def create_result_chart(data):
instrulist = list(set(d.instrument for d in data))
# x = ["{} P{}".format(localtime(d.upload_time).strftime("%Y-%m-%d %X"), d.platenum) for d in data]
x = ["{} P{}".format(d.upload_time.strftime("%Y-%m-%d %X"), d.platenum) for d in data]
# 均值直方图
b = Bar(init_opts=opts.InitOpts(width='1900px'))
b.add_xaxis(x)
for instru in instrulist:
mean = get_mean(data, instru)
b.add_yaxis(instru, mean)
b.set_global_opts(title_opts=opts.TitleOpts(title="结果mean图"))
# sd折线图
l = Line(init_opts=opts.InitOpts(width='1900px'))
l.add_xaxis(x)
for instru in instrulist:
sd = get_sd(data, instru)
l.add_yaxis(instru, sd, is_connect_nones=True)
l.set_global_opts(title_opts=opts.TitleOpts(title="结果sd图", pos_top='48%'))
grid = Grid(init_opts=opts.InitOpts(width='1900px'))
grid.add(b, grid_opts=opts.GridOpts(pos_bottom='60%'))
grid.add(l, grid_opts=opts.GridOpts(pos_top='60%'))
return grid
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 html_output(self, order, view, mode):
"""
output function of html
Args:
order(int): diversified_ranking use different order
view(View): view object
mode(str): single or multiple
Returns:
None
"""
instance = self.instance
data = {}
data['order'] = order
data['chartname'] = instance.table_name
data['describe'] = view.table.describe
data['x_name'] = view.fx.name
data['y_name'] = view.fy.name
data['chart'] = Chart.chart[view.chart]
data['classify'] = [v[0] for v in view.table.classes]
data['x_data'] = view.X
data['y_data'] = view.Y
data['title_top'] = 5
[chart, filename] = self.html_handle(data)
grid = Grid()
grid.add(chart,
grid_opts=opts.GridOpts(pos_bottom='20%', pos_top='20%'))
if mode == 'single':
self.page.add(grid) #the grid is added in the same page
elif mode == 'multiple':
grid.render('./html/' + self.table_name + '/' +
filename) #the grid is added in a new file
def grid(factor, factor2, instrument):
gridchart = Grid(init_opts=opts.InitOpts(width="100vw",
height="50vh",
animation_opts=opts.AnimationOpts(
animation=True)))
gridchart.add( #左边画最新数据
getplot(factor,
factor2,
kdata_part,
nnv,
new_p,
instrument,
xaxis_index=[0, 1]),
grid_opts=opts.GridOpts(pos_left="5%", pos_right="68%"))
gridchart.add( # 右边画全样本数据
getplot(factor,
factor2,
kdata_all,
nv,
new_a,
instrument,
xaxis_index=None),
grid_opts=opts.GridOpts(pos_left="40%", pos_right="5%"))
return gridchart
def KlinePlotting(target_file):
global trend_punishment
res = pickle.load(open(target_file, 'rb'))
c = (Line().add_xaxis([item[0] for item in res]).add_yaxis(
"打分",
[item[1] for item in res],
linestyle_opts=opts.LineStyleOpts(width=3, opacity=0.5),
).set_global_opts(
title_opts=opts.TitleOpts(title="打分"),
xaxis_opts=opts.AxisOpts(type_="category"),
datazoom_opts=[
opts.DataZoomOpts(
type_="slider",
xaxis_index=[0],
range_start=80,
range_end=100,
)
],
tooltip_opts=opts.TooltipOpts(
trigger="axis",
axis_pointer_type="cross",
background_color="rgba(245, 245, 245, 0.8)",
border_width=1,
border_color="#ccc",
textstyle_opts=opts.TextStyleOpts(color="#000"),
),
))
gridChart = Grid(init_opts=opts.InitOpts(width="1000px",
height="500px",
animation_opts=opts.AnimationOpts(
animation=False)))
gridChart.add(c, grid_opts=opts.GridOpts(pos_left="10%", pos_right="10%"))
gridChart.render("score_modified_plot_new_0.002.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 base_kline_grid_chart(df, signal_infos=[]):
""" 最最最基础的 K 线图，传入的 df 只需要有 open high low close volume 即可绘制 """
