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 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
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
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}",
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,
)
# 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(lines_file_dir)
# 用浏览器打开文件
webbrowser.open(lines_file_dir)
# # 地图中心
