json(), height=200, width=450).add_to(popup)
folium.Marker(pos.items(), popup=popup,icon=folium.Icon(color='green',icon='info-sign')).add_to(map)
wms = folium.features.WmsTileLayer('https://neo.sci.gsfc.nasa.gov/wms/wms',
name='MODIS Data',
format='image/png',
layers='MOD13A2_M_NDVI')
wms.add_to(map)
return map
def create_map(dataf, year):
state_geo = os.path.join('BIN/data/us-states.json')
with open('BIN/data/location_state.txt', 'r') as rd:
f = rd.read()
location = eval(f)
with open('BIN/data/states.txt', 'r') as rd:
f = rd.read()
states = eval(f)
prd = pd.read_csv('BIN/data/predictions_in_states.csv', index_col=0)
# Initialize the map:
m = folium.Map(location=[52, -112], zoom_start=3)
df = pd.read_csv('BIN/data/scores.csv', index_col=0)
# Add the color for the chloropleth:
m.choropleth(geo_data=state_geo,
name='choropleth',
data=df,
columns=['States', 'R^2'],
fill_color='GnBu',
key_on='feature.id',
fill_opacity=0.8,
line_opacity=0.6,
legend_name='R^2 prediction')
folium.LayerControl().add_to(m)
m.add_child(folium.LatLngPopup())
for s in states:
if s == 'NH' or s == 'WI' or s == 'WV' or s == 'DC':
continue
folium.RegularPolygonMarker(
[location[s][0], location[s][1]],
fill_color='#43d9de',
radius=4,
popup=folium.Popup(max_width=450).add_child(
folium.Vega(build_json_plot(dataf, prd, state=s),
width=250,
height=250))).add_to(m)
m.save('BIN/data/map.html')
return (m)
def get_polygons_layer(self,
clus_polygon_gdf,
clus_category_df,
layer_name=None,
marker_color='#43d9de'):
clus_polygon_gdf_json = clus_polygon_gdf.to_json()
layer = folium.GeoJson(clus_polygon_gdf_json, name=layer_name)
taret_idx = clus_polygon_gdf.index
target_df = clus_category_df.loc[taret_idx, :]
for idx in taret_idx:
folium.RegularPolygonMarker(
list(clus_polygon_gdf.loc[idx, 'centroid'])[::-1],
fill_color=marker_color,
radius=6,
popup=folium.Popup(max_width=400).add_child(
folium.Vega(self.get_vincent_bar_chart(target_df, idx),
width=400,
height=270))).add_to(layer)
return layer
def add_marker(self, serie, longitude, latitude, arrondissement):
liste_annee = []
liste_valeur = []
for annee in serie.index:
liste_annee.append(annee)
for valeur in serie.values:
liste_valeur.append(valeur)
scatter_points = {
'x': liste_annee,
'prix au m2': liste_valeur,
}
scatter_chart = vincent.Bar(scatter_points,
iter_idx='x',
width=300,
height=200)
scatter_chart.axis_titles(x='Année', y='Prix au m2')
scatter_chart.legend(title=arrondissement)
popup = folium.Popup()
folium.Vega(scatter_chart, width=400, height=250).add_to(popup)
folium.Marker([longitude, latitude], popup=popup).add_to(self.map)
def plotdataPopVega(data,vals):
'''
Fuction to create a data popup, as a time servies.
What then can be added to a marker
'''
df=data[vals]
df.fillna(value='null', inplace=True) # Does not handle missing values.
line=vincent.Line(df)
line.axis_titles(x="Time", y="Mass Concentration")
line.legend(title="Values")
#find the lenght of the data
width=len(df.index)
if width <500:
width=400
line.width=width
line.height=200
vega = folium.Vega(json.loads(line.to_json()), width="30%", height="10%")
popup = folium.Popup(max_width=line.width+75).add_child(vega)
return popup
def plot_bar_graphs(df_years, html_link, name, color):
print('>plotting map...')
