def line_markline():
obj_l = Line()
obj_l.add_xaxis(Faker.choose())
obj_l.add_yaxis("商家A",
Faker.values(),
markline_opts=opts.MarkLineOpts(data=[opts.MarkLineItem(type_="average")]),
# 该参数设置在这里只对商家A起作用
label_opts=opts.LabelOpts(is_show=False)
)
obj_l.add_yaxis("商家B",
Faker.values(),
markline_opts=opts.MarkLineOpts(data=[opts.MarkLineItem(type_="average")]),
)
obj_l.set_series_opts(
# 该参数设置在这里对商家A,商家B都起作用
# 设置该参数，则x对应的y值会显示出来，该参数默认为True
label_opts=opts.LabelOpts(is_show=False),
)
obj_l.set_global_opts(
title_opts=opts.TitleOpts(title="Line-MarkLine", subtitle="平均值的线"),
xaxis_opts=opts.AxisOpts(
name="x轴",
# 坐标轴两边留白策略，类目轴和非类目轴的设置和表现不一样,可设置x轴刻度顶格
boundary_gap=True,
# 类目轴中在 boundaryGap 为 true 的时候有效，可以保证刻度线和标签对齐。
axistick_opts=opts.AxisTickOpts(is_align_with_label=True)
),
)
return obj_l
def setrose():
x1 = ['2016年', '2017年', '2018年', '2019年', '2020年']
y1 = ['文学', 350.0, 345.0, 345.0, 355.0, 355.0]
y2 = ['理学', 285.0, 290.0, 280.0, 290.0, 288.0]
c = (Line(init_opts=opts.InitOpts(
width="1600px", height="800px")).add_xaxis(xaxis_data=x1).add_yaxis(
series_name="文学",
y_axis=y1,
markpoint_opts=opts.MarkPointOpts(data=[
opts.MarkPointItem(type_="max", name="最大值"),
opts.MarkPointItem(type_="min", name="最小值"),
]),
markline_opts=opts.MarkLineOpts(
data=[opts.MarkLineItem(type_="average", name="平均值")]),
).add_yaxis(
series_name="理学",
y_axis=y2,
markpoint_opts=opts.MarkPointOpts(data=[
opts.MarkPointItem(type_="max", name="最大值"),
opts.MarkPointItem(type_="min", name="最小值"),
]),
markline_opts=opts.MarkLineOpts(data=[
opts.MarkLineItem(type_="average", name="平均值"),
opts.MarkLineItem(symbol="none", x="90%", y="max"),
opts.MarkLineItem(symbol="circle", type_="max", name="最高点"),
]),
).set_global_opts(
title_opts=opts.TitleOpts(title="近5年理学/文学国家线趋势"),
tooltip_opts=opts.TooltipOpts(trigger="axis"),
toolbox_opts=opts.ToolboxOpts(is_show=True),
xaxis_opts=opts.AxisOpts(type_="category", boundary_gap=False),
))
return c
def line_markline() -> Line:
the_region = request.form["the_Race_selected"]
yy1 = []
yy2 = []
times = []
for t in list(time):
times.append(str(t))
for t in time:
df5 = dfs[dfs.Year == t]
yy1.append(sum(df5['Deaths'][(df['Sex'] == 'Female')]))
yy2.append(sum(df5['Deaths'][(df['Sex'] == 'Male')]))
c = (
Line()
.add_xaxis(times)
.add_yaxis(
"女性",
yy1,
markline_opts=opts.MarkLineOpts(data=[opts.MarkLineItem(type_="average")]),
)
.add_yaxis(
"男性",
yy2,
markline_opts=opts.MarkLineOpts(data=[opts.MarkLineItem(type_="average")]),
)
.set_global_opts(title_opts=opts.TitleOpts(title="1999-2017年{}的死亡人数".format(the_region)))
)
return c
def draw_charts(product_name, case_name, total_secs, qps, latency):
secs = range(0, int(total_secs), int(total_secs / 100))
secs.append(int(total_secs))
c = (Line(
init_opts=opts.InitOpts(width="1600px", height="800px")
).add_xaxis(xaxis_data=secs).add_yaxis(
series_name="QPS",
y_axis=qps,
markpoint_opts=opts.MarkPointOpts(data=[
opts.MarkPointItem(type_="max", name="Max Value"),
opts.MarkPointItem(type_="min", name="Min Value"),
]),
markline_opts=opts.MarkLineOpts(
data=[opts.MarkLineItem(type_="average", name="Average QPS")]),
).add_yaxis(
series_name="Latency",
y_axis=latency,
markline_opts=opts.MarkLineOpts(
data=[opts.MarkLineItem(type_="average", name="Average Latency")]),
).set_global_opts(
title_opts=opts.TitleOpts(title="QPS", subtitle="DML only"),
tooltip_opts=opts.TooltipOpts(trigger="axis"),
toolbox_opts=opts.ToolboxOpts(is_show=True),
