def test_effectscatter_item_base(fake_writer):
x_axis = Faker.choose()
chart_item = [
opts.EffectScatterItem(name=d[0], value=d[1])
for d in list(zip(x_axis, Faker.values()))
]
c = EffectScatter().add_xaxis(x_axis).add_yaxis("", chart_item)
c.render()
_, content = fake_writer.call_args[0]
assert_equal(c.theme, "white")
assert_equal(c.renderer, "canvas")
def drawEffectScatter(arr, value, name):
c1 = EffectScatter()
c1.add_xaxis(xaxis_data=arr)
c1.add_yaxis(
series_name=name,
y_axis=value,
# symbol='image://http://weizhendong.top/images/1.png',
symbol=SymbolType.ARROW,
# label_opts=opts.LabelOpts(is_show=False)
)
c1.set_global_opts(
title_opts=opts.TitleOpts(title=name),
datazoom_opts=opts.DataZoomOpts(),
)
c1.render("{0}.html".format(name))
def effectscatter_symbol() -> EffectScatter:
c = (
EffectScatter().add_xaxis(x[::1]) #设置横坐标数据，设为年份
.add_yaxis(
"四川特殊教育招生数(万人)",
[1.03, 1.04, 0.96, 0.81, 0.80, 0.82, 0.84, 0.68, 0.67, 0.65],
color="#1E90FF",
symbol_size=16,
symbol="arrow").add_yaxis(
"四川特殊教育专任教师数(万人)",
[0.30, 0.28, 0.25, 0.24, 0.22, 0.21, 0.19, 0.18, 0.17, 0.16],
color="#AB82FF",
symbol_size=16,
symbol="arrow").add_yaxis("四川特殊教育在校学生数(万人)", [
5.69, 5.35, 4.78, 4.33, 4.23, 4.37, 4.43, 4.09, 4.18, 4.18
],
color="#EE2222",
symbol_size=16,
symbol="arrow") # 设置纵坐标数据
.set_global_opts(
title_opts=opts.TitleOpts(
title='图1-6',
subtitle="四川特殊教育基本情况", # 设置标题与副标题样式，进行图例说明
subtitle_textstyle_opts=opts.TextStyleOpts(color="black",
font_size=15)),
xaxis_opts=opts.AxisOpts(splitline_opts=opts.SplitLineOpts(
is_show=True)),
yaxis_opts=opts.AxisOpts(splitline_opts=opts.SplitLineOpts(
is_show=True)),
))
return c #返回数据
def effectscatter_symbol():
c = (EffectScatter().add_xaxis(Faker.choose()).add_yaxis(
"", Faker.values(), symbol=SymbolType.ARROW).set_global_opts(
toolbox_opts=opts.ToolboxOpts(is_show=True),
title_opts=opts.TitleOpts(title="EffectScatter-不同Symbol"),
))
return c
def effectScatter1():
effectScatter = (
EffectScatter() #实例化Line
.add_xaxis(x2) #加入X轴数据
.add_yaxis("歌曲时长(m.s)", y2) #加入Y轴数据
.set_global_opts(title_opts=opts.TitleOpts(title="Line-歌曲时长")))
return effectScatter
def pyechart_EffectScatter_plot(self, filedata, para):
from pyecharts.charts import EffectScatter
file_name = '散点涟漪图.html'
path_plotly = self.path_dir_plotly_html + os.sep + file_name # 文件路径，前面是文件夹后面是文件名
costumeTheme = self.themedict[para['theme']]
# -----------------------------------------------------------------------
EffectScatter = (
EffectScatter(init_opts=opts.InitOpts(
theme=costumeTheme, width=para['width'],
height=para['height'])) # ThemeType.LIGHT para['theme']
)
chart = EffectScatter
df = pd.read_excel(filedata, sheet_name='Sheet1')
columns = df.columns.values.tolist()
xlabel = columns[0]
xdata = df[xlabel].values.tolist()
chart.add_xaxis(xaxis_data=xdata)
for i in range(1, len(columns)):
ylabel = columns[i]
ydata = df[ylabel].values.tolist()
chart.add_yaxis(series_name=ylabel, y_axis=ydata)
if para['x2y']:
chart.reversal_axis()
self.Common_code(chart, path_plotly, para)
print('成功绘制散点涟漪图')
return path_plotly # 返回该HTML文件路径
def read():
df = pd.read_csv('data_clean.csv')
types = get_type()
count = {}
for type in types:
type = type.lower()
for idx in df.index:
item = df.loc[idx].values[1:]
if item[1].lower().find(type) != -1:
if type not in count:
count[type] = 1
else:
count[type] += 1
count = list(count.items())
count.sort(key=lambda x: x[-1], reverse=True)