listOfMedians = prepare_for_data(df_years)
barGraphMap = Map(location = latlng, zoom_start = 10, tiles = 'Stamen Terrain')
for x,y,df,file,station,ttip in zip(coords['Lat_DD'], coords['Long_DD'], listOfMedians, file_names, coords['Station Number'], coords['Station label']):
bar = vincent.GroupedBar(df)
bar.axis_titles(x = "Index", y = name)
bar.width = 900
bar.height = 250
bar.legend(title='Year')
bar.to_json(file)
vis1 = os.path.join('/Users/fetch/Desktop/ProjectDirectory', file)
folium.Marker(location=[x,y],
icon = DivIcon(
icon_size=(28,25),
icon_anchor=(7, 20),
html='<div style="font-family: Rockwell; font-size: 9pt; color :#00664b">'+str(station)+'</div>',
),
popup=folium.Popup(max_width=1100).add_child(
folium.Vega(json.load(open(vis1)), width= 1000, height=300))
).add_to(barGraphMap)
barGraphMap.add_child(folium.CircleMarker([x,y], fill_color = color, color = color, radius = 22, tooltip = ttip))
barGraphMap.save(html_link+'.html')
import folium
import os
import json
#create a map object
m = folium.Map(location=[12.971599, 77.594566], zoom_start=12)
# vega data - load from json file
vis = os.path.join('datasets', 'vis.json')
folium.Marker([12.97, 77.69],
popup="<strong>Location 1</strong>",
tooltip="Click for more Info").add_to(m)
folium.Marker([13.03, 77.55],
popup="<strong>Location 2</strong>",
tooltip="Click for more Info",
icon=folium.Icon(icon='cloud')).add_to(m)
folium.Marker(
[12.92, 77.67],
popup=folium.Popup(max_width=450).add_child(
folium.Vega(json.load(open(vis)), width=450, height=250)),
).add_to(m)
# generate html file corresponding this map
m.save('map.html')
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
###############################################################################
import json
import folium
buoy_map = folium.Map(location=[46.3014, -123.7390], zoom_start=7,tiles='Stamen Terrain')
popup1 = folium.Popup(max_width=800,).add_child(folium.Vega( json.load(open('/gdata/folium/data/vis1.json')), width=500, height=250))
folium.RegularPolygonMarker([47.3489, -124.708], fill_color='#43d9de', radius=12, popup=popup1).add_to(buoy_map)
###############################################################################
popup2 = folium.Popup(max_width=800,).add_child(folium.Vega(json.load(open('/gdata/folium/data/vis2.json')), width=500, height=250))
folium.RegularPolygonMarker([44.639, -124.5339], fill_color='#43d9de', radius=12, popup=popup2).add_to(buoy_map)
popup3 = folium.Popup(max_width=800,).add_child(folium.Vega(json.load(open('/gdata/folium/data/vis3.json')), width=500, height=250))
folium.RegularPolygonMarker([46.216, -124.1280], fill_color='#43d9de', radius=12, popup=popup3).add_to(buoy_map)
buoy_map.save('/tmp/folium_xx_NOAA_buoys.html')
###############################################################################
antarctic_ice_edge = '/gdata/folium/data/antarctic_ice_edge.json'
antarctic_ice_shelf_topo = '/gdata/folium/data/antarctic_ice_shelf_topo.json'
m = folium.Map(
location=[-59.1759, -11.6016],
tiles='Mapbox Bright',
zoom_start=2
)
folium.GeoJson(
antarctic_ice_edge,
name='geojson'
).add_to(m)
folium.TopoJson(
open(antarctic_ice_shelf_topo),
'objects.antarctic_ice_shelf',
name='topojson'
).add_to(m)
}, {
"domain": {
"data": "table",
"field": "data.col"
},
"name": "color",
"range": "category20",
"type": "ordinal"
}],
"width":
400
}
#print(data)
marker_data.append(json.dumps(data))
#print('done')
get_data()
m = folium.Map(
location=[59.3242, 18.0659], # Stockholm
zoom_start=12)
for data in marker_data:
folium.Marker(location=[get_lat(), get_lon()],
popup=folium.Popup(max_width=450).add_child(
folium.Vega(data, width=450, height=250))).add_to(m)
#m
m.save('map.html')
def map_chloro(index_layer, index_plot, min_plot, output_folder):
"""
Create a Leaflet chloropeth map with Folium and Vega libraries. Each country has his own plot for the given data.
