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
def bar_same_series_gap() -> Bar:
c = (Bar().add_xaxis(Faker.choose()).add_yaxis(
"商家A", Faker.values(), category_gap="80%").set_global_opts(
title_opts=opts.TitleOpts(title="Bar-单系列柱间距离")))
return c
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="我是副标题")))
return c
from pyecharts.charts import Bar # 柱状图对象
from pyecharts import options # 配置对象
from pyecharts.globals import ThemeType
# 创建柱状图对象
(Bar({'theme': ThemeType.DARK})
# 添加横坐标
.add_xaxis(["冰箱", "洗衣机", "彩电", "空调", "电脑", "电饭煲"])
# 添加纵坐标（数据）可以添加多个
.add_yaxis("格力", [100, 200, 250, 150, 50, 300],
stack="strack").add_yaxis("小米", [200, 100, 50, 150, 240, 180],
stack="strack")
# 创建标题对象
.set_global_opts(
title_opts=options.TitleOpts(title="家电销售情况"),
# visualmap_opts=options.VisualMapOpts(max_=200), # 设置阈值
datazoom_opts=[options.DataZoomOpts()], # 拉的进度条
brush_opts=options.BrushOpts(), # 工具栏
yaxis_opts=options.AxisOpts(name="y轴名字"),
xaxis_opts=options.AxisOpts(name="x轴名字"),
).render("家电柱状图.html"))
def bar_is_selected() -> Bar:
c = (Bar().add_xaxis(Faker.choose()).add_yaxis(
"商家A", Faker.values()).add_yaxis(
"商家B", Faker.values(), is_selected=False).set_global_opts(
title_opts=opts.TitleOpts(title="Bar-默认取消显示某 Series")))
return c
datas = cursor.fetchall()
china_sql = 'select * from china_count'
cursor.execute(china_sql)
china_data = cursor.fetchone()
# 地图
map_obj = (
Map()
.add("", datas, "china")
.set_global_opts(
title_opts=opts.TitleOpts(title="疫情情况", subtitle="截止目前为止,全国共有累计确诊{}例,累计治愈{}例,累计死亡{}例,疑似{}例"
.format(china_data[0], china_data[1], china_data[2], china_data[3])
),
visualmap_opts=opts.VisualMapOpts(max_=3),
)
)
name_list = [name[0] for name in datas]
data_list = [name[1] for name in datas]
# 柱状图
bar_obj = (
Bar()
.add_xaxis(name_list)
.add_yaxis("各省份数据对比", data_list)
.set_global_opts(title_opts=opts.TitleOpts(title="各省份目前情况"),
datazoom_opts=[opts.DataZoomOpts()]
)
)
# page对象用于整合地图
p = Page(layout=Page.SimplePageLayout)
p.add(map_obj, bar_obj)
p.render("疫情实时监控.html")
def generate_price_bar_html(rows, title):
xx1 = []
xx2 = []
yy1 = []
yy2 = []
count = 0
for row in rows:
bond_code = row[0]
bond_code = trade_utils.rebuild_bond_code(bond_code)
count += 1
if count <= 20:
xx1.append(row[1].replace('转债', ''))
yy1.append({
'value': row[2],
'bond_code': bond_code,
'rise': row[3],
'premium': row[4]
})
else:
xx2.insert(0, row[1].replace('转债', ''))
yy2.insert(
0, {
'value': round(-float(row[2]) + 0, 2),
'bond_code': bond_code,
'rise': row[3],
'premium': row[4]
})
max_value = 0
size = len(yy1)
for i in range(size):
if yy1[i]['value'] + abs(yy2[i]['value']) > max_value:
max_value = yy1[i]['value'] + abs(yy2[i]['value'])
max_value = round(max_value * 0.7, 2) + 1
chart_id = str(abs(hash(title)))
bar = Bar(init_opts=opts.InitOpts(height='700px',
width='1424px',
theme=ThemeType.SHINE,
chart_id=chart_id))
add_popwin_js_code(bar, chart_id)
# 底部x轴
bar.add_xaxis(xx1)
# 顶部x轴
bar.extend_axis(
xaxis_data=xx2,
xaxis=opts.AxisOpts(type_="category",
position='top',
axislabel_opts=opts.LabelOpts(rotate=-60, )),
)
# 右侧y轴
bar.extend_axis(yaxis=opts.AxisOpts(
type_="value",
position="right",
name='可转债价格(元)',
name_gap=45,
name_location='middle',
min_=-max_value,
is_scale=True,
axisline_opts=opts.AxisLineOpts(
linestyle_opts=opts.LineStyleOpts(is_show=True, )),
# axislabel_opts=opts.LabelOpts(formatter="{value}"),
axislabel_opts=opts.LabelOpts(
formatter=JsCode("function(x){return (x + '元').substring(1);}")),
))
# 添加高价格的柱状图
bar.add_yaxis(
"高价格",
yy1,
bar_width=25,
category_gap='1%',
gap='1%',
label_opts=opts.LabelOpts(
is_show=True,
position="top",
formatter=JsCode("function(x){return x.data['value']+'元';}")),
)
# 添加低价格的柱状图
bar.add_yaxis(
"低价格",
yy2,
bar_width=25,
yaxis_index=1,
label_opts=opts.LabelOpts(
is_show=True,
position="bottom",
formatter=JsCode(
"function(x){return (x.data['value'] + '元').substring(1);}")),
)
# bar.reversal_axis()
bar.set_series_opts(
# itemstyle_opts=opts.ItemStyleOpts(
# color=JsCode(
# "function(x){return x.data['value']>0?'#c23531':'#1d953f'}"
# )
# )
)
bar.set_global_opts(
title_opts=opts.TitleOpts(
title="=========" + title + "=========",
pos_left='center',
pos_top='-1px',
),
tooltip_opts=opts.TooltipOpts(
is_show=True,
formatter=JsCode(
"function (params){return '名 称: ' + params.name + '<br/>' + '涨 跌: ' + params.data['rise'] + '%<br/>' + '溢价率: ' + params.data['premium']}"
)),
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, ),
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='可转债价格(元)',
# name_rotate=90,
name_gap=35,
name_location='middle',
# min_=0,
max_=max_value,
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']
)),
)
bar_html = bar.render_embed('template.html', html_utils.env)
return bar_html
import streamlit.components.v1 as components
from pyecharts import options as opts
from pyecharts.charts import Bar
c = (
Bar().add_xaxis(
["Microsoft", "Amazon", "IBM",
"Oracle", "Google", "Alibaba"]).add_yaxis(
'2017-2018 Revenue in (billion $)',
[21.2, 20.4, 10.3, 6.08, 4, 2.2]).set_global_opts(
title_opts=opts.TitleOpts(title="Top cloud providers 2018",
subtitle="2017-2018 Revenue"),
toolbox_opts=opts.ToolboxOpts()).render_embed(
) # generate a local HTML file
)
components.html(c, width=1000, height=1000)
def get_bar_data(request):
x_field = request.GET.get('x_choice')
y_field = request.GET.get('y_choice')
print(request.GET.get('begin_date'))
start_date = request.GET.get('begin_date').split('-')
end_date = request.GET.get('end_date').split('-')
start = datetime.date(int(start_date[0]), int(start_date[1]),
int(start_date[2]))
end = datetime.date(int(end_date[0]), int(end_date[1]), int(end_date[2]))
y_lists = []
x_list = []
b = Bar()
