def create_wordcloud(self, json_file, title='词云', column='title'):
text = self.get_text(json_file, column=column)
clear_word = self.split_word(text)
data = self.word_counter(clear_word)
wd = WordCloud()
wd.add(series_name=title, data_pair=data, word_size_range=[40, 150])
wd.set_global_opts(title_opts=opts.TitleOpts(title="你的文章词云", subtitle="基于你的博客数据生成", title_textstyle_opts=opts.TextStyleOpts(font_size=23)), tooltip_opts=opts.TooltipOpts(is_show=True))
# wd.render_notebook()
wd.render(self.path + os.sep + 'topic_wordcloud.html')
list = []
for i in range(len(jobNameList)):
list.append((jobNameList[i], MaxSalaryList[i]))
showJob = []
for i in range(showNum):
showJob.append(random.choice(list))
showJobnameArr = []
showSalArr = []
for (job, sal) in showJob:
showJobnameArr.append(job)
showSalArr.append(sal)
showlist = [str(i) for i in range(1, showNum + 1)]
(Line().set_global_opts(
tooltip_opts=opts.TooltipOpts(is_show=False),
xaxis_opts=opts.AxisOpts(type_="category"),
yaxis_opts=opts.AxisOpts(
type_="value",
axistick_opts=opts.AxisTickOpts(is_show=True),
splitline_opts=opts.SplitLineOpts(is_show=True),
),
title_opts=opts.TitleOpts(title="兰州最高薪水折线图")).add_xaxis(
xaxis_data=showlist).add_yaxis(
series_name="",
y_axis=showSalArr,
symbol="emptyCircle",
is_symbol_show=True,
is_smooth=True,
label_opts=opts.LabelOpts(is_show=False),
).render("./view/maxSalaryLineChart.html"))
48.7,
18.8,
6.0,
2.3,
],
label_opts=opts.LabelOpts(is_show=False),
).extend_axis(yaxis=opts.AxisOpts(
name="温度",
type_="value",
min_=0,
max_=25,
interval=5,
axislabel_opts=opts.LabelOpts(formatter="{value} °C"),
)).set_global_opts(
tooltip_opts=opts.TooltipOpts(is_show=True,
trigger="axis",
axis_pointer_type="cross"),
xaxis_opts=opts.AxisOpts(type_="category",
axispointer_opts=opts.AxisPointerOpts(
is_show=True, type_="shadow")),
yaxis_opts=opts.AxisOpts(
name="水量",
type_="value",
min_=0,
max_=250,
interval=50,
axislabel_opts=opts.LabelOpts(formatter="{value} ml"),
axistick_opts=opts.AxisTickOpts(is_show=True),
splitline_opts=opts.SplitLineOpts(is_show=True),
),
))
"abg": {
"backgroundColor": "#e3e3e3",
"width": "100%",
"align": "right",
"height": 22,
"borderRadius": [4, 4, 0, 0],
},
"hr": {
"borderColor": "#aaa",
"width": "100%",
"borderWidth": 0.5,
"height": 0,
},
"b": {
"fontSize": 16,
"lineHeight": 33
},
"per": {
"color": "#eee",
"backgroundColor": "#334455",
"padding": [2, 4],
"borderRadius": 2,
},
},
),
).set_global_opts(legend_opts=opts.LegendOpts(
pos_left='left', orient='vertical')).set_series_opts(
tooltip_opts=opts.TooltipOpts(
trigger='item', formatter="{a} <br/>{b}: {c} ({d}%)")).set_colors(
['#60acfc', '#32d3eb', '#5bc49f']).render('age_ratio.html'))
"Southern": "南区",
"Tai Po": "大埔",
"Tsuen Wan": "荃湾",
"Tuen Mun": "屯门",
"Wan Chai": "湾仔",
"Wong Tai Sin": "黄大仙",
"Yau Tsim Mong": "油尖旺",
"Yuen Long": "元朗",
}
(Map(init_opts=opts.InitOpts(width="1400px", height="800px")).add_js_funcs(
"echarts.registerMap('HK', {});".format(data)).add(
series_name="香港18区人口密度",
maptype="HK",
data_pair=MAP_DATA,
name_map=NAME_MAP_DATA,
is_map_symbol_show=False).set_global_opts(
title_opts=opts.TitleOpts(title="香港18区人口密度 （2011）",
subtitle="人口密度数据来自Wikipedia",
subtitle_link=WIKI_LINK),
tooltip_opts=opts.TooltipOpts(trigger="item",
formatter="{b}<br/>{c} (p / km2)"),
visualmap_opts=opts.VisualMapOpts(
min_=800,
max_=50000,
range_text=["High", "Low"],
is_calculable=True,
range_color=["lightskyblue", "yellow", "orangered"],
),
).render("population_density_of_HongKong.html"))
def v_tradecost(self, start=None, end=yesterdayobj(), rendered=True):
"""
visualization giving the average cost line together with netvalue line
:returns: pyecharts.line
"""
funddata = []
costdata = []
pprice = self.price[self.price["date"] <= end]
pcftable = self.cftable
if start is not None:
pprice = pprice[pprice["date"] >= start]
pcftable = pcftable[pcftable["date"] >= start]
for _, row in pprice.iterrows():
date = row["date"]
funddata.append(row["netvalue"])
cost = 0
if (date - self.cftable.iloc[0].date).days >= 0:
cost = self.unitcost(date)
costdata.append(cost)
coords = []
for i, r in pcftable.iterrows():
coords.append([
r.date, pprice[pprice["date"] <= r.date].iloc[-1]["netvalue"]
])
upper = self.cftable.cash.abs().max()
lower = self.cftable.cash.abs().min()
if upper == lower:
upper = 2 * lower
def marker_factory(x, y):
buy = self.cftable[self.cftable["date"] <= x].iloc[-1]["cash"]
if buy < 0:
color = "#ff7733"
else:
color = "#3366ff"
size = (abs(buy) - lower) / (upper - lower) * 5 + 5
return opts.MarkPointItem(
coord=[x.date(), y],
itemstyle_opts=opts.ItemStyleOpts(color=color),
# this nested itemstyle_opts within MarkPointItem is only supported for pyechart>1.7.1
symbol="circle",
symbol_size=size,
)
line = Line()
line.add_xaxis([d.date() for d in pprice.date])
line.add_yaxis(
series_name="基金净值",
y_axis=funddata,
is_symbol_show=False,
)
line.add_yaxis(
series_name="持仓成本",
y_axis=costdata,
is_symbol_show=False,
markpoint_opts=opts.MarkPointOpts(
data=[marker_factory(*c) for c in coords], ),
)
line.set_global_opts(
datazoom_opts=[
opts.DataZoomOpts(is_show=True,
type_="slider",
range_start=50,
range_end=100),
opts.DataZoomOpts(
is_show=True,
type_="slider",
orient="vertical",
range_start=50,
range_end=100,
),
],
tooltip_opts=opts.TooltipOpts(
is_show=True,
trigger="axis",
trigger_on="mousemove",
axis_pointer_type="cross",
),
)
if rendered:
return line.render_notebook()
else:
return line
def draw_line_charts(input_data,input_data2,canshu =[],path=''):
canshu_list = []
max0 = int(max(input_data[canshu].max())*1.001)
min0 = int(min(input_data[canshu].min())*0.995)
print(max0,min0)
for i in canshu:
canshu_dict0 = {}
canshu_dict0[i] = input_data[i]
canshu_list.append(canshu_dict0)
print(input_data.keys())
print(input_data.tail(100))
input_data['candle_begin_time'] = pd.to_datetime(input_data['candle_begin_time'])
time_list = input_data['candle_begin_time'].apply(lambda x: x.strftime('%Y-%m-%d %H:%M:%S')).values.tolist()
