def grid_graph(self, grid_graph=None):
# 创建组合图表画布对象
self.grid_chart = Grid(
init_opts=opts.InitOpts(width="1900px", height="900px"))
bar_data = [[
row["open_price"], row["close_price"], row["low_price"],
row["high_price"]
] for ix, row in self.bar_data.iterrows()]
self.grid_chart.add_js_funcs("var barData = {}".format(bar_data))
self.grid_chart.add(
chart=self.kline_chart,
grid_index=0,
grid_opts=opts.GridOpts(pos_left="5%",
pos_right="5%",
pos_top="10%",
height="60%"),
)
self.grid_chart.add(
chart=grid_graph,
grid_index=1,
grid_opts=opts.GridOpts(pos_left="5%",
pos_right="5%",
pos_top="75%",
height="20%"),
)
return self.grid_chart
def test_liquid_grid(fake_writer):
l1 = (
Liquid()
.add("lq", [0.6, 0.7], center=["60%", "50%"])
.set_global_opts(title_opts=opts.TitleOpts(title="多个 Liquid 显示"))
)
l2 = Liquid().add(
"lq",
[0.3254],
center=["25%", "50%"],
label_opts=opts.LabelOpts(
font_size=50,
formatter=JsCode(
"""function (param) {
return (Math.floor(param.value * 10000) / 100) + '%';
}"""
),
position="inside",
),
)
c = Grid().add(l1, grid_opts=opts.GridOpts()).add(l2, grid_opts=opts.GridOpts())
c.render()
_, content = fake_writer.call_args[0]
assert_in("center", content)
def grid_graph(self, grid_graph_1=None, grid_graph_2=None):
# 创建组合图表画布对象
self.grid_chart = Grid(init_opts=opts.InitOpts(
width="1900px", height="900px", chart_id=self.chart_id))
if self.grid_quantity == 0:
self.grid_chart.add(chart=self.kline_chart,
grid_opts=opts.GridOpts(pos_left="5%",
pos_right="5%",
pos_top="10%",
height="85%"),
is_control_axis_index=True)
return self.grid_chart
elif self.grid_quantity == 1:
self.grid_chart.add(
chart=self.kline_chart,
grid_index=0,
grid_opts=opts.GridOpts(pos_left="5%",
pos_right="5%",
pos_top="10%",
height="60%"),
)
self.grid_chart.add(
chart=grid_graph_1,
grid_index=1,
grid_opts=opts.GridOpts(pos_left="5%",
pos_right="5%",
pos_top="75%",
height="20%"),
)
return self.grid_chart
elif self.grid_quantity == 2:
self.grid_chart.add(
chart=self.kline_chart,
grid_index=0,
grid_opts=opts.GridOpts(pos_left="5%",
pos_right="5%",
pos_top="10%",
height="40%"),
)
self.grid_chart.add(
chart=grid_graph_1,
grid_index=1,
grid_opts=opts.GridOpts(pos_left="5%",
pos_right="5%",
pos_top="55%",
height="17.5%"),
)
self.grid_chart.add(
chart=grid_graph_2,
grid_index=2,
grid_opts=opts.GridOpts(pos_left="5%",
pos_right="5%",
pos_top="77.5%",
height="17.5%"),
)
return self.grid_chart
def grid(self, data=[]):
grid = Grid(init_opts=opts.InitOpts(**self.chart_opt))
for d in data:
grid.add(
d["chart"],
grid_opts=opts.GridOpts(**d["setting"]),
)
return grid
def test_grid_options(fake_writer):
bar = _chart_for_grid()
gc = Grid().add(
bar,
opts.GridOpts(pos_left="5%", pos_right="20%", is_contain_label=True))
gc.render()
_, content = fake_writer.call_args[0]
assert_in("containLabel", content)
def workarea_salary_chart(df):
# 转换类型为浮点型
df.low_salary, df.high_salary = df.low_salary.astype(
float), df.high_salary.astype(float)
# 分别求各地区平均最高薪资, 平均最低薪资
salary = df.groupby('workarea',
as_index=False)[['low_salary', 'high_salary'
]].mean() # 分别求各地区的岗位数量,并降序排列
print(salary)
workarea = df.groupby('workarea',
as_index=False)['name'].count().sort_values(
'name', ascending=False)
print(workarea)
workarea = pd.merge(workarea, salary, how='left', on='workarea') # 合并数据表
print(workarea)
workarea = workarea.head(20) # 用前20名进行绘图
