def get_eventbmap(pos) -> BMap:
#[(lo,la),(lo,la)]这种格式即可
BAIDU_AK = "HOTBRAfU1jGcQKHBX15ucKsfZO722eyN"
center = (117.20, 39.12)
c = BMap()
c.add_schema(
baidu_ak=BAIDU_AK,
center=[117.20, 39.12],
zoom=10,
is_roam=False,
)
sequence = []
for i in range(len(pos)):
c.add_coordinate(str(i), pos[i][0], pos[i][1]) #[(posindex,lo,la)]
sequence.append((str(i), 0.1)) #这里名字和pos要对应
c.add(
"投诉坐标", #系列名称
sequence,
type_="scatter", #"heatmap" 可以切换显示的类型热力图或散点图
label_opts=opts.LabelOpts(formatter="{b}"),
symbol_size=5)
c.set_series_opts(effect_opts=opts.EffectOpts(is_show=True),
label_opts=opts.LabelOpts(is_show=False))
c.add_control_panel(
scale_control_opts=opts.BMapScaleControlOpts(),
navigation_control_opts=opts.BMapNavigationControlOpts(),
maptype_control_opts=opts.BMapTypeControlOpts())
#c.set_global_opts(visualmap_opts=opts.VisualMapOpts())
return c
def test_bmap():
b = BMap()
b.add_schema(baidu_ak='iMplFNfYyAf4e7EleegtObtcOZdliriG',center=[116.395645,39.929986],zoom=9)
# 定义坐标对应的名称，添加到坐标库中 add_coordinate(name, lng, lat)
engine = create_engine('mysql+pymysql://root:123456@localhost:3306/house')
# 查询语句，选出employee表中的所有数据
sql = '''select * from 4sstore_beijing;'''
# read_sql_query的两个参数: sql语句， 数据库连接
df = pd.read_sql_query(sql, engine)
data_pair=[]
for index,row in df.iterrows():
b.add_coordinate(row['name'], float(row['lng']),float(row['lat']))
data_pair.append((row['name'], None))
# Geo 图类型，有 scatter, effectScatter, heatmap, lines 4 种，建议使用
# from pyecharts.globals import GeoType
# GeoType.GeoType.EFFECT_SCATTER，GeoType.HEATMAP，GeoType.LINES
# 将数据添加到地图上
b.add('bmap', data_pair, "scatter" ,symbol_size=5)
# 设置样式
b.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
b.set_global_opts(
visualmap_opts=opts.VisualMapOpts(is_piecewise=False),
title_opts=opts.TitleOpts(title="北京4s店分布")
)
return b
def bdapi():
friends = bot.friends(update=True)
dd1 = defaultdict(int) # 如果调用字典的key不存在的时候，默认value为0
for hy in friends:
# print(hy.city)
if '\u9fa5' >= hy.city >= '\u4e00': # 如果省份是汉字就保留，否则就舍弃，因为在中国地图上也不会显示
city = hy.city + "市" # 必须要加，不加会名字识别不到
dd1[city] += 1 # 每次进来如果key相同则进行累加
sj = [list(a) for a in zip(dd1.keys(), dd1.values())]
# 地图部分
BD_AK = "xC9wlIKWG31GZwZ87jFBGO6RycDZf7Ue"
MR_ZX = [108.9550557300, 34.3247934100] # 定义地图的默认中心位置
m3 = BMap()
# 进去的地图中心点 可以在网址查询 http://www.gpsspg.com/maps.htm 但是这里有一点需要注意，地图给出来的维度和经度，这里的顺序是经纬度，需要调整下
m3.add_schema(
baidu_ak=BD_AK,
center=MR_ZX,
zoom=8 # 缩放程度
)
m3.add(
series_name="",
data_pair=sj,
label_opts=opts.LabelOpts("{b}"),
)
m3.set_global_opts(
title_opts=opts.TitleOpts("百度API展示微信好友分布"),
visualmap_opts=opts.VisualMapOpts(
is_piecewise=True, max_=180,
pos_bottom=40), # 最后一个是设置提示距离底端的距离，防止百度的log将数据挡住
)
m3.render("./kshwy/map_bdapi.html")
webbrowser.open("E:\study\kshwy\map_bdapi.html", autoraise=True)
def get_bmap(pos) -> BMap:
BAIDU_AK = "HOTBRAfU1jGcQKHBX15ucKsfZO722eyN"
#center = (117.20, 39.12)
c = BMap()
c.add_schema(baidu_ak=BAIDU_AK,
center=[117.20, 39.12],
zoom=10,
is_roam=False)
sequence = []
for i in range(len(pos)):
#print(pos[i])
c.add_coordinate(pos[i][0], pos[i][2], pos[i][1])
sequence.append((str(i), 5))
c.add(
"投诉坐标", #系列名称
sequence,
type_="scatter", #"heatmap" 可以切换显示的类型热力图或散点图
label_opts=opts.LabelOpts(formatter="{b}"),
symbol_size=4 #scatter大小
)
#.add("bmap",
#[{"coord":[117.21, 39.13],"sim":10},{"coord":[117.20, 39.13],"sim":20},{"coord":[117.21, 39.12],"sim":5}],
#[list(z) for z in zip(Faker.provinces, Faker.values())],#zip将迭代对像打包成元组最后的结果是[['浙江',v1],['广东',v2]]
