def geo():
g = Geo(init_opts=opts.InitOpts(theme=ThemeType.PURPLE_PASSION))
g.add_schema(maptype='广州')
# 定义坐标对应的名称，添加到坐标库中 add_coordinate(name, lng, lat)
names = list(df['店铺名称'])
lng = list(df['经度'])
lat = list(df['纬度'])
for i in range(len(names)):
g.add_coordinate(names[i], lng[i], lat[i])
# 定义数据对，
data_pair = [(name, 50) for name in names]
# 将数据添加到地图上
g.add('', data_pair, type_=GeoType.EFFECT_SCATTER, symbol_size=3)
# 设置样式
g.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
g.set_global_opts(
visualmap_opts=opts.VisualMapOpts(is_show=False),
title_opts=opts.TitleOpts(title="广州美食分布"),
)
g.render('广州美食分布.html')
return g
def task():
city = '北京'
g = Geo(init_opts=opts.InitOpts(width = '1600px', height='800px'))
g.add_schema(maptype=city)
# 数据库连接
db = pymysql.connect(host = 'localhost', user='******', password='******', port=3306, db = 'educationalResource_bj')
cursor = db.cursor()
sql = 'select * from educationalResourceMap'
cursor.execute(sql)
results = cursor.fetchall()
db.close()
#print(results[0][1])
for result in results:
g.add_coordinate(result[1], result[6], result[7])
data_pair = []
for result in results:
judge = 0
if result[3] == 'gbyey':
judge = 10
elif result[3] == 'xx':
judge = 20
elif result[3] == 'zx':
judge = 30
elif result[3] == 'zyjyxx':
judge = 50
elif result[3] == 'gdyx':
judge = 80
else :
judge = 101
data_pair.append((result[1], judge))
#print(data_pair)
# 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
g.add('', data_pair, type_=GeoType.EFFECT_SCATTER, symbol_size=5)
# 设置样式
g.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
# 自定义分段 color 可以用取色器取色
pieces = [
{'min': 1, 'max': 10, 'label': '幼儿园', 'color': '#3700A4'},
{'min': 10, 'max': 20, 'label': '小学', 'color': '#81AE9F'},
{'min': 20, 'max': 30, 'label': '中学', 'color': '#E2C568'},
{'min': 30, 'max': 50, 'label': '职业技术学校', 'color': '#FCF84D'},
{'min': 50, 'max': 100, 'label': '高等院校', 'color': '#DD0200'},
{'min': 100, 'max': 200, 'label': '特殊教育学校', 'color': '#DD675E'},
]
# is_piecewise 是否自定义分段， 变为true 才能生效
g.set_global_opts(
visualmap_opts=opts.VisualMapOpts(is_piecewise=True, pieces=pieces),
title_opts=opts.TitleOpts(title="{}-学校分布".format(city)),
)
return g
def func3(path="temp.csv"):
win = tkinter.Tk()
x = win.winfo_screenwidth()
y = win.winfo_screenheight()
pieces = [
{
'min': 0,
'max': 0,
'label': '数据无效',
'color': '#00B2EE'
},
{
'min': 1,
'max': 1,
'label': '数据疑似为假',
'color': '#71C671'
},
{
'min': 2,
'max': 2,
'label': '数据疑似为真',
'color': '#CD4F39'
},
]
data = pd.read_csv(path) # 读取数据
geo_sight_coord = data[['dx', 'dy']] # 构造位置字典数据
# data_pair = [(str(i), 1) for i in range(len(geo_sight_coord))] # 构造项目租金数据
data_pair = []
for index, row in data.iterrows():
data_pair.append((str(index), int(row['flag_legal'])))
g = Geo(init_opts=opts.InitOpts(
page_title="车辆轨迹",
width=str(x / 1.1) + 'px',
height=str(y / 1.1) + 'px',
)) # 地理初始化
g.add_schema(maptype="江西") # 限定上海市范围
for key, value in geo_sight_coord.iterrows(): # 对地理点循环
g.add_coordinate(str(key), value['dx'], value['dy']) # 追加点位置
g.add("", data_pair, symbol_size=4) # 追加项目名称和租金
g.set_series_opts(
label_opts=opts.LabelOpts(is_show=False),
type='scatter',
) # 星散点图scatter
g.set_global_opts(visualmap_opts=opts.VisualMapOpts(
