def index(request):
targetProvinceName = 0
additionProvinceName = {"四川"}
gotProvinceName = {"四川"}
additionMapName = {"四川"}
v = pneumonia(targetProvinceName, additionProvinceName, gotProvinceName,
additionMapName)
tab = Tab("疫情地图豪华定制版")
tab.add(v.getSichuanMap("currentConfirmedCount", "现存确诊"), "四川现存")
tab.add(v.getSichuanMap("confirmedCount", "累计确诊"), "四川累计")
tab.add(v.getSichuanTable(), "四川表格")
tab.add(v.getChinaMap("currentConfirmedCount", "现存确诊"), "中国现存")
tab.add(v.getChinaMap("confirmedCount", "累计确诊"), "中国累计")
tab.add(v.getProvinceTable(), "各省表格")
tab.add(v.getWorldMap("currentConfirmedCount", "现存确诊"), "世界现存")
tab.add(v.getWorldMap("confirmedCount", "累计确诊"), "世界累计")
tab.add(v.getWorldTable(), "世界表格")
return HttpResponse(tab.render_embed())
def plot_all(data):
bonds_data = raw_data_process(data,rate_columns_names="票面利率(发行时)\r\n[单位] %")
bonds_data['省份'] = bonds_data['省份'].fillna("其他")
coupon_rate = coupon_rate_summary(bonds_data,group_name="Wind债券二级分类")
province_analysis = province_data_format(category_analysis(bonds_data, "省份")).round(4)
industry_analysis = category_analysis(bonds_data, "所属申万行业名称\r\n[行业级别] 一级行业")
province_analysis_2 = province_data_format(category_analysis(bonds_data, "省份",counts=False)).round(4)
industry_analysis_2 = category_analysis(bonds_data, "所属申万行业名称\r\n[行业级别] 一级行业",counts=False)
ranks_analysis = rank(bonds_data)
hotspot_analysis = usage_analysis(bonds_data)
daily, cummulative = daily_issue_analysis(bonds_data)
tc,tq = expireTime(bonds_data)
tab = Tab(page_title="疫情防控债统计分析")
tab.add(table_base(coupon_rate), "利率统计")
tab.add(rate_histogram_plot(bonds_data['票面利率(%)'].replace("--", np.nan).astype(np.float)), "利率分布直方图")
tab.add(province_analysis_plot(province_analysis), "发行省份统计（数量）")
tab.add(province_analysis_plot(province_analysis_2),"发行省份统计（金额）")
tab.add(issuer_industry_plot(industry_analysis), "发行行业统计（数量）")
tab.add(issuer_industry_plot(industry_analysis_2),"发行行业统计（金额）")
tab.add(word_clouds(hotspot_analysis), "资金用途热点")
tab.add(pie_plot(ranks_analysis.drop(["已公布评级","合计"])), "评级统计")
tab.add(pie_plot(tc),"债券期限统计（数量）")
tab.add(pie_plot(tq),"债券期限统计（金额）")
tab.add(daily_issue_plot(daily,cummulative),"每日发行状况统计")
# table
province_analysis['占比'] = (province_analysis['占比'].round(2)).astype(str)+"%"
province_analysis = province_analysis.sort_values(by='数量',ascending=False).astype(str)
tab.add(table_base(province_analysis),"发行省份统计表（数量）")
province_analysis_2['占比'] = (province_analysis_2['占比'].round(2)).astype(str)+"%"
province_analysis_2 = province_analysis_2.sort_values(by='金额(亿)',ascending=False).astype(str)
tab.add(table_base(province_analysis_2),"发行省份统计表（金额）")
industry_analysis.index.name = "行业(申万)"
industry_analysis["占比"] = (industry_analysis['占比']*100).round(2).astype('str')+"%"
industry_analysis = industry_analysis.sort_values(by='数量',ascending=False)
tab.add(table_base(industry_analysis),"发行行业统计表（数量）")
industry_analysis_2.index.name = "行业(申万)"
industry_analysis_2["占比"] = (industry_analysis_2['占比']*100).round(2).astype('str')+"%"
industry_analysis_2 = industry_analysis_2.sort_values(by='金额(亿)',ascending=False)
tab.add(table_base(industry_analysis_2),"发行行业统计表（金额）")
hotspot_analysis_df = pd.DataFrame(hotspot_analysis).rename(columns={0:"关键词",1:"权重"}).set_index("关键词").round(4)
tab.add(table_base(hotspot_analysis_df),"热点统计表")
tab.add(table_base(ranks_analysis),"债券评级统计表")
t = pd.concat([tc.rename("数量"),tq.rename("总金额(亿)")],axis=1).round(2)
tab.add(table_base(t),"债券期限统计表")
df = pd.concat([daily,cummulative],axis=1).round(2)
df.index = df.index.date.astype(str)
df.index.name = "日期"
tab.add(table_base(df),"每日发行状况统计表")
return tab
},
{
"min": int(max(job_num_list) / 5) * 4,
"max": max(job_num_list)
},
])))
return c
if __name__ == '__main__':
# area = input("please enter the area:")
# jobkey = input("please enter the jobkey:")
area = "上海"
jobkey = "Java"
data_exp = GetExpData(area, jobkey)
ex_list, ex_num_list = data_exp.get_data()
data_tem = GetTemData(area, jobkey)
tem_list, tem_num_list = data_tem.get_data()
data_area = GetAreaData(jobkey)
area_list, job_num_list = data_area.get_data()
data_pay = GetPayData(area, jobkey)
pay_list, range_list = data_pay.get_data()
tab = Tab()
tab.add(pie_radius(ex_list, ex_num_list), "工作经验")
tab.add(bar_base(pay_list, range_list), "工资待遇")
tab.add(wordcloud_base(tem_list, tem_num_list), "工作福利")
tab.add(map_visualmap(area_list, job_num_list), "全国岗位数量")
tab.render()
def draw_line_charts(input_data,input_data2,canshu =[],path=''):
canshu_list = []
max0 = int(max(input_data[canshu].max())*1.001)
min0 = int(min(input_data[canshu].min())*0.995)
print(max0,min0)
for i in canshu:
canshu_dict0 = {}
canshu_dict0[i] = input_data[i]
canshu_list.append(canshu_dict0)
print(input_data.keys())
print(input_data.tail(100))
input_data['candle_begin_time'] = pd.to_datetime(input_data['candle_begin_time'])
time_list = input_data['candle_begin_time'].apply(lambda x: x.strftime('%Y-%m-%d %H:%M:%S')).values.tolist()
tab = Tab()
line_base = Line(init_opts=opts.InitOpts(width="1500px", height="700px", theme=ThemeType.DARK)).add_xaxis(time_list)
line_base.add_yaxis(series_name="close:",
y_axis=input_data['close'].values,
is_symbol_show=False,
color="#FF0000")
line_base.set_global_opts(
datazoom_opts=[opts.DataZoomOpts(range_start=95,
range_end=100,), opts.DataZoomOpts(
type_="inside",
range_start=95,
range_end=100,
)],
title_opts=opts.TitleOpts(title="资金曲线变化"),
tooltip_opts=opts.TooltipOpts(trigger="axis", axis_pointer_type="cross"),
yaxis_opts=opts.AxisOpts(
is_scale=True,
axislabel_opts=opts.LabelOpts(is_show=False),
axisline_opts=opts.AxisLineOpts(is_show=False),
axistick_opts=opts.AxisTickOpts(is_show=False),
splitline_opts=opts.SplitLineOpts(is_show=False), ),
legend_opts=opts.LegendOpts(is_show=True) )
for ix,v in enumerate(canshu_list):
v0:str
value0:dict
for k,v0 in v.items():
name =k
value0 = v0
