def test_map_base():
c = Map().add("商家A",
[list(z) for z in zip(Faker.provinces, Faker.values())],
"china")
assert c.theme == "white"
assert c.renderer == "canvas"
c.render("render.html")
def create_province_map(df):
# 筛选数据
df = df[df["country"] == "中国"]
df1 = df.copy()
# 数据替换
df1["province"] = df1["province"].str.replace("省", "").str.replace(
"壮族自治区", "").str.replace("维吾尔自治区", "").str.replace("自治区", "")
# 分组计数
df_num = df1.groupby("province")["province"].agg(count="count")
df_province = df_num.index.values.tolist()
df_count = df_num["count"].values.tolist()
# 初始化配置
map = Map(init_opts=opts.InitOpts(width="800px", height="400px"))
# 中国地图
map.add("", [list(z) for z in zip(df_province, df_count)], "china")
# 设置全局配置项，标题、工具箱(下载图片)、颜色图例
map.set_global_opts(
title_opts=opts.TitleOpts(title="抖音大V省份分布情况",
pos_left="center",
pos_top="0"),
toolbox_opts=opts.ToolboxOpts(is_show=True,
feature={"saveAsImage": {}}),
# 设置数值范围0-600，is_piecewise标签值连续
visualmap_opts=opts.VisualMapOpts(max_=600, is_piecewise=False))
map.render("抖音大V省份分布情况.html")
def test_map_base():
c = Map().add("商家A",
[list(z) for z in zip(Faker.provinces, Faker.values())],
"china")
eq_(c.theme, "white")
eq_(c.renderer, "canvas")
c.render()
def sxcsfb():
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())]
print(sj)
# 地图部分
m2 = Map()
m2.add(series_name="微信好友",
data_pair=sj,
maptype="陕西",
is_map_symbol_show=True)
m2.set_global_opts(title_opts=opts.TitleOpts(
title="陕西好友分布",
subtitle=time.strftime("%Y-%m-%d %H:%M:%S",
time.localtime(time.time()))),
visualmap_opts=opts.VisualMapOpts(is_piecewise=True,
is_calculable=True,
max_=180))
m2.render("./kshwy/map_sxcsfb.html")
webbrowser.open("E:\study\kshwy\map_sxcsfb.html")
def sffb():
friends = bot.friends(update=True)
dy = defaultdict(
lambda: 0) # defaultdict的作用是在于，当字典里的key不存在但被查找时，返回的不是keyError而是一个默认值
for f in friends:
if '\u9fa5' >= f.province >= '\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
dy[f.province] += 1
attr = dy.keys()
value = dy.values()
dx = [list(dx1) for dx1 in zip(attr, value)
] # zip将可迭代的对象打包成一个一个的元组 如果各个迭代器的元素个数不一致，则返回列表长度与最短的对象相同
print(dx)
# 地图部分
m1 = Map()
m1.add(series_name="微信好友",
data_pair=dx,
maptype="china",
name_map={"key": "value"},
is_map_symbol_show=True)
m1.set_global_opts(
title_opts=opts.TitleOpts(title="小李的微信好友",
subtitle="2020-04-10"), # 设置标题和副标题在左侧
visualmap_opts=opts.VisualMapOpts(is_piecewise=True,
max_=250) # 是否分段,以及分段的最大值
)
m1.render('province.html')
webbrowser.open('province.html')
def dili(df):
df_sales = df
df_sales['订单创建时间'] = pd.to_datetime(df_sales['订单创建时间'])
df_sales = df_sales.set_index('订单创建时间')
df_sales_map = df_sales.groupby('收货地址')['收货地址'].count().sort_values(ascending=False)
plt.figure(figsize=(10, 5))
df_sales_map.plot(kind='bar', color='#87CEFA')
result = []
for i in df_sales_map.index:
if i.endswith('自治区'):
if i == '内蒙古自治区':
i = i[:3]
result.append(i)
else:
i = i[:2]
result.append(i)
else:
result.append(i)
df_sales_map.index = result
df_sales_map.index = df_sales_map.index.str.strip('省')
print(df_sales_map.index)
from pyecharts.charts import Map
sales_map = Map().add('订单数', [list(i) for i in df_sales_map.items()]).set_global_opts(
visualmap_opts=opts.VisualMapOpts(max_=max(df_sales_map) * 0.6)) # 设置最大数据范围
sales_map.render()
def visulize(
self,
city_name,