tab = Tab()
line_base = Line(init_opts=opts.InitOpts(width="1500px", height="700px", theme=ThemeType.DARK)).add_xaxis(time_list)
line_base.add_yaxis(series_name="close:",
y_axis=input_data['close'].values,
is_symbol_show=False,
color="#FF0000")
line_base.set_global_opts(
datazoom_opts=[opts.DataZoomOpts(range_start=95,
range_end=100,), opts.DataZoomOpts(
type_="inside",
range_start=95,
range_end=100,
)],
title_opts=opts.TitleOpts(title="资金曲线变化"),
tooltip_opts=opts.TooltipOpts(trigger="axis", axis_pointer_type="cross"),
yaxis_opts=opts.AxisOpts(
is_scale=True,
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=True) )
for ix,v in enumerate(canshu_list):
v0:str
value0:dict
for k,v0 in v.items():
name =k
value0 = v0
data0 = pd.DataFrame(value0)
data0.fillna(method='bfill',inplace=True)
# print(data0)
line_base.add_yaxis(
series_name=name,
y_axis=data0.values.tolist(),
yaxis_index=2,
linestyle_opts=opts.LineStyleOpts(),
label_opts=opts.LabelOpts(is_show=False),
)
line_base.extend_axis(
yaxis=opts.AxisOpts(
name="资金情况",
name_location="start",
type_="value",
max_= max0,
min_= min0,
is_inverse=False,
axistick_opts=opts.AxisTickOpts(is_show=False),
splitline_opts=opts.SplitLineOpts(is_show=False),))
tab.add(line_base,'资金变化')
table0 = Table().add(list(input_data2.columns), input_data2.values.tolist()).set_global_opts(
title_opts=ComponentTitleOpts(title="策略统计", subtitle=str(dt.datetime.now())))
table0.add_js_funcs('''
document.write("<p style='color:#FF0000;'>
注释：none </p>"
''')
tab.add(table0,'策略统计表')
html0 =tab.render(path)
if os.path.exists(html0):
print("ok!保存在：")
print(html0)
else:
print('保存失败！')
def musics_star_comments_count_scatter(id, num) -> Scatter:
(key,val,maxx,num) = musics_star_comments_count(id, num)
if key == 0:
return 0
x_data = list(map(float, key))
y_data = list(map(int, val))
c = (
Scatter() # init_opts=opts.InitOpts(width="1600px", height="1000px")
.add_xaxis(xaxis_data=x_data)
.add_yaxis(
series_name="评论数",
y_axis=y_data,
# symbol_size=20,
label_opts=opts.LabelOpts(is_show=False),
)
.set_series_opts()
.set_global_opts(
title_opts=opts.TitleOpts(title="评论数与评分的关系", subtitle="数据量：" + str(num),),
xaxis_opts=opts.AxisOpts(
# 坐标轴类型。可选：
# 'value': 数值轴，适用于连续数据。
# 'category': 类目轴，适用于离散的类目数据，为该类型时必须通过 data 设置类目数据。
# 'time': 时间轴，适用于连续的时序数据，与数值轴相比时间轴带有时间的格式化，在刻度计算上也有所不同，
# 例如会根据跨度的范围来决定使用月，星期，日还是小时范围的刻度。
# 'log' 对数轴。适用于对数数据。
type_="value",
name="评分",
splitline_opts=opts.SplitLineOpts(is_show=True),
max_=10,
min_=min(x_data) - 1
),
yaxis_opts=opts.AxisOpts(
type_="value",
name="评论数",
axistick_opts=opts.AxisTickOpts(is_show=True),
splitline_opts=opts.SplitLineOpts(is_show=True),
),
# tooltip_opts=opts.TooltipOpts(is_show=True),
tooltip_opts=opts.TooltipOpts(
trigger="item", formatter="评分 评论数 <br/>{c}条"
),
visualmap_opts=opts.VisualMapOpts(
# type_="size",
max_=int(maxx),
# min_=0,
range_color=[
"#313695",
"#4575b4",
"#74add1",
"#abd9e9",
"#e0f3f8",
"#ffffbf",
"#fee090",
"#fdae61",
"#f46d43",
"#d73027",
"#a50026",
],
),
toolbox_opts=opts.ToolboxOpts(is_show=True,pos_left="95%",
feature={
"dataZoom": {"yAxisIndex": "none"},
"dataView": {},
"restore": {},
"saveAsImage": {},
},
),
)
.dump_options_with_quotes()
)
return c
week_name_list = ["周一", "周二", "周三", "周四", "周五", "周六", "周日"]
high_temperature = [11, 11, 15, 13, 12, 13, 10]
low_temperature = [1, -2, 2, 5, 3, 2, 0]
(Line(init_opts=opts.InitOpts(width="1600px", height="800px")).add_xaxis(
xaxis_data=week_name_list).add_yaxis(
series_name="最高气温",
y_axis=high_temperature,
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(
data=[opts.MarkLineItem(type_="average", name="平均值")]),
).add_yaxis(
series_name="最低气温",
y_axis=low_temperature,
markpoint_opts=opts.MarkPointOpts(
data=[opts.MarkPointItem(value=-2, name="周最低", x=1, y=-1.5)]),
markline_opts=opts.MarkLineOpts(data=[
opts.MarkLineItem(type_="average", name="平均值"),
opts.MarkLineItem(symbol="none", x="90%", y="max"),
opts.MarkLineItem(symbol="circle", type_="max", name="最高点"),
]),
).set_global_opts(
title_opts=opts.TitleOpts(title="未来一周气温变化", subtitle="纯属虚构"),
tooltip_opts=opts.TooltipOpts(trigger="axis"),
toolbox_opts=opts.ToolboxOpts(is_show=True),
xaxis_opts=opts.AxisOpts(type_="category", boundary_gap=False),
).render("temperature_change_line_chart.html"))
def music_rating_pie(id,num) -> Pie:
(genre,num) = music_rating(id,num)
if genre == 0:
return 0
c = (
# Pie(init_opts=opts.InitOpts(width="", height="", bg_color=""))
Pie()
.add(
series_name="音乐评分",
data_pair=[list(z) for z in zip(["评分5", "评分4", "评分3", "评分2", "评分1"], list(map(float, genre)))],
center=["45%", "50%"],
radius=["40%", "55%"],
label_opts=opts.LabelOpts(
is_show=True,
position="outside",
formatter="{a|{a}}{abg|}\n{hr|}\n {b|{b}: }{c}% {per|{d}%} ",
background_color="#eee",
border_color="#aaa",
border_width=1,
border_radius=4,
rich={
"a": {"color": "#999", "lineHeight": 22, "align": "center"},
"abg": {
"backgroundColor": "#e3e3e3",
"width": "100%",
"align": "right",
"height": 22,
"borderRadius": [4, 4, 0, 0],
},
"hr": {
"borderColor": "#aaa",
"width": "100%",
"borderWidth": 0.5,
"height": 0,
},
"b": {"fontSize": 16, "lineHeight": 33},
"per": {
"color": "#eee",
"backgroundColor": "#334455",
"padding": [2, 4],
"borderRadius": 2,
},
},
),
)
.set_global_opts(
title_opts=opts.TitleOpts(
title="音乐评分分布",
subtitle="数据量：" + str(num),
# pos_left="center",
pos_left="38%",
# pos_top="20",
# title_textstyle_opts=opts.TextStyleOpts(color="#fff"),
),
legend_opts=opts.LegendOpts(is_show=True,
pos_top="20",
type_="scroll",
pos_left="85%",
orient="vertical"
),
toolbox_opts=opts.ToolboxOpts(is_show=True,pos_left="95%",
feature={
"dataZoom": {"yAxisIndex": "none"},
"restore": {},
"saveAsImage": {},
}, ),
)
.set_series_opts(
tooltip_opts=opts.TooltipOpts(
trigger="item", formatter="{a} <br/>{b}: {c}% ({d}%)"
),
# label_opts=opts.LabelOpts(color="rgba(1, 1, 1, 0.3)"),
)
.dump_options_with_quotes()
)
return c
def musics_star_pubdate_scatter(id, num) -> Scatter:
(key,val,num) = musics_star_pubdate(id, num)