if x_field == 'street' and y_field == 'property':
event_counts = Street.objects.all().values(
'name', 'number').order_by('-number')
for property in properties:
new_list = [property.name]
for street_node in event_counts:
new_len = 0
street = Street.objects.get(name=street_node['name'])
for community in Community.objects.filter(street=street):
new_len += len(
Event.objects.filter(community=community,
property=property,
create_time__range=(start, end)))
new_list.append(new_len)
# print(new_list)
y_lists.append(new_list)
for street_node in event_counts:
x_list.append(street_node['name'])
elif x_field == 'community' and y_field == 'property':
event_counts = Community.objects.all().values(
'name', 'number').order_by('-number')
for property in properties:
new_list = [property.name]
for street_node in event_counts:
community = Community.objects.get(name=street_node['name'])
new_len = len(
Event.objects.filter(
Q(create_time__range=(start, end))
& Q(community=community) & Q(property=property)))
new_list.append(new_len)
# print(new_list)
y_lists.append(new_list)
for community_node in event_counts:
x_list.append(community_node['name'])
elif x_field == 'dispose_unit' and y_field == 'property':
event_counts = DisposeUnit.objects.all().values(
'name', 'number').order_by('-number')
for property in properties:
new_list = [property.name]
limit = 0
for dispose_unit_node in event_counts:
dispose_unit = DisposeUnit.objects.get(
name=dispose_unit_node['name'])
if limit < 30:
new_len = len(
Event.objects.filter(
Q(create_time__range=(start, end))
& Q(dispose_unit=dispose_unit)
& Q(property=property)))
new_list.append(new_len)
new_len = 0
else:
new_len += len(
Event.objects.filter(
Q(create_time__range=(start, end))
& Q(dispose_unit=dispose_unit)
& Q(property=property)))
limit += 1
new_list.append(new_len)
y_lists.append(new_list)
limit = 0
for dispose_unit_node in event_counts:
if limit >= 30:
break
x_list.append(dispose_unit_node['name'])
limit += 1
x_list.append("其他")
elif x_field == 'main_type' and y_field == 'property':
event_counts = MainType.objects.all().values(
'name', 'number').order_by('-number')
for property in properties:
new_list = [property.name]
limit = 0
new_len = 0
for main_type_node in event_counts:
main_type = MainType.objects.get(name=main_type_node['name'])
for sub_type in SubType.objects.filter(main_type=main_type):
new_len += len(
Event.objects.filter(sub_type=sub_type,
property=property,
create_time__range=(start, end)))
if limit < 30:
limit += 1
new_list.append(new_len)
new_len = 0
# print(new_list)
new_list.append(new_len)
y_lists.append(new_list)
limit = 0
for main_type_node in event_counts:
if limit >= 30:
break
x_list.append(main_type_node['name'])
limit += 1
x_list.append('其他')
elif x_field == 'sub_type' and y_field == 'property':
event_counts = SubType.objects.all().values(
'name', 'number').order_by('-number')
for property in properties:
new_list = [property.name]
limit = 0
new_len = 0
for sub_type_node in event_counts:
sub_type = SubType.objects.get(name=sub_type_node['name'])
new_len += len(
Event.objects.filter(
Q(create_time__range=(start, end))
& Q(property=property) & Q(sub_type=sub_type)))
if limit < 60:
limit += 1
new_list.append(new_len)
new_len = 0
# print(new_list)
new_list.append(new_len)
y_lists.append(new_list)
limit = 0
for sub_type_node in event_counts:
if limit >= 60:
break
x_list.append(sub_type_node['name'])
limit += 1
x_list.append('其他')
elif x_field == 'street' and y_field == 'achieve':
event_counts = Street.objects.all().values(
'name', 'number').order_by('-number')
for achieve in achieves:
new_list = [achieve.name]
for street_node in event_counts:
new_len = 0
street = Street.objects.get(name=street_node['name'])
for community in Community.objects.filter(street=street):
new_len += len(
Event.objects.filter(community=community,
achieve=achieve,
create_time__range=(start, end)))
new_list.append(new_len)
# print(new_list)
y_lists.append(new_list)
for street_node in event_counts:
x_list.append(street_node['name'])
elif x_field == 'community' and y_field == 'achieve':
event_counts = Community.objects.all().values(
'name', 'number').order_by('-number')
for achieve in achieves:
new_list = [achieve.name]
for street_node in event_counts:
community = Community.objects.get(name=street_node['name'])
new_len = len(
Event.objects.filter(
Q(create_time__range=(start, end))
& Q(community=community) & Q(achieve=achieve)))
new_list.append(new_len)
# print(new_list)
y_lists.append(new_list)
for community_node in event_counts:
x_list.append(community_node['name'])
elif x_field == 'dispose_unit' and y_field == 'achieve':
event_counts = DisposeUnit.objects.all().values(
'name', 'number').order_by('-number')
for achieve in achieves:
new_list = [achieve.name]
limit = 0
for dispose_unit_node in event_counts:
dispose_unit = DisposeUnit.objects.get(
name=dispose_unit_node['name'])
if limit < 30:
new_len = len(
Event.objects.filter(
Q(create_time__range=(start, end))
& Q(dispose_unit=dispose_unit)
& Q(achieve=achieve)))
new_list.append(new_len)
new_len = 0
else:
new_len += len(
Event.objects.filter(
Q(create_time__range=(start, end))
& Q(dispose_unit=dispose_unit)
& Q(achieve=achieve)))
limit += 1
new_list.append(new_len)
y_lists.append(new_list)
limit = 0
for dispose_unit_node in event_counts:
if limit >= 30:
break
x_list.append(dispose_unit_node['name'])
limit += 1
x_list.append("其他")
elif x_field == 'main_type' and y_field == 'achieve':
event_counts = MainType.objects.all().values(
'name', 'number').order_by('-number')
for achieve in achieves:
new_list = [achieve.name]
limit = 0
new_len = 0
for main_type_node in event_counts:
main_type = MainType.objects.get(name=main_type_node['name'])