school_counts,
):
if (self.FlagOfProcess):
realcity = []
# 处理城市名，只保留两字或三字名称
for citys in city_name:
if "黑龙江" in citys:
a = "黑龙江"
elif "内蒙古" in citys:
a = "内蒙古"
else:
a = citys[4:6]
realcity.append(a)
# print(realcity)
# 将数据处理成列表
list1 = [[realcity[i], school_counts[i]]
for i in range(len(realcity))]
# print(list1)
map_1 = Map()
map_1.set_global_opts(
title_opts=opts.TitleOpts(title="2020高校分布"),
visualmap_opts=opts.VisualMapOpts(max_=40) # 最大数据范围
)
map_1.add("2020高校分布", list1, maptype="china")
# map_1.add_js_funcs("window.confirm()")
map_1.render('SchoolMap.html')
print("绘制成功，请在同级目录下查看 SchoolMap.html 文件！")
else:
print("出现错误，停止绘制图表！")
def confirmed_maps():
map_chart = Map()
map_chart.add(
'疫情地圖',
[list(i) for i in zip(city_confirmed.keys(), city_confirmed.values())],
maptype='台湾',
is_map_symbol_show=True,
label_opts=opts.LabelOpts(is_show=False))
map_chart.set_global_opts(title_opts=opts.TitleOpts(title='台灣'),
visualmap_opts=opts.VisualMapOpts(
is_piecewise=True,
pieces=[{
"min": 1,
"max": 50,
"label": "1-50人",
"color": "#FFE6BE"
}, {
"min": 51,
"max": 100,
"label": "51-100人",
"color": "#FFB769"
}, {
"min": 101,
"max": 300,
"label": "101-300人",
"color": "#FF8F66"
}, {
"min": 301,
"max": 1000,
"label": "301-1000人",
"color": "#ED514E"
}]))
map_chart.render('confirmed_map.html')
def test_map_base(fake_writer):
c = Map().add("商家A",
[list(z) for z in zip(Faker.provinces, Faker.values())],
"china")
c.render()
_, content = fake_writer.call_args[0]
eq_(c.theme, "white")
eq_(c.renderer, "canvas")
def chart(self, f): # unsupported city map for now
city, frequency = self.clean(f)[1], self.clean(f)[2]
data = [[city[i], frequency[i]] for i in range(len(city))]
map_1 = Map()
map_1.set_global_opts(
title_opts=opts.TitleOpts(title="2020年b站会员购商品线下分布"),
visualmap_opts=opts.VisualMapOpts(max_=100) # 最大数据范围
)
map_1.add("2020年b站会员购商品线下分布", data, maptype="china")
map_1.render('vip.html')
def render():
world_map = getData()
china_map = getChinaData()
map = Map(
options.InitOpts(bg_color="#87CEFA",
width="100%",
height="900px",
page_title='世界疫情分布'))
for item in maps:
map_data = {}
for item_map in world_map:
name = item_map['name'] # 国家名
count = int(item_map[item]) # 该国家人数
map_data[name] = count
map_data['中国'] = china_map[item]
map.add(
series_name=maps[item], # 名称
data_pair=list(map_data.items()), # 传入数据
maptype='world', # 地图类型
is_map_symbol_show=False, # 不显示标记
name_map=name_map)
map.set_global_opts(
visualmap_opts=options.VisualMapOpts(max_=1100000,
is_piecewise=True,
pieces=[
{
"min": 500000
},
{
"min": 200000,
"max": 499999
},
{
"min": 100000,
"max": 199999
},
{
"min": 50000,
"max": 99999
},
{
"min": 10000,
"max": 49999
},
{
"max": 9999
},
]))
map.set_series_opts(label_opts=options.LabelOpts(is_show=False)) # 不显示国家名
map.add_js_funcs("""document.body.style.backgroundColor="#87CEFA"
""")
map.render('map.html') # 命名并保存
def test_map_item_base(fake_writer):
location_name = ["广东"]
location_data = [[100, 200, 300, 400]]
mock_data = [
opts.MapItem(name=d[0], value=d[1])
for d in list(zip(location_name, location_data))
]
c = Map().add("商家A", mock_data, "china")
c.render()
_, content = fake_writer.call_args[0]
assert_equal(c.theme, "white")
assert_equal(c.renderer, "canvas")
def show_map_charts(df):
# 计数，[0:10]取前10
city_num = df['province'].value_counts() # 应该是按省份显示的