data0 = pd.DataFrame(value0)
data0.fillna(method='bfill',inplace=True)
# print(data0)
line_base.add_yaxis(
series_name=name,
y_axis=data0.values.tolist(),
yaxis_index=2,
linestyle_opts=opts.LineStyleOpts(),
label_opts=opts.LabelOpts(is_show=False),
)
line_base.extend_axis(
yaxis=opts.AxisOpts(
name="资金情况",
name_location="start",
type_="value",
max_= max0,
min_= min0,
is_inverse=False,
axistick_opts=opts.AxisTickOpts(is_show=False),
splitline_opts=opts.SplitLineOpts(is_show=False),))
tab.add(line_base,'资金变化')
table0 = Table().add(list(input_data2.columns), input_data2.values.tolist()).set_global_opts(
title_opts=ComponentTitleOpts(title="策略统计", subtitle=str(dt.datetime.now())))
table0.add_js_funcs('''
document.write("<p style='color:#FF0000;'>
注释：none </p>"
''')
tab.add(table0,'策略统计表')
html0 =tab.render(path)
if os.path.exists(html0):
print("ok!保存在：")
print(html0)
else:
print('保存失败！')
def line_detial_page(cpu, memory, upFlow, downFlow, fps, battery) -> Page:
line1 = line_cpu(cpu)
line2 = line_memory(memory)
line3 = line_upflow(upFlow)
table1 = table_base(data[0])
page = (Page().add(table1))
return page
if __name__ == '__main__':
print("----" * 20)
dataFile = readInfo("../info/info.pickle")
data = dataAnalysis(dataFile)
monkeyLog = monkeyLogAnalysis(data)
detial = line_detial_grid(cpu=data[1],
memory=data[2],
upFlow=data[3],
downFlow=data[4],
fps=data[5],
battery=data[6])
tab = Tab()
tab.add(table_base(data[0]), "汇总信息")
tab.add(detial, "详细信息")
tab.add(table_traces(monkeyLog), "日志记录")
tab.render()
def s_5():
with open(r'cus.csv', 'r') as f:
reader = csv.reader(f)
result = list(reader)
cus_list = [i[0] for i in result]
total_list = [float(i[1]) for i in result]
c = (EffectScatter(init_opts=opts.InitOpts(
width='1200px', height='800px')).add_xaxis(cus_list).add_yaxis(
"交易次数", total_list,
label_opts=opts.LabelOpts(is_show=False)).set_global_opts(
title_opts=opts.TitleOpts(title="本年度交易次数顾客排名分析-TOP20"),
xaxis_opts=opts.AxisOpts(splitline_opts=opts.SplitLineOpts(
is_show=True)),
yaxis_opts=opts.AxisOpts(splitline_opts=opts.SplitLineOpts(
is_show=True)),
))
return c
tab = Tab()
tab.add(liquid_0(), "年度关键指标")
tab.add(line_1(), "交易总额")
tab.add(line_2(), "交易次数")
tab.add(pie_3(), "交易种类")
tab.add(s_4(), "省份排名分析")
tab.add(s_5(), "顾客排名分析")
tab.render("tab_base.html")
# In[ ]:
def dong_scatter3(qian_n=10,
data={},
data2=[],
info='',
print_data=[0, 0, 0],
path0=''):
from pyecharts import options as opts
from pyecharts.commons.utils import JsCode
from pyecharts.charts import Scatter, Timeline, Line, Page, Tab
from pyecharts.components import Table
# endlist = ['canshu', 'celue_name', '预测_s_Time', '最终预测值1', 'hg最终预测值1',
# '未来end_3', '未来max_back_3', '未来sharp_rate_3', '未来trade_nums_3', '未来win_rate_3']
data['aim'] = data['canshu'] + data['celue_name']
dict0 = {
'hg最终预测值1': '回归=预测值',
'最终预测值1': '分类=预测值',
'预测_s_Time': 's_Time',
'未来end_3': '未来=月终收益',
'未来max_back_3': '未来=最大回撤',
'未来sharp_rate_3': '未来=夏普率',
'未来trade_nums_3': '未来=交易次数',
'未来win_rate_3': '未来=胜率'
}
data.rename(columns=dict0, inplace=True)
data = data.applymap(lambda x: round(x, 3) if isinstance(x, float) else x)
# print(data.columns)
# print(data.tail())
title = f'动态图:{qian_n}:'
df = data
# data['回归=预测值'] = data['回归=预测值'].apply(lambda x: int(x))
# df['未来=月终收益']=df['未来=月终收益'].apply(lambda x: int(x))
# df['回归=预测值']=df['回归=预测值'].apply(lambda x: int(x))
df['s_Time'] = pd.to_datetime(
df['s_Time']) # .apply(lambda x:x.strftime(format="%Y-%m-%d"))
df.sort_values(by=['s_Time'], ascending=True, inplace=True)
tl = Timeline()
tl_fl = Timeline()
timelist = list(set(df['s_Time'].tolist()))
# sorted(timelist)
# df_date = [time.strftime('%Y-%m-%d',time.localtime(i/1000000000) ) for i in timelist]
# print(df_date)
tjdf = pd.DataFrame()
timelist = sorted(timelist)
# 时间散点图
df_zong = pd.DataFrame()
for k, i in enumerate(timelist):
df1 = df[df['s_Time'] == i]
df0 = df1[df1['分类=预测值'] == df1['分类=预测值'].max()]
tjindex = [x for x in list(df.keys()) if str(x).startswith('未来')]
# print(df[df['s_Time']==i][tjindex].mean())
print(i)
df0.sort_values(by=['回归=预测值'], ascending=True, inplace=True)
print(df0.tail(30))
print(df0.shape)
df0_ = df0.iloc[-1 * qian_n:]
tjdf[i] = df0_[tjindex].mean()
df_zong = df_zong.append(df0_)
xdata = [t for t in df0['回归=预测值'].values.tolist()]
yd1 = df0[['未来=月终收益', '回归=预测值', 'aim']]
if yd1.shape[0] > qian_n:
ydata1 = yd1.iloc[:].values.tolist()
ydata2 = yd1.iloc[-1 * qian_n:].values.tolist()
min_pre = yd1.iloc[-1 * qian_n:]['回归=预测值'].mean()
max_pre = yd1.iloc[-1 * qian_n:]['回归=预测值'].max()
else:
ydata1 = yd1.values.tolist()
# ydata2 = yd1.values.tolist()
min_pre = df0['回归=预测值'].mean()
max_pre = df0['回归=预测值'].max()
xdata2 = [t for t in df1['分类=预测值'].values.tolist()]
yd2 = df1[['未来=月终收益', '分类=预测值', 'aim']].values.tolist()
# print(ydata)
Scatter0 = (
Scatter().add_xaxis(xdata).add_yaxis(
'未来=月终收益',
ydata1,
label_opts=opts.LabelOpts(is_show=False, ),
)
# .add_yaxis('未来=月终收益', ydata2, label_opts=opts.LabelOpts(is_show=False, ), symbol='triangle')
.set_global_opts(
xaxis_opts=opts.AxisOpts(
name='预测值：',
type_="value",
axistick_opts=opts.AxisTickOpts(is_show=True)),
yaxis_opts=opts.AxisOpts(
name='真实值：',
type_="value",
axistick_opts=opts.AxisTickOpts(is_show=True)),
title_opts=opts.TitleOpts(title=f"{title}==:{i}", pos_left=30),
tooltip_opts=opts.TooltipOpts(formatter=JsCode(
"function (params) { return '==真实：'+params.value[1] +' <br/>== 预测：'+ params.value[2]+' <br/>== 策略：'+ params.value[3];}"
), ),
visualmap_opts=opts.VisualMapOpts(min_=min_pre, max_=max_pre),
))
min_pre = df0['未来=月终收益'].mean()
max_pre = df0['未来=月终收益'].max()
Scatter1 = (Scatter().add_xaxis(xdata2).add_yaxis(
'未来=月终收益',
yd2,
label_opts=opts.LabelOpts(is_show=False, ),
).set_global_opts(
xaxis_opts=opts.AxisOpts(
name='预测值：',
type_="value",
axistick_opts=opts.AxisTickOpts(is_show=True)),
yaxis_opts=opts.AxisOpts(
name='真实值：',
