def generate_line_html(rows, select=None):
# 用散点图展示
line = Line(opts.InitOpts(height='700px', width='1424px', theme=ThemeType.LIGHT))
x = []
y1 = []
y2 = []
y3 = []
for row in rows:
x.append(row['时间'])
y1.append(row['我的累积收益率'])
y2.append(row['可转债指数累积收益率'])
y3.append(row['沪深300累积收益率'])
line.add_xaxis(x)
line.add_yaxis("我的涨跌", y1)
line.add_yaxis("可转债指数涨跌", y2)
line.add_yaxis("沪深300涨跌", y3)
line.set_global_opts(
title_opts=opts.TitleOpts(title="收益率曲线", pos_left='center', pos_top=-5),
tooltip_opts=opts.TooltipOpts(
trigger='axis',
formatter=JsCode(
"function (params) {"
"return '<table style=\"width:150px;\">'+"
"'<tr ><td style=\"height:20px;background-color:white;border:0px\" colspan=2>'+ params[0].data[0] +'</td></tr>' +"
"'<tr ><td style=\"height:15px;background-color:white;border:0px;text-align: right;color:'+params[0].color+'\">我的</td><td style=\"height:15px;background-color:white;border:0px\">' + params[0].value[1] + '%</td></tr>' +"
"'<tr><td style=\"height:15px;background-color:white;border:0px;text-align: right;color:'+params[1].color+'\">可转债指数</td><td style=\"height:15px;background-color:white;border:0px\">' + params[1].value[1] + '%</td></tr>' +"
"'<tr><td style=\"height:15px;background-color:white;border:0px;text-align: right;color:'+params[2].color+'\">沪深300</td><td style=\"height:15px;background-color:white;border:0px\">' + params[2].value[1] + '%</td></tr>' +"
"'</table>';}"
)
),
legend_opts=opts.LegendOpts(
pos_top=20,
# selected_mode='single'
),
datazoom_opts={'start': 0, 'end': 100},
toolbox_opts=opts.ToolboxOpts(feature={
'dataZoom': {},
}
),
# visualmap_opts=opts.VisualMapOpts(
# type_="color", max_=150, min_=20, dimension=1
# ),
xaxis_opts=opts.AxisOpts(
# data=None,
type_='time',
name='时间',
name_gap=30,
is_scale=True,
name_location='middle',
splitline_opts=opts.SplitLineOpts(is_show=False),
# axislabel_opts=opts.LabelOpts(formatter="{value}"), #echarts.format.formatTime('yy-MM-dd', value*1000)
axisline_opts=opts.AxisLineOpts(
is_on_zero=False,
symbol=['none', 'arrow']
)
),
yaxis_opts=opts.AxisOpts(
type_='value',
name='收益率(%)',
name_rotate=90,
name_gap=55,
name_location='middle',
is_scale=True,
axislabel_opts=opts.LabelOpts(formatter='{value}%'),
splitline_opts=opts.SplitLineOpts(is_show=False),
axisline_opts=opts.AxisLineOpts(
is_on_zero=False,
symbol=['none', 'arrow']
)
)
)
line.set_series_opts(
symbol='none',
smooth=False,
label_opts=opts.LabelOpts(is_show=False),
)
line_html = line.render_embed('template.html', env)
return line_html
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 = True,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),
],
)
)
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",
axislabel_opts = opts.LabelOpts(is_show = False),
),
yaxis_opts = opts.AxisOpts(
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 = kline.overlap(kline_line)
bar_1 = (
Bar()
.add_xaxis(xaxis_data = data["times"])
.add_yaxis(
series_name = "Volumn",
y_axis = data["vols"],
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",
axislabel_opts = opts.LabelOpts(is_show = False),
),
legend_opts = opts.LegendOpts(is_show = False),
)
)
bar_2 = (
Bar()
.add_xaxis(xaxis_data = data["times"])
.add_yaxis(
series_name = "MACD",
y_axis = data["macds"],
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",
axislabel_opts = opts.LabelOpts(is_show = False),
),
yaxis_opts = opts.AxisOpts(
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"],
label_opts = opts.LabelOpts(is_show =False),
)
.set_global_opts(
legend_opts = opts.LegendOpts(is_show = False)
)
)
overlap_bar_line = bar_2.overlap(line_2)
grid_chart = Grid(init_opts = opts.InitOpts(width = "1400px",height = "800px",theme = ThemeType.ESSOS))
grid_chart.add_js_funcs("var barData={}".format(data["datas"]))
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("professional_kline_chart.html")
def kline(stock_data: pd.DataFrame):
"""
@input: column name ('date', 'open', 'close', 'min', 'max', 'Trading_Volume')
"""
def calculate_ma(day_count: int, price: list):
result: List[Union[float, str]] = []
for i in range(len(price)):
if i < day_count:
result.append("-")
continue
sum_total = 0.0
for j in range(day_count):
sum_total += float(price[i - j])
result.append(abs(float("%.3f" % (sum_total / day_count))))
return result
def process_stock_data(data):
data = data[["date", "open", "close", "min", "max", "Trading_Volume"]]
data.columns = ["date", "open", "close", "low", "high", "volume"]
if is_datetime(data["date"]):
data_times = data["date"].dt.strftime("%Y-%m-%d").to_list()
else:
data_times = data["date"].to_list()
values = data.values.tolist()
volumes = []
for i, tick in enumerate(data.values.tolist()):
volumes.append([i, tick[5], 1 if tick[1] > tick[2] else -1])
return {
"categoryData": data_times,
"values": values,
"volumes": volumes,
}
chart_data = process_stock_data(stock_data)
kline_data = [data[1:-1] for data in chart_data["values"]]
kline = Kline(init_opts=opts.InitOpts(animation_opts=opts.AnimationOpts(
animation=False), ))
kline.add_xaxis(xaxis_data=chart_data["categoryData"])
kline.add_yaxis(
series_name="kline",
y_axis=kline_data,
itemstyle_opts=opts.ItemStyleOpts(color="#ec0000", color0="#00da3c"),
)
kline.set_global_opts(
legend_opts=opts.LegendOpts(is_show=True, pos_left="center"),
datazoom_opts=[
opts.DataZoomOpts(
is_show=False,