:param index_layer: index of the data in the DATA_PATH csv
:type index_layer: int
:param index_plot: index of the data in the DATA_PATH csv
:type index_plot: int
:param min_plot: the minimal value of y axes for which the data will be process
:type min_plot: float
:param output_folder: name of the folder where to save the map
:type output_folder: str
:return: nothing
:rtype: None
"""
print('Loading capitals data...')
# open the json file with the json data of capitals
capitals_json = open('utilitaires/data/data_capitals.geojson')
capitals = json.load(capitals_json)
print("Loading countries data...")
# open the json file with dates of quarantine
country_json = open('utilitaires/data/data_country.geojson')
pays = json.load(country_json)
# gestion des titres pour la couche des pays
if index_layer == 2:
titre_layer = "Nouveaux cas recensés dans la journée d hier"
titre_fichier = "nouveaux_cas"
elif index_layer == 3:
titre_layer = "Nouveaux décès recensés dans la journée d hier"
titre_fichier = "nouveaux_deces"
elif index_layer == 4:
titre_layer = "Nombre de cas total recensés"
titre_fichier = "cas_total"
elif index_layer == 5:
titre_layer = "Nombre de décès total recensés"
titre_fichier = "deces_total"
elif index_layer == 6:
titre_layer = "Nombre de cas recensés la veillepour 10000 habitants"
titre_fichier = "nouveaux_cas_10000"
elif index_layer == 7:
titre_layer = "Nombre de décès recensés la veille pour 10000 habitants"
titre_fichier = "nouveaux_deces_10000"
elif index_layer == 8:
titre_layer = "Nombre total de cas pour 10000 habitants"
titre_fichier = "cas_total_10000"
elif index_layer == 9:
titre_layer = "Nombre total de décès pour 10000 habitants"
titre_fichier = "deces_total_10000"
# gestion des titres pour les graphiques
if index_plot == 2:
x_plot = "Jours depuis le premier jour ayant " + str(
min_plot) + " nouveaux cas recensés la veille"
y_plot = "Nombre de nouveaux cas recensés la veille"
elif index_plot == 3:
x_plot = "Jours depuis le premier jour ayant " + str(
min_plot) + " nouveaux décès recensés la veille"
y_plot = "Nombre de nouveaux décès recensés la veille"
elif index_plot == 4:
x_plot = "Jours depuis un total de " + str(min_plot) + " cas"
y_plot = "Nombre de cas recensés "
elif index_plot == 5:
x_plot = "Jours depuis un total de " + str(min_plot) + " décès"
y_plot = "Nombre de décès recensés "
elif index_plot == 6:
x_plot = "Jours depuis le premier jour ayant " + str(
min_plot) + " nouveaux cas recensés la veille pour 10000 habitants"
y_plot = "Nombre de nouveaux cas recensés la veille pour 10000 habitants"
elif index_plot == 7:
x_plot = "Jours depuis le premier jour ayant " + str(
min_plot
) + " nouveaux décès recensés la veille pour 10000 habitants"
y_plot = "Nombre de nouveaux décès recensés la veille pour 10000 habitants"
elif index_plot == 8:
x_plot = "Jours depuis un total de " + str(
min_plot) + " cas pour 10000 habitants"
y_plot = "Nombre de cas recensés pour 10000 habitants"
elif index_plot == 9:
x_plot = "Jours depuis un total de " + str(
min_plot) + " décès pour 10000 habitants"
y_plot = "Nombre de décès recensés pour 10000 habitants"
print("Processing your given dataset...")