# OHLC
kline_chart = ohlc_kline_chart(df, signal_infos=signal_infos)
# Volume
volume_chart = volume_bar_chart(df)
# 把 ohlc 和 volume 图组合起来
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="55%"),
)
grid_chart.add(
volume_chart,
grid_opts=opts.GridOpts(pos_left="10%",
pos_right="8%",
pos_top="70%",
height="15%"),
)
return grid_chart
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 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 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 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 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 draw_charts():
# #fn = get_dss() +'backtest/fut/m/' + 'm_01_05.csv'
# fn = get_dss() +'backtest/fut/m/' + 'day_m.csv'
# df1 = pd.read_csv(fn)
# df1['datetime'] = df1['date'] + ' ' + df1['time']
# #print(df1.head())
# kline1 = gen_kline_one(df1)
#
# fn = get_dss() +'backtest/fut/y/' + 'day_y.csv'
# df2 = pd.read_csv(fn)
# df2['datetime'] = df2['date'] + ' ' + df2['time']
# #print(df1.head())
# kline2 = gen_kline_two(df2)
#--------------------------------------------------------------------------
fn = 'bar_kamaresid_raw_duo_CF.csv'
fn = 'bar_kamaresid_raw_duo_m.csv'
df = pd.read_csv(fn)
df.columns = ['date','time','close','q1','q2','resid']
df1 = df.loc[:,['date','time','close']]
df2 = df.loc[:,['date','time','resid']]
df2['close'] = df2['resid']
print(df1.head(3))
print(df2.head(3))
s1 = 'close'
s2 = 'resid'
line1 = gen_line_one(df1)
line2 = 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%" ),
)
fn = get_dss( )+ 'backtest/render/k_k_' + s1 + '_' + s2 + '.html'
grid_chart.render(fn)
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 grid(self):
line = Line('折线图', width=1200)
attr = ['周一', '周二', '周三', '周四', '周五', '周六', '周日']
line.add('最高气温', attr, [11, 12, 14, 11, 12, 15, 16], mark_point=['max', 'min'], \
mark_line=['average'])
line.add('最低气温', attr, [1, -1, 2, 5, 2, 3, 0], mark_point=['max', 'min'], \
mark_line=['average'], legend_pos='20%')
attr = ['衬衫', '羊毛衫', '雪纺衫', '裤子', '高跟鞋', '袜子']
v1 = [11, 12, 14, 10, 11, 10]
pie = Pie('饼图', title_pos='55%')
pie.add('', attr, v1, radius=[45, 65], center=[65, 50], legend_pos='80%', \
legend_orient='vertical')
grid = Grid()
grid.add(line, grid_right='55%')
grid.add(pie, grid_left='60%')
def makechart(sumary,day,shdeath,shcured,shsum,shpending,newconfirmed,newpending):
title = "上海新型冠状病毒统计"
grid_chart = Grid()
grid_chart.add(
total_trend(day,shdeath,shcured,shsum,shpending),
grid_opts=GridOpts(height="57%")
)
grid_chart.add(
new_trend(day,newconfirmed,newpending),
grid_opts=GridOpts(pos_top="75%",)
)
page = Page(page_title=title)
page.add(
grid_chart,
# calendar_base(),
map_visualmap(sumary),
)
page.render(outfile)
def to_treemap(data, proj, version):
complexity = {'name': f'{proj}-{version}', 'children': []}
extract(complexity, 'complexity', data)
maintainability = {'name': f'{proj}-{version}', 'children': []}
extract(maintainability, 'maintainability', data)
testability = {'name': f'{proj}-{version}', 'children': []}
extract(testability, 'testability', data)
readability = {'name': f'{proj}-{version}', 'children': []}
extract(readability, 'readability', data)
reusability = {'name': f'{proj}-{version}', 'children': []}
extract(reusability, 'reusability', data)
inheritance = {'name': f'{proj}-{version}', 'children': []}
extract(inheritance, 'inheritance', data)
