def dayloss_line_base(self, x, y, t, st):
c = (Line(
init_opts=opts.InitOpts(width="1000px", height="500px")
).add_xaxis(x).add_yaxis("线损率(%)", y, is_smooth=True).set_global_opts(
tooltip_opts=opts.TooltipOpts(is_show=True,
trigger="axis",
axis_pointer_type="cross"),
title_opts=opts.TitleOpts(title=t, subtitle=st),
xaxis_opts=opts.AxisOpts(
axislabel_opts=opts.LabelOpts(rotate=-90),
max_interval=366,
type_="category",
axispointer_opts=opts.AxisPointerOpts(is_show=True,
type_="shadow"),
),
yaxis_opts=opts.AxisOpts(
axislabel_opts=opts.LabelOpts(formatter="{value} %"),
axistick_opts=opts.AxisTickOpts(is_show=True),
splitline_opts=opts.SplitLineOpts(is_show=True),
),
toolbox_opts=opts.ToolboxOpts(feature=opts.ToolBoxFeatureOpts(
save_as_image=opts.ToolBoxFeatureSaveAsImageOpts(
pixel_ratio=2, background_color='white'),
restore=opts.ToolBoxFeatureRestoreOpts(is_show=False),
data_view=opts.ToolBoxFeatureDataViewOpts(is_show=False),
data_zoom=opts.ToolBoxFeatureDataZoomOpts(is_show=False),
magic_type=opts.ToolBoxFeatureMagicTypeOpts(is_show=False),
brush=opts.ToolBoxFeatureBrushOpts(type_='clear'),
))).set_series_opts(
label_opts=opts.LabelOpts(is_show=False),
markline_opts=opts.MarkLineOpts(
data=[
opts.MarkLineItem(y=6),
],
label_opts=opts.LabelOpts(is_show=False),
),
))
return c
def __init__(self, title: str, output: str):
self.title = title
self.output = output
self.datas = []
self.chart = (Line(init_opts=opts.InitOpts(
page_title=self.title,
theme=ThemeType.DARK,
width="1280px",
height="720px",
)).set_global_opts(
title_opts=opts.TitleOpts(title=self.title),
xaxis_opts=opts.AxisOpts(type_="value",
is_scale=True,
split_number=10,
name="bitrate/kbps"),
yaxis_opts=opts.AxisOpts(type_="value", is_scale=True,
name="vmaf"),
toolbox_opts=opts.ToolboxOpts(
is_show=True,
orient="vertical",
pos_left="right",
feature=opts.ToolBoxFeatureOpts(
save_as_image=opts.ToolBoxFeatureSaveAsImageOpts(
title="save as image", is_show=True),
restore=opts.ToolBoxFeatureRestoreOpts(is_show=False),
data_zoom=opts.ToolBoxFeatureDataZoomOpts(is_show=False),
data_view=opts.ToolBoxFeatureDataViewOpts(
is_show=False, is_read_only=True, title="data"),
magic_type=opts.ToolBoxFeatureMagicTypeOpts(is_show=False),
brush=opts.ToolBoxFeatureBrushOpts(type_=[]))),
legend_opts=opts.LegendOpts(pos_top="bottom"),
tooltip_opts=opts.TooltipOpts(
is_show=True,
formatter=pyecharts_utils.JsCode(
"function(x) {return x.seriesName + '<br/>bitrate '+ x.data[0] + ' kbps<br/>vmaf ' + x.data[1];}"
))))
# 配置各个工具箱
feature=opts.ToolBoxFeatureOpts(
# 保存工具
opts.ToolBoxFeatureSaveAsImageOpts(
# 是否显示
is_show=True,
# 提示语
title="保存为图片",
),
# 还原工具
opts.ToolBoxFeatureRestoreOpts(
# 是否显示该工具
is_show=True,
# 提示语
title="还原",
),
# 数据视图工具
opts.ToolBoxFeatureDataViewOpts(
# 是否显示该工具
is_show=True,
# 提示语
title="数据视图",
# 是否不可编辑
is_read_only=False,
),
# 缩放工具配置项，直角坐标系适用
opts.ToolBoxFeatureDataZoomOpts(
def plot_ground_truth_analysis(
all_score_columns: typing.List[str],
title: str,
output_path: str,
df: pd.DataFrame,
add_wrong_candidates: bool = False,
wrong_candidates_score_column: str = None,
) -> None:
"""
use pyechart to plot html interactive figure
"""
df_processed = copy.deepcopy(df)
for each_col in df_processed.columns:
df_processed[each_col] = pd.to_numeric(df_processed[each_col],
errors='ignore')
xaxis_labels = []
memo = defaultdict(list)
groupby_res = df_processed[df_processed["evaluation_label"] ==
1].groupby(["column", "row"])