# création d'un fichier csv temporaire contenant les données pour la carte selon l'index_layer
with open(DATA_PATH, 'r') as f:
f_o = csv.reader(f)
next(f_o)
with open('data.csv', "w") as f_csv:
writer = csv.writer(f_csv)
writer.writerow(["Country", "Chiffre"])
for line in f_o:
# data.csv prend en valeur la date de la veille avec le nombre correspondant, hors World
if line[0] == str(YESTERDAY_CUT) and line[1] != 'World':
writer.writerow([line[1], line[index_layer]])
# ouverture et lecture du fichier csv créé
data = f'data.csv'
world_data = pd.read_csv(data)
print("Creating the countries's layer of the map...")
# initialisation de la carte et des paramètres généraux
map = folium.Map(location=[48, 0], zoom_start=3)
# création du fonds de carte coloré en fonction des valeurs de la veille
folium.Choropleth(geo_data=pays,
name='choropleth',
data=world_data,
columns=['Country', 'Chiffre'],
key_on='feature.id',
fill_color='OrRd',
fill_opacity=0.7,
line_opacity=0.2,
legend_name=titre_layer).add_to(map)
print("Processing plots for each country...")
# création d'un point par pays (sur la capitale) pour accueillir les graphiques ensuite
for pays in capitals["features"]:
# création d'une liste vide qui accueillera un dictionnaire par jour pour le pays donné
data_country = []
# création d'un booléen pour savoir si le pays aura un graphique
data_plot = False
with open(DATA_PATH, 'r') as f_e:
f_o = csv.reader(f_e)
next(f_o)
# la variable i permet non pas d'afficher le jour précis sur l'axe x, mais le numéro du jour en fonction
# de la réalisation du paramètre min_plot
i = 1
for line in f_o:
if pays['properties']['country'] == line[1] and float(
line[index_plot]) >= float(min_plot):
dico = {
"col": "Nombre",
"idx": i,
"val": float(line[index_plot])
}
data_country.append(dico)
i += 1
data_plot = True
# créer le fichier json nécessaire à Vega pour faire les graphiques
if data_plot:
with open('country.json', "w") as f:
data = {
"axes": [{
"scale": "x",
"title": x_plot,
"type": "x"
}, {
"scale": "y",
"title": y_plot,
"type": "y",
"grid": True
}],
"data": [{
"name": "table",
"values": data_country
}],
"height":
400,
"legends": [],
"marks": [{
"from": {
"data": "table",
"transform": [{
"keys": ["data.col"],
"type": "facet"
}]
},
"marks": [{
"properties": {
"enter": {
"stroke": {
"field": "data.col",
"scale": "color"
},
"strokeWidth": {
"value": 2
},
"x": {
"field": "data.idx",
"scale": "x"
},
"y": {
"field": "data.val",
"scale": "y"
}
}
},
"type": "line"
}],
"type":
"group"
}],
"padding":
"auto",
"scales": [{
"domain": {
"data": "table",
"field": "data.idx"
},
"name": "x",
"range": "width",
"nice": True
}, {
"domain": {
"data": "table",
"field": "data.val"
},
"name": "y",
"nice": True,
"range": "height"
}, {
"domain": {
"data": "table",
"field": "data.col"
},
"name": "color",
"range": "category20",
"type": "ordinal"
}],
"width":
800
}
# écriture du dictionnaire dans un fichier json
f.write(json.dumps(data))
# ouverture et lecture de ce nouveau fichier json...
with open('country.json', 'r') as f:
data_json = json.load(f)
custom_icon = folium.features.CustomIcon(
icon_image='utilitaires/img/coro.png', icon_size=(14, 14))
# ... pour le donner à folium.Vega dans le marker placé sur la capitale de chaque pays
folium.Marker(location=pays['geometry']['coordinates'],
popup=folium.Popup(max_width=900).add_child(
folium.Vega(data_json, width=900, height=450)),
icon=custom_icon).add_to(map)
os.remove('country.json')
print("Creating the HTML file...")