type_="inside",
xaxis_index=[0, 1],
range_start=85,
range_end=100,
),
opts.DataZoomOpts(
is_show=True,
xaxis_index=[0, 1],
type_="slider",
pos_top="85%",
range_start=85,
range_end=100,
),
],
yaxis_opts=opts.AxisOpts(
is_scale=True,
splitarea_opts=opts.SplitAreaOpts(
is_show=True, areastyle_opts=opts.AreaStyleOpts(opacity=1)),
),
tooltip_opts=opts.TooltipOpts(
trigger="axis",
axis_pointer_type="cross",
background_color="rgba(245, 245, 245, 0.8)",
border_width=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": "#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",
),
)
close = np.array(chart_data["values"])[:, 2]
ma_items = [5, 10, 20, 60]
line = Line(init_opts=opts.InitOpts(animation_opts=opts.AnimationOpts(
animation=False), )).add_xaxis(xaxis_data=chart_data["categoryData"])
for ma in ma_items:
line.add_yaxis(
series_name="MA" + str(ma),
y_axis=calculate_ma(day_count=ma, price=close),
is_smooth=True,
is_symbol_show=False,
is_hover_animation=False,
linestyle_opts=opts.LineStyleOpts(width=3, opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
)
line.set_global_opts(xaxis_opts=opts.AxisOpts(type_="category"))
bar = Bar(init_opts=opts.InitOpts(animation_opts=opts.AnimationOpts(
animation=False), ))
bar.add_xaxis(xaxis_data=chart_data["categoryData"])
bar.add_yaxis(
series_name="Volume",
y_axis=chart_data["volumes"],
xaxis_index=1,
yaxis_index=1,
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=False),
)
overlap_kline_line = kline.overlap(line)
grid_chart = Grid(init_opts=opts.InitOpts(
width="1000px",
height="800px",
animation_opts=opts.AnimationOpts(animation=False),
))
grid_chart.add(
overlap_kline_line,
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%"),
)
grid_chart.render("kline.html")
display(HTML(filename="kline.html"))
return grid_chart
for key in source:
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="1280px", height="720px")).add(
series_name="option",
data=tree_map_data["children"],
visual_min=300,
leaf_depth=1,
label_opts=opts.LabelOpts(position="middle"),
).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"))
print(china_data)
# 全国疫情地图
# 自定义的每一段的范围，以及每一段的特别的样式。
# pieces = [
# {'min': 10000, 'color': '#540d0d'},
# {'max': 9999, 'min': 1000, 'color': '#9c1414'},
# {'max': 999, 'min': 500, 'color': '#d92727'},
# {'max': 499, 'min': 100, 'color': '#ed3232'},
# {'max': 99, '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]
p = Pie(init_opts=opts.InitOpts(height="500px"))
p.add("累计确诊",[list(z) for z in zip(labels,counts)],center=["40%","70%"])
# 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
p.set_series_opts(label_opts=opts.LabelOpts(font_size=12,formatter="{b}: {c}"),
is_show=False)
#全局配置项,可配置标题、动画、坐标轴、图例等
p.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)) #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")
p.render(path='html/全国实时确诊数据饼图.html')
def generate_bar_html(rows, title):
x = []
y = []
up_count = 0
down_count = 0
for row in rows:
x.append(row[0])
y.append({'value': row[1], 'range': [row[2], row[3]]})
if row[3] < 0:
down_count += row[1]
elif row[2] > 0:
up_count += row[1]
bar = Bar(init_opts=opts.InitOpts(height='700px',
width='1424px',
theme=ThemeType.SHINE,
chart_id="cb_tree_map"))
bar.add_xaxis(x)
bar.add_yaxis(
"个数",
y,
yaxis_index=0,
bar_width=50,
category_gap='1%',
gap='1%',
label_opts=opts.LabelOpts(
position="top",
formatter=JsCode("function(x){return x.data[0];}")),
)
bar.set_series_opts(itemstyle_opts=opts.ItemStyleOpts(color=JsCode(
"function(x){return x.data['range'][0]=='0'?'lightgray':(x.data['range'][0] < 0 ? 'lightgreen':'lightcoral')}"
)))
bar.set_global_opts(
title_opts=opts.TitleOpts(
title="=========" + title + "=========",
subtitle="(跌:" + str(down_count) + " 涨:" + str(up_count) + ")",
subtitle_textstyle_opts=opts.TextStyleOpts(font_weight='bold',
font_size='15px'),
pos_left='center',
pos_top='-1px',
),
tooltip_opts=opts.TooltipOpts(is_show=False),
legend_opts=opts.LegendOpts(is_show=False),
xaxis_opts=opts.AxisOpts(
# data=None,
# type_='category',
# name_gap=0,
# name_rotate=90,
# axislabel_opts=opts.LabelOpts(
# rotate=-60,
# ),
name='价格涨跌区间',
name_gap=35,
is_scale=True,
name_location='middle',
splitline_opts=opts.SplitLineOpts(is_show=False),
axisline_opts=opts.AxisLineOpts(is_on_zero=True,
# symbol=['none', 'arrow']
)),
yaxis_opts=opts.AxisOpts(
type_='value',
name='个数',
# name_rotate=90,
name_gap=35,
name_location='middle',
# min_=0,
# max_=max_value,
is_scale=True,
axislabel_opts=opts.LabelOpts(formatter='{value}'),
splitline_opts=opts.SplitLineOpts(is_show=False),
axisline_opts=opts.AxisLineOpts(is_on_zero=False,
# symbol=['none', 'arrow']
)),
)
bar_html = bar.render_embed('template.html', html_utils.env)
return bar_html
def get_year_chart(year: str):
map_data = [[[x["name"], x["value"]] for x in d["data"]] for d in MapData
if d["time"] == year][0]
min_data, max_data = (minNum, maxNum)
data_mark: List = []
i = 0
for x in time_list:
if x == year:
data_mark.append(total_num[i])
else:
data_mark.append("")
i = i + 1
map_chart = (Map().add(
series_name="",
data_pair=map_data,
maptype="world",
zoom=1,
center=[119.5, 34.5],
is_map_symbol_show=False,
itemstyle_opts={
"normal": {
"areaColor": "#323c48",
"borderColor": "#404a59"
},
"emphasis": {
"label": {
"show": Timeline
},
"areaColor": "rgba(255,255,255, 0.5)",
},
},
).set_global_opts(