# 返回index列表和value列表组成的元组[('北京', 62), ('上海', 50)]，再将元组转换成列表[['北京', 62], ['上海', 50]]
city_list = [
list(z) for z in zip(city_num.index.tolist(), city_num.values.tolist())
]
map1 = Map(init_opts=opts.InitOpts(width='1350px', height='750px'))
map1.add(series_name='评论数量', data_pair=city_list, maptype='china')
map1.set_global_opts(title_opts=opts.TitleOpts(title='评论用户国内城市分布'),
visualmap_opts=opts.VisualMapOpts(max_=50),
toolbox_opts=opts.ToolboxOpts())
map1.render('charts/map_chart.html')
def chinaMap(value):
province_distribution = {
'河南': 45.23,
'北京': 37.56,
'河北': 21,
'辽宁': 12,
'江西': 6,
'上海': 20,
'安徽': 10,
'江苏': 16,
'湖南': 9,
'浙江': 13,
'海南': 2,
'广东': 22,
'湖北': 8,
'黑龙江': 11,
'澳门': 1,
'陕西': 11,
'四川': 7,
'内蒙古': 3,
'重庆': 3,
'云南': 6,
'贵州': 2,
'吉林': 3,
'山西': 12,
'山东': 11,
'福建': 4,
'青海': 1,
'天津': 1,
'其他': 1
}
#provice = list(province_distribution.keys())
#values = list(province_distribution.values())
provice = list()
values = list()
for item in value:
values.append(item['total']['confirm'])
provice.append(item['name'])
#print(provice)
# print(values)
map = Map("疫情地图", width=1200, height=600)
map.add("疫情",
provice,
values,
visual_range=[0, 500],
maptype='china',
is_visualmap=True,
visual_text_color='#000',
is_label_show=True)
map.render(path="中国地图.html")
def show_map_charts():
city_num = df['city_dealed'].value_counts()
city_num.drop('国外', inplace=True)
city_num.drop('未填写', inplace=True)
map1 = Map(init_opts=opts.InitOpts(width='1350px', height='750px'))
map1.add("", [
list(z) for z in zip(city_num.index.tolist(), city_num.values.tolist())
],
maptype='china')
map1.set_global_opts(title_opts=opts.TitleOpts(title='评论者国内城市分布'),
visualmap_opts=opts.VisualMapOpts(max_=50),
toolbox_opts=opts.ToolboxOpts())
map1.render()
def DrawMap(city_names):
# 假设省会城市最高温度代表该省最高温度
attrs = ['北京', '天津', '上海', '重庆',
'河北', '山西', '辽宁', '吉林',
'黑龙江', '江苏', '浙江', '安徽',
'福建', '江西', '山东', '河南',
'湖北', '湖南', '广东', '四川']
values = []
for cn in city_names:
data = qr.get_data(cn)
values.append(data['Htemperature'])
map_ = Map('气温分布图', width=1200, height=600)
map_.add('', attrs, values, maptype='china', is_visualmap=True, visual_text_color='#000')
map_.render('weatherMap.html')
def college_map():
db = DataBase()
sql = "select area, count(id) from college group by area"
db.cur.execute(sql)
ret = db.cur.fetchall()
db.close()
college_map = Map(init_opts=opts.InitOpts(width="1400px", height="900px")) \
.set_global_opts(title_opts=opts.TitleOpts(title="全国高校分布图"), visualmap_opts=opts.VisualMapOpts(max_=150), )
college_map.add(
'高校数量',
list(ret),
).set_series_opts(label_opts=opts.LabelOpts(
is_show=True, color="#00f", formatter="{b}:{c}"))
college_map.render('college_map.html')
def get_data():
# API地址
url = 'https://api.inews.qq.com/newsqa/v1/automation/foreign/country/ranklist'
# 添加请求头
headers = {
'User-Agent': 'Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/70.0.3538.25 Safari/537.36 Core/1.70.3741.400 QQBrowser/10.5.3863.400'
}
# 获取结果
res = requests.get(url=url, headers=headers).text
# 转为json
data = json.loads(res)
# 获取数据
name = jsonpath.jsonpath(data, '$..name')
confirm = jsonpath.jsonpath(data, '$..confirm')
# 配对
data_list = zip(name, confirm)
# print(list(data_list))
# 绘制地图
map = Map(init_opts=opts.InitOpts(width="1900px", height="900px", page_title="全球疫情分布图", bg_color="#ADD8E6", theme="white")).add(