xaxis_opts=opts.AxisOpts(type_="category", boundary_gap=False),
).render("sysbench-" + product_name + '-' + case_name + ".html"))
def kline_base(mydate, data, name) -> Kline:
kline = (
Kline()
.add_xaxis(mydate)
.add_yaxis("%s" % name, data, markpoint_opts=opts.MarkLineOpts(
data=[opts.MarkLineItem(type_="max", value_dim="close")]
), markline_opts=opts.MarkLineOpts(
data=[opts.MarkLineItem(type_="max", value_dim="close")]
),
itemstyle_opts=opts.ItemStyleOpts(
color="#ec0000",
color0="#00da3c",
border_color="#8A0000",
border_color0="#008F28",
),
)
.set_global_opts(
yaxis_opts=opts.AxisOpts(is_scale=True,
splitarea_opts=opts.SplitAreaOpts(
is_show=True, areastyle_opts=opts.AreaStyleOpts(opacity=1)
),
),
xaxis_opts=opts.AxisOpts(is_scale=True,
axislabel_opts=opts.LabelOpts(rotate=-30)),
title_opts=opts.TitleOpts(title="股票走势"),
datazoom_opts=[opts.DataZoomOpts()],
toolbox_opts=opts.ToolboxOpts(is_show=True),
)
)
return kline
def line_markline() -> Line:
c = (Line().add_xaxis(Faker.choose()).add_yaxis(
"商家A",
Faker.values(),
markline_opts=opts.MarkLineOpts(
data=[opts.MarkLineItem(type_="average")]),
).add_yaxis(
"商家B",
Faker.values(),
markline_opts=opts.MarkLineOpts(
data=[opts.MarkLineItem(type_="average")]),
).set_global_opts(title_opts=opts.TitleOpts(title="Line-MarkLine")))
return c
def line_markpoint() -> Line:
c = (
Line().add_xaxis(Faker.choose()).add_yaxis(
"商家A",
Faker.values(),
is_smooth=True,
# linestyle_opts=opts.LineStyleOpts(width=3),
markline_opts=opts.MarkLineOpts(
data=[
opts.MarkLineItem(type_="min"),
opts.MarkLineItem(type_="max")
],
label_opts=opts.LabelOpts(
is_show=True,
position="end",
),
),
).add_yaxis(
"商家B",
Faker.values(),
is_smooth=True,
# linestyle_opts=opts.LineStyleOpts(width=3),
markline_opts=opts.MarkLineOpts(
data=[
opts.MarkLineItem(type_="min"),
opts.MarkLineItem(type_="max")
],
label_opts=opts.LabelOpts(
is_show=True,
position="end",
),
),
).set_series_opts(
areastyle_opts=opts.AreaStyleOpts(opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
).set_global_opts(
title_opts=opts.TitleOpts(
title="Line-面积图（紧贴 Y 轴）",
pos_left="center",
),
xaxis_opts=opts.AxisOpts(
axistick_opts=opts.AxisTickOpts(is_align_with_label=True),
is_scale=False,
boundary_gap=False,
),
tooltip_opts=opts.TooltipOpts(trigger="axis",
axis_pointer_type="cross"),
))
return c
def barSpeed(data) -> Bar:
"""
-- 按列车类型统计运行速度
:param data:list:[[X轴-类型名称], [Y轴-速度]]
:return: Bar
"""
speed_bar = (
Bar(init_opts=opts.InitOpts(width='1000px',
height='500px')).add_xaxis(data[0]).
add_yaxis("车次数量", data[1], color='#8c4356').set_global_opts(
legend_opts=opts.LegendOpts(is_show=False), # 不显示图例
xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(
rotate=0)), # 横坐标标签倾斜
yaxis_opts=opts.AxisOpts(name='公里/小时'),
title_opts=opts.TitleOpts(
title='按列车类型统计运行速度',
title_textstyle_opts=opts.TextStyleOpts(
font_size=20))).set_series_opts(
label_opts=opts.LabelOpts(is_show=False),
markline_opts=opts.MarkLineOpts(
symbol='circle',
precision=0,
data=[
opts.MarkLineItem(name='平均',
type_='average')
]), # 平均值线
markpoint_opts=opts.MarkPointOpts(data=[
opts.MarkPointItem(type_="max", name="Max"),
opts.MarkPointItem(type_="min", name="Min")
]),
))
return speed_bar
def barDistance(data) -> Bar:
"""
-- 按运行里程区间统计车次数量
:param data:list:[[X轴-里程区间], [Y轴-车次数量]]
:return: Bar
"""
distance_bar = (
Bar(init_opts=opts.InitOpts(width='1000px',
height='500px')).add_xaxis(data[0]).