is_piecewise=True,
pieces=pieces,
pos_left='left',
pos_bottom="100px",
),
title_opts=opts.TitleOpts(title="车辆轨迹"))
g.render(path="车辆加油地图.html")
def locate_on_map(addr, longtitude, latitude):
g = Geo().add_schema(maptype="上海")
g.add_coordinate(addr, longtitude, latitude)
data_pair = [(addr, 1)]
g.add('', data_pair, type_=GeoType.EFFECT_SCATTER, symbol_size=20)
g.set_series_opts(label_opts=options.LabelOpts(is_show=False))
g.set_global_opts(title_opts=options.TitleOpts(title="地图标点测试"))
g.render_notebook()
print("process finished")
def quakes():
# 对地震数据进行绘图
quakes_data = pd.read_csv("quakes.csv")
latitude = quakes_data['lat'].values
longitude = quakes_data['long'].values
mag = quakes_data['mag'].values
index = list(range(len(mag)))
#可视化
geo = Geo()
geo.add_schema(maptype='world')
for i in range(len(index)):
geo.add_coordinate(index[i], longitude[i], latitude[i])
data_pair = [list(z) for z in zip(index, mag)]
# 将数据添加进图中
geo.add('震源', data_pair, symbol_size=3)
geo.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
# 设置显示区间
pieces = [
{
'max': 4,
'label': '4级以下',
'color': '#50A3BA'
},
{
'min': 4,
'max': 5,
'label': '4-5级',
'color': '#E2C568'
},
{
'min': 5,
'max': 6,
'label': '5-6级',
'color': '#D94E5D'
},
{
'min': 6,
'max': 7,
'label': '6-7级',
'color': '#3700A4'
},
{
'min': 7,
'label': '7级以上',
'color': '#81AE9F'
},
]
geo.set_global_opts(visualmap_opts=opts.VisualMapOpts(is_piecewise=True,
pieces=pieces),
title_opts=opts.TitleOpts(title="震源分布"))
#导出图像
geo.render('地震地点分布.html')
def drawGeoMap(excel_path, mtype='china'):
data = xlrd.open_workbook(excel_path)
table = data.sheet_by_index(0)
nrow = table.nrows
dates = table.row_values(0)
# print(dates)
allData = {}
for i in range(1, nrow):
aNameData = {}
sname = f"{table.cell(i,0).value} {table.cell(i,1).value}"
for j in range(2, len(dates)):
value = table.cell(i,j).value
try:
value = json.loads(value)
value = value[1:4]
except Exception as e:
print(f"error: {e}")
pass
aNameData[dates[j]] = value
allData[sname] = aNameData
# print(f'学生数：{len(allData)}')
title = '2021年1月21日能仔位置'
geo = Geo(init_opts=opts.InitOpts(width='1000px', height='580px', page_title=title, theme=ThemeType.LIGHT))
geo.add_schema(maptype=mtype)
for name, data in allData.items():
for date, location in data.items():
aNameLocation = []
try:
longitude, latitude, address = location
except Exception as e:
print(f"Error: {e}")
longitude, latitude, address = DEFAULT_Location
# aNameLocation.append(('%s %s\n' % (name, date), address))
# geo.add_coordinate('%s %s' % (name, date), longitude, latitude)
sno, sname = name.split(' ')
# print(sno, sname)
aNameLocation.append(('%s %s' % (sno, sname), date))
geo.add_coordinate('%s %s' % (sno, sname), longitude, latitude)
geo.add(name, aNameLocation, type_=ChartType.EFFECT_SCATTER)
geo.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
geo.set_global_opts(legend_opts=opts.LegendOpts(type_='scroll', orient='vertical', pos_left='left', pos_top='10%', is_show=False), title_opts=opts.TitleOpts(title=title))
map_path = os.path.join(dir_path, '2021年1月21日能仔位置地图.html')
geo.render(map_path)
# os.system(f'"C:\Program Files (x86)\Google\Chrome\Application\chrome.exe" {os.path.abspath(map_path)}')
print('done.')