for key, each_group in reversed(tuple(groupby_res)):
if add_wrong_candidates:
df_wrong_examples = df_processed[(df_processed["column"] == key[0]) &
(df_processed["row"] == key[1]) &
(df_processed["evaluation_label"] == -1)] \
.sort_values(by=[wrong_candidates_score_column], ascending=False).iloc[:3, :]
# add wrong candidate information
for _, each_row in df_wrong_examples.iterrows():
longest_string = max(each_row["kg_labels"].split("|"),
key=len)
xaxis_labels.append(each_row["label_clean"] +
" \n({})".format(longest_string))
for each_score_column in all_score_columns:
memo[each_score_column].append("{:.2f}".format(
each_row[each_score_column]))
# add ground truth information
xaxis_labels.append(each_group["label_clean"].iloc[0])
for each_score_column in all_score_columns:
memo[each_score_column].append("{:.2f}".format(
each_group[each_score_column].iloc[0]))
# build figure
bar = Bar()
bar.add_xaxis(xaxis_labels)
for k, v in memo.items():
bar.add_yaxis(k, v)
# set the global options
bar.set_global_opts(
title_opts=opts.TitleOpts(title=title, pos_left='40%'),
legend_opts=opts.LegendOpts(pos_left="center",
pos_top="bottom",
orient='horizontal'),
brush_opts=opts.BrushOpts(),
toolbox_opts=opts.ToolboxOpts(feature=opts.ToolBoxFeatureOpts(
save_as_image=opts.ToolBoxFeatureSaveAsImageOpts(
title="save as image"),
magic_type=opts.ToolBoxFeatureMagicTypeOpts(
line_title="switch to line chart",
bar_title="switch to bar chart",
stack_title="switch to stacked values",
tiled_title="switch to tiled values"),
data_zoom=opts.ToolBoxFeatureDataZoomOpts(
zoom_title="zoom in", back_title="zoom reset"),
restore=opts.ToolBoxFeatureRestoreOpts(title="reset"),
data_view=opts.ToolBoxFeatureDataViewOpts(
title="Data table view",
lang=["Table view", "Close", "Refresh"],
),
brush=opts.ToolBoxFeatureBrushOpts(
rect_title="rectangle choice",
polygon_title="polygon choice",
clear_title="clear choices",
keep_title="keep choices"))),
datazoom_opts=opts.DataZoomOpts(orient="vertical"),
# yaxis_opts=opts.AxisOpts(name='labels', name_gap=5000, name_rotate=15),
tooltip_opts=opts.TooltipOpts(is_show=True,
trigger="axis",
axis_pointer_type="shadow"),
xaxis_opts=opts.AxisOpts(axistick_opts=opts.AxisTickOpts(
is_inside=True,
length=850,
linestyle_opts=opts.LineStyleOpts(type_="dotted",
opacity=0.5))))
# do not shown bar label values
bar.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
bar.reversal_axis()
grid = (Grid(init_opts=opts.InitOpts(
width="1500px",
height="1000px",
page_title="Table Linker visualization page")).add(
bar,
grid_opts=opts.GridOpts(pos_top='5%',
pos_bottom="10%",
pos_right='5%',
pos_left="20%")))
grid.render(output_path + ".html")
def day_sxdl(self, x, mc, a, b, c, k):
c = (Line(
init_opts=opts.InitOpts(width="1000px", height="500px")
).add_xaxis(x).add_yaxis("电压A", a, is_smooth=True).add_yaxis(
"电压B", b, is_smooth=True).add_yaxis(
"电压C", c, is_smooth=True).set_global_opts(
tooltip_opts=opts.TooltipOpts(is_show=True,
trigger="axis",
axis_pointer_type="cross"),
title_opts={
'text': mc,
'subtext': None
},
xaxis_opts=opts.AxisOpts(
axislabel_opts=opts.LabelOpts(rotate=-90),
max_interval=366,
type_="category",
axispointer_opts=opts.AxisPointerOpts(is_show=True,
type_="shadow"),
),
yaxis_opts=opts.AxisOpts(
axislabel_opts=opts.LabelOpts(formatter="{value} v"),
axistick_opts=opts.AxisTickOpts(is_show=True),
splitline_opts=opts.SplitLineOpts(is_show=True),
),
toolbox_opts=opts.
