def drawGeoMap(self, aStuData):
sno, sname = aStuData['info'].split(SPLIT_CHAR)
sdata = aStuData['data']
try:
geo = Geo()
def add_data(date, location):
# print(location)
try:
longitude, latitude, address = location
except:
longitude, latitude, address = DEFAULT_Location
geo.add_coordinate(date, longitude, latitude)
geo.add(date, [(date, address)], type_=ChartType.EFFECT_SCATTER)
geo.add_schema(maptype="china")
# add data
for date, data in sdata.items():
add_data(date, data)
title = f'{sno} {sname} 位置动态'
geo.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
geo.set_global_opts(legend_opts=opts.LegendOpts(orient='vertical', pos_left='left', pos_top='10%'), title_opts=opts.TitleOpts(title=title))
html_path = ospath_join(self.mapsDir, f'{sno}_{sname}.html')
geo.render(html_path)
except Exception as e:
log.error(f'地图生成失败: {sno} {sname} - 可能是包含了文件名不可用的特殊字符', exc_info=True)
self._signal.emit(f'错误：{sno} {sname} 地图生成失败！可能原因：工号或提交人姓名中包含文件名不可用的特殊字符，如：*。请修改后重试！')
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 get_salary_city(self):
sql = """
select city,cast(sum((max_salary+min_salary)/2)/count(*) as signed) salary from lagou_job GROUP BY city order by salary desc
"""
cursor.execute(sql)
key = []
values = []
for info in cursor.fetchall():
if not info[0] == '海外':
key.append(info[0])
values.append(info[1])
max = values[0]
geo = Geo(init_opts=opts.InitOpts(width="1600px", height="1000px"))
geo.add_schema(
maptype="china",
itemstyle_opts=opts.ItemStyleOpts(color="#DDF8FF",
border_color="#111"),
)
geo.add("薪资水平", [list(z) for z in zip(key, values)],
type_="effectScatter")
geo.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
geo.set_global_opts(visualmap_opts=opts.VisualMapOpts(max_=max),
title_opts=opts.TitleOpts(title="各城市工资水平,单位K"))
file = data_store + "\\" + "salary.html"
check_path(file)
geo.render(file)
def generate_drug_map(date, type='疫苗'):
data = fetch_drug_date(date, type)
points = []
for country, num in data:
poins.append('1', random.sample(random_points[country], num))
count = [1] * len(data)
addresses = []
json_data = {}
for address in test_data:
json_data[address[0]] = [address[1], address[2]]
addresses.append(address[0])
json_str = json.dumps(json_data, ensure_ascii=False, indent=4)
with open('test_data.json', 'w', encoding='utf-8') as json_file:
json_file.write(json_str)
geo = Geo()
geo.add_schema(maptype='world')
geo.add_coordinate_json(json_file='test_data.json')
geo.add('研发中的{}类药物'.format(type), [list(z) for z in zip(addresses, data)], type_=ChartType.EFFECT_SCATTER, color='#1e90ff',
symbol_size=10, symbol='diamond')
geo.set_series_opts(label_opts=opts.LabelOpts(is_show=False), effect_opts=opts.EffectOpts(scale=5))
geo.set_global_opts(title_opts=opts.TitleOpts(title="Geo-EffectScatter"),
tooltip_opts=opts.TooltipOpts(is_show=False))
geo.render(path='3.html')
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 geo_base(data):
city_list, province_list = data
# 好友全国省份分布图
geo = Geo(init_opts=opts.InitOpts(theme="vintage"))
for city in city_list:
try:
geo.add_schema(maptype="china",
itemstyle_opts=opts.ItemStyleOpts(color="gray"))
geo.add("微信好友分布地图", [city],
type_="effectScatter",
symbol_size=10)
geo.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
geo.set_global_opts(
visualmap_opts=opts.VisualMapOpts(),
title_opts=opts.TitleOpts(title="微信好友分布地图"),
)
except:
pass
print("正在制作好友全国分布图")
