def signal_overview(price,
trades=None,
supplements=None,
plot_options={},
f_name="1213.html"):
figs_counts = len(supplements)
legends_heights = (figs_counts * 5)
left = 90 - legends_heights
height = left / figs_counts
sub_plots = []
fig_size = plot_options.setdefault("fig_size", ("1000px", "600px"))
x_data = price.index.tolist()
y_data = price.values.tolist()
marks = supplements.get("marks")
with_marks = True if marks is not None else False
if with_marks:
_ = pd.Series(index=price.index, data=np.arange(len(price)))
marks_data = [
opts.MarkPointItem(coord=[(x_data[int(l)]), (y_data[int(l)])],
itemstyle_opts=opts.ItemStyleOpts(
color=plot_options.setdefault(
v[0], colors[i]),
opacity=0.6),
symbol_size=20,
symbol="pin")
for i, v in enumerate(marks.items())
for l in _.reindex(v[1]).dropna().values.tolist()
]
else:
marks_data = []
with_trades = True if isinstance(trades, pd.Series) else False
if with_trades:
e_loc = [
price.index.get_loc(i)
for i in trades.index.get_level_values(0).unique()
]
ex_loc = [
price.index.get_loc(i)
for i in trades.index.get_level_values(1).unique()
]
pairs_loc = list(zip(e_loc, ex_loc))
(pairs_loc)
_b = [{"gt": p[0], "lte": p[1], "color": "#CD6839"} for p in pairs_loc]
_c = [{
"gt": pairs_loc[i][-1],
"lte": p[0],
"color": "#4A708B"
} for i, p in enumerate(pairs_loc[1:])]
_a = [{
"lte": pairs_loc[0][0],
"color": "#4A708B"
}, {
"gt": pairs_loc[-1][-1],
"color": colors[0]
}]
pieces = _a + _b + _c
else:
pieces = []
ds = np.unique(price.index.date)
decor_ = price.groupby(
price.index.date).apply(lambda x: [x.index[0], x.index[-1]]).loc[[
l[1] for l in list(filter(lambda x: x[0] % 2 == 0, enumerate(ds)))
]]
data_zoom_axis = np.arange(figs_counts).astype(int).tolist()
line = (Line(
init_opts=opts.InitOpts(width=fig_size[0], height=fig_size[1])
).add_xaxis(xaxis_data=x_data).add_yaxis(
series_name="价格",
y_axis=y_data,
yaxis_index=0,
is_smooth=True,
is_symbol_show=False,
is_hover_animation=False,
linestyle_opts=opts.LineStyleOpts(width=2),
markpoint_opts=opts.MarkPointOpts(data=marks_data, ),
).set_global_opts(
title_opts=opts.TitleOpts(title="价格",
pos_left='center',
pos_top=str(figs_counts * 5) + "%"),
toolbox_opts=opts.ToolboxOpts(is_show=True),
tooltip_opts=opts.TooltipOpts(
trigger="axis",
axis_pointer_type="cross",
border_width=1,
),
legend_opts=opts.LegendOpts(pos_left="left"),
axispointer_opts=opts.AxisPointerOpts(is_show=True,
link=[{
"xAxisIndex": "all"
}]),
datazoom_opts=[
opts.DataZoomOpts(is_show=True,
is_realtime=True,
xaxis_index=data_zoom_axis,
pos_top="5%",
range_start=0,
range_end=100,
orient="horizontal",
type_="inside"),
opts.DataZoomOpts(type_="slider",
xaxis_index=data_zoom_axis,
pos_bottom="bottom"),
],
yaxis_opts=opts.AxisOpts(
is_scale=True,
axistick_opts=opts.AxisTickOpts(is_inside=False),
axispointer_opts=opts.AxisPointerOpts(is_show=True),
axisline_opts=opts.AxisLineOpts(symbol='none'),
splitline_opts=opts.SplitLineOpts(is_show=True)),
xaxis_opts=opts.AxisOpts(is_show=False, ),
visualmap_opts=opts.VisualMapOpts(
is_show=False,
is_piecewise=with_trades,
dimension=0,
pieces=pieces,
),
).set_series_opts(markarea_opts=opts.MarkAreaOpts(data=[
opts.MarkAreaItem(
name="",
x=(d[0], d[1]),
itemstyle_opts=opts.ItemStyleOpts(color="#A1A9AF", opacity=0.2),
) for d in decor_
])))
sub_plots.append(line)
if with_marks:
_ = supplements.pop("marks")
if len(supplements) > 0:
last_pic = list(supplements.keys())[-1]
for i, s_1 in enumerate(supplements.items()):
k, v = s_1[0], s_1[1]
print(k)
ls = Line(
init_opts=opts.InitOpts(width=fig_size[0], height=fig_size[1]))
ls = ls.add_xaxis(xaxis_data=price.index.tolist())
for j, s_2 in enumerate(v.items()):
n, l = s_2[0], s_2[1]
print(n)
num = i * len(s_1) + j
ls.add_yaxis(
series_name=n,
y_axis=l.tolist(),
yaxis_index=len(sub_plots),
linestyle_opts=opts.LineStyleOpts(
width=2, color=plot_options.setdefault(n,
colors[num])),
label_opts=opts.LabelOpts(is_show=False),
)
s = legends_heights + height * len(sub_plots)
ls = ls.set_global_opts(
title_opts=opts.TitleOpts(title=k,
pos_left='center',
pos_top=str(s) + "%"),
legend_opts=opts.LegendOpts(pos_left="left",
pos_top=str(len(sub_plots) * 5) +
"%"),
yaxis_opts=opts.AxisOpts(
is_scale=True,
axistick_opts=opts.AxisTickOpts(is_inside=False),
axispointer_opts=opts.AxisPointerOpts(is_show=True),
axisline_opts=opts.AxisLineOpts(symbol='none'),
splitline_opts=opts.SplitLineOpts(is_show=True)),
xaxis_opts=opts.AxisOpts(is_show=(k == last_pic), ),
).set_series_opts(markarea_opts=opts.MarkAreaOpts(
data=[
opts.MarkAreaItem(
name="",
x=(d[0], d[1]),
itemstyle_opts=opts.ItemStyleOpts(color="#A1A9AF",
opacity=0.2),
) for d in decor_
],
is_silent=False,
))
sub_plots.append(ls)
pos = [
i + "%" for i in (height * np.arange(len(sub_plots)) +
legends_heights + 4.3).astype(str)
]
g = (Grid(init_opts=opts.InitOpts(width=fig_size[0], height=fig_size[1])))
for i, v in enumerate(sub_plots):
g.add(
chart=v,
