def table_base(sql, titl) -> Table:
table = Table()
# engine = create_engine("mysql+pymysql://root:root@localhost:3306/mysql", encoding="utf-8")
# session = sessionmaker(bind=engine)
# df = pd.read_sql(sql, engine)
# rows=df.values.tolist()
# headers=df.columns.tolist()
with SQLPoll() as db:
students = db.fetch_all(sql, None)
headers = []
for student in students:
headers = list(student.keys())
break
c = []
rows = []
for student in students:
c = list(student.values())
rows += [c]
if len(rows) == 0:
headers = ['记录', '数量']
rows = [['无', 0]]
table.add(
headers,
rows).set_global_opts(title_opts=opts.ComponentTitleOpts(title=titl))
return table
def table_base() -> Table:
table = Table()
heardes = [
"课题名称", "课题编号", "年份", "负责人", "负责单位", "课题类型", "领域", "课题方向", "关键词", "URL"
]
res_file = "..\\000LocalData\\caict_k\\research_subject_intergrate.csv"
res_file_read = open(res_file, 'r', encoding='utf-8')
res_list = []
temp_list = []
res_name_list = []
for line in res_file_read.readlines():
line = line.strip().split("|")
if line[1] not in res_name_list:
temp_list.append(line[0])
temp_list.append(line[1].replace("--", "-", 10))
temp_list.append(line[2])
temp_list.append(line[3])
temp_list.append(line[4])
temp_list.append(line[5])
temp_list.append(line[6])
temp_list.append(line[7])
temp_list.append(line[8])
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
def table_base(data) -> Table:
table = Table()
level = ['较低:0~3k', '一般:3k~5k', '中等:5k~8k', '较高:8k~12k', '优秀:12k以上']
city = ['beijing', "shanghai", 'guang', 'shen', 'cheng']
table.add(level, data[1]).set_global_opts(
title_opts=opts.ComponentTitleOpts(title="表格", subtitle="城市"))
return table
def get_eventtable(rows) -> Table:
table = Table()
headers = ["序号", "日期", "举报内容", "所属区域", "维度", "经度"]
for r in rows:
c = r[2]
cl = list(c)
n_w = len(cl)
idx = int((n_w - 1) / 30) #每40个词分行
if (idx < 1):
continue
for i in range(idx):
cl.insert((i + 1) * 30, "\n")
r[2] = "".join(cl)
'''
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],
["Sydney", 2058, 4336374, 1214.8],
["Melbourne", 1566, 3806092, 646.9],
["Perth", 5386, 1554769, 869.4],
]
'''
table.add(headers, rows).set_global_opts(
title_opts=ComponentTitleOpts(title="详细举报数据"), )
return table
def table_traces(rows) -> Table:
table = Table()
headers = ["设备名", "MonkeyError", "条数"]
table.add(
headers,
rows).set_global_opts(title_opts=ComponentTitleOpts(title="错误日志收集"))
return table
def table_base(header,row) -> Table:
table = Table()
headers = header
rows = row
table.add(headers, rows).set_global_opts(
title_opts=opts.ComponentTitleOpts(title="Table")
)
return table
def table_base(header,row) -> Table:
table = Table(page_title='未完成项',js_host=r"D:\JGY\600-Data\004-auxiliary辅助文件\\")
headers = header
rows = row
table.add(headers, rows).set_global_opts(
title_opts=opts.ComponentTitleOpts(title="Table")
)
return table
def _gen_table() -> Table:
table = Table()
headers = ["City name", "Area", "Population", "Annual Rainfall"]
rows = [["Brisbane", 5905, 1857594, 1146.4],
["Perth", 5386, 1554769, 869.4]]
table.add(headers, rows)
return table
def getGuanjian():
data = pd.DataFrame(pd.read_excel(excel_dir,))
mean = data.mean()
print(type(mean))
table = (Table().add(headers=['关键维度满意度得分','平均分'],rows=[['质量满意度','4.254902'],['包装满意度','4.078431'],['物流速度满意度','3.803922'],['售后满意度','4.117647'],['价格满意度','4.058824'],['支付满意度','4.117647'],]))
table.render(path='keyScore.html')
table1 = (Table().add(headers=['关键维度满意度比例','达到满意'],rows=[['质量满意度','0.86'],['包装满意度','0.76'],['物流速度满意度','0.70'],['售后满意度','0.80'],['价格满意度','0.76'],['支付满意度','0.84'],]))
table1.render(path='keyScale.html')