make_snapshot(driver, geo.render(), "geo.png")
# 广东好友热力图
# geo = Geo(init_opts=opts.InitOpts(theme="vintage"))
# for city in city_list:
# try:
# geo.add_schema(maptype="广东", itemstyle_opts=opts.ItemStyleOpts(color="gray"))
# geo.add("广东好友热力图", [city], type_="heatmap", symbol_size=10)
# geo.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
# geo.set_global_opts(visualmap_opts=opts.VisualMapOpts(), title_opts=opts.TitleOpts(title="热力图"),
# toolbox_opts=opts.ToolboxOpts())
# except:
# pass
#
# print("正在制作好友广东热力图")
# make_snapshot(driver, geo.render(), "heat.png")
# 好友全国地理图
maps = Map()
maps.add("", province_list, "china")
maps.set_global_opts(title_opts=opts.TitleOpts(title="微信好友分布图"),
visualmap_opts=opts.VisualMapOpts())
print("正在制作好友地理图")
make_snapshot(driver, geo.render(), "map.png")
# 词云图
c = (WordCloud().add("",
city_list,
word_size_range=[15, 50],
shape="diamond",
word_gap=10).set_global_opts(
title_opts=opts.TitleOpts(title="diamond")))
print("正在制作好友城市词云图")
make_snapshot(driver, c.render(), "world.png")
def base_info():
"""地图参数配置"""
geo = Geo(is_ignore_nonexistent_coord=False)
geo.width = "1440px"
geo.height = "680px"
geo.add_schema(maptype="china") \
.add(series_name="新增确诊人数", data_pair=confirmed) \
.set_series_opts(label_opts=opts.LabelOpts(is_show=False, background_color='#61a0a8')) \
.set_global_opts(visualmap_opts=opts.VisualMapOpts(),
title_opts=opts.TitleOpts(title="中国新冠疫情动态\n数据更新时间：\n" + time))
return geo
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 base_info():
geo = Geo()
geo.add_schema(maptype="河南") # 加入自定义的点，格式为
# 地图形式 type_=ChartType.HEATMAP
geo.add("geo", data, type_=ChartType.EFFECT_SCATTER)
# is_show：是否显示经纬度
geo.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
geo.set_global_opts(visualmap_opts=opts.VisualMapOpts(max_=10),
title_opts=opts.TitleOpts(title="Geo-河南省地图"))
return geo
def out_data(date_number):
city_list = ('南宁市', '柳州市', '桂林市', '梧州市', '北海市', '防城港市', '钦州市', '贵港市',
'玉林市', '百色市', '贺州市', '河池市', '来宾市', '崇左市')
city_od_list_pro = []
city_od_values_pro = []
for i in city_list:
city = i
workbook = xlrd.open_workbook(f'.\\{path}\\{city}-迁入来源地规模指数.xlsx')
sheet = workbook.sheet_by_index(0)
city_od_list = []
city_od_values = []
a = 0
cols = sheet.col_values(1)[1:]
cols_date = sheet.col_values(date_number + 1)[1:]
for j in cols:
city_od = (city, j)
city_od_v = (j, cols_date[a])
city_od_list.append(city_od)
city_od_values.append(city_od_v)
a += 1
city_od_list_pro.append(city_od_list)
city_od_values_pro.append(city_od_values)
od_list = []
os_values = []
for i in range(14):
for j in range(14):
od_list.append(city_od_list_pro[i][j])
os_values.append(city_od_values_pro[i][j])
# print(od_list)
# print(os_values)
geo = Geo()
# 地图类型，世界地图可换为world
geo.add_schema(maptype="广西")
# 添加数据点
geo.add("%", os_values, type_=ChartType.EFFECT_SCATTER)
# 添加流向，type_设置为LINES，涟漪配置为箭头，提供的标记类型包括 'circle', 'rect', 'roundRect', 'triangle',
# 'diamond', 'pin', 'arrow', 'none'
geo.add("迁出城市",
od_list,
type_=ChartType.LINES,
effect_opts=opts.EffectOpts(symbol=SymbolType.ARROW,
symbol_size=5,
color="yellow"),
linestyle_opts=opts.LineStyleOpts(curve=0.2),
is_large=True)
# 不显示标签
geo.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
# 设置图标标题，visualmap_opts=opts.VisualMapOpts()为左下角的视觉映射配置项
geo.set_global_opts(visualmap_opts=opts.VisualMapOpts(),
title_opts=opts.TitleOpts(title="广西地市迁徙数据"))
# 直接在notebook里显示图表
geo.render_notebook()