for sub_type in SubType.objects.filter(main_type=main_type):
new_len += len(
Event.objects.filter(sub_type=sub_type,
achieve=achieve,
create_time__range=(start, end)))
if limit < 30:
limit += 1
new_list.append(new_len)
new_len = 0
# print(new_list)
new_list.append(new_len)
y_lists.append(new_list)
limit = 0
for main_type_node in event_counts:
if limit >= 30:
break
x_list.append(main_type_node['name'])
limit += 1
x_list.append('其他')
elif x_field == 'sub_type' and y_field == 'achieve':
event_counts = SubType.objects.all().values(
'name', 'number').order_by('-number')
for achieve in achieves:
new_list = [achieve.name]
limit = 0
new_len = 0
for sub_type_node in event_counts:
sub_type = SubType.objects.get(name=sub_type_node['name'])
new_len += len(
Event.objects.filter(
Q(create_time__range=(start, end)) & Q(achieve=achieve)
& Q(sub_type=sub_type)))
if limit < 60:
limit += 1
new_list.append(new_len)
new_len = 0
# print(new_list)
new_list.append(new_len)
y_lists.append(new_list)
limit = 0
for sub_type_node in event_counts:
if limit >= 60:
break
x_list.append(sub_type_node['name'])
limit += 1
x_list.append('其他')
b.add_xaxis(x_list)
for y_list in y_lists:
b.add_yaxis(y_list[0], y_list[1:], stack="stack1", category_gap="60%")
b.set_series_opts(label_opts=opts.LabelOpts(is_show=False)) \
.set_global_opts(
xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(rotate=-15)),
# title_opts=opts.TitleOpts(title="处理事件最多的部门"),
datazoom_opts=[opts.DataZoomOpts(), opts.DataZoomOpts(type_="inside")], )
c = (b)
grid = Grid()
grid.add(c, grid_opts=opts.GridOpts(pos_bottom="20%"))
c = grid.dump_options_with_quotes()
return HttpResponse(c, content_type='application/json')
def html_handle(self, data):
"""
convert function to html by pyecharts
Args:
data(dict): the data info
Returns:
chart: chart generated by pyecharts: Bar, Pie, Line or Scatter
filename: html file name
"""
filename = self.table_name + str(data['order']) + '.html'
margin = str(data['title_top']) + '%'
# 设置图标基本属性
if data['chart'] == 'bar':
chart = (Bar().set_series_opts(label_opts=opts.LabelOpts(
is_show=False)).set_global_opts(
title_opts=opts.TitleOpts(title=data['chartname'],
subtitle=data['describe'],
pos_left='center',
pos_top=margin),
xaxis_opts=opts.AxisOpts(name=data['x_name']),
yaxis_opts=opts.AxisOpts(
name=data['y_name'],
splitline_opts=opts.SplitLineOpts(is_show=True))))
elif data['chart'] == 'pie':
chart = (Pie().set_global_opts(
title_opts=opts.TitleOpts(title=data['chartname'],
subtitle=data['describe'],
pos_left='center',
pos_top=margin)))
elif data['chart'] == 'line':
chart = (Line().set_series_opts(label_opts=opts.LabelOpts(
is_show=False)).set_global_opts(
title_opts=opts.TitleOpts(title=data['chartname'],
subtitle=data['describe'],
pos_left='center',
pos_top=margin),
xaxis_opts=opts.AxisOpts(name=data['x_name']),
yaxis_opts=opts.AxisOpts(
name=data['y_name'],
splitline_opts=opts.SplitLineOpts(is_show=True))))
elif data['chart'] == 'scatter':
chart = (Scatter().set_series_opts(label_opts=opts.LabelOpts(
is_show=False)).set_global_opts(
title_opts=opts.TitleOpts(title=data['chartname'],
subtitle=data['describe'],
pos_left='center',
pos_top=margin),
xaxis_opts=opts.AxisOpts(
type_='value',
name=data['x_name'],
splitline_opts=opts.SplitLineOpts(is_show=True)),
yaxis_opts=opts.AxisOpts(
type_='value',
name=data['y_name'],
splitline_opts=opts.SplitLineOpts(is_show=True))))
else:
print("not valid chart")
if not data["classify"]: # 在图片上只需展示一组数据
attr = data["x_data"][0] # 横坐标
val = data["y_data"][0] # 纵坐标
if data['chart'] == 'bar':
chart.add_xaxis(attr).add_yaxis(
"", val, label_opts=opts.LabelOpts(is_show=False))
elif data['chart'] == 'line':
chart.add_xaxis(attr).add_yaxis(
"", val, label_opts=opts.LabelOpts(is_show=False))
elif data['chart'] == 'pie':
chart.add("", [list(z) for z in zip(attr, val)])
elif data['chart'] == 'scatter':
if isinstance(attr[0], str):
attr = [x for x in attr if x != '']
attr = list(map(float, attr))
if isinstance(val[0], str):
val = [x for x in val if x != '']
val = list(map(float, val))
chart.add_xaxis(attr).add_yaxis(
"", val, label_opts=opts.LabelOpts(is_show=False))
else: # 在图片上需要展示多组数据
attr = data["x_data"][0] # 横坐标
for i in range(len(data["classify"])): # 循环输出每组数据
val = data["y_data"][i] # 每组纵坐标的值
name = (data["classify"][i][0] if type(data["classify"][i])
== type(('a', 'b')) else data["classify"][i])
if i == 0:
if data['chart'] != 'pie' and data['chart'] != 'scatter':
chart.add_xaxis(attr)
if data['chart'] == 'bar':
chart.add_yaxis(name,
val,
stack="stack1",
label_opts=opts.LabelOpts(is_show=False))
elif data['chart'] == 'line':
chart.add_yaxis(name,
val,
label_opts=opts.LabelOpts(is_show=False))
elif data['chart'] == 'pie':
chart.add("", [list(z) for z in zip(attr, val)])
elif data['chart'] == 'scatter':
attr_scatter = data["x_data"][i]
if isinstance(attr_scatter[0],
str): # 去除散点图的空点，并将字符类型转化为数字类型
attr_scatter = [x for x in attr_scatter if x != '']
attr_scatter = list(map(float, attr_scatter))
if isinstance(val[0], str):
val = [x for x in val if x != '']
val = list(map(float, val))
chart.add_xaxis(attr_scatter).add_yaxis(
name, val, label_opts=opts.LabelOpts(is_show=False))
return chart, filename
def show_visualizations(self, number=-1):
"""
show the charts in jupyter notebook.
Args:
number(int): the index of chart to be shown in jupyter notebook.
If number == -1, show all the charts in jupyter notebook.
Returns:
page(Page()): an object of class Page in pyecharts, containing the chart(s)
to be shown in jupyter notebook.
"""
instance = self.instance
if number > instance.view_num:
print(
"In function show_visualizations: Error, input number greater than the view numbers."