add_yaxis("车次数量", data[1], color='#425066').set_global_opts(
legend_opts=opts.LegendOpts(is_show=False), # 不显示图例
xaxis_opts=opts.AxisOpts(
axislabel_opts=opts.LabelOpts(rotate=30),
name='公里'), # 横坐标标签倾斜
yaxis_opts=opts.AxisOpts(name='次'),
title_opts=opts.TitleOpts(
title='按运行里程区间统计车次数量',
title_textstyle_opts=opts.TextStyleOpts(
font_size=20))).set_series_opts(
label_opts=opts.LabelOpts(is_show=False),
markline_opts=opts.MarkLineOpts(
symbol='circle',
precision=0,
data=[
opts.MarkLineItem(name='平均',
type_='average')
]), # 平均值线
markpoint_opts=opts.MarkPointOpts(data=[
opts.MarkPointItem(type_="max", name="Max"),
opts.MarkPointItem(type_="min", name="Min")
]),
))
return distance_bar
def genLine(self,x_list=None,y_orderdicts=None,title='线形示例',subtitle='副标题示例',is_smooth=True,average = None):
'''
折线图 平滑曲线
应用场景 趋势图
param x_list x轴的值 type list
param y_dicts key是类别，value是y轴的值 type orderdict
param title 图表的题目 type str
param is_smooth 是否平滑 默认是 type bool
param average 平均数的基准标记线 type number
'''
x_list = x_list or Faker.choose()
y_orderdicts = y_orderdicts or {i: Faker.values() for i in range(1, 3)}
c = (
Line()
.add_xaxis(x_list)
.set_global_opts(title_opts=opts.TitleOpts(title=title,subtitle =subtitle))
)
for key, value in y_orderdicts.items():
if average:
# c.add_yaxis(key, value,is_smooth=True,markline_opts=opts.MarkLineOpts(data=[opts.MarkLineItem(type_="average")]))
c.add_yaxis(key, value,is_smooth=True,markline_opts=opts.MarkLineOpts(data=average))
else:
c.add_yaxis(key, value,is_smooth=True)
chart_name = f'static/chart{self.index}_line'
make_snapshot(snapshot,c.render(f'{chart_name}.html'),f'{chart_name}.png',is_remove_html=True)
self.index += 1
def get_line(x, y, y_name, x_name):
print(y_name)
new_bar = (Bar().add_xaxis(x))
for y_label in y:
new_bar.add_yaxis(
y_label,
y[y_label],
color="#409EFF",
label_opts=opt.LabelOpts(),
# is_smooth=True,
markline_opts=opt.MarkLineOpts(
data=[opt.MarkLineItem(type_="average")]),
markpoint_opts=opt.MarkPointOpts(
data=[opt.MarkPointItem(type_='max', name="最大值")]))
new_bar.set_series_opts(areastyle_opts=opt.AreaStyleOpts(opacity=0.5),
label_opts=opt.LabelOpts(is_show=False))
new_bar.set_global_opts(
datazoom_opts=opt.DataZoomOpts(type_='inside'),
legend_opts=opt.LegendOpts(is_show=True),
yaxis_opts=opt.AxisOpts(
name=y_name, name_textstyle_opts=opt.TextStyleOpts(color="black")),
xaxis_opts=opt.AxisOpts(
name=x_name,
name_textstyle_opts=opt.TextStyleOpts(color="black"),
type_="category",
)),
return new_bar.dump_options_with_quotes()
def report(request):
"""返回慢SQL历史趋势"""
checksum = request.GET.get('checksum')
cnt_data = ChartDao().slow_query_review_history_by_cnt(checksum)
pct_data = ChartDao().slow_query_review_history_by_pct_95_time(checksum)
cnt_x_data = [row[1] for row in cnt_data['rows']]
cnt_y_data = [int(row[0]) for row in cnt_data['rows']]
pct_y_data = [str(row[0]) for row in pct_data['rows']]
line = Line(init_opts=opts.InitOpts(width='800', height='380px'))
line.add_xaxis(cnt_x_data)
line.add_yaxis("慢查次数",
cnt_y_data,
is_smooth=True,
markline_opts=opts.MarkLineOpts(data=[
opts.MarkLineItem(type_="max", name='最大值'),
opts.MarkLineItem(type_="average", name='平均值')
]))
line.add_yaxis("慢查时长(95%)",
pct_y_data,
is_smooth=True,
is_symbol_show=False)
line.set_series_opts(areastyle_opts=opts.AreaStyleOpts(opacity=0.5, ))
line.set_global_opts(
title_opts=opts.TitleOpts(title='SQL历史趋势'),
legend_opts=opts.LegendOpts(selected_mode='single'),
xaxis_opts=opts.AxisOpts(
axistick_opts=opts.AxisTickOpts(is_align_with_label=True),
is_scale=False,
boundary_gap=False,
),
)
result = {"status": 0, "msg": '', "data": line.render_embed()}
return HttpResponse(json.dumps(result), content_type='application/json')
def show_bar():
bar = (Bar().add_xaxis(bar_city).add_yaxis(
"Average Income",
bar_income).add_yaxis("Employed People", bar_employ).add_yaxis(
"Covid Pos",
bar_co_p, stack="stack1").add_yaxis("Covid Neg",
bar_co_n,
stack="stack1").add_yaxis(
"Crime Pos",
bar_cr_p,
stack="stack2").