grid = Grid()
bar = Bar()
grid.theme = themes
line = Line()
line1 = Line()
bar.add_xaxis(workarea.workarea.tolist())
bar.add_yaxis("岗位需求量", workarea.name.tolist())
bar.set_global_opts(
title_opts=opts.TitleOpts(
title="岗位需求量排名前20地区的平均薪资水平状况",
title_link='./chart/workarea_salary_chart.html',
title_target='blank',
pos_left='left',
),
tooltip_opts=opts.TooltipOpts(trigger="axis",
axis_pointer_type="cross",
is_show=True), # 交叉指向工具
legend_opts=opts.LegendOpts(pos_left="80%",
orient="vertical",
pos_top="3%"),
)
# bar.set_series_opts(is_show=True, position='rightTop')
print((workarea.name).tolist())
print((round(workarea.high_salary * 1000)).tolist())
# 在bar上增加Y轴，在line图上选择对应的轴向
line.add_xaxis(workarea.workarea.tolist())
line.add_yaxis("平均最高薪资", (round(workarea.high_salary * 1000)).tolist(),
yaxis_index=0)
line1.add_xaxis(workarea.workarea.tolist())
line1.add_yaxis("平均最低薪资", (round(workarea.low_salary * 1000)).tolist(),
yaxis_index=0)
# 把line添加到bar上
bar.overlap(line)
bar.overlap(line1)
# 这里如果不需要grid也可以，直接设置bar的格式，然后显示bar即可
#bar.render_notebook()
grid.add(chart=bar, grid_opts=opts.GridOpts(), is_control_axis_index=True)
# grid.render("./chart/workarea_salary_chart.html")
return grid
def to_single_html(self):
if len(self.charts) == 0:
self.generate_views()
page = Page()
self.page = Page()
for chart in self.charts:
grid = Grid()
grid.add(chart,
grid_opts=opts.GridOpts(pos_bottom='20%', pos_top='20%'))
self.page.add(grid)
self.page.render(self.charts_dir + self.name + '.html')
def bar_base(self) -> Bar:
start = datetime.datetime.now()
dispose_unit = []
done = []
undo = []
count_dispose_unit = 30
event_counts = Event.objects.filter(Q(achieve=1)).values('dispose_unit').annotate(count=Count('dispose_unit')).values('dispose_unit', 'count').order_by('-count')
event_counts = list(event_counts)
sum_of_done = 0
sum_of_undo = 0
# print(len(event_counts))
# print(event_counts)
for i in range(0,count_dispose_unit):
undo_event = Event.objects.filter(Q(achieve=3) & Q(dispose_unit = event_counts[i]['dispose_unit']))
undo_event_num = len(undo_event)
dispose_unit_name = DisposeUnit.objects.filter(id=event_counts[i]['dispose_unit']).values("name")[0]['name']
dispose_unit.append(dispose_unit_name)
done.append(event_counts[i]['count'])
undo.append(undo_event_num)
for i in range(count_dispose_unit,len(event_counts)):
undo_event = Event.objects.filter(Q(achieve=3) & Q(dispose_unit=event_counts[i]['dispose_unit']))
undo_event_num = len(undo_event)
sum_of_done = sum_of_done + event_counts[i]['count']
sum_of_undo = sum_of_undo + undo_event_num
other_unit_num = len(event_counts) - count_dispose_unit
dispose_unit.append("其他"+str(other_unit_num)+"个部门总和")
done.append(sum_of_done)
undo.append(sum_of_undo)
c = (
Bar()
.add_xaxis(dispose_unit)
.add_yaxis("按期完成", done, stack="stack1",category_gap="60%")
.add_yaxis("逾期完成", undo, stack="stack1",category_gap="60%")
.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
.set_global_opts(
xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(rotate=-15)),
title_opts=opts.TitleOpts(title="处理事件最多的部门"),
datazoom_opts=[opts.DataZoomOpts(), opts.DataZoomOpts(type_="inside")],
)
)
grid = Grid()
grid.add(c, grid_opts=opts.GridOpts(pos_bottom="20%"))
c = grid.dump_options_with_quotes()
end = datetime.datetime.now()
print("Bar: " + str(end - start))
return c
