def image_host_apps(doc_path, host_apps, host):
if host_apps.get(host['id']) is None:
return
data_inner = []
data_outer = []
for i in host_apps.get(host['id']):
number = 0
for x in i.get('apps'):
number = number + x.get('number')
data_outer.append([x.get('name'), x.get('number')])
data_inner.append(["★%s" % (i.get('user')), number])
options = opts.InitOpts(js_host=os.path.join(os.getcwd(), "scripts/javascript/"),
animation_opts=opts.AnimationOpts(animation=False))
pipe = (
Pie(options)
.add('', data_inner, radius=["0%", "30%"],
label_opts=opts.LabelOpts(formatter='{b}'))
.add('', data_outer, radius=['55%', '80%'],
label_opts=opts.LabelOpts(formatter='{b}：{c} ({d}%)', font_size=13))
.set_global_opts(legend_opts=opts.LegendOpts(orient='vertical', pos_left=0),
title_opts=opts.TitleOpts(title="应用分布", pos_left='center'))
)
make_snapshot(snapshot, pipe.render(os.path.join(doc_path, "render_%s.html" % (host['ip'].replace(".", "_")))),
os.path.join(doc_path, "host_app_%s.png" % (host['ip'].replace(".", "_"))), pixel_ratio=1, delay=1,
is_remove_html=True)
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 bar_base_with_animation() -> Bar:
c = (Bar(init_opts=opts.InitOpts(animation_opts=opts.AnimationOpts(
animation_delay=1000, animation_easing="elasticOut"))).add_xaxis(
x).add_yaxis("北京", beijing).add_yaxis(
"新疆", xingjiang).set_global_opts(title_opts=opts.TitleOpts(
title="北京与新疆教育经费支出对比", subtitle="缺2012年及2018年")))
return c
def bars(name, dict_values):
# 链式调用
c = (
Bar(init_opts=opts.InitOpts( # 初始配置项
theme=ThemeType.MACARONS,
animation_opts=opts.AnimationOpts(
animation_delay=1000,
animation_easing="cubicOut" # 初始动画延迟和缓动效果
))).add_xaxis(xaxis_data=name) # x轴
.add_yaxis(series_name="股票型", yaxis_data=dict_values['股票型']) # y轴
.add_yaxis(series_name="混合型", yaxis_data=dict_values['混合型']) # y轴
.add_yaxis(series_name="债券型", yaxis_data=dict_values['债券型']) # y轴
.add_yaxis(series_name="指数型", yaxis_data=dict_values['指数型']) # y轴
.add_yaxis(series_name="QDII型", yaxis_data=dict_values['QDII型']) # y轴
.set_global_opts(
title_opts=opts.TitleOpts(
title='涨跌幅',
subtitle='李运辰绘制', # 标题配置和调整位置
title_textstyle_opts=opts.TextStyleOpts(
font_family='SimHei',
font_size=25,
font_weight='bold',
color='red',
),
pos_left="90%",
pos_top="10",
),
xaxis_opts=opts.AxisOpts(name='阶段',
axislabel_opts=opts.LabelOpts(rotate=45)),
# 设置x名称和Label rotate解决标签名字过长使用
yaxis_opts=opts.AxisOpts(name='涨跌点'),
).render("基金各个阶段涨跌幅.html"))
def draw_bar3D(cls, title: str, data: pd.DataFrame) -> Bar3D:
"""
根据df内容绘制3D柱状图
:param title: 标题
:param data: 包含三轴数据的dataframe index为x轴 column为Y轴 value为z轴
:return:
"""
data_list = []
index_list = data.index.tolist()
column_list = data.columns.tolist()
# 获取dataframe最大最小值
min_data = data.min().min()
max_data = data.max().max()
# 遍历dataframe，准备待操作数组
for i in range(len(index_list)):
for j in range(len(column_list)):
# 记录 XYZ
temp_list = [index_list[i], column_list[j], data.iloc[i, j]]
# print(i,j,index_list[i],column_list[j])
data_list.append(temp_list)
c = (
Bar3D(init_opts=opts.InitOpts(
width=DEFAULT_WIDTH,
animation_opts=opts.AnimationOpts(
animation_delay=200,
animation_easing="bounceOut"), # 增加启动动效
)).add(
series_name=title,
data=data_list,
xaxis3d_opts=opts.Axis3DOpts(type_="category",
data=index_list),
yaxis3d_opts=opts.Axis3DOpts(type_="category",
data=column_list),
zaxis3d_opts=opts.Axis3DOpts(type_="value"),
).set_series_opts(label_opts=opts.LabelOpts(is_show=True)).