from pyecharts.charts import EffectScatter
EffectScatter().add_xaxis([item[0] for item in count]).add_yaxis("", [item[1] for item in count]).render(
"effectscatter_base.html")
count = {}
for idx in df.index:
item = df.loc[idx].values[1:]
xl = item[3]
if xl not in count:
count[xl] = 1
else:
count[xl] += 1
Line().add_xaxis(list(count.keys())).add_yaxis('', list(count.values())).render('学历.html')
def effectscatter_symbol() -> EffectScatter:
c = (
EffectScatter()
.add_xaxis(Faker.choose())
.add_yaxis("", Faker.values(), symbol=SymbolType.ARROW)
).set_global_opts(title_opts=opts.TitleOpts(title="EffectScatter-不同Symbol"))
return c
def effectscatter_base() -> EffectScatter:
c = (
EffectScatter()
.add_xaxis(Faker.choose())
.add_yaxis("", Faker.values())
.set_global_opts(title_opts=opts.TitleOpts(title="EffectScatter-基本示例"))
)
return c
def effectscatter_base() -> EffectScatter:
d = (EffectScatter().add_xaxis([
'2001', '2002', '2003', '2004', '2005', '2006', '2007', '2008', '2009',
'2010', '2011', '2012', '2013', '2014', '2015', '2016', '2017'
]).add_yaxis("China", China).add_yaxis("Angola", Angola).add_yaxis(
"Ethiopia", Ethiopia).add_yaxis("Zambia", Zambia).set_global_opts(
title_opts=opts.TitleOpts(title="四国人均GDP")))
return render_template("showchart3.html", bar_data3=d.dump_options())
def effectscatter_splitline() -> EffectScatter:
c = (EffectScatter().add_xaxis(Faker.choose()).add_yaxis(
"", Faker.values()).set_global_opts(
title_opts=opts.TitleOpts(title="EffectScatter-显示分割线"),
xaxis_opts=opts.AxisOpts(splitline_opts=opts.SplitLineOpts(
is_show=True)),
yaxis_opts=opts.AxisOpts(splitline_opts=opts.SplitLineOpts(
is_show=True)),
))
return c
def effectscatter_splitline() -> EffectScatter:
c = (EffectScatter().add_xaxis(x3).add_yaxis("泰国汇率", 泰国).set_global_opts(
title_opts=opts.TitleOpts(title="1987-2001泰国汇率变化"),
toolbox_opts=opts.ToolboxOpts(),
datazoom_opts=opts.DataZoomOpts(),
xaxis_opts=opts.AxisOpts(splitline_opts=opts.SplitLineOpts(
is_show=True)),
yaxis_opts=opts.AxisOpts(splitline_opts=opts.SplitLineOpts(
is_show=True)),
))
return c
def world_per1():
data_str = df4.to_html()
d = (EffectScatter().add_xaxis([
'2001', '2002', '2003', '2004', '2005', '2006', '2007', '2008', '2009',
'2010', '2011', '2012', '2013', '2014', '2015', '2016', '2017'
]).add_yaxis("China", China).add_yaxis("Angola", Angola).add_yaxis(
"Ethiopia", Ethiopia).add_yaxis("Zambia", Zambia).set_global_opts(
title_opts=opts.TitleOpts(title="四国人均GDP")))
return render_template('results4.html',
bar_data3=d.dump_options(),
the_select_region2=world_per,
the_res=data_str)
def get_picture2():
df = pd.read_csv(UPLOAD_FOLDER + '地区生产总值.csv', encoding='gbk')
地区 = list(df.地区)
地区生产总值 = list(df.地区生产总值)
c = (
EffectScatter()
.add_xaxis(地区)
.add_yaxis("地区生产总值", 地区生产总值)
.set_global_opts(title_opts=opts.TitleOpts(title="EffectScatter-基本示例"))
)
fig = c.render('./temp/example.html');
return fig
def effectscatter_symbol() -> EffectScatter:
df = pd.read_csv('data4.csv', encoding='utf8', index_col="名称")
省 = list(df.loc["省"].values)[-24:]
平均月薪 = list(df.loc["平均月薪"].values)[-24:]
c = (EffectScatter().add_xaxis(省).add_yaxis(
"平均月薪", 平均月薪, symbol=SymbolType.ARROW)).set_global_opts(
title_opts=opts.TitleOpts(title=""))
c.render("./templates/symbol.html")
with open("./templates/symbol.html", encoding="utf8", mode="r") as f:
sym = "".join(f.readlines())
return render_template(
'python_effectscatter_symbol.html',