add_yaxis("Crime Neg", bar_cr_n, stack="stack2").set_global_opts(
title_opts=opts.TitleOpts(title="City Income"),
yaxis_opts=opts.AxisOpts(name="Income(k)/Employ(10k)/PosVSNeg"),
xaxis_opts=opts.AxisOpts(name="City"),
datazoom_opts=opts.DataZoomOpts(orient="horizontal",
range_start=0,
range_end=100),
toolbox_opts=opts.ToolboxOpts(),
).set_series_opts(
label_opts=opts.LabelOpts(is_show=False),
markline_opts=opts.MarkLineOpts(data=[
opts.MarkLineItem(type_="average", name="Average Value"),
]),
))
return bar
def develop_select() -> 'html':
the_region = request.form["the_region_selected"]
dfs_develop = df_develop.query("region=='{}'".format(the_region))
c = (
Bar(init_opts=opts.InitOpts(theme=ThemeType.VINTAGE))
.add_xaxis(dfs_develop['年'].tolist())
.add_yaxis("", dfs_develop['count'].tolist())
.set_global_opts(title_opts=opts.TitleOpts(title=the_region, subtitle=""))
.set_series_opts(
label_opts=opts.LabelOpts(is_show=False),
markline_opts=opts.MarkLineOpts(
data=[
opts.MarkLineItem(type_="average", name="平均值"),
]
),
)
)
c.render("./static/tmp/echarts_develop.html")
with open("./static/tmp/echarts_develop.html", encoding="utf8", mode="r") as f:
plot_all = "".join(f.readlines())
data_str = dfs_develop.to_html()
return render_template('pyecharts.html',
myechart=plot_all,
the_res=data_str,
the_select_region=regions_available_develop,
bottom_title="分析:柱状图",
mark="develop",
)
def line_base(self, date: list, price: list) -> Line:
line = (
Line()
.add_xaxis(xaxis_data=date)
.add_yaxis(
series_name="价格",
y_axis=price,
markpoint_opts=opts.MarkPointOpts(
data=[
opts.MarkPointItem(type_="max", name="最大值"),
opts.MarkPointItem(type_="min", name="最小值"),
]
),
markline_opts=opts.MarkLineOpts(
data=[opts.MarkLineItem(type_="average", name="平均值")]
),
)
.set_global_opts(
title_opts=opts.TitleOpts(title="历史价格记录"),
tooltip_opts=opts.TooltipOpts(trigger="axis"),
toolbox_opts=opts.ToolboxOpts(is_show=False),
xaxis_opts=opts.AxisOpts(type_="category", boundary_gap=False),
)
)
return line
def draw_balance_line(xaxis,
yaxis,
title="消费统计",
markline=None,
width=2000) -> Line:
"""
x = [月_日, 月_日, 月_日, ....]
y = [(title1, [num1, num2, num3, num4, ...]), (title2, [num1, num2, num3, num4, ...])]