#[[117.21, 39.13],[117.20, 39.12],[117.22, 39.10],[117.25, 39.11],[117.19, 39.02],[117.20, 39.12]],
#type_="heatmap",
#label_opts=opts.LabelOpts(formatter="{b}"))
c.set_series_opts(
effect_opts=opts.EffectOpts(is_show=True),
label_opts=opts.LabelOpts(is_show=False),
)
c.add_control_panel(
scale_control_opts=opts.BMapScaleControlOpts(),
navigation_control_opts=opts.BMapNavigationControlOpts(),
maptype_control_opts=opts.BMapTypeControlOpts())
#copyright_control_opts=opts.BMapCopyrightTypeOpts(copyright_="我的")
#geo_location_control_opts=opts.BMapGeoLocationControlOpts()
#overview_map_opts=opts.BMapOverviewMapControlOpts(is_open=True),
#c.set_global_opts(visualmap_opts=opts.VisualMapOpts(pos_left="right"))
return c
})
coord = prepare_coord(load_street_pickle())
for s, (long, lat) in coord.items():
m.add_coordinate(s, long, lat)
speed, n_slice = read_speed()
# Match T-GCN expand 3 times... (don't know why)
idx_map = list(range(0, 114 - 12 - 3 - 1)) + list(range(114, 143 - 12 - 3))
pred_speed, n_slice1 = read_pred_speed()
assert len(idx_map) == n_slice1
m.add(series_name="actual",
type_="heatmap",
data_pair=get_data_pair(speed, 0)).add(
series_name="predict",
type_="heatmap",
data_pair=get_data_pair(pred_speed, 0),
).add_control_panel(
copyright_control_opts=opts.BMapCopyrightTypeOpts(position=3),
maptype_control_opts=opts.BMapTypeControlOpts(
type_=BMapType.MAPTYPE_CONTROL_DROPDOWN),
scale_control_opts=opts.BMapScaleControlOpts(),
overview_map_opts=opts.BMapOverviewMapControlOpts(is_open=True),
navigation_control_opts=opts.BMapNavigationControlOpts(),
geo_location_control_opts=opts.BMapGeoLocationControlOpts(),
).set_global_opts(
visualmap_opts=opts.VisualMapOpts(
min_=0,
max_=60,
range_color=['blue', 'blue', 'green', 'yellow', 'red']),
title_opts=opts.TitleOpts(title="Traffic Status of Xi'An"))
m.render('test.html')
from pyecharts.charts import BMap
from pyecharts import options as opts
from parse_xml import get_coordinate_list
baidu_log_offset = 0.010598
baidu_lat_offset = 0.00203
# 不习惯链式调用的开发者依旧可以单独调用方法
bmap = BMap(init_opts=opts.InitOpts(width='2000px', height='1000px'))
bmap.add_schema(baidu_ak='GvrsNgKWZokjb1yG7gOphfmHFsrBn8AL',
center=[113.975994, 22.59844],
zoom=16)
coordinate_list = get_coordinate_list('build/my-data.xml')
bunch_points = [[i[2], 1] for i in coordinate_list]
for i, v in enumerate(coordinate_list):
bmap.add_coordinate(v[2], baidu_log_offset + v[0], baidu_lat_offset + v[1])
bmap.add('shenzhen', bunch_points, label_opts=opts.LabelOpts(is_show=False))
bmap.set_global_opts(legend_opts=opts.LegendOpts(is_show=False))
bmap.render()
def plot_map(point_data, path):
point_data = point_data[point_data['remarks'] != 'no_result']
data = point_data[['lat_BaiDu', 'long_BaiDu', 'behavior', 'label']].copy()
data.columns = ['lat', 'lon', 'behavior', 'label']
# 地图中心
map_center = [data['lon'].mean(), data['lat'].mean()]
# 初始化百度地图
bd_map = BMap(init_opts=opts.InitOpts(width="1920px", height="1080px"))
bd_map.add_schema(baidu_ak=KEY,
center=map_center,
zoom=8, is_roam=True,
map_style=None,
)
color = ['red', 'green', 'blue', 'yellow', 'gold', 'cyan', 'magenta', 'purple']
behavior_list = data.drop_duplicates(['behavior'])['behavior'].copy() # 剔除重复的数据文件路径
i = 0
for behavior in behavior_list:
behavior_data = data[data['behavior'] == behavior]
# behavior_color = color_choice() # 从color列表中随机抽取一个颜色
behavior_color = color[i]
i = i + 1
map_data = []
# 利用BMap.add_coordinate 将坐标值赋值给一个地点名称，并增加近BMap对象地理信息中
for j in range(len(behavior_data)):