def draw_map(data_pair, data_coordinate) -> Geo: # ->常常出现在python函数定义的函数名后面，为函数添加元数据,描述函数的返回类型，从而方便开发人员使用。
g = Geo()
# 选择要显示的地图
g.add_schema(maptype="重庆", center=[106.531638,29.561081], itemstyle_opts=opts.ItemStyleOpts(color="#575D57"))
# 使用add_coordinate(name, lng, lat)添加坐标点和坐标名称
for item in data_coordinate:
g.add_coordinate(item['name'], item['lng'], item['lat'])
# 将数据添加到定义的地图上
g.add('', data_pair, type_=GeoType.EFFECT_SCATTER, symbol_size=5) # type_=ChartType.HEATMAP
# 设置样式
g.set_series_opts(label_opts=opts.LabelOpts(is_show=False))\
.set_global_opts(visualmap_opts=opts.VisualMapOpts(min_=60, max_ = 200, is_piecewise=True),title_opts=opts.TitleOpts(title="重庆房源分布图"))
return g
def insert_loc():
g = Geo()
data_pair = []
g.add_schema(maptype='china')
airport_df = pd.read_excel('CN_airport_info.xlsx', usecols='B,D,E,F,S')
for i in range(len(airport_df)):
g.add_coordinate(airport_df.iloc[i, 1], float(airport_df.iloc[i, 3]),
float(airport_df.iloc[i, 2]))
data_pair.append((airport_df.iloc[i, 1], int(airport_df.iloc[i, 4])))
g.add('', data_pair, type_=GeoType.EFFECT_SCATTER, symbol_size=7)
g.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
g.set_global_opts(
visualmap_opts=opts.VisualMapOpts(is_piecewise=False),
title_opts=opts.TitleOpts(title="全国机场分布图"),
)
return g
def plot_geolines(plotting_data, geo_cities_coords):
c = Geo(init_opts=opts.InitOpts(width='1500px', height='700px'))
c.add_schema(
maptype="china",
itemstyle_opts=opts.ItemStyleOpts(color="#F0CA00", border_color="#111"))
# 所有坐标点加进地图
for i in range(0, len(geo_cities_coords)):
c.add_coordinate(list(geo_cities_coords.keys())[i], list(geo_cities_coords.values())[i][0],
list(geo_cities_coords.values())[i][1])
c.add('仓网结构', plotting_data,
type_=GeoType.LINES,
linestyle_opts=opts.LineStyleOpts(width=1),
label_opts=opts.LabelOpts(is_show=False),
##trail_length=0,
is_polyline=True)
c.set_global_opts(title_opts=opts.TitleOpts(title="仓网结构"))
c.render('二级仓网结构.html')
def draw_with_echarts_scatter():
'''使用pyecharts画散点图'''
try:
device_csv_dir = r'E:/test_opencv/车辆经常停留位置/all_device_data_csv/'
pyecharts_device_html_dir = r'E:/test_opencv/车辆经常停留位置/pyecharts_device_html/'
if not os.path.exists(device_csv_dir):
os.makedirs(device_csv_dir)
if not os.path.exists(pyecharts_device_html_dir):
os.makedirs(pyecharts_device_html_dir)
for item in os.listdir(device_csv_dir):
csvlName = device_csv_dir + item
df = pd.read_csv(csvlName, encoding='utf-8', low_memory=False)
# X = df.drop_duplicates(subset=['longitude', 'latitude'])
X = df
device_id = X['device_id'].iloc[0] # 取组内第一个device_id用于存csv用
staff_id = X['staff_id'].iloc[0] # 取组内第一个staff_id用于存csv用
staff_name = X['staff_name'].iloc[0] # 取组内第一个staff_name用于存csv用
car_id = X['car_id'].iloc[0] # 取组内第一个car_id用于存csv用
car_num = X['car_num'].iloc[0] # 取组内第一个car_num用于存csv用
create_time_1 = X['create_time_1'].iloc[0] # 取组内第一个create_time_1用于存csv用
g = Geo()
g.add_schema(maptype="china")
# 给所有点附上标签 'create_time'
for index, row in X.iterrows():
g.add_coordinate(row['create_time'], row['remain_longitude'], row['remain_latitude'])
create_time = X.create_time.values.tolist()
# 给每个点的值赋为 1
data_list = [[item, 1] for item in create_time]
# 画图
# g.add('', data_list, type_=GeoType.HEATMAP, symbol_size=2)
g.add('', data_list, type_=GeoType.EFFECT_SCATTER, symbol_size=2)
g.set_series_opts(label_opts=options.LabelOpts(is_show=False))
#heatmap
# g.set_global_opts(visualmap_opts=options.VisualMapOpts(), title_opts=options.TitleOpts(title=staff_name+"_" + str(car_num) + '_' + str(create_time_1) +'_heatmap' ,pos_left='50%',pos_top='20'))
#scatter
g.set_global_opts(title_opts=options.TitleOpts(title=staff_name+"_" + str(car_num) + '_' + str(create_time_1) +'_scatter' ,pos_left='50%',pos_top='20'))
# 保存结果到 html
result = g.render(pyecharts_device_html_dir + str(staff_name) + '_' +str(car_num) + '_' + str(create_time_1) + '.html')
except Exception as ex:
logger.error("Call method draw_with_echarts_scatter() error!")
