def province_map(cities_data, province) -> Map:
if province == '香港':
opt = [{
"min": 11,
"color": '#731919'
}, {
"min": 6,
"max": 10,
"color": '#f08f7f'
}, {
"min": 1,
"max": 5,
"color": '#ffb86a'
}, {
"value": 0,
"color": '#ffffff'
}]
else:
opt = [{
"min": 100,
"color": '#731919'
}, {
"min": 10,
"max": 99,
"color": '#f08f7f'
}, {
"min": 1,
"max": 9,
"color": '#ffb86a'
}, {
"value": 0,
"color": '#ffffff'
}]
c = (Map().add(
"確診人數", cities_data, province,
is_map_symbol_show=False).set_series_opts(label_opts=opts.LabelOpts(
is_show=True, font_size=9)).set_global_opts(
visualmap_opts=opts.VisualMapOpts(max_=200,
is_piecewise=True,
pieces=opt),
title_opts=opts.TitleOpts(title=province + "疫情情况"),
legend_opts=opts.LegendOpts(is_show=False)))
return c
def get_covid_oversea_country(request):
file = open("country.txt", encoding='UTF-8')
country_list = []
while 1:
line = file.readline()
if not line:
break
else:
country_list.append(line.replace("\n", ""))
file.close()
all_covid = db.covid_oversea.find({}, {'text'}).limit(500)
all_text = ''
for item in all_covid:
all_text = all_text + item['text']
jieba.load_userdict('country.txt')
all_text = re.sub(
"[A-Za-z0-9\!\%\[\]\,\。\<\>\#\@\&\/\-\=\"\:\?\'\;\.\➕\_\～\，\·\！\）\（\？\{\}\【\】\°\(\)]",
"", all_text)
words = jieba.cut(all_text)
word_list = []
for word in words:
if word in country_list:
word_list.append(word)
data = pd.DataFrame({'country': word_list})
country_serise = data['country'].value_counts()
country_name_list = []
country_value_list = []
for i in country_serise.index:
country_name_list.append(country_chinese_english[str(i)])
country_value_list.append(int(country_serise[i]))
c = (Map(init_opts=opts.InitOpts(width="1000px", height="580px")).add(
"国家", [list(z) for z in zip(country_name_list, country_value_list)],
"world",
is_roam=False,
is_map_symbol_show=False).set_series_opts(label_opts=opts.LabelOpts(
is_show=False)).set_global_opts(
title_opts=opts.TitleOpts(title=""),
visualmap_opts=opts.VisualMapOpts(item_height=150,
item_width=20,
pos_left=100,
pos_bottom=50),
legend_opts=opts.LegendOpts(is_show=False)))
return HttpResponse(json.dumps({'Code': 1, 'Data': c.render_embed()}))
def timeline_map_zrs():
tl = Timeline()
for i in range(2008, 2018):
map0 = (Map().add(
"世界抑郁症总人数",
list(
zip(list(df_zrs.region.unique()),
list(df_zrs["{}".format(i)]))),
"world",
is_map_symbol_show=False).set_series_opts(
label_opts=opts.LabelOpts(is_show=False)).set_global_opts(
title_opts=opts.TitleOpts(title="{}年世界抑郁症总人数".format(i)),
visualmap_opts=opts.VisualMapOpts(
min_=0, max_=df_zrs["2017"].max(), series_index=0),
))
tl.add(map0, "{}年".format(i))
return tl
def hotmap_location(self, filename):
df = pd.read_excel(filename)
row_locations = df['location']
row_location_list = []
for row_t in row_locations:
row_location_list.append(row_t)
num_count = Counter(row_location_list)
location = []
num = [] #解析字典
for key, value in num_count.items():
location.append(key)
num.append(value)
c = (Map().add("", [list(z) for z in zip(location, num)],
"china").set_global_opts(
title_opts=opts.TitleOpts(title="淘宝T恤卖家分布图"),
visualmap_opts=opts.VisualMapOpts(max_=200)))
c.render(path='saler_location.html')
def timeline_map() -> Timeline:
tl = Timeline()
df = dfdemo()
for i in range(2009, 2018):
map0 = (Map().add(
"国际旅游收入",
list(zip(list(df.CountryName), list(df["{}".format(i)]))),