if key == 0:
return 0
x_data = list(map(str, key))
y_data = list(map(float, val))
c = (
Scatter() # init_opts=opts.InitOpts(width="1600px", height="1000px")
.add_xaxis(xaxis_data=x_data)
.add_yaxis(
series_name="评分",
y_axis=y_data,
# symbol_size=20,
label_opts=opts.LabelOpts(is_show=False),
)
.set_series_opts()
.set_global_opts(
title_opts=opts.TitleOpts(title="发行时间与评分的关系", subtitle="数据量：" + str(num),),
xaxis_opts=opts.AxisOpts(
# type_="value",
type_='time',
name="发行时间",
splitline_opts=opts.SplitLineOpts(is_show=True),
# max_=10,
# min_=min(y_data)-1
),
yaxis_opts=opts.AxisOpts(
type_="value",
name="评分",
axistick_opts=opts.AxisTickOpts(is_show=True),
splitline_opts=opts.SplitLineOpts(is_show=True),
max_=10,
min_=min(y_data) - 1
),
tooltip_opts=opts.TooltipOpts(
is_show=True, trigger="item", formatter="发行时间 评分 <br/>{c}分"
),
visualmap_opts=opts.VisualMapOpts(
max_=10,
range_color=[
"#313695",
"#4575b4",
"#74add1",
"#abd9e9",
"#e0f3f8",
"#ffffbf",
"#fee090",
"#fdae61",
"#f46d43",
"#d73027",
"#a50026",
],
),
toolbox_opts=opts.ToolboxOpts(is_show=True,pos_left="95%",
feature={
"dataZoom": {"yAxisIndex": "none"},
"dataView": {},
"restore": {},
"saveAsImage": {},
},
),
)
.dump_options_with_quotes()
)
return c
# visualMap 组件离容器右侧的距离
pos_right='right',
),
tooltip_opts=opts.TooltipOpts(
# 提示框浮层的背景颜色。
background_color='white',
# 提示框浮层的边框宽。
border_width=1,
# 文字样式配置项，参考 `series_options.TextStyleOpts`
textstyle_opts=opts.TextStyleOpts(color='#00C791'),
# 回调函数，回调函数格式：
# (params: Object|Array) => string
# 参数 params 是 formatter 需要的单个数据集。
formatter=(JsCode("""
function(params){
return params.name + ' : '
+ '<br/>'
+ '现存确诊：'+params.data.value[3]
+ '<br/>'
+ '累积确诊：'+params.data.value[0]
+ '<br/>'
+ '死亡人数：'+params.data.value[1]
+ '<br/>'
+ '治愈人数：'+params.data.value[2];
}
"""))))
# 默认将会在根目录下生成一个 render.html 的文件，支持 path 参数，设置文件保存位置
.render("China_2019-nCov_map.html"), )
#system函数可以将字符串转化成命令在服务器上运行，其会创建一个子进程在系统上执行命令行，子进程的执行结果无法影响主进程；
["2015/11/09", 15, "DD"],
["2015/11/10", 35, "DD"],
["2015/11/11", 38, "DD"],
["2015/11/12", 22, "DD"],
["2015/11/13", 16, "DD"],
["2015/11/14", 7, "DD"],
["2015/11/15", 2, "DD"],
["2015/11/16", 17, "DD"],
["2015/11/17", 33, "DD"],
["2015/11/18", 4, "DD"],
["2015/11/19", 32, "DD"],
["2015/11/20", 26, "DD"],
["2015/11/21", 35, "DD"],
["2015/11/22", 40, "DD"],
["2015/11/23", 32, "DD"],
["2015/11/24", 26, "DD"],
["2015/11/25", 22, "DD"],
["2015/11/26", 16, "DD"],
["2015/11/27", 22, "DD"],
["2015/11/28", 10, "DD"],
]
(ThemeRiver().add(
series_name=x_data,
data=y_data,
singleaxis_opts=opts.SingleAxisOpts(pos_top="50",
pos_bottom="50",
type_="time"),
).set_global_opts(tooltip_opts=opts.TooltipOpts(
trigger="axis", axis_pointer_type="line")).render("theme_river.html"))
def draw_chan(stock, stock_df, kc, end_time, period): #
stock_df_original = stock_df[[
'date', 'open', 'close', 'low', 'high', 'money'
]]
stock_df_bi = kc.getFenBI_df()[['date', 'chan_price']]
stock_df_xd = kc.getFenDuan_df()[['date', 'chan_price']]
kline = (
# Kline({"theme": ThemeType.DARK})
Kline().add_xaxis(
xaxis_data=stock_df_original['date'].tolist(),
).add_yaxis(
series_name=stock,
y_axis=stock_df_original[['open', 'close', 'low',
'high']].tolist(),
itemstyle_opts=opts.ItemStyleOpts(
color="#ec0000",
color0="#00da3c"), #添加用 Hex 字符串表示的红和绿两种颜色，对应着 K线涨和跌的颜色。
).set_global_opts(
title_opts=opts.TitleOpts(title="缠论", ),
xaxis_opts=opts.AxisOpts(type_="category"),
# 分割线
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),
toolbox_opts=opts.ToolboxOpts(),
datazoom_opts=[
opts.DataZoomOpts(
is_show=False,
type_="inside",
#xaxis_index=[0, 1],
range_start=
0, #添加两个「数据区域缩放」功能，一个看的到（用鼠标拉缩图最下面的 slider），一个看不到（用鼠标直接在图中拉缩），并且设置 xaxis_index =[0,1]，表示用 K 线图（index 0）来控制柱状图（index 1）。
range_end=100,
),
opts.DataZoomOpts(
is_show=True,
#xaxis_index=[0, 1],
type_="slider",
pos_top="90%",
range_start=
0, #index为1和2的两幅图的数据局部伸缩跟着index0那幅图，这样就实现了用一根x轴的slider可以任意缩放三幅图的数据
range_end=100,
),
],
tooltip_opts=opts.TooltipOpts(
trigger="axis",
axis_pointer_type="cross", #将两幅图的提示框合并在一起。
background_color="rgba(245, 245, 245, 0.8)",
border_width=1,
border_color="#ccc",
textstyle_opts=opts.TextStyleOpts(color="#000"),
),
visualmap_opts=opts.VisualMapOpts(
is_show=False,
dimension=2,
series_index=3,
is_piecewise=True,
pieces=[
{
"value": -1,
"color": "#ec0000"
},
{
"value": 1,
"color": "#00da3c"
},
],
), #坐标轴指示器配置和区域选择组件配置使得数据和轴可以一起联动。
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",
),
))
Biline = (
Line().add_xaxis(xaxis_data=stock_df_bi['date'].tolist()).add_yaxis(
series_name='笔',
y_axis=stock_df_bi['chan_price'].tolist(),
is_smooth=False,
is_connect_nones=True,
is_hover_animation=False,
#linestyle_opts=opts.LineStyleOpts(color="red",width=2, type_="dashed"),
linestyle_opts=opts.LineStyleOpts(color="red",
width=2,
opacity=0.5),
label_opts=opts.LabelOpts(is_show=True),
)
# .set_global_opts(xaxis_opts=opts.AxisOpts(type_="category"))
)
overlap = kline.overlap(Biline)
crp = CentralRegionProcess(kc.getFenDuan_df(), kc, isdebug=False)
crp.define_central_region()
stock_zs_x, stock_zs_y = crp.convert_to_graph_data()
if (stock_df_xd is not None) and (len(stock_zs_x) != 0):
# print('if')
for i in range(0, len(stock_zs_x), 2):
XD_line = (
Line().add_xaxis(xaxis_data=stock_df_xd['date'].tolist()).
add_yaxis(
series_name='段',
y_axis=stock_df_xd['chan_price'].tolist(),
is_smooth=False,
is_connect_nones=True,
is_hover_animation=False,
#linestyle_opts=opts.LineStyleOpts(color="red",width=2, type_="dashed"),
linestyle_opts=opts.LineStyleOpts(color="blue",
width=2,
opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
).set_series_opts(markarea_opts=opts.MarkAreaOpts(
is_silent=True,
data=[[{
'xAxis': stock_zs_x[i],
'yAxis': stock_zs_y[i]
}, {
'xAxis': stock_zs_x[i + 1],
'yAxis': stock_zs_y[i + 1]
}]]))
# .set_global_opts(xaxis_opts=opts.AxisOpts(type_="category"))
)
elif stock_df_xd is not None:
# print('elif')
XD_line = (
Line().add_xaxis(xaxis_data=stock_df_xd['date'].tolist()).