logger.error("Exception:" + str(ex))
raise ex
def get_geo_base(self, _data):
'''
plot geo-HeatMap
:param _data: gps对应的城市分布(dataframe)
:return: geo城市分布图
'''
register_url(self.echarts_url)
geo = Geo(init_opts=opts.InitOpts(width="1000px",
height="600px",
page_title=self.country,
bg_color=''))
geo.add_schema(maptype=self.country)
for _city, _gps in self.geo_dict.items():
geo.add_coordinate(name=_city, longitude=_gps[0], latitude=_gps[1])
geo.add(series_name=self.series_name,
data_pair=_data,
type_=ChartType.HEATMAP)
geo.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
geo.set_global_opts(
visualmap_opts=opts.VisualMapOpts(max_=self.threshold_plot),
title_opts=opts.TitleOpts(title="Geo-HeatMap"))
return geo
class NetworkVisualization(object):
"""
class which show the demand heat map and the allocation network
"""
def __init__(self, data_class, json_file=None):
"""
Initialization the class with data_class and json_file parameters, and add the customized city into Geo class
:param data_class: the data_class
:param json_file:
"""
self.geo = Geo()
self.data_class = data_class
self.json_file = json_file
if self.json_file is not None:
self._load_coordinate_json()
else:
if not hasattr(self.data_class, 'city'):
raise ValueError(
'the city and corresponding longitude and latitude should be included in data_class!'
)
else:
self._load_coordinate()
def _load_coordinate(self):
# add the customized city with the corresponding longitude and latitude
if len(self.data_class.city) != len(self.data_class.longitude):
raise ValueError(
'the length of city and longitude and latitude should be same !'
)
for city, longitude, latitude in zip(self.data_class.city,
self.data_class.longitude,
self.data_class.latitude):
self.geo.add_coordinate(city, longitude, latitude)
def _load_coordinate_json(self):
# add the customized city with the longitude and latitude json file to Geo
# json file contents: { 'city name':[120.123,32.345] }
self.geo.add_coordinate_json(self.json_file)
def heat_map(self, city, demand) -> Geo:
c = (Geo().add_schema(maptype="china").add(
"demand heat map ", [list(z) for z in zip(city, demand)],
type_=ChartType.HEATMAP,
symbol_size=8).set_series_opts(label_opts=opts.LabelOpts(
is_show=False)).set_global_opts(
visualmap_opts=opts.VisualMapOpts(max_=max(demand) * 1.2),
title_opts=opts.TitleOpts(title="demand heat map "),
))
return c
def net_work_map(self, source_list, cost, destination_list, demand, source,
destination) -> Geo:
"""
output the network map based on the source and destination pair.