type_="value",
axistick_opts=opts.AxisTickOpts(is_show=True)),
title_opts=opts.TitleOpts(title=f"{title}==:{i}", pos_left=30),
tooltip_opts=opts.TooltipOpts(formatter=JsCode(
"function (params) { return '==真实：'+params.value[1] +' <br/>== 预测：'+ params.value[2]+' <br/>== 策略：'+ params.value[3];}"
), ),
visualmap_opts=opts.VisualMapOpts(min_=min_pre, max_=max_pre),
))
tl.add(Scatter0, f"{i}")
tl_fl.add(Scatter1, f"{i}")
tjdf.fillna(0, inplace=True)
tjdf = tjdf.applymap(lambda x: round(x, 3) if isinstance(x, float) else x)
# print(tjdf)
# exit()
tl2 = Timeline()
for wl in tjdf.index:
wl0 = tjdf.loc[wl]
# print(wl0)
line = Line().add_xaxis(list(wl0.index)).add_yaxis(wl, wl0.values.tolist()) \
.set_global_opts(title_opts=opts.TitleOpts(title=f'前{qian_n}名策略\n统计量：{wl}—变化', pos_left=30))
tl2.add(line, wl)
# 统计表格
talble = Table()
# [t.strftime(format="%Y-%m-%d")for t in tjdf.keys()]
tjdf.columns = tjdf.columns.map(lambda x: x.strftime(format="%Y-%m-%d"))
tjdf['统计量名称'] = tjdf.index
tjdf['平均总计'] = tjdf.mean(axis=1)
# print(tjdf['总计'])
# exit()
# print(tjdf.values)
tjdf = tjdf.applymap(lambda x: round(x, 3) if isinstance(x, float) else x)
tjdf_columns = list(tjdf.keys())[-2:] + list(tjdf.keys())[:-2]
tjdf = tjdf[tjdf_columns]
talble.add(list(tjdf.keys()), tjdf.values.tolist()).set_global_opts(
title_opts=opts.ComponentTitleOpts(title=f"前{qian_n}名策略的真实未来，的历史统计表格"))
# 策略推荐表格
talble2 = Table()
tjcelue_data = df[df['s_Time'] == timelist[-1]][[
's_Time', 'canshu', 'celue_name', '分类=预测值', '回归=预测值'
]]
tjcelue_data = tjcelue_data[tjcelue_data['分类=预测值'].notnull()]
tjcelue_data.sort_values(by='分类=预测值', ascending=True, inplace=True)
tjcelue_data = tjcelue_data.iloc[-1 * qian_n:]
talble2.add(
tjcelue_data.columns.tolist(),
tjcelue_data.values.tolist()).set_global_opts(
title_opts=opts.ComponentTitleOpts(title=f"推荐前{qian_n}名策略"))
talble3 = Table()
headers_list = ['介绍：', '本次筛选个数', '过滤因子']
match_df = [print_data]
talble3.add(headers_list, match_df).set_global_opts(
title_opts=opts.ComponentTitleOpts(title=f"整体介绍"))
talble4 = Table()
new_columns = list(data2.keys())
newdata = data2.values.tolist()
talble4.add(new_columns,
newdata).set_global_opts(title_opts=opts.ComponentTitleOpts(
title=f"逐月—策略详情：文件位置：" + path0.split('\\')[0]))
tab0 = Tab()
tab0.add(tl, '回归')
tab0.add(tl_fl, '分类')
tab0.add(tl2, '逐月统计图')
tab0.add(talble, '逐月统计表格')
tab0.add(talble2, '最新月推荐策略')
tab0.add(talble3, '备注描述')
tab0.add(talble4, '每月策略详情表')
tab0.render(path0 + f"{info}.html")
print(path0 + f"{info}.html")
return df_zong
def map_world2() -> Map:
c = (Map().add("2016年女性性自杀率（每10万人）",
[list(z) for z in zip(df1["国家"], df1["女性自杀率"])],
"world").set_series_opts(label_opts=opts.LabelOpts(
is_show=False)).set_global_opts(
title_opts=opts.TitleOpts(title="2016年女性自杀率"),
visualmap_opts=opts.VisualMapOpts(min_=0, max_=30),
))
return c
map_world2().render()
tab = Tab()
tab.add(map_world(), "2016年总自杀率")
tab.add(map_world1(), "2016年男性自杀率")
tab.add(map_world2(), "2016年女性自杀率")
tab.render('p2.html')
@app.route('/', methods=['GET'])
def get_out():
data_str = df.to_html()
regions_available = regions_available_loaded # 下拉选单有内容
return render_template(
'p1.html',
the_res=data_str, # 表
the_select_region=regions_available)
def anylize_to_html_01(data={}, info=''):
from pyecharts import options as opts
from pyecharts.commons.utils import JsCode
from pyecharts.charts import Scatter, Bar, Timeline, Line, Page, Tab
from pyecharts.components import Table
tab0 = Tab()
data.loc[(data['max_back_1'] != 0),
'赢撤比'] = -1 * data['end_1'] / data['max_back_1']
data.loc[(data['max_back_1'] == 0), '赢撤比'] = 0
data = data[data['end_1'] != 0]
clm = list(set(data['celue_name'].tolist()))
print(clm)
title = f'所有参数的，每个指标的统计状态:'
tjdf = pd.DataFrame()
i = 0
tj_index = {
'end_1': '最终收益',
'sharp_rate_1': '夏普率',
'max_back_1': '最大回撤',
'win_rate_2': '胜率',
'trade_nums_1': '交易次数',
'profit_days_1': '盈利天数',
'赢撤比': '赢撤比'
}
s_tj_index = []
for clm0 in clm:
print(clm0)
df = data[data['celue_name'] == clm0].copy()
for k, v in df.groupby('para1'):
print(k)
index_0 = i + 1
i = i + 1
tjdf.loc[index_0, 'para1'] = k
tjdf.loc[index_0, 'clm'] = clm0
for tj_ix in tj_index.keys():
tjdf.loc[index_0, tj_index[tj_ix]] = v[tj_ix].mean()
tjdf.loc[index_0, '赢撤比2'] = -1 * v['end_1'].mean(
) / v['max_back_1'].mean() if v['max_back_1'].mean() != 0 else 0
print(tjdf)
print(tjdf.keys())
tj_index2 = list(tjdf.keys())
tj_index2.remove('para1')
tj_index2.remove('clm')
clm2 = tj_index2
for x, clm0 in enumerate(clm):
df0 = tjdf[tjdf['clm'] == clm0].copy()
tl = Timeline()
for k, i in enumerate(tj_index2):
xdata, ydata1 = df0['para1'], df0[['para1', i]]
print(xdata)
print(ydata1)
# exit()
bar0 = (Bar().add_xaxis(xdata.tolist()).add_yaxis(
'均值',
ydata1.values.tolist(),
label_opts=opts.LabelOpts(is_show=False, ),
).set_global_opts(
xaxis_opts=opts.AxisOpts(
offset=0,
name='参数名：',
type_="value",
is_show=True,
axistick_opts=opts.AxisTickOpts(is_show=True)),
yaxis_opts=opts.AxisOpts(
offset=0,
name='均值：',
type_="value",
axistick_opts=opts.AxisTickOpts(is_show=True)),
title_opts=opts.TitleOpts(title=f"{title}==:{i}"),
legend_opts=opts.LegendOpts(pos_right=True),
tooltip_opts=opts.TooltipOpts(formatter=JsCode(
"function (params) { return '==para1：'+params.value[0] +' <br/>== 指标：'+ params.value[1]}"
), ),
))
tl.add(bar0, f"{i}")
tab0.add(tl, clm0)
for x, clm0 in enumerate(clm2):
tl = Timeline()
tjdf0 = tjdf[tjdf['最终收益'] != 0].copy()
tjdf0.sort_values(clm0, ascending=True, inplace=True)
df0 = tjdf0.iloc[-1 * int(tjdf0.shape[0] * 0.12):].copy()
index_list = list(df0.keys())
index_list.remove('clm')
df0 = df0[index_list]
for k, i in enumerate(index_list):
xdata, ydata1 = df0['para1'], df0[[i, 'para1']]
print(ydata1)
sca0 = (Scatter().add_xaxis(xdata.tolist()).add_yaxis(
'分布值',
ydata1.values.tolist(),
label_opts=opts.LabelOpts(is_show=False, ),
).set_global_opts(
xaxis_opts=opts.AxisOpts(