name = behavior + str(j)
longitude = behavior_data['lon'].iloc[j]
latitude = behavior_data['lat'].iloc[j]
bd_map.add_coordinate(name=name,
longitude=longitude,
latitude=latitude
)
map_data.append((name, 1))
# 将地点增加到百度地图
bd_map = bd_map.add(
series_name=behavior,
type_="scatter",
data_pair=map_data,
symbol_size=20,
effect_opts=opts.EffectOpts(),
label_opts=opts.LabelOpts(formatter="{b}", position="left", is_show=False),
itemstyle_opts=opts.ItemStyleOpts(color=behavior_color),
)
# 显示聚类分析的结果
label_list = data.drop_duplicates(['label'])['label'].copy()
map_data = []
for label in label_list:
if label == -1:
continue
else:
label_data = data[data['label'] == label]
# 将聚类点命名增加至地图地名空间
name = '高风险路段_' + str(label)
bd_map.add_coordinate(name=name,
longitude=label_data['lon'].mean(),
latitude=label_data['lat'].mean()
)
map_data.append((name, len(label_data)))
# 将地点增加到百度地图
bd_map = bd_map.add(
series_name='高风险行为频发路段',
type_="scatter",
data_pair=map_data,
symbol_size=50,
# blur_size=500,
is_selected=True, # 是否选中图例
effect_opts=opts.EffectOpts(),
label_opts=opts.LabelOpts(formatter="{b}", position="right", is_show=False),
itemstyle_opts=opts.ItemStyleOpts(color='rgba(138,43,226, 0.8)'),
)
# 将绘图结果输出至网页文件
bd_map = bd_map.add_control_panel(maptype_control_opts=opts.BMapTypeControlOpts(position=1),
)
# 设置图例
bd_map = bd_map.set_global_opts(legend_opts=opts.LegendOpts(is_show=True,
item_height=50,
),
title_opts=opts.TitleOpts(title="新奥危险货物运输高风险路段聚类分析图"),
)
bd_map = bd_map.render(path)
# 用浏览器打开文件
webbrowser.open(path)
return bd_map
def plot_scatter_map(route_data, path):
route_data = route_data[route_data['speed'] > 10.0]
data = route_data[['lat_BaiDu', 'long_BaiDu', 'behavior', 'label']].copy()
data.columns = ['lat', 'lon', 'behavior', 'label']
# 地图中心
map_center = [data['lon'].mean(), data['lat'].mean()]
# 初始化百度地图
bd_map = BMap(init_opts=opts.InitOpts(width="1920px", height="1080px"))
bd_map.add_schema(
baidu_ak=KEY,
center=map_center,
zoom=8,
is_roam=True,
map_style=None,
)
color = [
'red', 'green', 'blue', 'yellow', 'gold', 'cyan', 'magenta', 'purple'
]
# 将数据分为异常路段和正常路段
normal_route = data[data['label'] <= -0.0].copy()
risk_route = data[data['label'] > 0.0].copy()
# 绘制正常路段散点图，颜色为绿色
normal_map_data = []
# 利用BMap.add_coordinate 将坐标值赋值给一个地点名称，并增加近BMap对象地理信息中
for i in range(len(normal_route)):
name = 'low_risk' + str(i)
longitude = normal_route['lon'].iloc[i]
latitude = normal_route['lat'].iloc[i]
bd_map.add_coordinate(name=name,
longitude=longitude,
latitude=latitude)
normal_map_data.append((name, -1))
# 绘制高风险路段散点图，颜色为红色
risk_map_data = []
# 利用BMap.add_coordinate 将坐标值赋值给一个地点名称，并增加近BMap对象地理信息中
for j in range(len(risk_route)):
name = 'high_risk' + str(j)
longitude = risk_route['lon'].iloc[j]
latitude = risk_route['lat'].iloc[j]
bd_map.add_coordinate(name=name,
longitude=longitude,
latitude=latitude)
risk_map_data.append((name, -1))
# 将低风险轨迹散点标识在百度地图上
bd_map = bd_map.add(
series_name='低风险路段',
type_="scatter",
data_pair=normal_map_data,
symbol_size=10,
effect_opts=opts.EffectOpts(),
label_opts=opts.LabelOpts(formatter="{b}",
position="left",
is_show=False),
itemstyle_opts=opts.ItemStyleOpts(color='green'),
)
# 将高风险轨迹散点标识在百度地图上
bd_map = bd_map.add(
series_name='高风险路段',
type_="scatter",
data_pair=risk_map_data,
symbol_size=20,
effect_opts=opts.EffectOpts(),
label_opts=opts.LabelOpts(formatter="{b}",
position="left",
is_show=False),
itemstyle_opts=opts.ItemStyleOpts(color='red'),
)
# 将绘图结果输出至网页文件
bd_map = bd_map.add_control_panel(