def weak_user(title):
all_day = parse_day(7)
x_data = [str(i.day) + '日' for i in all_day]
y_data = [get_long(i) for i in all_day]
bar = (Bar(init_opts=opts.InitOpts(
theme=ThemeType.DARK, width="1400px",
height='1000px')).add_xaxis(x_data).add_yaxis(
"增长用户",
[i[1] for i in y_data],
yaxis_index=0,
color="#d14a61",
).extend_axis(yaxis=opts.AxisOpts(
type_="value",
name="活跃用户",
min_=0,
max_=max([i[0] for i in y_data]),
position="left",
axisline_opts=opts.AxisLineOpts(linestyle_opts=opts.LineStyleOpts(
color="#675bba")),
axislabel_opts=opts.LabelOpts(formatter="{value} 人"),
splitline_opts=opts.SplitLineOpts(
is_show=True, linestyle_opts=opts.LineStyleOpts(opacity=1)),
)).set_global_opts(
yaxis_opts=opts.AxisOpts(
name="增长用户",
type_="value",
min_=0,
max_=max([i[1] for i in y_data]),
position="right",
offset=80,
axisline_opts=opts.AxisLineOpts(
linestyle_opts=opts.LineStyleOpts(color="#5793f3")),
axislabel_opts=opts.LabelOpts(formatter="{value} 人"),
),
title_opts=opts.TitleOpts(title=title),
tooltip_opts=opts.TooltipOpts(trigger="axis",
axis_pointer_type="cross"),
))
line = (Line(init_opts=opts.InitOpts(
theme=ThemeType.DARK, width="1400px", height='1000px')).add_xaxis(
x_data).add_yaxis(
"活跃用户",
[i[0] for i in y_data],
yaxis_index=1,
color="#675bba",
label_opts=opts.LabelOpts(is_show=False),
))
bar.overlap(line)
grid = Grid(init_opts=opts.InitOpts(
theme=ThemeType.DARK, width="1400px", height='1000px'))
grid.add(bar, opts.GridOpts(), is_control_axis_index=True)
return grid
def bolling_backtest_grid_chart(df):
"""完整布林线回测图，传入的 df 需要有 open high low close volume upper median lower signal equity_curve 即可绘制 """
# 布林线的 OHLC 图
kline_chart = bolling_kline_chart(df, 3) # 这里要控制3个轴同时缩放
# Volume
volume_chart = volume_bar_chart(df)
# 资金曲线
equity_chart = equity_line_chart(df)
# 组合起来
grid_chart = Grid(init_opts=opts.InitOpts(
width="1000px",
height="800px",
bg_color="#ffffff",
animation_opts=opts.AnimationOpts(animation=False),
))
grid_chart.add(
kline_chart,
grid_opts=opts.GridOpts(pos_left="10%", pos_right="8%", height="40%"),
)
grid_chart.add(
volume_chart,
grid_opts=opts.GridOpts(pos_left="10%",
pos_right="8%",
pos_top="55%",
height="10%"),
)
grid_chart.add(
equity_chart,
grid_opts=opts.GridOpts(pos_left="10%",
pos_right="8%",
pos_top="75%",
height="10%"),
)
return grid_chart
def draw(self, symbol, fn_render):
fn = get_dss() +'fut/bar/day_' + symbol + '.csv'
df = pd.read_csv(fn)
df1 = df.loc[:,['date','time','close']]
n = 30
close_list = df.apply(lambda record: float(record['close']), axis=1).tolist()
close_list = np.array(close_list)
ma_arr = talib.SMA(close_list, n)
df['ma'] = ma_arr
df['close'] = df['close'] - df['ma']
df2 = df.loc[:,['date','time','close']]
line1 = self.gen_line_one(df1, symbol)
line2 = self.gen_line_two(df2)
grid_chart = Grid(
init_opts=opts.InitOpts(
width="1300px",
height="700px",
#animation_opts=opts.AnimationOpts(animation=False),
)
)
grid_chart.add(
line1,
grid_opts=opts.GridOpts(pos_left="5%", pos_right="3%", height="39%"),
)
grid_chart.add(
line2,
grid_opts=opts.GridOpts(
pos_left="5%", pos_right="3%", pos_top="53%", height="39%" ),
)
grid_chart.render(fn_render)
def draw_charts(fn1, fn2):
df1 = pd.read_csv(fn1)
df1['datetime'] = df1['date'] + ' ' + df1['time']
#print(df1.head())
kline1 = gen_kline_one(df1)
df2 = pd.read_csv(fn2)
df2['datetime'] = df2['date'] + ' ' + df2['time']