# 生成html文件，可传入位置参数
geo.render(f".\\out_od\\广西-2020.10.{date_number}迁出OD图.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 base_info():
print(data)
geo = Geo()
geo.add_schema(maptype="china") # 加入自定义的点，格式为
# geo.add_coordinate("自定义点", 80.39770014211535,39.90779994986951)
# 地图形式 type_=ChartType.HEATMAP
geo.add("geo", data, type_=ChartType.EFFECT_SCATTER)
# is_show：是否显示经纬度
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-基本示例"))
return geo
def base_info():
geo = Geo()
geo.width = "1440px"
geo.height = "680px"
geo.add_schema(maptype="china")
geo.add("确诊人数", confirmed, type_=ChartType.EFFECT_SCATTER)
geo.add("疑似人数", suspect, type_=ChartType.EFFECT_SCATTER)
geo.add("死亡人数", dead, type_=ChartType.EFFECT_SCATTER)
geo.add("治愈人数", healed, type_=ChartType.EFFECT_SCATTER)
geo.set_series_opts(
label_opts=opts.LabelOpts(is_show=False, background_color='#61a0a8'))
geo.set_global_opts(visualmap_opts=opts.VisualMapOpts(),
title_opts=opts.TitleOpts(
title="中国ncov-2019疫情动态\n上一次数据更新时间：\n" + time))
return geo
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 geo_base():
city_data = get_data()
print(city_data)
geo = Geo(init_opts=opts.InitOpts(theme="vintage"))
for city in city_data:
try:
geo.add_schema(maptype="china",
itemstyle_opts=opts.ItemStyleOpts(color="gray"))
geo.add("微信好友分布地图", [city], type_="effectScatter", symbol_size=10)
geo.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
geo.set_global_opts(
visualmap_opts=opts.VisualMapOpts(),
title_opts=opts.TitleOpts(title="来自中南海的瓶子"),
)
except Exception as e:
print(city)
pass
geo.render("ms_geo.html")
def geo_heatmap(k, v) -> Geo:
try:
c = Geo()
c.add_schema(maptype="china")
c.add(
"ratio",
[list(z) for z in zip(k, v)],
type_=ChartType.HEATMAP,
)
except TypeError:
print("地址有误，开始排错......")
with open("place.txt", mode="a") as f:
i = 0
c = Geo()
c.add_schema(maptype="china")
while i < len(k):
try:
c.add(
"ratio",
[list(z) for z in zip(k[:i], v[:i])],
type_=ChartType.HEATMAP,
)
except:
print(k[i - 1])
f.writelines(k[i - 1])
f.writelines('\n')
del k[i - 1]
del v[i - 1]
i -= 1
continue
else:
i += 1
geo_heatmap(k, v)
else:
c.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
timen = datetime.datetime.now().strftime('%Y-%m-%d')
timea = datetime.datetime.now().strftime('%Y-%m-%d %H.%M.%S')
c.set_global_opts(
visualmap_opts=opts.VisualMapOpts(),
title_opts=opts.TitleOpts(title="2019nCoV{} BY PZW".format(timen)))
Geo.render(c, path='{}HEATMAP.html'.format(timea))
print('渲染完毕')
def get_company_city(self):
sql = """
select city,count(distinct url) as company_count from lagou_company GROUP BY city
"""
result = cursor.execute(sql)
key = []
values = []
for info in cursor.fetchall():
if info[0] != '海外' and re.match('[\u4e00-\u9fa5]', info[0]):
key.append(info[0])
values.append(info[1])
geo = Geo(init_opts=opts.InitOpts(width="1600px", height="1000px"))
geo.add_schema(maptype="china")
geo.add("公司分布", [list(z) for z in zip(key, values)], type_="heatmap")
geo.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
geo.set_global_opts(visualmap_opts=opts.VisualMapOpts(),
title_opts=opts.TitleOpts(title="各城市公司分布"))
file = data_store + "\\" + "company.html"
check_path(file)
geo.render(file)
def plot():
# 初始化图表信息
geo = Geo(init_opts=opts.InitOpts(width="96vw",
height="96vh",
page_title="教育网拓扑图",
animation_opts=opts.AnimationOpts(
animation=False)))
# 添加地图
geo.add_schema(maptype="china")