ToolboxOpts(feature=opts.ToolBoxFeatureOpts(
save_as_image=opts.ToolBoxFeatureSaveAsImageOpts(
pixel_ratio=2, background_color='white'),
restore=opts.ToolBoxFeatureRestoreOpts(is_show=False),
data_view=opts.ToolBoxFeatureDataViewOpts(
is_show=False),
data_zoom=opts.ToolBoxFeatureDataZoomOpts(
is_show=False),
magic_type=opts.ToolBoxFeatureMagicTypeOpts(
is_show=False),
brush=opts.ToolBoxFeatureBrushOpts(type_='clear'),
))).set_series_opts(label_opts=opts.LabelOpts(
is_show=False), ).extend_axis(
yaxis=opts.AxisOpts(name='方差',
axislabel_opts=opts.LabelOpts(
formatter="{value} "))))
line1 = Line().add_xaxis(x).add_yaxis(
"方差",
k,
yaxis_index=1,
is_smooth=True,
linestyle_opts=opts.LineStyleOpts(
color="black", width=2, type_="solid")).set_series_opts(
label_opts=opts.LabelOpts(is_show=False), )
c.overlap(line1)
return c
# def day_loss_1(self,x,y,t,st,z,m):
# c = self.dayloss_line_base(x,y,t,st)
# c = c.extend_axis(
# yaxis=opts.AxisOpts(
# axislabel_opts=opts.LabelOpts(formatter="{value} kWh")
# )
# ).extend_axis(
# yaxis=opts.AxisOpts(
# name = '数据完整率',
# position = 'right',
# offset = 62,
# axislabel_opts=opts.LabelOpts(formatter="{value} "),
# axisline_opts=opts.AxisLineOpts(
# linestyle_opts=opts.LineStyleOpts(color="#675bba")
# ),
# )
# )
# line = Line().add_xaxis(x).add_yaxis("供入电量", z,yaxis_index=1,is_smooth=True).set_series_opts(
# label_opts=opts.LabelOpts(is_show=False), )
# line_1 = Line().add_xaxis(x).add_yaxis("数据完整率", m,yaxis_index=2).set_series_opts(
# label_opts=opts.LabelOpts(is_show=False), )
# c.overlap(line)
# c.overlap(line_1)
# return c
# def day_loss_2(self,x,y,t,st,a,b):
# c = self.dayloss_line_base(x,y,t,st)
# c = c.extend_axis(
# yaxis=opts.AxisOpts(
# axislabel_opts=opts.LabelOpts(formatter="{value} kWh")
# )
# )
# line = Line().add_xaxis(a).add_yaxis("用电量", b,yaxis_index=1,is_smooth=True).set_series_opts(
# label_opts=opts.LabelOpts(is_show=False), )
# c.overlap(line)
# return c
# class electricity():
# """docstring for electricity"""
# def __init__(self,df):
# self.df = df
# def day_data(self,a):
# tq = self.df[self.df.测量点号==a][['数据时间','正向']].set_index('数据时间').sort_values(by='数据时间')
# x = [i[-5:] for i in tq.index]
# y = [float(i[0]) for i in tq.values]
# t = a
# return x,y,t
# def measure_num(self):
# num = set(i for i in self.df.测量点号)
# num = list(num)[1:]
# return num
# class relation(object):
# """docstring for relation"""
# def curr_relation(self,x,y,a,b):
# lineloss = pd.Series(y,index=x)
# day_electricity = pd.Series(b,index= a)
# c = lineloss.corr(day_electricity)
# # c1 = lineloss.cov(day_electricity)
# return c
# IO = '石鼓12月.xls'
# k = []
# day_loss = data(IO)
# tq_index = day_loss.day_lineloss_index()
# for i in tq_index:
# x,y,a,z,m = data(IO).day_lineloss(i)
# nc = relation().curr_relation(x,y,x,z)
# c = drawing().day_loss_1(x,y, a+ str(nc),z,m)
# k.append(c)
# drawing().all_day_lineloss(k,'石鼓12')
# IO = '石鼓12月.xls'
# name = '宝丰12.xlsx'
# num = electricity(name).measure_num()
# day_loss = data(IO)
# a,b,j,z,m = data(IO).day_lineloss('宝丰公用台变')
# k1 = []
# for i in num:
# x,y,t = electricity(name).day_data(i)
# c = relation().curr_relation(a,b,x,y)
# c1 = drawing().day_loss_2(a,b,str(t)+' & '+str(c),x,y)
# k1.append(c1)
# ny = np.array(electricity(name).day_data(92)[1]) + np.array(electricity(name).day_data(37)[1])
# c = relation().curr_relation(a,b,x,ny)
# c1 = drawing().day_loss_2(a,b,'9237'+str(c),x,ny)