#print(df1.head())
kline2 = gen_kline_two(df2)
grid_chart = Grid(
init_opts=opts.InitOpts(
width="1300px",
height="700px",
#animation_opts=opts.AnimationOpts(animation=False),
)
)
grid_chart.add(
kline1,
grid_opts=opts.GridOpts(pos_left="5%", pos_right="3%", height="39%"),
)
grid_chart.add(
kline2,
grid_opts=opts.GridOpts(
pos_left="3%", pos_right="5%", pos_top="53%", height="39%" ),
)
fn = get_dss( ) + 'backtest/render/bar2.html'
grid_chart.render(fn)
def draw_charts():
vtSymbol = 'CF005'
fn = get_dss() + 'fut/bar/min5_' + vtSymbol + '.csv'
df1 = pd.read_csv(fn)
df1 = df1[df1.date >= '2019-11-20']
df1['datetime'] = df1['date'] + ' ' + df1['time']
# print(df1.head())
fn = get_dss() + 'fut/engine/rsiboll/signal_rsiboll_duo_deal_CF.csv'
df2 = pd.read_csv(fn)
kline = gen_kline(df1)
line_rsi = gen_rsi(df1)
line_atr = gen_atr(df1)
scatter_deal_one = gen_poit_one(df2)
scatter_deal_two = gen_poit_two(df2)
scatter_deal_three = gen_poit_three(df2)
kline_scatter = kline.overlap(scatter_deal_one)
kline_scatter = kline_scatter.overlap(scatter_deal_two)
kline_scatter = kline_scatter.overlap(scatter_deal_three)
grid_chart = Grid(init_opts=opts.InitOpts(
width="1000px",
height="700px",
animation_opts=opts.AnimationOpts(animation=False),
))
grid_chart.add(
kline_scatter,
grid_opts=opts.GridOpts(pos_left="10%", pos_right="8%", height="45%"),
)
grid_chart.add(
line_rsi,
grid_opts=opts.GridOpts(pos_left="10%",
pos_right="8%",
pos_top="57%",
height="17%"),
)
grid_chart.add(
line_atr,
grid_opts=opts.GridOpts(pos_left="10%",
pos_right="8%",
pos_top="76%",
height="17%"),
)
grid_chart.render("k_deal_rsi_atr.html")
def draw(self, symbol, fn_render):
fn = get_dss() + 'fut/bar/day_' + symbol + '.csv'
df1 = pd.read_csv(fn)
# print(df1.head())
price_min = int(df1.close.min() * 0.99)
price_max = df1.close.max()
kline = self.gen_kline(df1, symbol)
line_cci = self.gen_cci(df1, 100)
grid_chart = Grid(init_opts=opts.InitOpts(
width="1390px",
height="700px",
animation_opts=opts.AnimationOpts(animation=False),
))
grid_chart.add(
kline,
grid_opts=opts.GridOpts(pos_left="10%",
pos_right="8%",
height="60%"),
)
grid_chart.add(
line_cci,
grid_opts=opts.GridOpts(pos_left="10%",
pos_right="8%",
pos_top="75%",
height="17%"),
)
grid_chart.render(fn_render)
def overlap_line_scatter(day_data, week_data) -> Bar:
# 处理日数据
x_day = list(day_data[16:-7].index)
for i in range(0, len(x_day)):
x_day[i] = datetime.datetime.strftime(x_day[i], "%H:%M")
Y_day = list(day_data[16:-7])
for i in range(0, len(Y_day)):
Y_day[i] = round(Y_day[i])
# 处理周数据
print(week_data)
x_week = list(week_data.index)
Y_week = list(week_data['counts'])
print(Y_week)
bar = (Bar().add_xaxis(x_day).add_yaxis(
"未来一天",
Y_day).set_global_opts(title_opts=opts.TitleOpts(title="咨询量预测统计")))
line = (Line().add_xaxis(x_week).add_yaxis(
"未来一周", Y_week, symbol="triangle", symbol_size=20).set_global_opts(
legend_opts=opts.LegendOpts(pos_top="48%")))
grid = (Grid().add(bar, grid_opts=opts.GridOpts(pos_bottom="60%")).add(
line, grid_opts=opts.GridOpts(pos_top="60%")))
return grid
def overlap_line_scatter(day_data) -> Bar:
# 处理日数据
x_day = list(day_data[16:-7].index)
for i in range(0, len(x_day)):
x_day[i] = datetime.datetime.strftime(x_day[i], "%H:%M")
Y_day = list(day_data[16:-7])
for i in range(0, len(Y_day)):
Y_day[i] = round(Y_day[i])