def test_table_base(fake_writer):
table = Table()
headers = ["City name", "Area", "Population", "Annual Rainfall"]
rows = [["Brisbane", 5905, 1857594, 1146.4],
["Perth", 5386, 1554769, 869.4]]
table.add(headers, rows).render()
_, content = fake_writer.call_args[0]
assert_in("fl-table", content)
def createtable(data):
# 生成pyecharts表格
table = Table()
headers = ["提交日期","仪器编号","板号","样品数量","结果均值","ISD"]
# rows = [[localtime(d.upload_time).strftime("%Y-%m-%d %X"), d.instrument, d.platenum, d.num, d.mean, d.sd] for d in data]
rows = [[d.upload_time.strftime("%Y-%m-%d %X"), d.instrument, d.platenum, d.num, d.mean, d.sd] for d in data]
table.add(headers, rows).set_global_opts(title_opts=ComponentTitleOpts(title="结果分布表"))
return table
def _create_table() -> 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],
]
table.add(headers, rows)
return table
def table_base():
table = Table()
headers = ["地区", "现有确诊", "累计死亡", "累计治愈"]
rows = []
table.add(headers, rows).set_global_opts(
title_opts=ComponentTitleOpts(title="Table", subtitle="副标题"))
# table.render("table_base.html")
return rows
def table_base(rows) -> Table:
table = Table()
headers = [
"设备名", "设备号", "CPU", "总内存", "分辨率", "网络", "耗时(s)", "CPU峰值", "CPU均值",
"内存峰值", "内存均值", "fps峰值", "fps均值", "开始电量", "结束电量", "上行流量峰值", "上行流量均值",
"下行流量峰值", "下行流量均值"
]
table.add(headers, rows).set_global_opts(
title_opts=ComponentTitleOpts(title="手机汇总信息", subtitle=""))
return table
def table_base() -> Table:
table = Table()
headers = ["国家", "2012", "2013", "2014", "2015", "2016", "2017", "2018"]
rows = [
["中国", 3.867, 4.158, 4.301, 3.953, 3.685, 4.107, 4.622],
]
table.add(headers, rows).set_global_opts(
title_opts=opts.ComponentTitleOpts(title="近年中国贸易总额/万亿美元"))
return table
def table_base():
table = Table()
headers = ["地区", "确诊", "死亡", "治愈"]
rows = [["湖北", 13522, 414, 397], ["浙江", 829, 0, 60], ["广东", 813, 0, 24],
["河南", 675, 2, 20], ["湖南", 593, 0, 29], ["安徽", 480, 0, 20],
["江西", 476, 2, 19], ["重庆", 344, 2, 9]]
table.add(headers, rows).set_global_opts(
title_opts=ComponentTitleOpts(title="Table", subtitle="副标题"))
# table.render("table_base.html")
return rows
def draw_Table(Time_List, Score_List, Comment_List, Update_List): #绘制评论表格 table = Table() headers = ["评论时间", "情绪值", "评论内容"] rows = [] for index in range(0,1800,30): temp = [Time_List[index], Score_List[index], Comment_List[index]] rows.append(temp) table.add(headers, rows).set_global_opts( title_opts=ComponentTitleOpts(title="时间-情绪-评论文本 抽样数据") ) return table
def get_table_base(self, _data):
'''
plot geo-detail
:param _data: gps对应的城市分布(dataframe)
:return: 数据示例
'''
table = Table()
headers = ["City name", "Number"]
rows = _data
table.add(headers, rows).set_global_opts(
title_opts=opts.ComponentTitleOpts(title="Table-Details"))
return table
def table_base():
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)
def create_base_table(title, x_list, data_list):
headers = [" "] + x_list
rows = []
for key in data_list.keys():
#print(key)
row = []
row.append(key)
for x in data_list[key]:
row.append(x)
#print(row)
rows.append(row)
table = Table()
table.add(headers, rows)
table.set_global_opts(
title_opts=opts.ComponentTitleOpts(title=title)
)
src_path = "./test/"
html_file_name = src_path + title + ".html"
img_file_name = src_path + title + ".png"
table.render(html_file_name)
path_wkimg = r'C:\Program Files\wkhtmltopdf\bin\wkhtmltoimage.exe' # 工具路径
cfg = imgkit.config(wkhtmltoimage=path_wkimg)
imgkit.from_file(html_file_name, img_file_name, config=cfg)
#make_snapshot(snapshot, table.render(html_file_name), img_file_name)
print(img_file_name+"生成完毕...")