:param xaxis: x轴
:param yaxis: y轴
:param title: 标题
:param markline: 标记辅助线
:param width: 宽
:return: Line
"""
line = Line()
line.add_xaxis(xaxis)
for name, axis in yaxis:
line.add_yaxis(name, axis, is_symbol_show=True)
line.set_global_opts(title_opts=opts.TitleOpts(title=title, ),
datazoom_opts=[
opts.DataZoomOpts(range_start=0, range_end=100),
opts.DataZoomOpts(type_="inside")
],
tooltip_opts=opts.TooltipOpts(
trigger='axis', axis_pointer_type='shadow'))
line.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
if markline is not None:
line.set_series_opts(markline_opts=opts.MarkLineOpts(
data=[opts.MarkLineItem(y=markline, name='预算')]))
return line
def plot_one_y(df, title: str):
df.index = pd.to_datetime(df['date'])
df.sort_index(inplace=True)
df = df.drop(['date'], axis=1)
return (Line(
init_opts=opts.InitOpts(width="1200px", height="400px")).add_xaxis(
xaxis_data=df.index.strftime(
'%Y-%m-%d').values.tolist()).add_yaxis(
series_name=title.upper(),
y_axis=np.round(df['r2'].values, 2).tolist(),
is_smooth=True,
label_opts=opts.LabelOpts(is_show=False),
linestyle_opts=opts.LineStyleOpts(width=2),
markline_opts=opts.MarkLineOpts(
data=[opts.MarkLineItem(type_="average")]),
).set_global_opts(
datazoom_opts=opts.DataZoomOpts(),
legend_opts=opts.LegendOpts(pos_bottom="0%",
pos_right='45%'),
title_opts=opts.TitleOpts(
title=title.upper(),
pos_left='0%',
),
tooltip_opts=opts.TooltipOpts(trigger="axis",
axis_pointer_type="cross"),
toolbox_opts=opts.ToolboxOpts(is_show=True),
xaxis_opts=opts.AxisOpts(boundary_gap=False),
yaxis_opts=opts.AxisOpts(
axislabel_opts=opts.LabelOpts(formatter="{value}"),
splitline_opts=opts.SplitLineOpts(is_show=True),
),
).set_series_opts(markpoint_opts=opts.MarkPointOpts(data=[
opts.MarkPointItem(type_='max', name='Max'),
opts.MarkPointItem(type_='min', name='Min')
]), ))
def student_history_ranking(student_id: int) -> dict:
result = {}
result_chart = {}
grades = StudentGrade.query.filter_by(
student_ID=student_id, subject='理科').order_by(StudentGrade.test_time).all()
test_names = [t.test_name for t in Test.query.order_by(Test.test_time).all()]
print(test_names)
for subject in Subject.li_all_subject():
result[subject] = []
for grade in grades:
grade: StudentGrade
result[subject].append(grade.__dict__[subject + '_ranking'])
for subject in Subject.li_all_subject():
line = (
Line()
.add_xaxis(test_names)
.add_yaxis(
'排名',
result[subject],
markline_opts=opts.MarkLineOpts(data=[opts.MarkLineItem(type_="average")]), )
.set_global_opts(
title_opts=opts.TitleOpts(
title=Subject.en2cn(subject) + '科历次成绩走势',
subtitle='虚线为平均排名'
),
)
)
result_chart[subject] = line
return result_chart
def displayECLine(request, t_inner_id):
query_type, inner_code = t_inner_id[0], t_inner_id[1:]
if query_type=='P':
sql_query_1 = f'''SELECT mall_name, goods_name, prescription FROM pdd_rep_goods WHERE pdd_inner_code='{inner_code}';'''
sql_query_2 = f'''SELECT update_time, price FROM pdd_rep_price WHERE pdd_inner_code='{inner_code}';'''
else:
sql_query_1 = f'''SELECT shop_name, goods_name, three_cat FROM jd_rep_goods WHERE jd_inner_code='{inner_code}';'''
sql_query_2 = f'''SELECT update_time, price FROM jd_rep_price WHERE jd_inner_code='{inner_code}';'''
supplier, mds_name, mds_spec = query_mysql_reptile(sql_query_1)[0]
list_temp = DataFrame(list(query_mysql_reptile(sql_query_2)))
date_list, pric_list = list_temp[0].tolist(), list_temp[1].tolist()
c = (
Line()
.add_xaxis(date_list)
.add_yaxis(supplier, pric_list,
linestyle_opts=opts.LineStyleOpts(color="#6699cc"),