folium.LayerControl().add_to(map)
os.mkdir(output_folder)
map.save(output_folder + '/map_' + titre_fichier + '.html')
country_json.close()
capitals_json.close()
os.remove('data.csv')
# -*- coding: utf-8 -*-
print('=' * 40)
print(__file__)
from helper.textool import get_tmp_file
################################################################################
import folium
import json
map_a = folium.Map(location=[46.3014, -123.7390],
zoom_start=7,
tiles='Stamen Terrain')
popup1 = folium.Popup(max_width=800, ).add_child(
folium.Vega(json.load(open('/gdata/folium/data/vis1.json')),
width=500,
height=250))
folium.RegularPolygonMarker([47.3489, -124.708],
fill_color='#ff0000',
radius=12,
popup=popup1).add_to(map_a)
################################################################################
popup2 = folium.Popup(max_width=800, ).add_child(
folium.Vega(json.load(open('/gdata/folium/data/vis2.json')),
width=500,
height=250))
folium.RegularPolygonMarker([44.639, -124.5339],
fill_color='#00ff00',
radius=12,
'x': segment['Distance / km'],
'y': segment['elevation'],
}
WIDTH = 400
HEIGHT = 200
line = vincent.Line(data, iter_idx="x", width=WIDTH, height=HEIGHT)
line.axis_titles(x='Distance / km', y='Elevation / m')
line.x_axis_properties(title_offset=2)
line.y_axis_properties(title_offset=-10)
line_json = line.to_json()
line_dict = json.loads(line_json)
popup = folium.Popup(max_width=WIDTH + 50, show=True)
chart = folium.Vega(line_dict, width=WIDTH + 50, height=HEIGHT + 50)
chart.add_to(popup)
marker = folium.Marker(
location=segment['latlon'][idx],
popup=popup,
icon=folium.Icon(icon='star'),
)
marker.add_to(the_map)
# To store the map as a HTML page:
# the_map.save('map_006_chart1.html')
# To display the map in a Jupyter notebook:
the_map
def create_map(results, cities, statename, time):
## Gets the geoJSON for state and cities calling functions
transformCities(cities)
transformStates(statename)
## Creates the pup-up graph for the city results using vincent package
## looping over the results dictionary
for cityid, df in results.iteritems():
line = vincent.Line(df[['@realDonaldTrump', '@HillaryClinton']])
line.axis_titles(x='date', y='normalized weighted composite score')
line.legend(title='Queries')
line.width=400
line.height=200
line.axes[0].properties = AxisProperties(
labels=PropertySet(angle=ValueRef(value=45),
align=ValueRef(value='left')))
line.colors(brew='Set1')
line.to_json('data/'+cityid+time+'.json')
## Creates the map outline
m = folium.Map([34.569728, -106.119447], tiles="Mapbox Bright", zoom_start=5, min_zoom=5)
fg = folium.map.FeatureGroup().add_to(m)
## Adds the states looping over the transformed GeoJSON file
## and sets colour based on time string 'after' for states of
## California and Texas
geo_json_states = json.load(open('data/us_states/us_states.json'))
for feature in geo_json_states['features']:
if time == "after" and feature['properties']['NAME'] == 'California':
fg.add_child(MultiPolygon(_locations_mirror(feature['geometry']['coordinates']),
color='blue', weight=0))
elif time == "after" and feature['properties']['NAME'] == 'Texas':
fg.add_child(MultiPolygon(_locations_mirror(feature['geometry']['coordinates']),
color='red', weight=0))
else:
fg.add_child(MultiPolygon(_locations_mirror(feature['geometry']['coordinates']),
color='grey', weight=0))
## Add the cities layer looping over the transformed GeoJSON file
geo_json_cities = json.load(open('data/us_cities/us_cities.json'))