the_sym=sym,
)
def s_4():
with open(r'pro.csv', 'r') as f:
reader = csv.reader(f)
result = list(reader)
pro_list = [i[0] for i in result]
total_list = [float(i[1]) for i in result]
p = (EffectScatter(init_opts=opts.InitOpts(
width='1200px', height='800px')).add_xaxis(pro_list).add_yaxis(
"交易额", total_list,
label_opts=opts.LabelOpts(is_show=False)).set_global_opts(
title_opts=opts.TitleOpts(title="本年度交易额省份排名分析"),
xaxis_opts=opts.AxisOpts(splitline_opts=opts.SplitLineOpts(
is_show=True)),
yaxis_opts=opts.AxisOpts(splitline_opts=opts.SplitLineOpts(
is_show=True)),
))
return p
def index_bar():
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'))
r = (
EffectScatter().add_xaxis(ted['views'].values.tolist()).add_yaxis(
'商家A', ted['comments'].values.tolist()).set_global_opts(
title_opts=opts.TitleOpts(title="EffectScatter-基本示例"),
xaxis_opts=opts.AxisOpts(
type_="value", # x轴数据类型是连续型的
min_=0 # x轴范围最小为20
)))
return render_template('index.html',
myechart=r.render_embed(),
select=list(res.keys()))
def overlap_net_pnl(self):
# 在K线图绘制累计净盈亏曲线(注：kline的datazoom_opts为单图形模式)
if self.grid == True:
print("grid应为False！")
return
bar_data = pd.merge(self.bar_data,
self.trade_data.net_pnl.cumsum(),
how="outer",
left_index=True,
right_index=True)
bar_data["net_pnl"].fillna(method="ffill", inplace=True)
bar_data["net_pnl"].fillna(value=0, inplace=True)
self.kline_chart.extend_axis(yaxis=opts.AxisOpts(position="right"))
net_pnl_line = (Line().add_xaxis(
xaxis_data=self.bar_data_datetime).add_yaxis(
series_name="Net_pnl",
y_axis=bar_data.net_pnl.apply(
lambda x: round(x, 2)).values.tolist(),
yaxis_index=1,
label_opts=opts.LabelOpts(is_show=False),
is_symbol_show=False,
linestyle_opts=opts.LineStyleOpts(width=3,
opacity=0.5,
type_="solid",
color="#8A0000"),
))
signal_scatter = (EffectScatter().add_xaxis(xaxis_data=list(
self.trade_data.index.strftime("%Y-%m-%d %H:%M"))).add_yaxis(
series_name="Signal",
y_axis=self.trade_data.signal.values.tolist(),
yaxis_index=1,
symbol_size=5,
label_opts=opts.LabelOpts(is_show=True, font_size=9),
itemstyle_opts=opts.ItemStyleOpts(color="#0000FF",
opacity=0.9),
).set_global_opts(legend_opts=opts.LegendOpts(is_show=False)))
self.kline_chart.overlap(signal_scatter)
self.kline_chart.overlap(net_pnl_line)
# .set_global_opts(
# title_opts=opts.TitleOpts(title="标题"),
# xaxis_opts=opts.AxisOpts(splitline_opts=opts.SplitLineOpts(is_show=True)),
# yaxis_opts=opts.AxisOpts(splitline_opts=opts.SplitLineOpts(is_show=True)),
# )
# .render("分割线.html")
# )
# c = (
# Scatter()
# .add_xaxis(Faker.choose())
# .add_yaxis("1", Faker.values())
# .add_yaxis("2", Faker.values())
# .set_global_opts(
# title_opts=opts.TitleOpts(title="标题"),
# visualmap_opts=opts.VisualMapOpts(type_="size", max_=150, min_=20),
# )
# .render("凸出大小散点图.html")
# )
# c = (
# EffectScatter()
# .add_xaxis(Faker.choose())
# .add_yaxis("", Faker.values())
# .set_global_opts(title_opts=opts.TitleOpts(title="散点图"))
# .render("动态散点图.html")
# )
c = (EffectScatter().add_xaxis(Faker.choose()).add_yaxis(
"", Faker.values(), symbol=SymbolType.ARROW).set_global_opts(
title_opts=opts.TitleOpts(title="标题")).render("箭头动态散点图.html"))
def test_effectscatter_base():
c = EffectScatter().add_xaxis(Faker.choose()).add_yaxis("", Faker.values())
eq_(c.theme, "white")
eq_(c.renderer, "canvas")
c.render()
def generate_kchart(self,MA=[5,10,21],renderfile=False) -> Grid:
if self.exists == False:
return None
# reassign cursor tick to trigger populate attributes like self.histBar and etc.
self.cursorTick = self._cursorTick
df_toPlot = self.allBars.loc[self.allBars['unit_intdate'] <= self.cursorTick['unit_intdate']]
# 输入时间序列中带有未来日期，则取当下能取到的最新online数据模拟
# if len(self.allBars.loc[self.allBars['unit_intdate'] == self.cursorTick['unit_intdate']]) == 0:
# df_liveQuote = self.get_livebar(assign_timestamp=self.cursorTick['timeAxis'])
# if len(df_liveQuote) > 0:
# df_toPlot = df_toPlot.append(df_liveQuote, ignore_index = True, sort=False)
str_secname = self.sec_name
str_plotname = '{} - {}'.format(self.ts_code,str_secname)
'''-----------------画图部分---------------------'''
lst_ohlcv=df_toPlot.loc[:,['open','close','low','high']].values.tolist()
lst_vol=list(df_toPlot['vol'].values)
lst_amount=list(df_toPlot['amount'].values)
# 需从处理过的histbar中获得，由于长度比allbar短1，需将值list添加一个空值补齐
lst_peaks=list(abs(self.histBars['peaks'].values))
# lst_pivotdown=[*(float('nan') if i<0 else i for i in list(self.histBars['pivots'].values)),float('nan')]
lst_pivotdown=[*(float('nan') if i<0 else i for i in list(self.histBars['valid_pivots'].values)),float('nan')]
# lst_pivotup=[*(float('nan') if i>0 else abs(i) for i in list(self.histBars['pivots'].values)),float('nan')]
lst_pivotup=[*(float('nan') if i>0 else abs(i) for i in list(self.histBars['valid_pivots'].values)),float('nan')]
lst_xaxis=list(df_toPlot['unit_intdate'].astype(str)) # xaxis values has to be STRING type, int will not work
def calculate_ma(day_count: int, d):
result: List[Union[float, str]] = []
for i in range(len(d)):
if i < day_count:
result.append("-")
continue
sum_total = 0.0
for j in range(day_count):
sum_total += float(d[i - j][1])
result.append(abs(float("%.3f" % (sum_total / day_count))))
return result
kline = (Kline()
.add_xaxis(lst_xaxis)
.add_yaxis(series_name=str_secname, y_axis=lst_ohlcv,
markpoint_opts=opts.MarkPointOpts(
data=[opts.MarkPointItem(type_="min",value_dim="close"),
opts.MarkPointItem(type_="max",value_dim="close")],
symbol_size = [20, 20], #表示标记宽为 20，高为 10
),
itemstyle_opts=opts.ItemStyleOpts(
color="#ec0000",
color0="#00da3c",
border_color="#ec0000",
border_color0="#00da3c",
),)
.set_global_opts(
title_opts=opts.TitleOpts(
title=str_plotname,
subtitle='MA='+str(MA),
),
xaxis_opts=opts.AxisOpts(type_="category"),
yaxis_opts=opts.AxisOpts(
is_scale=True,
splitarea_opts=opts.SplitAreaOpts(
is_show=True, areastyle_opts=opts.AreaStyleOpts(opacity=1)
),
),
legend_opts=opts.LegendOpts(is_show=True, pos_top='10%', pos_left="center"),
datazoom_opts=[
opts.DataZoomOpts(
is_show=False,
type_="inside",
xaxis_index=[0,1,2],
range_start=75,
range_end=100,
),
opts.DataZoomOpts(
is_show=True,
xaxis_index=[0,1,2],
type_="slider",
pos_top="90%",
range_start=75,
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",font_size=10),
),
# 阴量绿阳量红
visualmap_opts=opts.VisualMapOpts(
is_show=False,
dimension=2,
series_index=list(map(lambda x: x+len(MA),[4,5])), #动态算出vol和amt柱状图的series index
is_piecewise=True,
pieces=[
{"value": 1, "color": "#ec0000"},
{"value": -1, "color": "#00da3c"},
],
),
axispointer_opts=opts.AxisPointerOpts(
is_show=True,
link=[{"xAxisIndex": "all"}],
label=opts.LabelOpts(background_color="#777"),
),
brush_opts=opts.BrushOpts(
x_axis_index="all",
brush_link="all",
out_of_brush={"colorAlpha": 0.1},
brush_type="lineX",
),
)
)
trendline = (
Line()
.add_xaxis(lst_xaxis)
.add_yaxis('高低点', lst_peaks,
itemstyle_opts=opts.ItemStyleOpts(color="green"))
)
for i in MA:
if i is not None:
trendline.add_yaxis(
series_name='MA'+str(i),
y_axis=calculate_ma(day_count=i, d=lst_ohlcv),