series_name='世界疫情分布图', data_pair=data_list, maptype='world', name_map=nameMap, is_map_symbol_show=False)
# 设置系列配置项
map.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
# 设置全局配置项
# map.set_global_opts(title_opts=opts.TitleOpts(title="国外疫情情况"),
# visualmap_opts=opts.VisualMapOpts(max_=2000000, is_piecewise=True))
map.set_global_opts(title_opts=opts.TitleOpts(title="国外疫情情况"),
visualmap_opts=opts.VisualMapOpts(pieces=[
{"min": 500000, "color": "#800000"},
{"min": 100000, "max": 500000,
"color": "#CC0000"},
{"min": 50000, "max": 100000,
"color": "#FF0000"},
{"min": 10000, "max": 50000,
"color": "#FF4500"},
{"min": 1000, "max": 10000, "color": "#FF7F50"},
{"min": 100, "max": 1000, "color": "#FFA07A"},
{"max": 100, "color": "#FFCCCC"},
],
is_piecewise=True)) # 显示分段式图例
# 生成文件
map.render('世界疫情分布图.html')
def analysis(self):
"""
数据分析可视化
:return:
"""
data_list = [(i[0], i[3]) for i in self.result]
with open('CountryMap.json', 'r') as f:
country_map = json.load(f)
map = Map(options.InitOpts(width='1366px', height='768px'))
map.add("确诊病例", data_list, 'world', name_map=country_map, is_map_symbol_show=False)
map.set_series_opts(label_opts=options.LabelOpts(is_show=False))
map.set_global_opts(
title_opts=options.TitleOpts(title='全球疫情数据可视化展示'),
visualmap_opts=options.VisualMapOpts(max_=100000)
)
map.render('World_Epidemic_Analysis.html')
def draw_map_world(data, to_file, svg_name, label_name, number_max):
"""
画地图
:param data:
:param to_file:
:param svg_name:
:param label_name: 图例名称
:param number_max: 最大值
:return:
"""
geo = Map(init_opts=opts.InitOpts(width="800px", height="600px", bg_color='rgb(255, 255, 255)')) \
.add(label_name, data, maptype="world") \
.set_series_opts(label_opts=opts.LabelOpts(is_show=False),
showLegendSymbol=False) \
.set_global_opts(legend_opts=opts.LegendOpts(item_width=50,
item_height=30,
textstyle_opts=opts.TextStyleOpts(font_size=30)),
visualmap_opts=opts.VisualMapOpts(min_=0,
max_=int(number_max),
background_color='rgb(255, 255, 255)',
is_piecewise=True,
item_width=50,
item_height=30,
textstyle_opts=opts.TextStyleOpts(font_size=30)),
toolbox_opts=opts.ToolboxOpts(
feature=opts.ToolBoxFeatureOpts(
data_zoom=opts.ToolBoxFeatureDataZoomOpts(is_show=False),
# brush=opts.ToolBoxFeatureBrushOpts(is_show=False),
)
),
)
# geo.render(to_file)
make_snapshot(snapshot, geo.render(to_file), svg_name) # 生成svg图片
def analysisArea(self, title='分析好友地域分布', friends_info=None):
area_infos = {'未知': 0}
for item in friends_info.get('province'):
if not item: area_infos['未知'] += 1
else:
if item in area_infos: area_infos[item] += 1
else: area_infos[item] = 1
map_ = Map(init_opts=dict(theme='purple-passion', page_title=title))
map_.add(title,
data_pair=tuple(zip(area_infos.keys(), area_infos.values())),
maptype='china')
map_.set_global_opts(
title_opts=opts.TitleOpts(title=title),
visualmap_opts=opts.VisualMapOpts(max_=200),
)
map_.render(os.path.join(self.savedir, '%s.html' % title))
def plt_data(name, confirm_all):
data_list = list(zip(name, confirm_all))
map = Map(opts.InitOpts(width='1600px',
height='600px')).add(series_name='累计确诊',
data_pair=data_list,
maptype="china",
is_map_symbol_show=False)
map.set_series_opts(label_opts=opts.LabelOpts(is_show=True))
map.set_global_opts(title_opts=opts.TitleOpts("中国疫情"),