add_yaxis(
series_name='段',
y_axis=stock_df_xd['chan_price'].tolist(),
is_smooth=False,
is_connect_nones=True,
is_hover_animation=False,
#linestyle_opts=opts.LineStyleOpts(color="red",width=2, type_="dashed"),
linestyle_opts=opts.LineStyleOpts(color="blue",
width=2,
opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
))
overlap = overlap.overlap(XD_line)
# MA_line = (
# Line()
# .add_xaxis(xaxis_data=stock_df_original['date'].tolist(),)
# .add_yaxis(
# series_name="MA5",
# y_axis= talib.SMA(stock_df['close'],timeperiod = 5).tolist(),
# is_smooth=True,
# is_hover_animation=False,
# linestyle_opts=opts.LineStyleOpts(width=1, opacity=0.5),
# label_opts=opts.LabelOpts(is_show=False),
# )
# .add_yaxis(
# series_name="MA10",
# y_axis= talib.SMA(stock_df['close'],timeperiod = 10).tolist(),
# is_smooth=True,
# is_hover_animation=False,
# linestyle_opts=opts.LineStyleOpts(width=1, opacity=0.5),
# label_opts=opts.LabelOpts(is_show=False),
# )
# .add_yaxis(
# series_name="MA13",
# y_axis= talib.SMA(stock_df['close'],timeperiod = 13).tolist(),
# is_smooth=True,
# is_hover_animation=False,
# linestyle_opts=opts.LineStyleOpts(width=1, opacity=0.5),
# label_opts=opts.LabelOpts(is_show=False),
# )
# .add_yaxis(
# series_name="MA34",
# y_axis= talib.SMA(stock_df['close'],timeperiod = 34).tolist(),
# is_smooth=True,
# is_hover_animation=False,
# linestyle_opts=opts.LineStyleOpts(width=1, opacity=0.5),
# label_opts=opts.LabelOpts(is_show=False),
# )
# .set_global_opts(xaxis_opts=opts.AxisOpts(type_="category"))
# )
# Volume_Bar = (
# Bar()
# .add_xaxis(xaxis_data=stock_df_original['date'].tolist())
# .add_yaxis(
# series_name="money",
# yaxis_data=stock_df_original['money'].tolist(),
# xaxis_index=2,
# yaxis_index=2,
# label_opts=opts.LabelOpts(is_show=False),
# markline_opts=opts.MarkLineOpts(
# data=[opts.MarkLineItem(type_="average", name="平均值")]
# ),
# itemstyle_opts=opts.ItemStyleOpts(
# color=JsCode(
# """
# function(params) {
# var colorList;
# if (params.data >= 0) {
# colorList = '#ef232a';
# } else {
# colorList = '#14b143';
# }
# return colorList;
# }
# """
# # """
# # function(params) {
# # var colorList;
# # if (barData[params.dataIndex][1] > barData[params.dataIndex][0]) {
# # colorList = '#ef232a';
# # } else {
# # colorList = '#14b143';
# # }
# # return colorList;
# # }
# # """
# )
# ),
# )
# .set_global_opts(
# title_opts=opts.TitleOpts(
# title='',
# ),
# xaxis_opts=opts.AxisOpts(
# type_="category",
# grid_index=2,
# is_scale=True,
# # axislabel_opts=opts.LabelOpts(is_show=False),
# 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(
# 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),
# splitarea_opts=opts.SplitAreaOpts(
# is_show=True,
# areastyle_opts=opts.AreaStyleOpts(opacity=1)
# ),
# ),
# # xaxis_opts=opts.AxisOpts(is_scale=True),
# legend_opts=opts.LegendOpts(is_show=False),
# datazoom_opts=opts.DataZoomOpts(type_="inside"),
# )
# )
#
# overlap = overlap.overlap(Volume_Bar)
print("it's here")
if len(stock_zs_x) != 0:
for i in range(0, len(stock_zs_x), 2):
zs1_line = (Line().add_xaxis(
xaxis_data=stock_zs_x[i:i + 2], ).add_yaxis(
series_name="中枢",
y_axis=np.append(stock_zs_y[i], stock_zs_y[i]),
is_smooth=False,
is_hover_animation=False,
linestyle_opts=opts.LineStyleOpts(color="red",
width=1.5,
opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
).set_global_opts(xaxis_opts=opts.AxisOpts(type_="category")))
zs2_line = (Line().add_xaxis(
xaxis_data=stock_zs_x[i:i + 2], ).add_yaxis(
series_name="中枢",
y_axis=np.append(stock_zs_y[i + 1:i + 2],
stock_zs_y[i + 1:i + 2]),
is_smooth=False,
is_hover_animation=False,
linestyle_opts=opts.LineStyleOpts(color="red",
width=1.5,
opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
).set_global_opts(xaxis_opts=opts.AxisOpts(type_="category")))
zs3_line = (Line().add_xaxis(xaxis_data=np.append(
stock_zs_x[i:i + 1], stock_zs_x[i:i + 1])).add_yaxis(
series_name="中枢",
y_axis=stock_zs_y[i:i + 2],
is_smooth=False,
is_hover_animation=False,
linestyle_opts=opts.LineStyleOpts(color="red",
width=1.5,
opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
).set_global_opts(xaxis_opts=opts.AxisOpts(type_="category")))
zs4_line = (Line().add_xaxis(xaxis_data=np.append(
stock_zs_x[i + 1:i + 2], stock_zs_x[i + 1:i + 2])).add_yaxis(
series_name="中枢",
y_axis=stock_zs_y[i:i + 2],
is_smooth=False,
is_hover_animation=False,
linestyle_opts=opts.LineStyleOpts(color="red",
width=1.5,
opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
).set_global_opts(xaxis_opts=opts.AxisOpts(type_="category")))
zs1 = zs1_line.overlap(zs2_line)
zs2 = zs3_line.overlap(zs4_line)
zs = zs1.overlap(zs2)
overlap.overlap(zs)
overlap.render("diagram/{0}@{1}#{2}.html".format(stock[0:6],
end_time[0:10], period))
print("it's done")
def world_data_all():
###############################################
world_message = pd.DataFrame(pd.read_excel(readPath+'/世界总体疫情信息/世界总体疫情信息.xlsx')) # 获取当前工作路径下的data数据
data_gb = world_message.groupby(by='continent') ####将世界总体的信息以不同的洲进行分组
world_gb = data_gb.agg(np.sum)
world_gb
continent = ['亚洲', '其他', '北美洲', '南美洲', '大洋洲', '欧洲', '非洲']
world_continent = continent
continent_isUpdated = list(world_gb['isUpdated'])
continent_confirmAdd = list(world_gb['confirmAdd'])
continent_confirm = list(world_gb['confirm'])
continent_dead = list(world_gb['dead'])
continent_heal = list(world_gb['heal'])
continent_nowConfirm = list(world_gb['nowConfirm'])
continent_nowConfirmCompare = list(world_gb['nowConfirmCompare'])
continent_ConfirmCompare = list(world_gb['confirmCompare'])
continent_healCompare = list(world_gb['healCompare'])
continent_deadCompare = list(world_gb['deadCompare'])
Line_World = (
Line()
.add_xaxis(xaxis_data=world_continent)
# .add_yaxis(series_name='新增确诊',yaxis_data=world_confirmAdd)
.add_yaxis(series_name='确诊', y_axis=continent_confirm, label_opts=opts.LabelOpts(is_show=False))
.add_yaxis(series_name='死亡', y_axis=continent_dead, label_opts=opts.LabelOpts(is_show=False))
.add_yaxis(series_name='治愈', y_axis=continent_heal, label_opts=opts.LabelOpts(is_show=False))
.add_yaxis(series_name='现存确诊', y_axis=continent_nowConfirm, label_opts=opts.LabelOpts(is_show=False))
.set_global_opts(title_opts=opts.TitleOpts(title='各大洲总信息', pos_left='50%', pos_top='10%'),