"world",
is_map_symbol_show=False).set_series_opts(
label_opts=opts.LabelOpts(is_show=False)).set_global_opts(
title_opts=opts.TitleOpts(
title="2009-2017年世界各国国际旅游收入（占总出口的百分比）".format(i),
subtitle_textstyle_opts=opts.TextStyleOpts(
color="red", font_size=10, font_style="italic")),
visualmap_opts=opts.VisualMapOpts(series_index=0, max_=20),
))
tl.add(map0, "{}".format(i))
return tl
def map_film() -> Map:
df = pd.read_csv('./data/movie2.csv', encoding='utf-8', delimiter="\t", error_bad_lines=False)
df = df.drop(columns='Unnamed: 2')
# 求pname的个数
counts = df['pname'].value_counts()
# print(counts)
province = list(counts.index)
province_values = list(counts.values)
province_values = [str(i) for i in province_values]
c = (
Map()
.add("", [list(z) for z in zip(province, province_values)], "china")
.set_global_opts(
title_opts=opts.TitleOpts(title="全国影院分布情况"),
visualmap_opts=opts.VisualMapOpts(max_=110),
)
)
return c.render()
def map_world() -> Timeline:
tl = Timeline()
for i in range(2006, 2018):
c = (
Map().add("中小学女生与男生的入学比例（%）",
list(
zip(list(df3['CountryName']),
list(df3["{}".format(i)]))),
"world",
is_map_symbol_show=False)
# .add("2017", [list(z) for z in zip(df3.CountryName, 女性受教育程度)], "world")
.set_series_opts(label_opts=opts.LabelOpts(
is_show=False)).set_global_opts(
title_opts=opts.TitleOpts(title="Map- 中小学女生与男生的入学比例（%）"),
visualmap_opts=opts.VisualMapOpts(min_=0.9, max_=1.08),
))
tl.add(c, "{}年".format(i))
return tl
def china_map_NewAddConfirm():
map_NewAddConfirm = (
Map()
.add("", [list(z) for z in zip(data_pro_newly.name, data_pro_newly.confirm)], "china")
.set_global_opts(title_opts=opts.TitleOpts(title="中国疫情地图（新增确诊人数）"),
visualmap_opts = opts.VisualMapOpts(is_piecewise = True, pieces=[
{"min": 500, "color": "#4F070D"},
{"min": 300, "max": 499, "color": "#780707"}, # 数据范围分段，分颜色，可以根据数据大小具体分配大小
{"min": 200, "max": 299, "color": "#B40404"},
{"min": 100, "max": 199, "color": "#CD1111"},
{"min": 1, "max": 99, "color": "#F68181"},
{"max": 0, "min": 0, "label": "0", "color": "#FFFFFF"},
]))
)
page.add(map_NewAddConfirm) # 将图像加入同一页
if flag: dwf.write_to_file(savaPath + '/中国疫情地图（新增确诊人数）.txt', str(map_NewAddConfirm.dump_options_with_quotes()))
return map_NewAddConfirm
def map_world1() -> Timeline:
tl = Timeline()
for i in range(2015, 2019):
c = (
Map().add("女性参会人数占比%",
list(
zip(list(df2['CountryName']),
list(df2["{}".format(i)]))),
"world",
is_map_symbol_show=False)
# .add("2017", [list(z) for z in zip(df2.CountryName, 女性参会人数占比)], "world")
.set_series_opts(label_opts=opts.LabelOpts(
is_show=False)).set_global_opts(
title_opts=opts.TitleOpts(title="Map- 国家议会中妇女席位的比例（%）"),
visualmap_opts=opts.VisualMapOpts(min_=7, max_=50),
))
tl.add(c, "{}年".format(i))
return tl
def map_base():
chinaMap = (
Map()
# .add("累计确诊", maptype="world")
.add("累计确诊", [list(z) for z in zip(province_name, province_total_confirm)], maptype="china", is_roam=True)
.set_global_opts(
title_opts=opts.TitleOpts(title="中国累计确诊数据"),
visualmap_opts=opts.VisualMapOpts(max_=3600,
is_piecewise=True,
pieces=[{"max": 0, "label": '0人'}, {"min": 1, "max": 9, "label": '1-9人'},
{"min": 10, "max": 99, "label": '10-99人'},
{"min": 100, "max": 499, "label": '100-499人'},
{"min": 500, "max": 999, "label": '500-999人'},
{"min": 1000, "max": 9999, "label": '1000-9999人'},