offset=0,
name='参数名：',
type_="value",
is_show=True,
axistick_opts=opts.AxisTickOpts(is_show=True)),
yaxis_opts=opts.AxisOpts(
offset=0,
name='分布值：',
type_="value",
axistick_opts=opts.AxisTickOpts(is_show=True)),
title_opts=opts.TitleOpts(title=f"{title}==:{i}"),
legend_opts=opts.LegendOpts(pos_right=True),
tooltip_opts=opts.TooltipOpts(formatter=JsCode(
"function (params) { return '==para1：'+params.value[0] +' <br/>== 指标：'+ params.value[1]}"
), ),
))
tl.add(sca0, f"{i}")
tab0.add(tl, clm0)
tab0.render(f"{info}.html")
print(f"{info}.html")
return
def tab(filename="info"):
'''
分页组件
'''
tab = Tab()
tab.add(liquid_data_precision("111"), "前日留存")
tab.add(bar_datazoom_slider("222"), "分端留存")
tab.add(table_base("222"), "明细")
tab.add(line_markpoint("222"), "人群留存")
tab.add(pie_rosetype("222"), "城市")
tab.add(grid_mutil_yaxis("222"), "新增留存")
tab.render_notebook()
tab.render('{}.html'.format(filename))
line = (Line()
.add_xaxis(xaxis_data=x_axis_data)
.add_yaxis(series_name=legend_name, y_axis=y_axis_data,
markpoint_opts=opts.MarkPointOpts(data=[opts.MarkPointItem(type_="max"),
opts.MarkPointItem(type_="min")])
)
)
return line
"""建立看板，可切换不同的折线图进行观察，因为后面要添加多张折线图
"""
tab = Tab()
# 逐个创建新的曲线并添加至tab看板下
for weather_key, weather_value in weather_dict.items():
# 把weather_key中的下划线换成空格呈现在最终的折线图中
weather_key_modified = weather_key.replace("_", " ")
tab.add(
chart=create_line(x_axis_data=month, y_axis_data=weather_value, legend_name=weather_key_modified),
tab_name=weather_key_modified
)
# 保存路径
resultPath = "./result"
if not os.path.exists(path=resultPath):
os.mkdir(path=resultPath)
# 保存文件名
resultFileName = "weather_tab.html"
# 执行多折线图看板的保存
tab.render(path=os.path.join(resultPath, resultFileName))
def index():
tab = Tab()
tab.add(grid_horizontal(), "Income_Group")
tab.add(grid_horizontal1(), "Region")
tab.add(timeline_map(), "map")
tab.add(bar_base(), "map-data story")
tab.add(timeline_pie(), "pie")
tab.add(line_base(), "pie-data story")
return Markup(tab.render_embed())
def plot_performance_overview(dict_results, plot_tymod, p):
if plot_tymod == "pic":
figs = {}
for k, v in dict_results.items():
v = v.unstack()
series = v.index.get_level_values(0).unique() if isinstance(
v.index, pd.MultiIndex) else [k]
c = 3
r = np.ceil(len(series) / c)
fig = plt.figure(figsize=(8, 6))
plt.subplots_adjust(hspace=0.3)
for i, _s in enumerate(series):
_data = v.loc[_s] if isinstance(v.index, pd.MultiIndex) else v
X, Y = np.meshgrid(_data.index, _data.columns, indexing='ij')
Z = _data.values
pos = int(r * 100 + c * 10 + (i + 1))
ax = fig.add_subplot(pos, projection='3d')
ax.plot_surface(X, Y, Z, color='b')
ax.set_title("%s" % _s, fontdict={"size": 8})
figs[k] = fig
fig.savefig("%s_%s.png" % (p, k))
return figs
elif plot_tymod == 'dyn-3d':
results = {}
for k, perf in dict_results.items():
perf = perf.unstack()
c = (Surface3D(init_opts=opts.InitOpts(
width="1000px", height="500px")).set_global_opts(
title_opts=opts.TitleOpts(title="绩效-曲面波图（%s）" % k),
visualmap_opts=opts.VisualMapOpts(
max_=perf.values.max(),
min_=perf.values.min(),
range_color=Faker.visual_color),
toolbox_opts=opts.ToolboxOpts(is_show=True),
))
series = perf.index.get_level_values(0).unique() if isinstance(
perf.index, pd.MultiIndex) else [k]
for i, _s in enumerate(series):
_data = perf.loc[_s] if isinstance(perf.index,
pd.MultiIndex) else perf
X, Y = np.meshgrid(_data.index, _data.columns)
d = list(
zip(X.flatten().astype(str),
Y.flatten().astype(str),
_data.values.T.flatten().astype(np.float)))
c = c.add(
series_name=str(_s),
shading="color",
data=d,
xaxis3d_opts=opts.Axis3DOpts(type_="category"),
yaxis3d_opts=opts.Axis3DOpts(type_="category"),
grid3d_opts=opts.Grid3DOpts(width=100,
height=50,
depth=100),
)
results[k] = c
tab = Tab()
for k, v in results.items():
tab.add(v, k)
tab.render("%s.html" % p)
elif plot_tymod == "dyn-hm":
tab = Tab()
for k, v in dict_results.items():
v = v.unstack()
_tl = _timeline(v, name=k)
tab.add(_tl, k)
tab.render("%s.html" % p)
return data_pair
# 用于添加所有绘制出来的漏斗图
tab = Tab()
for monthSheetName in salesBook.sheetnames:
# 获取该表下用于绘图的数据对
data_pair = read_data_pair(salesBook[monthSheetName])
# 绘制漏斗图，要求：
# 1、隐藏图例；
# 2、每张图标题设置为：f"{月份}销售"
# 3、图中每个漏斗项中间的距离为10
# 4、多漏斗图用看板展示；
# 5、保存
funnel = (Funnel().set_global_opts(
title_opts=opts.TitleOpts(title=f"{monthSheetName}销售"),
legend_opts=opts.LegendOpts(is_show=False)).add(series_name="",
data_pair=data_pair,
gap=10))
tab.add(chart=funnel, tab_name=f"{monthSheetName}")
# 保存看板图
resultPath = "./result"
if not os.path.exists(resultPath):
os.mkdir(path=resultPath)
resultFileName = "year_funnel_tab.html"
tab.render(path=os.path.join(resultPath, resultFileName))
def all():
tab = Tab(page_title='2019年安全公众号统计分析')
tab.add(article_day_3d(), "公众号文章发布统计")
tab.add(account_article_num(), "公众号收录文章数")
tab.add(article_day_num(), "每天收录文章统计")
tab.add(article_hour_num(), "文章发布时间段")
tab.add(article_source_url(), "公众号文章来源")
tab.add(zhaopin(), "公众号文章主题统计")
tab.add(article_author(), "勤奋作者")
tab.render(path='./static/year/2019.html')
print('生成完毕')
def anylize_to_html_02(data={}, fl_style='para1', info='', add_info=''):
# 根据生成的原始数据
from pyecharts import options as opts
from pyecharts.commons.utils import JsCode
from pyecharts.charts import Scatter, Bar, Timeline, Line, Page, Tab
from pyecharts.components import Table
tab0 = Tab()
data.loc[(data['max_back_1'] != 0),
'赢撤比'] = -1 * data['end_1'] / data['max_back_1']
data.loc[(data['max_back_1'] == 0), '赢撤比'] = 0
data = data[data['end_1'] != 0]
clm = list(set(data['celue_name'].tolist()))
print(clm)
title = f'所有参数的，每个指标的统计状态:'
tjdf = pd.DataFrame()
tj_dict = {}
# 生成分类数据tjdf
i = 0
tj_index = {
'end_1': '最终收益',
'sharp_rate_1': '夏普率',
'max_back_1': '最大回撤',
'win_rate_2': '胜率',
'trade_nums_1': '交易次数',
'profit_days_1': '盈利天数',
'赢撤比': '赢撤比'
}
for clm0 in clm:
print(clm0)
df = data[data['celue_name'] == clm0].copy()