maptype_control_opts=opts.BMapTypeControlOpts(position=1), )
# 设置图例
bd_map = bd_map.set_global_opts(
legend_opts=opts.LegendOpts(
is_show=True,
item_height=50,
),
title_opts=opts.TitleOpts(title="新奥危险货物运输高风险路段聚类分析图"),
)
# 将地图加载到网页
bd_map = bd_map.render(path)
# 用浏览器打开文件
webbrowser.open(path)
return bd_map
# series_name='低风险路段',
# type_="scatter",
# data_pair=normal_map_data,
# symbol_size=10,
# effect_opts=opts.EffectOpts(),
# label_opts=opts.LabelOpts(formatter="{b}", position="left", is_show=False),
# itemstyle_opts=opts.ItemStyleOpts(color='green'),
# )
# 将高风险轨迹散点标识在百度地图上
bd_map = bd_map.add(
series_name='高风险路段',
type_="scatter",
data_pair=risk_map_data,
symbol_size=20,
effect_opts=opts.EffectOpts(),
label_opts=opts.LabelOpts(formatter="{b}",
position="left",
is_show=False),
itemstyle_opts=opts.ItemStyleOpts(color='red'),
)
# 将低风险路线标识在百度地图上
bd_map = bd_map.add(
series_name='低风险路段',
type_="lines",
is_polyline=True,
data_pair=all_map_data,
linestyle_opts=opts.LineStyleOpts(opacity=0.8, width=5),
effect_opts=opts.EffectOpts(),
label_opts=opts.LabelOpts(formatter="{b}",
output = 'json'
ak = 'Z2hzbMkqq4hgAS0IPHgRYPoRfjP9QBee'
url = 'http://api.map.baidu.com/place/v2/search?q=华中科技大学食堂®ion=武汉&output=' + output + '&ak=' + ak
r = requests.get(url)
r_js = r.json()
for i in range(0, 9):
names = r_js['results'][i]['name']
lng_ = r_js['results'][i]['location']['lng']
lat_ = r_js['results'][i]['location']['lat']
print(names, lng_, lat_)
c = BMap()
c.width = "1000px"
c.height = "800px"
c.add_schema(baidu_ak='Z2hzbMkqq4hgAS0IPHgRYPoRfjP9QBee',
center=[114.421214, 30.519139],
zoom=17)
for i in range(0, 9):
c.add_coordinate(r_js['results'][i]['name'],
r_js['results'][i]['location']['lng'],
r_js['results'][i]['location']['lat'])
c.add("华中科技大学食堂", [(r_js['results'][i]['name'], "#404a59")],
color="#404a59")
c.set_global_opts(title_opts=opts.TitleOpts(title="BMap-基本示例"),
visualmap_opts=opts.VisualMapOpts(is_piecewise=True))
c.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
c.render()
'''driver = webdriver.Chrome()
driver.get("F:///pyecharts/untitled/html/road.html")
driver.get("http://127.0.0.1:8848/untitled/road.html")'''
def route_plot(data, map_center):
# 初始化百度地图
bd_map = BMap(init_opts=opts.InitOpts(width="1920px", height="1080px"))
bd_map.add_schema(
baidu_ak=KEY,
center=map_center,
zoom=10,
is_roam=True,
map_style={
"styleJson": [{
"featureType": "estatelabel",
"elementType": "labels.text.fill",
"stylers": {
"color": "#8d694eff"
}
}, {
"featureType": "restaurantlabel",
"elementType": "labels",
"stylers": {
"visibility": "off"
}
}, {
"featureType": "restaurantlabel",
"elementType": "labels.icon",
"stylers": {
"visibility": "off"
}
}, {
"featureType": "lifeservicelabel",
"elementType": "labels",
"stylers": {
"visibility": "off"
}
}, {
"featureType": "lifeservicelabel",
"elementType": "labels.icon",
"stylers": {
"visibility": "off"
}
}, {
"featureType": "transportationlabel",
"elementType": "labels.icon",
"stylers": {
"visibility": "off"
}
}, {
"featureType": "transportationlabel",
"elementType": "labels",
"stylers": {
"visibility": "on"
}
}, {
"featureType": "financelabel",
"elementType": "labels.icon",
"stylers": {
"visibility": "off"
}
}, {
"featureType": "financelabel",
"elementType": "labels",
"stylers": {
"visibility": "off"
}
}, {
"featureType": "land",
"elementType": "geometry",
"stylers": {
"color": "#ffffffff"
}
}, {
"featureType": "building",
"elementType": "geometry.topfill",
"stylers": {
"color": "#e7dfd6ff"
}