grid_opts=opts.GridOpts(pos_left=50,
pos_right=50,
pos_top=pos[i],
height=str(height - 5) + "%"),
)
g.render(f_name)
def plot_kline(ka, bs=None, file_html="kline.html", width="1400px", height="680px"):
"""
:param ka: KlineAnalyze
:param bs: pd.DataFrame
买卖点，包含三个字段 ["操作提示", "交易时间", "交易价格"]
:param file_html: str
:param width: str
:param height: str
:return: None
"""
df = pd.DataFrame(ka.kline)
df = macd(df)
x = df.dt.to_list()
title = "%s | %s 至 %s" % (ka.symbol, ka.start_dt, ka.end_dt)
kline = (
Kline()
.add_xaxis(xaxis_data=x)
.add_yaxis(
series_name="",
y_axis=df[['open', 'close', 'low', 'high']].values.tolist(),
itemstyle_opts=opts.ItemStyleOpts(
color="#ef232a",
color0="#14b143",
border_color="#ef232a",
border_color0="#14b143",
),
)
.set_series_opts(
markarea_opts=opts.MarkAreaOpts(is_silent=True)
)
.set_global_opts(
title_opts=opts.TitleOpts(title=title, pos_left="0"),
xaxis_opts=opts.AxisOpts(
type_="category",
is_scale=True,
boundary_gap=False,
axisline_opts=opts.AxisLineOpts(is_on_zero=False),
splitline_opts=opts.SplitLineOpts(is_show=False),
split_number=20,
min_="dataMin",
max_="dataMax",
),
yaxis_opts=opts.AxisOpts(
is_scale=True, splitline_opts=opts.SplitLineOpts(is_show=True),
axislabel_opts=opts.LabelOpts(is_show=True, position="inside")
),
tooltip_opts=opts.TooltipOpts(trigger="axis", axis_pointer_type="line"),
datazoom_opts=[
opts.DataZoomOpts(
is_show=False, type_="inside", xaxis_index=[0, 0], range_end=100
),
opts.DataZoomOpts(
is_show=True, xaxis_index=[0, 1], pos_top="96%", range_end=100
),
opts.DataZoomOpts(is_show=False, xaxis_index=[0, 2], range_end=100),
],
# 三个图的 axis 连在一块
axispointer_opts=opts.AxisPointerOpts(
is_show=True,
link=[{"xAxisIndex": "all"}],
label=opts.LabelOpts(background_color="#777"),
),
)
)
kline_line = (
Line()
.add_xaxis(xaxis_data=x)
.add_yaxis(
series_name="笔",
y_axis=df.bi.tolist(),
is_smooth=False,
is_connect_nones=True,
symbol='diamond',
symbol_size=8,
linestyle_opts=opts.LineStyleOpts(opacity=1, type_='dotted', width=2),
label_opts=opts.LabelOpts(is_show=False),
)
.add_yaxis(
series_name="线段",
y_axis=df.xd.tolist(),
is_smooth=False,
is_connect_nones=True,
symbol='triangle',
symbol_size=12,
linestyle_opts=opts.LineStyleOpts(opacity=1, type_='solid', width=2),
label_opts=opts.LabelOpts(is_show=True, position='right'),
)
.set_global_opts(
xaxis_opts=opts.AxisOpts(
type_="category",
grid_index=1,
axislabel_opts=opts.LabelOpts(is_show=False),
),
yaxis_opts=opts.AxisOpts(
grid_index=1,
split_number=3,
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, position="inside"),
),
)
)
# Overlap Kline + Line
overlap_kline_line = kline.overlap(kline_line)
if isinstance(bs, pd.DataFrame) and len(bs) > 0:
c = (
Scatter()
.add_xaxis(bs['交易时间'].to_list())
.add_yaxis(
"买卖点",
bs['交易价格'].to_list(),
label_opts=opts.LabelOpts(
is_show=True,
position="left",
formatter=JsCode(
"function(params){return bsName[params.dataIndex][0];}"
)
),
))
overlap_kline_line = overlap_kline_line.overlap(c)
# draw volume
bar_1 = (
Bar()
.add_xaxis(xaxis_data=x)
.add_yaxis(
series_name="Volumn",
yaxis_data=df.vol.tolist(),
xaxis_index=1,
yaxis_index=1,
label_opts=opts.LabelOpts(is_show=False),
itemstyle_opts=opts.ItemStyleOpts(
color=JsCode(
"""
function(params) {
var colorList;
if (barData[params.dataIndex][1] > barData[params.dataIndex][0]) {
colorList = '#ef232a';
} else {
colorList = '#14b143';
}
return colorList;
}
"""
)
),
)
.set_global_opts(
xaxis_opts=opts.AxisOpts(
type_="category",
grid_index=1,
axislabel_opts=opts.LabelOpts(is_show=False),
),
yaxis_opts=opts.AxisOpts(
axislabel_opts=opts.LabelOpts(is_show=True, position='inside')
),
legend_opts=opts.LegendOpts(is_show=False),
)
)
# Bar-2 (Overlap Bar + Line)
bar_2 = (
Bar()
.add_xaxis(xaxis_data=x)
.add_yaxis(
series_name="MACD",
yaxis_data=df.macd.tolist(),
xaxis_index=2,
yaxis_index=2,
label_opts=opts.LabelOpts(is_show=False),
itemstyle_opts=opts.ItemStyleOpts(
color=JsCode(
"""
function(params) {
var colorList;
if (params.data >= 0) {
colorList = '#ef232a';
} else {
colorList = '#14b143';
}
return colorList;
}
"""
)
),
)
.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, position="inside"),
),
legend_opts=opts.LegendOpts(is_show=False),
)
)
line_2 = (
Line()
.add_xaxis(xaxis_data=x)
.add_yaxis(
series_name="DIF",
y_axis=df['diff'].tolist(),
xaxis_index=2,
yaxis_index=2,
label_opts=opts.LabelOpts(is_show=False),
)
.add_yaxis(
series_name="DEA",
y_axis=df['dea'].tolist(),
xaxis_index=2,
yaxis_index=2,
label_opts=opts.LabelOpts(is_show=False),
)
.set_global_opts(legend_opts=opts.LegendOpts(is_show=False))
)
# draw MACD
overlap_bar_line = bar_2.overlap(line_2)
# 最后的 Grid
grid_chart = Grid(init_opts=opts.InitOpts(width=width, height=height, page_title=title))
grid_chart.add_js_funcs("var barData = {}".format(df[['open', 'close', 'low', 'high']].values.tolist()))
if isinstance(bs, pd.DataFrame) and len(bs) > 0:
grid_chart.add_js_funcs("var bsName = {}".format(bs[["操作提示", "交易价格"]].values.tolist()))
grid_chart.add(
overlap_kline_line,
grid_opts=opts.GridOpts(pos_left="3%", pos_right="1%", height="60%"),
)
grid_chart.add(
bar_1,
grid_opts=opts.GridOpts(pos_left="3%", pos_right="1%", pos_top="71%", height="10%"),
)
grid_chart.add(
overlap_bar_line,
grid_opts=opts.GridOpts(pos_left="3%", pos_right="1%", pos_top="82%", height="14%"),
)
grid_chart.render(path=file_html)
def kline_pro(kline: List[dict],
fx: List[dict] = None,
bi: List[dict] = None,
xd: List[dict] = 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 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"
}])
dz_inside = opts.DataZoomOpts(False,
"inside",
xaxis_index=[0, 1, 2],
range_start=80,
range_end=100)
dz_slider = opts.DataZoomOpts(True,
"slider",
xaxis_index=[0, 1, 2],
pos_top="96%",
pos_bottom="0%",
range_start=80,
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['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['vol'],
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)
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)
# 均线图
# ------------------------------------------------------------------------------------------------------------------
chart_ma = Line()
chart_ma.add_xaxis(xaxis_data=dts)
ma_keys = {"MA5": ma5, "MA34": ma34, "MA233": ma233}
ma_colors = ["#39afe6", "#da6ee8", "#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:
fx_dts = [x['dt'] for x in fx]
fx_val = [x['fx'] 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 bi:
bi_dts = [x['dt'] for x in bi]
bi_val = [x['bi'] for x in bi]
chart_bi = Scatter()
chart_bi.add_xaxis(bi_dts)
chart_bi.add_yaxis(series_name="BI",
y_axis=bi_val,
is_selected=True,
symbol="diamond",
symbol_size=10,
label_opts=label_not_show_opts,
itemstyle_opts=opts.ItemStyleOpts(
color="rgba(184, 117, 225, 1.0)", ))
chart_bi.set_global_opts(xaxis_opts=grid0_xaxis_opts,
legend_opts=legend_not_show_opts)
chart_k = chart_k.overlap(chart_bi)
if xd:
xd_dts = [x['dt'] for x in xd]
xd_val = [x['xd'] for x in xd]
chart_xd = Scatter()
chart_xd.add_xaxis(xd_dts)
chart_xd.add_yaxis(series_name="XD",
y_axis=xd_val,
is_selected=True,
symbol="triangle",
symbol_size=10,
itemstyle_opts=opts.ItemStyleOpts(
color="rgba(37, 141, 54, 1.0)", ))
chart_xd.set_global_opts(xaxis_opts=grid0_xaxis_opts,
legend_opts=legend_not_show_opts)
chart_k = chart_k.overlap(chart_xd)
if bs:
b_dts = [x['dt'] for x in bs if x['mark'] == 'buy']
if len(b_dts) > 0:
b_val = [x['price'] for x in bs if x['mark'] == '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['dt'] for x in bs if x['mark'] == 'sell']
if len(s_dts) > 0:
s_val = [x['price'] for x in bs if x['mark'] == '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
# -*- coding: utf-8 -*-
from pyecharts import options as opts
from pyecharts.charts import Geo
from pyecharts.globals import ChartType
import ElectricityData
c = (Geo().add_schema(maptype="china").add(
"geo",
[
list(z) for z in zip(ElectricityData.provinces,
ElectricityData.GWP2017heatmapvalues)
],
type_=ChartType.HEATMAP,
).set_series_opts(label_opts=opts.LabelOpts(is_show=False)).set_global_opts(
visualmap_opts=opts.VisualMapOpts(
min_=0,
max_=1.2,
),
title_opts=opts.TitleOpts(title="Geo-HeatMap"),
).render("GWP2017heatmap.html"))
red_dict['{}'.format(i[0])] = i[1]
for j in blue_counter.most_common():
blue_dict['{}'.format(j[0])] = j[1]
# 对红蓝球号码和次数重新进行排序
red_list = sorted(red_counter.most_common(), key=lambda number: number[0])
blue_list = sorted(blue_counter.most_common(),
key=lambda number: number[0])
red_bar = Bar()
red_x = ['{}'.format(str(x[0])) for x in red_list]
red_y = ['{}'.format(str(x[1])) for x in red_list]
red_bar.add_xaxis(red_x)
red_bar.add_yaxis('红色球出现次数', red_y)
red_bar.set_global_opts(
title_opts=opts.TitleOpts(title='大乐透彩票', subtitle='近14年数据'),
toolbox_opts=opts.ToolboxOpts(),
yaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(
formatter="{value}/次")),
xaxis_opts=opts.AxisOpts(name='开奖号码'))
red_bar.set_series_opts(markpoint_opts=opts.MarkPointOpts(data=[
opts.MarkPointItem(type_='max', name='最大值'),
opts.MarkPointItem(type_='min', name='最小值')
]))
blue_bar = Bar()
blue_x = ['{}'.format(str(x[0])) for x in blue_list]
blue_y = ['{}'.format(str(x[1])) for x in blue_list]
blue_bar.add_xaxis(blue_x)
blue_bar.add_yaxis('蓝色球出现次数',
blue_y,
#!/usr/bin/env python
# coding=utf-8
import pyecharts.options as opts
from pyecharts.charts import Funnel
"""
Gallery 使用 pyecharts 1.0.0
参考地址: https://echarts.baidu.com/examples/editor.html?c=funnel
目前无法实现的功能:
1、暂时无法对漏斗图的长宽等范围操作进行修改
"""
x_data = ["展现", "点击", "访问", "咨询", "订单"]
y_data = [100, 80, 60, 40, 20]
data = [[x_data[i], y_data[i]] for i in range(len(x_data))]
(Funnel(init_opts=opts.InitOpts(width="1600px", height="800px")).add(
series_name="",
data_pair=data,
gap=2,
tooltip_opts=opts.TooltipOpts(trigger="item",
formatter="{a} <br/>{b} : {c}%"),
label_opts=opts.LabelOpts(is_show=True, position="inside"),