for feature in geo_json_cities['features']:
## Sets colour variable based on most positive nwcs in the analysis
## timeframe
places = open('data/' + cities, 'r')
for line in places:
attr = line.split(";")
if attr[0] == feature['properties']['NAME10']:
city_id = attr[1]
if sum(results[city_id]['@realDonaldTrump']) > sum(results[city_id]['@HillaryClinton']):
colour = 'red'
else:
colour = 'blue'
## Adds the polygon with the set colour
fg.add_child(MultiPolygon(
_locations_mirror(feature['geometry']['coordinates']),
color=colour,
weight=0,
popup = folium.Popup(max_width=650).add_child(folium.Vega(json.load(open('data/'+city_id+time+'.json')),
width=620, height=270)),))
# Saves the map in html format using the string time in filename
m.save('sentiment_'+time+'.html')
title_cancel='Exit me',
force_separate_button=True).add_to(stamen)
FloatImage(url1, bottom=0.08, left=87.4).add_to(stamen)
FloatImage(url2, bottom=19, left=82.5).add_to(stamen)
FloatImage(url3, bottom=93, left=7.7).add_to(stamen)
plugins.Search(yamunanagar, search_zoom=10, geom_type='Polygon').add_to(stamen)
folium.Marker(location=[30.50666, 76.52114],
icon=icon1,
popup=folium.Popup(max_width=500).add_child(
folium.Vega(json.load(open(interpolate)),
width=500,
height=260))).add_to(stamen)
folium.CircleMarker(location=[30.445122, 76.672211],
radius=13,
fill_color='purple',
color='purple',
popup=folium.Popup(max_width=500).add_child(
folium.Vega(json.load(open(stroke)),
width=500,
height=260))).add_to(stamen)
folium.Marker(location=[30.423809, 76.522522],
icon=icon2,
popup=folium.Popup(max_width=500).add_child(
folium.Vega(json.load(open(interpolate)),
radius=75,
location=row,
popup='This is the sensing radius',
color='#3186cc',
fill=True,
fill_color='#3186cc'
).add_to(Radii)
# test markers for vega/altair visualization:
folium.Marker(
location=[6.942236, 80.615474],
icon=folium.Icon(color='blue', icon='bar-chart',
prefix='fa'),
popup=folium.Popup(max_width=500).add_child(
folium.Vega(vis1, width=500, height=250))
).add_to(dat_grphs)
folium.Marker(
location=[6.941506, 80.619474],
icon=folium.Icon(color='blue', icon='bar-chart',
prefix='fa'),
popup=folium.Popup(max_width=500).add_child(
folium.Vega(vis2, width=500, height=250))
).add_to(dat_grphs)
folium.Marker(
location=[6.942936, 80.610974],
icon=folium.Icon(color='blue', icon='bar-chart',
prefix='fa'),
popup=folium.Popup(max_width=500).add_child(
folium.Marker([-1.307466,36.826537],
popup='<strong>Location Three</strong>',
tooltip=tooltip,
icon=folium.Icon(icon='cloud',color='red')
).add_to(myMap)
folium.Marker([-1.304481,36.823405],
popup='<strong>Location Four</strong>',
tooltip=tooltip,
icon=customIcon
).add_to(myMap)
# 1.303683, 36.824670
folium.Marker([-1.303683,36.824670],
popup=folium.Popup(max_width=450).add_child(folium.Vega(json.load(open(vis)),width=450,height=250))
).add_to(myMap)
# Encircle a region
folium.CircleMarker(
location=[-1.311509,36.814945],
radius=50,
color='#42bca',
fill=True,
popup="Qwetu Wilson View",
fill_color="#42bca"
).add_to(myMap)
#Load GeoJSON
import os
import json
import requests
import folium
url = 'https://raw.githubusercontent.com/python-visualization/folium/master/examples/data'
vis1 = json.loads(requests.get(f'{url}/vis1.json').text)
vis2 = json.loads(requests.get(f'{url}/vis2.json').text)
vis3 = json.loads(requests.get(f'{url}/vis3.json').text)
# 在图中标记三个位置
m = folium.Map(location=[46.3014, -123.7390],