# 处理周数据
# print(week_data)
# x_week = list(week_data.index)
# Y_week = list(week_data['counts'])
# print(Y_week)
bar = (
Bar().add_xaxis(x_day).add_yaxis("未来一天", Y_day)
# .set_global_opts(title_opts=opts.TitleOpts(title="“及时倾听”半点客户量预测"))
# .set_series_opts(width=900,height=200)
)
# line = (
# Line()
# .add_xaxis(x_week)
# .add_yaxis("未来一周", Y_week, symbol="triangle", symbol_size=20)
# .set_global_opts(legend_opts=opts.LegendOpts(pos_top="48%"))
# )
grid = (Grid().add(bar, grid_opts=opts.GridOpts(pos_bottom="5%")))
return grid
def index_bar_every_4():
df2 = pd.read_csv(r'./static/data/house_price.csv')
df = pd.read_csv(r'./static/data/divorce.csv')
bar = (Bar().add_xaxis(df.province.values.tolist()).add_yaxis(
"离婚年均增长率", df.dir.values.tolist()).set_global_opts(
title_opts=opts.TitleOpts(title="中国分省离婚年均增长率情况"),
xaxis_opts=opts.AxisOpts(name_rotate=60,
name="省份",
axislabel_opts={"rotate": 45})))
line = (Line().add_xaxis(df2.province.values.tolist()).add_yaxis(
"房价年均增长率", df2.increase.values.tolist()).set_global_opts(
title_opts=opts.TitleOpts(title="中国分省住宅商品房价格年均增长率情况",
pos_top="48%"),
legend_opts=opts.LegendOpts(pos_top="48%"),
xaxis_opts=opts.AxisOpts(name_rotate=60,
name="省份",
axislabel_opts={"rotate": 45})))
grid = (Grid().add(bar, grid_opts=opts.GridOpts(pos_bottom="60%")).add(
line, grid_opts=opts.GridOpts(pos_top="60%")))
return render_template('index.html',
myechart=grid.render_embed(),
text1='''离婚年均增长率相对较低的省份：北京、上海、新疆、黑龙江、吉林、内蒙古；
但其实，北京、上海的房价年均增长率是非常高的，且新疆、黑龙江这些省份的房价年均增长率也是处于中游水平；
可以很明显地从整个图中看出，离婚年均增长率和房价年均增长率之间的关联性不大。''')
def index_bar_every_1_tp():
ted = pd.read_csv("./static/data/ted_main.csv")
ted['film_date'] = ted['film_date'].apply(
lambda x: datetime.datetime.fromtimestamp(int(x)).strftime('%d-%m-%Y'))
ted['published_date'] = ted['published_date'].apply(
lambda x: datetime.datetime.fromtimestamp(int(x)).strftime('%d-%m-%Y'))
views_ted = ted[[
'main_speaker', 'title', 'published_date', 'views', 'comments', 'tags',
'speaker_occupation', 'num_speaker'
]].sort_values(by='views', ascending=False)
views_ted_1 = ted[[
'main_speaker', 'title', 'published_date', 'views', 'comments', 'tags',
'speaker_occupation', 'num_speaker'
]].sort_values(by='comments', ascending=False)
bar = (Bar().add_xaxis(
views_ted.head(10).main_speaker.values.tolist()).add_yaxis(
"top10",
views_ted.head(10).views.values.tolist()).set_global_opts(
title_opts=opts.TitleOpts(
title="浏览量和评论数TOP10视频")).set_series_opts(
label_opts=opts.LabelOpts(is_show=True), ))
bar1 = (Bar().add_xaxis(
views_ted_1.head(10).main_speaker.values.tolist()).add_yaxis(
"top10",
views_ted_1.head(10).comments.values.tolist()).set_global_opts(
title_opts=opts.TitleOpts(
title="浏览量和评论数TOP10视频")).set_series_opts(
label_opts=opts.LabelOpts(is_show=True), ))
grid = (Grid().add(bar, grid_opts=opts.GridOpts(pos_bottom="60%")).add(
bar1, grid_opts=opts.GridOpts(pos_top="60%")))
return render_template('index.html',
myechart=grid.render_embed(),
select=list(res.keys()))
def grid_chart(dataframe) -> Grid:
grid = (
Grid(init_opts=opts.InitOpts(
width="1200px",
height="800px",
#设置动画
animation_opts=opts.AnimationOpts(animation_delay=1000,
animation_easing="elasticOut"),
)).add(
line_datazoom_CO2(dataframe), # 图表实例，仅 `Chart` 类或者其子类
# grid 组件离容器右侧的距离。
# right 的值可以是像 20 这样的具体像素值，可以是像 '20%' 这样相对于容器高宽的百分比。
grid_opts=opts.GridOpts(pos_top="4%", height="24%")).