treemap = TreeMap()
for indicator in [
'complexity', 'maintainability', 'testability', 'readability',
'reusability', 'inheritance'
]:
treemap = treemap.add(series_name=indicator,
data=[locals()[indicator]],
leaf_depth=2,
roam=False,
label_opts=opts.LabelOpts(position='inside'))
treemap = (treemap.set_global_opts(
tooltip_opts=opts.TooltipOpts(formatter='{b}<br/>{a}: {c}'),
toolbox_opts=opts.ToolboxOpts(feature=opts.ToolBoxFeatureOpts(
magic_type=opts.ToolBoxFeatureMagicTypeOpts(is_show=False),
data_zoom=opts.ToolBoxFeatureDataZoomOpts(is_show=False),
brush=opts.ToolBoxFeatureBrushOpts(type_='clear'))),
legend_opts=opts.LegendOpts(is_show=True,
selected_mode='single',
pos_top='7%',
orient='horizontal',
padding=0),
title_opts=opts.TitleOpts(
title=f'Code Quality Treemap of {proj}-{version}',
pos_left='center')))
grid = Grid()
grid.add(treemap, grid_opts=opts.GridOpts(pos_top='100%'))
return grid
def margin_chart(chart: pyecharts.charts.chart.Chart,
margin: list = []) -> Grid:
"""
Description:
1. Arange `chart` in a grid to set the margin
Params:
chart
margin: [top, left, bottom, right]
Return:
grid
"""
margin = margin or [0, 0, 0, 0]
grid = Grid()
grid.add(chart,
grid_opts=opts.GridOpts(pos_top=margin[0] or None,
pos_left=margin[1] or None,
pos_bottom=margin[2] or None,
pos_right=margin[3] or None))
return grid
def to_linechart(data, proj):
versions = [version['version_name'] for version in data]
linechart = Line().add_xaxis(xaxis_data=versions)
for indicator in [
'complexity', 'maintainability', 'testability', 'readability',
'reusability', 'inheritance'
]:
linechart = linechart.add_yaxis(
series_name=indicator,
y_axis=[round(version[indicator], 2) for version in data],
is_smooth=True,
label_opts=opts.LabelOpts(is_show=False),
linestyle_opts=opts.LineStyleOpts(width=2),
)
linechart = (linechart.set_global_opts(
tooltip_opts=opts.TooltipOpts(trigger="axis",
axis_pointer_type="cross"),
xaxis_opts=opts.AxisOpts(boundary_gap=False),
yaxis_opts=opts.AxisOpts(
axislabel_opts=opts.LabelOpts(formatter="{value}"),
splitline_opts=opts.SplitLineOpts(is_show=True),
),
toolbox_opts=opts.ToolboxOpts(feature=opts.ToolBoxFeatureOpts(
magic_type=opts.ToolBoxFeatureMagicTypeOpts(is_show=False),
data_zoom=opts.ToolBoxFeatureDataZoomOpts(is_show=False),
brush=opts.ToolBoxFeatureBrushOpts(type_='clear'))),
legend_opts=opts.LegendOpts(is_show=True,
pos_top='middle',
pos_left='1%',
orient='vertical',
padding=0),
datazoom_opts=opts.DataZoomOpts(type_='slider',
range_start=0,
range_end=100),
title_opts=opts.TitleOpts(title=f'Code Quality Linechart of {proj}',
pos_left='center')))
grid = Grid()
grid.add(linechart, grid_opts=opts.GridOpts(pos_left='150'))
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 drawChart(self,
data=None,
ma=None,
vol=None,
blist=None,
slist=None,
positlist=None):
koverlap = self.kline(data=data,
blist=blist,
slist=slist,
positlist=positlist)
datazoom = [
opts.DataZoomOpts(
pos_bottom="0px",
range_start=50.0,
range_end=100.00,
)
]
koverlap.set_global_opts(
xaxis_opts=opts.AxisOpts(is_scale=True),
yaxis_opts=opts.AxisOpts(
is_scale=True,
splitarea_opts=opts.SplitAreaOpts(
is_show=True,
areastyle_opts=opts.AreaStyleOpts(opacity=1)),
),
datazoom_opts=datazoom,
title_opts=opts.TitleOpts(title="eeeee"),
)
grid = Grid(init_opts=opts.InitOpts())
if koverlap != None:
grid.add(
koverlap,
grid_opts=opts.GridOpts(pos_top="5%"),
)
return grid
def gen_grid_chart(