)
return Page()
if self.rank_method == methods_of_ranking[3]: # diversified_ranking
G = myGraph(instance.view_num)
for i in range(instance.view_num):
view = instance.tables[instance.views[i].table_pos].views[
instance.views[i].view_pos]
G.addNode(view)
G.getSim()
result = G.getTopK(instance.view_num)
if number != -1:
begin = number - 1
end = number
else:
begin = 0
end = instance.view_num
page = Page()
for order in range(begin, end):
if self.rank_method == methods_of_ranking[
3]: # diversified_ranking
view = G.nodes[result[order]]
else:
view = instance.tables[instance.views[order].table_pos].views[
instance.views[order].view_pos]
data = {}
data['order'] = order
data['chartname'] = instance.table_name
data['describe'] = view.table.describe
data['x_name'] = view.fx.name
data['y_name'] = view.fy.name
data['chart'] = Chart.chart[view.chart]
data['classify'] = [v[0] for v in view.table.classes]
data['x_data'] = view.X
data['y_data'] = view.Y
data['title_top'] = 5
# 以下代码与html_handle相似
margin = str(data['title_top']) + '%'
if data['chart'] == 'bar':
chart = (Bar().set_series_opts(label_opts=opts.LabelOpts(
is_show=False)).set_global_opts(
title_opts=opts.TitleOpts(title=data['chartname'],
subtitle=data['describe'],
pos_left='center',
pos_top=margin),
xaxis_opts=opts.AxisOpts(name=data['x_name']),
yaxis_opts=opts.AxisOpts(
name=data['y_name'],
splitline_opts=opts.SplitLineOpts(is_show=True))))
elif data['chart'] == 'pie':
chart = (Pie().set_global_opts(
title_opts=opts.TitleOpts(title=data['chartname'],
subtitle=data['describe'],
pos_left='center',
pos_top=margin)))
elif data['chart'] == 'line':
chart = (Line().set_series_opts(label_opts=opts.LabelOpts(
is_show=False)).set_global_opts(
title_opts=opts.TitleOpts(title=data['chartname'],
subtitle=data['describe'],
pos_left='center',
pos_top=margin),
xaxis_opts=opts.AxisOpts(name=data['x_name']),
yaxis_opts=opts.AxisOpts(
name=data['y_name'],
splitline_opts=opts.SplitLineOpts(is_show=True))))
elif data['chart'] == 'scatter':
chart = (Scatter().set_series_opts(label_opts=opts.LabelOpts(
is_show=False)).set_global_opts(
title_opts=opts.TitleOpts(title=data['chartname'],
subtitle=data['describe'],
pos_left='center',
pos_top=margin),
xaxis_opts=opts.AxisOpts(
type_='value',
name=data['x_name'],
splitline_opts=opts.SplitLineOpts(is_show=True)),
yaxis_opts=opts.AxisOpts(
type_='value',
name=data['y_name'],
splitline_opts=opts.SplitLineOpts(is_show=True))))
else:
print("not valid chart")
if not data["classify"]:
attr = data["x_data"][0]
val = data["y_data"][0]
if data['chart'] == 'bar':
chart.add_xaxis(attr).add_yaxis(
"", val, label_opts=opts.LabelOpts(is_show=False))
elif data['chart'] == 'line':
chart.add_xaxis(attr).add_yaxis(
"", val, label_opts=opts.LabelOpts(is_show=False))
elif data['chart'] == 'pie':
chart.add("", [list(z) for z in zip(attr, val)])
elif data['chart'] == 'scatter':
if isinstance(attr[0], str):
attr = [x for x in attr if x != '']
attr = list(map(float, attr))
if isinstance(val[0], str):
val = [x for x in val if x != '']
val = list(map(float, val))
chart.add_xaxis(attr).add_yaxis(
"", val, label_opts=opts.LabelOpts(is_show=False))
page.add(chart)
else:
attr = data["x_data"][0]
for i in range(len(data["classify"])):
val = data["y_data"][i]
name = (data["classify"][i][0] if type(data["classify"][i])
== type(('a', 'b')) else data["classify"][i])
if i == 0:
if data['chart'] != 'pie' and data[
'chart'] != 'scatter':
chart.add_xaxis(attr)
if data['chart'] == 'bar':
chart.add_yaxis(
name,
val,
stack="stack1",
label_opts=opts.LabelOpts(is_show=False))
elif data['chart'] == 'line':
chart.add_yaxis(
name,
val,
label_opts=opts.LabelOpts(is_show=False))
elif data['chart'] == 'pie':
chart.add("", [list(z) for z in zip(attr, val)])
elif data['chart'] == 'scatter':
attr_scatter = data["x_data"][i]
if isinstance(attr_scatter[0], str):
attr_scatter = [x for x in attr_scatter if x != '']
attr_scatter = list(map(float, attr_scatter))
if isinstance(val[0], str):
val = [x for x in val if x != '']
val = list(map(float, val))
chart.add_xaxis(attr_scatter).add_yaxis(
name,
val,
label_opts=opts.LabelOpts(is_show=False))
page.add(chart)
return page
def analyse_data():
client = pymongo.MongoClient("mongodb://localhost:27017/")
db = client['testcomment']
col = db[collection_name]
result = col.find({}, {'_id': 0, '评论日期': 1, "评论": 1, "追加评论": 1})
comment_list = []
date_list = []
date_dic = {}
for date in result: # 把日期和评论字数放入列表中
date_list.append(date['评论日期'])
comment_list.append(len(date['评论']))
if len(date['追加评论']) != 0:
comment_list.append(len(date['追加评论']))
for date in date_list: # 统计日销量
if date in date_dic:
date_dic[date] += 1
else:
date_dic[date] = 1
# date_list = list(date_dic)
data_dic = {}
arr = np.array(list(date_dic)) # 给日期排序
date_list = arr[np.argsort(
[datetime.strptime(i, '%m.%d') for i in list(date_dic)])].tolist()
for i in date_list:
data_dic[i] = date_dic[i]
# s = pd.Series(data_dic) # series表示一维数组
# comment_list.sort()
word_num_dic = {
'0-10': 0,
'11-20': 0,
'21-30': 0,
'31-40': 0,
'41-50': 0,
'51-60': 0,
'61-70': 0,
'71-80': 0,
'91-100': 0,
'101-110': 0,
'111-120': 0,
'121-130': 0,
'131-140': 0,
'141-150': 0,
'151-160': 0,
'161-170': 0,
'171-180': 0,
'181-190': 0,
'191-200': 0,
'201-300': 0
}
for i in word_num_dic: # 统计字数
for j in comment_list:
if j >= int(i.split('-')[0]) and j <= int(i.split('-')[-1]):