title_opts=opts.TitleOpts(
title="" + str(year) + "全球主要国家(除中国外感染超过300例)NCP实时动态",
subtitle="",
pos_left="center",
pos_top="top",
title_textstyle_opts=opts.TextStyleOpts(
font_size=25, color="rgba(255,255,255, 0.9)"),
),
tooltip_opts=opts.TooltipOpts(
is_show=True,
formatter=JsCode("""function(params) {
if ('value' in params.data) {
return params.data.value[2] + ': ' + params.data.value[0];
}
}"""),
),
visualmap_opts=opts.VisualMapOpts(
is_calculable=True,
dimension=0,
pos_left="30",
pos_top="center",
range_text=["High", "Low"],
range_color=["lightskyblue", "yellow", "orangered"],
textstyle_opts=opts.TextStyleOpts(color="#ddd"),
min_=min_data,
max_=max_data,
),
))
line_chart = (Line().add_xaxis(time_list).add_yaxis(
"", total_num).add_yaxis(
"",
data_mark,
markpoint_opts=opts.MarkPointOpts(
data=[opts.MarkPointItem(type_="max")]),
).set_series_opts(label_opts=opts.LabelOpts(
is_show=False)).set_global_opts(
title_opts=opts.TitleOpts(title="全球主要国家(除中国外)NCP实时动态(单位: 百人)",
pos_left="72%",
pos_top="5%")))
bar_x_data = [x[0] for x in map_data]
bar_y_data = [{"name": x[0], "value": x[1][0]} for x in map_data]
bar = (Bar().add_xaxis(xaxis_data=bar_x_data).add_yaxis(
series_name="",
yaxis_data=bar_y_data,
label_opts=opts.LabelOpts(is_show=True,
position="right",
formatter="{b} : {c}"),
).reversal_axis().set_global_opts(
xaxis_opts=opts.AxisOpts(max_=maxCount,
axislabel_opts=opts.LabelOpts(is_show=False)),
yaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(is_show=False)),
tooltip_opts=opts.TooltipOpts(is_show=False),
visualmap_opts=opts.VisualMapOpts(
is_calculable=True,
dimension=0,
pos_left="10",
pos_top="top",
range_text=["High", "Low"],
range_color=["lightskyblue", "yellow", "orangered"],
textstyle_opts=opts.TextStyleOpts(color="#ddd"),
min_=min_data,
max_=max_data,
),
))
pie_data = [[x[0], x[1][0]] for x in map_data]
pie = (Pie().add(
series_name="",
data_pair=pie_data,
radius=["15%", "35%"],
center=["80%", "82%"],
itemstyle_opts=opts.ItemStyleOpts(border_width=1,
border_color="rgba(0,0,0,0.5)"),
).set_global_opts(
tooltip_opts=opts.TooltipOpts(is_show=True, formatter="{b} {d}%"),
legend_opts=opts.LegendOpts(is_show=False),
))
grid_chart = (Grid().add(
bar,
grid_opts=opts.GridOpts(pos_left="10",
pos_right="45%",
pos_top="50%",
pos_bottom="5"),
).add(
line_chart,
grid_opts=opts.GridOpts(pos_left="65%",
pos_right="80",
pos_top="10%",
pos_bottom="50%"),
).add(pie, grid_opts=opts.GridOpts(pos_left="45%", pos_top="60%")).add(
map_chart, grid_opts=opts.GridOpts()))
return grid_chart
def god_j_mtm_adapt_bolling_chart(df):
"""子母布林自适应完整图"""
# 母布林线上轨、下轨数据
p_uppers = pyecharts_float_values_data(df, 'upper')
p_lower = pyecharts_float_values_data(df, 'lower')
# 子布林线上轨、中轨、下轨数据
uppers = pyecharts_float_values_data(df, 'up', int(1e15))
lowers = pyecharts_float_values_data(df, 'dn', int(1e15))
medians = pyecharts_float_values_data(df, 'mtm_mean', int(1e15))
# 信号信息
signal_infos = bolling_signals_data(df)
# k 线
p_bolling_line = Line().add_xaxis(
xaxis_data=pyecharts_time_data(df)).add_yaxis(
y_axis=p_uppers,
series_name='pUpper',
markpoint_opts=opts.MarkPointOpts(data=[{
'value': info[2],
"coord": [info[0], info[1]],
"itemStyle": {
"color": info[3]
}
} for info in signal_infos]),
is_symbol_show=False,
label_opts=None,
).add_yaxis(
y_axis=p_lower,
series_name='pLower',
is_symbol_show=False,
label_opts=None,
)
ohlc_chart = ohlc_kline_chart(df, x_axis_count=3)
ohlc_chart.overlap(p_bolling_line)
# 因子通道
indicator_line_chart = Line().add_xaxis(
xaxis_data=pyecharts_time_data(df)).add_yaxis(
series_name='Upper',
y_axis=uppers,
is_smooth=True,
is_symbol_show=False,
is_hover_animation=False,
label_opts=opts.LabelOpts(is_show=False),
).add_yaxis(
series_name='Lower',
y_axis=lowers,
is_smooth=True,
is_symbol_show=False,
is_hover_animation=False,
label_opts=opts.LabelOpts(is_show=False),
).add_yaxis(
series_name='Meian',
y_axis=medians,
is_smooth=True,
is_symbol_show=False,
is_hover_animation=False,
label_opts=opts.LabelOpts(is_show=False),
).set_global_opts(legend_opts=opts.LegendOpts(is_show=False), )
# 资金曲线
equity_chart = equity_line_chart(df)
grid_chart = Grid(init_opts=opts.InitOpts(
width="1000px",
height="800px",
bg_color="#ffffff",
animation_opts=opts.AnimationOpts(animation=False),
))
grid_chart.add(
ohlc_chart,
grid_opts=opts.GridOpts(pos_left="10%", pos_right="8%", height="30%"),
)
grid_chart.add(
indicator_line_chart,
grid_opts=opts.GridOpts(pos_left="10%",
pos_right="8%",
pos_top="43%",
height="30%"),
)
grid_chart.add(
equity_chart,
grid_opts=opts.GridOpts(pos_left="10%",
pos_right="8%",
pos_top="80%",
height="10%"),
)
return grid_chart
def generate_line_html2(rows, select=None):
line = Line(
opts.InitOpts(height='500px', width='1424px', theme=ThemeType.LIGHT))
x = []
y1 = []
y2 = []
min_y = 10000
max_y = 0
for row in rows:
x.append(row['date'])
mid_price = row['mid_price']
avg_premium = row['avg_premium']
if mid_price > max_y:
max_y = mid_price
elif mid_price < min_y:
min_y = mid_price
y1.append(mid_price)
y2.append(avg_premium)
delta = max_y - min_y
interval = round(delta / 5, 2)
star_up1 = round(min_y + interval * 1, 2)
star_up2 = round(min_y + interval * 2, 2)
star_up3 = round(min_y + interval * 3, 2)
star_up4 = round(min_y + interval * 4, 2)
line.add_xaxis(x)
line.extend_axis(
yaxis=opts.AxisOpts(type_='value',
name='溢价率平均值(%)',