tooltip_opts=opts.TooltipOpts(axis_pointer_type='cross'))
)
Pie_continent_confirm = (
Pie()
.add(series_name='死亡', data_pair=[(i, j) for i, j in zip(world_continent, continent_dead)],
rosetype='radius')
# .add(series_name='确诊',data_pair=[(i,j) for i,j in zip(continent_continent,continent_confirm)])
)
Pie_continent_heal = (
Pie()
.add(series_name='治愈', data_pair=[(i, j) for i, j in zip(world_continent, continent_heal)],
rosetype='radius')
)
Pie_continent_nowconfirm = (
Pie()
.add(series_name='现存确诊', data_pair=[(i, j) for i, j in zip(world_continent, continent_nowConfirm)],
rosetype='radius')
)
# Pie_continent_confirm.render(savaPath + '\\daliy_changes_view\\各大洲确诊信息.html')
# Pie_continent_heal.render(savaPath + '\\daliy_changes_view\\各大洲治愈信息.html')
# Pie_continent_nowconfirm.render(savaPath + '\\daliy_changes_view\\各大洲现存确诊信息.html')
# Line_World.render(savaPath + '\\daliy_changes_view\\各大洲总信息.html')
if flag:
dwf.write_to_file(savaPath + '/各大洲确诊信息.txt',str(Pie_continent_confirm.dump_options_with_quotes()))
dwf.write_to_file(savaPath + '/各大洲治愈信息.txt',str(Pie_continent_heal.dump_options_with_quotes()))
dwf.write_to_file(savaPath + '/各大洲现存确诊信息.txt',str(Pie_continent_nowconfirm.dump_options_with_quotes()))
dwf.write_to_file(savaPath + '/各大洲总信息.txt',str(Line_World.dump_options_with_quotes()))
page.add(Pie_continent_confirm)
page.add(Pie_continent_heal)
page.add(Pie_continent_nowconfirm)
page.add(Line_World)
'elementType': 'labels.text.fill',
'stylers': {
'visibility': 'off'
}
}]
}))
for index, row in X.iterrows():
c = c.add_coordinate(index, row['y'], row['x'])
for group in X.groupby('labels'):
c = c.add(series_name=group[0],
type_="scatter",
is_large=True,
large_threshold=2000,
symbol_size=5,
label_opts=opts.LabelOpts(is_show=False),
tooltip_opts=opts.TooltipOpts(formatter='{c}'),
data_pair=[
list(z)
for z in zip(list(group[1].index), group[1]['labels'])
])
c = (c.add_control_panel(
navigation_control_opts=opts.BMapNavigationControlOpts(
position=BMapType.ANCHOR_TOP_LEFT),
maptype_control_opts=opts.BMapTypeControlOpts()))
c = c.set_global_opts(title_opts=opts.TitleOpts(title="Clusters of Crime"))
c.render(name + 'scatter.html')
# In[393]:
map_c = pd.read_csv('dbscanoutput.csv')[:10000]
map_c.columns = ['x', 'y', 'labels']
]
],
"outliers": [],
},
]
c = (Boxplot(
init_opts=opts.InitOpts(width="1600px", height="800px")).add_xaxis(
xaxis_data=axis_data).add_yaxis(
series_name="category0",
y_axis=data[0]["boxData"],
tooltip_opts=opts.TooltipOpts(
formatter=JsCode("""function(param) { return [
'Experiment ' + param.name + ': ',
'upper: ' + param.data[0],
'Q1: ' + param.data[1],
'median: ' + param.data[2],
'Q3: ' + param.data[3],
'lower: ' + param.data[4]
].join('<br/>') }""")),
).add_yaxis(
series_name="category1",
y_axis=data[1]["boxData"],
tooltip_opts=opts.TooltipOpts(
formatter=JsCode("""function(param) { return [
'Experiment ' + param.name + ': ',
'upper: ' + param.data[0],
'Q1: ' + param.data[1],
'median: ' + param.data[2],
'Q3: ' + param.data[3],
'lower: ' + param.data[4]
pos_top="40px",
pos_left="center",
title="TITLE",
subtitle="SUBTITLE",
title_textstyle_opts=txt_opts
)
# %%
# ## SERIES OPTIONS
# ### Explaination Options
TOOlTIP = {
"click": opts.TooltipOpts(
is_show=True,
trigger="item",
trigger_on="click",
axis_pointer_type="shadow",
formatter="名称：{a} <br/> {b}: {c}%"
),
"mouse": opts.TooltipOpts(
is_show=True,
trigger="axis",
trigger_on="mousemove",
axis_pointer_type="cross",
formatter="名称：{a} <br/> {b}: {c}%"
),
"hidden": opts.TooltipOpts(
is_show=False
)
}
LABEL_OPTS = {
def grid_muti_yaxis():
x_data = ["{}月".format(i) for i in range(1, 13)]
bar = (
Bar()
.add_xaxis(x_data)
.add_yaxis(
"蒸发量",
[2.0, 4.9, 7.0, 23.2, 25.6, 76.7, 135.6, 162.2, 32.6, 20.0, 6.4, 3.3],
yaxis_index=0,
color="#d14a61",
)
.add_yaxis(
"降水量",
[2.6, 5.9, 9.0, 26.4, 28.7, 70.7, 175.6, 182.2, 48.7, 18.8, 6.0, 2.3],
yaxis_index=1,
color="#5793f3",
)
.extend_axis(
yaxis=opts.AxisOpts(
name="蒸发量",
type_="value",
min_=0,
max_=250,
position="right",
axisline_opts=opts.AxisLineOpts(
linestyle_opts=opts.LineStyleOpts(color="d14a61")
),
axislabel_opts=opts.LabelOpts(formatter="{value} ml"),
)
)
.extend_axis(
yaxis=opts.AxisOpts(
type_="value",
name="温度",
min_=0,
max_=25,
position="left",
axisline_opts=opts.AxisLineOpts(
linestyle_opts=opts.LineStyleOpts(color="#675bba")
),
axislabel_opts=opts.LabelOpts(color="#{value} °C"),
splitline_opts=opts.SplitLineOpts(
is_show=True, linestyle_opts=opts.LineStyleOpts(opacity=1)
),
)
)
.set_global_opts(
yaxis_opts=opts.AxisOpts(
name="降水量",
min_=0,
max_=250,
position="right",
offset=80,
axisline_opts=opts.AxisLineOpts(
linestyle_opts=opts.LineStyleOpts(color="#5793f3")
),
axislabel_opts=opts.LabelOpts(formatter="{value} ml"),
),
title_opts=opts.TitleOpts(title="Grid-多 Y 轴示例"),
tooltip_opts=opts.TooltipOpts(trigger="axis", axis_pointer_type="cross"),
)
)
line = (
Line()
.add_xaxis(x_data)
.add_yaxis(
"平均温度",
[2.0, 2.2, 3.3, 4.5, 6.3, 10.2, 20.3, 23.4, 23.0, 16.5, 12.0, 6.2],
yaxis_index=2,
color="#675bba",
label_opts=opts.LabelOpts(is_show=False),
)
)
bar.overlap(line)
return Grid().add(bar, opts.GridOpts(pos_left="5%", pos_right="20%"), is_control_axis_index=True)
1、迷之颜色映射的问题
"""
x_data = ["直接访问", "邮件营销", "联盟广告", "视频广告", "搜索引擎"]
y_data = [335, 310, 274, 235, 400]
data_pair = [list(z) for z in zip(x_data, y_data)]
data_pair.sort(key=lambda x: x[1])
(Pie(init_opts=opts.InitOpts(
width="1600px", height="800px", bg_color="#2c343c")).add(
series_name="访问来源",
data_pair=data_pair,
rosetype="radius",
radius="55%",
center=["50%", "50%"],
label_opts=opts.LabelOpts(is_show=False, position="center"),
).set_global_opts(
title_opts=opts.TitleOpts(
title="Customized Pie",
pos_left="center",
pos_top="20",
title_textstyle_opts=opts.TextStyleOpts(color="#fff"),
),
legend_opts=opts.LegendOpts(is_show=False),
).set_series_opts(
tooltip_opts=opts.TooltipOpts(trigger="item",
formatter="{a} <br/>{b}: {c} ({d}%)"),
label_opts=opts.LabelOpts(color="rgba(255, 255, 255, 0.3)"),
).render("customized_pie.html"))
def draw_charts(kline_data ,canshu ={} ,canshu2={},vol_bar=False,markline_show1 =False ,markline_show2 =False, path = '0501'):
'''
df['candle_begin_time','open','high','low','close','volume']