:param source_list: the unique source list
:param cost: the cost of source list
:param destination_list: the unique destination list
:param demand: the demand of destination
:param source: the all source point
:param destination: the all destination point
:return:
"""
c = (
Geo().add_schema(maptype="china").add(
"", [z for z in zip(source_list, cost)],
type_=ChartType.SCATTER,
color='green',
symbol_size=5,
symbol="image://..\\icon\\warehouse_1.png").add(
"",
[z for z in zip(destination_list, demand)],
type_=ChartType.SCATTER,
color='black',
symbol_size=5,
# symbol="image://..\\icon\\customer.png"
).add(
"Network",
[list(z) for z in zip(source, destination)],
type_=ChartType.LINES,
symbol=[None, 'arrow'],
symbol_size=3,
effect_opts=opts.EffectOpts(
period=20,
scale=1,
symbol="image://..\\icon\\car.png",
symbol_size=15,
color="blue",
trail_length=0),
linestyle_opts=opts.LineStyleOpts(curve=0.1, opacity=0.3),
).set_series_opts(label_opts=opts.LabelOpts(
is_show=False)).set_global_opts(
title_opts=opts.TitleOpts(title="Allocation Network"),
visualmap_opts=opts.VisualMapOpts(is_piecewise=False,
max_=max(demand),
type_='size',
range_size=[10,
20])))
return c
def networkcivilisation(self):
"""
画出仓网覆盖现状
"""
# 获取RDC仓位置和城市经纬度坐标点
RDC_cords = {self.GIS[rdc]['city_name_cn'].split("市")[0] + '仓' : (self.GIS[rdc]['lng'], self.GIS[rdc]['lat']) \
for rdc in self.rdc}
City_cords = {
customer: (self.GIS[customer]['lng'], self.GIS[customer]['lat'])
for customer in self.customer
}
# 初始化地图
c = Geo(init_opts=opts.InitOpts(width='1500px', height='700px'))
c.add_schema(maptype="china",
itemstyle_opts=opts.ItemStyleOpts(color="#F0CA00",
border_color="#111"))
# 所有城市、RDC仓的坐标加进地图中
for key, value in RDC_cords.items():
c.add_coordinate(key, value[0], value[1])
for key, value in City_cords.items():
c.add_coordinate(key, value[0], value[1])
# 地图中加RDC仓的点
c.add("RDC仓",
[[z,1] for z in RDC_cords.keys()],
type_="scatter", label_opts=opts.LabelOpts(formatter="{b}", color='red', font_size=16, font_weight='bold',\
position = 'right')
, color='#3481B8', symbol='pin',
# 标记图形形状,circle，pin，rect，diamon，roundRect，arrow，triangle
symbol_size=16
)
#加需求点
non_rdc_customer = set(self.customer) - set(self.rdc)
c.add("需求点", [[z, 1] for z in non_rdc_customer],
type_="scatter",
label_opts=opts.LabelOpts(is_show=False),
color='blue',
blur_size=1,
symbol_size=5)
# 循环添加每一个覆盖
colors = [
"#F70000", "#373F43", "#0A0A0D", "#3E753B", "#3481B8", "#904684",
"#D47479", "#a21c68", "#e38105", "#42d1bd"
]
m = 0
for rdc in self.rdc:
flow = []
for key, value in self.demand.items():
if key[0] == rdc:
flow.append((key[0], key[1]))
c.add(
series_name=self.GIS[rdc]['city_name_cn'].split('市')[0] + '仓',
data_pair=flow,
type_=GeoType.LINES,
linestyle_opts=opts.LineStyleOpts(width=0.8, color=colors[m]),
label_opts=opts.LabelOpts(is_show=False),
##trail_length=0,
is_polyline=True)
m += 1
c.set_global_opts(title_opts=opts.TitleOpts(title="现状仓网覆盖"))
c.render("现状仓网覆盖.html")
from pyecharts.charts import Geo
from pyecharts.globals import ChartType
from pyecharts import options as opts
# bar = Bar()
# bar.add_xaxis(["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"])
# bar.add_yaxis("商家A", [5, 20, 36, 10, 75, 90])
# render 会生成本地 HTML 文件，默认会在当前目录生成 render.html 文件
# 也可以传入路径参数，如 bar.render("mycharts.html")
# bar.render()
citys = ['丰县', '沛县', '铜山区', '贾汪区', '鼓楼区', '泉山区', '云龙区', '邳州市', '新沂市', '睢宁县']