# k1.append(c1)
# drawing().all_day_lineloss(k1,'宝丰12')
# k2 = []
# for i in num:
# x,y,t = electricity(name).day_data(i)
# c = relation().curr_relation(a,b,x,y)
# k2.append(c)
# print(str(c) + 10*'-'+str(i))
# print(10*'--')
# k3 = []
# nyy= []
# for i in num:
# x,y,t = electricity(name).day_data(i)
# c = relation().curr_relation(a,b,x,y)
# print(c[0])
# if c[0] > 0.5:
# nyy.append(np.array(y))
# c1 = drawing().day_loss_2(a,b,t,x,y)
# k3.append(c1)
# c2 = drawing().day_loss_2(a,b,'all',x,sum(nyy))
# k3.append(c2)
# drawing().all_day_lineloss(k3,'test_class_5')
# c = relation().curr_relation(a,b,x,sum(nyy))
# print(c)
def html_plot(file):
sp = file.split(".")
with open(file) as f:
l = f.readlines()
i = 0
ks = {}
for line in l:
if i == 0:
head = line.strip().split("\t")
i = 1
else:
t = line.strip().split("\t")
name = t[0]
del t[0]
ks[name] = t
bar = Bar(init_opts=opts.InitOpts(
page_title=sp[0] + "-ks.htmlpolt", width="1500px", height="700px"))
bar.add_xaxis(head)
for key, value in ks.items():
bar.add_yaxis(key,
value,
category_gap=0,
stack="stack1",
is_large=True)
bar.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
bar.set_global_opts(
title_opts=opts.TitleOpts(title=sp[0] + ".ks.polt",
subtitle="Plot by Tree2gd v1.0"),
toolbox_opts=opts.ToolboxOpts(
is_show=True,
pos_top='10%',
orient='vertical',
pos_left='right',
feature=opts.ToolBoxFeatureOpts(
save_as_image=opts.ToolBoxFeatureSaveAsImageOpts(
title="Save as png"),
restore=opts.ToolBoxFeatureRestoreOpts(title="Recovery"),
data_view=opts.ToolBoxFeatureDataViewOpts(
title="Data", lang=['Data View', 'Close', 'Refresh']),
data_zoom=opts.ToolBoxFeatureDataZoomOpts(is_show=False),
magic_type=opts.ToolBoxFeatureMagicTypeOpts(
line_title="Switch to line plot",
bar_title="Switch to histogram plot",
stack_title="Switch to stack plot",
tiled_title="Switch to tiled plot"),
brush=opts.ToolBoxFeatureBrushOpts(
rect_title="Rectangle selection",
polygon_title="Circle selection",
line_x_title="X line selection",
line_y_title="Y line selection",
keep_title="Keep selection",
clear_title="Clear selection"),
)),
legend_opts=opts.LegendOpts(pos_left='right', ),
datazoom_opts=[
opts.DataZoomOpts(range_start=0, range_end=60),
opts.DataZoomOpts(type_="inside")
],
xaxis_opts=opts.AxisOpts(name="Ks"),
)
bar.render(os.sep.join(["..", "ks_html_plot",
sp[0] + ".ks.plot.html"]))
def day_sxdl(self, x, mc, a, b, c, m, n, rca, rcb, rcc, j, rcj):
c = (Line(
init_opts=opts.InitOpts(width="1000px", height="500px")
).add_xaxis(x).add_yaxis(
"电流A",
a,
is_smooth=True,
itemstyle_opts=opts.ItemStyleOpts(color='black')).add_yaxis(
"电流B",
b,
is_smooth=True,
itemstyle_opts=opts.ItemStyleOpts(color='blue')).add_yaxis(
"电流C",
c,
is_smooth=True,
itemstyle_opts=opts.ItemStyleOpts(
color='green')).set_global_opts(
tooltip_opts=opts.TooltipOpts(
is_show=True,
trigger="axis",
axis_pointer_type="cross"),
title_opts=opts.TitleOpts(
title=mc,
subtitle='A: ' + str(rca)[:12] + ' ' + 'B: ' +
str(rcb)[:12] + ' ' + 'C: ' + str(rcc)[:12] +
' '),
xaxis_opts=opts.AxisOpts(
axislabel_opts=opts.LabelOpts(rotate=-90),
max_interval=366,
type_="category",
axispointer_opts=opts.AxisPointerOpts(
is_show=True, type_="shadow"),
),
yaxis_opts=opts.AxisOpts(
axislabel_opts=opts.LabelOpts(
formatter="{value} A"),
axistick_opts=opts.AxisTickOpts(is_show=True),
splitline_opts=opts.SplitLineOpts(
is_show=True),
),
toolbox_opts=opts.