# 添加所有点的地理信息
geo.add_coordinate_json("./data/data.json")
# 画中间节点
geo.add("Route", [(i, "Route") for i in Nodes_Route],
color="Red",
point_size=4,
symbol_size=4,
effect_opts=opts.EffectOpts(is_show=False))
# 画目的节点
geo.add("Dst", [(i, "Dst") for i in Nodes_Dst],
color="SeaGreen",
point_size=4,
symbol_size=4,
effect_opts=opts.EffectOpts(is_show=False))
# 画线 展示拓扑信息
geo.add("",
list(Trace),
type_=ChartType.LINES,
effect_opts=opts.EffectOpts(is_show=False, symbol_size=2),
linestyle_opts=opts.LineStyleOpts(curve=0.1))
geo.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
geo.set_global_opts(title_opts=opts.TitleOpts(title="教育网拓扑图"))
# 生成图表到指定文件
geo.render("./dist/render.html")
def plot_geo_heatmap(all_df):
"""画热力图
Args:
all_df (dataframe): 每日数据图
"""
def valid_area(x, v):
if x.get_coordinate(v) is not None:
return 'Y'
else:
return 'N'
c = Geo(init_opts=opts.InitOpts(width='800px', height='600px'))
all_df['valid'] = all_df['name'].apply(lambda x: valid_area(c, x))
all_df['plot_v'] = np.log2(all_df['confirm_all'] / 50 + 1) * 10
c.add_schema(maptype="china")
c.add(
"ratio",
all_df.loc[all_df['valid'] == 'Y', ['name', 'plot_v']].values,
type_=ChartType.HEATMAP,
)
c.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
c.set_global_opts(
visualmap_opts=opts.VisualMapOpts(),
legend_opts=opts.LegendOpts(is_show=False),
title_opts=opts.TitleOpts(
pos_left='center',
title='2020 Novel coronavirus pneumonia (NCP) ',
title_link=
'https://news.qq.com/zt2020/page/feiyan.htm?from=timeline&isappinstalled=0'
))
# map_p = Map()
# map_p.set_global_opts(title_opts=opts.TitleOpts(title="实时疫情图"), visualmap_opts=opts.VisualMapOpts(max_=100))
# map_p.add("确诊", data, maptype="china")
Geo.render(c, path='E:/HEATMAP.html')
def country_job_num(self):
df = pd.read_csv(
"D:\PythonFile\LagouSpider\imags\CsvFile\LagouPosition.csv")
citys = df.city
count = Counter(citys)
# count = count.most_common(len(count))
city_list = []
for city in count:
city_list.append([city, count[city]])
map = Geo()
map.add_schema("china")
map.add("geo", city_list, type_=ChartType.EFFECT_SCATTER)
map.set_series_opts(label_opts=opt.LabelOpts(is_show=False))
map.set_global_opts(
visualmap_opts=opt.VisualMapOpts(),
title_opts=opt.TitleOpts(title="Python开发工程师分布情况"),
)
map.render(
path="D:\PythonFile\LagouSpider\imags\pic/Python开发工程师分布情况.html")
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
def in_data(city, date_number):
workbook = xlrd.open_workbook(f'.\\{path}\\{city}-迁入来源地规模指数.xlsx')
sheet = workbook.sheet_by_index(0)
city_od_list = []
city_od_values = []
a = 0
cols = sheet.col_values(1)[1:]
cols_date = sheet.col_values(date_number + 1)[1:]
for j in cols:
city_od = (j, city)
city_od_v = (j, cols_date[a])
city_od_list.append(city_od)
city_od_values.append(city_od_v)
a += 1
geo = Geo()
# 地图类型，世界地图可换为world
geo.add_schema(maptype="广西")
# 添加数据点
geo.add("%", city_od_values, type_=ChartType.EFFECT_SCATTER)
# 添加流向，type_设置为LINES，涟漪配置为箭头，提供的标记类型包括 'circle', 'rect', 'roundRect', 'triangle',
# 'diamond', 'pin', 'arrow', 'none'
geo.add("迁入城市",
city_od_list,
type_=ChartType.LINES,
effect_opts=opts.EffectOpts(symbol=SymbolType.ARROW,
symbol_size=5,
color="yellow"),
linestyle_opts=opts.LineStyleOpts(curve=0.2),
is_large=True)
# 不显示标签
geo.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
# 设置图标标题，visualmap_opts=opts.VisualMapOpts()为左下角的视觉映射配置项
geo.set_global_opts(visualmap_opts=opts.VisualMapOpts(),