values = [120, 160, 180, 200, 180, 190, 130, 160, 140, 170]
geo = Geo()
geo.add_schema(maptype="徐州")
geo.add_coordinate("某地", 117.3, 34.3)
print([list(z) for z in zip(citys, values)])
geo.add("test", [list(z) for z in zip(citys, values)],
type_=ChartType.EFFECT_SCATTER)
geo.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
geo.set_global_opts(visualmap_opts=opts.VisualMapOpts(),
title_opts=opts.TitleOpts(title="Geo地图示例"))
geo.render("测试.html")
gys_unuse_df['CDC_Name'].isna())].drop_duplicates()['城市代码']
# 初始化地图
c = Geo(init_opts=opts.InitOpts(width='1200px', height='700px'))
c.add_schema(maptype="china",
itemstyle_opts=opts.ItemStyleOpts(color="#F0CA00",
border_color="#111"))
# CDC位置加入地图坐标
CDC_cords = {
GIS[gys_total.iloc[i]['城市代码']]['city_name_cn']:
(gys_total.iloc[i]['CDC_LGT'], gys_total.iloc[i]['CDC_LAT'])
for i in range(len(gys_total))
} ##会自动去重
for key, value in CDC_cords.items():
c.add_coordinate(key, value[0], value[1])
# 未被选择的CDC加入地图坐标
unuse_cdc_sku = {'763':'包装胶袋','746':'包装胶袋','754':'包装胶袋','591':'防护用品,封箱胶纸,配饰用品,称重工具,贴纸类',\
'553':'称重工具','762':'封条类','532':'封条类','577':'封条类,一次性编织袋','592':'封箱胶纸',\
'535':'配饰用品','631':'夏装','756':'配饰用品,贴纸类,纸质运单'}
unuse_cdc_cords = {GIS[key]['city_name_cn'] + ':' + value : (GIS[key]['lng'], GIS[key]['lat']) \
for key,value in unuse_cdc_sku.items()}
for key, value in unuse_cdc_cords.items():
c.add_coordinate(key, value[0], value[1])
# RDC位置加入地图坐标
RDC_cords = {GIS[gys_rdc.iloc[i]['RDC_Name']]['city_name_cn'].split('市')[0] + '仓': [gys_rdc.iloc[i]['RDC_LGT'],\
gys_rdc.iloc[i]['RDC_LAT']] for i in range(len(gys_rdc))} ##会自动去重
for key, value in RDC_cords.items():
c.add_coordinate(key, value[0], value[1])
def geo():
place_avaliable = passenger_spider(None, all=True)
place_no_weather = passenger_spider_no_weather()
city = '上海'
g = Geo(init_opts=opts.InitOpts(
width="100%", height="100%", bg_color="#12406d"))
g.add_schema(maptype=city,
itemstyle_opts=opts.ItemStyleOpts(color="#135dbe",
border_color="#fff"),
zoom=1.2)
# 定义坐标对应的名称，添加到坐标库中 add_coordinate(name, lng, lat)
for k, v in jingwei_dict.items():
g.add_coordinate(k, float(v[1]), float(v[0]))
for item in place_no_weather:
g.add_coordinate(item["NAME"], float(item["LOCATION_X"]),
float(item["LOCATION_Y"]))
coordinates = []
for k in jingwei_dict.keys():
data_pairs = []
data_pairs.append(k)
for p in place_avaliable:
if k == p["NAME"]:
if not p.get("MAX_NUM"):
data_pairs.append(0.0)
else:
data_pairs.append(
round(int(p["NUM"]) / int(p["MAX_NUM"]), 2))
break
if len(data_pairs) == 1:
data_pairs.append(0.0)
data_pairs = tuple(data_pairs)
coordinates.append(data_pairs)
for item in place_no_weather:
data_pairs = []
data_pairs.append(item["NAME"])
if (not item["MAX_NUM"]) or (item["MAX_NUM"] == ""):
data_pairs.append(0)
else:
data_pairs.append(round(
int(item["NUM"]) / int(item["MAX_NUM"]), 2))
data_pairs = tuple(data_pairs)
coordinates.append(data_pairs)
# 定义数据对，
# data_pair = [
# ('湖南省长沙市雨花区跳马镇仙峰岭', 25),
# ('湖南省长沙市宁乡市横市镇藕塘', 5),
# ('湖南省长沙市长沙县黄花镇新塘铺长沙黄花国际机场', 20)
# ]
data_pair = coordinates
# 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
g.add('', data_pair, type_=ChartType.EFFECT_SCATTER)
# 设置样式
g.set_series_opts(
label_opts=opts.LabelOpts(is_show=False, formatter="{b}:{c}"))
# 自定义分段 color 可以用取色器取色
pieces = [
{
'max': 0.1,
'label': '10%以下',
'color': '#98F5FF'
},
{
'min': 0.1,
'max': 0.3,