overlap_kline_line: Kline,
width: str,
height: str,
bar_plot_list: typing.List[Bar],
filename: str,
) -> Grid:
index = 0
grid_chart = Grid(init_opts=opts.InitOpts(
width=width,
height=height,
animation_opts=opts.AnimationOpts(animation=False),
))
pos_top_list, height_list = gen_pos_top_height(plot_list=bar_plot_list)
grid_chart.add(
overlap_kline_line,
grid_opts=opts.GridOpts(
pos_left="10%",
pos_right="8%",
pos_top=f"{pos_top_list[index]}%",
height=f"{height_list[index]}%",
),
)
index += 1
bar_plot_list = filter_bar_plot(bar_plot_list)
for bar_plot in bar_plot_list:
grid_chart.add(
bar_plot,
grid_opts=opts.GridOpts(
pos_left="10%",
pos_right="8%",
pos_top=f"{pos_top_list[index]}%",
height=f"{height_list[index]}%",
),
)
index += 1
grid_chart.render(filename)
return grid_chart
def makechart(data, debug=False):
day, shsum, shpending, shcured, shdeath, shsum_date, new_date, newconfirmed, newpending = data_process(
data)
title = "上海新型冠状病毒统计"
grid_chart = Grid()
grid_chart.add(total_trend(day, shdeath, shcured, shsum, shpending),
grid_opts=GridOpts(height="57%"))
grid_chart.add(new_trend(day, newconfirmed, newpending),
grid_opts=GridOpts(pos_top="75%", ))
bk = bkey()
if debug:
ak = bk.ak_dev
else:
ak = bk.ak_web
get_detail()
with open(shsumary, 'r', encoding='utf-8') as f:
j = json.loads(f.read())
sumary = [(k, j[k]) for k in j.keys()]
tab = Tab(page_title=title)
tab.add(grid_chart, "趋势")
tab.add(map_visualmap(sumary), "区县分布")
tab.add(bmap_base(BAIDU_AK=ak), '详细地图')
tab.render(outfile)
def ice_ga_shrinkage_chart(df):
"""ice ga shrinkage"""
os_ma = pyecharts_float_values_data(df, 'os_ma', 6)
# 信号信息
signal_infos = bolling_signals_data(df)
# k 线
ohlc_chart = ohlc_kline_chart(df,
x_axis_count=3,
signal_infos=signal_infos)
# os_ma
os_ma_line = Line().add_xaxis(
xaxis_data=pyecharts_time_data(df)).add_yaxis(
y_axis=os_ma,
series_name='os_ma',
is_symbol_show=False,
label_opts=None,
is_smooth=True,
)
# 资金曲线
equity_chart = equity_line_chart(df)
grid_chart = Grid(init_opts=opts.InitOpts(
width="1000px",
height="600px",
bg_color="#ffffff",
animation_opts=opts.AnimationOpts(animation=False),
))
grid_chart.add(
ohlc_chart,
grid_opts=opts.GridOpts(pos_left="10%",
pos_right="8%",
pos_top="5%",
height="30%"),
)
grid_chart.add(
os_ma_line,
grid_opts=opts.GridOpts(pos_left="10%",
pos_right="8%",
pos_top="40%",
height="20%"),
)
grid_chart.add(
equity_chart,
grid_opts=opts.GridOpts(pos_left="10%",
pos_right="8%",
pos_top="70%",
height="18%"),
)
return grid_chart
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_charts():
vtSymbol = 'm'
fn = get_dss() + 'backtest/fut/m/day_' + vtSymbol + '.csv'
df1 = pd.read_csv(fn)
#df1 = df1[df1.date >= '2019-10-12']
df1['datetime'] = df1['date'] + ' ' + df1['time']
# print(df1.head())
kline = gen_kline(df1)
line_rsi = gen_rsi(df1)
line_atr = gen_atr(df1)
grid_chart = Grid(init_opts=opts.InitOpts(
width="1500px",
height="700px",
animation_opts=opts.AnimationOpts(animation=False),
))
grid_chart.add(
kline,
grid_opts=opts.GridOpts(pos_left="3%", pos_right="8%", height="45%"),
)
grid_chart.add(
line_rsi,
grid_opts=opts.GridOpts(pos_left="3%",
pos_right="8%",
pos_top="57%",
height="17%"),
)
grid_chart.add(
line_atr,
grid_opts=opts.GridOpts(pos_left="3%",
pos_right="8%",
pos_top="76%",
height="17%"),
)
fn = get_dss() + 'backtest/render/bar_ti_ti.html'
grid_chart.render(fn)
def kline_pro(kline: List[dict],