for k, v in df.groupby(fl_style):
print(k)
index_0 = i + 1
i = i + 1
tjdf.loc[index_0, fl_style] = k
tjdf.loc[index_0, 'clm'] = clm0
for tj_ix in tj_index.keys():
tjdf.loc[index_0, tj_index[tj_ix]] = v[tj_ix].mean()
tjdf.loc[index_0, '赢撤比2'] = -1 * v['end_1'].mean(
) / v['max_back_1'].mean() if v['max_back_1'].mean() != 0 else 0
print(tjdf)
print(tjdf.keys())
# exit()
filter_col = '夏普率'
df0_dict = {} #策略名+参数的字典。
tjdf2 = pd.DataFrame()
for x, clm0 in enumerate(clm):
# 每个策略类，按照filter_col排序，根据每个策略回测数量，选取比较高的排名的策略
df0 = tjdf[tjdf['clm'] == clm0].copy()
df0.sort_values(filter_col, ascending=True, inplace=True)
df0 = df0.iloc[-1 * int(df0.shape[0] * 0.35):]
df0_dict[str(df0.iloc[-1]['clm'])] = list(
sorted(set(df0[fl_style].tolist())))
tjdf2 = tjdf2.append(df0, ignore_index=True)
tjdf2.sort_values('最终收益', ascending=True, inplace=True)
tjdf2 = tjdf2.iloc[int(tjdf2.shape[0] * 0.1):]
print(tjdf2)
tj_index2 = list(tjdf2.keys())
tj_index2.remove(fl_style)
tj_index2.remove('clm')
clm2 = tj_index2
for x, clm0 in enumerate(clm2):
tl = Timeline()
tjdf0 = tjdf2[tjdf2['最终收益'] != 0].copy()
tjdf0.sort_values(clm0, ascending=True, inplace=True)
# df0 = tjdf0.iloc[-1*int(tjdf0.shape[0]*0.12):].copy()
df0 = tjdf0
index_list = list(df0.keys())
index_list.remove('clm')
df0 = df0[index_list]
for k, i in enumerate(index_list):
xdata, ydata1 = df0[fl_style], df0[[i, fl_style]]
print(add_info)
# print( ydata1)
sca0 = (Scatter().add_js_funcs(
f"document.write('%s')" % add_info
).add_xaxis(xdata.tolist()).add_yaxis(
'分布值',
ydata1.values.tolist(),
label_opts=opts.LabelOpts(is_show=False, ),
).set_global_opts(
xaxis_opts=opts.AxisOpts(
offset=0,
name='参数名：',
type_="value",
is_show=True,
axistick_opts=opts.AxisTickOpts(is_show=True)),
yaxis_opts=opts.AxisOpts(
offset=0,
name='分布值：',
type_="value",
axistick_opts=opts.AxisTickOpts(is_show=True)),
title_opts=opts.TitleOpts(title=f"{title}==:{i}"),
legend_opts=opts.LegendOpts(pos_right=True),
tooltip_opts=opts.TooltipOpts(formatter=JsCode(
"function (params) { return '==canshu：'+params.value[0] +' <br/>== 指标：'+ params.value[1]}"
), ),
))
tl.add(sca0, f"{i}")
tab0.add(tl, clm0)
tl.render(f"{info}.html")
print(f"{info}.html")
return df0_dict
def Create_Combine_Compare(exam_name):
tab = Tab()
for i in range(len(exam_name)):
tab.add(ALL_Exam_Tab[i], exam_name[i])
tab.render("成绩对比结果.html")
def index():
tab = Tab()
tab.add(grid_mutil_yaxis(), "美元指数及GDP增长率")
tab.add(line_areastyle_boundary_gap(), "1971-1990GDP增长率")
tab.add(bar_stack1(), "1986-2005外资净投入")
tab.add(effectscatter_splitline(), "1987-2001泰国汇率变化")
tab.add(bar_base(), "中美外资净投入对比")
tab.add(pie_rosetype(), "2018石油产量")
tab.add(line_base(), "21世纪历年国际油价/美元")
tab.add(line_base2(), "近年委内瑞拉通货膨胀率")
tab.add(Pie_base(), "2018中国产业加值占GDP比重%")
tab.add(Pie_base1(), "2018美国产业加值占GDP比重%")
tab.add(bar_datazoom_slider(), "中国贸易总额")
tab.add(line_markpoint(), "中国贸易总额增长率")
tab.add(pie_base(), "主要国家贸易占比")
tab.add(table_base(), "历年中国贸易总额")
tab.add(timeline_map(data), "2012-2018GDP增长率")
return Markup(tab.render_embed())
title='图1-6',
subtitle="四川特殊教育基本情况", # 设置标题与副标题样式，进行图例说明
subtitle_textstyle_opts=opts.TextStyleOpts(color="black",
font_size=15)),
xaxis_opts=opts.AxisOpts(splitline_opts=opts.SplitLineOpts(
is_show=True)),
yaxis_opts=opts.AxisOpts(splitline_opts=opts.SplitLineOpts(
is_show=True)),
))
return c #返回数据
effectscatter_symbol().render_notebook()
tab = Tab() #添加tab
tab.add(teachers_map(), "特殊教育专任教师数")
tab.add(schools_map(), "各省特殊教育学校数")
tab.add(recruit_students_map(), "各省特殊教育招生数")
tab.add(all(), "教育支出与特殊教育在校学生详情")
tab.add(finacial_education_map(), "地方财政教育支出")
tab.add(four_compare(), "四个不同省份对比")
tab.add(effectscatter_symbol(), "四川特殊教育基本情况")
tab.render_notebook()
@app.route('/', endpoint='r1', redirect_to='/index')
def root():
return render_template('index.html')
@app.route('/index')
def get_line_charts():
hb_confirmed = [int(x) for x in np.array(get_hubei_data())[:, 0]]
cq_confirmed = [int(x) for x in np.array(get_chongqin_data())[:, 0]]
tot_confirmed = [int(x) for x in np.array(get_total_data())[:, 0]]
hb_cured = [int(x) for x in np.array(get_hubei_data())[:, 1]]
cq_cured = [int(x) for x in np.array(get_chongqin_data())[:, 1]]
tot_cured = [int(x) for x in np.array(get_total_data())[:, 1]]
hb_dead = [int(x) for x in np.array(get_hubei_data())[:, 2]]
cq_dead = [int(x) for x in np.array(get_chongqin_data())[:, 2]]
tot_dead = [int(x) for x in np.array(get_total_data())[:, 2]]
line_chart_1 = (
Line(init_opts=opts.InitOpts())
.add_xaxis(time_list)
.add_yaxis('湖北',hb_confirmed, color='#ff6361')
.add_yaxis('重庆', cq_confirmed, color='#ffa600')
.add_yaxis('全国', tot_confirmed, color='#bc5090')
.set_global_opts(
title_opts=opts.TitleOpts(title='2-8至2-22之间累计确诊病例变化趋势',
pos_left='20%', pos_top='5%'),
tooltip_opts=opts.TooltipOpts(trigger="axis", axis_pointer_type='shadow'),
legend_opts=opts.LegendOpts(orient='horizontal', pos_left='60%', pos_top='5%'),
yaxis_opts=opts.AxisOpts(
name='人数',
type_='value',
axistick_opts=opts.AxisTickOpts(is_show=True),
splitline_opts=opts.SplitLineOpts(is_show=True),
)
)
.set_series_opts(label_opts=opts.LabelOpts(is_show=False), splitline_opts=opts.SplitLineOpts(is_show=True))
)
line_chart_2 = (
Line()
.add_xaxis(time_list)
.add_yaxis('湖北', hb_cured, color='#ff6361')
.add_yaxis('重庆', cq_cured, color='#ffa600')
.add_yaxis('全国', tot_cured, color='#bc5090')
.set_global_opts(
title_opts=opts.TitleOpts(title='2-8至2-22之间累计治愈病例变化趋势',
pos_left='20%', pos_top='5%'),
tooltip_opts=opts.TooltipOpts(trigger="axis", axis_pointer_type='shadow'),
legend_opts=opts.LegendOpts(orient='horizontal', pos_left='60%', pos_top='5%'),
yaxis_opts=opts.AxisOpts(
name='人数',
type_='value',
axistick_opts=opts.AxisTickOpts(is_show=True),
splitline_opts=opts.SplitLineOpts(is_show=True),
)
)
.set_series_opts(label_opts=opts.LabelOpts(is_show=False), splitline_opts=opts.SplitLineOpts(is_show=True))