is_smooth=True,
is_hover_animation=False,
linestyle_opts=opts.LineStyleOpts(width=1, opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
)
trendline.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
trendline.set_global_opts(legend_opts=opts.LegendOpts(is_show=False))
keyPoints = (
EffectScatter()
.add_xaxis(lst_xaxis)
.add_yaxis("末跌高", lst_pivotdown,symbol=SymbolType.ARROW,symbol_rotate=180,symbol_size=5,itemstyle_opts=opts.ItemStyleOpts(color="purple"))
.add_yaxis("末升低", lst_pivotup,symbol=SymbolType.ARROW,symbol_size=5,itemstyle_opts=opts.ItemStyleOpts(color="blue"))
.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
)
vol_bar = (
Bar()
.add_xaxis(lst_xaxis)
.add_yaxis(
series_name="交易量",
y_axis=[
[i, lst_vol[i], 1 if lst_ohlcv[i][0] < lst_ohlcv[i][1] else -1]
for i in range(len(lst_vol))
],
xaxis_index=1,
yaxis_index=1,
label_opts=opts.LabelOpts(is_show=False),
)
.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_="最低",
max_="最高",
),
yaxis_opts=opts.AxisOpts(
grid_index=1,
is_scale=True,
split_number=2,
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),
)
# .add_yaxis("交易量", lst_vol,itemstyle_opts=opts.ItemStyleOpts(color="#456A76"))
)
amnt_bar = (
Bar()
.add_xaxis(lst_xaxis)
.add_yaxis(
series_name="交易额",
y_axis=[
[i, lst_amount[i], 1 if lst_ohlcv[i][0] < lst_ohlcv[i][1] else -1]
for i in range(len(lst_amount))
],
xaxis_index=2,
yaxis_index=2,
label_opts=opts.LabelOpts(is_show=False),
)
.set_global_opts(
xaxis_opts=opts.AxisOpts(
type_="category",
is_scale=True,
grid_index=2,
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_="最低",
max_="最高",
),
yaxis_opts=opts.AxisOpts(
grid_index=2,
is_scale=True,
split_number=2,
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),
)
# .add_yaxis("交易额", lst_amount,itemstyle_opts=opts.ItemStyleOpts(color="#456A76"))
)
priceChart = kline.overlap(trendline).overlap(keyPoints)
gridChart = Grid()
gridChart.add(
priceChart,
grid_opts=opts.GridOpts(pos_left="10%", pos_right="8%", pos_bottom='40%'),
)
gridChart.add(
vol_bar,
grid_opts=opts.GridOpts(pos_left="10%", pos_right="8%", pos_top="60%", height='15%'),
)
gridChart.add(
amnt_bar,
grid_opts=opts.GridOpts(pos_left="10%", pos_right="8%", pos_top="75%"),
)
fname = '{}{}.html'.format(cfg.PATH_ANAFILE,'kline')
gridChart.render(fname) if renderfile else None
self.kchart = gridChart # 将结果输出到analytics对象属性中用于function以外的调用
return self
def K(self) -> Kline:
data = self.data[self.prices_cols].values.tolist()
c = (Kline().add_xaxis(self.dateindex).add_yaxis(
"k线", data).set_global_opts(
title_opts=opts.TitleOpts(title=self.title, pos_left="0"),
legend_opts=opts.LegendOpts(is_show=False,
pos_bottom=10,
pos_left="center"),
datazoom_opts=[
opts.DataZoomOpts(
is_show=True,
xaxis_index=[0, 1],
type_="slider",
pos_top="85%",
range_start=0,
range_end=100,
),
],
xaxis_opts=opts.AxisOpts(
axislabel_opts=opts.LabelOpts(is_show=False),
axistick_opts=opts.AxisTickOpts(is_show=False),
),
yaxis_opts=opts.AxisOpts(
is_scale=True,
splitarea_opts=opts.SplitAreaOpts(
is_show=True,
areastyle_opts=opts.AreaStyleOpts(opacity=1)),
),
tooltip_opts=opts.TooltipOpts(
trigger="axis",
axis_pointer_type="cross",
background_color="rgba(245, 245, 245, 0.8)",
border_width=2,
border_color="#ccc",
textstyle_opts=opts.TextStyleOpts(color="#000"),
),
visualmap_opts=opts.VisualMapOpts(
is_show=False,
dimension=2,
series_index=5,
is_piecewise=True,
pieces=[
{
"value": 1,
"color": "#00da3c"
},
{
"value": -1,
"color": "#ec0000"
},
],
),
axispointer_opts=opts.AxisPointerOpts(
is_show=True,
link=[{
"xAxisIndex": "all"
}],