set_global_opts(
title_opts=opts.TitleOpts(title=title, pos_left="0%"),
toolbox_opts=opts.ToolboxOpts(), # 显示工具箱
tooltip_opts=opts.TooltipOpts(is_show=True),
axispointer_opts=opts.AxisPointerOpts(
is_show=True, type_="none"), # 指针移动时显示所有数值
legend_opts=opts.LegendOpts(
is_show=True,
selected_mode="multiple",
# pos_bottom="0%",
# pos_right="0%",
# orient="vertical",
), # 显示图例说明
# datazoom_opts=[
# opts.DataZoomOpts(
# range_start=0, range_end=100, orient="vertical", pos_left="2%"
# ),
# opts.DataZoomOpts(range_start=0, range_end=100, orient="horizontal"),
# ], # 增加缩放配置横纵轴都支持缩放
visualmap_opts=opts.VisualMapOpts(max_=max_data, min_=min_data)
# visualmap_opts=opts.VisualMapOpts(type_="color", max_=1, min_=-1),
))
return c
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 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 bars(name, dict_values):
# 链式调用
c = (
Bar(init_opts=opts.InitOpts( # 初始配置项
theme=ThemeType.MACARONS,
animation_opts=opts.AnimationOpts(
animation_delay=1000,
animation_easing="cubicOut" # 初始动画延迟和缓动效果
))).add_xaxis(xaxis_data=name) # x轴
.add_yaxis(series_name="up主昵称", yaxis_data=dict_values) # y轴
.set_global_opts(
title_opts=opts.TitleOpts(
title='李运辰',
subtitle='up视频数', # 标题配置和调整位置
title_textstyle_opts=opts.TextStyleOpts(
font_family='SimHei',
font_size=25,
font_weight='bold',
color='red',
),
pos_left="90%",
pos_top="10",
),
xaxis_opts=opts.AxisOpts(name='up主昵称',
axislabel_opts=opts.LabelOpts(rotate=45)),
# 设置x名称和Label rotate解决标签名字过长使用
yaxis_opts=opts.AxisOpts(name='大学生学习视频视频数'),
).render("up主大学生学习视频视频数.html"))
def test_bar_base_with_animation(fake_writer):
c = (Bar(init_opts=opts.InitOpts(animation_opts=opts.AnimationOpts(
animation_delay=1000))).add_xaxis(["A", "B", "C"]).add_yaxis(
"series0", [1, 2, 4]).add_yaxis("series1", [2, 3, 6]))
c.render()
_, content = fake_writer.call_args[0]
assert_in("animationDelay", content)
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 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 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 bar_datazoom_inside() -> Timeline:
tl = Timeline()
for i in range(2014, 2020):
c = (
Bar(
init_opts=opts.InitOpts(
animation_opts=opts.AnimationOpts(
animation_delay=1000, animation_easing="elasticOut"
)
)
)
.add_xaxis(list(zip(list(data总.set_index('类别').index))))
.add_yaxis("显示",list(data总["{}".format(i)]))
.set_global_opts(
title_opts=opts.TitleOpts(title="纵横小说月票榜"),
datazoom_opts=opts.DataZoomOpts(type_="inside"),
visualmap_opts=opts.VisualMapOpts(type_="color", max_=250000, min_=200,pos_right='20',pos_top='middle'),
toolbox_opts=opts.ToolboxOpts(),
)
.set_series_opts(
label_opts=opts.LabelOpts(is_show=False),
markpoint_opts=opts.MarkPointOpts(
data=[
opts.MarkPointItem(type_="max", name="最大值"),
opts.MarkPointItem(type_="min", name="最小值"),
]
),
)
)
tl.add(c, "{}年".format(i))
return tl
def bar_base_with_animation() -> Bar:
c = (Bar(init_opts=opts.InitOpts(animation_opts=opts.AnimationOpts(
animation_delay=1000, animation_easing="elasticOut"))).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="Bar-动画配置基本示例", subtitle="我是副标题")))
return c
def bar_base_with_animation(x_values, one) -> Bar:
c = (Bar(init_opts=opts.InitOpts(animation_opts=opts.AnimationOpts(