'label': '10%-30%',
'color': '#0f0'
},
{
'min': 0.3,
'max': 0.5,
'label': '30%-50%',
'color': 'orange'
},
{
'min': 0.5,
'max': 0.7,
'label': '50%-70%',
'color': '#E2C568'
},
{
'min': 0.7,
'max': 0.8,
'label': '70%-80%',
'color': '#FCF84D'
},
{
'min': 0.8,
'max': 0.9,
'label': '80%-90%',
'color': '#3700A4'
},
{
'min': 0.9,
'max': 1,
'label': '90%-100%',
'color': '#DD675E'
},
{
'min': 1,
'label': '100%以上',
'color': '#f00'
} # 有下限无上限
]
# is_piecewise 是否自定义分段， 变为true 才能生效
g.set_global_opts(
visualmap_opts=opts.VisualMapOpts(is_piecewise=True,
pieces=pieces,
textstyle_opts=opts.TextStyleOpts(
color="#fff")))
return g
center=[116.46, 39.92],
zoom=3,
itemstyle_opts=opts.ItemStyleOpts(color="#fff",
border_width=1,
border_color="#1E90FF"),
emphasis_label_opts=opts.LabelOpts(is_show=False),
emphasis_itemstyle_opts=opts.ItemStyleOpts(color="#FFFACD"),
label_opts=opts.LabelOpts(is_show=True,
font_size=16,
font_style='italic',
color='#1E90FF'),
)
stations = []
for idx, row in df.iterrows():
geo.add_coordinate(row['站点名称'], row['经度'], row['纬度'])
stations.append([row['站点名称'], 1])
for line in lines:
# 线图
geo.add(
line,
# 数据格式（from， to）
data_pair=line,
type_='lines',
label_opts=opts.LabelOpts(is_show=False),
symbol_size=8,
symbol='emptyCircle',
is_polyline=False,
linestyle_opts=line_style,
effect_opts={
'geo',
prov_tuple,
).set_series_opts(label_opts=opts.LabelOpts(is_show=False)).set_global_opts(
visualmap_opts=opts.VisualMapOpts(),
title_opts=opts.TitleOpts(title="Geo-基本示例")).render("./省份分布.html"))
#-----------------------------------------
#世界地图
from pyecharts.globals import ChartType, SymbolType
geo = Geo()
geo.add_schema(maptype="world")
# 可以在网上查找城市的经纬度
geo.add_coordinate(name="洛杉矶", longitude=-118.15, latitude=34.04)
geo.add_coordinate(name="巴塞罗那", longitude=2.17, latitude=41.38)
geo.add_coordinate(name="孟买", longitude=72.87, latitude=19.07)
geo.add_coordinate(name="墨尔本", longitude=144.96, latitude=-37.81)
geo.add_coordinate(name="圣彼得堡", longitude=30.36, latitude=59.93)
geo.add_coordinate(name="斯图加特", longitude=9.18, latitude=48.77)
geo.add_coordinate(name="圣地亚哥", longitude=-117.16, latitude=32.71)
geo.add_coordinate(name="里约热内卢", longitude=-43.17, latitude=-22.90)
geo.add_coordinate(name="伊尔库斯克", longitude=104.28, latitude=52.28)
geo.add_coordinate(name="曼谷", longitude=100.50, latitude=13.75)
geo.add_coordinate(name="开普敦", longitude=18.42, latitude=-33.92)
geo.add_coordinate(name="阿布扎比", longitude=54.37, latitude=24.45)
geo.add_coordinate(name="檀香山", longitude=-157.85, latitude=21.30)
geo.add_coordinate(name="安克雷奇", longitude=-149.90, latitude=61.21)
geo.add_coordinate(name="东京", longitude=139.76, latitude=35.68)
geo.add_coordinate(name="北京", longitude=116.40, latitude=39.90)
cities_coor = [("华盛顿", -77.0, 38.5), ("北京", 116.3, 39.5),
("伦敦", 0, 52.0), ("香港", 114.2, 22.3),
("巴西利亚", -47.5, -15.4), ("堪培拉", 149.0, -35.1)]
cities_value = [(x[0], int(x[2])) for x in cities_coor]
cities_arrow = []
for city in cities_coor:
if city[0] != "香港":
cities_arrow.append((city[0], "香港"))
geo = Geo()
geo.add_schema(maptype="world")
for city in cities_coor:
geo.add_coordinate(city[0], city[1], city[2])
geo.add(
"city",
cities_value,
type_=ChartType.EFFECT_SCATTER,
color="green",
label_opts=opts.LabelOpts(
is_show=True,
font_family="Microsoft YaHei",
position="left",
formatter="{b}"))
geo.add(
"impact",
cities_arrow,