name_rotate=90,
name_gap=55,
name_location='middle',
is_scale=True,
axislabel_opts=opts.LabelOpts(
formatter='{value}%'),
splitline_opts=opts.SplitLineOpts(is_show=False),
axisline_opts=opts.AxisLineOpts(
is_on_zero=False, symbol=['none', 'arrow'])))
line.add_yaxis("价格中位数", y1, yaxis_index=0)
line.add_yaxis("溢价率平均值", y2, yaxis_index=1)
line.set_global_opts(
title_opts=opts.TitleOpts(title="用价格中位数估值",
pos_left='center',
pos_top=-5),
tooltip_opts=opts.TooltipOpts(trigger='axis', ),
legend_opts=opts.LegendOpts(
is_show=True,
pos_top=20,
# pos_bottom=-50,
selected_mode='multiple'),
datazoom_opts={
'start': 0,
'end': 100
},
toolbox_opts=opts.ToolboxOpts(feature={
'dataZoom': {},
}),
xaxis_opts=opts.AxisOpts(
# data=None,
type_='time',
name='时间',
name_gap=30,
is_scale=True,
name_location='middle',
splitline_opts=opts.SplitLineOpts(is_show=False),
# axislabel_opts=opts.LabelOpts(formatter="{value}"), #echarts.format.formatTime('yy-MM-dd', value*1000)
axisline_opts=opts.AxisLineOpts(is_on_zero=False,
symbol=['none', 'arrow'])),
yaxis_opts=opts.AxisOpts(
type_='value',
name='价格中位数(元)',
name_rotate=90,
name_gap=55,
name_location='middle',
is_scale=True,
axislabel_opts=opts.LabelOpts(formatter='{value}元'),
splitline_opts=opts.SplitLineOpts(is_show=False),
axisline_opts=opts.AxisLineOpts(is_on_zero=False,
symbol=['none', 'arrow'])),
visualmap_opts=opts.VisualMapOpts(is_show=True,
type_='color',
pos_top='50',
pos_left='5',
min_=min_y,
max_=max_y,
is_piecewise=True,
split_number=5,
series_index=0,
pieces=[
{
'min': min_y,
'max': star_up1,
'color': '#93CE07',
'label': '★★★★★'
},
{
'min': star_up1,
'max': star_up2,
'color': '#FBDB0F',
'label': '★★★★'
},
{
'min': star_up2,
'max': star_up3,
'color': '#FC7D02',
'label': '★★★'
},
{
'min': star_up3,
'max': star_up4,
'color': '#FD0100',
'label': '★★'
},
{
'min': star_up4,
'max': max_y,
'color': '#AC3B2A',
'label': '★'
},
]),
)
line.set_colors(['lightcoral', 'lightskyblue'])
line.set_series_opts(
symbol='none',
smooth=False,
linestyle_opts=opts.LineStyleOpts(width=2, ),
label_opts=opts.LabelOpts(is_show=False),
markline_opts=opts.MarkLineOpts(
is_silent=True,
symbol='none',
label_opts=opts.LabelOpts(
position='end',
is_show=False,
formatter=JsCode("function (params){return params.name}")),
linestyle_opts=opts.LineStyleOpts(
color='#333',
type_='dashed',
),
data=[
opts.MarkLineItem(y=min_y, name='0%'),
opts.MarkLineItem(y=star_up1, name='20%'),
opts.MarkLineItem(y=star_up2, name='40%'),
opts.MarkLineItem(y=star_up3, name='60%'),
opts.MarkLineItem(y=star_up4, name='80%'),
opts.MarkLineItem(y=max_y, name='100%'),
]),
)
line_html = line.render_embed('template.html', env)
return line_html
def book_rating_pie(id,num) -> Pie:
(genre,num) = book_rating(id,num)
if genre == 0:
return 0
c = (
# Pie(init_opts=opts.InitOpts(width="", height="", bg_color=""))
Pie()
.add(
series_name="图书评分",
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="图书评分分布",
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 ohlc_kline_chart(df, x_axis_count=2, signal_infos=[]):
"""创建一个 OHLC K线图，并返回
:time_series 时间数组
:ohlc_data K线数据
:x_axis_count 控制要缩放的 X 轴数量
"""
time_series = pyecharts_time_data(df)
ohlc_data = pyecharts_ohlc_data(df)
kline = (Kline().add_xaxis(xaxis_data=time_series).add_yaxis(
series_name="K线",
y_axis=ohlc_data,
yaxis_index=2,
itemstyle_opts=opts.ItemStyleOpts(
color="#314555",
color0="#ec0000",
border_color="#314555",
border_color0="#ec0000",
),
markpoint_opts=opts.MarkPointOpts(data=[{
'value': info[2],
"coord": [info[0], info[1]],
"itemStyle": {
"color": info[3]
}
} for info in signal_infos]),
).set_global_opts(
legend_opts=opts.LegendOpts(
is_show=True,
pos_top=10,
pos_left="center",
),
title_opts=opts.TitleOpts(title=""),
datazoom_opts=[
opts.DataZoomOpts(
is_show=True,
xaxis_index=list(range(0, x_axis_count)),
type_="slider",
pos_top="90%",
range_start=50,
range_end=100,
),
],
xaxis_opts=opts.AxisOpts(
is_scale=True,
splitline_opts=opts.SplitLineOpts(
is_show=True,
linestyle_opts=opts.LineStyleOpts(type_='dotted')),
),
yaxis_opts=opts.AxisOpts(
is_scale=True,
splitarea_opts=opts.SplitAreaOpts(
is_show=True, areastyle_opts=opts.AreaStyleOpts(opacity=1)),
),
tooltip_opts=opts.TooltipOpts(
trigger="axis",
axis_pointer_type="cross",
background_color="rgba(245, 245, 245, 0.8)",
border_width=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": "#314555"
},
{
"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",
),
))
return kline
def get_themeriver(data, lengend, num_frames):
'''
data = [
["2015/11/08", 10, "DQ"],
["2015/11/09", 15, "DQ"],
["2015/11/10", 35, "DQ"],
["2015/11/14", 7, "DQ"],
["2015/11/15", 2, "DQ"],
["2015/11/16", 17, "DQ"],
["2015/11/17", 33, "DQ"],
["2015/11/18", 40, "DQ"],
["2015/11/19", 32, "DQ"],
["2015/11/20", 26, "DQ"],
["2015/11/08", 35, "TY"],
["2015/11/09", 36, "TY"],
["2015/11/10", 37, "TY"],
["2015/11/11", 22, "TY"],
["2015/11/12", 24, "TY"],
["2015/11/13", 26, "TY"],
["2015/11/14", 34, "TY"],
["2015/11/15", 21, "TY"],
["2015/11/16", 18, "TY"],
["2015/11/17", 45, "TY"],
["2015/11/18", 32, "TY"],
["2015/11/19", 35, "TY"],
["2015/11/20", 30, "TY"],
["2015/11/08", 21, "SS"],
["2015/11/09", 25, "SS"],
["2015/11/10", 27, "SS"],
["2015/11/11", 23, "SS"],
["2015/11/12", 24, "SS"],
["2015/11/13", 21, "SS"],
["2015/11/14", 35, "SS"],
["2015/11/15", 39, "SS"],
["2015/11/16", 40, "SS"],