word_num_dic[i] += 1
# print(ndic)
bar1 = ( # 生成柱状图
Bar().add_xaxis(list(data_dic.keys())).add_yaxis(
'商品', list(data_dic.values())).set_global_opts( # 设置全局参数
title_opts=opts.TitleOpts(title='商品日销量'),
yaxis_opts=opts.AxisOpts(splitline_opts=opts.SplitLineOpts(
is_show=True)), # 添加纵坐标标线
datazoom_opts=[
opts.DataZoomOpts(range_start=10,
range_end=80,
is_zoom_lock=False)
] # 添加滑动条
))
bar1.chart_id = '6f180e81787a48539de004c1eb847c1e'
bar2 = ( # 生成柱状图
Bar({
'theme': ThemeType.MACARONS
}).add_xaxis(list(word_num_dic.keys())).add_yaxis(
'商品', list(word_num_dic.values())).set_global_opts( # 设置全局参数
title_opts=opts.TitleOpts(title='字数统计'),
yaxis_opts=opts.AxisOpts(splitline_opts=opts.SplitLineOpts(
is_show=True)),
datazoom_opts=[
opts.DataZoomOpts(range_start=10,
range_end=80,
is_zoom_lock=False)
]))
bar2.chart_id = '9a412341322b4d92bba49e3fd932a7f0'
pie1 = ( # 生成柱状图
Pie().add(series_name='用户身份',
data_pair=[('超级会员', vip_num), ('普通用户', comment_num - vip_num)
]).set_global_opts( # 设置全局参数
title_opts=opts.TitleOpts(title='超级会员比例'), ))
pie1.chart_id = '7ed5d413532646909f127fd36f584081'
pie2 = ( # 生成柱状图
Pie(init_opts=opts.InitOpts(theme=ThemeType.WESTEROS)).add(
series_name='有无图片',
data_pair=[('有图评价', img_num), ('无图评价', comment_num - img_num)
]).set_global_opts( # 设置全局参数
title_opts=opts.TitleOpts(title='有图评价比例'), ))
pie2.chart_id = 'aa3f038854e144dbaa6e9892d4ca31fc'
pie3 = ( # 生成柱状图
Pie(init_opts=opts.InitOpts(theme=ThemeType.INFOGRAPHIC)).add(
series_name='是否追加',
data_pair=[('追加评价', append_num), ('普通评价', comment_num - append_num)
]).set_global_opts( # 设置全局参数
title_opts=opts.TitleOpts(title='追加评价比例'), ))
pie3.chart_id = '235863de6fe74a2aa9273e30ed4a7f66'
page = Page(page_title='评论分析报告')
page.add(bar1, bar2, pie1, pie2, pie3)
page.render()
Page.save_resize_html(cfg_file=r'chart_config.json')
with open('resize_render.html', 'r+', encoding='utf-8') as f:
f.seek(573)
html = f.read()
f.seek(573)
f.write(' <h1>评价分析结果：</h1>\n <p>' + content + '</p>\n ')
f.write(html)
print(
r'已完成分析 请打开E:\Python\learnPython\testspider\FinalProject\resize_render.html查看分析结果'
)
from pyecharts import options as opts
from pyecharts.charts import Bar
from pyecharts.faker import Faker
c = (Bar().add_xaxis(Faker.choose()).add_yaxis(
"商家A", Faker.values()).add_yaxis("商家B", Faker.values()).set_global_opts(
title_opts=opts.TitleOpts(title="Bar-MarkLine（自定义）")).set_series_opts(
label_opts=opts.LabelOpts(is_show=False),
markline_opts=opts.MarkLineOpts(
data=[opts.MarkLineItem(y=50, name="yAxis=50")]),
).render("Bar_markline_custom.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 paint_bar(self,
x: list,
collects: list,
title: str,
mark_point: bool = False):
bar = Bar(init_opts=opts.InitOpts(theme=ThemeType.LIGHT))
bar.add_xaxis(x)
for collect in collects:
for i, (name, unit, data) in enumerate(collect):
bar.add_yaxis(f'{name}-单位：{unit}', data, yaxis_index=i)
if i != 0:
bar.extend_axis(yaxis=opts.AxisOpts(
name='',
type_='value',
position='right',
))
bar.set_global_opts(title_opts=opts.TitleOpts(title=title,
pos_left='5%'),
legend_opts=opts.LegendOpts(pos_bottom='0'))
bar.set_series_opts(
label_opts=opts.LabelOpts(position='top'),
tooltip_opts=opts.TooltipOpts(formatter=f'{{b}}年{{a}}：{{c}}'))
if mark_point:
bar.set_series_opts(
markpoint_opts=opts.MarkPointOpts(data=[
opts.MarkPointItem(type_='max', name='最大值'),
opts.MarkPointItem(type_='min', name='最小值')
],
symbol_size=80))
return bar
def get_year_chart(year: int):
map_data = [
[[x["name"], x["value"]] for x in d["data"]] for d in data if d["time"] == year
][0]
min_data, max_data = (
min([d[1][0] for d in map_data]),
max([d[1][0] for d in map_data]),
)
map_chart = (
Map()
.add(
series_name="",
data_pair=map_data,
label_opts=opts.LabelOpts(is_show=False),
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="1980年以来中国各省GDP排名变化情况",
subtitle="GDP单位:亿元",
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="10",
pos_top="center",
range_text=["High", "Low"],
range_color=["lightskyblue", "yellow", "orangered"],
textstyle_opts=opts.TextStyleOpts(color="#ddd"),
min_=min_data,
max_=max_data,
),
)
)
bar_x_data = [x[0] for x in map_data]
# 这里注释的部分会导致 label 和 value 与 饼图不一致
# 使用下面的 List[Dict] 就可以解决这个问题了。
# bar_y_data = [x[1][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_index=1,
y_axis=bar_y_data,
label_opts=opts.LabelOpts(
is_show=True, position="right", formatter="{b}: {c}"
),
)
.reversal_axis()
.set_global_opts(
xaxis_opts=opts.AxisOpts(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="center",
range_text=["High", "Low"],
range_color=["lightskyblue", "yellow", "orangered"],
textstyle_opts=opts.TextStyleOpts(color="#ddd"),
min_=min_data,
max_=max_data,
),
graphic_opts=[
opts.GraphicGroup(
graphic_item=opts.GraphicItem(
rotation=JsCode("Math.PI / 4"),
bounding="raw",
right=110,
bottom=110,
z=100,
),
children=[
opts.GraphicRect(
graphic_item=opts.GraphicItem(left="center", top="center", z=100),
graphic_shape_opts=opts.GraphicShapeOpts(width=400, height=50),
graphic_basicstyle_opts=opts.GraphicBasicStyleOpts(
fill="rgba(0,0,0,0.3)"
),
),
opts.GraphicText(
graphic_item=opts.GraphicItem(left="center", top="center", z=100),
graphic_textstyle_opts=opts.GraphicTextStyleOpts(
text=f"{str(year)} 年",
font="bold 26px Microsoft YaHei",
graphic_basicstyle_opts=opts.GraphicBasicStyleOpts(fill="#fff"),
),
),
],
)
],
)
)
pie_data = [[x[0], x[1][0]] for x in map_data]