animation_delay=1000, animation_easing="elasticOut"))).add_xaxis(
x_values).add_yaxis("2019", one).set_global_opts(
title_opts=opts.TitleOpts(title=""),
datazoom_opts=opts.DataZoomOpts(),
))
return c
def render_filter_legend_py():
with st.echo("below"):
c = (Bar(init_opts=opts.InitOpts(animation_opts=opts.AnimationOpts(
animation_delay=1000, animation_easing="elasticOut"))).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="Bar-动画配置基本示例", subtitle="我是副标题")))
st_pyecharts(c)
def gen_bar_plot(
chart_data: typing.Dict[str, typing.List[typing.List[typing.Union[float,
int]]]],
index: int,
column: str,
label: str,
) -> typing.Tuple[Bar, int]:
if column in chart_data.keys():
bar = Bar(init_opts=opts.InitOpts(animation_opts=opts.AnimationOpts(
animation=False), ))
xaxis_data = [label for i in range(len(chart_data[column]))]
bar.add_xaxis(xaxis_data=xaxis_data)
bar.add_yaxis(
series_name="Value",
y_axis=chart_data[column],
xaxis_index=index,
yaxis_index=index,
label_opts=opts.LabelOpts(is_show=False),
)
bar.set_global_opts(
xaxis_opts=opts.AxisOpts(
type_="category",
is_scale=True,
grid_index=1,
boundary_gap=False,
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=False),
split_number=20,
min_="dataMin",
max_="dataMax",
),
yaxis_opts=opts.AxisOpts(
name=label,
name_textstyle_opts=opts.TextStyleOpts(
color="red",
font_style="normal",
font_weight="normal",
font_family="Arial",
font_size=12,
),
grid_index=1,
is_scale=True,
split_number=2,
name_gap=0,
axislabel_opts=opts.LabelOpts(is_show=False),
axisline_opts=opts.AxisLineOpts(is_show=False),
axistick_opts=opts.AxisTickOpts(is_show=False),
splitline_opts=opts.SplitLineOpts(is_show=False),
),
legend_opts=opts.LegendOpts(is_show=False),
)
return bar, index + 1
else:
return None, index
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 grid_chart(self):
"""
组合图
"""
grid_chart = Grid(
init_opts=opts.InitOpts(
width="1000px",
height="800px",
animation_opts=opts.AnimationOpts(animation=False),
)
)
return grid_chart
def bar_with_animation():
bar = Bar(init_opts=opts.InitOpts(
theme='light',
width='1000px',
height='600px',
animation_opts=opts.AnimationOpts(
animation_delay=1000, # 动画延时
animation_easing='bounceIn')))
bar.add_xaxis(Faker.choose())
bar.add_yaxis('A', Faker.values())
bar.add_yaxis('B', Faker.values())
return bar
def bar_base_with_animation(title, x, y, y_label) -> Bar:
c = (
Bar(init_opts=opts.InitOpts(animation_opts=opts.AnimationOpts(
animation_delay=1000,
animation_easing="elasticOut"))).add_xaxis(x).add_yaxis(
y_label,
y).reversal_axis().set_series_opts(label_opts=opts.LabelOpts(
position="right")).set_global_opts(
title_opts=opts.TitleOpts(title=title),
# datazoom_opts=opts.DataZoomOpts(),
))
return c
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 bar_base_with_animation():
obj_bar = Bar(
init_opts=opts.InitOpts(
animation_opts=opts.AnimationOpts(
animation_delay=1000,
animation_easing="elasticOut"
)
)
)
obj_bar.add_xaxis(Faker.choose())
obj_bar.add_yaxis("A", Faker.values())
obj_bar.add_yaxis("B", Faker.values())
obj_bar.set_global_opts(title_opts = opts.TitleOpts(title = "Bar-动画配置基本示例", subtitle = "副标题"))
return obj_bar
def bar_border_radius():
c = (
Bar(init_opts=opts.InitOpts(