label=opts.LabelOpts(background_color="#777"),
),
brush_opts=opts.BrushOpts(
x_axis_index="all",
brush_link="all",
out_of_brush={"colorAlpha": 0.1},
brush_type="lineX",
),
))
if len(self.klines) != 0:
kLine = Line().add_xaxis(self.dateindex)
for i in self.klines:
kLine.add_yaxis(
i,
round(self.data[i], self.precision).values.tolist(),
is_smooth=True,
is_symbol_show=False,
is_hover_animation=False,
label_opts=opts.LabelOpts(is_show=True),
linestyle_opts=opts.LineStyleOpts(type_='solid', width=2),
)
kLine.set_global_opts(
xaxis_opts=opts.AxisOpts(type_="category", is_show=False))
c.overlap(kLine)
if 'BUY' in self.data.columns:
v1 = self.data[self.data['BUY'] == True].index.strftime(
"%Y-%m-%d").tolist()
v2 = self.data[self.data['BUY'] == True]['close']
es_buy = (EffectScatter().add_xaxis(v1).add_yaxis(
"", v2, symbol=SymbolType.ARROW, is_selected=True))
c.overlap(es_buy)
if 'SELL' in self.data.columns:
v1 = self.data[self.data['SELL'] == True].index.strftime(
"%Y-%m-%d").tolist()
v2 = self.data[self.data['SELL'] == True]['low']
es_sell = (EffectScatter().add_xaxis(v1).add_yaxis("", v2))
c.overlap(es_sell)
return c
from pywebio.output import put_html
from pyecharts import options as opts
from pyecharts.charts import EffectScatter
from pyecharts.faker import Faker
c = (
EffectScatter()
.add_xaxis(Faker.choose())
.add_yaxis("", Faker.values())
.set_global_opts(title_opts=opts.TitleOpts(title="EffectScatter-基本示例"))
)
c.width = "100%"
put_html(c.render_notebook())
def test_effectscatter_base(fake_writer):
c = EffectScatter().add_xaxis(Faker.choose()).add_yaxis("", Faker.values())
c.render()
_, content = fake_writer.call_args[0]
eq_(c.theme, "white")
eq_(c.renderer, "canvas")
line.render_notebook()
line = (
Line()
.add_xaxis(x)
.add_yaxis('china', data_china, is_step=True)
.set_global_opts(title_opts=opts.TitleOpts(title="Line - 阶梯折线图"))
)
line.render_notebook()
# 散点图
scatter = (
EffectScatter()
.add_xaxis(x)
.add_yaxis("", data_china)
.set_global_opts(title_opts=opts.TitleOpts(title="EffectScatter - 基本示例"))
)
scatter.render_notebook()
scatter = (
EffectScatter()
.add_xaxis(x)
.add_yaxis("china", data_china, symbol=SymbolType.ARROW)
.set_global_opts(
title_opts=opts.TitleOpts(title="EffectScatter - 显示分割线"),
xaxis_opts=opts.AxisOpts(splitline_opts=opts.SplitLineOpts(is_show=True)),
yaxis_opts=opts.AxisOpts(splitline_opts=opts.SplitLineOpts(is_show=True)),
)
)
v2 = [10, 25, 8, 60, 20, 80]
scatter = (Scatter().add_xaxis(Faker.choose()).add_yaxis(
"商家A", Faker.values()).add_yaxis("aa", v1).set_global_opts(
title_opts=opts.TitleOpts(title="散点图", pos_top="50%"),
legend_opts=opts.LegendOpts(pos_top="50%", pos_left="20%")))
scatter.render("scatter.html")
v3 = [11, 11, 15, 13, 12, 13, 10]
v4 = [1, -2, 2, 5, 3, 2, 0]
es = (EffectScatter().add_xaxis(
Faker.choose()).add_yaxis("不同Symbol",
Faker.values(),
symbol=SymbolType.ARROW).set_global_opts(
title_opts=opts.TitleOpts(title="动态散点图",
pos_top="50%",
pos_left="50%"),
legend_opts=opts.LegendOpts(pos_top="50%",
pos_left="70%")))
es.render("es.html")
grid = (Grid(init_opts=opts.InitOpts(width="1200px", height="720px")).add(
bar, grid_opts=opts.GridOpts(pos_right="60%", pos_bottom="60%")).add(
line, grid_opts=opts.GridOpts(pos_left="60%", pos_bottom="60%")).add(
scatter,
grid_opts=opts.GridOpts(pos_right="60%", pos_top="60%")).add(
es, grid_opts=opts.GridOpts(pos_left="60%", pos_top="60%")))
grid.render("duotu.html")
def effectscatter_base():
c = (EffectScatter().add_xaxis(Faker.choose()).add_yaxis(
"", Faker.values()).set_global_opts(
toolbox_opts=opts.ToolboxOpts(is_show=True),