[['candle_begin_time','open','high','low','close','volume']]
kdata = df[['open', 'high', 'low', 'close']].values.tolist()
df['candle_begin_time'].values.tolist()
:return:
'''
df = kline_data.copy()
df['candle_begin_time'] = pd.to_datetime(df['candle_begin_time'])
time_list = df['candle_begin_time'].apply(lambda x: x.strftime('%Y-%m-%d %H:%M:%S')).values.tolist()
vol = df['volume'].values.tolist()
kline = Kline()
kline.add_xaxis(xaxis_data=time_list)
signal_pos_list = []
if 'signal' in df.columns:
print('signal,存在！')
df['pos'] = df['signal'].shift(1)
for i in df[df['pos'] > 0].index:
bar0 = df.iloc[i]
sig_pos0 = opts.MarkPointItem(name="做多",
coord=[bar0['candle_begin_time'].strftime('%Y-%m-%d %H:%M:%S'),bar0.low -2],
value='买',
symbol ='circle',
symbol_size =[20,40],
itemstyle_opts = {'color': 'red'})
signal_pos_list.append(sig_pos0)
for i in df[df['pos'] < 0].index:
bar0 = df.iloc[i]
sig_pos0 = opts.MarkPointItem(name="做空",
coord=[bar0['candle_begin_time'].strftime('%Y-%m-%d %H:%M:%S'),bar0.high +3],
value='卖',
symbol='circle',
symbol_size=[20, 40],
itemstyle_opts={'color': 'blue'})
signal_pos_list.append(sig_pos0)
for i in df[df['pos'] == 0].index:
bar0 = df.iloc[i]
sig_pos0 = opts.MarkPointItem(name="平仓",
coord=[bar0['candle_begin_time'].strftime('%Y-%m-%d %H:%M:%S'),bar0.low - 2],
value='平',
symbol ='triangle',
symbol_size =[20,40],
itemstyle_opts = {'color': 'green'})
signal_pos_list.append(sig_pos0)
else :
df['pos'] =None
markline =[]
if markline_show1 and ('signal' in df.columns) :
area_index_list =[i for i in df[(df['pos'] ==0)|(df['pos'] >0)|(df['pos'] <0)].index]
for ix,i in enumerate(area_index_list):
if ix+1 > len(area_index_list)-1:
break
i_now = df.iloc[area_index_list[ix]]
i_next = df.iloc[area_index_list[ix+1]]
if (i_now['pos'] >0) or (i_now['pos'] <0) :
log_info = f"价差：={i_next['open']-i_now['open']}--({i_next['open']}-{i_now['open']})"
else :
log_info =f"平仓：{i_next['open']}---开仓：{i_now['open']}"
sig_area = [{"xAxis": i_now['candle_begin_time'].strftime('%Y-%m-%d %H:%M:%S'),
"yAxis": i_now['open'] ,
"value": None},
{"xAxis": i_next['candle_begin_time'].strftime('%Y-%m-%d %H:%M:%S'),
"yAxis": i_now['open'],
"value":log_info}]
sig_area_v = [{"xAxis": i_next['candle_begin_time'].strftime('%Y-%m-%d %H:%M:%S'),
"yAxis": i_now['open'] ,
"value": None},
{"xAxis": i_next['candle_begin_time'].strftime('%Y-%m-%d %H:%M:%S'),
"yAxis": i_next['open'],
"value":None}]
markline.append(sig_area)
markline.append(sig_area_v)
elif markline_show2 and ('signal' in df.columns):
area_index_list =[i for i in df[(df['pos'] ==0)|(df['pos'] >0)|(df['pos'] <0)].index]
for ix,i in enumerate(area_index_list):
i_now = df.iloc[area_index_list[ix]]
i_1_now = df.iloc[area_index_list[ix-1]] if ix != 0 else 0
if i_now['pos'] == 0:
log_info = f"交易价: {round(i_now['open'], 1)} +- 2__盈亏:{round(i_now['open'], 1) - round(i_1_now['open'], 1)}"
else:
log_info = f"交易价: {round(i_now['open'],1)} +- 2"
sig_area = [{"xAxis": i_now['candle_begin_time'].strftime('%Y-%m-%d %H:%M:%S'),
"yAxis": i_now['open'] ,
"value": None},
{"xAxis": (i_now['candle_begin_time']+timedelta(minutes=15)).strftime('%Y-%m-%d %H:%M:%S'),
"yAxis": i_now['open'],
"value":log_info}]
markline.append(sig_area)
colors = {'red': 'rgb((220,20,60)','red2': 'rgb((250,20,40)',
'yangzi': 'rgb(255,0,255)', 'zi': 'rgb(128,0,128)',
'sehnzi': 'rgb(148,0,211)', 'blue': 'rgb(0,0,255)',
'qing': 'rgb(0,255,255)', 'black': 'rgb(0,0,0)',
'shengreen': 'rgb(157,255,212)', 'green': 'rgb(0,255,0)',
'yellow': 'rgb(255,255,0)', 'glod': 'rgb(218,165,32)',
'orange': 'rgb(255,165,0)', 'brown': 'rgb(165,42,42)'}
kline.add_xaxis(xaxis_data=time_list)
kline.add_yaxis(series_name="oclh",
xaxis_index=1,
yaxis_index=1,
y_axis =df.loc[:, ['open', 'close', 'low', 'high']].values.tolist(),
markline_opts=opts.MarkLineOpts(data=markline,
linestyle_opts=opts.LineStyleOpts(type_='dotted',width=3,color='red',opacity=0.5),
label_opts=opts.LabelOpts(position="right", color="blue", font_size=13),),
#官网给出的样本
markpoint_opts=opts.MarkPointOpts(data=signal_pos_list),
itemstyle_opts=opts.ItemStyleOpts(color="#ec0090", color0="#00aa3c"), )
kline.set_global_opts(
legend_opts=opts.LegendOpts(is_show=True,pos_top=30, pos_left="left",orient='vertical'),
datazoom_opts=[ opts.DataZoomOpts(
is_show=False,
type_="inside",
xaxis_index=[0, 1],
range_start=90,
range_end=100,
orient='vertical'),
opts.DataZoomOpts(
is_show=True,
xaxis_index=[0, 1],
type_="slider",
pos_top="20%",
range_start=90,
range_end=100,orient='vertical'),],
yaxis_opts =opts.AxisOpts(
is_scale=True,
splitarea_opts=opts.SplitAreaOpts(is_show=True, areastyle_opts=opts.AreaStyleOpts(opacity=1)),),
title_opts=opts.TitleOpts(
title = 'K_line',
pos_top='middle',
title_textstyle_opts=opts.TextStyleOpts(
color='black',font_weight='bold' ,font_size=20)),
tooltip_opts=opts.TooltipOpts(
trigger="axis",
trigger_on='"mousemove"',#click|mousemove
axis_pointer_type="cross",
is_show_content=True,
is_always_show_content=True,
background_color="rgba(20, 105, 245, 0.1)",
border_width=1,
border_color= "#ccc",
position = ['70%','30%'],
textstyle_opts=opts.TextStyleOpts(font_size=10,color="#000"),),
visualmap_opts=opts.VisualMapOpts(
is_show=True,
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(canshu.keys())>0:
cos = list(colors.values())
line = Line()
for k,v in canshu.items():
line.add_xaxis(xaxis_data=time_list)
line.set_global_opts(xaxis_opts=opts.AxisOpts(type_="category"))
co = cos.pop()
line.add_yaxis(
series_name=k,
y_axis = [y for y in v.values.tolist() if y != np.nan],
xaxis_index=1,
yaxis_index=1,
is_smooth=False,
is_connect_nones=False,# 是否连接空数据
is_symbol_show=False,#是否显示值的位置，默认显示。
color = co,
is_hover_animation = False, # 是否开启 hover 在拐点标志上的提示动画效果。
linestyle_opts=opts.LineStyleOpts(width=2, opacity=0.9,color=co),
label_opts=opts.LabelOpts(is_show =True,position='middle',distance=2,rotate=5,color = 'rgb(165,42,42)'),
itemstyle_opts=opts.ItemStyleOpts(color=co),)
kline = kline.overlap(line)
grid_chart = Grid(init_opts=opts.InitOpts(width = "1500px",height= "700px",theme=ThemeType.DARK))
grid_chart.add(kline,grid_opts=opts.GridOpts(pos_left="10%", pos_right="8%",pos_top='4%' ,height="70%"))
if( vol_bar == True) or (len(canshu2.keys())==0):
bar = Bar()
bar.add_xaxis(xaxis_data=time_list)