add(
line_datazoom_temp(dataframe), # 图表实例，仅 `Chart` 类或者其子类
# grid 组件离容器右侧的距离。
# right 的值可以是像 20 这样的具体像素值，可以是像 '20%' 这样相对于容器高宽的百分比。
grid_opts=opts.GridOpts(pos_bottom="39%", height="24%")
).add(
bar_datazoom_rh(dataframe), # 图表实例，仅 `Chart` 类或者其子类
# grid 组件离容器右侧的距离。
# right 的值可以是像 20 这样的具体像素值，可以是像 '20%' 这样相对于容器高宽的百分比。
grid_opts=opts.GridOpts(pos_bottom="6%",
height="24%")).dump_options_with_quotes())
return grid
def get_chart_of_date(date):
# 一个只标注出对应年份，其他是空字符串的list
date_mark = [0] * len(date_list)
date_mark[date_list.index(date)] = dayly_count_list[date_list.index(date)]
# print(date_mark)
line_chart = (Line().add_xaxis(date_list).add_yaxis(
"", dayly_count_list,
linestyle_opts=opts.LineStyleOpts(width=3)).add_yaxis(
"",
date_mark,
markpoint_opts=opts.MarkPointOpts(
data=[opts.MarkPointItem(type_='max')])).set_series_opts(
label_opts=opts.LabelOpts(is_show=False)).set_global_opts(
title_opts=opts.TitleOpts(
title='2017年海口市5至10月正常天气滴滴每日出行总量', pos_top='50%')))
bar_chart = (Bar().add_xaxis(xaxis_data=hour_24_list).add_yaxis(
series_name='',
yaxis_data=hourly_count_list[date_list.index(date)],
label_opts=opts.LabelOpts(is_show=True,
position='top',
formatter='{c}'),
).set_global_opts(
xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(is_show=True)),
yaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(is_show=True)),
title_opts=opts.TitleOpts(title=date + '各时段出行量 天气：' +
weather_list[date_list.index(date)] + ', ' +
weekday_list[date_list.index(date)])))
grid = (Grid().add(bar_chart,
grid_opts=opts.GridOpts(pos_bottom="60%")).add(
line_chart, grid_opts=opts.GridOpts(pos_top="60%")))
return grid
def index_bar_every_X():
ted = pd.read_csv("./static/data/ted_main.csv")
ted['film_date'] = ted['film_date'].apply(
lambda x: datetime.datetime.fromtimestamp(int(x)).strftime('%d-%m-%Y'))
ted['published_date'] = ted['published_date'].apply(
lambda x: datetime.datetime.fromtimestamp(int(x)).strftime('%d-%m-%Y'))
month_order = [
'Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct',
'Nov', 'Dec'
]
# day_order = ['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun']
ted['month'] = ted['film_date'].apply(
lambda x: month_order[int(x.split('-')[1]) - 1])
month_df = pd.DataFrame(ted['month'].value_counts()).reset_index()
month_df.columns = ['month', 'talk_times']
ted['years'] = ted['published_date'].apply(lambda x: x.split('-')[2])
year_df = pd.DataFrame(ted['years'].value_counts().reset_index())
year_df.columns = ['years', 'times']
bar = (Bar().add_xaxis(month_df.month.values.tolist()).add_yaxis(
"", month_df.talk_times.values.tolist()).set_global_opts(
title_opts=opts.TitleOpts()).set_series_opts(
label_opts=opts.LabelOpts(is_show=True), ))
bar1 = (Line().add_xaxis(year_df.years.values.tolist()[::-1]).add_yaxis(
"",
year_df.times.values.tolist()[::-1]).set_global_opts(
title_opts=opts.TitleOpts()).set_series_opts(
label_opts=opts.LabelOpts(is_show=True), ))
grid = (Grid().add(bar, grid_opts=opts.GridOpts(pos_bottom="60%")).add(
bar1, grid_opts=opts.GridOpts(pos_top="60%")))
return render_template('index.html',
myechart=grid.render_embed(),
select=list(res.keys()))
def get_date_chart(date: str):
grid_chart = (
Grid()
.add(
get_provinces_daily_bar_chart(date),
grid_opts=opts.GridOpts(
pos_left="10", pos_right="45%", pos_top="50%", pos_bottom="5"
),
)
.add(
get_China_sum_line_chart(date),
grid_opts=opts.GridOpts(
pos_left="65%", pos_right="80", pos_top="5%", pos_bottom="65%"
),
)
.add(
get_provinces_daily_pie_chart(date),
grid_opts=opts.GridOpts(
pos_left="45%", pos_top="65%"
)
)
.add(
get_provinces_daily_map_chart(date),
grid_opts=opts.GridOpts(
pos_left='10'
)
)
)
return grid_chart
def create_grid_bar_and_bar(title, x_list, data_list, data_list_2, data_unit_list):
# 1.先生成上方的柱状图
bar1 = Bar()
bar1.add_xaxis(x_list)
for key in data_list.keys():
bar1.add_yaxis(key, data_list[key])
bar1.set_global_opts(title_opts=opts.TitleOpts(title=title),
legend_opts=opts.LegendOpts(pos_left="25%", pos_top="5%"),
yaxis_opts=opts.AxisOpts(name="单位:" + data_unit_list[0]
, is_scale=True))
# 2.下方柱状图
bar2 = Bar()
bar2.add_xaxis(x_list)
for key in data_list_2.keys():
bar2.add_yaxis(key, data_list_2[key])
bar2.set_global_opts(title_opts=opts.TitleOpts(title=title),
legend_opts=opts.LegendOpts(pos_left="25%", pos_top="55%"),
yaxis_opts=opts.AxisOpts(name="单位:" + data_unit_list[1],
is_scale=True))
# 3.生成最终的组合图
c = (
Grid(init_opts=opts.InitOpts(theme=THEME_TYPE,height="700px"))
.add(bar1, grid_opts=opts.GridOpts(pos_bottom="60%"))
.add(bar2, grid_opts=opts.GridOpts(pos_top="60%"))
)
src_path = "./指标-年度-图片生成/"
html_file_name = src_path + title + ".html"
img_file_name = src_path + title + ".png"
make_snapshot(snapshot, c.render(html_file_name), img_file_name)
print(img_file_name+"生成完毕...")