title_opts=opts.TitleOpts(title="EffectScatter-基本示例")))
return c
def effect_scatter_chart():
# 涟漪散点图
effect_scatter = EffectScatter()
x_y_axis = get_chart_data()
effect_scatter.add_xaxis(x_y_axis[0])
for j in x_y_axis[1]:
for key, value in j.items():
effect_scatter.add_yaxis(key,
value,
label_opts=opts.LabelOpts(is_show=False))
effect_scatter.set_series_opts(markpoint_opts=opts.MarkPointOpts(data=[
opts.MarkPointItem(type_="max", name="最大值"),
opts.MarkPointItem(type_="min", name="最小值"),
]), )
effect_scatter.set_global_opts(
title_opts=opts.TitleOpts(
title="API耗时统计",
subtitle="生产环境",
pos_left="30%",
title_textstyle_opts=opts.TextStyleOpts(color='red'),
subtitle_textstyle_opts=opts.TextStyleOpts(color='blue')),
xaxis_opts=opts.AxisOpts(
name="运行时间",
type_="category",
boundary_gap=True,
# axislabel_opts=opts.LabelOpts(rotate=15)
),
yaxis_opts=opts.AxisOpts(name="实际的响应时间（单位：秒）",
# min_=0,
# max_=20
),
legend_opts=opts.LegendOpts(type_='scroll',
selected_mode='multiple',
pos_left='right',
pos_top='10%',
orient='vertical'),
tooltip_opts=opts.TooltipOpts(trigger="axis",
axis_pointer_type="line"),
toolbox_opts=opts.ToolboxOpts(is_show=True, pos_left='right'),
visualmap_opts=opts.VisualMapOpts(is_show=True,
type_="size",
min_=0,
max_=20,
range_text=["最大值", "最小值"]),
datazoom_opts=[opts.DataZoomOpts(range_start=50, range_end=100)],
)
effect_scatter = effect_scatter.dump_options_with_quotes()
return effect_scatter
def __draw_zs_area(_ka):
lines = []
scatters = []
for _zs in _ka.zs_list:
x_start = _zs['start_point']['dt']
if _zs['zs_finished']:# 中枢完成
x_end = _zs['end_point']['dt']
chart_b = EffectScatter()
if 'buy3' in _zs:
chart_b.add_xaxis([_zs['buy3']['dt']])
chart_b.add_yaxis(series_name=_ka.freq if aggregation else "B", y_axis=[_zs['buy3']['xd']], is_selected=False, symbol="circle", symbol_size=8,
itemstyle_opts=opts.ItemStyleOpts(color="red",))
chart_b.set_global_opts(xaxis_opts=grid0_xaxis_opts, legend_opts=legend_not_show_opts)
scatters.append(chart_b)
elif 'sell3' in _zs:
chart_b.add_xaxis([_zs['sell3']['dt']])
chart_b.add_yaxis(series_name=_ka.freq if aggregation else "S", y_axis=[_zs['sell3']['xd']], is_selected=False, symbol="circle", symbol_size=8,
itemstyle_opts=opts.ItemStyleOpts(color="green",))
chart_b.set_global_opts(xaxis_opts=grid0_xaxis_opts, legend_opts=legend_not_show_opts)
scatters.append(chart_b)
elif len(_zs['points'])>=5:# 中枢成立但未完成，有3笔或段以上
x_end = _zs['points'][-1]['dt']
else: # 中枢未完成，且未确定
continue
ZD = _zs['ZD']
ZG = _zs['ZG']
area_data=[[ {'xAxis': x_start, 'yAxis': ZD, 'value': ZD }, {'xAxis': x_end, 'yAxis': ZG, 'value': ZG }]]
line = (Line()
.add_xaxis([x_start, x_end])
.add_yaxis(series_name=_ka.freq if aggregation else "ZS", y_axis=[ZD, ZG], symbol='none'
"""
, markpoint_opts = opts.MarkPointOpts(
data=[
opts.MarkPointItem(type_="max", name="ZG"),
opts.MarkPointItem(type_="min", name="ZD"),
]
)
"""
, markline_opts=opts.MarkLineOpts(
label_opts=opts.LabelOpts(
position="middle", color="blue", font_size=15,
),
linestyle_opts=opts.LineStyleOpts(type_="dashed"),
data=area_data,
symbol=["none", "none"],
)
)
.set_series_opts(
markarea_opts=opts.MarkAreaOpts(data=area_data, itemstyle_opts=opts.ItemStyleOpts(color="#dcdcdc",opacity=0.1))
))
lines.append(line)
return lines, scatters
def plot(self,
area=['V', 'M'],
width=1000,
height=680,
klines=[],
vlines=[]) -> Grid:
'''
@params:
- area : list #显示区域
'V' 交易量
'M' k线+MACD
FieldName: string Dataframe中的字段名
[Field1,Field2,...] Dataframe中的字段名列表，将显示在一个区域
width: int #图表宽度 px
height:int #图表高度 px
klines:str #K线区域显示的数据，Dataframe中的字段名，如MA...
vline: str #Volume区域显示的数据，Dataframe中的字段名，如MA...