["2015/11/17", 36, "SS"],
["2015/11/18", 33, "SS"],
["2015/11/19", 43, "SS"],
["2015/11/20", 40, "SS"],
["2015/11/14", 7, "QG"],
["2015/11/15", 2, "QG"],
["2015/11/16", 17, "QG"],
["2015/11/17", 33, "QG"],
["2015/11/18", 40, "QG"],
["2015/11/19", 32, "QG"],
["2015/11/20", 26, "QG"],
["2015/11/21", 35, "QG"],
["2015/11/22", 40, "QG"],
["2015/11/23", 32, "QG"],
["2015/11/24", 26, "QG"],
["2015/11/25", 22, "QG"],
["2015/11/08", 10, "SY"],
["2015/11/09", 15, "SY"],
["2015/11/10", 35, "SY"],
["2015/11/11", 38, "SY"],
["2015/11/12", 22, "SY"],
["2015/11/13", 16, "SY"],
["2015/11/14", 7, "SY"],
["2015/11/15", 2, "SY"],
["2015/11/16", 17, "SY"],
["2015/11/17", 33, "SY"],
["2015/11/18", 40, "SY"],
["2015/11/19", 32, "SY"],
["2015/11/20", 26, "SY"],
["2015/11/21", 35, "SY"],
["2015/11/22", 4, "SY"],
["2015/11/23", 32, "SY"],
["2015/11/24", 26, "SY"],
["2015/11/25", 22, "SY"],
["2015/11/08", 10, "DD"],
["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"],
]
'''
themeriver = (
ThemeRiver().add(
#["DQ", "TY", "SS", "QG", "SY", "DD"],
lengend,
data,
singleaxis_opts=opts.SingleAxisOpts(
pos_bottom="50%", min_=1, max_=num_frames), #可以通过调整坐标轴来调整图的位置
label_opts=opts.LabelOpts(is_show=False),
tooltip_opts=opts.TooltipOpts(formatter="{a}") #不能用 不知道为什么
).set_global_opts(
title_opts=opts.TitleOpts(title="事件链河流图", subtitle="图例按突发性程度降序排序"),
#tooltip_opts=opts.TooltipOpts(is_show=True,trigger="item",formatter='{@[0]}'),#formatter目前没效果？？？
legend_opts=opts.LegendOpts(type_='scroll',
pos_left="5%",
orient='vertical',
pos_top="55%"))
#datazoom_opts=opts.DataZoomOpts())#图例设置
)
return themeriver
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
print(data["vols"])
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("1.professional_kline_chart.html")
# 添加列
df['价格标签'] = price_cut
cut_purchase = df.groupby('价格标签')['销售额'].sum()
print(cut_purchase)
# 绘制饼图可以清晰地看出各价格区间货品销售额在总体占比情况：
data_pair = [
list(z)
for z in zip(cut_purchase.index.tolist(), cut_purchase.values.tolist())
]
# 绘制饼图
pie1 = Pie(init_opts=opts.InitOpts(width='1350px', height='750px'))
pie1.add('', data_pair, radius=['35%', '60%'])
pie1.set_global_opts(title_opts=opts.TitleOpts(title='不同价格区间的销售额整体表现'),
legend_opts=opts.LegendOpts(orient='vertical',
pos_top='15%',
pos_left='2%'))
pie1.set_series_opts(label_opts=opts.LabelOpts(formatter="{b}:{d}%"))
pie1.set_colors([
'#EF9050', '#3B7BA9', '#6FB27C', '#FFAF34', '#D8BFD8', '#00BFFF', '#7FFFAA'
])
pie1.render()
# 玩一个词云图
def get_cut_words(content_series):
# 读入停用词表
stop_words = []
with open(
r"C:/Users/Maggie/Desktop/Udemy-desktop/xiaoxiangxueyuan/Python认知打卡课课程资料包/资料代码整理/数据分析/解读乐高/stop_words.txt",
'r',
"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="right", orient="vertical"),title_opts=opts.TitleOpts(title="面向用例比例"))
.set_series_opts(
tooltip_opts=opts.TooltipOpts(
trigger="item", formatter="{a} <br/>{b}: {c} ({d}%)"
)
)
.render("nested_pies.html")
)
def generate_scatter_html_with_multi_tables(tables,
title="可转债分布情况",
subtitle=None,
select=None,
use_personal_features=True,
price_field_name='转债价格'):
chart_id = str(abs(hash(title)))
scatter = Scatter(
opts.InitOpts(height='700px',
width='1424px',
theme=ThemeType.LIGHT,
chart_id=chart_id))
scatter.add_js_funcs('chart_' + chart_id +
""".on('click', function(params){
// alert(params)
popWin.showWin("1200","600", params['data']['value'][3]);
})
""")
for label, table in tables.items():
if select is not None and label not in select:
continue
x = []
y = []
point_items = []
rows = table._rows
for row in rows:
record = get_record(table, row)
if record['溢价率'] is None:
continue
x1 = record[price_field_name]
x.append(x1)
y1 = record['溢价率'].replace('%', '') * 1
amount = record.get("持有数量", 0)
bond_name = record['名称'].replace('转债', '')
bond_code = record['bond_code']
bond_code = trade_utils.rebuild_bond_code(bond_code)
y.append([y1])
# todo symbol 轮出:triangle 轮入:diamond
if use_personal_features and record.get(
'hold_id') is not None and amount > 0:
point_items.append(
opts.MarkPointItem(
coord=[x1, y1],
symbol_size=amount,
itemstyle_opts=opts.ItemStyleOpts(
opacity=0.5,
border_color='#000',
border_width=1,
),
value=[bond_name, x1, y1, bond_code, amount]))
else:
point_items.append(
opts.MarkPointItem(
coord=[x1, y1],
# symbol_size=symbol_size,
itemstyle_opts=opts.ItemStyleOpts(
# color='#fff',
# border_color='#000'
),
value=[bond_name, x1, y1, bond_code]))
scatter.add_xaxis(x)
scatter.add_yaxis(
label,
y,
label_opts=opts.LabelOpts(is_show=False),
markpoint_opts=opts.MarkPointOpts(
symbol='circle',
symbol_size=10,
data=point_items,
label_opts=opts.LabelOpts(
position='bottom',
formatter=JsCode(
'function(params){return params.value[0]}'))),
markline_opts=opts.MarkLineOpts(
linestyle_opts=opts.LineStyleOpts(type_='dashed'),
is_silent=True,
label_opts=opts.LabelOpts(
position='end',
formatter=JsCode(
"function(params){return params.data['name'];}")),
data=[
opts.MarkLineItem(x=utils.trade_utils.MID_X,
name='中位数(' +
str(utils.trade_utils.MID_X) + '元)'),
opts.MarkLineItem(y=utils.trade_utils.MID_Y,
name='中位数(' +
str(utils.trade_utils.MID_Y) + '%)'),
]))
# scatter.add_xaxis(x)
scatter.set_global_opts(
title_opts=opts.TitleOpts(title=title,
subtitle=subtitle,
pos_left='center'),
tooltip_opts=opts.TooltipOpts(formatter=get_hover_js_code()),
legend_opts=opts.LegendOpts(pos_bottom=-5,