visualmap_opts=opts.VisualMapOpts(
range_color=Faker.visual_color, max_=1000)) # 图例显示
map.render("html/中国疫情分布图.html")
plt.rcParams['font.sans-serif'] = ['FangSong']
plt.bar(range(len(confirm_all)), confirm_all, tick_label=name)
plt.xticks(rotation=270)
plt.savefig("imgdata/中国疫情柱状图")
print("中国数据可视化成功，文件名：中国疫情分布图.html、中国疫情柱状图.png")
def draw():
get_data.GetCondition()
result = {'province': [], 'province_confirmedCount': []}
result['province'].append('台湾')
result['province_confirmedCount'].append(18)
result['province'].append('香港')
result['province_confirmedCount'].append(56)
result['province'].append('澳门')
result['province_confirmedCount'].append(10)
# 批量删除多余字符的函数
def replace_something(source_str, replace_list):
for line in replace_list:
source_str = source_str.replace(line, "")
return source_str
with open("virus.csv", 'r', encoding='UTF-8') as csvFile:
reader = csv.reader(csvFile) # 读取文件数据
for item in reader:
if reader.line_num == 1:
continue
# 定义删除的字串列表
replace_list = ['省', '市', '壮族自治区', '维吾尔自治区', '回族自治区', '自治区']
# 调用删除字串的函数
province_name = replace_something(item[0], replace_list)
if len(result['province']) == 34:
break
if province_name in result['province']:
continue
else:
result['province'].append(province_name)
result['province_confirmedCount'].append(int(item[2]))
# 绘制地图
from pyecharts import options as opts
from pyecharts.charts import Map
map = Map()
map.add("确诊人数", [
list(z)
for z in zip(result['province'], result['province_confirmedCount'])
], 'china')
map.set_global_opts(
title_opts=opts.TitleOpts(title="疫情地图"),
visualmap_opts=opts.VisualMapOpts(max_=2000),
)
map.render(path="疫情地图.html")
def display_recent_global_distribution(pic_file, maxCount=200, **kwargs):
"""
display the distribution of recent total numbers of confirmed patients.
Parameters
----------
pic_file: str
absolute path of the generated figure.
maxCount: int
maximumn count of colorbar. (default: 200)
"""
conn = db.connect(dbFile)
cu = conn.cursor()
OverallDf = pd.read_sql_query("""select * from Region_Data""", conn)
OverallDf['updateTime'] = OverallDf['updateTime'].astype('int64')
recentData = OverallDf.groupby('provinceShortName').apply(
lambda t: t[t['updateTime'] == t['updateTime'].max()])
recentData = recentData.groupby('country').agg({
'confirmedCount': 'sum',
'suspectedCount': 'sum',
'updateTime': 'mean'
})
recentData['date'] = pd.to_datetime(recentData['updateTime'] / 1000,
unit='s')
time = recentData[recentData.index == '中国']['updateTime']
data = [[
searchCountryENName(recentData.index[i]),
int(recentData['confirmedCount'][i])
] for i in range(recentData.shape[0])]
map_3 = Map()
map_3.add("{0} worldwide COVID-19 patients distribution".format(
recentData['date'][1].strftime('%Y-%m-%d')),
data,
maptype='world',
is_map_symbol_show=False)
map_3.set_series_opts(
label_opts=opts.LabelOpts(is_show=False, font_size=100))
map_3.set_global_opts(visualmap_opts=opts.VisualMapOpts(max_=maxCount),
title_opts=opts.TitleOpts(title=""))
if 'notebook' in kwargs.keys():
if kwargs['notebook']:
map_3.render_notebook()
else:
html_file = '{0}.html'.format(os.path.splitext(pic_file)[0])
tmpHtmlFile = map_3.render()
shutil.move(tmpHtmlFile, html_file)
make_snapshot(snapshot,
file_name=html_file,
output_name=pic_file,
is_remove_html=False,