{"min": 10000, "label": '10000人及以上'}]),
)
)
return chinaMap
def Birth_map() -> Timeline:
a = Timeline()
for i in range(2010, 2018):
map0 = (Map().add(
"世界青春期女性生育率",
list(zip(list(青春生育率.CountryName), list(青春生育率["{}".format(i)]))),
"world",
is_map_symbol_show=False).set_series_opts(
label_opts=opts.LabelOpts(is_show=False)).set_global_opts(
title_opts=opts.TitleOpts(
title="世界青春期女性生育率".format(i),
subtitle_textstyle_opts=opts.TextStyleOpts(
color="red", font_size=10, font_style="italic")),
visualmap_opts=opts.VisualMapOpts(series_index=0,
max_=187),
))
a.add(map0, "{}".format(i))
return a.render_embed()
def china_map_picture(self,seriesname:list, x_data, y_data):
print(x_data)
print(y_data)
c = (
Map(init_opts=self.init_opts)
.add(seriesname[0], [list(z) for z in zip(x_data, y_data)], "china")
.set_global_opts(title_opts=opts.TitleOpts(title=self.title, subtitle=self.subtitle),
# 分段类型
visualmap_opts=opts.VisualMapOpts(max_=200)
# 添加logo
# graphic_opts=self.logo
)
)
# 返回option
return c
def China(self,area,confirmed,time):
pieces=[
{'max':100000,'min':10001,'label':'>50000','color':'#8A0808'},
{'max':10000,'min':5001,'label':'100000-10000','color':'#B40404'},
{'max':5000,'min':1001,'label':'5000-1001','color':'#DF0101'},
{'max':1000,'min':101,'label':'1000-101','color':'#F5A9A9'},
{'max':100,'min':1,'label':'100-1','color':'#F5A9A9'},
{'max':0,'min':0,'label':'0','color':'#FFFFFF'},
]
c=(
Map(init_opts=opts.InitOpts(width='1000px',height='880px'))
.add("累计确诊人数",[list(z) for z in zip(area,confirmed)],"china")
.set_global_opts(
title_opts=opts.TitleOpts(title="中国疫情图",subtitle='截至'+time+'中国疫情分布图'),
visualmap_opts=opts.VisualMapOpts(max_=200,is_piecewise=True,pieces=pieces),
)
.render("中国疫情地图.html")
)
def city(self,city,confirmed,province,time):
pieces = [
{'max': 50000, 'min': 1001, 'label': '>1000', 'color': '#8A0808'},
{'max': 100, 'min': 51, 'label': '100-51', 'color': '#B40404'},
{'max': 50, 'min': 11, 'label': '50-11', 'color': '#DF0101'},
{'max': 10, 'min': 2, 'label': '10-2', 'color': '#F5A9A9'},
{'max': 5, 'min': 1, 'label': '5-1', 'color': '#F5A9A9'},
{'max': 0, 'min': 0, 'label': '0', 'color': '#FFFFFF'},
]
c = (
Map(init_opts=opts.InitOpts(width='1000px', height='880px'))
.add("累计确诊人数", [list(z) for z in zip(city, confirmed)], province)
.set_global_opts(
title_opts=opts.TitleOpts(title=province+"疫情图", subtitle='截至' + time + '疫情分布图'),
visualmap_opts=opts.VisualMapOpts(max_=200, is_piecewise=True, pieces=pieces),
)
.render(province+"疫情地图.html")
)
def getMap():
# load data from redis
number = r.get(provinceNumberKey)
data_pair = []
for i in range(0, int(number)):
key = 'province-data-' + str(i)
data_pair.append([
r.hget(key, 'province_cn'),
r.hget(key, 'number'),
])
# adjust the color range based on the confirmed case
range_color = []
i = 0
while i <= 1000:
if i == 0:
range_color.append('#ffffff')
if i <= 10 and i > 0:
range_color.append('#f2dadb')
if i > 10 and i <= 99:
range_color.append('#ce8a86')
if i > 99 and i <= 980:
range_color.append('#bf5a53')
i = i + 1
map = (Map(init_opts=opts.InitOpts(
width='100%',
height='600px',
page_title='coronavirus-map',
)).add(
zoom=1.1,
is_roam=False,
maptype="china",
data_pair=data_pair,
series_name="confirmed case",
).set_global_opts(visualmap_opts=opts.VisualMapOpts(
min_=0,
max_=1000,
is_show=True,
pos_left='left',