# selected_mode='single'
),
toolbox_opts=opts.ToolboxOpts(feature={
'dataZoom': {},
}),
# visualmap_opts=opts.VisualMapOpts(
# type_="color", max_=150, min_=20, dimension=1
# ),
xaxis_opts=opts.AxisOpts(
# data=None,
type_='value',
name='转债价格(元)',
name_gap=30,
is_scale=True,
name_location='middle',
splitline_opts=opts.SplitLineOpts(is_show=False),
axislabel_opts=opts.LabelOpts(formatter='{value}元'),
axisline_opts=opts.AxisLineOpts(is_on_zero=False,
symbol=['none', 'arrow'])),
yaxis_opts=opts.AxisOpts(
type_='value',
name='转股溢价率(%)',
name_rotate=90,
name_gap=35,
name_location='middle',
is_scale=True,
axislabel_opts=opts.LabelOpts(formatter='{value}%'),
splitline_opts=opts.SplitLineOpts(is_show=False),
axisline_opts=opts.AxisLineOpts(is_on_zero=False,
symbol=['none', 'arrow'])))
scatter_html = scatter.render_embed('template.html', env)
return scatter_html
from pywebio.output import put_html
from pyecharts import options as opts
from pyecharts.charts import Pie
from pyecharts.faker import Faker
c = (Pie().add(
"",
[list(z) for z in zip(Faker.choose(), Faker.values())],
center=["35%", "50%"],
).set_global_opts(
title_opts=opts.TitleOpts(title="Pie-调整位置"),
legend_opts=opts.LegendOpts(pos_left="15%"),
).set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {c}")))
c.width = "100%"
put_html(c.render_notebook())
if __name__ == '__main__':
print(year_store_query())
dataX, dataY1, dataY2 = year_store_query()
radar = Radar(init_opts=opts.InitOpts(
width="1280px", height="720px", bg_color="#CCCCCC"))
radar.add_schema(
schema=[
opts.RadarIndicatorItem(name="门店订单金额", max_=34000),
opts.RadarIndicatorItem(name="门店顾客数", max_=10000),
opts.RadarIndicatorItem(name="门店商品类型", max_=10000),
opts.RadarIndicatorItem(name="门店订单量", max_=10000)
],
splitarea_opt=opts.SplitAreaOpts(
is_show=True, areastyle_opts=opts.AreaStyleOpts(opacity=1)),
textstyle_opts=opts.TextStyleOpts(color="#fff"),
)
radar.add(
series_name="Store 1",
data=[list(dataY1)],
linestyle_opts=opts.LineStyleOpts(color="#CD0000"),
)
radar.add(
series_name="Store 2",
data=[list(dataY2)],
linestyle_opts=opts.LineStyleOpts(color="#5CACEE"),
)
radar.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
radar.set_global_opts(title_opts=opts.TitleOpts(title="2005年门店竞争优势多维分析"),
legend_opts=opts.LegendOpts())
radar.render('radar.html')
def genGeo(data):
c = (
Geo(init_opts=opts.InitOpts(
width='1800px',
height='1000px',
bg_color='#0F1C3C',
)).add_schema(
maptype='china',
itemstyle_opts=opts.ItemStyleOpts(
border_color=JsCode("""
new echarts.graphic.LinearGradient(0, 0, 0, 1, [{
offset: 0,
color: '#00F6FF'
}, {
offset: 1,
color: '#53D9FF'
}], false)
"""),
color0='rgba(10,76,139,1)',
border_width=1.0,
opacity=0.8,
color="#012366",
),
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),
zoom=1.2,
).add( # 添加散点图
"",
data,
symbol_size=20,
color='#EA7552',
label_opts=opts.LabelOpts(
is_show=True,
color="#0f0",
border_width=2.,
font_size=18,
position="inside",
formatter=JsCode("""
function f(param){
return param.name + ','+ param.value[2]/10
}
"""),
)).set_series_opts(
range_size=[12, 40],
itemstyle_opts=opts.ItemStyleOpts(
border_width=1.0,
border_color='#215495',
),
areastyle_opts=opts.AreaStyleOpts(
color={
"x":
0,
"y":
0,
"x2":
0,
"y2":
1,
"colorStops": [{
"offset": 0,
"color": '#073684'
}, {
"offset": 1,
"color": '#061E3D'
}],
})).set_global_opts(
title_opts=opts.TitleOpts(
title="全国本科院校分布图",
pos_left='40%',
pos_top='10%',
title_textstyle_opts=opts.TextStyleOpts(
color='white', font_size=20)),
visualmap_opts=opts.VisualMapOpts(
is_show=False,
type_='size',
min_=4 * 10,
max_=80 * 10,
),
legend_opts=opts.LegendOpts(is_show=False),
tooltip_opts=opts.TooltipOpts(trigger="item",
formatter=JsCode("""
function f(param){
return param.name + ','+ param.value[2]/10
}
"""))).render("university-geo.html"))
def costEffectiveLine() -> Line:
sql = "select area,AVG(rent)/area from house where area >= 5 group by area;"
with connection.cursor() as cursor:
cursor.execute(sql)
rows = cursor.fetchall()
rows = sorted(rows)
data = dict(rows)
x_data = data.keys()
y_data = data.values()
# 画图
c = (Line().add_xaxis(xaxis_data=x_data).add_yaxis(
series_name="面积性价比",
y_axis=y_data,
is_smooth=True,
is_symbol_show=True,
symbol="circle",
symbol_size=6,
linestyle_opts=opts.LineStyleOpts(color="#c82c42"),
label_opts=opts.LabelOpts(is_show=False),
itemstyle_opts=opts.ItemStyleOpts(color="red",
border_color="#c82c42",
border_width=3),
tooltip_opts=opts.TooltipOpts(is_show=True,
background_color="#196286"),
areastyle_opts=opts.AreaStyleOpts(opacity=0.5, color="#c82c42"),
).set_global_opts(
title_opts=opts.TitleOpts(
title="面积性价比折线图",
pos_top="3%",
pos_left="center",
title_textstyle_opts=opts.TextStyleOpts(color="#666666",
font_size=16),
),
xaxis_opts=opts.AxisOpts(
name="面积",
type_="category",
boundary_gap=False,
axislabel_opts=opts.LabelOpts(margin=30, color="#666666"),
axisline_opts=opts.AxisLineOpts(is_show=True),
axistick_opts=opts.AxisTickOpts(
is_show=True,
length=25,
linestyle_opts=opts.LineStyleOpts(color="#666666"),
),
),
yaxis_opts=opts.AxisOpts(
name="价格/（月租金/平方米）",
type_="value",
position="left",
axislabel_opts=opts.LabelOpts(margin=20, color="#666666"),
axisline_opts=opts.AxisLineOpts(
linestyle_opts=opts.LineStyleOpts(width=2, color="#666666")),
axistick_opts=opts.AxisTickOpts(
is_show=True,
length=15,