)
line_chart_3 = (
Line()
.add_xaxis(time_list)
.add_yaxis('湖北', hb_dead, color='#ff6361')
.add_yaxis('重庆', cq_dead, color='#ffa600')
.add_yaxis('全国', tot_dead, color='#bc5090')
.set_global_opts(
title_opts=opts.TitleOpts(title='2-8至2-22之间累计死亡病例变化趋势',
pos_left='20%', pos_top='5%'),
tooltip_opts=opts.TooltipOpts(trigger="axis", axis_pointer_type='shadow'),
legend_opts=opts.LegendOpts(orient='horizontal', pos_left='60%', pos_top='5%'),
yaxis_opts=opts.AxisOpts(
name='人数',
type_='value',
axistick_opts=opts.AxisTickOpts(is_show=True),
splitline_opts=opts.SplitLineOpts(is_show=True),
)
)
.set_series_opts(label_opts=opts.LabelOpts(is_show=False), splitline_opts=opts.SplitLineOpts(is_show=True))
)
tab = Tab(page_title='湖北、重庆、全国病例变化趋势')
tab.add(line_chart_1, '累计确诊人数')
tab.add(line_chart_2, '累计治愈人数')
tab.add(line_chart_3, '累计死亡人数')
return tab
temp_list.append(line[10])
# print(temp_list)
res_list.append(temp_list)
temp_list = []
res_name_list.append(line[1])
table.add(heardes, res_list).set_global_opts(
title_opts=opts.ComponentTitleOpts(title="院软课题知识数据表（2010-2019）"))
return table
if __name__ == "__main__":
time_start = time.time() # 记录启动时间
# generate_json()
# year_str = "2014"
# opts_title = year_str + "年院软课题知识网络拓扑图绘制"
# graph_rs(year_str, opts_title).render("..\\000LocalData\\caict_k\\Graph_rs_render.html")
# 开始尝试tab选项卡多图的展示
tab = Tab(page_title="院软课题知识网络拓扑图绘制(2010-2019)")
for year_item in range(2019, 2009, -1):
opts_title = str(year_item) + "年院软课题知识网络拓扑图绘制"
tab.add(graph_rs(str(year_item), opts_title), str(year_item) + "年")
tab.add(table_base(), "数据查询表")
tab.render("..\\000LocalData\\caict_k\\CAICT_rs_graph_force.html")
time_end = time.time() # 记录程序结束时间
print("=>Scripts Finish, Time Consuming:", (time_end - time_start), "S")
""""
cnzz
<script type="text/javascript">var cnzz_protocol = (("https:" == document.location.protocol) ? "https://" : "http://");document.write(unescape("%3Cspan id='cnzz_stat_icon_4586080'%3E%3C/span%3E%3Cscript src='" + cnzz_protocol + "s11.cnzz.com/stat.php%3Fid%3D4586080%26show%3Dpic2' type='text/javascript'%3E%3C/script%3E"));</script>
"""
def tab_product_base():
"""
It is used to respond to requests for chart parameters.
Returns
-------
tab : TYPE-echarts parameters
return echarts parameters.
"""
tab = Tab()
tab.add(hist_product_feature_base(), "Product Module")
tab.add(hist_product_edc_feature_base(), "EDC Feature")
tab.add(hist_product_deployment_base(), "Product Deployment")
tab.add(hist_product_support_base(),"Product Support")
tab.add(hist_product_train_base(), "Product Train")
tab.add(radar_product_rating_base(),"Product Rating")
return tab
def draw_charts(kline_data ,canshu ={} ,canshu2={},vol_bar=False,markline_show1 =False ,markline_show2 =False, path = '0501'):
'''
df['candle_begin_time','open','high','low','close','volume']
[['candle_begin_time','open','high','low','close','volume']]
kdata = df[['open', 'high', 'low', 'close']].values.tolist()
df['candle_begin_time'].values.tolist()
:return:
'''
df = kline_data.copy()
df['candle_begin_time'] = pd.to_datetime(df['candle_begin_time'])
time_list = df['candle_begin_time'].apply(lambda x: x.strftime('%Y-%m-%d %H:%M:%S')).values.tolist()
vol = df['volume'].values.tolist()
kline = Kline()
kline.add_xaxis(xaxis_data=time_list)
signal_pos_list = []
if 'signal' in df.columns:
print('signal,存在！')
df['pos'] = df['signal'].shift(1)
for i in df[df['pos'] > 0].index:
bar0 = df.iloc[i]
sig_pos0 = opts.MarkPointItem(name="做多",
coord=[bar0['candle_begin_time'].strftime('%Y-%m-%d %H:%M:%S'),bar0.low -2],
value='买',
symbol ='circle',
symbol_size =[20,40],
itemstyle_opts = {'color': 'red'})
signal_pos_list.append(sig_pos0)
for i in df[df['pos'] < 0].index:
bar0 = df.iloc[i]
sig_pos0 = opts.MarkPointItem(name="做空",
coord=[bar0['candle_begin_time'].strftime('%Y-%m-%d %H:%M:%S'),bar0.high +3],
value='卖',
symbol='circle',
symbol_size=[20, 40],
itemstyle_opts={'color': 'blue'})
signal_pos_list.append(sig_pos0)
for i in df[df['pos'] == 0].index:
bar0 = df.iloc[i]
sig_pos0 = opts.MarkPointItem(name="平仓",
coord=[bar0['candle_begin_time'].strftime('%Y-%m-%d %H:%M:%S'),bar0.low - 2],
value='平',
symbol ='triangle',
symbol_size =[20,40],
itemstyle_opts = {'color': 'green'})
signal_pos_list.append(sig_pos0)
else :
df['pos'] =None
markline =[]
if markline_show1 and ('signal' in df.columns) :
area_index_list =[i for i in df[(df['pos'] ==0)|(df['pos'] >0)|(df['pos'] <0)].index]
for ix,i in enumerate(area_index_list):
if ix+1 > len(area_index_list)-1:
break
i_now = df.iloc[area_index_list[ix]]
i_next = df.iloc[area_index_list[ix+1]]
if (i_now['pos'] >0) or (i_now['pos'] <0) :
log_info = f"价差：={i_next['open']-i_now['open']}--({i_next['open']}-{i_now['open']})"
else :
log_info =f"平仓：{i_next['open']}---开仓：{i_now['open']}"
sig_area = [{"xAxis": i_now['candle_begin_time'].strftime('%Y-%m-%d %H:%M:%S'),
"yAxis": i_now['open'] ,
"value": None},
{"xAxis": i_next['candle_begin_time'].strftime('%Y-%m-%d %H:%M:%S'),
"yAxis": i_now['open'],
"value":log_info}]
sig_area_v = [{"xAxis": i_next['candle_begin_time'].strftime('%Y-%m-%d %H:%M:%S'),
"yAxis": i_now['open'] ,
"value": None},
{"xAxis": i_next['candle_begin_time'].strftime('%Y-%m-%d %H:%M:%S'),
"yAxis": i_next['open'],
"value":None}]
markline.append(sig_area)
markline.append(sig_area_v)
elif markline_show2 and ('signal' in df.columns):
area_index_list =[i for i in df[(df['pos'] ==0)|(df['pos'] >0)|(df['pos'] <0)].index]
for ix,i in enumerate(area_index_list):
i_now = df.iloc[area_index_list[ix]]
i_1_now = df.iloc[area_index_list[ix-1]] if ix != 0 else 0
if i_now['pos'] == 0:
log_info = f"交易价: {round(i_now['open'], 1)} +- 2__盈亏:{round(i_now['open'], 1) - round(i_1_now['open'], 1)}"
else:
log_info = f"交易价: {round(i_now['open'],1)} +- 2"
sig_area = [{"xAxis": i_now['candle_begin_time'].strftime('%Y-%m-%d %H:%M:%S'),