def table_base() -> Table:
table = Table()
headers = ["年份", "华南理工大学", "深圳大学", "广州工业大学", "五邑大学"]
rows = [
["2019", 55, 169, 377, 745],
["2018", 58, 167, 452, 792],
["2017", 87, 432, 406, 768],
["2016", 58, 470, 350, 800],
]
table.add(headers, rows).set_global_opts(
title_opts=ComponentTitleOpts(title="江门一中最低录取排名（不包含自主招生）")
)
return table
def table_base(sql, titl) -> Table:
# for arg in args:
# print(arg)
table = Table()
engine = create_engine(
"mysql+pymysql://root:[email protected]:3306/mysql", encoding="utf-8")
session = sessionmaker(bind=engine)
df = pd.read_sql(sql, engine)
rows = df.values.tolist()
headers = df.columns.tolist()
if len(rows) == 0:
rows = [['无', 0]]
table.add(
headers,
rows).set_global_opts(title_opts=opts.ComponentTitleOpts(title=titl))
return table
def image_info(root, path='image_info.html', show_original=False):
"""Show image visualize.
Args:
root: An image file root.
path: result save a html file.
show_original: whether show original image shape.
Returns:
A pyecharts polt object.
"""
shape = read_image(root).shape
info = PIL.Image.open(root).info
width = '' if show_original else str(360)+'px'
height = '' if show_original else str(int(360/shape[1]*shape[0]))+'px'
image_charts = image_base(root, '', '', width, height)
headers = ["Attribute", "Info"]
rows = [['path', root],
['size', str(round(os.stat(root).st_size/1024/1024, 2))+'M'],
['shape', str(read_image(root).shape)],
['jfif', info['jfif']],
['jfif_version', info['jfif_version']],
['dpi', info['dpi']],
['jfif_unit', info['jfif_unit']],
['jfif_density', info['jfif_density']]
]
table_charts = Table().add(headers, rows)
page = Page(layout=Page.SimplePageLayout).add(*[image_charts, table_charts])
return page.render(path)
def _plot_txt_time(self, stats, ax=None, **kwargs):
"""
Outputs the statistics for various time frames.
"""
returns = stats['returns']
mly_ret = perf.aggregate_returns(returns, 'monthly')
yly_ret = perf.aggregate_returns(returns, 'yearly')
mly_pct = mly_ret[mly_ret >= 0].shape[0] / float(mly_ret.shape[0])
mly_avg_win_pct = np.mean(mly_ret[mly_ret >= 0])
mly_avg_loss_pct = np.mean(mly_ret[mly_ret < 0])
mly_max_win_pct = np.max(mly_ret)
mly_max_loss_pct = np.min(mly_ret)
yly_pct = yly_ret[yly_ret >= 0].shape[0] / float(yly_ret.shape[0])
yly_max_win_pct = np.max(yly_ret)
yly_max_loss_pct = np.min(yly_ret)
header = ["Performance", "Value"]
rows = [["Winning Months %", "{:.0%}".format(mly_pct)],
["Average Winning Month %", "{:.2%}".format(mly_avg_win_pct)],
["Average Losing Month %", "{:.2%}".format(mly_avg_loss_pct)],
["Best Month %", "{:.2%}".format(mly_max_win_pct)],
["Worst Month %", "{:.2%}".format(mly_max_loss_pct)],
["Winning Years %", '{:.0%}'.format(yly_pct)],
["Best Year %", '{:.2%}'.format(yly_max_win_pct)],
["Worst Year %", '{:.2%}'.format(yly_max_loss_pct)]]
table = (Table().add(header, rows).set_global_opts(
title_opts=opts.ComponentTitleOpts(title="Time")))
return table
def table(self):
table = Table()
headers = ["项目", "数值", "开始时间", "结束时间"]
rows = [
["最终收益", "{:.2f}%".format(self.endassets*100),"",""],
["交易次数", "{}".format(len(self.tradings)),"",""],