**kwargs)
def map_total_cases(csv):
"""
Description: exports an html file with an interactive map showing the latest total cases
registered per country in the planet
in a two-ax chart for countries with p-score > 1
:param owid_data: OWID main coronavirus dataset in a pandas dataframe format,
it can be already processed with clean_data()
:export: HTML file with a pyecharts interactive map
"""
input_df = pd.read_csv(csv)
if input_df.index.name == "date":
input_df = input_df[["location", "total_cases"]].dropna()
else:
input_df = input_df[["date", "location", "total_cases"]].set_index("date").dropna()
map_df = input_df.loc[input_df.groupby('location').total_cases.idxmax()].head(
len(input_df["location"].unique()) - 1)
map_df.reset_index(drop=True, inplace=True)
country = list(map_df["location"])
totalcases = list(map_df["total_cases"])
list1 = [[country[i], totalcases[i]] for i in range(len(country))]
map_1 = Map(init_opts=opts.InitOpts(width="1000px", height="460px"))
map_1.add("Total Confirmed Cases",
list1,
maptype="world",
is_map_symbol_show=False)
map_1.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
map_1.set_global_opts(
visualmap_opts=opts.VisualMapOpts(max_=1100000, is_piecewise=True, pieces=[
{"min": 1000000},
{"min": 600000, "max": 999999},
{"min": 200000, "max": 599999},
{"min": 50000, "max": 119999},
{"min": 10000, "max": 49999},
{"max": 9999}, ]),
title_opts=opts.TitleOpts(
title='Casos confirmados de COVID-19',
subtitle="Últimos datos disponibles",
pos_left="center",
padding=0,
item_gap=2,
title_textstyle_opts=opts.TextStyleOpts(color="darkblue",
font_weight="bold",
font_family="Courier New",
font_size=30),
subtitle_textstyle_opts=opts.TextStyleOpts(color="grey",
font_weight="bold",
font_family="Courier New",
font_size=13)),
legend_opts=opts.LegendOpts(is_show=False))
return map_1.render('map_cases.html')
def make_geo_map(self):
"""
生成世界地图，根据各国电影发行量
:return:
"""
# print(get_current_time() + '|-------> 正在生成 世界各国电影发行量 图表...')
# 导入TOP500电影数据
csv_path = os.path.abspath(os.path.join(os.path.dirname("__file__"), os.path.pardir, "moviespider", "movie_info_top500.csv"))
rows = pd.read_csv(csv_path, encoding='utf-8', dtype=str)
# 分析并统计数据
col_country = rows['国别'].to_frame()
res = col_country.groupby('国别')['国别'].count().sort_values(ascending=False)
raw_data = [i for i in res.items()]
# 导入映射数据，英文名 -> 中文名
country_name = pd.read_json('countries_zh_to_en.json', orient='index')
stand_data = [i for i in country_name[0].items()]
# 数据转换
res_code = []
for raw_country in raw_data:
for stand_country in stand_data:
if stand_country[1] in raw_country[0]:
res_code.append(stand_country[0])
code = pd.DataFrame(res_code).groupby(0)[0].count().sort_values(ascending=False)
data = []
for k, v in code.items():
data.append([k, v])
# 制作图表
c = Map()
c.add("电影发行量", data, "world")
c.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
c.set_global_opts(title_opts=opts.TitleOpts(title="电影TOP500榜单中 - 世界各国电影发行量"),
visualmap_opts=opts.VisualMapOpts(max_=55))
htmlPath = os.path.abspath(os.path.join(os.path.abspath(os.path.dirname(os.path.dirname(__file__))), "analyse_data", "世界各国电影发行量.html"))
pngPath = os.path.abspath(os.path.join(os.path.abspath(os.path.dirname(os.path.dirname(__file__))), "analyse_data", "世界各国电影发行量.png"))
# 生成html
c.render(htmlPath)