animation_opts=opts.AnimationOpts( # 柱状图显示效果动画控制代码
animation_delay=500,
animation_easing="cubicOut"),
theme=ThemeType.MACARONS, # 柱状图显示主题
page_title="Bar Learn", # 设置html页面标题
))
# .reversal_axis() # 翻转XY轴
.add_xaxis(["草莓", "芒果", "葡萄", "雪梨", "西瓜", "柠檬", "车厘子"]).add_yaxis(
"A",
Faker.values(),
category_gap="50%", # 柱间距对应的控制代码
markpoint_opts=opts.MarkPointOpts(),
is_selected=True # A系列柱子是否显示对应的控制代码
).set_global_opts(
title_opts=opts.TitleOpts( # 标题
title="Bar-参数使用例子", subtitle="副标题"),
toolbox_opts=opts.ToolboxOpts(), # toolbox 工具箱配置
yaxis_opts=opts.AxisOpts(position="right", name="Y轴"), # Y轴右侧控制
datazoom_opts=opts.DataZoomOpts(), # 数据区域放大缩小设置
).set_series_opts(
itemstyle_opts={ # A系列柱子颜色渐变对应的控制代码
"normal": {
"color":
JsCode("""
new echarts.graphic.LinearGradient(
0, 0, 0, 1, [{
offset: 0,
color: 'rgba(0, 244, 255, 1)'}, {
offset: 1,
color: 'rgba(0, 77, 167, 1)'}], false)"""),
"barBorderRadius": [6, 6, 6, 6],
"shadowColor":
'rgb(0, 160, 221)',
}
},
# A系列柱子最大和最小值标记点对应的控制代码
markpoint_opts=opts.MarkPointOpts(data=[
opts.MarkPointItem(type_="max", name="最大值"),
opts.MarkPointItem(type_="min", name="最小值"),
]),
# A系列柱子最大和最小值标记线对应的控制代码
markline_opts=opts.MarkLineOpts(data=[
opts.MarkLineItem(type_="min", name="最小值"),
opts.MarkLineItem(type_="max", name="最大值")
])))
return c
def country_base() -> Bar:
results0 = countries.find()
dis0 = []
for result in results0:
dis0.append(result['distance'])
c = (
Bar(init_opts=opts.InitOpts(
animation_opts=opts.AnimationOpts(
animation_delay=500,
animation_easing="elasticOut", # 延时动画效果
animation_duration_update=1000), )).add_xaxis(
["中国", "美国"]).add_yaxis("评分差距", dis0).set_global_opts(
title_opts=opts.TitleOpts(title="国家")))
return c
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 image_host_resource(doc_path, hosts_metrics, host):
options = opts.InitOpts(js_host=os.path.join(os.getcwd(), "scripts/javascript/"),
animation_opts=opts.AnimationOpts(animation=False))
line1 = (
Line(options)
.add_xaxis(hosts_metrics[host['ip']]['metrics']['datetime'])
.add_yaxis("CPU", hosts_metrics[host['ip']]['metrics']['cpu'], is_symbol_show=False, is_smooth=True,
areastyle_opts=opts.AreaStyleOpts(color=utils.JsCode(
"new echarts.graphic.LinearGradient(0,0,0,1,[{offset:0,color:'rgb(255,158,68)'},{offset:1,color:'rgb(255,70,131)'}])"),
opacity=0.5))
.add_yaxis("Memory", hosts_metrics[host['ip']]['metrics']['memory'], is_symbol_show=False, is_smooth=True)
.set_series_opts(linestyle_opts=opts.LineStyleOpts(width=2))
.set_global_opts(
title_opts=opts.TitleOpts(title="主机资源使用率 (%s)" % (host['ip']),
title_textstyle_opts=opts.TextStyleOpts(font_size=13),
subtitle=" 数据来源-健康度平台"),
xaxis_opts=opts.AxisOpts(type_="time"),
yaxis_opts=opts.AxisOpts(
axislabel_opts=opts.LabelOpts(formatter=utils.JsCode("function(val){return val + '%';}")))
)
)
line2 = (
Line(options)
.add_xaxis(hosts_metrics[host['ip']]['metrics']['datetime'])
.add_yaxis("Process", hosts_metrics[host['ip']]['metrics']['process'], is_symbol_show=False, is_smooth=True,
areastyle_opts=opts.AreaStyleOpts(color=utils.JsCode(
"new echarts.graphic.LinearGradient(0,0,0,1,[{offset:0,color:'#fff'},{offset:1,color:'#61a0a8'}])"),
opacity=0.5)
)
.set_series_opts(linestyle_opts=opts.LineStyleOpts(width=2))