range_color=range_color),
title_opts=opts.TitleOpts(title='coronavirus-map')))
return map.render()
def map_visualmap(df, up_to_date, map_name, title_suffix='') -> Map:
now_num = (df['confirmed_num'] - df['cured_num'] - df['dead_num']).tolist()
c = (Map(init_opts=opts.InitOpts()).add('现有确诊人数', [
list(z) for z in zip(df['area_name'].tolist(), now_num)
], map_name).add('累计确诊人数', [
list(z)
for z in zip(df['area_name'].tolist(), df['confirmed_num'].tolist())
], map_name).add('累计治愈人数', [
list(z)
for z in zip(df['area_name'].tolist(), df['cured_num'].tolist())
], map_name).add('累计死亡人数', [
list(z) for z in zip(df['area_name'].tolist(), df['dead_num'].tolist())
], map_name).set_series_opts(label_opts=opts.LabelOpts(
is_show=False)).set_global_opts(
title_opts=opts.TitleOpts(title=f"新型冠状肺炎最新情况（中国内地）{title_suffix}",
subtitle=f'数据更新至{up_to_date}',
subtitle_link='https://tushare.pro/',
pos_left='center',
title_textstyle_opts={
'fontWeight': 'bolder',
'fontSize': 40,
'color': '#BC3B20'
},
subtitle_textstyle_opts={
'fontSize': 20,
'fontStyle': 'normal',
'color': 'lightgrey',
'fontFamily': 'SimSun'
}),
legend_opts=opts.LegendOpts(pos_top='middle',
pos_right='10%',
orient='vertical',
textstyle_opts={'fontSize': 20},
selected_mode='single'),
visualmap_opts=opts.VisualMapOpts(
max_=sorted(now_num)[-2]
if len(now_num) > 1 else sorted(now_num)[-1],
pos_top='middle',
pos_left='5%',
textstyle_opts={'color': 'white'},
range_text=['High', 'Low']),
tooltip_opts=opts.TooltipOpts(trigger='item',
formatter='{b}</br>{a}:{c}')))
return c
def drawmap(maptype, map_province_list, map_province_data):
# 拿到想要的数据之后，接下来我们开始画地图
title = ''
if maptype == 'china':
title = '全国疫情可视化数据展示'
else:
title = maptype
ditu = (
Map(
init_opts=opts.InitOpts(
width='900px',
height='600px',
page_title=title
)
)
.add(
series_name='确诊人数',
data_pair=[list(z) for z in zip(map_province_list, map_province_data)],
maptype=maptype,
is_map_symbol_show=False # 是否显示地图上的小红点
)
.set_global_opts(
legend_opts=opts.LegendOpts(
is_show=False
),
visualmap_opts=opts.VisualMapOpts(
is_piecewise=True,
pieces=[
{'min': 0, 'max': 0, 'label': '0', 'color': '#ffffff'},
{'min': 1, 'max': 9, 'label': '1-9', 'color': '#FFE5DB'},
{'min': 10, 'max': 99, 'label': '10-99', 'color': '#FF9985'},
{'min': 100, 'max': 999, 'label': '100-999', 'color': '#F57567'},
{'min': 1000, 'max': 9999, 'label': '1000-9999', 'color': '#E64546'},
{'min': 10000, 'label': '>= 10000', 'color': '#B80909'}
],
# pos_top='center',
textstyle_opts=opts.TextStyleOpts(
color='#ffffff'
)
)
)
)
return ditu
def map_base() -> Map:
# 数据暂时写死：后续可以结合实时数据，进行更新
# 省和直辖市
province_distribution = {
'河南': 566,
'北京': 212,
'河北': 21,
'辽宁': 12,
'江西': 391,
'上海': 203,
'安徽': 408,
'江苏': 271,
'湖南': 521,
'浙江': 724,
'海南': 2,
'广东': 725,
'湖北': 11177,
'黑龙江': 121,
'澳门': 1,
'陕西': 128,
'四川': 254,
'内蒙古': 3,
'重庆': 312,
'云南': 6,
'贵州': 2,
'吉林': 3,
'山西': 12,
'山东': 259,
'福建': 179,
'青海': 1,
'天津': 1,
'其他': 1
}
provice = list(province_distribution.keys())
values = list(province_distribution.values())
c = (Map().add("确诊人数", [list(z) for z in zip(provice, values)],
"china").set_series_opts(label_opts=opts.LabelOpts(
is_show=False)).set_global_opts(
title_opts=opts.TitleOpts(title="全国实时疫情分布地图"),
visualmap_opts=opts.VisualMapOpts(max_=1000),
))
return c
def film_map():
df = pd.read_csv('./data/movie2.csv', encoding='utf-8', delimiter="\t", error_bad_lines=False)
