def timeline():
val = request.args.get('select')
isMap = request.args.get('isMap')
fileName = val
if isMap != 'true':
fileName += fileSuffix
print(fileName)
with open(dataDir + "/" + fileName) as f:
f_csv = csv.reader(f)
head = []
city = {}
cityX = []
i = 0
rows = []
for row in f_csv:
if i == 0:
head = row[1:len(row)]
print(head)
else:
rows.append(row)
cityX.append(row[0])
i = +1
k = 1
city = {}
for year in head:
city[year] = [row[k] for row in rows]
k = k + 1
x = cityX
tl = Timeline()
for year in head:
if isMap == 'true':
cityMap = []
i = 0
maxInt = 0
t = Timeline()
for c in x:
if city[year][i] and city[year][i] != '':
if maxInt == 0:
maxInt = int(city[year][i])
else:
ci = int(city[year][i])
if ci > maxInt:
maxInt = ci
cityMap.append([c, city[year][i]])
i = i + 1
map0 = (Map().add(mapTips, cityMap, "china").set_global_opts(
title_opts=opts.TitleOpts(title=val),
visualmap_opts=opts.VisualMapOpts(max_=maxInt),
))
tl.add(map0, year)
else:
bar = (Bar().add_xaxis(x).add_yaxis(
val,
city[year]).set_global_opts(title_opts=opts.TitleOpts(val)))
tl.add(bar, year)
# return Markup(tl.render_embed())
return tl.dump_options_with_quotes()
def member_month_workhour():
p = PJinfo()
m = p.member_workhour_get()
tl = Timeline()
li_month = sorted(m.keys(), reverse=True)
for i in li_month:
month = m[i].sort_values(0, axis=1, ascending=False)
fig = Bar()
fig.add_xaxis(month.columns.tolist())
fig.add_yaxis('投入工时', month.values.flatten().tolist())
fig.set_global_opts(
title_opts=opts.TitleOpts(title=f"2020年{i}月人员工时投入"),
yaxis_opts=opts.AxisOpts(name='工时'),
)
tl.add(fig, time_point=f'{i}月')
return tl.dump_options_with_quotes()
def ChinaMaptime():
data = pd.read_excel(savePath + '中国各省市(区)合成表.xlsx', index_col=False)
data['date'] = str('2020/') + data['date']
data['date'] = pd.to_datetime(data['date'])
data['date'] = pd.PeriodIndex(data['date'], freq='D') # 转换为时间
class_list = list(data['date'].drop_duplicates())
class_list = sorted(class_list)
t1 = Timeline()
for i in class_list:
data1 = pd.read_excel(savePath + '中国时间轴/%s.xlsx' % (i), index=False)
data_name = data1['country']
data_confirm = data1['confirm']
chinaMap = (
Map()
.add("累计确诊", [list(z) for z in zip(data_name, data_confirm)], maptype="china")
.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
.set_global_opts(
title_opts=opts.TitleOpts(title="{}中国累计确诊数据".format(i)),
visualmap_opts=opts.VisualMapOpts(is_piecewise=True,
pieces=[{"max": 0, "label": '0人', "color": "#FFFAFA"},
{"min": 1, "max": 9, "label": '1-9人', "color": "#F08080"},
{"min": 10, "max": 99, "label": '10-99人', "color": "#BC8F8F"},
{"min": 100, "max": 499, "label": '100-499人',
"color": "#A52A2A"},
{"min": 500, "max": 999, "label": '500-999人',
"color": "#B22222"},
{"min": 1000, "max": 9999, "label": '1000-9999人',
"color": "#8B0000"},
{"min": 10000, "label": '10000人及以上', "color": "#800000"}]),
)
)
t1.add(chinaMap, "".format(i))
path = savePath + '时间轴图像/'
isExists = os.path.exists(path) # 判断当前目录是否有时间轴图像文件夹，如果没有则创建
if not isExists:
os.mkdir(path) # 在当前路径创建新文件夹analisis,用于存放生成的图像数据
# t1.render(path+'中国总体疫情变化.html')
if flag:
dwf.write_to_file(path + '中国总体疫情变化.txt', str(t1.dump_options_with_quotes()))