percent_sum = sum([x[1][1] for x in map_data])
rest_value = 0
for d in map_data:
rest_percent = 100.0
rest_percent = rest_percent - percent_sum
rest_value = d[1][0] * (rest_percent / d[1][1])
pie_data.append(["其他省份", rest_value])
pie = (
Pie()
.add(
series_name="",
data_pair=pie_data,
radius=["12%", "20%"],
center=["75%", "85%"],
itemstyle_opts=opts.ItemStyleOpts(
border_width=1, border_color="rgba(0,0,0,0.3)"
),
)
.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="70%", pos_bottom="5"
),
)
.add(pie, grid_opts=opts.GridOpts())
.add(map_chart, grid_opts=opts.GridOpts())
)
return grid_chart
def paint_bar_stack_with_line(self, x: list, children: dict, parents: dict,
sub_title: str):
for (parent_name, unit), item in children.items():
bar = Bar(init_opts=opts.InitOpts(theme=ThemeType.MACARONS))
bar.add_xaxis(x)
line = Line()
line.add_xaxis(x)
child_names = []
for child_name, data, overall in item:
bar.add_yaxis(child_name, data, stack='stack1')
line.add_yaxis(f'{child_name}占比', overall, yaxis_index=1)
child_names.append(child_name)
bar.add_yaxis(parent_name,
parents[parent_name],
stack='stack1',
yaxis_index=0)
bar.set_global_opts(
title_opts=opts.TitleOpts(title='，'.join(child_names),
subtitle=sub_title,
pos_left='5%'),
legend_opts=opts.LegendOpts(pos_bottom='0'))
bar.set_series_opts(label_opts=opts.LabelOpts(is_show=False),
tooltip_opts=opts.TooltipOpts(
formatter=f'{{b}}年{{a}}：{{c}}{unit}'),
itemstyle_opts=opts.ItemStyleOpts(opacity=0.5))
bar.extend_axis(yaxis=opts.AxisOpts(
type_='value',
name='所占比例',
min_=0,
max_=1,
position='right',
splitline_opts=opts.SplitLineOpts(
is_show=True, linestyle_opts=opts.LineStyleOpts(
opacity=1))))
bar.overlap(line)
yield bar
from pyecharts import options as opts
from pyecharts.charts import Bar, Line
from pyecharts.faker import Faker
v1 = [2.0, 4.9, 7.0, 23.2, 25.6, 76.7, 135.6, 162.2, 32.6, 20.0, 6.4, 3.3]
v2 = [2.6, 5.9, 9.0, 26.4, 28.7, 70.7, 175.6, 182.2, 48.7, 18.8, 6.0, 2.3]
v3 = [2.0, 2.2, 3.3, 4.5, 6.3, 10.2, 20.3, 23.4, 23.0, 16.5, 12.0, 6.2]
bar = (Bar().add_xaxis(Faker.months).add_yaxis("蒸发量", v1).add_yaxis(
"降水量", v2).extend_axis(yaxis=opts.AxisOpts(
axislabel_opts=opts.LabelOpts(
formatter="{value} °C"), interval=5)).set_series_opts(
label_opts=opts.LabelOpts(is_show=False)).set_global_opts(
title_opts=opts.TitleOpts(title="Overlap-bar+line"),
yaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(
formatter="{value} ml")),
))
line = Line().add_xaxis(Faker.months).add_yaxis("平均温度", v3, yaxis_index=1)
bar.overlap(line)
bar.render("overlap_bar_line.html")
def find_value_render(query_filter, x_label, x, result, Data, query,
table_path, answer):
colorList = ['#f36c6c', '#e6cf4e', '#20d180', '#0093ff']
# x = ['GDP', 'Industry', 'Architecture', 'Service']
# y = [990865, 317109, 70904, 534233]
# x1 = ["周一"]
# y1 = [11]
Y = []
# for i in range(len(y)):
# if i==0:
# Y.append(
# opts.BarItem(
# name=x[i],
# value=round(y[i], 2),
# label_opts=opts.LabelOpts(position="insideTop"),
# itemstyle_opts={
# "normal": {
# "color": colorList[0],
# "barBorderRadius": [30, 30, 30, 30],
# }
# }
# ))
# else:
# Y.append(
# opts.BarItem(
# name=x[i],
# value=round(y[i], 2),
# label_opts=opts.LabelOpts(position="insideTop"),
# itemstyle_opts={
# "normal": {
# "color": "blue",
# "barBorderRadius": [30, 30, 30, 30],
# }
# }
# ))
bar1 = Bar()
bar1.add_xaxis(x)
bar1.add_yaxis(result[0][0],
y_axis=result[0][1],
label_opts=opts.LabelOpts(position="insideTop"))
bar1.set_global_opts(
yaxis_opts=opts.AxisOpts(is_show=False),
xaxis_opts=opts.AxisOpts(
axisline_opts=opts.AxisLineOpts(is_show=False),
axispointer_opts=opts.AxisPointerOpts(is_show=False),
axistick_opts=opts.AxisTickOpts(is_show=False)),
title_opts=opts.TitleOpts(subtitle="When the search condition is " +
query_filter,
pos_left='center'),
graphic_opts=[
opts.GraphicText(
graphic_item=opts.GraphicItem(
left="center",
top="bottom",
z=100,
),
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' + "\n" + 'A:' + ' ' +
answer
],
font="14px Microsoft YaHei",
graphic_basicstyle_opts=opts.GraphicBasicStyleOpts(
fill="#333")))
])
bar2 = Bar()
bar2.add_xaxis(x)
for i in range(len(result)):
bar2.add_yaxis(result[i][0], y_axis=result[i][1])
bar2.set_global_opts(
yaxis_opts=opts.AxisOpts(is_show=False),
xaxis_opts=opts.AxisOpts(
axisline_opts=opts.AxisLineOpts(is_show=False),
axispointer_opts=opts.AxisPointerOpts(is_show=False),
axistick_opts=opts.AxisTickOpts(is_show=False)),
graphic_opts=[
opts.GraphicText(
graphic_item=opts.GraphicItem(
left="center",
top="bottom",
z=100,
),
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' + "\n" + "Q:" + ' ' + query + '\n' + "\n" + 'A:' +
' ' + answer
],
font="16px Microsoft YaHei",
graphic_basicstyle_opts=opts.GraphicBasicStyleOpts(
fill="#333")))
])
Label = []
data = []
for i in range(len(Data)):
Label.append(str(Data[i][0]))
data.append(Data[i][1])
data = list(map(list, zip(*data)))
parallel = Parallel()
parallel_axis = []
for i in range(len(Label)):
parallel_axis.append({"dim": i, "name": Label[i]})
parallel.add_schema(schema=parallel_axis)
parallel.add("", data, is_smooth=True)
parallel.set_global_opts(graphic_opts=[
opts.GraphicText(
graphic_item=opts.GraphicItem(
left="center",
top="bottom",
z=100,
),
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")))
])
grid0 = Grid(init_opts=opts.InitOpts(
width="100%",