df = df.drop(columns='Unnamed: 2')
# 求pname的个数
counts = df['pname'].value_counts()
# print(counts)
counts = counts.astype(str)
province = list(counts.index)
province_values = list(counts.values)
map = (
Map()
.add("全国影院分布", list(zip(province, province_values)), "china", is_map_symbol_show=False)
.set_global_opts(
title_opts=opts.TitleOpts(title="全国影院分布情况", subtitle="",
subtitle_textstyle_opts=opts.TextStyleOpts(color="blue", font_size=18)),
visualmap_opts=opts.VisualMapOpts(min_=0, max_=110, series_index=0),
)
)
return map.render_embed()
def china_map_heal():
map_heal = (
Map()
.add("", [list(z) for z in zip(data_pro_tota.name, data_pro_tota.heal)], "china")
.set_global_opts(title_opts=opts.TitleOpts(title="中国疫情地图（累计治愈人数）"),
visualmap_opts = opts.VisualMapOpts(is_piecewise = True, pieces=[
{"min": 10000, "color": "#006400"},
{"min": 1000, "max": 9999, "color": "#008B00"}, # 数据范围分段，分颜色，可以根据数据大小具体分配大小
{"min": 500, "max": 999, "color": "#00CD66"},
{"min": 100, "max": 499, "color": "#00EE76"},
{"min": 10, "max": 99, "color": "#00FF00"},
{"min": 1, "max": 9, "color": "#00FA9A"},
{"max": 0, "min": 0, "label": "0", "color": "#FFFFFF"},
]))
)
page.add(map_heal) # 将图像加入同一页
if flag: dwf.write_to_file(savaPath + '/中国疫情地图（累计治愈人数）.txt', str(map_heal.dump_options_with_quotes()))
return map_heal
def timeline_map() -> Timeline:
tl = Timeline()
for i in range(2007, 2019):
map0 = (
Map()
.add(
"high-tech export", list(zip(list(df.Country_Name_en), list(df["{}".format(i)]))), "world",
is_map_symbol_show=False)
.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
.set_global_opts(
title_opts=opts.TitleOpts(title="{}高科技出口".format(i), subtitle="",
subtitle_textstyle_opts=opts.TextStyleOpts(color="red", font_size=18,
font_style="italic")),
visualmap_opts=opts.VisualMapOpts(min_=0, max_=30),
)
)
tl.add(map0, "{}".format(i))
return tl
def timeline_map_c() -> Timeline:
tl = Timeline()
for i in range(2009, 2020):
map0 = (Map().add(
"女性劳动力参与率",
(list(zip(list(dfc1.country), list(dfc1["{}".format(i)])))),
"world",
is_map_symbol_show=False).set_global_opts(
title_opts=opts.TitleOpts(
title="".format(i),
subtitle="",
subtitle_textstyle_opts=opts.TextStyleOpts(
color="blue", font_size=16, font_style="italic")),
visualmap_opts=opts.VisualMapOpts(min_=0,
max_=100,
series_index=0),
))
tl.add(map0, "{}".format(i))
return tl.render_embed()
def analysisProvince():
province_count = df.groupby(
'province', as_index=True)['province'].count().sort_values()
data = list(
map(lambda x: x if x != '' else '未知', list(province_count.index)))
# 画图
page = Page()
chart1 = (Map().add(
"人数", [list(z) for z in zip(data, list(province_count))],
"china").set_global_opts(title_opts=opts.TitleOpts(title="好友分布(中国地图)"),
visualmap_opts=opts.VisualMapOpts(
max_=200, is_piecewise=False)))
page.add(chart1)
chart2 = (Bar().add_xaxis(data).add_yaxis(
'人数', list(province_count)).set_global_opts(title_opts=opts.TitleOpts(
title="好友分布柱状图")))
page.add(chart2)
page.render('好友分布分析.html')
def hotmap(year):
timeline_1 = Timeline()
for i in range(0, 4):
if i == 0:
a = 'confirmed'
elif i == 1:
a = 'suspected'
elif i == 2:
a = 'dead'
elif i == 3:
a = 'healed'
list_i = listfilter2(i)
map_1 = (
Map().set_global_opts(
title_opts=opts.TitleOpts(title="2020年全国各省疫情"),
visualmap_opts=opts.VisualMapOpts() # 最大数据范围