def grid_mutil_yaxis() -> Grid:
x_data = ["{}月".format(i) for i in range(1, 13)]
bar = (Bar().add_xaxis(x_data).add_yaxis(
"蒸发量",
[2.0, 4.9, 7.0, 23.2, 25.6, 76.7, 135.6, 162.2, 32.6, 20.0, 6.4, 3.3],
yaxis_index=0,
color="#d14a61",
).add_yaxis(
"降水量",
[2.6, 5.9, 9.0, 26.4, 28.7, 70.7, 175.6, 182.2, 48.7, 18.8, 6.0, 2.3],
yaxis_index=1,
color="#5793f3",
).extend_axis(yaxis=opts.AxisOpts(
name="蒸发量",
type_="value",
min_=0,
max_=250,
position="right",
axisline_opts=opts.AxisLineOpts(linestyle_opts=opts.LineStyleOpts(
color="#d14a61")),
axislabel_opts=opts.LabelOpts(formatter="{value} ml"),
)).extend_axis(yaxis=opts.AxisOpts(
type_="value",
name="温度",
min_=0,
max_=25,
position="left",
axisline_opts=opts.AxisLineOpts(linestyle_opts=opts.LineStyleOpts(
color="#675bba")),
axislabel_opts=opts.LabelOpts(formatter="{value} °C"),
splitline_opts=opts.SplitLineOpts(
is_show=True, linestyle_opts=opts.LineStyleOpts(opacity=1)),
)).set_global_opts(
yaxis_opts=opts.AxisOpts(
name="降水量",
min_=0,
max_=250,
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)
def grid_liquid_favorate_rate() -> Grid:
l1 = (Liquid().add(
"好评率",
[favorate_rate],
center=["60%", "50%"],
label_opts=opts.LabelOpts(
font_size=50,
formatter=JsCode("""function (param) {
return (Math.floor(param.value * 10000) / 100) + '%';
}"""),
position="inside",
),
).set_global_opts(title_opts=opts.TitleOpts(title="好评率和差评率")))
l2 = (Liquid().add(
"差评率",
[poor_rate],
center=["25%", "50%"],
label_opts=opts.LabelOpts(
font_size=50,
formatter=JsCode("""function (param) {
return (Math.floor(param.value * 10000) / 100) + '%';
}"""),
position="inside",
),
))
grid = Grid().add(l2,
grid_opts=opts.GridOpts()).add(l1,
grid_opts=opts.GridOpts())
return grid
def grid_vertical():
bar = (
Bar()
.add_xaxis(Faker.choose())
.add_yaxis("商家A", Faker.values())
.add_yaxis("商家B", Faker.values())
.set_global_opts(title_opts=opts.TitleOpts(title="Grid-Bar"))
)
line = (
Line()
.add_xaxis(Faker.choose())
.add_yaxis("商家A", Faker.values())
.add_yaxis("商家B", Faker.values())
.set_global_opts(
title_opts=opts.TitleOpts(title="Grid-Line", pos_top="48%"),
legend_opts=opts.LegendOpts(pos_top="48%"),
)
)
grid = (
Grid()
.add(bar, grid_opts=opts.GridOpts(pos_bottom="60%"))
.add(line, grid_opts=opts.GridOpts(pos_top="60%"))
)
return grid
def grid_horizontal():
scatter = (
Scatter()
.add_xaxis(Faker.choose())
.add_yaxis("商家A", Faker.values())
.add_yaxis("商家B", Faker.values())
.set_global_opts(
toolbox_opts=opts.ToolboxOpts(is_show=True),
title_opts=opts.TitleOpts(title="Grid-Scatter"),
legend_opts=opts.LegendOpts(pos_left="20%"),
)
)
line = (
Line()
.add_xaxis(Faker.choose())
.add_yaxis("商家A", Faker.values())
.add_yaxis("商家B", Faker.values())
.set_global_opts(
toolbox_opts=opts.ToolboxOpts(is_show=True),
title_opts=opts.TitleOpts(title="Grid-Line", pos_right="5%"),
legend_opts=opts.LegendOpts(pos_right="20%"),
)
)
grid = (
Grid()
.add(scatter, grid_opts=opts.GridOpts(pos_left="55%"))
.add(line, grid_opts=opts.GridOpts(pos_right="55%")))
return grid
def test_grid_control_axis_index():
bar = _chart_for_grid()