itemstyle_opts=opts.ItemStyleOpts(border_color="#fff", border_width=1),
).set_global_opts(title_opts=opts.TitleOpts(
title="漏斗图", subtitle="纯属虚构")).render("funnel_chart.html"))
from pyecharts import options as opts
from pyecharts.charts import Geo
from pyecharts.faker import Faker
c = (
Geo()
.add_schema(maptype="china")
.add("geo", [list(z) for z in zip(Faker.provinces, Faker.values())])
.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
.set_global_opts(
visualmap_opts=opts.VisualMapOpts(is_piecewise=True),
title_opts=opts.TitleOpts(title="Geo-VisualMap（分段型）"),
)
.render("geo_visualmap_piecewise.html")
)
def safe_message_count(self):
print('本月基本信息统计')
print('*************************************')
df = pd.read_sql(f'select * from {self.table}', self.conn)
safe_message_num = df['id'].count()
print('本月安全息息条数：', safe_message_num)
print('*************************************')
print('本月发生不同性质的安全问题总数')
group = df.groupby(by='性质').count()['id']
print(group)
print('*************************************')
safe_message_score = df['分值'].sum()
print(f'本月安全信息总分值为{safe_message_score}分')
df['time'] = pd.to_datetime(df['检查时间'])
print('**************其他功能****************')
print('1、查询本月趋势图')
print('2、本月小时趋势图')
print('3、责任车间统计')
print('4、责任人岗位统计')
print('5、返回主菜单')
p = input('请输入选择编号')
if p == '1':
df['time'] = df['time'].apply(lambda x: x.strftime('%Y%m%d'))
ya = []
for i in df['time'].unique():
i = str(i)
try:
bkk = df[df['time'] == i]['性质'].value_counts()['A类']
ya.append(str(bkk))
except:
bkk = 0
ya.append(str(bkk))
# print(ya, len(ya))
yb = []
for i in df['time'].unique():
i = str(i)
try:
bkk = df[df['time'] == i]['性质'].value_counts()['B类']
yb.append(str(bkk))
except:
bkk = 0
yb.append(str(bkk))
# print(yb, len(yb))
yc = []
for i in df['time'].unique():
i = str(i)
try:
bkk = df[df['time'] == i]['性质'].value_counts()['C类']
yc.append(str(bkk))
except:
bkk = 0
yc.append(str(bkk))
# print(yc, len(yc))
yd = []
for i in df['time'].unique():
i = str(i)
try:
bkk = df[df['time'] == i]['性质'].value_counts()['D类']
yd.append(str(bkk))
except:
bkk = 0
yd.append(str(bkk))
# print(yd, len(yd))
x = df['time'].unique()
a = []
for i in x:
a.append(str(i))
(Line(init_opts=opts.InitOpts(
width="1600px", height="800px")).add_xaxis(a).add_yaxis(
"A类", ya).add_yaxis("B类",
yb).add_yaxis("C类",
yc).add_yaxis("D类", yd).
set_global_opts(
title_opts=opts.TitleOpts(
title="现场安全信息性质分布(月)",
subtitle=f"统计时间：{self.table_name}"),
tooltip_opts=opts.TooltipOpts(trigger="axis"),
xaxis_opts=opts.AxisOpts(
axislabel_opts=opts.LabelOpts(rotate=45), name="日期"),
# toolbox_opts=opts.ToolBoxFeatureSaveAsImageOpts(background_color= "auto",type_='jpg'),
toolbox_opts=opts.ToolboxOpts(
is_show=True,
feature={
"saveAsImage": {
'backgroundColor': 'white'
},
"dataZoom": {},
"restore": {},
"magicType": {
"show": True,
"type": ["line", "bar"]
},
"dataView": {}
})).render(f"safe_message_month_{self.table_name}.html"))
print('已画图成功')
elif p == '2':
df['time_H'] = df['time'].apply(lambda x: x.hour)
x = df['time_H'].sort_values(ascending=True).unique()
# print(df['time'],x)
ya = []
for i in x:
try:
bkk = df[df['time_H'] == i]['性质'].value_counts()['A类']
ya.append(str(bkk))
except:
bkk = 0
ya.append(str(bkk))
# print(ya, len(ya))
yb = []
for i in x:
try:
bkk = df[df['time_H'] == i]['性质'].value_counts()['B类']
yb.append(str(bkk))
except:
bkk = 0
yb.append(str(bkk))
# print(yb, len(yb))
yc = []
for i in x:
try:
bkk = df[df['time_H'] == i]['性质'].value_counts()['C类']
yc.append(str(bkk))
except:
bkk = 0
yc.append(str(bkk))
# print(yc, len(yc))
yd = []
for i in x:
try:
bkk = df[df['time_H'] == i]['性质'].value_counts()['D类']
yd.append(str(bkk))
except:
bkk = 0
yd.append(str(bkk))
# print(yd, len(yd))
a = []
for i in x:
m = str(i) + ':00'
a.append(m)
(Line(init_opts=opts.InitOpts(width="1600px", height="800px")).
add_xaxis(a).add_yaxis("A类", ya).add_yaxis("B类", yb).add_yaxis(
"C类", yc).add_yaxis("D类", yd).set_global_opts(
title_opts=opts.TitleOpts(
title="现场安全信息性质分布(小时)",
subtitle=f"统计时间：{self.table_name}"),
tooltip_opts=opts.TooltipOpts(trigger="axis"),
xaxis_opts=opts.AxisOpts(name="时间"),
toolbox_opts=opts.ToolboxOpts(
is_show=True,
feature={
"saveAsImage": {
'backgroundColor': 'white'
},
"dataZoom": {},
"restore": {},
"magicType": {
"show": True,
"type": ["line", "bar"]
},
"dataView": {}
})).render(
f"safe_message_hour_{self.table_name}.html"))
print('已画图成功')
elif p == '3':
group = df.groupby(by=df['录入科室']).count()['性质']
x = group.index.tolist()
y = group.values.tolist()
# print(x)
# print(y)
(Line(
init_opts=opts.InitOpts(width="1600px", height="800px")
).add_xaxis(x).add_yaxis("数量", y).set_global_opts(
title_opts=opts.TitleOpts(title="现场安全信息车间统计分布",
subtitle=f"统计时间：{self.table_name}"),
tooltip_opts=opts.TooltipOpts(trigger="axis"),
toolbox_opts=opts.ToolboxOpts(is_show=True,
feature={
"saveAsImage": {
'backgroundColor':
'white'
},
"dataZoom": {},
"restore": {},
"magicType": {