zoom_start=7,
tiles='Stamen Terrain')
folium.Marker(location=[47.3489, -124.708],
popup=folium.Popup(max_width=450).add_child(
folium.Vega(vis1, width=450, height=250))).add_to(m)
folium.Marker(location=[44.639, -124.5339],
popup=folium.Popup(max_width=450).add_child(
folium.Vega(vis2, width=450, height=250))).add_to(m)
folium.Marker(location=[46.216, -124.1280],
popup=folium.Popup(max_width=450).add_child(
folium.Vega(vis3, width=450, height=250))).add_to(m)
m.save("test1.html")
chania.height = 200
platanias = vincent.StackedBar(df_platanias)
platanias.axis_titles(x='Tierarten', y='Anzahl Operationen')
platanias.legend(title='platanias')
platanias.scales['x'].padding = 0.2
platanias.colors(brew='Pastel1')
platanias.width = 350
platanias.height = 200
kreta = folium.Map(location=[35.3220, 25.1001],
tiles="Stamen Terrain",
zoom_start=8)
popup1 = folium.Popup(max_width=800, ).add_child(
folium.Vega(heraklion, width=500, height=250))
popup2 = folium.Popup(max_width=800, ).add_child(
folium.Vega(agiosNikolaos, width=500, height=250))
popup3 = folium.Popup(max_width=800, ).add_child(
folium.Vega(sitia, width=500, height=250))
popup4 = folium.Popup(max_width=800, ).add_child(
folium.Vega(kalyves, width=500, height=250))
popup5 = folium.Popup(max_width=800, ).add_child(
folium.Vega(rethymno, width=500, height=250))
popup6 = folium.Popup(max_width=800, ).add_child(
folium.Vega(chania, width=500, height=250))
import pandas as pd
import vincent
sbucks = pd.read_csv('starbucks_india.csv')
map_mumbai = folium.Map(location=[19.0760, 72.8777])
for index, row in sbucks.iterrows():
lis = [
row['Mon'], row['Tue'], row['Wed'], row['Thu'], row['Fri'], row['Sat'],
row['Sun']
]
x = range(len(lis))
bar = vincent.Bar(lis, width=440, height=200)
bar.axis_titles(x='Day of Week - ' + row['Name'], y='Foot fall')
bar.to_json('vega.json')
popup_ = folium.Popup(max_width=800).add_child(
folium.Vega(bar, width=500, height=250))
if sum(lis) > 2600:
folium.Marker([row['Latitude'], row['Longitude']],
popup=popup_,
icon=folium.Icon(color='red',
icon='star')).add_to(map_mumbai)
else:
folium.Marker([row['Latitude'], row['Longitude']],
popup=popup_,
icon=folium.Icon(color='green',
icon='star-empty')).add_to(map_mumbai)
map_mumbai.save('starbucks_india.html')
coordinates = (20.5937, 78.9629)
map1 = folium.Map(location=coordinates, zoom_start=4.5)
cases_num = df_list[0]["Total Confirmed cases*"]
# In[13]:
labels = ["Total Confirmed cases*", "Cured/Discharged/Migrated", "Death"]
sizes = [
df_list[0]["Total Confirmed cases*"][36],
df_list[0]["Cured/Discharged/Migrated*"][36], df_list[0]["Deaths**"][36]
]
data = {labels[0]: sizes[0], labels[1]: sizes[1], labels[2]: sizes[2]}
a = vincent.Pie(data, width=500, height=300)
a.legend("Covid 19 India")
popup = folium.Popup(width=500, height=100)
folium.Vega(a).add_to(popup)
# In[14]:
def popups(id):
labels = ["Total Confirmed cases", "Cured/Discharged/Migrated", "Death"]
sizes = [
df_list[0]["Total Confirmed cases*"][id],
df_list[0]["Cured/Discharged/Migrated*"][id],
df_list[0]["Deaths**"][id]
]
data = {labels[0]: sizes[0], labels[1]: sizes[1], labels[2]: sizes[2]}
a = vincent.Pie(data, width=500, height=300)
a.legend(state["Name"][i])
popup = folium.Popup(width=550, height=100)
# macro = MacroElement()
# macro._template = Template(template)
# m.get_root().add_child(macro)
# m
#End of adding legend
#Adding graph
#Graph feature
df = pd.read_csv("sample1.csv")
print(df.to_string())