label_opts=opts.LabelOpts(is_show=True, position="bottom", color="#aabcfe", rotate = '-20'),
markline_opts=opts.MarkLineOpts(data=[
opts.MarkLineItem(type_="average", name="平均 Average")
# opts.MarkLineItem(type_="min", name="最小 Min Value"),
# opts.MarkLineItem(type_="max", name="最大 Max Value")
],
linestyle_opts=opts.LineStyleOpts(type_='dashed', color="rgba(235,85,128,0.87)"), is_silent=True))
.set_global_opts(xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(rotate=-20)),
title_opts=opts.TitleOpts(title='\n' + mds_name , pos_left='10%', subtitle=mds_spec)
)
)
return HttpResponse(c.render_embed())
def displayAnaLine(request, gid):
# 功能 获取信息显示在折线图上方
# 返回 supplier:供应商名称层 mds_name:商品名称 mds_spec:商品规格
sql_query_1 = f'''SELECT search_app_tb_supplier.supp_name, search_app_tb_goods.goods_name, search_app_tb_goods.goods_spec FROM (search_app_tb_supplier INNER JOIN search_app_tb_goods ON search_app_tb_supplier.supp_id = search_app_tb_goods.supp_id) WHERE search_app_tb_goods.goods_id={gid}'''
supplier, mds_name, mds_spec = query_mysql_analyze(sql_query_1)[0]
# 功能 获取折线图的 x轴数据(日期) y轴数据(价格)
# 返回 日期列表 价格列表
sql_query_2 = f'''SELECT search_app_tb_price.update_time, search_app_tb_price.tax_cost FROM (search_app_tb_supplier INNER JOIN search_app_tb_price ON search_app_tb_supplier.supp_id = search_app_tb_price.supp_id) INNER JOIN search_app_tb_goods ON (search_app_tb_goods.goods_id = search_app_tb_price.goods_id) AND (search_app_tb_supplier.supp_id=search_app_tb_goods.supp_id) WHERE search_app_tb_goods.goods_id={gid} ORDER BY search_app_tb_price.update_time ASC'''
list_temp = DataFrame(list(query_mysql_analyze(sql_query_2)))
date_list, pric_list = list_temp[0].tolist(), list_temp[1].tolist()
# 获取所有数据集 开始渲染
c = (
Line()
.add_xaxis(date_list)
.add_yaxis(supplier, pric_list,
linestyle_opts=opts.LineStyleOpts(color="#6699cc"),
label_opts=opts.LabelOpts(is_show=True, position="bottom", color="#aabcfe", rotate = '-20'),
markline_opts=opts.MarkLineOpts(data=[opts.MarkLineItem(type_="average", name="平均 Average")
# opts.MarkLineItem(type_="min", name="最小 Min Value"),
# opts.MarkLineItem(type_="max", name="最大 Max Value")
],
linestyle_opts=opts.LineStyleOpts(type_='dashed', color="rgba(235,85,128,0.87)"), is_silent=True))
.set_global_opts(xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(rotate=-20)),
title_opts=opts.TitleOpts(title=mds_name , pos_left='80%', subtitle=mds_spec)
)
)
return HttpResponse(c.render_embed())
def valuation_report():
"""
股票池估值报告
:return:
"""
df = db.read("valuation_report")
x, y = df['name'].values.tolist(), df['rate'].values.tolist()
series_name = "高估比例"
title = "股票池估值报告"
subtitle = datetime.now().strftime("%Y%m%d")
bar = (
Bar(init_opts={"height": "700px", "bg_color": "white", "theme": ThemeType.LIGHT})
.add_xaxis(x)
.add_yaxis(series_name, y)
.set_series_opts(
label_opts=opts.LabelOpts(position="right"),
markline_opts=opts.MarkLineOpts(
data=[opts.MarkLineItem(x=9, name=""), opts.MarkLineItem(x=-9, name="")],
linestyle_opts={'color': 'rgb(220, 20, 60)'}
)
)
.set_global_opts(title_opts=opts.TitleOpts(title=title, subtitle=subtitle))
.reversal_axis()
)
# make_snapshot(snapshot, bar.render(), output_path)
return bar
def account_article_num():
account = db.session.query(WechatArticle.account, func.count(WechatArticle.id)) \
.filter(extract('year', WechatArticle.publish_time) == '2019').group_by(
WechatArticle.account).order_by(desc(func.count(WechatArticle.id))).all()
keys = []
values = []
for key, value in account:
keys.append(key)
values.append(value)
print('文章公众号总数', len(keys), sum(values), values)
bar = (Bar({
"width": "1500px",