"show": True,
"type": ["line", "bar"]
},
"dataView": {}
}),
# xaxis_opts=opts.AxisOpts(name_rotate=60, name="车间名称",axislabel_opts={"rotate": 45}))
xaxis_opts=opts.AxisOpts(
axislabel_opts=opts.LabelOpts(rotate=30),
name="车间名称")).render(
f"safe_message_chejian_{self.table_name}.html"))
print('已画图成功')
elif p == '4':
group = df.groupby(by=df['责任人岗位']).count()['性质']
x = group.index.tolist()
y = group.values.tolist()
# print(x)
# print(y)
(Line(
init_opts=opts.InitOpts(width="1600px", height="800px")
).add_xaxis(x).add_yaxis("数量", y).set_global_opts(
title_opts=opts.TitleOpts(title="现场安全信息责任人岗位统计分布",
subtitle=f"统计时间：{self.table_name}"),
tooltip_opts=opts.TooltipOpts(trigger="axis"),
toolbox_opts=opts.ToolboxOpts(is_show=True,
feature={
"saveAsImage": {
'backgroundColor':
'white'
},
"dataZoom": {},
"restore": {},
"magicType": {
"show": True,
"type": ["line", "bar"]
},
"dataView": {}
}),
# xaxis_opts=opts.AxisOpts(name_rotate=60, name="车间名称",axislabel_opts={"rotate": 45}))
xaxis_opts=opts.AxisOpts(
axislabel_opts=opts.LabelOpts(rotate=20),
name="责任人岗位")).render(
f"safe_message_duty_{self.table_name}.html"))
print('已画图成功')
else:
self.run()
from pywebio.output import put_html
from pyecharts import options as opts
from pyecharts.charts import Gauge
c = (Gauge().add("", [("完成率", 66.6)]).set_global_opts(
title_opts=opts.TitleOpts(title="Gauge-基本示例")))
c.width = "100%"
put_html(c.render_notebook())
from pywebio.output import put_html
import math
from pyecharts import options as opts
from pyecharts.charts import Polar
data = []
for i in range(361):
t = i / 180 * math.pi
r = math.sin(2 * t) * math.cos(2 * t)
data.append([r, i])
c = (Polar().add_schema(
angleaxis_opts=opts.AngleAxisOpts(start_angle=0, min_=0)).add(
"flower", data,
label_opts=opts.LabelOpts(is_show=False)).set_global_opts(
title_opts=opts.TitleOpts(title="Polar-Flower")))
c.width = "100%"
put_html(c.render_notebook())
#-------------------------------------------------------------------------------------
n = time.strftime("%Y-%m-%d") + "-daily.csv"
data = pd.read_csv(n)
date_list = list(data['date'])
confirm_list = list(data['confirm'])
suspect_list = list(data['suspect'])
dead_list = list(data['dead'])
heal_list = list(data['heal'])
print(date_list) # 日期
print(confirm_list) # 确诊数据
print(suspect_list) # 疑似数据
print(dead_list) # 死亡数据
print(heal_list) # 治愈数据
#-------------------------------------------------------------------------------------
# 第二步：绘制柱状图
#-------------------------------------------------------------------------------------
line = (
Line().add_xaxis(date_list).add_yaxis('确诊数据', confirm_list).add_yaxis(
'疑似数据', suspect_list, is_smooth=True) #平滑
.add_yaxis('死亡数据', dead_list).add_yaxis(
'治愈数据',
heal_list).set_series_opts(label_opts=opts.LabelOpts(is_show=False))
# 设置x轴标签旋转角度
.set_global_opts(
xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(rotate=-30)),
yaxis_opts=opts.AxisOpts(name='人数', min_=3),
title_opts=opts.TitleOpts(title='2019-nCoV疫情曲线图')))
line.render('2019-nCoV疫情曲线图.html')
for i in range(2):
values = df.iloc[:, i].unique()
for value in values:
dic = {}
dic['name'] = value
nodes.append(dic)
print(nodes)
links = []
for i in df.values:
dic = {}
# source, target, value
dic['source'] = i[0]
dic['target'] = i[1]
dic['value'] = i[2]
links.append(dic)
print(links)
pic = (Sankey().add(
'',
nodes,
links,
linestyle_opt=opts.LineStyleOpts(opacity=0.3, curve=0.5, color="source"),
label_opts=opts.LabelOpts(position="right"),
node_gap=30,
).set_global_opts(title_opts=opts.TitleOpts(title='熬夜原因桑基图')))
pic.render('sankey.html')
# 案例002添加标题选项
# 输出文件为图片
from pyecharts.charts import Bar
from pyecharts import options as opts
# 导入输出图片工具
# 使用snapshot-selenium 渲染图片
from pyecharts.render import make_snapshot
from snapshot_selenium import snapshot
# 创建一个柱状图Bar实例
bar = (
Bar()
# 添加X轴数据
.add_xaxis(["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"])
# 添加Y轴数据,系列的名称
.add_yaxis("商家A",
[5, 20, 36, 10, 75, 90]).add_yaxis("商家B",
[8, 15, 60, 20, 25, 30])
# 添加标题
.set_global_opts(
title_opts=opts.TitleOpts(title="主标题: 双十一销量", subtitle="副标题:服饰类")))
# render 会生成本地 HTML 文件，默认会在当前目录生成 render.html 文件
# 也可以传入路径参数，自定义名称，如 bar.render("mycharts.html")
bar.render()
# 输出保存为图片
# make_snapshot(snapshot, bar.render(), "003_Options配置项_自定义样式_保存图片.png")
def draw_chart():
kline = (
Kline().add_xaxis(xaxis_data=data["times"]).add_yaxis(
series_name="",
y_axis=data["datas"],
itemstyle_opts=opts.ItemStyleOpts(
color="#ef232a",
color0="#14b143",
border_color="#ef232a",
border_color0="#14b143",
),
markpoint_opts=opts.MarkPointOpts(data=[
opts.MarkPointItem(type_="max", name="最大值"),
opts.MarkPointItem(type_="min", name="最小值"),
]),
markline_opts=opts.MarkLineOpts(
label_opts=opts.LabelOpts(position="middle",
color="blue",
font_size=15),
data=split_data_part(),
symbol=["circle", "none"],
),
).set_series_opts(markarea_opts=opts.MarkAreaOpts(
is_silent=True, data=split_data_part())).