).add(a, list_i, maptype="china"))
timeline_1.add(map_1, "{}".format(a))
timeline_1.render("province_confirmed.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 sichuanmap():
c = (Map().add("橙子销量", [list(z) for z in zip(distincts, value_sichuan)],
"四川").set_global_opts(
title_opts=opts.TitleOpts(title="四川销售分布"),
visualmap_opts=opts.VisualMapOpts(max_=maxvalue1,
is_piecewise=True,
pieces=[{
"min": 0,
"max": 1000
}, {
"min": 1000,
"max": 3000
}, {
"min":
300000,
"max":
maxvalue1
}]),
).render("map_sichuan.html"))
def get_total_confirm_map():
total_confirm_dict = get_api.get_total_confirm_dict(api_url) #分省统计确诊患者总数字典
total_confirm_list = [[x, total_confirm_dict[x]]
for x in list(total_confirm_dict)] #分省统计确诊患者总数列表
data_update_time = get_api.get_data_update_time(api_url) #api数据更新时间
total_confirm_map = ( #全国确诊统计图
Map(init_opts=opts.InitOpts()).add(
"确诊患者", total_confirm_list, "china").set_global_opts(
title_opts=opts.TitleOpts(
title="各省累计确诊患者数",
subtitle="数据更新时间：{}".format(data_update_time),
pos_right="center",
pos_top="5%",
title_link='/now_page/',
title_target='self'),
visualmap_opts=opts.VisualMapOpts(max_=1500),
legend_opts=opts.LegendOpts(is_show=False)))
total_confirm_map.chart_id = 'totalconfirmmap'
return total_confirm_map
def timeline_map():
tl = Timeline()
for i in range(2015, 2018):
map0 = (Map().add("世界世界肺癌生存率",
list(zip(list(cf.地区), list(cf["{}年".format(i)]))),
"world",
is_map_symbol_show=False).set_global_opts(
title_opts=opts.TitleOpts(
title="{}年世界肺癌生存率".format(i),
subtitle="",
subtitle_textstyle_opts=opts.TextStyleOpts(
color="red",
font_size=18,
font_style="italic")),
visualmap_opts=opts.VisualMapOpts(min_=5,
max_=40),
))
tl.add(map0, "{}年".format(i))
return tl
def timeline_map() -> Timeline:
tl = Timeline()
for i in range(2000, 2017):
map0 = (
Map()
.add(
"", list(zip(list(df2.国家), list(df2["{}".format(i)]))), "world", is_map_symbol_show=False
)
.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
.set_global_opts(
title_opts=opts.TitleOpts(title="2000年-2017年世界各国因吸烟死亡人数".format(i), subtitle="",
subtitle_textstyle_opts=opts.TextStyleOpts(color="black", font_size=16,
font_style="italic")),
visualmap_opts=opts.VisualMapOpts(max_=266, min_=15),
)
)
tl.add(map0, "{}年".format(i))
return render_template('yx.html')
def map_gd_disease_dis() -> Map:
# ->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。
ls_gd_cities = [name + '市' for name in dic_gd_cfm.keys()]
# Python3使用dict.keys()返回的是迭代器(如同遍历一般)
map = (
Map(init_opts=opts.InitOpts(width="900px", height="700px")).add(
'广东省', [list(z) for z in zip(ls_gd_cities, dic_gd_cfm.values())],
'广东',
is_map_symbol_show=False)
# Python3的keys(), values(), items()返回的都是迭代器，如果需要像Python2一样返回列表，只要传给list就行了
# is_map_symbol_show=False 表示不显示符号
.set_series_opts(label_opts=opts.LabelOpts(
is_show=True, formatter='{b}\n{c}例')) # 设置图例
# 地图 : {a}（系列名称），{b}（区域名称），{c}（合并数值）, {d}（无）
.set_global_opts(
title_opts=opts.TitleOpts(
title='广东省新型冠状病毒疫情地图（确诊数）',
subtitle='数能工作室制作\r\n最新更新时间%s\r\n数据来源腾讯' % format(timestamp)),
visualmap_opts=opts.VisualMapOpts(
is_show=True,
split_number=6,
is_piecewise=True, # 是否为分段型
pos_top='center',
pieces=[{
'min': 50
}, {
'min': 30,
'max': 49
}, {
'min': 20,
'max': 29
}, {
'min': 10,
'max': 19
}, {
'min': 1,
'max': 9
}, {
'value': 0,
"label": '无确诊病例',
"color": 'green'
}])))
return map