fx: List[dict] = None,
xd=None,
bs: List[dict] = None,
title: str = "缠中说禅K线分析",
width: str = "1400px",
height: str = '580px') -> Grid:
"""绘制缠中说禅K线分析结果
:param kline: K线
:param fx: 分型识别结果
:param bi: 笔识别结果
:param xd: 线段识别结果
:param zs: 中枢
:param bs: 买卖点
:param title: 图表标题
:param width: 图表宽度
:param height: 图表高度
:return: 用Grid组合好的图表
"""
# 配置项设置
# ------------------------------------------------------------------------------------------------------------------
bg_color = "#1f212d" # 背景
up_color = "#F9293E"
down_color = "#00aa3b"
init_opts = opts.InitOpts(bg_color=bg_color,
width=width,
height=height,
animation_opts=opts.AnimationOpts(False))
title_opts = opts.TitleOpts(
title=title,
pos_top="1%",
title_textstyle_opts=opts.TextStyleOpts(color=up_color, font_size=20),
subtitle_textstyle_opts=opts.TextStyleOpts(color=down_color,
font_size=12))
label_not_show_opts = opts.LabelOpts(is_show=False)
legend_not_show_opts = opts.LegendOpts(is_show=False)
red_item_style = opts.ItemStyleOpts(color=up_color)
green_item_style = opts.ItemStyleOpts(color=down_color)
k_style_opts = opts.ItemStyleOpts(color=up_color,
color0=down_color,
border_color=up_color,
border_color0=down_color,
opacity=0.8)
legend_opts = opts.LegendOpts(is_show=True,
pos_top="1%",
pos_left="30%",
item_width=14,
item_height=8,
textstyle_opts=opts.TextStyleOpts(
font_size=12, color="#0e99e2"))
brush_opts = opts.BrushOpts(tool_box=["rect", "polygon", "keep", "clear"],
x_axis_index="all",
brush_link="all",
out_of_brush={"colorAlpha": 0.1},
brush_type="lineX")
axis_pointer_opts = opts.AxisPointerOpts(is_show=True,
link=[{
"xAxisIndex": "all"
}])
range_start = int(100 - 216 / len(kline) * 100)
dz_inside = opts.DataZoomOpts(False,
"inside",
xaxis_index=[0, 1, 2],
range_start=range_start,
range_end=100)
dz_slider = opts.DataZoomOpts(True,
"slider",
xaxis_index=[0, 1, 2],
pos_top="96%",
pos_bottom="0%",
range_start=range_start,
range_end=100)
yaxis_opts = opts.AxisOpts(is_scale=True,
axislabel_opts=opts.LabelOpts(
color="#c7c7c7",
font_size=8,
position="inside"))
grid0_xaxis_opts = opts.AxisOpts(
type_="category",
grid_index=0,
axislabel_opts=label_not_show_opts,
split_number=20,
min_="dataMin",
max_="dataMax",
is_scale=True,
boundary_gap=False,
axisline_opts=opts.AxisLineOpts(is_on_zero=False))
tool_tip_opts = opts.TooltipOpts(
trigger="axis",
axis_pointer_type="cross",
background_color="rgba(245, 245, 245, 0.8)",
border_width=1,
border_color="#ccc",
position=JsCode("""
function (pos, params, el, elRect, size) {
var obj = {top: 10};
obj[['left', 'right'][+(pos[0] < size.viewSize[0] / 2)]] = 30;
return obj;
}
"""),
textstyle_opts=opts.TextStyleOpts(color="#000"),
)
# 数据预处理
# ------------------------------------------------------------------------------------------------------------------
# dts = [x.get('dt', x['date']) for x in kline]
try:
dts = [x['date'] for x in kline]
except:
dts = [x['dt'] for x in kline]
# k_data = [[x['open'], x['close'], x['low'], x['high']] for x in kline]
k_data = [
opts.CandleStickItem(
name=i, value=[x['open'], x['close'], x['low'], x['high']])
for i, x in enumerate(kline)
]
vol = []
for i, row in enumerate(kline):
item_style = red_item_style if row['close'] > row[
'open'] else green_item_style
bar = opts.BarItem(name=i,
value=row['volume'],
itemstyle_opts=item_style,
label_opts=label_not_show_opts)
vol.append(bar)
close = np.array([x['close'] for x in kline], dtype=np.double)