set_global_opts(
title_opts=opts.TitleOpts(title="K线周期图表", pos_left="0"),
xaxis_opts=opts.AxisOpts(
type_="category",
is_scale=True,
boundary_gap=False,
axisline_opts=opts.AxisLineOpts(is_on_zero=False),
splitline_opts=opts.SplitLineOpts(is_show=False),
split_number=20,
min_="dataMin",
max_="dataMax",
),
yaxis_opts=opts.AxisOpts(
is_scale=True,
splitline_opts=opts.SplitLineOpts(is_show=True)),
tooltip_opts=opts.TooltipOpts(trigger="axis",
axis_pointer_type="line"),
datazoom_opts=[
opts.DataZoomOpts(is_show=False,
type_="inside",
xaxis_index=[0,
0],
range_end=100),
opts.DataZoomOpts(is_show=True,
xaxis_index=[0,
1],
pos_top="97%",
range_end=100),
opts.DataZoomOpts(is_show=False,
xaxis_index=[0,
2],
range_end=100),
],
# 三个图的 axis 连在一块
# axispointer_opts=opts.AxisPointerOpts(
# is_show=True,
# link=[{"xAxisIndex": "all"}],
# label=opts.LabelOpts(background_color="#777"),
# ),
))
kline_line = (Line().add_xaxis(xaxis_data=data["times"]).add_yaxis(
series_name="MA5",
y_axis=calculate_ma(day_count=5),
is_smooth=True,
linestyle_opts=opts.LineStyleOpts(opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
).set_global_opts(
xaxis_opts=opts.AxisOpts(
type_="category",
grid_index=1,
axislabel_opts=opts.LabelOpts(is_show=False),
),
yaxis_opts=opts.AxisOpts(
grid_index=1,
split_number=3,
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),
),
))
# Overlap Kline + Line
overlap_kline_line = kline.overlap(kline_line)
# Bar-1
bar_1 = (
Bar().add_xaxis(xaxis_data=data["times"]).add_yaxis(
series_name="Volumn",
y_axis=data["vols"],
xaxis_index=1,
yaxis_index=1,
label_opts=opts.LabelOpts(is_show=False),
# 根据 echarts demo 的原版是这么写的
# itemstyle_opts=opts.ItemStyleOpts(
# color=JsCode("""
# function(params) {
# var colorList;
# if (data.datas[params.dataIndex][1]>data.datas[params.dataIndex][0]) {
# colorList = '#ef232a';
# } else {
# colorList = '#14b143';
# }
# return colorList;
# }
# """)
# )
# 改进后在 grid 中 add_js_funcs 后变成如下
itemstyle_opts=opts.ItemStyleOpts(color=JsCode("""
function(params) {
var colorList;
if (barData[params.dataIndex][1] > barData[params.dataIndex][0]) {
colorList = '#ef232a';
} else {
colorList = '#14b143';
}
return colorList;
}
""")),
).set_global_opts(
xaxis_opts=opts.AxisOpts(
type_="category",
grid_index=1,
axislabel_opts=opts.LabelOpts(is_show=False),
),
legend_opts=opts.LegendOpts(is_show=False),
))
# Bar-2 (Overlap Bar + Line)
bar_2 = (Bar().add_xaxis(xaxis_data=data["times"]).add_yaxis(
series_name="MACD",
y_axis=data["macds"],
xaxis_index=2,
yaxis_index=2,
label_opts=opts.LabelOpts(is_show=False),
itemstyle_opts=opts.ItemStyleOpts(color=JsCode("""
function(params) {
var colorList;
if (params.data >= 0) {
colorList = '#ef232a';
} else {
colorList = '#14b143';
}
return colorList;
}
""")),
).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),
),
legend_opts=opts.LegendOpts(is_show=False),
))
line_2 = (Line().add_xaxis(xaxis_data=data["times"]).add_yaxis(
series_name="DIF",
y_axis=data["difs"],
xaxis_index=2,
yaxis_index=2,
label_opts=opts.LabelOpts(is_show=False),
).add_yaxis(
series_name="DIF",
y_axis=data["deas"],
xaxis_index=2,
yaxis_index=2,
label_opts=opts.LabelOpts(is_show=False),
).set_global_opts(legend_opts=opts.LegendOpts(is_show=False)))
# 最下面的柱状图和折线图
overlap_bar_line = bar_2.overlap(line_2)
# 最后的 Grid
grid_chart = Grid(init_opts=opts.InitOpts(width="1400px", height="800px"))
# 这个是为了把 data.datas 这个数据写入到 html 中,还没想到怎么跨 series 传值
# demo 中的代码也是用全局变量传的
grid_chart.add_js_funcs("var barData = {}".format(data["datas"]))
# K线图和 MA5 的折线图
grid_chart.add(
overlap_kline_line,
grid_opts=opts.GridOpts(pos_left="3%", pos_right="1%", height="60%"),
)
# Volumn 柱状图
grid_chart.add(
bar_1,
grid_opts=opts.GridOpts(pos_left="3%",
pos_right="1%",
pos_top="71%",
height="10%"),
)
# MACD DIFS DEAS
grid_chart.add(
overlap_bar_line,
grid_opts=opts.GridOpts(pos_left="3%",
pos_right="1%",
pos_top="82%",
height="14%"),
)
grid_chart.render("professional_kline_chart.html")
total_profit.append(result[3])
# 月定投收益
result_month = calculate_found_profit_by_month(start_date, end_date)
total_amount.append(result_month[2])
total_profit.append(result_month[3])
return total_amount, total_profit
total_amount, total_profit = calculate_found_profit_week_month()
# 这部分代码在 jupyter 中 run
line = (Line().add_xaxis(list(found_date_price.keys())).add_yaxis(
'price',
list(found_date_price.values()),
label_opts=opts.LabelOpts(is_show=False)).set_global_opts(
title_opts=opts.TitleOpts(title=f'{foundCode}基金走势图'),
xaxis_opts=opts.AxisOpts(splitline_opts=opts.SplitLineOpts(
is_show=True)),
yaxis_opts=opts.AxisOpts(splitline_opts=opts.SplitLineOpts(
is_show=True)),
))
#line.render_notebook()
x = ['周一', '周二', '周三', '周四', '周五', '月定投']
bar = (Bar().add_xaxis(x).add_yaxis('投资金额', total_amount).add_yaxis(
'投资收益', total_profit).set_global_opts(
title_opts=opts.TitleOpts(title="投资总额 & 投资收益"),
xaxis_opts=opts.AxisOpts(splitline_opts=opts.SplitLineOpts(
is_show=True)),
yaxis_opts=opts.AxisOpts(splitline_opts=opts.SplitLineOpts(
is_show=True)),
[57.1, 78254, "Milk Tea"],
[74.4, 41032, "Cheese Cocoa"],
[50.1, 12755, "Cheese Brownie"],
[89.7, 20145, "Matcha Cocoa"],
[68.1, 79146, "Tea"],
[19.6, 91852, "Orange Juice"],
[10.6, 101852, "Lemon Juice"],