["平均收益", "{:.2f}%".format(self.averageassets*100),"",""],
["开仓次数", "{}".format(self.openpositions),"",""],
["清仓次数", "{}".format(self.clearance),"",""],
["手续费", "{:.2f}".format(self.data["scomm"].sum()+self.data["bcomm"].sum()),"",""],
["最大收益", "{:.2f}%".format(self.maxassets["value"]*100),self.maxassets["begin"],self.maxassets["end"]],
["最大回撤", "{:.2f}%".format(self.minassets["value"]*100),self.minassets["begin"],self.minassets["end"]],
]
table.add(headers, rows)
return table
def plot_portfolio(self):
# 持仓
total_value = glovar.context.portfolio.total_value
if len(glovar.context.value_history)==0:
day_return = total_value/glovar.context.portfolio.starting_cash-1
else:
day_return = total_value/glovar.context.value_history[-1][-1]-1
headers = ['股票代码','股票名称','持仓数量','开仓时间','收益率','资金占比']
rows = []
value_count = 0
for security in glovar.context.portfolio.positions.index:
position = glovar.context.portfolio.positions.loc[security,:]
if security in self.stock_info.index.values:
name = self.stock_info.loc[security, 'display_name']
else:
name = security[:-5]
position_amount = position.amount
position_value = position.value
profit = (position.price-position.avg_cost)*position.amount*position.multiplier*position.side
init_time = position.init_time.strftime('%Y-%m-%d %H:%M:%S')
returns_rate = '{:.2%}'.format(profit/position_value)
position_rate = '{:.2%}'.format(position_value/total_value)
value_count += position_value
rows.append([security, name, position_amount, init_time, returns_rate, position_rate])
total_position_rate = value_count/total_value
title = '{} 当天收益率：{:.2%} 总资产：{:.0f} 持仓比率：{:.2%} 持股{}只'.format(glovar.context.current_dt.strftime('%Y-%m-%d %H:%M:%S'),
day_return, total_value, total_position_rate, len(glovar.context.portfolio.positions))
(
Table()
.add(headers, rows)
.set_global_opts(title_opts=opts.ComponentTitleOpts(title=title))
.render(sys.path[0]+'/img/{}/{}/持仓.html'.format(self.run_params['mode'], self.run_params['strategy']))
)
def get_table_base(self, _df, title_name='数据分布', subtitle_name=''):
'''
数据详情表 （未使用）
:param _df:
:param title_name:
:param subtitle_name:
:return:
'''
_df = _df.reset_index()
table = Table()
headers = _df.columns.tolist()
rows = _df.values
table.add(
headers, rows).set_global_opts(title_opts=opts.ComponentTitleOpts(
title="Table-{}".format(title_name), subtitle=subtitle_name))
return table
def table_picture(self, headers, data):
'''
数据类似
headers = ["City name", "Area", "Population", "Annual Rainfall"]
data = [
["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],
]
'''
headers = [""]
data = [
[]
]
table = (
Table()
.add(headers=headers, rows=data)
.set_global_opts(title_opts=ComponentTitleOpts(title=self.title, subtitle=self.subtitle),
# 添加logo
graphic_opts=self.logo
)
)
return table
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=ComponentTitleOpts(
title="Table-基本示例", subtitle="我是副标题支持换行哦"))
return table
def table_base(data):
data = data.reset_index().round(4)
headers = data.columns.tolist()
cont = data.values.tolist()
table = Table().add(headers, cont).set_global_opts(
title_opts=ComponentTitleOpts(title="", subtitle="数据来源:WIND")
).set_global_opts()
return table