bar.add_yaxis(
series_name="volume",
y_axis=vol,
xaxis_index=1,
yaxis_index=2,
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(
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=True), )
grid_chart.add(bar,grid_opts=opts.GridOpts(pos_left="10%", pos_right="8%", pos_top="80%", height="15%"),)
if len(canshu2.keys())>0 :
line2 = Line()
line2.add_xaxis(xaxis_data=time_list)
for i, k in enumerate(canshu2.keys()):
co = random.choice(list(colors.values()))
line2.add_yaxis(
series_name=k,
y_axis=[y for y in canshu2[k].values.tolist()],
xaxis_index=1,
yaxis_index=i + 1,
is_smooth=True,
is_symbol_show=False,
is_hover_animation=True,
color=co,
z_level=0,
linestyle_opts=opts.LineStyleOpts(width=2, opacity=0.5, color=co),
label_opts=opts.LabelOpts(is_show=False), )
if k == list(canshu2.keys())[-1]: continue
line2.extend_axis(yaxis=opts.AxisOpts(name=k, type_="value", position="right", min_=None, max_=None,
axisline_opts=opts.AxisLineOpts(linestyle_opts=opts.LineStyleOpts(color=co)),
axislabel_opts=opts.LabelOpts(formatter="{value}"), ))
line2.set_global_opts(xaxis_opts=opts.AxisOpts(
type_="category", is_scale=True, grid_index=1, split_number=20, min_="dataMin", max_="dataMax",
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), ),
yaxis_opts=opts.AxisOpts(name=k, grid_index=i + 1, position='right',
splitarea_opts=opts.SplitAreaOpts(is_show=True, areastyle_opts=opts.AreaStyleOpts(opacity=1)),
min_=None, max_=None, is_scale=True, offset=50, ), )
grid_chart.add(line2,grid_opts=opts.GridOpts(pos_left="10%", pos_right="8%", pos_top="75%", height="23%"),is_control_axis_index=True)
tab = Tab()
zijin_data = (kline_data['per_lr'].cumsum())
zijin_data.fillna(method='ffill', inplace=True)
zijin_line = Line(init_opts=opts.InitOpts(width = "1500px",height= "700px",theme=ThemeType.DARK)).add_xaxis(time_list)
zijin_line.add_yaxis(series_name="zijin_line:", y_axis=zijin_data.values, color="#FF0000")
zijin_line.set_global_opts(
datazoom_opts=[opts.DataZoomOpts(), opts.DataZoomOpts(type_="inside")],
title_opts=opts.TitleOpts(title="资金曲线变化"),
tooltip_opts=opts.TooltipOpts(trigger="axis", axis_pointer_type="cross"),
yaxis_opts = opts.AxisOpts(
is_scale=True,
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=True)
)
tab.add(grid_chart,'kline' )
tab.add(zijin_line,'资金曲线变化' )
tab.render(f"{path}.html")
html0 = tab.render(f"{path}.html")
if os.path.exists(html0):
print("ok!保存在：")
print(html0)
else:
print('保存失败！')
def genGeo(data, datapair):
c = (
Geo(init_opts=opts.InitOpts(
bg_color='#344b58',
))
.add_schema(
maptype="china",
itemstyle_opts=opts.ItemStyleOpts(
border_color='#344b58',
border_width=2.0,
area_color='rgba(0,0,0,0)',
color="#eeeeee",
),
label_opts=opts.LabelOpts(
is_show=False,
color='rgba(255,144,128,1)',
),
emphasis_itemstyle_opts=opts.ItemStyleOpts(
area_color='rgba(0,0,0,0)'
),
emphasis_label_opts=opts.LabelOpts(
is_show=False
))
.add( # 添加动态散点图
"",
data,
type_=ChartType.EFFECT_SCATTER,
# symbol_size=20,
effect_opts=opts.EffectOpts(
period=5,
scale=5,
brush_type='fill'
),
label_opts=opts.LabelOpts(
is_show=True,
formatter="{b}",
color="rgba(255,144,128,1)",
border_width=2.,
font_size=18,
)
)
.add( # 添加线段图
"geo",
datapair,
type_=ChartType.LINES,
symbol_size=7,
effect_opts=opts.EffectOpts(
symbol="circle",
symbol_size=8,
trail_length=0.08,
color="rgba(255,144,128,1)"
),
linestyle_opts=opts.LineStyleOpts(
curve=0.5,
width=2.5,
opacity=0.8,
color=JsCode(
"""new echarts.graphic.LinearGradient(0, 0, 0, 1, [{
offset: 0,
color: '#00acea'
}, {
offset: 1,
color: '#FF8C00'
}])"""
)
),
label_opts=opts.LabelOpts(
is_show=False
)
)
.set_global_opts(
title_opts=opts.TitleOpts(
title="十一人口流动图"
),
visualmap_opts=opts.VisualMapOpts(
min_=0,
max_=1000,
textstyle_opts=opts.TextStyleOpts(
color='white'
)
),
legend_opts=opts.LegendOpts(
is_show=False
),
tooltip_opts=opts.TooltipOpts(
trigger="item",
formatter='{b}'
),
)
)
tl.add(c, "{}年".format(series[i]))
def draw_charts(x_axis_list, oclh_data_list, volume_list, tech_line_dict):
upColor = '#ec0000'
downColor = '#00da3c'
upBorderColor = '#8A0000'
downBorderColor = '#008F2'
kline = (Kline().add_xaxis(x_axis_list).add_yaxis(
series_name="kline",
y_axis=oclh_data_list,
markpoint_opts=opts.MarkPointOpts(
data=gen_mark_points_data_list())).set_series_opts(
itemstyle_opts=opts.ItemStyleOpts(
color=upColor,
color0=downColor,
border_color=upBorderColor,
border_color0=downBorderColor), ).set_global_opts(
legend_opts=opts.LegendOpts(pos_bottom=10,
pos_left="center"),
xaxis_opts=opts.AxisOpts(is_scale=True),
yaxis_opts=opts.AxisOpts(
is_scale=True,
splitarea_opts=opts.SplitAreaOpts(
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=1,
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": downColor
},
{
"value": -1,
"color": upColor
},
],
),
datazoom_opts=[
opts.DataZoomOpts(
type_="inside",
xaxis_index=[0, 1],
range_start=98,
range_end=100,
),
opts.DataZoomOpts(
type_="slider",
xaxis_index=[0, 1],
range_start=98,
range_end=100,
pos_top='85%',
)
],
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",
),
title_opts=opts.TitleOpts(title="Kline")))
if tech_line_dict:
line = (Line().add_xaxis(xaxis_data=x_axis_list).set_global_opts(
xaxis_opts=opts.AxisOpts(type_="category")))
[
line.add_yaxis(
series_name=name,
y_axis=value_list,
is_smooth=True,
is_hover_animation=False,
linestyle_opts=opts.LineStyleOpts(width=3, opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
) for name, value_list in tech_line_dict.items()
]
kline = kline.overlap(line)
bar = (Bar().add_xaxis(x_axis_list).add_yaxis(
series_name="成交量",
yaxis_data=volume_list,
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_="dataMin",
max_="dataMax",
),
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),
))
grid_chart = Grid(init_opts=opts.InitOpts(
width="2000px",
height="800px",
animation_opts=opts.AnimationOpts(animation=False),
))
grid_chart.add(kline,
grid_opts=opts.GridOpts(pos_left="10%",
pos_right="8%",
height="50%"))
grid_chart.add(bar,
grid_opts=opts.GridOpts(pos_left="10%",
pos_right="8%",
pos_top="63%",
height="16%"))
folder_path = get_folder_path("backtesting_result_pyecharts")
file_path = f"{folder_path}/render.html"
grid_chart.render(path=file_path)
return file_path
def draw_bar(cls, title: str, xaxis_data: List[str],