gc = Grid().add(bar,
opts.GridOpts(pos_left="5%", pos_right="20%"),
is_control_axis_index=True)
expected_idx = (0, 1, 2)
for idx, series in enumerate(gc.options.get("series")):
assert_equal(series.get("yAxisIndex"), expected_idx[idx])
def draw_charts():
pz = 'CF'
vtSymbol = 'CF'
fn = get_dss() + 'backtest/fut/' + pz + '/day_' + vtSymbol + '.csv'
df1 = pd.read_csv(fn)
# df1 = df1[df1.date >= '2019-01-20']
#df1['datetime'] = df1['date'] + ' ' + df1['time']
df1['datetime'] = df1['date']
price_min = int(df1.close.min() * 0.99)
price_max = df1.close.max()
#fn = get_dss( )+ 'fut/engine/aberration_raw/signal_aberration_raw_duo_deal_CF.csv'
#fn = get_dss( )+ 'fut/engine/aberration_raw/signal_aberration_raw_kong_deal_CF.csv'
fn = get_dss(
) + 'fut/engine/aberration_enhance/signal_aberration_enhance_duo_deal_CF.csv'
#fn = get_dss( )+ 'fut/engine/aberration_enhance/signal_aberration_enhance_kong_deal_CF.csv'
df2 = pd.read_csv(fn)
dt_list = df2['datetime'].tolist()
dt_list = [dt[:10] for dt in dt_list]
# print(dt_list)
df2['datetime'] = dt_list
line = gen_line(df1, vtSymbol, price_min, price_max)
line_atr = gen_atr(df1, 10)
line_boll = gen_boll(df1, 10, 2)
line = line.overlap(line_boll)
scatter_open = gen_poit_open(df2)
scatter_close = gen_poit_close(df2)
line = line.overlap(scatter_open)
line = line.overlap(scatter_close)
grid_chart = Grid(init_opts=opts.InitOpts(
width="1300px",
height="700px",
animation_opts=opts.AnimationOpts(animation=False),
))
grid_chart.add(
line,
grid_opts=opts.GridOpts(pos_left="10%", pos_right="8%", height="60%"),
)
grid_chart.add(
line_atr,
grid_opts=opts.GridOpts(pos_left="10%",
pos_right="8%",
pos_top="76%",
height="17%"),
)
fn = get_dss() + 'backtest/render/k_deal_aberration_' + vtSymbol + '.html'
grid_chart.render(fn)
def show_score_top():
# 查找上映年份集合
showtime = select_showtime()
# 生成时间轴的图
timeline = Timeline(
init_opts=opts.InitOpts(page_title="豆瓣电影TOP250-评分TOP10的电影", ))
for year in showtime:
film_tuple = select_film(year)
date_list = select_data(year)
timeline.add_schema(is_auto_play=True, play_interval=1000)
# 柱状图初始化
bar = Bar()
# 横坐标
bar.add_xaxis(film_tuple[0])
# 纵坐标
bar.add_yaxis(
"",
date_list,
# 数据靠右显示
label_opts=opts.LabelOpts(is_show=True, position='right'))
# 横纵坐标翻转
bar.reversal_axis()
# 全局配置
bar.set_global_opts(
# 标题
title_opts=opts.TitleOpts(
title="豆瓣电影TOP250-第{}年评分TOP10的电影".format(year),
pos_left='center'),
# 横坐标隐藏
xaxis_opts=opts.AxisOpts(is_show=False,
max_=select_film(year)[2],
min_=(float(select_film(year)[3]) - 0.1),
split_number=10),
# 纵坐标
yaxis_opts=opts.AxisOpts(
max_=9,
# 字体大小
axislabel_opts=opts.LabelOpts(font_size=10),
# 隐藏坐标轴
axisline_opts=opts.AxisLineOpts(is_show=False),
# 隐藏刻度
axistick_opts=opts.AxisTickOpts(is_show=False)))
# 组合组件
grid = (Grid().add(bar,
grid_opts=opts.GridOpts(pos_top='8%',
pos_bottom='12%',
pos_left='25%')))
timeline.add(grid, "{}年".format(year))
timeline.add_schema(is_auto_play=True,
play_interval=1000,
is_loop_play=False,
width='820px',
pos_left='60px')
# 生成HTML
html = "pages/iframes/score_top.html"
timeline.render("./templates/" + html)
return html