diff, dea, macd = MACD(close)
ma5 = SMA(close, timeperiod=5)
ma34 = SMA(close, timeperiod=34)
ma55 = SMA(close, timeperiod=55)
ma233 = SMA(close, timeperiod=233)
macd_bar = []
for i, v in enumerate(macd.tolist()):
item_style = red_item_style if v > 0 else green_item_style
bar = opts.BarItem(name=i,
value=round(v, 4),
itemstyle_opts=item_style,
label_opts=label_not_show_opts)
macd_bar.append(bar)
diff = diff.round(4)
dea = dea.round(4)
# K 线主图
# ------------------------------------------------------------------------------------------------------------------
chart_k = Kline()
chart_k.add_xaxis(xaxis_data=dts)
chart_k.add_yaxis(series_name="Kline",
y_axis=k_data,
itemstyle_opts=k_style_opts)
chart_k.set_global_opts(legend_opts=legend_opts,
datazoom_opts=[dz_inside, dz_slider],
yaxis_opts=yaxis_opts,
tooltip_opts=tool_tip_opts,
axispointer_opts=axis_pointer_opts,
brush_opts=brush_opts,
title_opts=title_opts,
xaxis_opts=grid0_xaxis_opts)
if xd:
index = 0
zs_colors = [
"yellow", "white", '#f034c1', "#7944b7", "#468b58", "#c17f2f",
"#9EA0A1"
]
data = []
temp_xd = xd
while temp_xd:
zs_color = zs_colors[index % len(zs_colors)]
data = data + [
opts.MarkAreaItem(
name='XD{}'.format(index),
x=(x['xd_list'][0]['date'], x['xd_list'][-1]['date']),
y=(x['ZG']['value'], x['ZD']['value']),
label_opts=opts.LabelOpts(color=zs_color),
itemstyle_opts=opts.ItemStyleOpts(
color=zs_color,
opacity=0.2,
)) for x in temp_xd.zs_list
]
temp_xd = temp_xd.next
index = index + 1
chart_k.set_series_opts(
markarea_opts=opts.MarkAreaOpts(is_silent=True, data=data))
# 均线图
# ------------------------------------------------------------------------------------------------------------------
chart_ma = Line()
chart_ma.add_xaxis(xaxis_data=dts)
ma_keys = {"MA5": ma5, "MA34": ma34, "MA55": ma55, "MA233": ma233}
ma_colors = ["#39afe6", "#da6ee8", "#A02128", "#00940b"]
for i, (name, ma) in enumerate(ma_keys.items()):
chart_ma.add_yaxis(series_name=name,
y_axis=ma,
is_smooth=True,
is_selected=False,
symbol_size=0,
label_opts=label_not_show_opts,
linestyle_opts=opts.LineStyleOpts(
opacity=0.8, width=1.0, color=ma_colors[i]))
chart_ma.set_global_opts(xaxis_opts=grid0_xaxis_opts,
legend_opts=legend_not_show_opts)
chart_k = chart_k.overlap(chart_ma)
# 缠论结果
# ------------------------------------------------------------------------------------------------------------------
if fx:
try:
fx_dts = [x['date'] for x in fx]
except:
fx_dts = [x['dt'] for x in fx]
fx_val = [x['value'] for x in fx]
chart_fx = Scatter()
chart_fx.add_xaxis(fx_dts)
chart_fx.add_yaxis(series_name="FX",
y_axis=fx_val,
is_selected=False,
symbol="circle",
symbol_size=6,
label_opts=label_not_show_opts,
itemstyle_opts=opts.ItemStyleOpts(
color="rgba(152, 147, 193, 1.0)", ))
chart_fx.set_global_opts(xaxis_opts=grid0_xaxis_opts,
legend_opts=legend_not_show_opts)
chart_k = chart_k.overlap(chart_fx)
if xd:
index = 0
xd_colors = zs_colors
while xd:
xd_dts = [x['date'] for x in xd]
xd_val = [x['value'] for x in xd]
chart_xd = Line()
chart_xd.add_xaxis(xd_dts)
xd_color = xd_colors[index % len(xd_colors)]
chart_xd.add_yaxis(
series_name="XD{}".format(index),
y_axis=xd_val,
is_selected=True,
symbol="triangle",
symbol_size=10,
linestyle_opts=opts.LineStyleOpts(color=xd_color,
width=index + 1,
type_="solid"),
itemstyle_opts=opts.ItemStyleOpts(color=xd_color))
chart_xd.set_global_opts(xaxis_opts=grid0_xaxis_opts,
legend_opts=legend_not_show_opts)