# 生成png
# make_snapshot(snapshot, c.render(), pngPath)
# print(get_current_time() + '|-------> 已生成 世界各国电影发行量 图表...')
return c
def nowconfirm_map(data_json,virus_nowconfirm_dict):
map = Map(init_opts=opts.InitOpts(width="1900px", height="900px"))
map.set_global_opts(
title_opts=opts.TitleOpts(title="实时疫情地图 %s\n\n累计确诊 %d\n现有确诊 %d\n现有疑似 %d\n输入病例 %d\n累计治愈 %d\n累计死亡 %d" \
%(data_json['lastUpdateTime'],data_json['chinaTotal']['confirm'],data_json['chinaTotal']['nowConfirm'], \
data_json['chinaTotal']['suspect'],data_json['chinaTotal']['importedCase'], \
data_json['chinaTotal']['heal'],data_json['chinaTotal']['dead'])),
visualmap_opts=opts.VisualMapOpts(max_=3600, is_piecewise=True,
pieces=[
{"max": 1999999, "min": 1000, "label": "10000人及以上", "color": "#8A0808"},
{"max": 999, "min": 200, "label": "200-999人", "color": "#B40404"},
{"max": 199, "min": 50, "label": "50-199人", "color": "#DF0101"},
{"max": 49, "min": 30, "label": "30-49人", "color": "#F78181"},
{"max": 29, "min": 10, "label": "10-29人", "color": "#F5A9A9"},
{"max": 9, "min": 0, "label": "1-9人", "color": "#FFFFCC"},
], ) #最大数据范围，分段
)
virus_nowconfirm = list(virus_nowconfirm_dict.items())
# {names[i]:v[i] for i in range(len(names))}
map.add("现有确诊", data_pair=virus_nowconfirm, maptype="china", is_roam=True)
map.render('html\全国实时现有确诊疫情地图.html')
def analyseLocation(friends):
keys = []
values = []
province = list(map(lambda x: x['Province'], friends[1:]))
for i in set(province):
keys.append(i)
values.append(province.count(i))
print(keys)
print(values)
maps = Map('中国地图', '中国地图', width=1200, height=600)
maps.add("",
keys,
values,
visual_range=[0, 50],
maptype='china',
is_visualmap=True,
visual_text_color='#000')
maps.show_config()
maps.render(path="localtion.html")
def display_recent_overall_distribution(pic_file, maxCount=500, **kwargs):
"""
display the distribution of recent total numbers of nation-wide confirmed
patients in China.
Parameters
----------
pic_file: str
absolute path of the generated figure.
maxCount: int
maximumn count of colorbar. (default: 500)
"""
conn = db.connect(dbFile)
cu = conn.cursor()
cu.execute("""select provinceShortName, confirmedCount
from Region_Data
where updateTime in (select max(updateTime)
from Region_Data r_d
where r_d.country='中国' and r_d.region_id=Region_Data.region_id)
group by Region_Data.region_id;
""")
recentProvinceData = cu.fetchall()
cu.execute("""select max(updateTime) from Region_Data;""")
recentTime = cu.fetchone()
recentTimeObj = dt.datetime.utcfromtimestamp(int(recentTime[0]) / 1000)
# color-plot
list1 = [[recentProvinceData[i][0], recentProvinceData[i][1]]
for i in range(len(recentProvinceData))]
map_1 = Map()
map_1.add("{0}全国各省感染总人数".format(recentTimeObj.strftime('%y-%m-%d')),
list1,
maptype="china",
is_map_symbol_show=False)
map_1.set_global_opts(title_opts=opts.TitleOpts(
title="{0}全国各省感染总人数".format(recentTimeObj.strftime('%y-%m-%d'))),
visualmap_opts=opts.VisualMapOpts(max_=maxCount))
if 'notebook' in kwargs.keys():
if kwargs['notebook']:
map_1.render_notebook()
else:
html_file = '{0}.html'.format(os.path.splitext(pic_file)[0])
tmpHtmlFile = map_1.render()
shutil.move(tmpHtmlFile, html_file)
make_snapshot(snapshot,
file_name=html_file,
output_name=pic_file,
is_remove_html=False,
**kwargs)
def promap():
# attr, value要显示的数值
value = [20, 100]
attr = ['余杭区', '萧山区']
# 图框的基本特性
m = Map('杭州地图示例图', width=600, height=400)
# 添加数据到图框中
m.add('',
attr,
value,
maptype=u'杭州',
visual_range=[0, 100],
is_visualmap=True,
visual_text_color='#000')
# show_config() 打印输出图表的所有配置项
m.show_config()
# render() 生成 .html 文件
m.render()