title_opts=opts.TitleOpts(title=f"{city}迁入数据"))
# 直接在notebook里显示图表
geo.render_notebook()
# 生成html文件，可传入位置参数
geo.render(f".\\in_od\\{city}-2020.10.{date_number}迁入OD图.html")
def generate_geo_png(geodata, geomax, geomin, geotitle, display_id,
map_data_title):
from pyecharts import options as opts
from pyecharts.charts import Map, Geo
from pyecharts.render import make_snapshot
# from pyecharts_snapshot.main import make_a_snapshot
from snapshot_selenium import snapshot
geo = Geo()
geo.add_coordinate_json(
'/home/CitizenScience/backend/backend/geoCoordJson.js')
geo.add_schema(maptype="china")
print(geodata)
print(map_data_title)
geo.add(" ", geodata, symbol_size=10)
geo.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
geo.set_global_opts(
visualmap_opts=opts.VisualMapOpts(max_=geomax, min_=geomin))
figure_url = "/home/CitizenScience/file/visualization/" + str(
display_id) + '_geo.png'
# figure_url = str(display_id) + '_geo.png'
make_snapshot(snapshot, geo.render(), figure_url, 2)
# geo.render(path='render.html')
# cmd = "snapshot render.html png"
# try:
# isRun = os.system(cmd)
# print("截图完成")
# except:
# print("截图失败")
# cmd = "mv output.png ../file/visualization/{}".format(figure_url)
# try:
# isRun = os.system(cmd)
# print("移动完成")
# except:
# print("移动失败")
# make_a_snapshot('geo2.html', 'geo3.png')
return figure_url
from pyecharts import options as opts
from pyecharts.charts import Geo, Page
from pyecharts.globals import ChartType, SymbolType
#定义地理图
geo = Geo()
#设置画布宽度
geo.width = "800px"
#设置画布高度
geo.height = "600px"
#全局设置项
geo.set_global_opts(visualmap_opts = opts.VisualMapOpts(max_=100),
title_opts=opts.TitleOpts(title="Geo-流向图"))
#添加主题，中国地图，填充及边界颜色设置
geo.add_schema(
maptype="china",
itemstyle_opts=opts.ItemStyleOpts( border_color="#111", color="#454545"),
)
#添加系列
geo.add(
"",
[("成都",10),("合肥",20),("宁波",30),("武汉",40),("西安",50),("郑州",60),("大连",70),("济南",80)],
type_=ChartType.EFFECT_SCATTER, #散点图的一种形式
label_opts=opts.LabelOpts(is_show = True), #不显示数值则设置为False
# color="red"
)
geo.add(
"",
[("北京", 100), ("上海", 100)],
type_=ChartType.HEATMAP, #散点的另一种形式
label_opts=opts.LabelOpts(is_show=False)
)
from pyecharts import options as opts
from pyecharts.charts import Geo
from pyecharts.globals import ChartType, SymbolType
c = Geo()
c.add_schema(
maptype="china",
itemstyle_opts=opts.ItemStyleOpts(color="#323c48", border_color="gray"),
is_roam=True,
zoom=1,
)
c.add("港口", [
("天津", 27),
("唐山", 10),
("沧州", 6),
("日照", 5),
("大连", 5),
("秦皇岛", 4),
("连云港", 4),
("青岛", 3),
("镇江", 3),
("福州", 3),
("潍坊", 2),
("广州", 2),
("厦门", 2),
("北海", 1),
("黄埔", 1),
("秦皇岛", 1),
("南通", 1),
("杭州", 1),
("上海", 1),
addr_dic[provice] = 0
addr_dic[provice] += 1
# print(addr_dic)
# print(addr_dic.items())
sort_addr = sorted(addr_dic.items(), key=lambda item: item[1],
reverse=True)[0:10]
print(sort_addr)
# addr_names = [item[0] for item in sort_addr]
# addr_counts = [item[1] for item in sort_addr]
# print(addr_names, addr_counts)
from pyecharts.charts import Geo
from pyecharts.options import LabelOpts, VisualMapOpts, TitleOpts
from pyecharts.globals import ChartType
geo = Geo()
geo.add_schema(maptype="china")
geo.add("地点统计", sort_addr, type_=ChartType.EFFECT_SCATTER)
geo.set_series_opts(label_opts=LabelOpts(is_show=False))
geo.set_global_opts(
visualmap_opts=VisualMapOpts(is_piecewise=True, max_=sort_addr[0][1]),
title_opts=TitleOpts(title="撩课Python统计"),
)
geo.render("地点统计-geo图.html")