height="100%",
renderer=globals.RenderType.SVG,
))
grid0.add(bar1, grid_opts={'left': '20%', 'bottom': '34%'})
option0 = grid0.dump_options_with_quotes()
option0 = json.loads(option0)
grid = Grid(init_opts=opts.InitOpts(
width="100%",
height="100%",
renderer=globals.RenderType.SVG,
))
grid.add(bar2, grid_opts={'left': '15%', 'bottom': '50%'})
option1 = grid.dump_options_with_quotes()
option1 = json.loads(option1)
grid1 = Grid(init_opts=opts.InitOpts(
width="100%",
height="100%",
renderer=globals.RenderType.SVG,
))
grid1.add(parallel, grid_opts={'left': '30%', 'bottom': '34%'})
option2 = grid1.dump_options_with_quotes()
option2 = json.loads(option2)
option = {"option": [option1, option2], "query": query}
return option
def generate_a_view(data):
# 设置图标基本属性
margin = '5%'
if data['chart'] == 'bar':
chart = (Bar().set_series_opts(label_opts=opts.LabelOpts(
is_show=False)).set_global_opts(
title_opts=opts.TitleOpts(title=data['chartname'],
subtitle=data['describe'],
pos_left='center',
pos_top=margin),
xaxis_opts=opts.AxisOpts(name=data['x_name']),
yaxis_opts=opts.AxisOpts(
name=data['y_name'],
splitline_opts=opts.SplitLineOpts(is_show=True))))
elif data['chart'] == 'pie':
chart = (Pie().set_global_opts(
title_opts=opts.TitleOpts(title=data['chartname'],
subtitle=data['describe'],
pos_left='center',
pos_top=margin)))
elif data['chart'] == 'line':
chart = (Line().set_series_opts(label_opts=opts.LabelOpts(
is_show=False)).set_global_opts(
title_opts=opts.TitleOpts(title=data['chartname'],
subtitle=data['describe'],
pos_left='center',
pos_top=margin),
xaxis_opts=opts.AxisOpts(name=data['x_name']),
yaxis_opts=opts.AxisOpts(
name=data['y_name'],
splitline_opts=opts.SplitLineOpts(is_show=True))))
elif data['chart'] == 'scatter':
chart = (Scatter().set_series_opts(label_opts=opts.LabelOpts(
is_show=False)).set_global_opts(
title_opts=opts.TitleOpts(title=data['chartname'],
subtitle=data['describe'],
pos_left='center',
pos_top=margin),
xaxis_opts=opts.AxisOpts(
type_='value',
name=data['x_name'],
splitline_opts=opts.SplitLineOpts(is_show=True)),
yaxis_opts=opts.AxisOpts(
type_='value',
name=data['y_name'],
splitline_opts=opts.SplitLineOpts(is_show=True))))
else:
print("not valid chart")
# 添加数据
attr = data["x_data"] # 横坐标
val = data["y_data"] # 纵坐标
if data['chart'] == 'bar':
chart.add_xaxis(attr).add_yaxis(
"", val, label_opts=opts.LabelOpts(is_show=False))
elif data['chart'] == 'line':
chart.add_xaxis(attr).add_yaxis(
"", val, label_opts=opts.LabelOpts(is_show=False))
elif data['chart'] == 'pie':
chart.add("", [list(z) for z in zip(attr, val)])
elif data['chart'] == 'scatter':
if isinstance(attr[0], str):
attr = [x for x in attr if x != '']
attr = list(map(float, attr))
if isinstance(val[0], str):
val = [x for x in val if x != '']
val = list(map(float, val))
chart.add_xaxis(attr).add_yaxis(
"", val, label_opts=opts.LabelOpts(is_show=False))
return chart
from pywebio.output import put_html
from pyecharts import options as opts
from pyecharts.charts import Bar
from pyecharts.faker import Faker
c = (Bar().add_xaxis(
Faker.choose()).add_yaxis("商家A", Faker.values()).add_yaxis(
"商家B", Faker.values()).reversal_axis().set_series_opts(
label_opts=opts.LabelOpts(position="right")).set_global_opts(
title_opts=opts.TitleOpts(title="Bar-翻转 XY 轴")))
c.width = "100%"
put_html(c.render_notebook())
import pandas as pd
import numpy as np
from pyecharts import options as opts
from pyecharts.charts import Bar
from pyecharts.options import LabelOpts
from pyecharts.globals import ThemeType
# 读取excel数据
df = pd.read_excel("648(1)(1).xls")
# print(df)
# 获取ｘ数据
time_x = df.iloc[6:, 0].values.tolist()[0:-1:5]
# print(time_x)
# print(len(time_x))
# 获取y轴数据
val_y = df.iloc[6:, 1].values.reshape((5, -1))
avg = np.mean(val_y, axis=0).tolist()
# print(avg)
# # print(val_y)
# print(len(avg))
c = (Bar(init_opts=opts.InitOpts(
theme=ThemeType.LIGHT)).add_xaxis(time_x).add_yaxis(
"第一列", avg).set_global_opts(
title_opts=opts.TitleOpts(title="第一列"),
datazoom_opts=opts.DataZoomOpts(type_="inside"),
).set_series_opts(
LabelOpts(is_show=False)).render("html/bar_datazoom_inside.html"))
def bar_base() -> Bar:
c = (Bar().add_xaxis(["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"]).add_yaxis(
"商家A", [randrange(0, 100) for _ in range(6)]).add_yaxis(
"商家B", [randrange(0, 100) for _ in range(6)]).set_global_opts(
title_opts=opts.TitleOpts(title="Bar-基本示例", subtitle="我是副标题")))
return c
def test_bar_default_remote_host(fake_writer):
c = Bar().add_xaxis(["A", "B", "C"]).add_yaxis("series0", [1, 2, 4])
c.render()
eq_(c.js_host, "https://assets.pyecharts.org/assets/")
_, content = fake_writer.call_args[0]
assert_in("https://assets.pyecharts.org/assets/echarts.min.js", content)
def bar_histogram() -> Bar:
c = (Bar().add_xaxis(Faker.choose()).add_yaxis(
"商家A", Faker.values(), category_gap=0,
color=Faker.rand_color()).set_global_opts(title_opts=opts.TitleOpts(
title="Bar-直方图")))
return c
age_num = sorted(list(age_distribution.index))
age_counts = [int(age_distribution[i]) for i in age_num]
# 年龄分布柱状图
title = 'PYTHON'
subtitle = '年龄分布要求'
age_bar = (
Bar().add_xaxis(age_num[:-1]) # 剔除“年龄不限”类别
.add_yaxis('', age_counts[:-1]).set_series_opts(
label_opts=opts.LabelOpts(is_show=False),
markpoint_opts=opts.MarkPointOpts(data=[
opts.MarkPointItem(type_='max', name='最大值'),
opts.MarkPointItem(type_='min', name='最小值')
]),