.set_global_opts(
xaxis_opts=opts.AxisOpts(type_="time", position="top", axislabel_opts=opts.LabelOpts(is_show=False)),
yaxis_opts=opts.AxisOpts(is_inverse=True),
title_opts=opts.TitleOpts(title="应用运行数量(所有)", pos_bottom="0%",
title_textstyle_opts=opts.TextStyleOpts(font_size=13)),
legend_opts=opts.LegendOpts(pos_bottom="0%")
)
)
grid = (
Grid(options)
.add(line1, grid_opts=opts.GridOpts(pos_top=50, pos_left=50, pos_right=50, pos_bottom="50%"))
.add(line2, grid_opts=opts.GridOpts(pos_top="58%", pos_left=50, pos_right=50, pos_bottom=30))
)
make_snapshot(snapshot, grid.render(os.path.join(doc_path, "render_%s.html" % (host['ip'].replace(".", "_")))),
os.path.join(doc_path, "host_res_%s.png" % (host['ip'].replace(".", "_"))), pixel_ratio=1, delay=1,
is_remove_html=True)
def type_base() -> Bar:
results1 = types.find()
dis1 = []
for result in results1:
dis1.append(result['distance'])
c = (
Bar(init_opts=opts.InitOpts(
animation_opts=opts.AnimationOpts(
animation_delay=500,
animation_easing="elasticOut", # 延时动画效果
animation_duration_update=1000), )).add_xaxis(
["喜剧", "动作", "家庭", "战争",
"惊悚"]).add_yaxis("评分差距", dis1).set_global_opts(
title_opts=opts.TitleOpts(title="类型")))
return c
def draw_network(self, layout="force", render=False):
"""将网络可视化输出"""
if self.scale > 10000: # 对于太大的网络，可视化意义不大且效率极低，故只对小型网络进行可视化
raise ValueError("网络规模太大("+str(self.scale)+")，不支持可视化！请抽取更小的子网络进行可视化")
g = self.network
if self._community is None:
self._community = community.best_partition(g)
if layout.lower() != 'force' and layout.lower() != 'circular':
raise ValueError("没有这种布局！布局请选择force或circular")
# 获取节点名称映射
names = nx.get_node_attributes(g, 'name')
nodes = [{'name': names[n], 'symbolSize': (math.log2(nx.degree(g, n, weight=self.weight_type)+1))*6,
'category': self._community[n], 'value':nx.degree(g, n, weight=self.weight_type)}
for n in g.nodes()]
links = [{'source': names[e[0]], 'target': names[e[1]]} for e in g.edges(data=True)]
categories = [{'category': self._community[n], 'name': self._community[n]} for n in g.nodes()]
graph = (
Graph(
init_opts=opts.InitOpts(
width="1200px", height="1000px",
animation_opts=opts.AnimationOpts(
animation=False,
animation_threshold=10,
animation_duration=1,
),
)
)
.add(
"",
nodes=nodes,
links=links,
categories=categories,
repulsion=50,
linestyle_opts=opts.LineStyleOpts(curve=0.2),
label_opts=opts.LabelOpts(is_show=False),
)
.set_global_opts(
legend_opts=opts.LegendOpts(is_show=True, orient="vertical", pos_left="2%", pos_top="10%"),
)
)
if render:
graph.render(self.net_type+'.html')
return graph
def gen_line_plot(
stock_data: pd.DataFrame,
chart_data: typing.Dict[str, typing.List[typing.List[typing.Union[float,
int]]]],
) -> Line:
ma_items = [5, 10, 20, 60]
line = Line(init_opts=opts.InitOpts(animation_opts=opts.AnimationOpts(
animation=False), )).add_xaxis(xaxis_data=chart_data["categoryData"])
for ma in ma_items:
line.add_yaxis(
series_name=f"MA{ma}",
y_axis=ta.trend.sma_indicator(stock_data["close"], ma).round(2),
is_smooth=True,
is_symbol_show=False,
is_hover_animation=False,
linestyle_opts=opts.LineStyleOpts(width=3, opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
)
line.set_global_opts(xaxis_opts=opts.AxisOpts(type_="category"))
return line