}, {
"featureType": "building",
"elementType": "geometry.sidefill",
"stylers": {
"color": "#f4ebe2ff"
}
}, {
"featureType": "building",
"elementType": "geometry.stroke",
"stylers": {
"color": "#b9a797ff"
}
}, {
"featureType": "estatelabel",
"elementType": "labels.text.stroke",
"stylers": {
"color": "#ebe1d8ff"
}
}, {
"featureType": "estatelabel",
"elementType": "labels.icon",
"stylers": {
"visibility": "off"
}
}, {
"featureType": "estatelabel",
"elementType": "labels.text",
"stylers": {
"fontsize": "28"
}
}, {
"featureType": "manmade",
"elementType": "labels.text.fill",
"stylers": {
"color": "#72533aff"
}
}, {
"featureType": "manmade",
"elementType": "geometry",
"stylers": {
"color": "#ecececff"
}
}, {
"featureType": "manmade",
"elementType": "labels.text",
"stylers": {
"fontsize": "36"
}
}, {
"featureType": "manmade",
"elementType": "labels.text.stroke",
"stylers": {
"color": "#b6997fff"
}
}, {
"featureType": "green",
"elementType": "geometry",
"stylers": {
"color": "#ecececff"
}
}, {
"featureType": "education",
"elementType": "geometry",
"stylers": {
"color": "#ecececff"
}
}, {
"featureType": "medical",
"elementType": "geometry",
"stylers": {
"color": "#ecececff"
}
}, {
"featureType": "scenicspots",
"elementType": "geometry",
"stylers": {
"color": "#ecececff"
}
}, {
"featureType": "entertainment",
"elementType": "geometry",
"stylers": {
"visibility": "off"
}
}, {
"featureType": "estate",
"elementType": "geometry",
"stylers": {
"color": "#ecececff"
}
}, {
"featureType": "shopping",
"elementType": "geometry",
"stylers": {
"visibility": "on",
"color": "#ecececff"
}
}, {
"featureType": "transportation",
"elementType": "geometry",
"stylers": {
"color": "#ecececff"
}
}, {
"featureType": "transportation",
"elementType": "labels.text.fill",
"stylers": {
"color": "#72533aff"
}
}, {
"featureType": "transportation",
"elementType": "labels.text.stroke",
"stylers": {
"color": "#b6997fff"
}
}, {
"featureType": "transportation",
"elementType": "labels.text",
"stylers": {
"fontsize": "36"
}
}, {
"featureType": "medical",
"elementType": "labels.text.fill",
"stylers": {
"color": "#72533aff"
}
}, {
"featureType": "medical",
"elementType": "labels.text.stroke",
"stylers": {
"color": "#b6997fff"
}
}, {
"featureType": "medical",
"elementType": "labels.text",
"stylers": {
"fontsize": "36"
}
}, {
"featureType": "education",
"elementType": "labels.text.fill",
"stylers": {
"color": "#72533aff"
}
}, {
"featureType": "education",
"elementType": "labels.text.stroke",
"stylers": {
"color": "#b6997fff"
}
}, {
"featureType": "education",
"elementType": "labels.text",
"stylers": {
"fontsize": "36"
}
}, {
"featureType": "carservicelabel",
"elementType": "labels",
"stylers": {
"visibility": "off"
}
}, {
"featureType": "carservicelabel",
"elementType": "labels.icon",
"stylers": {
"visibility": "off"
}
}, {
"featureType": "shoppinglabel",
"elementType": "labels.icon",
"stylers": {
"visibility": "off"
}
}, {
"featureType": "hotellabel",
"elementType": "labels.icon",
"stylers": {
"visibility": "off"
}
}, {
"featureType": "governmentlabel",
"elementType": "labels.icon",
"stylers": {
"visibility": "off"
}
}, {
"featureType": "companylabel",
"elementType": "labels.icon",
"stylers": {
"visibility": "off"
}
}, {
"featureType": "businesstowerlabel",
"elementType": "labels.icon",
"stylers": {
"visibility": "off"
}
}, {
"featureType": "entertainmentlabel",
"elementType": "labels",
"stylers": {
"visibility": "off"
}
}, {
"featureType": "entertainmentlabel",