# Let's create the vincent chart.
scatter_chart = vincent.Bar(df[['Concentration']], width=600,
height=300).axis_titles(x='Days',
y='Concentrations')
# Let's convert it to JSON.
scatter_json = scatter_chart.to_json()
# Let's convert it to dict.
scatter_dict = json.loads(scatter_json)
# m = folium.Map([43, -100], zoom_start=4)
# Let's create a Vega popup based on df.
popup = folium.Popup(max_width=650)
folium.Vega(scatter_json, height=350, width=650).add_to(popup)
folium.Marker([51.525211, -0.033503], popup=popup).add_to(m)
#Graph feature ends
m.save('map.html') #Render map
print('Map rendering completed')
import folium
import pandas as pd
import vincent
sbucks = pd.read_csv('starbucks_india.csv')
map_pune = folium.Map(location = [18.5204, 73.8567])
for index, row in sbucks.iterrows():
lis = [row['Mon'], row['Tue'], row['Wed'], row['Thu'], row['Fri'], row['Sat'], row['Sun']]
x = range(len(lis))
bar = vincent.Bar(lis, width=440, height=200)
bar.axis_titles(x='Day of week - ' +row['Name'], y='Foot fall')
bar.to_json('vega.json')
popup_ = folium.Popup(max_width=800).add_child(folium.Vega(bar, width=500, height=250))
if sum(lis) > 2600:
folium.Marker([row['Latitude'], row['Longitude']], popup=popup_,icon=folium.Icon(color='red', icon='star')).add_to(map_pune)
else:
folium.Marker([row['Latitude'], row['Longitude']], popup=popup_,icon=folium.Icon(color='green', icon='star-empty')).add_to(map_pune)
map_pune.save('starbucks_india_pune.html')
vis2 = os.path.join('data', 'vis2.json')
vis3 = os.path.join('data', 'vis3.json')
vis4 = os.path.join('data', 'vis4.json')
vis5 = os.path.join('data', 'vis5.json')
vis6 = os.path.join('data', 'vis6.json')
vis7 = os.path.join('data', 'vis7.json')
vis8 = os.path.join('data', 'vis8.json')
vis9 = os.path.join('data', 'vis9.json')
overlay = os.path.join('data', 'overlay.json')
folium.Marker([44.78964787826317, 20.4664977583781],
tooltip='Autokomanda',
icon=folium.Icon(prefix='fa', icon='bus', markerColor='red'),
popup=folium.Popup(max_width=450).add_child(
folium.Vega(json.load(open(vis9)), width=450,
height=280))).add_to(m),
folium.Marker([44.75617744172545, 20.4769512336477],
tooltip='Banjica',
icon=folium.Icon(prefix='fa', icon='bus', markerColor='blue'),
popup=folium.Popup(max_width=450).add_child(
folium.Vega(json.load(open(vis8)), width=450,
height=280))).add_to(m),
folium.Marker([44.768087391424814, 20.49229493597008],
tooltip='Brace Jerkovic',
icon=folium.Icon(prefix='fa', icon='bus', markerColor='green'),
popup=folium.Popup(max_width=450).add_child(
folium.Vega(json.load(open(vis7)), width=450,
height=280))).add_to(m),
folium.Marker([44.8157349019317, 20.491060018369023],
tooltip='Bogoslovija',
icon=folium.Icon(prefix='fa', icon='bus', markerColor='purple'),