[32.7, 20112, "Walnut Brownie"],
]).add_yaxis(
series_name="",
y_axis=[],
encode={
"x": "amount",
"y": "product"
},
label_opts=opts.LabelOpts(is_show=False),
).set_global_opts(
title_opts=opts.TitleOpts(title="Dataset normal bar example"),
xaxis_opts=opts.AxisOpts(name="amount"),
yaxis_opts=opts.AxisOpts(type_="category"),
visualmap_opts=opts.VisualMapOpts(
orient="horizontal",
pos_left="center",
min_=10,
max_=100,
range_text=["High Score", "Low Score"],
dimension=0,
range_color=["#D7DA8B", "#E15457"],
),
).render("dataset_bar_1.html"))
from pyecharts import options as opts
from pyecharts.charts import Liquid
c = (Liquid().add(
"lq", [0.6, 0.7, 0.8],
is_outline_show=False).set_global_opts(title_opts=opts.TitleOpts(
title="Liquid-无边框")).render("Liquid_without_outline.html"))
if base_path != "":
path = base_path + "." + key
else:
path = key
if re.match(r"/^\$/", key):
pass
else:
child = {"name": path, "children": []}
target["children"].append(child)
if isinstance(source[key], dict):
convert(source[key], child, path)
else:
target["value"] = source["$count"]
convert(source=data, target=tree_map_data, base_path="")
(TreeMap(init_opts=opts.InitOpts(width="1200px", height="720px")).add(
series_name="option",
data=tree_map_data["children"],
visual_min=300,
leaf_depth=1,
# 标签居中为 position = "inside"
label_opts=opts.LabelOpts(position="inside"),
).set_global_opts(
legend_opts=opts.LegendOpts(is_show=False),
title_opts=opts.TitleOpts(title="Echarts 配置项查询分布",
subtitle="2016/04",
pos_left="leafDepth"),
).render("echarts_option_query.html"))
from pyecharts import options as opts
from pyecharts.charts import Bar
from pyecharts.faker import Faker
c = (
Bar()
.add_xaxis(Faker.choose())
.add_yaxis("商家A", Faker.values(), gap="0%")
.add_yaxis("商家B", Faker.values(), gap="0%")
.set_global_opts(title_opts=opts.TitleOpts(title="Bar-不同系列柱间距离"))
.render("Bar_different_series_gap.html")
)
import pyecharts.options as opts
from pyecharts.charts import Line
from pyecharts.faker import Faker
c = (Line().add_xaxis(Faker.choose()).add_yaxis(
"商家A", Faker.values(), is_step=True).set_global_opts(
title_opts=opts.TitleOpts(title="Line-阶梯图")).render("Line_step.html"))
# -*- coding:utf-8 -*-
from pyecharts import options as opts
from pyecharts.charts import Bar
from pyecharts.faker import Faker
c = (
Bar().add_xaxis(Faker.choose()).add_yaxis("商家A",
Faker.values(),
stack="stack1").add_yaxis(
"商家B",
Faker.values(),
stack="stack1").add_yaxis(
"商家C", Faker.values())
#系列配置项 LabelOpts: 标签配置项
.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
#全局配置项 TitleOpts：标题配置项
.set_global_opts(title_opts=opts.TitleOpts(
title="Bar-堆叠数据（部分）")).render("bar_stack1.html"))
},
{
'name': '续费后50万'
},
{
'name': '续费后包年'
},
{
'name': '续费后单价极低'
},
]
pic = (
Sankey().add(
'', #图例名称
nodes, #传入节点数据
linkes, #传入边和流量数据
#设置透明度、弯曲度、颜色
linestyle_opt=opts.LineStyleOpts(opacity=0.3,
curve=0.5,
color="source"),
#标签显示位置
label_opts=opts.LabelOpts(position="right"),
#节点之前的距离
node_gap=30,
# 鼠标 hover 到节点或边上，相邻接的节点和边高亮的交互，默认关闭，可手动开启
focus_node_adjacency="allEdges").set_global_opts(
title_opts=opts.TitleOpts(title='企业版合同续签客户流向')))
#输出文件
pic.render(r'C:\Users\YAO\AppData\Local\Temp\test.html')
[2309.16, 2286.6, 2264.83, 2333.29],
[2282.17, 2263.97, 2253.25, 2286.33],
[2255.77, 2270.28, 2253.31, 2276.22],
]
c=(
Kline()
.add_xaxis(["2017/7/{}".format(i + 1) for i in range(31)])
.add_yaxis("kline",
data,
itemstyle_opts=opts.ItemStyleOpts(
color="#ec0000",
color0="#00da3c",
border_color="#8A0000",
border_color0="#008F28",
)
)
.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=[opts.DataZoomOpts(type_="inside")],
title_opts=opts.TitleOpts(title="K-line"),
)
)
c.render("k-line.html")
).add_yaxis(
series_name="联盟广告",
stack="总量",
y_axis=[220, 182, 191, 234, 290, 330, 310],
label_opts=opts.LabelOpts(is_show=False),
).add_yaxis(
series_name="视频广告",
stack="总量",
y_axis=[150, 232, 201, 154, 190, 330, 410],
label_opts=opts.LabelOpts(is_show=False),
).add_yaxis(
series_name="直接访问",
stack="总量",
y_axis=[320, 332, 301, 334, 390, 330, 320],
label_opts=opts.LabelOpts(is_show=False),
).add_yaxis(
series_name="搜索引擎",
stack="总量",
y_axis=[820, 932, 901, 934, 1290, 1330, 1320],
label_opts=opts.LabelOpts(is_show=False),
).set_global_opts(
title_opts=opts.TitleOpts(title="折线图堆叠"),
tooltip_opts=opts.TooltipOpts(trigger="axis"),
yaxis_opts=opts.AxisOpts(
type_="value",
axistick_opts=opts.AxisTickOpts(is_show=True),
splitline_opts=opts.SplitLineOpts(is_show=True),
),
xaxis_opts=opts.AxisOpts(type_="category", boundary_gap=False),
).render("stacked_line_chart.html"))
'min': 10,
'color': '#f27777'
},
{
'max': 9,
'min': 1,
'color': '#f7adad'
},
{
'max': 0,
'color': '#f7e4e4'
},
]
labels = [data[0] for data in china_data]
counts = [data[1] for data in china_data]
m = Map()
m.add("累计确诊", [list(z) for z in zip(labels, counts)], 'china')
#系列配置项,可配置图元样式、文字样式、标签样式、点线样式等
m.set_series_opts(label_opts=opts.LabelOpts(font_size=12), is_show=False)
#全局配置项,可配置标题、动画、坐标轴、图例等
m.set_global_opts(
title_opts=opts.TitleOpts(title='全国实时确诊数据', subtitle='数据来源：丁香园'),
legend_opts=opts.LegendOpts(is_show=False),
visualmap_opts=opts.VisualMapOpts(
pieces=pieces,
is_piecewise=True, #是否为分段型
is_show=True)) #是否显示视觉映射配置