- sample:
chart=data.plot(area=[['small_pct','medium_pct','big_pct','super_pct'],'V','cci'],vlines=['vMA5','vMA10'],klines=['MA5','MA10'])
'''
self.klines = klines
self.vlines = vlines
grid = (Grid(init_opts=opts.InitOpts(
width=str(width) + "px",
height=str(height) + "px",
animation_opts=opts.AnimationOpts(animation=False),
)))
c = self.K()
iTop = 10
iButton = 10
iWindows = len(area)
iStep = 0
if iWindows == 0:
grid.add(c,
grid_opts=opts.GridOpts(pos_top="2%", pos_bottom="10%"))
elif iWindows > 1:
grid.add(c,
grid_opts=opts.GridOpts(pos_top="2%", pos_bottom="50%"))
iStep = int(30 / iWindows)
iButton = 50
else:
grid.add(c,
grid_opts=opts.GridOpts(pos_top="1%", pos_bottom="30%"))
iStep = 15
iButton = 70
icount = 0
for w in area:
#print(iStep)
if type(w) == list:
window = Line().add_xaxis(self.dateindex)
for l in w:
window.add_yaxis(
series_name=l,
y_axis=round(self.data[l],
self.precision).values.tolist(),
is_smooth=True,
is_symbol_show=False,
is_hover_animation=False,
label_opts=opts.LabelOpts(is_show=False),
linestyle_opts=opts.LineStyleOpts(type_='solid',
width=2))
window.axislabel_opts = opts.LabelOpts(is_show=False),
window.set_global_opts(
datazoom_opts=[opts.DataZoomOpts()],
xaxis_opts=opts.AxisOpts(
type_="category",
axislabel_opts=opts.LabelOpts(is_show=False),
),
legend_opts=opts.LegendOpts(orient='vertical',
pos_left="top",
pos_top=str(iButton) + "%"),
)
elif w == 'V':
window = self.V()
#grid.add(v,grid_opts=opts.GridOpts(pos_top= str(iButton)+'%',height=str(iStep)+'%'))
elif w == 'M':
window = self.MACD()
#grid.add(macd,grid_opts=opts.GridOpts(pos_top= str(iButton)+'%',height=str(iStep)+'%'))
else:
window = Line().add_xaxis(self.dateindex)
window.add_yaxis(
series_name=w,
y_axis=round(self.data[w], self.precision).values.tolist(),
is_smooth=True,
is_symbol_show=False,
is_hover_animation=False,
label_opts=opts.LabelOpts(is_show=False),
linestyle_opts=opts.LineStyleOpts(type_='solid', width=2))
window.axislabel_opts = opts.LabelOpts(is_show=True),
window.set_global_opts(
datazoom_opts=[opts.DataZoomOpts()],
xaxis_opts=opts.AxisOpts(
type_="category",
axislabel_opts=opts.LabelOpts(is_show=False),
),
legend_opts=opts.LegendOpts(orient='horizontal',
pos_left=str(icount + 20) +
"%"),
)
if '_' + w + '_flag' in self.data.columns:
#print("find_flag")
v1 = self.data[self.data['_' + w + '_flag'] ==
True].index.strftime("%Y-%m-%d").tolist()
v2 = self.data[self.data['_' + w + '_flag'] == True][w]
c_flag = (EffectScatter().add_xaxis(v1).add_yaxis("", v2))
window.overlap(c_flag)
#grid.add(vLine,grid_opts=opts.GridOpts(pos_top= str(iButton)+'%',height=str(iStep)+'%'))
icount += 1
#最后一行加上x刻度
if icount == iWindows:
window.options['xAxis'][0]['axisLabel'].opts['show'] = True
grid.add(window,
grid_opts=opts.GridOpts(pos_top=str(iButton) + '%',
height=str(iStep) + '%'))
iButton = iButton + iStep
#grid.grid_opts=opts.GridOpts(pos_left="8%", pos_right="8%", height="50%"),
grid.options['dataZoom'][0].opts['xAxisIndex'] = list(
range(0, iWindows + 1))
return grid