"height": "700px"
}).add_xaxis(keys).add_yaxis(
series_name="文章数量(/篇)", yaxis_data=values,
category_gap='10%').set_global_opts(
title_opts=opts.TitleOpts(title="2019年公众号收录文章数",
pos_top='10%',
pos_left='43%'),
xaxis_opts=opts.AxisOpts(name='公众号名称',
name_rotate=-90,
axislabel_opts={'rotate': -45}),
yaxis_opts=opts.AxisOpts(name="文章数量(/篇)"),
legend_opts=opts.LegendOpts(is_show=False),
datazoom_opts=[opts.DataZoomOpts(type_="inside")],
).set_series_opts(
label_opts=opts.LabelOpts(is_show=False),
markline_opts=opts.MarkLineOpts(data=[
opts.MarkLineItem(type_="average", name="平均值"),
]),
))
# bar.render(path='./static/year/account_article_num.html')
print('生成公众号收录文章数')
return bar
def bar_select() -> 'html':
# 获取用户输入的选项
the_region = request.form["the_region_selected"]
# 根据用户输入选项提取 对应数据
dfs_bar = df_bar.query("region=='{}'".format(the_region))
# 创建图表对象并配置参数
c = (
Bar()
.add_xaxis(dfs_bar['年'].tolist()) # 配置x轴数据
.add_yaxis(the_region, dfs_bar['count'].tolist())# 配置y轴数据
.set_global_opts(title_opts=opts.TitleOpts(title=the_region, subtitle=""))# 配置标题
.set_series_opts(
label_opts=opts.LabelOpts(is_show=False),
markline_opts=opts.MarkLineOpts(
data=[
opts.MarkLineItem(type_="average", name="平均值"),
]
),
)
)
# 生成图表html文件
c.render("./static/tmp/echarts_bar.html")
# 读取生成好的图表
with open("./static/tmp/echarts_bar.html", encoding="utf8", mode="r") as f:
plot_all = "".join(f.readlines())
# 准备模板显示用的 表格数据
data_str = dfs_bar.to_html()
return render_template('pyecharts.html',
myechart=plot_all,# 图表
the_res=data_str, # 表格数据
the_select_region=regions_available_bar,#下拉框数据
bottom_title="分析:柱状图",# 图表下标题数据
mark="bar",# 用于给模板html识别显示柱状图对应的from标签
)
def createPlotLine(df, xAxisTitle, yAxisTitle, title=None):
line = Line()
line.add_xaxis(df[xAxisTitle].tolist())
line.add_yaxis(yAxisTitle, df[yAxisTitle].tolist())
if not title:
title = ""
line.set_global_opts(
tooltip_opts=opts.TooltipOpts(trigger='axis', axis_pointer_type='cross'),
title_opts=opts.TitleOpts(title=title)
)
ptg75 = df[yAxisTitle].quantile(.75)
ptg50 = df[yAxisTitle].quantile(.5)
ptg25 = df[yAxisTitle].quantile(.25)
line.set_series_opts(
markline_opts=opts.MarkLineOpts(
data=[
opts.MarkLineItem(y=ptg75, name="75%"),
opts.MarkLineItem(y=ptg50, name="50%"),
opts.MarkLineItem(y=ptg25, name="25%")
]
),
)
return line
def line_pf() -> Line:
c = (Line(opts.InitOpts(
bg_color='#FFFFFF', width='800px',
height='500px')).add_xaxis(arr).add_yaxis(
"功率因数",
val[13],
color='LightSkyBlue',
is_symbol_show=False,
markline_opts=opts.MarkLineOpts(
data=[opts.MarkLineItem(type_="average")]),
areastyle_opts=opts.AreaStyleOpts(opacity=0.4),
markpoint_opts=opts.MarkPointOpts(data=[
opts.MarkPointItem(type_="max"),
opts.MarkPointItem(type_="min")
])).add_yaxis(
"下限(0.9)",
paras[5],
linestyle_opts=opts.LineStyleOpts(width=1.5, type_='dashed'),
is_symbol_show=False).set_series_opts(
label_opts=opts.LabelOpts(is_show=False)).set_global_opts(
title_opts=opts.TitleOpts(
title='功率因数趋势图',
subtitle='日期：' + df.Date.iloc[0] + ' to ' +
df.Date.iloc[-1],
)))
return c
def barCompanyOut(data) -> Bar:
# 全国民航机场进出港航班数量(TOP 15)
company_out_bar = (
Bar(init_opts=opts.InitOpts(width='1000px',
height='500px')).add_xaxis(data[0]).
add_yaxis("航空公司", data[1], color='#574266').set_global_opts(
legend_opts=opts.LegendOpts(is_show=False), # 不显示图例
datazoom_opts=[
opts.DataZoomOpts(),
opts.DataZoomOpts(type_="inside")
], # DataZoom类型
title_opts=opts.TitleOpts(
title='本周航空公司出港航班数量统计',
title_textstyle_opts=opts.TextStyleOpts(
font_size=20), # 设置标题大小
subtitle='{}-{}，本周发送航班最多的航空公司是：{}，{}，{}。[数据来源：ctrip.com]'.