chart_k = chart_k.overlap(chart_xd)
xd = xd.next
index = index + 1
if bs:
b_dts = [x['date'] for x in bs if x['bs'] == 'buy']
if len(b_dts) > 0:
b_val = [x['value'] for x in bs if x['bs'] == 'buy']
chart_b = Scatter()
chart_b.add_xaxis(b_dts)
chart_b.add_yaxis(series_name="BUY",
y_axis=b_val,
is_selected=False,
symbol="arrow",
symbol_size=8,
itemstyle_opts=opts.ItemStyleOpts(
color="#f31e1e", ))
chart_b.set_global_opts(xaxis_opts=grid0_xaxis_opts,
legend_opts=legend_not_show_opts)
chart_k = chart_k.overlap(chart_b)
s_dts = [x['date'] for x in bs if x['bs'] == 'sell']
if len(s_dts) > 0:
s_val = [x['value'] for x in bs if x['bs'] == 'sell']
chart_s = Scatter()
chart_s.add_xaxis(s_dts)
chart_s.add_yaxis(series_name="SELL",
y_axis=s_val,
is_selected=False,
symbol="pin",
symbol_size=12,
itemstyle_opts=opts.ItemStyleOpts(
color="#45b97d", ))
chart_s.set_global_opts(xaxis_opts=grid0_xaxis_opts,
legend_opts=legend_not_show_opts)
chart_k = chart_k.overlap(chart_s)
# 成交量图
# ------------------------------------------------------------------------------------------------------------------
chart_vol = Bar()
chart_vol.add_xaxis(dts)
chart_vol.add_yaxis(series_name="Volume", y_axis=vol, bar_width='60%')
chart_vol.set_global_opts(
xaxis_opts=opts.AxisOpts(
type_="category",
grid_index=1,
axislabel_opts=opts.LabelOpts(is_show=True,
font_size=8,
color="#9b9da9"),
),
yaxis_opts=yaxis_opts,
legend_opts=legend_not_show_opts,
)
# MACD图
# ------------------------------------------------------------------------------------------------------------------
chart_macd = Bar()
chart_macd.add_xaxis(dts)
chart_macd.add_yaxis(series_name="MACD", y_axis=macd_bar, bar_width='60%')
chart_macd.set_global_opts(
xaxis_opts=opts.AxisOpts(
type_="category",
grid_index=2,
axislabel_opts=opts.LabelOpts(is_show=False),
),
yaxis_opts=opts.AxisOpts(
grid_index=2,
split_number=4,
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=True, color="#c7c7c7"),
),
legend_opts=opts.LegendOpts(is_show=False),
)
line = Line()
line.add_xaxis(dts)
line.add_yaxis(series_name="DIFF",
y_axis=diff,
label_opts=label_not_show_opts,
is_symbol_show=False,
linestyle_opts=opts.LineStyleOpts(opacity=0.8,
width=1.0,
color="#da6ee8"))
line.add_yaxis(series_name="DEA",
y_axis=dea,
label_opts=label_not_show_opts,
is_symbol_show=False,
linestyle_opts=opts.LineStyleOpts(opacity=0.8,
width=1.0,
color="#39afe6"))
chart_macd = chart_macd.overlap(line)
grid0_opts = opts.GridOpts(pos_left="0%",
pos_right="1%",
pos_top="12%",
height="58%")
grid1_opts = opts.GridOpts(pos_left="0%",
pos_right="1%",
pos_top="74%",
height="8%")
grid2_opts = opts.GridOpts(pos_left="0%",
pos_right="1%",
pos_top="86%",
height="10%")
grid_chart = Grid(init_opts)
grid_chart.add(chart_k, grid_opts=grid0_opts)
grid_chart.add(chart_vol, grid_opts=grid1_opts)
grid_chart.add(chart_macd, grid_opts=grid2_opts)
return grid_chart
def plot_kline(df, rendered=True, col=""):
"""
针对 dataframe 直接画出标准看盘软件的上k线图下成交量图的形式
:param df:
:param rendered:
:param col:
:return:
"""
kline = (
Kline()
.add_xaxis(xaxis_data=list(df["date"]))
.add_yaxis(
series_name="",
itemstyle_opts=opts.ItemStyleOpts(
color="#ef232a",
color0="#14b143",
border_color="#ef232a",
border_color0="#14b143",
),
y_axis=list(zip(df["open"], df["close"], df["high"], df["low"])),
markpoint_opts=opts.MarkPointOpts(
data=[
opts.MarkPointItem(type_="max", name="最大值"),