ToolboxOpts(feature=opts.ToolBoxFeatureOpts(
save_as_image=opts.
ToolBoxFeatureSaveAsImageOpts(
pixel_ratio=2, background_color='white'),
restore=opts.ToolBoxFeatureRestoreOpts(
is_show=False),
data_view=opts.ToolBoxFeatureDataViewOpts(
is_show=False),
data_zoom=opts.ToolBoxFeatureDataZoomOpts(
is_show=False),
magic_type=opts.ToolBoxFeatureMagicTypeOpts(
is_show=False),
brush=opts.ToolBoxFeatureBrushOpts(
type_='clear'),
))).set_series_opts(
label_opts=opts.LabelOpts(is_show=False), ).
extend_axis(yaxis=opts.AxisOpts(name='线损率',
axislabel_opts=opts.LabelOpts(
formatter="{value} %"))))
line = (Line(init_opts=opts.InitOpts(
width="1000px", height="500px")).add_xaxis(m).add_yaxis(
"线损率",
n,
is_smooth=True,
linestyle_opts=opts.LineStyleOpts(color="red",
width=2,
type_="solid")
).set_series_opts(
label_opts=opts.LabelOpts(is_show=False), ).set_global_opts(
tooltip_opts=opts.TooltipOpts(is_show=True,
trigger="axis",
axis_pointer_type="cross"),
title_opts=opts.TitleOpts(title=mc,
subtitle='相关性系数: ' +
str(rcj)[:12]),
xaxis_opts=opts.AxisOpts(
axislabel_opts=opts.LabelOpts(rotate=-90),
max_interval=366,
type_="category",
axispointer_opts=opts.AxisPointerOpts(is_show=True,
type_="shadow"),
),
yaxis_opts=opts.AxisOpts(
axislabel_opts=opts.LabelOpts(formatter="{value} %"),
axistick_opts=opts.AxisTickOpts(is_show=True),
splitline_opts=opts.SplitLineOpts(is_show=True),
),
toolbox_opts=opts.