linestyle_opts=opts.LineStyleOpts(color="#666666"),
),
),
legend_opts=opts.LegendOpts(is_show=False),
).dump_options_with_quotes())
return c
map = Map(
options.InitOpts(width='1200px',
height='600px',
bg_color="#ffffff",
page_title='中国疫情地图'))
map.add(
series_name="累计确诊", #标签名称
data_pair=china_data.items(), #传入数据
is_map_symbol_show=False, #不显示标记
maptype='china', #地图类型
)
map.set_global_opts(title_opts=options.TitleOpts(title='中国新冠肺炎累计确诊图',
pos_left='center'),
legend_opts=options.LegendOpts(is_show=True,
pos_left='left',
pos_top='60%'),
visualmap_opts=options.VisualMapOpts(max_=1100000,
is_piecewise=True,
pieces=[{
"max":
10,
'color':
'#fdebcf',
'label':
'10人以下'
}, {
"min":
10,
"max":
100,
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 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 genetateLineArea(self,dates,scores,trainning):
x_data = dates
y_data = scores
background_color_js = (
"new echarts.graphic.LinearGradient(0, 0, 0, 1, "
"[{offset: 0, color: '#0a243D'}, {offset: 1, color: '#133457'}], false)"
)
area_color_js = (
"new echarts.graphic.LinearGradient(0, 0, 0, 1, "
"[{offset: 0, color: '#FF6295'}, {offset: 1, color: '#3fbbff0d'}], false)"
)
c = (
Line(init_opts=opts.InitOpts(bg_color=JsCode(background_color_js)))
.add_xaxis(xaxis_data=x_data)
.add_yaxis(
series_name="注册总量",
y_axis=y_data,
is_smooth=True,
is_symbol_show=True,
symbol="circle",
symbol_size=6,
linestyle_opts=opts.LineStyleOpts(color="#fff"),
label_opts=opts.LabelOpts(is_show=True, position="top", color="white"),
itemstyle_opts=opts.ItemStyleOpts(
color="red", border_color="#fff", border_width=3
),
tooltip_opts=opts.TooltipOpts(is_show=False),
areastyle_opts=opts.AreaStyleOpts(color=JsCode(area_color_js), opacity=1),
)
.set_global_opts(
title_opts=opts.TitleOpts(
title=trainning+"\n历史成绩",
pos_bottom="5%",
pos_left="center",
title_textstyle_opts=opts.TextStyleOpts(color="#fff", font_size=22),
),
xaxis_opts=opts.AxisOpts(
type_="category",
boundary_gap=False,
axislabel_opts=opts.LabelOpts(margin=30, color="#ffffff63"),
axisline_opts=opts.AxisLineOpts(is_show=False),
axistick_opts=opts.AxisTickOpts(
is_show=True,
length=25,
linestyle_opts=opts.LineStyleOpts(color="#ffffff1f"),
),
splitline_opts=opts.SplitLineOpts(
is_show=True, linestyle_opts=opts.LineStyleOpts(color="#ffffff1f")
),
),
yaxis_opts=opts.AxisOpts(
type_="value",
position="right",
axislabel_opts=opts.LabelOpts(margin=20, color="#ffffff63"),
axisline_opts=opts.AxisLineOpts(
linestyle_opts=opts.LineStyleOpts(width=2, color="#fff")
),
axistick_opts=opts.AxisTickOpts(
is_show=True,
length=15,
linestyle_opts=opts.LineStyleOpts(color="#ffffff1f"),
),
splitline_opts=opts.SplitLineOpts(
is_show=True, linestyle_opts=opts.LineStyleOpts(color="#ffffff1f")
),
),
legend_opts=opts.LegendOpts(is_show=False),
)
.render("C:\\Users\\dell\\Desktop\\line.html")
# .render("./datavisual/"+trainning+".html")
)
gl.set_value("filename",trainning+".html")
from pywebio.output import put_html
import json
from pyecharts import options as opts
from pyecharts.charts import Graph
with open("les-miserables.json", "r", encoding="utf-8") as f:
j = json.load(f)
nodes = j["nodes"]
links = j["links"]
categories = j["categories"]
c = (Graph(init_opts=opts.InitOpts(width="1000px", height="600px")).add(
"",
nodes=nodes,
links=links,
categories=categories,
layout="circular",
is_rotate_label=True,
linestyle_opts=opts.LineStyleOpts(color="source", curve=0.3),
label_opts=opts.LabelOpts(position="right"),
).set_global_opts(
title_opts=opts.TitleOpts(title="Graph-Les Miserables"),
legend_opts=opts.LegendOpts(orient="vertical",
pos_left="2%",
pos_top="20%"),
))
c.width = "100%"
put_html(c.render_notebook())
def plot(session, individual):
"""
visualizing data of individual in session
:param session:
:param individual:
:return:
"""
eeg = SEED_IV(session=session,
individual=individual,
modal='concat',
shuffle=False,
balance=False,
normalization=1)
X, Y = eeg.get_X_Y()
eeg = X[:, :310]
eye = X[:, 310:]
eeg = eeg.reshape(-1, 62, 5)
b, n, d = eeg.shape
page = Page()
# 眼动特征可视化
# 1-12瞳孔直径
ling_pupil = (Line().add_xaxis([i for i in range(b)]).add_yaxis(
'pupil diameter 1',
numpy_vector_to_python(eye[:, 0]),
label_opts=opts.LabelOpts(is_show=False)).add_yaxis(
'pupil diameter 2',
numpy_vector_to_python(eye[:, 1]),
label_opts=opts.LabelOpts(is_show=False)).add_yaxis(
'pupil diameter 3',
numpy_vector_to_python(eye[:, 2]),
label_opts=opts.LabelOpts(is_show=False)).add_yaxis(
'pupil diameter 4',
numpy_vector_to_python(eye[:, 3]),
label_opts=opts.LabelOpts(is_show=False)).add_yaxis(
'pupil diameter mean 1',
numpy_vector_to_python(eye[:, 4]),
label_opts=opts.LabelOpts(is_show=False)).add_yaxis(
'pupil diameter mean 2',
numpy_vector_to_python(eye[:, 5]),
label_opts=opts.LabelOpts(
is_show=False)).add_yaxis(
'pupil diameter mean 3',
numpy_vector_to_python(eye[:, 6]),
label_opts=opts.LabelOpts(is_show=False)).
add_yaxis('pupil diameter mean 4',
numpy_vector_to_python(eye[:, 7]),
label_opts=opts.LabelOpts(
is_show=False)).add_yaxis(
'pupil diameter std 1',
numpy_vector_to_python(eye[:, 8]),
label_opts=opts.LabelOpts(
is_show=False)).add_yaxis(
'pupil diameter std 2',
numpy_vector_to_python(eye[:, 9]),
label_opts=opts.LabelOpts(
is_show=False)).add_yaxis(
'pupil diameter std 3',
numpy_vector_to_python(
eye[:, 10]),
label_opts=opts.LabelOpts(
is_show=False)).