"elementType": "labels.icon",
"stylers": {
"visibility": "off"
}
}, {
"featureType": "medicallabel",
"elementType": "labels.icon",
"stylers": {
"visibility": "off"
}
}, {
"featureType": "educationlabel",
"elementType": "labels.icon",
"stylers": {
"visibility": "off"
}
}, {
"featureType": "scenicspotslabel",
"elementType": "labels.icon",
"stylers": {
"visibility": "off"
}
}, {
"featureType": "airportlabel",
"elementType": "labels.icon",
"stylers": {
"visibility": "off"
}
}, {
"featureType": "airportlabel",
"elementType": "labels.text",
"stylers": {
"fontsize": "36"
}
}, {
"featureType": "airportlabel",
"elementType": "labels.text.fill",
"stylers": {
"color": "#72533aff"
}
}, {
"featureType": "airportlabel",
"elementType": "labels.text.stroke",
"stylers": {
"color": "#b6997fff"
}
}, {
"featureType": "scenicspotslabel",
"elementType": "labels.text",
"stylers": {
"fontsize": "28"
}
}, {
"featureType": "scenicspotslabel",
"elementType": "labels.text.fill",
"stylers": {
"color": "#4a4a4aff"
}
}, {
"featureType": "scenicspotslabel",
"elementType": "labels.text.stroke",
"stylers": {
"color": "#ffffffff"
}
}, {
"featureType": "educationlabel",
"elementType": "labels.text.fill",
"stylers": {
"color": "#8d694eff"
}
}, {
"featureType": "educationlabel",
"elementType": "labels.text.stroke",
"stylers": {
"color": "#ebe1d8ff"
}
}, {
"featureType": "educationlabel",
"elementType": "labels.text",
"stylers": {
"fontsize": "26"
}
}, {
"featureType": "medicallabel",
"elementType": "labels.text.fill",
"stylers": {
"color": "#8d694eff"
}
}, {
"featureType": "medicallabel",
"elementType": "labels.text.stroke",
"stylers": {
"color": "#ebe1d8ff"
}
}, {
"featureType": "medicallabel",
"elementType": "labels.text",
"stylers": {
"fontsize": "24"
}
}, {
"featureType": "businesstowerlabel",
"elementType": "labels.text.stroke",
"stylers": {
"color": "#ebe1d8ff"
}
}, {
"featureType": "businesstowerlabel",
"elementType": "labels.text.fill",
"stylers": {
"color": "#8d694eff"
}
}, {
"featureType": "businesstowerlabel",
"elementType": "labels.text",
"stylers": {
"fontsize": "24"
}
}, {
"featureType": "companylabel",
"elementType": "labels",
"stylers": {
"visibility": "off"
}
}, {
"featureType": "hotellabel",
"elementType": "labels.text.fill",
"stylers": {
"color": "#8d694eff"
}
}, {
"featureType": "hotellabel",
"elementType": "labels.text.stroke",
"stylers": {
"color": "#ebe1d8ff"
}
}, {
"featureType": "hotellabel",
"elementType": "labels.text",
"stylers": {
"fontsize": "24"
}
}, {
"featureType": "shoppinglabel",
"elementType": "labels.text.fill",
"stylers": {
"color": "#8d694eff"
}
}, {
"featureType": "shoppinglabel",
"elementType": "labels.text.stroke",
"stylers": {
"color": "#ebe1d8ff"
}
}, {
"featureType": "transportationlabel",
"elementType": "labels.text.fill",
"stylers": {
"color": "#4a4a4aff"
}
}, {
"featureType": "transportationlabel",
"elementType": "labels.text",
"stylers": {
"fontsize": "24"
}
}, {
"featureType": "scenicspots",
"elementType": "labels.text.fill",
"stylers": {
"color": "#72533aff"
}
}, {
"featureType": "scenicspots",
"elementType": "labels.text.stroke",
"stylers": {
"color": "#b6997fff"
}
}, {
"featureType": "scenicspots",
"elementType": "labels.text",
"stylers": {
"fontsize": "36"
}
}, {
"featureType": "governmentlabel",
"elementType": "labels.text.fill",
"stylers": {
"color": "#4a4a4aff"
}
}, {
"featureType": "scenicspotslabel",
"elementType": "labels",
"stylers": {
"visibility": "off"
}
}, {
"featureType": "district",
