def timeline(list, title, name):
attr = Faker.choose()
tl = Timeline()
for i in list:
x_list = ['每日死亡', '累计死亡', '每日确诊', '累计确诊']
y_list = []
for x in range(5, 9):
y_list.append(i[x])
histogram = (Bar().add_xaxis(x_list).add_yaxis(
name, y_list, color=Faker.rand_color()))
tl.add(histogram, "{}年".format(transform_time(i[0])))
return tl
def CumulativeConfirmed_histogram(list, title, name):
x_list = []
y_list = []
for i in list:
x_list.append(transform_time(i[0]))
y_list.append(i[8])
histogram = (Bar().add_xaxis(x_list).add_yaxis(
'累计确诊', y_list,
color=Faker.rand_color()).set_global_opts(datazoom_opts=[
opts.DataZoomOpts(),
opts.DataZoomOpts(type_="inside")
], ).set_series_opts(label_opts=opts.LabelOpts(is_show=False)))
return histogram
def COVID_Data(file, day, timeStamp):
#定义一个总的列表，里面存放所有数据，再定义一个字典，存放时间和每天的数据，再一个列表存放每天每个国家的数据
#累计确诊 TC ,新增确诊 NC ,累计死亡 TD ,新增死亡 ND
# 这是总列表
TC_Total_list = []
NC_Total_list = []
TD_Total_list = []
ND_Total_list = []
# 四个数据最大数
TC_max = 0
NC_max = 0
TD_max = 0
ND_max = 0
# 四个数据，每个数据的每天总和的列表
TC_day_sum = []
NC_day_sum = []
TD_day_sum = []
ND_day_sum = []
while day <= timeStamp:
# 这是字典，存放每天的数据
TC_day_dict = {"time": transform_time(day)}
NC_day_dict = {"time": transform_time(day)}
TD_day_dict = {"time": transform_time(day)}
ND_day_dict = {"time": transform_time(day)}
# 这是列表，被上面那个字典包含
TC_day_data = []
NC_day_data = []
TD_day_data = []
ND_day_data = []
# 每天的总和
TC = 0
NC = 0
TD = 0
ND = 0
#先计算下四个数据每日总和
for i in range(len(file)):
if day == file[i][0]: # 时间戳是不是一样
if ND_max <= file[i][5]:
ND_max = file[i][5]
if TD_max <= file[i][6]:
TD_max = file[i][6]
if NC_max <= file[i][7]:
NC_max = file[i][7]
if TC_max <= file[i][8]:
TC_max = file[i][8]
ND = ND + file[i][5]
TD = TD + file[i][6]
NC = NC + file[i][7]
TC = TC + file[i][8]
for i in range(len(file)):
# 这是每天每个国家的数据列表
TC_list = []
NC_list = []
TD_list = []
ND_list = []
# 字典
TC_dict = {}
NC_dict = {}
TD_dict = {}
ND_dict = {}
if day == file[i][0]: # 时间戳是不是一样
name = file[i][2]
tc = file[i][8]
if TC != 0:
percent_1 = tc / TC * 100
else:
percent_1 = 0
TC_list.append(tc)
TC_list.append(percent_1)
TC_list.append(name)
TC_dict['name'] = name
TC_dict['value'] = TC_list
TC_day_data.append(TC_dict)
nc = file[i][7]
if NC != 0:
percent_2 = nc / NC * 100
else:
percent_2 = 0
NC_list.append(nc)
NC_list.append(percent_2)
NC_list.append(name)
NC_dict['name'] = name
NC_dict['value'] = NC_list
NC_day_data.append(NC_dict)
td = file[i][6]
if TD != 0:
percent_3 = td / TD * 100
else:
percent_3 = 0
TD_list.append(td)
TD_list.append(percent_3)
TD_list.append(name)
TD_dict['name'] = name
TD_dict['value'] = TD_list
TD_day_data.append(TD_dict)
nd = file[i][5]
if ND != 0:
percent_4 = nd / ND * 100
else:
percent_4 = 0
ND_list.append(nd)
ND_list.append(percent_4)
ND_list.append(name)
ND_dict['name'] = name
ND_dict['value'] = ND_list
ND_day_data.append(ND_dict)
day = day + 86400000
TC_day_sum.append(TC)
TC_day_dict['data'] = TC_day_data
TC_Total_list.append(TC_day_dict)
NC_day_sum.append(NC)
NC_day_dict['data'] = NC_day_data
NC_Total_list.append(NC_day_dict)
TD_day_sum.append(TD)
TD_day_dict['data'] = TD_day_data
TD_Total_list.append(TD_day_dict)
ND_day_sum.append(ND)
ND_day_dict['data'] = ND_day_data
ND_Total_list.append(ND_day_dict)
data = [
TC_Total_list, TC_max, TC_day_sum, NC_Total_list, NC_max, NC_day_sum,
TD_Total_list, TD_max, TD_day_sum, ND_Total_list, ND_max, ND_day_sum
]
return data
f = open(
os.path.dirname(os.getcwd()) + '/crawler/ByTime_COVID-19.json', 'r')
# 获得一个字典格式的数据
data = json.load(f)
# 从字典中提取出疫情数据所在的列表
file = data['rows']
time_list = []
Day = 1578441600000 # 初始日期2020年1月8号
# 读取运行代码时的时间，并保留年月日，时间变成8：00：00，然后扩大1000倍，因为json文件里的时间戳到毫秒，并变成字符串格式
now_time = datetime.datetime.now()
timeStr = datetime.datetime.strftime(now_time, '%Y-%m-%d 08:00:00')
timeStamp = int(
time.mktime(time.strptime(timeStr, '%Y-%m-%d %H:%M:%S')) * 1000)
while Day <= timeStamp:
time_list.append(transform_time(Day))
Day = Day + 86400000
data = COVID_Data(file, 1578441600000, timeStamp)
#累计确诊
timeline_1 = Timeline(init_opts=opts.InitOpts(width="calc(100vw)",
height="1000px",
theme=ThemeType.DARK,
page_title='全球疫情累计确诊'), )
for y in time_list:
g = get_day_chart(day=y,
data=data[0],
minNum=0,
maxNum=data[1],
total_num=data[2])