add_yaxis('pupil diameter std 4',
numpy_vector_to_python(eye[:, 11]),
label_opts=opts.LabelOpts(
is_show=False)).set_global_opts(
title_opts=opts.TitleOpts(
title='pupil diameter feature'),
legend_opts=opts.LegendOpts()))
page.add(ling_pupil)
# dispersion
line_dispersion = (Line().add_xaxis([i for i in range(b)]).add_yaxis(
'dispersion 1',
numpy_vector_to_python(eye[:, 12]),
label_opts=opts.LabelOpts(is_show=False)).add_yaxis(
'dispersion 2',
numpy_vector_to_python(eye[:, 13]),
label_opts=opts.LabelOpts(is_show=False)).add_yaxis(
'dispersion 3',
numpy_vector_to_python(eye[:, 14]),
label_opts=opts.LabelOpts(is_show=False)).add_yaxis(
'dispersion 4',
numpy_vector_to_python(eye[:, 15]),
label_opts=opts.LabelOpts(is_show=False)).set_global_opts(
title_opts=opts.TitleOpts(title='dispersion'),
legend_opts=opts.LegendOpts()))
page.add(line_dispersion)
#fixation duration
line_fixation = (Line().add_xaxis([i for i in range(b)]).add_yaxis(
'fixation 1',
numpy_vector_to_python(eye[:, 16]),
label_opts=opts.LabelOpts(is_show=False)).add_yaxis(
'fixation 2',
numpy_vector_to_python(eye[:, 17]),
label_opts=opts.LabelOpts(is_show=False)).set_global_opts(
title_opts=opts.TitleOpts(title='fixation'),
legend_opts=opts.LegendOpts()))
page.add(line_fixation)
# saccade
line_saccade = (Line().add_xaxis([i for i in range(b)]).add_yaxis(
'saccade 1',
numpy_vector_to_python(eye[:, 18]),
label_opts=opts.LabelOpts(is_show=False)).add_yaxis(
'saccade 2',
numpy_vector_to_python(eye[:, 19]),
label_opts=opts.LabelOpts(is_show=False)).add_yaxis(
'saccade 3',
numpy_vector_to_python(eye[:, 20]),
label_opts=opts.LabelOpts(is_show=False)).add_yaxis(
'saccade 4',
numpy_vector_to_python(eye[:, 21]),
label_opts=opts.LabelOpts(is_show=False)).set_global_opts(
title_opts=opts.TitleOpts(title='saccade'),
legend_opts=opts.LegendOpts()))
page.add(line_saccade)
# event statistics
line_event = (Line().add_xaxis([i for i in range(b)]).add_yaxis(
'event 1',
numpy_vector_to_python(eye[:, 22]),
label_opts=opts.LabelOpts(is_show=False)).add_yaxis(
'event 2',
numpy_vector_to_python(eye[:, 23]),
label_opts=opts.LabelOpts(is_show=False)).add_yaxis(
'event 3',
numpy_vector_to_python(eye[:, 24]),
label_opts=opts.LabelOpts(is_show=False)).add_yaxis(
'event 4',
numpy_vector_to_python(eye[:, 25]),
label_opts=opts.LabelOpts(is_show=False)).add_yaxis(
'event 5',
numpy_vector_to_python(eye[:, 26]),
label_opts=opts.LabelOpts(is_show=False)).add_yaxis(
'event 6',
numpy_vector_to_python(eye[:, 27]),
label_opts=opts.LabelOpts(
is_show=False)).add_yaxis(
'event 7',
numpy_vector_to_python(eye[:, 28]),
label_opts=opts.LabelOpts(is_show=False)).
add_yaxis(
'event 8',
numpy_vector_to_python(eye[:,
29]),
label_opts=opts.LabelOpts(
is_show=False)).add_yaxis(
'event 9',
numpy_vector_to_python(eye[:,
30]),
label_opts=opts.LabelOpts(
is_show=False)).set_global_opts(
title_opts=opts.TitleOpts(title='event'),
legend_opts=opts.LegendOpts()))
page.add(line_event)
# 62个电极的信息，每个电极的信息，画出五种波
for i in range(62):
line = (Line().add_xaxis([i for i in range(b)]).add_yaxis(
'delta wave',
numpy_vector_to_python(eeg[:, i, 0]),
color='red',
label_opts=opts.LabelOpts(is_show=False)).add_yaxis(
'theta wave',
numpy_vector_to_python(eeg[:, i, 1]),
color='yellow',
label_opts=opts.LabelOpts(is_show=False)).add_yaxis(
'alpha wave',
numpy_vector_to_python(eeg[:, i, 2]),
color='green',
label_opts=opts.LabelOpts(is_show=False)).add_yaxis(
'beta wave',
numpy_vector_to_python(eeg[:, i, 3]),
color='blue',
label_opts=opts.LabelOpts(is_show=False)).add_yaxis(
'gamma wave',
numpy_vector_to_python(eeg[:, i, 4]),
color='black',
label_opts=opts.LabelOpts(
is_show=False)).set_global_opts(
title_opts=opts.TitleOpts(
title='electrode %d' % i),
legend_opts=opts.LegendOpts()))
page.add(line)
save_path = '../../chart/Raw_data_visualization/%d/%d' % (session,
individual)
if not os.path.exists(save_path):
os.makedirs(save_path)
page.render(
path=os.path.join(save_path, '%d_%d_norm_feature.html' %
(session, individual)))
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 = "1200px",
height: str = '680px') -> 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['buy'] 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['sell'] 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
'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=True),
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='data/全国实时确诊数据.html')
def draw_charts():
kline_data = [data[1:-1] for data in chart_data["values"]]
kline = (Kline().add_xaxis(
xaxis_data=chart_data["categoryData"]).add_yaxis(
series_name="Dow-Jones index",
y_axis=kline_data,
itemstyle_opts=opts.ItemStyleOpts(color="#ec0000",
color0="#00da3c"),
).set_global_opts(
legend_opts=opts.LegendOpts(is_show=False,
pos_bottom=10,
pos_left="center"),
datazoom_opts=[
opts.DataZoomOpts(
is_show=False,
type_="inside",
xaxis_index=[0, 1],
range_start=98,
range_end=100,
),
opts.DataZoomOpts(
is_show=True,
xaxis_index=[0, 1],
type_="slider",
pos_top="85%",
range_start=98,
range_end=100,
),
],
yaxis_opts=opts.AxisOpts(
is_scale=True,
splitarea_opts=opts.SplitAreaOpts(
is_show=True,
areastyle_opts=opts.AreaStyleOpts(opacity=1)),
),
tooltip_opts=opts.TooltipOpts(
trigger="axis",
axis_pointer_type="cross",
background_color="rgba(245, 245, 245, 0.8)",
border_width=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": "#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",
),
))
line = (Line().add_xaxis(xaxis_data=chart_data["categoryData"]).add_yaxis(
series_name="MA5",
y_axis=calculate_ma(day_count=5, data=chart_data),
is_smooth=True,
is_hover_animation=False,
linestyle_opts=opts.LineStyleOpts(width=3, opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
).add_yaxis(
series_name="MA10",
y_axis=calculate_ma(day_count=10, data=chart_data),
is_smooth=True,
is_hover_animation=False,
linestyle_opts=opts.LineStyleOpts(width=3, opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
).add_yaxis(
series_name="MA20",
y_axis=calculate_ma(day_count=20, data=chart_data),
is_smooth=True,
is_hover_animation=False,
linestyle_opts=opts.LineStyleOpts(width=3, opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
).add_yaxis(
series_name="MA30",
y_axis=calculate_ma(day_count=30, data=chart_data),
is_smooth=True,
is_hover_animation=False,
linestyle_opts=opts.LineStyleOpts(width=3, opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
).set_global_opts(xaxis_opts=opts.AxisOpts(type_="category")))
bar = (Bar().add_xaxis(xaxis_data=chart_data["categoryData"]).add_yaxis(
series_name="Volume",
y_axis=chart_data["volumes"],
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),
))
# Kline And Line
overlap_kline_line = kline.overlap(line)
# Grid Overlap + Bar
grid_chart = Grid(init_opts=opts.InitOpts(
width="1000px",
height="800px",
animation_opts=opts.AnimationOpts(animation=False),
))
grid_chart.add(
overlap_kline_line,
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%"),
)
grid_chart.render("professional_kline_brush.html")
def drawFunnel(title, data, savedir='./results'):
checkDir(savedir)
funnel = (Funnel(init_opts=options.InitOpts(theme=ThemeType.MACARONS))
.add('', [list(item) for item in data.items()], label_opts=options.LabelOpts(position="inside"))
.set_global_opts(title_opts=options.TitleOpts(title=title, pos_left='center'), legend_opts=options.LegendOpts(orient='vertical', pos_top='15%', pos_left='2%')))
funnel.render(os.path.join(savedir, title+'.html'))