ToolboxOpts(feature=opts.ToolBoxFeatureOpts(
save_as_image=opts.ToolBoxFeatureSaveAsImageOpts(
pixel_ratio=2, background_color='white'),
restore=opts.ToolBoxFeatureRestoreOpts(is_show=False),
data_view=opts.ToolBoxFeatureDataViewOpts(
is_show=False),
data_zoom=opts.ToolBoxFeatureDataZoomOpts(
is_show=False),
magic_type=opts.ToolBoxFeatureMagicTypeOpts(
is_show=False),
brush=opts.ToolBoxFeatureBrushOpts(type_='clear'),
))).set_series_opts(label_opts=opts.LabelOpts(
is_show=False), ).extend_axis(
yaxis=opts.AxisOpts(name='方差',
axislabel_opts=opts.LabelOpts(
formatter="{value} "))))
line3 = (Line().add_xaxis(m).add_yaxis(
"线损率",
n,
yaxis_index=1,
is_smooth=True,
is_selected=False,
linestyle_opts=opts.LineStyleOpts(
color="red", width=2, type_="dotted")).set_series_opts(
label_opts=opts.LabelOpts(is_show=False), ))
line1 = Line().add_xaxis(x).add_yaxis(
"方差",
j,
yaxis_index=1,
is_smooth=True,
linestyle_opts=opts.LineStyleOpts(
color="black", width=2, type_="solid")).set_series_opts(
label_opts=opts.LabelOpts(is_show=False), )
d = line.overlap(line1)
c.overlap(line3)
return c, d
def Plot(self, jine: list, hushu: list, coef: list, date :str = None, title : str = None, size_def = lambda x: 50 if 10 + x > 50 else 10 + x):
##配置全局变量
j_list = [0 for _ in jine]
h_list = [0 for _ in hushu]
plot = Scatter(init_opts = opts.InitOpts(width = self.width, height = self.height, theme = self.theme))
plot.set_global_opts(
title_opts=opts.TitleOpts(title=title),
xaxis_opts = opts.AxisOpts(type_="value",
splitline_opts=opts.SplitLineOpts(is_show=False),
min_ = self.x_min, max_ = self.x_max,
name = self.data_name[self.x_index]),
yaxis_opts = opts.AxisOpts(type_="value",
splitline_opts=opts.SplitLineOpts(is_show=False),
min_ = self.y_min, max_ = self.y_max,
name = self.data_name[self.y_index]),
toolbox_opts = opts.ToolboxOpts(orient = 'horizontal', pos_top = 0,
feature = opts.ToolBoxFeatureOpts(
save_as_image = opts.ToolBoxFeatureSaveAsImageOpts(type_ = "jpeg", pixel_ratio = 4, background_color = 'white'),
restore = opts.ToolBoxFeatureRestoreOpts(),
data_zoom = opts.ToolBoxFeatureDataZoomOpts(),
data_view = opts.ToolBoxFeatureDataViewOpts(is_show = False),
magic_type = opts.ToolBoxFeatureMagicTypeOpts(is_show = False),
brush = opts.ToolBoxFeatureBrushOpts(type_=[]))),
tooltip_opts=opts.TooltipOpts(is_show=True, trigger="item", axis_pointer_type="cross")
)
##逐个添加点
for item in self.data_value:
#数据处理
local = str(item[self.local_index])
num = float(item[self.x_index])
gold = float(item[self.y_index])
name = str(item[self.name_index])
size = 0.
for s in range(len(self.size_index)):
size += item[self.size_index[s]]*self.size_coef[s]
tips = '日期:{}'.format(date)
for s in range(len(self.data_name)):
tips += '<br>'
tips += '{}:{}'.format(self.data_name[s], item[s])
#添加点及其tips
plot.add_xaxis([num])
plot.add_yaxis(local, [gold], symbol_size = size_def(size),
tooltip_opts=opts.TooltipOpts(formatter = tips),
label_opts=opts.LabelOpts(formatter = name))
#计算达标人数
for i in range(len(j_list)):
if gold >= jine[i]:
j_list[i] += 1
for i in range(len(h_list)):
if num >= hushu[i]:
h_list[i] += 1
##添加达标线及其注释点
for item in range(len(j_list)):
plot.add_xaxis([self.x_max])
plot.add_yaxis('达标线', [jine[item]], symbol_size = 25, symbol = 'pin',
label_opts = opts.LabelOpts(is_show = False),
tooltip_opts = opts.TooltipOpts(formatter = '达标人数:{}<br>未达标人数:{}'.format(j_list[item], self.num-j_list[item])),
markline_opts = opts.MarkLineOpts(data = [opts.MarkLineItem(y = jine[item], name = '金额达标线{}'.format(i+1))],
label_opts = opts.LabelOpts(is_show = False),
symbol_size = 0))
for item in range(len(h_list)):
plot.add_xaxis([hushu[item]])
plot.add_yaxis('达标线', [self.y_max], symbol_size = 25, symbol = 'pin',
label_opts = opts.LabelOpts(is_show = False),
tooltip_opts = opts.TooltipOpts(formatter = '奖励系数:x{}<br>达标人数:{}<br>未达标人数:{}'.format(coef[item], h_list[item], self.num-h_list[item])),
markline_opts = opts.MarkLineOpts(data = [opts.MarkLineItem(x = hushu[item], name = '户数达标线{}'.format(i+1))],
label_opts = opts.LabelOpts(is_show = False),
symbol_size = 0))
return plot