type_=ChartType.LINES,
from pyecharts.render import make_snapshot # 快照
from snapshot_selenium import snapshot
from pyecharts.globals import ChartType, SymbolType # 全局配置
data = pd.read_excel("D:\数有引力\魔都商圈\办公项目.xlsx") # 读取数据
geo_sight_coord = {
data['项目名称'][i].strip(): [data['经度'][i], data['纬度'][i]]
for i in range(len(data))
} # 构造位置字典数据
data_pair = [(data['项目名称'][i].strip(), data['日租金'][i])
for i in range(len(data))] # 构造项目租金数据
g = Geo() # 地理初始化
g.add_schema(maptype="上海") # 限定上海市范围
for key, value in geo_sight_coord.items(): # 对地理点循环
g.add_coordinate(key, value[0], value[1]) # 追加点位置
g.add("", data_pair, symbol_size=2) # 追加项目名称和租金
g.set_series_opts(label_opts=opts.LabelOpts(is_show=False),
type='scatter') # 星散点图scatter
pieces = [
{
'max': 3,
'label': '<3',
'color': '#00B2EE'
},
{
'min': 3,
'max': 6,
'3~6': 'love',
long_list = sheet.col_values(21)#经度数据
city1 = sheet.col_values(2)#起飞城市
city2 = sheet.col_values(8)#降落城市
speed = sheet.col_values(22)#飞机速度
vspeed = sheet.col_values(23)#垂直速度
height = sheet.col_values(24)#飞行高度
uptime = sheet.col_values(26)#时间
c = Geo()
# 加载图表模型中的中国地图
c.add_schema(maptype="china")
i = 1
for i in range(1,10):
c.add_coordinate("航班号" + flightnumber_list[i], long_list[i], lat_list[i])
# 为自定义的点添加属性
c.add("geo", [("航班号" + flightnumber_list[i], "航空公司："+aircname[i]+" "+city1[i]+"飞往"+city2[i]+" 飞行速度："+str(speed[i])+" 垂直速度："+str(vspeed[i])+" 高度："+str(height[i])+" 时间"+uptime[i])])
# 设置坐标属性
c.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
# 设置全局属性
c.set_global_opts(
visualmap_opts=opts.VisualMapOpts(),
title_opts=opts.TitleOpts(title="航班信息可视化"),
)
# 在 html(浏览器) 中渲染图表
c.render()
# 在 Jupyter Notebook 中渲染图表
y = x[0] + '仓'
rdc_name.append(y)
rdc_value.append(1)
#初始化地图
c = Geo(init_opts=opts.InitOpts(width='1500px', height='700px'))
c.add_schema(maptype="china",
itemstyle_opts=opts.ItemStyleOpts(color="#F0CA00",
border_color="#111"))
#所有城市、RDC仓的坐标加进地图中
City = list(City_cords.keys())
JW = list(City_cords.values())
RDC_JW = list(RDC_cords.values())
for i in range(0, len(City)):
c.add_coordinate(City[i], JW[i][0], JW[i][1])
for i in range(0, len(RDC_city)):
c.add_coordinate(rdc_name[i], RDC_JW[i][0], RDC_JW[i][1])
#地图中加RDC仓的点
c.add(
"RDC仓",
[list(z) for z in zip(rdc_name, rdc_value)],
type_="scatter",
label_opts=opts.LabelOpts(formatter="{b}",
color='red',
font_size=16,
font_weight='bold'),
color='#3481B8',
symbol='pin',
# 标记图形形状,circle，pin，rect，diamon，roundRect，arrow，triangle
result = g.render(r'E:\test_opencv\轨迹分析\1.html')
'''
#热力图
import pandas as pd
from pyecharts.charts import Geo
from pyecharts.globals import GeoType
from pyecharts import options as opts
df = pd.read_csv(
r'E:\test_opencv\轨迹分析\abnormal_device_csv\17201100972_202005.csv',
encoding='utf-8')
X = df.drop_duplicates(subset=['longitude', 'latitude'])
g = Geo()
g.add_schema(maptype="china")
# 给所有点附上标签 'upload_time'
for index, row in X.iterrows():
g.add_coordinate(row['upload_time'], row['longitude'], row['latitude'])
upload_time = X.upload_time.values.tolist()
# 给每个点的值赋为 1
data_list = [[item, 1] for item in upload_time]
# 画图
g.add('', data_list, type_=GeoType.HEATMAP, symbol_size=2)
g.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
g.set_global_opts(visualmap_opts=opts.VisualMapOpts(),
title_opts=opts.TitleOpts(title="Geo-HeatMap",
pos_left='50%',
pos_top='20'))
# 保存结果到 html
result = g.render(r'E:\test_opencv\轨迹分析\1.html')