format(
data[2], data[3], data[4], data[5],
data[6]))).set_series_opts(
label_opts=opts.LabelOpts(is_show=False), # 不显示标签值
markpoint_opts=opts.MarkPointOpts(data=[
opts.MarkPointItem(type_="max", name="Max"),
opts.MarkPointItem(type_="min", name="Min"),
]), # 最大值，最小值
markline_opts=opts.MarkLineOpts(
symbol='circle',
precision=0,
data=[
opts.MarkLineItem(name='平均',
type_='average')
]) # 平均值线
))
return company_out_bar
def Getline(dataX, dataY, n=[0], T=""):
chinaLine = (
Line(init_opts=opts.InitOpts(theme=ThemeType.INFOGRAPHIC))
.add_xaxis(dataX)
.set_global_opts(
title_opts=opts.TitleOpts(title="{}月情感变化{}".format(n[0], T)),
toolbox_opts=opts.ToolboxOpts(is_show=True),
)
.set_series_opts(
label_opts=opts.LabelOpts(is_show=False),
)
)
for i in range(len(dataY)):
chinaLine.add_yaxis('{}月情感变化'.format(n[i]), dataY[i], is_symbol_show=False, label_opts=opts.LabelOpts(is_show=True),
markline_opts=opts.MarkLineOpts(
data=[
opts.MarkLineItem(type_="average", name="平均值"),
opts.MarkLineItem(symbol="none", x="90%", y="max"),
# opts.MarkLineItem(symbol="circle", type_="max", name="最高点"),
]
),
markpoint_opts=opts.MarkPointOpts(data=[opts.MarkPointItem(type_="max"), ]), is_smooth=True)
#
chinaLine.render()
return chinaLine.render_embed()
def barAirPort(self, data) -> Bar:
# 全国民航机场进出港航班数量(TOP 15)
airport_bar = (
Bar(init_opts=opts.InitOpts(
width='1000px', height='500px')).add_xaxis(
data[0][0][:15]).add_yaxis("%s" % data[2],
data[0][1][:15],
color='#425066').
set_global_opts(
legend_opts=opts.LegendOpts(is_show=False), # 不显示图例
xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(
rotate=30)), # 横坐标标签倾斜
title_opts=opts.TitleOpts(
title='全国民航机场%s航班数量(TOP 15)' % data[2],
title_textstyle_opts=opts.TextStyleOpts(
font_size=20), # 设置标题大小
subtitle='{}，全国共{}航班{}架次，{}，{}，{}是数量最多的三个。[数据来源：ctrip.com]'
.format(self.today_ymd, data[2], data[1], data[0][0][0],
data[0][0][1], data[0][0][2]))
).set_series_opts(markline_opts=opts.MarkLineOpts(
symbol='circle',
precision=0,
data=[opts.MarkLineItem(name='平均', type_='average')]) # 平均值线
))
return airport_bar
def get_month_overlap_chart(month: int) -> Bar:
bar = (Bar().add_xaxis(xaxis_data=name_list).add_yaxis(
series_name="销量",
y_axis=total_data[month],
label_opts=opts.LabelOpts(is_show=True),
).set_global_opts(
title_opts=opts.TitleOpts(title="2020年{}月各尺寸销量".format(month)),
tooltip_opts=opts.TooltipOpts(is_show=True,
trigger="axis",
axis_pointer_type="shadow"),
xaxis_opts=opts.AxisOpts(name='单位: 英寸'),
yaxis_opts=opts.AxisOpts(name='销量: 台')).set_series_opts(
label_opts=opts.LabelOpts(is_show=True),
markline_opts=opts.MarkLineOpts(data=[
opts.MarkLineItem(type_="average", name="平均值"),
]),
))
pie = (Pie().add(
series_name="销售占比",
data_pair=[["小屏(<=38寸)", classified_data[month][0]],
["中等大小屏幕", classified_data[month][1]],
["大屏(>=65寸)", classified_data[month][2]]],
center=["25%", "35%"],
radius="28%",
).set_series_opts(
tooltip_opts=opts.TooltipOpts(is_show=True, trigger="item")))
return bar.overlap(pie)
def barTime(self, data) -> Bar:
# 进出港航班数量按小时统计
time_bar = (
Bar(init_opts=opts.InitOpts(width='1000px', height='500px')).
add_xaxis(data[0][0]).add_yaxis(
"{}航班数量".format(data[2]), data[0][1],
color='#1685a9').set_series_opts(
label_opts=opts.LabelOpts(is_show=False),
markpoint_opts=opts.MarkPointOpts(data=[
opts.MarkPointItem(type_="max", name="Max"),
opts.MarkPointItem(type_="min", name="Min"),
]),
markline_opts=opts.MarkLineOpts(
symbol='circle',
precision=0,
data=[opts.MarkLineItem(name='平均', type_='average')])).
set_global_opts(
legend_opts=opts.LegendOpts(is_show=False), # 不显示图例
xaxis_opts=opts.AxisOpts(name_rotate=30), # 【【【X坐标轴标签倾斜，不好使】】】
title_opts=opts.TitleOpts(
title='{}航班数量按小时统计'.format(data[2]),
title_textstyle_opts=opts.TextStyleOpts(
font_size=20), # 设置标题大小
subtitle='{}，全国共{}航班{}架次。[数据来源：ctrip.com]'.format(
self.today_ymd, data[2], data[1]))))
return time_bar