yaxis_data: pd.DataFrame) -> Bar:
"""
根据df内容绘制柱状图
:param title: 标题
:param xaxis_data: 横轴数据
:param yaxis_data: 纵轴绘制数据
:return:
"""
# 获取dataframe最大最小值
min_data = yaxis_data.min().min()
max_data = yaxis_data.max().max()
c = (
Bar(init_opts=opts.InitOpts(
width=DEFAULT_WIDTH,
animation_opts=opts.AnimationOpts(
animation_delay=200,
animation_easing="bounceOut"), # 增加启动动效
)).add_xaxis(xaxis_data).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=DATAZOOM_VERTICAL_POS_LEFT,
),
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(type_="color", max_=1, min_=-1),
# visualmap_opts = opts.VisualMapOpts(max_ = max_data, min_ = min_data)
))
# 遍历dataframe 依次添加数据到y轴
column_list = yaxis_data.columns.tolist()
for column in column_list:
c.add_yaxis(
column,
yaxis_data[column].tolist(),
# markpoint_opts=opts.MarkPointOpts(
# data=[
# opts.MarkPointItem(type_="min"),
# opts.MarkPointItem(type_="max"),
# ]
# ),
)
return c
750,
800,
700,
600,
400,
]
c = (Line().add_xaxis(xaxis_data=x_data).add_yaxis(
series_name="用电量",
y_axis=y_data,
is_smooth=True,
label_opts=opts.LabelOpts(is_show=False),
linestyle_opts=opts.LineStyleOpts(width=2),
).set_global_opts(
title_opts=opts.TitleOpts(title="一天用电量分布", subtitle="纯属虚构"),
tooltip_opts=opts.TooltipOpts(trigger="axis", axis_pointer_type="cross"),
xaxis_opts=opts.AxisOpts(boundary_gap=False),
yaxis_opts=opts.AxisOpts(
axislabel_opts=opts.LabelOpts(formatter="{value} W"),
splitline_opts=opts.SplitLineOpts(is_show=True),
),
visualmap_opts=opts.VisualMapOpts(
is_piecewise=True,
dimension=0,
pieces=[
{
"lte": 6,
"color": "green"
},
{
"gt": 6,
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 = ka.to_df(use_macd=True, ma_params=(
5,
20,
))
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 find_proportion_render(x, y, z, part_name, main_name, query, table_path,
answer):
x = list(map(str, x))
y = list(map(float, y))
z = list(map(float, z))
colorList = ['#f36c6c', '#e6cf4e', '#20d180', '#0093ff']
y_part = []
y_main = []
for i in range(len(y)):
percentage = y[i] / z[i] * 100
percentage = str(percentage).split('.')[0] + '.' + str(
percentage).split('.')[1][:2]
y_part.append(
opts.BarItem(
name=x[i],
value=y[i],
label_opts=opts.LabelOpts(position="insideTop",
formatter=str(percentage) + "%"),
itemstyle_opts=opts.ItemStyleOpts(color=colorList[2])))
y_main.append(opts.BarItem(
name=x[i],
value=z[i] - y[i],
))
bar = Bar()
bar.add_xaxis(x)
bar.add_yaxis('',
y_axis=y_part,
stack='stack1',
label_opts=opts.LabelOpts(position="insideTop"),
itemstyle_opts=opts.ItemStyleOpts(color=colorList[2]),
tooltip_opts=opts.TooltipOpts(formatter=part_name + ':{c}')),
bar.add_yaxis('',
y_axis=y_main,
stack='stack1',
label_opts=opts.LabelOpts(is_show=False),
color="gray",
tooltip_opts=opts.TooltipOpts(formatter=main_name + ':{c}'))
bar.set_global_opts(
datazoom_opts=[
opts.DataZoomOpts(range_start=10, range_end=90),
opts.DataZoomOpts(type_="inside")
],
legend_opts=opts.LegendOpts(is_show=False),
graphic_opts=[
opts.GraphicText(
graphic_item=opts.GraphicItem(
left="center",
top="bottom",
),
graphic_textstyle_opts=opts.GraphicTextStyleOpts(
# 可以通过jsCode添加js代码，也可以直接用字符串
text=[
'\n' + "Q:" + ' ' + query + '\n' + 'A:' + ' ' + answer
],
font="14px Microsoft YaHei",
graphic_basicstyle_opts=opts.GraphicBasicStyleOpts(
fill="#333")))
])
bar.render("aggregation.html")
grid = Grid(init_opts=opts.InitOpts(
width="100%",
height="100%",
renderer=globals.RenderType.SVG,
))
grid.add(bar, grid_opts={'left': '20%', 'bottom': '34%'})
option1 = grid.dump_options_with_quotes()
option1 = json.loads(option1)
option = {"option": [option1], "query": query}
return option
+(latExtent[0] + lngIndex * cellSizeCoord[0]).toFixed(6),
+(lngExtent[0] + latIndex * cellSizeCoord[1]).toFixed(6)
]));
}
""").add(
series_name="",
data_pair=j["data"],
type_=ChartType.CUSTOM,
render_item=JsCode("renderItem"),
itemstyle_opts=opts.ItemStyleOpts(color="yellow"),
encode={
"tooltip": 2
},
).set_global_opts(
title_opts=opts.TitleOpts(title="BMap-Custom 图"),
tooltip_opts=opts.TooltipOpts(is_show=True, formatter=None),
visualmap_opts=opts.VisualMapOpts(
is_piecewise=True,
pos_top="10",
pos_left="10",
is_inverse=True,
pieces=[{
"value": i,
"color": color_list[i]
} for i in range(len(color_list))],
dimension=2,
border_color="#ccc",
border_width=2,
background_color="#eee",
range_opacity=0.7,
),
from pyecharts.faker import Faker
# from pyecharts.charts import Page # 顺序多图
# from pyecharts.globals import SymbolType
# from pyecharts.charts import Page, WordCloud
# from pyecharts.render import make_snapshot
# from snapshot_selenium import snapshot # 渲染图片
bar = Bar(init_opts=opts.InitOpts(theme=ThemeType.LIGHT)) # 设置主题
bar.add_xaxis(['衬衫', '羊毛衫', '学纺纱', '裤子', '高跟鞋', '袜子'])
bar.add_yaxis('商家A', [5, 20, 36, 10, 75, 90])
bar.add_yaxis('商家B', [15, 6, 45, 20, 35, 66])
# 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 bar.render("mycharts.html")
bar.set_global_opts(title_opts=opts.TitleOpts(title='主标题', subtitle='副标题'),
toolbox_opts=opts.ToolboxOpts(is_show=True),
tooltip_opts=opts.TooltipOpts(is_show=True),
visualmap_opts=opts.VisualMapOpts(is_show=True),
datazoom_opts=opts.DataZoomOpts(is_show=True))
bar.render(path='C:\\Users\\tangyun\\Desktop\\生成图像\\figure1.html')
# make_snapshot(snapshot, bar.render(), 'bar.png')
def bar_base() -> Bar:
c = (Bar().add_xaxis(Faker.choose()).add_yaxis(
'商家A',
Faker.values()).add_yaxis('商家B', Faker.values()).set_global_opts(
title_opts=opts.TitleOpts(title='Bar-基本示例', subtitle='副标题'),
toolbox_opts=opts.ToolboxOpts(is_show=True)))
return c
import pyecharts.options as opts
from pyecharts.charts import Tree
async def get_json_data(url: str) -> dict:
async with ClientSession(connector=TCPConnector(ssl=False)) as session:
async with session.get(url=url) as response:
return await response.json()
# 获取官方的数据
data = asyncio.run(
get_json_data(url="https://echarts.baidu.com/examples/data/asset/data/flare.json")
)
(
Tree(init_opts=opts.InitOpts(width="1400px", height="800px"))
.add(
series_name="",
data=[data],
pos_top="18%",
pos_bottom="14%",
layout="radial",
symbol="emptyCircle",
symbol_size=7,
)
.set_global_opts(
tooltip_opts=opts.TooltipOpts(trigger="item", trigger_on="mousemove")
)
.render("Radial_tree.html")
)