#render（）会生成本地 HTML 文件，默认会在当前目录生成 render.html 文件，也可以传入路径参数，如 m.render("mycharts.html")
m.render(path='html/全国实时确诊数据.html')
def heat_map(data: List[dict],
x_label: List[str] = None,
y_label: List[str] = None,
title: str = "热力图",
width: str = "900px",
height: str = "680px") -> HeatMap:
"""绘制热力图
:param data: 用于绘制热力图的数据，示例如下
[{'x': '0hour', 'y': '0day', 'heat': 11},
{'x': '0hour', 'y': '1day', 'heat': 40},
{'x': '0hour', 'y': '2day', 'heat': 38},
{'x': '0hour', 'y': '3day', 'heat': 36},
{'x': '0hour', 'y': '4day', 'heat': 11}]
:param x_label: x轴标签
:param y_label: y轴标签
:param title: 图表标题
:param width: 图表宽度
:param height: 图表高度
:return: 图表
"""
value = [[s['x'], s['y'], s['heat']] for s in data]
heat = [s['heat'] for s in data]
if not x_label:
x_label = sorted(list(set([s['x'] for s in data])))
if not y_label:
y_label = sorted(list(set([s['y'] for s in data])))
vis_map_opts = opts.VisualMapOpts(pos_left="90%",
pos_top="20%",
min_=min(heat),
max_=max(heat))
title_opts = opts.TitleOpts(title=title)
init_opts = opts.InitOpts(page_title=title, width=width, height=height)
dz_inside = opts.DataZoomOpts(False,
"inside",
xaxis_index=[0],
range_start=80,
range_end=100)
dz_slider = opts.DataZoomOpts(True,
"slider",
xaxis_index=[0],
pos_top="96%",
pos_bottom="0%",
range_start=80,
range_end=100)
legend_opts = opts.LegendOpts(is_show=False)
hm = HeatMap(init_opts=init_opts)
hm.add_xaxis(x_label)
hm.add_yaxis("heat",
y_label,
value,
label_opts=opts.LabelOpts(is_show=True, position="inside"))
hm.set_global_opts(title_opts=title_opts,
visualmap_opts=vis_map_opts,
legend_opts=legend_opts,
xaxis_opts=opts.AxisOpts(grid_index=0),
datazoom_opts=[dz_inside, dz_slider])
return hm
("物业维修", 37),
("婚庆服务", 24),
("二手回收", 24),
("鲜花配送", 3),
("维修服务", 3),
("殡葬服务", 1),
("求职创业", 874),
("创业", 363),
("目标职位", 162),
("目标行业", 50),
("兼职", 21),
("期望年薪", 20),
("实习", 16),
("雇主类型", 10),
("星座运势", 789),
("星座", 316),
("算命", 303),
("解梦", 196),
("风水", 93),
("面相分析", 47),
("手相", 32),
("公益", 90),
]
with open("wordcloud_image_base64.json", "r", encoding="utf-8") as f:
j = json.load(f)
c = (WordCloud().add(
"", words, word_size_range=[12, 55],
mask_image=j["base64"]).set_global_opts(title_opts=opts.TitleOpts(
title="WordCloud-自定义图片")).render("Wordcloud_custom_mask_image.html"))
def sankey_vertical() -> Sankey:
colors = [
"#67001f",
"#b2182b",
"#d6604d",
"#f4a582",
"#fddbc7",
"#d1e5f0",
"#92c5de",
"#4393c3",
"#2166ac",
"#053061",
]
nodes = [
{
"name": "a"
},
{
"name": "b"
},
{
"name": "a1"
},
{
"name": "b1"
},
{
"name": "c"
},
{
"name": "e"
},
]
links = [
{
"source": "a",
"target": "a1",
"value": 5
},
{
"source": "e",
"target": "b",
"value": 3
},
{
"source": "a",
"target": "b1",
"value": 3
},
{
"source": "b1",
"target": "a1",
"value": 1
},
{
"source": "b1",
"target": "c",
"value": 2
},
{
"source": "b",
"target": "c",
"value": 1
},
]
c = (Sankey().set_colors(colors).add(
"sankey",
nodes=nodes,
links=links,
pos_bottom="10%",
focus_node_adjacency="allEdges",
orient="vertical",
linestyle_opt=opts.LineStyleOpts(opacity=0.2,
curve=0.5,
color="source"),
label_opts=opts.LabelOpts(position="top"),
).set_global_opts(
title_opts=opts.TitleOpts(title="Sankey-Vertical"),
tooltip_opts=opts.TooltipOpts(trigger="item", trigger_on="mousemove"),
))
return c
def plot_performance_overview(dict_results, plot_tymod, p):
if plot_tymod == "pic":
figs = {}
for k, v in dict_results.items():
v = v.unstack()
series = v.index.get_level_values(0).unique() if isinstance(
v.index, pd.MultiIndex) else [k]
c = 3
r = np.ceil(len(series) / c)
fig = plt.figure(figsize=(8, 6))
plt.subplots_adjust(hspace=0.3)
for i, _s in enumerate(series):
_data = v.loc[_s] if isinstance(v.index, pd.MultiIndex) else v
X, Y = np.meshgrid(_data.index, _data.columns, indexing='ij')
Z = _data.values
pos = int(r * 100 + c * 10 + (i + 1))
ax = fig.add_subplot(pos, projection='3d')
ax.plot_surface(X, Y, Z, color='b')
ax.set_title("%s" % _s, fontdict={"size": 8})
figs[k] = fig
fig.savefig("%s_%s.png" % (p, k))
return figs
elif plot_tymod == 'dyn-3d':
results = {}
for k, perf in dict_results.items():
perf = perf.unstack()
c = (Surface3D(init_opts=opts.InitOpts(
width="1000px", height="500px")).set_global_opts(
title_opts=opts.TitleOpts(title="绩效-曲面波图（%s）" % k),
visualmap_opts=opts.VisualMapOpts(
max_=perf.values.max(),
min_=perf.values.min(),
range_color=Faker.visual_color),
toolbox_opts=opts.ToolboxOpts(is_show=True),
))
series = perf.index.get_level_values(0).unique() if isinstance(
perf.index, pd.MultiIndex) else [k]
for i, _s in enumerate(series):
_data = perf.loc[_s] if isinstance(perf.index,
pd.MultiIndex) else perf
X, Y = np.meshgrid(_data.index, _data.columns)
d = list(
zip(X.flatten().astype(str),
Y.flatten().astype(str),
_data.values.T.flatten().astype(np.float)))
c = c.add(
series_name=str(_s),
shading="color",
data=d,
xaxis3d_opts=opts.Axis3DOpts(type_="category"),
yaxis3d_opts=opts.Axis3DOpts(type_="category"),
grid3d_opts=opts.Grid3DOpts(width=100,
height=50,
depth=100),
)
results[k] = c
tab = Tab()
for k, v in results.items():
tab.add(v, k)
tab.render("%s.html" % p)
elif plot_tymod == "dyn-hm":
tab = Tab()
for k, v in dict_results.items():
v = v.unstack()
_tl = _timeline(v, name=k)
tab.add(_tl, k)
tab.render("%s.html" % p)