"yAxis": i_now['open'] ,
"value": None},
{"xAxis": (i_now['candle_begin_time']+timedelta(minutes=15)).strftime('%Y-%m-%d %H:%M:%S'),
"yAxis": i_now['open'],
"value":log_info}]
markline.append(sig_area)
colors = {'red': 'rgb((220,20,60)','red2': 'rgb((250,20,40)',
'yangzi': 'rgb(255,0,255)', 'zi': 'rgb(128,0,128)',
'sehnzi': 'rgb(148,0,211)', 'blue': 'rgb(0,0,255)',
'qing': 'rgb(0,255,255)', 'black': 'rgb(0,0,0)',
'shengreen': 'rgb(157,255,212)', 'green': 'rgb(0,255,0)',
'yellow': 'rgb(255,255,0)', 'glod': 'rgb(218,165,32)',
'orange': 'rgb(255,165,0)', 'brown': 'rgb(165,42,42)'}
kline.add_xaxis(xaxis_data=time_list)
kline.add_yaxis(series_name="oclh",
xaxis_index=1,
yaxis_index=1,
y_axis =df.loc[:, ['open', 'close', 'low', 'high']].values.tolist(),
markline_opts=opts.MarkLineOpts(data=markline,
linestyle_opts=opts.LineStyleOpts(type_='dotted',width=3,color='red',opacity=0.5),
label_opts=opts.LabelOpts(position="right", color="blue", font_size=13),),
#官网给出的样本
markpoint_opts=opts.MarkPointOpts(data=signal_pos_list),
itemstyle_opts=opts.ItemStyleOpts(color="#ec0090", color0="#00aa3c"), )
kline.set_global_opts(
legend_opts=opts.LegendOpts(is_show=True,pos_top=30, pos_left="left",orient='vertical'),
datazoom_opts=[ opts.DataZoomOpts(
is_show=False,
type_="inside",
xaxis_index=[0, 1],
range_start=90,
range_end=100,
orient='vertical'),
opts.DataZoomOpts(
is_show=True,
xaxis_index=[0, 1],
type_="slider",
pos_top="20%",
range_start=90,
range_end=100,orient='vertical'),],
yaxis_opts =opts.AxisOpts(
is_scale=True,
splitarea_opts=opts.SplitAreaOpts(is_show=True, areastyle_opts=opts.AreaStyleOpts(opacity=1)),),
title_opts=opts.TitleOpts(
title = 'K_line',
pos_top='middle',
title_textstyle_opts=opts.TextStyleOpts(
color='black',font_weight='bold' ,font_size=20)),
tooltip_opts=opts.TooltipOpts(
trigger="axis",
trigger_on='"mousemove"',#click|mousemove
axis_pointer_type="cross",
is_show_content=True,
is_always_show_content=True,
background_color="rgba(20, 105, 245, 0.1)",
border_width=1,
border_color= "#ccc",
position = ['70%','30%'],
textstyle_opts=opts.TextStyleOpts(font_size=10,color="#000"),),
visualmap_opts=opts.VisualMapOpts(
is_show=True,
dimension=2,
series_index=5,
is_piecewise=True,
pieces=[{"value": 1, "color": "#00da3c"},{"value": -1, "color": "#ec0000"},],),
axispointer_opts=opts.AxisPointerOpts(
is_show=True,
link=[{"xAxisIndex": "all"}],
label=opts.LabelOpts(background_color="#777"),),
brush_opts=opts.BrushOpts(
x_axis_index="all",
brush_link="all",
out_of_brush={"colorAlpha": 0.1},
brush_type="lineX",),
)
if len(canshu.keys())>0:
cos = list(colors.values())
line = Line()
for k,v in canshu.items():
line.add_xaxis(xaxis_data=time_list)
line.set_global_opts(xaxis_opts=opts.AxisOpts(type_="category"))
co = cos.pop()
line.add_yaxis(
series_name=k,
y_axis = [y for y in v.values.tolist() if y != np.nan],
xaxis_index=1,
yaxis_index=1,
is_smooth=False,
is_connect_nones=False,# 是否连接空数据
is_symbol_show=False,#是否显示值的位置，默认显示。
color = co,
is_hover_animation = False, # 是否开启 hover 在拐点标志上的提示动画效果。
linestyle_opts=opts.LineStyleOpts(width=2, opacity=0.9,color=co),
label_opts=opts.LabelOpts(is_show =True,position='middle',distance=2,rotate=5,color = 'rgb(165,42,42)'),
itemstyle_opts=opts.ItemStyleOpts(color=co),)
kline = kline.overlap(line)
grid_chart = Grid(init_opts=opts.InitOpts(width = "1500px",height= "700px",theme=ThemeType.DARK))
grid_chart.add(kline,grid_opts=opts.GridOpts(pos_left="10%", pos_right="8%",pos_top='4%' ,height="70%"))
if( vol_bar == True) or (len(canshu2.keys())==0):
bar = Bar()
bar.add_xaxis(xaxis_data=time_list)
bar.add_yaxis(
series_name="volume",
y_axis=vol,
xaxis_index=1,
yaxis_index=2,
label_opts=opts.LabelOpts(is_show=False), )
bar.set_global_opts(xaxis_opts=opts.AxisOpts(
type_="category",
is_scale=True,
grid_index=1,
boundary_gap=False,
axisline_opts=opts.AxisLineOpts(is_on_zero=False),
axistick_opts=opts.AxisTickOpts(is_show=False),
splitline_opts=opts.SplitLineOpts(is_show=False),
axislabel_opts=opts.LabelOpts(is_show=False),
split_number=20,
min_="dataMin",
max_="dataMax", ),
yaxis_opts=opts.AxisOpts(
grid_index=1,
is_scale=True,
split_number=2,
axislabel_opts=opts.LabelOpts(is_show=False),
axisline_opts=opts.AxisLineOpts(is_show=False),
axistick_opts=opts.AxisTickOpts(is_show=False),
splitline_opts=opts.SplitLineOpts(is_show=False), ),
legend_opts=opts.LegendOpts(is_show=True), )
grid_chart.add(bar,grid_opts=opts.GridOpts(pos_left="10%", pos_right="8%", pos_top="80%", height="15%"),)
if len(canshu2.keys())>0 :
line2 = Line()
line2.add_xaxis(xaxis_data=time_list)
for i, k in enumerate(canshu2.keys()):
co = random.choice(list(colors.values()))
line2.add_yaxis(
series_name=k,
y_axis=[y for y in canshu2[k].values.tolist()],
xaxis_index=1,
yaxis_index=i + 1,
is_smooth=True,
is_symbol_show=False,
is_hover_animation=True,
color=co,
z_level=0,
linestyle_opts=opts.LineStyleOpts(width=2, opacity=0.5, color=co),
label_opts=opts.LabelOpts(is_show=False), )
if k == list(canshu2.keys())[-1]: continue
line2.extend_axis(yaxis=opts.AxisOpts(name=k, type_="value", position="right", min_=None, max_=None,
axisline_opts=opts.AxisLineOpts(linestyle_opts=opts.LineStyleOpts(color=co)),
axislabel_opts=opts.LabelOpts(formatter="{value}"), ))
line2.set_global_opts(xaxis_opts=opts.AxisOpts(
type_="category", is_scale=True, grid_index=1, split_number=20, min_="dataMin", max_="dataMax",
axisline_opts=opts.AxisLineOpts(is_on_zero=False),
axistick_opts=opts.AxisTickOpts(is_show=False),
splitline_opts=opts.SplitLineOpts(is_show=False),
axislabel_opts=opts.LabelOpts(is_show=False), ),
yaxis_opts=opts.AxisOpts(name=k, grid_index=i + 1, position='right',
splitarea_opts=opts.SplitAreaOpts(is_show=True, areastyle_opts=opts.AreaStyleOpts(opacity=1)),
min_=None, max_=None, is_scale=True, offset=50, ), )