).set_global_opts(
title_opts=opts.TitleOpts(title=title,
subtitle=subtitle,
pos_left='center'),
xaxis_opts=opts.AxisOpts(
name='年龄段',
position='center',
axislabel_opts=opts.LabelOpts(formatter="{value}岁")),
yaxis_opts=opts.AxisOpts(name='职位数', ),
)
# .render(title + '_' + subtitle + '.html')
)
# In[4]:
# 获得各经验段职位数量
experience_dist = df3['experience_required'].value_counts()
def bar_different_series_gap() -> Bar:
c = (Bar().add_xaxis(Faker.choose()).add_yaxis(
"商家A", Faker.values(),
gap="0%").add_yaxis("商家B", Faker.values(), gap="0%").set_global_opts(
title_opts=opts.TitleOpts(title="Bar-不同系列柱间距离")))
return c
from pyecharts.charts import Bar
bar = Bar()
bar.add_xaxis(["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"])
bar.add_yaxis("商家sfdsdfA", [522, 20, 36, 10, 75, 90])
# 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.render()
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",
t_seq: List[int] = None,
width: str = "1400px",
height: str = '580px') -> Grid:
"""绘制缠中说禅K线分析结果
:param kline: K线
:param fx: 分型识别结果
:param bi: 笔识别结果
{'dt': Timestamp('2020-11-26 00:00:00'),
'fx_mark': 'd',
'start_dt': Timestamp('2020-11-25 00:00:00'),
'end_dt': Timestamp('2020-11-27 00:00:00'),
'fx_high': 144.87,
'fx_low': 138.0,
'bi': 138.0}
:param xd: 线段识别结果
:param bs: 买卖点
:param title: 图表标题
:param t_seq: 均线系统
: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)
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)
if not t_seq:
t_seq = [10, 20, 60, 120, 250]
ma_keys = dict()
for t in t_seq:
ma_keys[f"MA{t}"] = SMA(close, timeperiod=t)
for i, (name, ma) in enumerate(ma_keys.items()):
chart_ma.add_yaxis(series_name=name, y_axis=ma, is_smooth=True,
is_selected=True, symbol_size=0, label_opts=label_not_show_opts,
linestyle_opts=opts.LineStyleOpts(opacity=0.8, width=1))
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 = Line()
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)", ),
linestyle_opts=opts.LineStyleOpts(width=1.5))
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 = Line()
chart_xd.add_xaxis(xd_dts)
chart_xd.add_yaxis(series_name="XD", y_axis=xd_val, is_selected=True, symbol="triangle", symbol_size=10,
itemstyle_opts=opts.ItemStyleOpts(color="rgba(37, 141, 54, 1.0)", ))
chart_xd.set_global_opts(xaxis_opts=grid0_xaxis_opts, legend_opts=legend_not_show_opts)
chart_k = chart_k.overlap(chart_xd)
if bs:
b_dts = [x['dt'] for x in bs if x['mark'] == 'buy']
if len(b_dts) > 0:
b_val = [x['price'] for x in bs if x['mark'] == 'buy']
chart_b = Scatter()
chart_b.add_xaxis(b_dts)
chart_b.add_yaxis(series_name="BUY", y_axis=b_val, is_selected=False, symbol="arrow", symbol_size=8,
itemstyle_opts=opts.ItemStyleOpts(color="#f31e1e", ))
chart_b.set_global_opts(xaxis_opts=grid0_xaxis_opts, legend_opts=legend_not_show_opts)
chart_k = chart_k.overlap(chart_b)
s_dts = [x['dt'] for x in bs if x['mark'] == 'sell']
if len(s_dts) > 0:
s_val = [x['price'] for x in bs if x['mark'] == 'sell']
chart_s = Scatter()
chart_s.add_xaxis(s_dts)
chart_s.add_yaxis(series_name="SELL", y_axis=s_val, is_selected=False, symbol="pin", symbol_size=12,
itemstyle_opts=opts.ItemStyleOpts(color="#45b97d", ))
chart_s.set_global_opts(xaxis_opts=grid0_xaxis_opts, legend_opts=legend_not_show_opts)
chart_k = chart_k.overlap(chart_s)
# 成交量图
# ------------------------------------------------------------------------------------------------------------------
chart_vol = Bar()
chart_vol.add_xaxis(dts)
chart_vol.add_yaxis(series_name="Volume", y_axis=vol, bar_width='60%')
chart_vol.set_global_opts(
xaxis_opts=opts.AxisOpts(
type_="category",
grid_index=1,
axislabel_opts=opts.LabelOpts(is_show=True, font_size=8, color="#9b9da9"),
),
yaxis_opts=yaxis_opts, legend_opts=legend_not_show_opts,
)
# MACD图
# ------------------------------------------------------------------------------------------------------------------
chart_macd = Bar()
chart_macd.add_xaxis(dts)
chart_macd.add_yaxis(series_name="MACD", y_axis=macd_bar, bar_width='60%')
chart_macd.set_global_opts(
xaxis_opts=opts.AxisOpts(
type_="category",
grid_index=2,
axislabel_opts=opts.LabelOpts(is_show=False),
),
yaxis_opts=opts.AxisOpts(
grid_index=2,
split_number=4,
axisline_opts=opts.AxisLineOpts(is_on_zero=False),
axistick_opts=opts.AxisTickOpts(is_show=False),
splitline_opts=opts.SplitLineOpts(is_show=False),
axislabel_opts=opts.LabelOpts(is_show=True, color="#c7c7c7"),
),
legend_opts=opts.LegendOpts(is_show=False),
)
line = Line()
line.add_xaxis(dts)
line.add_yaxis(series_name="DIFF", y_axis=diff, label_opts=label_not_show_opts, is_symbol_show=False,
linestyle_opts=opts.LineStyleOpts(opacity=0.8, width=1.0, color="#da6ee8"))
line.add_yaxis(series_name="DEA", y_axis=dea, label_opts=label_not_show_opts, is_symbol_show=False,
linestyle_opts=opts.LineStyleOpts(opacity=0.8, width=1.0, color="#39afe6"))
chart_macd = chart_macd.overlap(line)
grid0_opts = opts.GridOpts(pos_left="0%", pos_right="1%", pos_top="12%", height="58%")
grid1_opts = opts.GridOpts(pos_left="0%", pos_right="1%", pos_top="74%", height="8%")
grid2_opts = opts.GridOpts(pos_left="0%", pos_right="1%", pos_top="86%", height="10%")
grid_chart = Grid(init_opts)
grid_chart.add(chart_k, grid_opts=grid0_opts)
grid_chart.add(chart_vol, grid_opts=grid1_opts)
grid_chart.add(chart_macd, grid_opts=grid2_opts)
return grid_chart
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")