opts.MarkPointItem(type_="min", name="最小值"),
],
symbol="pin",
symbol_size=[56, 40],
# label_opts=opts.LabelOpts(color="#CCFFFF",position=["top", "bottom"])
),
)
.set_global_opts(
datazoom_opts=[
opts.DataZoomOpts(
is_show=True,
type_="slider",
range_start=50,
range_end=100,
xaxis_index=[0, 1],
),
opts.DataZoomOpts(
is_show=False,
type_="inside",
range_start=50,
range_end=100,
xaxis_index=1,
),
],
tooltip_opts=opts.TooltipOpts(
is_show=True,
trigger="axis",
trigger_on="mousemove",
axis_pointer_type="cross",
),
)
)
if col:
line = (
Line()
.add_xaxis(xaxis_data=list(df["date"]))
.add_yaxis(
series_name="",
y_axis=list(df[col]),
is_smooth=True,
linestyle_opts=opts.LineStyleOpts(opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
)
)
kline = kline.overlap(line)
if "volume" in df.columns:
vl = list(df["volume"])
elif "amount" in df.columns:
vl = list(df["amount"])
else:
vl = [0 for _ in range(len(df))]
bar = (
Bar()
.add_js_funcs("var barData = {}".format(list(df["close"] - df["open"])))
.add_xaxis(xaxis_data=list(df["date"]))
.add_yaxis(
series_name="",
yaxis_data=vl,
label_opts=opts.LabelOpts(is_show=False),
itemstyle_opts=opts.ItemStyleOpts(
color=JsCode(
"""
function(params) {
var colorList;
if (barData[params.dataIndex]>0) {
colorList = '#ef232a';
} else {
colorList = '#14b143';
}
return colorList;
}
"""
)
),
)
.set_global_opts(
tooltip_opts=opts.TooltipOpts(
is_show=True,
trigger="axis",
trigger_on="mousemove",
axis_pointer_type="cross",
),
)
)
grid_chart = Grid()
grid_chart.add_js_funcs("var barData = {}".format(list(df["close"] - df["open"])))
grid_chart.add(
kline,
grid_opts=opts.GridOpts(
pos_left="10%", pos_right="1%", pos_top="2%", height="65%"
),
)
grid_chart.add(
bar,
grid_opts=opts.GridOpts(
pos_left="10%", pos_right="1%", pos_top="71%", height="22%"
),
)
if rendered:
return grid_chart.render_notebook()
else:
return grid_chart
def bar_base(self) -> Bar:
# start = datetime.datetime.now()
#
# dispose_unit = []
# done = []
# undo = []
# count_dispose_unit = 30
#
# event_counts = Event.objects.all().values('dispose_unit').annotate(count=Count('dispose_unit')).values('dispose_unit', 'count').order_by('-count')
# print(event_counts)
# 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))
start = datetime.date(2016, 6, 6)
end = datetime.date(2019, 6, 6)
y_lists = []
x_list = []
b = Bar()
event_counts = DisposeUnit.objects.all().values(
'name', 'number').order_by('-number')
for achieve in achieves:
new_list = [achieve.name]
limit = 0
for dispose_unit_node in event_counts:
dispose_unit = DisposeUnit.objects.get(
name=dispose_unit_node['name'])
if limit < 30:
new_len = len(
Event.objects.filter(
Q(create_time__range=(start, end))
& Q(dispose_unit=dispose_unit)
& Q(achieve=achieve)))
new_list.append(new_len)
new_len = 0
else:
new_len += len(
Event.objects.filter(
Q(create_time__range=(start, end))
& Q(dispose_unit=dispose_unit)
& Q(achieve=achieve)))
limit += 1
new_list.append(new_len)
y_lists.append(new_list)
limit = 0
for dispose_unit_node in event_counts:
if limit >= 30:
break
x_list.append(dispose_unit_node['name'])
limit += 1
x_list.append("其他")
b.add_xaxis(x_list)
for y_list in y_lists:
b.add_yaxis(y_list[0],
y_list[1:],
stack="stack1",
category_gap="60%")
b.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")], )
c = (b)
grid = Grid()
grid.add(c, grid_opts=opts.GridOpts(pos_bottom="20%"))
c = grid.dump_options_with_quotes()
return c