grid_chart.add(line2,grid_opts=opts.GridOpts(pos_left="10%", pos_right="8%", pos_top="75%", height="23%"),is_control_axis_index=True)
tab = Tab()
zijin_data = (kline_data['per_lr'].cumsum())
zijin_data.fillna(method='ffill', inplace=True)
zijin_line = Line(init_opts=opts.InitOpts(width = "1500px",height= "700px",theme=ThemeType.DARK)).add_xaxis(time_list)
zijin_line.add_yaxis(series_name="zijin_line:", y_axis=zijin_data.values, color="#FF0000")
zijin_line.set_global_opts(
datazoom_opts=[opts.DataZoomOpts(), opts.DataZoomOpts(type_="inside")],
title_opts=opts.TitleOpts(title="资金曲线变化"),
tooltip_opts=opts.TooltipOpts(trigger="axis", axis_pointer_type="cross"),
yaxis_opts = opts.AxisOpts(
is_scale=True,
axislabel_opts=opts.LabelOpts(is_show=False),
axisline_opts=opts.AxisLineOpts(is_show=False),
axistick_opts=opts.AxisTickOpts(is_show=False),
splitline_opts=opts.SplitLineOpts(is_show=False), ),
legend_opts = opts.LegendOpts(is_show=True)
)
tab.add(grid_chart,'kline' )
tab.add(zijin_line,'资金曲线变化' )
tab.render(f"{path}.html")
html0 = tab.render(f"{path}.html")
if os.path.exists(html0):
print("ok!保存在：")
print(html0)
else:
print('保存失败！')
line = Line(init_opts=opts.InitOpts(width="1200px", height="600px"))
line.add_xaxis(dates)
line.add_yaxis("确诊", cc_data[0], is_symbol_show=False)
line.add_yaxis("死亡", cc_data[1], is_symbol_show=False)
line.set_global_opts(
title_opts=opts.TitleOpts(title="疫情状况",
pos_top="top",
pos_left="center"),
legend_opts=opts.LegendOpts(pos_top="5%", pos_left="center"),
)
tab = Tab()
tab.add_js_funcs(
"var div = document.createElement('div'); var divs = document.createAttribute('style'); divs.value = 'text-align:center'; div.setAttributeNode(divs); var divc = document.createAttribute('class'); divc.value = 'button_group'; div.setAttributeNode(divc); var but = document.createElement('button'); but.innerHTML='"
+ topics[0] +
"'; but.onclick = function() { window.location.href='index0.html' }; div.appendChild(but); var but = document.createElement('button'); but.innerHTML='"
+ topics[1] +
"'; but.onclick = function() { window.location.href='index1.html' }; div.appendChild(but); var but = document.createElement('button'); but.innerHTML='"
+ topics[2] +
"'; but.onclick = function() { window.location.href='index2.html' }; div.appendChild(but); var but = document.createElement('button'); but.innerHTML='"
+ topics[3] +
"'; but.onclick = function() { window.location.href='index3.html' }; div.appendChild(but); var but = document.createElement('button'); but.innerHTML='"
+ topics[4] +
"'; but.onclick = function() { window.location.href='index4.html' }; div.appendChild(but); document.body.appendChild(div);"
)
tab.add(chart=theme_river, tab_name="议题变化")
tab.add(chart=bar, tab_name="情绪变化")
tab.add(chart=line, tab_name="疫情状况")
tab.add(chart=wc_tl, tab_name="词频统计")
tab.render("index" + str(topic) + ".html")
# 计算未提交人数
df['已提交'] = df.apply(lambda x: x.value_counts().get(0, 0), axis=1)
print(df)
df['提交率'] = 1 - df.已提交 / (df.columns.size - 1)
df['提交率'] = df['提交率'].apply(lambda x: format(x, '.2'))
print(df.提交率)
xaxis = [
'1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1',
'1'
]
for i in range(16):
xaxis[i] = '第' + str(i + 1) + '次'
print(xaxis)
def line_xaxis_type() -> Line:
c = (Line().add_xaxis(xaxis).add_yaxis(
'1班_赵丽华', submission_rate(dfz)).add_yaxis(
"4班_李烨", submission_rate(dfz)).set_global_opts(
title_opts=opts.TitleOpts(title="正确率"), ))
return c
tab = Tab()
tab.add(timeline_bar(), "作业平均分对比")
tab.add(line_xaxis_type(), '作业提交率')
tab.render()
import data_spider
import data_analyze
import china_dailyData_line
import china_map
import overseas_map
import overseas_bar
import china_pie
import overseas_worldCloud
from pyecharts.charts import Bar, Grid, Line, Pie, Tab
#数据爬取及分析
data_spider.run()
data_analyze.run()
#数据可视化呈现
tab = Tab()
tab.add(china_map.plot_china_provinces(), "中国疫情数据地图")
tab.add(china_dailyData_line.plot_chinaDayData(),"中国每日疫情数据折线图")
tab.add(china_pie.plot_chinaPie(),"国内省份死亡/治愈人数饼图")
tab.add(overseas_bar.plot_overseasBar(), "海外疫情数据条形图")
tab.add(overseas_map.plot_overseasMap(),"海外疫情数据地图")
tab.add(overseas_worldCloud.plot_countryWordCloud(),"海外疫情国家词云图")
#tab.add(china_ratePie.plot_healDeadPie(), "pie-example")
tab.render("show_view.html")
position="right",
offset=80,
axisline_opts=opts.AxisLineOpts(linestyle_opts=opts.LineStyleOpts(
color="#5793f3")),
axislabel_opts=opts.LabelOpts(formatter="{value} ml"),
),
title_opts=opts.TitleOpts(title="Grid-多 Y 轴示例"),
tooltip_opts=opts.TooltipOpts(trigger="axis",
axis_pointer_type="cross"),
))
line = (Line().add_xaxis(x_data).add_yaxis(
"平均温度",
[2.0, 2.2, 3.3, 4.5, 6.3, 10.2, 20.3, 23.4, 23.0, 16.5, 12.0, 6.2],
yaxis_index=2,
color="#675bba",
label_opts=opts.LabelOpts(is_show=False),
))
bar.overlap(line)
return Grid().add(bar,
opts.GridOpts(pos_left="5%", pos_right="20%"),
is_control_axis_index=True)
tab = Tab()
tab.add(bar_datazoom_slider(), "bar-example")
tab.add(line_markpoint(), "line-example")
tab.add(pie_rosetype(), "pie-example")
tab.add(grid_mutil_yaxis(), "grid-example")
tab.render("Tab_base.html")
radius=["30%", "75%"],
center=["75%", "50%"],
rosetype="area",
).set_global_opts(title_opts=opts.TitleOpts(title="Pie-玫瑰图示例")))
return c
def table_base() -> Table:
table = Table()
headers = ["City name", "Area", "Population", "Annual Rainfall"]
rows = [
["Brisbane", 5905, 1857594, 1146.4],
["Adelaide", 1295, 1158259, 600.5],
["Darwin", 112, 120900, 1714.7],
["Hobart", 1357, 205556, 619.5],
["Sydney", 2058, 4336374, 1214.8],
["Melbourne", 1566, 3806092, 646.9],
["Perth", 5386, 1554769, 869.4],
]
table.add(headers, rows).set_global_opts(
title_opts=opts.ComponentTitleOpts(title="Table"))
return table
tab = Tab()
tab.add(bar_datazoom_slider(), "bar-example")
tab.add(line_markpoint(), "line-example")
tab.add(pie_rosetype(), "pie-example")
tab.add(table_base(), "table-example")
tab.render("tab_render.html")