"elementType": "labels.text.fill",
"stylers": {
"color": "#ffffffff"
}
}, {
"featureType": "district",
"elementType": "labels.text.stroke",
"stylers": {
"color": "#72533aff",
"weight": "3.5"
}
}, {
"featureType": "town",
"elementType": "labels.text.stroke",
"stylers": {
"color": "#72533aff",
"weight": "3"
}
}, {
"featureType": "town",
"elementType": "labels.text.fill",
"stylers": {
"color": "#ffffffff"
}
}, {
"featureType": "village",
"elementType": "labels.text.stroke",
"stylers": {
"color": "#ffffffff",
"weight": "2.5"
}
}, {
"featureType": "village",
"elementType": "labels.text.fill",
"stylers": {
"color": "#72533aff",
"weight": "40"
}
}, {
"featureType": "village",
"elementType": "labels.text",
"stylers": {
"fontsize": "20"
}
}, {
"featureType": "highway",
"elementType": "geometry.fill",
"stylers": {
"color": "#fdf0daff"
}
}, {
"featureType": "highway",
"elementType": "geometry.stroke",
"stylers": {
"color": "#ffd993ff"
}
}, {
"featureType": "highway",
"elementType": "labels.text.fill",
"stylers": {
"color": "#000000ff"
}
}, {
"featureType": "highway",
"elementType": "labels.text.stroke",
"stylers": {
"color": "#ffffffff"
}
}, {
"featureType": "nationalway",
"elementType": "geometry.fill",
"stylers": {
"color": "#fdf0daff"
}
}, {
"featureType": "nationalway",
"elementType": "geometry.stroke",
"stylers": {
"color": "#ffd993ff"
}
}, {
"featureType": "nationalway",
"elementType": "labels.text.fill",
"stylers": {
"color": "#000000ff"
}
}, {
"featureType": "nationalway",
"elementType": "labels.text.stroke",
"stylers": {
"color": "#ffffffff"
}
}, {
"featureType": "provincialway",
"elementType": "geometry.stroke",
"stylers": {
"color": "#ffd993ff"
}
}, {
"featureType": "provincialway",
"elementType": "geometry.fill",
"stylers": {
"color": "#fdf0daff"
}
}, {
"featureType": "provincialway",
"elementType": "labels.text.fill",
"stylers": {
"color": "#000000ff"
}
}, {
"featureType": "provincialway",
"elementType": "labels.text.stroke",
"stylers": {
"color": "#ffffffff"
}
}, {
"featureType": "subway",
"elementType": "geometry.fill",
"stylers": {
"color": "#f5a117ff"
}
}, {
"featureType": "manmade",
"elementType": "labels",
"stylers": {
"visibility": "on"
}
}]
},
)
# 剔除重复的数据文件路径
truck_list = data.drop_duplicates(['truck_license'
])['truck_license'].to_list()
# 将数据根据时间间隔大于10分钟的规则进行分割，保存进list
# 将每一段行程数据保存进一个列表
trip_list = []
for truck in truck_list:
print(truck)
truck_temp = data[data['truck_license'] == truck].copy()
# 剔除车辆静止时的数据
truck_temp = truck_temp[truck_temp['distance_gps'] > 20].copy()
# 将行程数据按照时间序列排序
truck_temp = truck_temp.sort_values(by='time')
# 通过相邻GPS坐标计算的行程距离
truck_temp = gps_distance(earth_r=EARTH_REDIUS, data=truck_temp)
# 以间距大于100米采样
# truck_temp = truck_temp[truck_temp['distance_gps'] > 100].copy()
truck_temp = gps_distance(earth_r=EARTH_REDIUS, data=truck_temp)
# 重新计算提出零速度后的相邻数据行之间的时间间隔
truck_temp = driving_time(truck_temp)
# 获取行程间隔数据的索引列表，初定为间距2km以上时认为是分隔的路线
time_cut_index = truck_temp[truck_temp['distance_gps'] > 2000].index
index_bp = truck_temp.index[0]
index_ep = truck_temp.index[len(truck_temp) - 1]
if len(time_cut_index) > 1:
for i in range(len(time_cut_index)):
if i == 0:
trip_temp_data = truck_temp.loc[index_bp:time_cut_index[i]]
trip_temp_data = trip_temp_data.drop(
index=[time_cut_index[i]])
else:
trip_temp_data = truck_temp.loc[
time_cut_index[i - 1]:time_cut_index[i]]
trip_temp_data = trip_temp_data.drop(
index=[time_cut_index[i]])
# 只有具备一定长度的路线才被绘制，初定为至少包括5个轨迹坐标点
if len(trip_temp_data) > 50:
bd_map = bd_map.add(
series_name='低风险路段' + truck + str(i),
type_="lines",
is_polyline=True,
data_pair=[
route_to_baidu_line(trip_temp_data, color='green')
],
linestyle_opts=opts.LineStyleOpts(opacity=0.8,
width=5),
effect_opts=opts.EffectOpts(),
label_opts=opts.LabelOpts(formatter="{b}",
position="left",
is_show=False),
itemstyle_opts=opts.ItemStyleOpts(color='green'),
# 如果不是最新版本的话可以注释下面的参数（效果差距不大）
progressive=200,
progressive_threshold=500,
)
print('低风险路段_' + truck + '_', str(i))
print(route_to_baidu_line(trip_temp_data, color='green'))
# 设置图例
bd_map = bd_map.set_global_opts(
legend_opts=opts.LegendOpts(
is_show=False, # 不显示标签
),
title_opts=opts.TitleOpts(title="新奥危险货物运输高风险路段聚类分析图"),
)
return bd_map
def locate():
# 对数据进行绘图
station_data = pd.read_csv("station.csv",header = 0, sep = '\t')
latitude = station_data['lat'].values
longitude = station_data['lng'].values
index = station_data['station_id'].values
length = len(latitude)
mag = [1] * length
#可视化
BMaps = BMap()
data_pair = dict.fromkeys(index, 0)
user = pd.read_csv("mobile.csv",header = 0, sep = '\t')
locate = user['station_id'].values
count = user['count'].values
for i in range(len(locate)):
try:
if count[i] >= 10:
data_pair[locate[i]] += int(count[i])
except:
pass
for i in range(len(index)):
BMaps.add_coordinate(index[i], longitude[i], latitude[i])
BMaps.add_schema(baidu_ak = 'UoW1wfwAiRoBPYeh52XzdUGGoByxmUN2',
center=(121.37, 31.23),
zoom=10,
map_style={
"styleJson": [
{
"featureType": "water",
"elementType": "all",
"stylers": {"color": "#031628"},
},
{
"featureType": "land",
"elementType": "geometry",
"stylers": {"color": "#000102"},
},
{
"featureType": "highway",
"elementType": "all",
"stylers": {"color": "#50A3BA"},
},
{
"featureType": "local",
"elementType": "geometry",
"stylers": {"color": "#000000"},
},
{
"featureType": "railway",
"elementType": "geometry.fill",
"stylers": {"color": "#000000"},
},
{
"featureType": "railway",
"elementType": "geometry.stroke",
"stylers": {"color": "#08304b"},
},
{
"featureType": "building",
"elementType": "geometry.fill",
"stylers": {"color": "#000000"},
},
{
"featureType": "all",
"elementType": "labels.text.fill",
"stylers": {"color": "#857f7f"},
},
{
"featureType": "all",
"elementType": "labels.text.stroke",
"stylers": {"color": "#000000"},
},
{
"featureType": "building",
"elementType": "geometry",
"stylers": {"color": "#022338"},
},
{
"featureType": "green",
"elementType": "geometry",
"stylers": {"color": "#062032"},
},
{
"featureType": "boundary",
"elementType": "all",
"stylers": {"color": "#465b6c"},
},
{
"featureType": "manmade",
"elementType": "all",
"stylers": {"color": "#022338"},
},
{
"featureType": "label",
"elementType": "all",
"stylers": {"visibility": "on"},
},
]
})
# 添加数据点
data_pair = [list(z) for z in zip(data_pair.keys(), data_pair.values())]
BMaps.add('次数', data_pair,type_="scatter",symbol_size=3, label_opts=opts.LabelOpts(is_show=False))
pieces = [
{'max': 10000, 'label': '10000以下', 'color': '#50A3BA'},
{'min': 10000, 'max': 20000, 'label': '10000-20000', 'color': '#E2C568'},
{'min': 20000, 'max': 30000, 'label': '20000-30000', 'color': '#D94E5D'},
{'min': 30000, 'max': 40000, 'label': '30000-40000', 'color': '#3700A4'},
{'min': 40000, 'label': '40000+', 'color': '#81AE9F'},
]
BMaps.set_global_opts(
visualmap_opts=opts.VisualMapOpts(is_piecewise=True, pieces= pieces, pos_top='top', pos_left = 'right'),
title_opts=opts.TitleOpts(title="基站频率分布>10")
)
#导出图像
BMaps.render('基站频率分布10.html')
