def create_result_chart(data):
instrulist = list(set(d.instrument for d in data))
# x = ["{} P{}".format(localtime(d.upload_time).strftime("%Y-%m-%d %X"), d.platenum) for d in data]
x = ["{} P{}".format(d.upload_time.strftime("%Y-%m-%d %X"), d.platenum) for d in data]
# 均值直方图
b = Bar(init_opts=opts.InitOpts(width='1900px'))
b.add_xaxis(x)
for instru in instrulist:
mean = get_mean(data, instru)
b.add_yaxis(instru, mean)
b.set_global_opts(title_opts=opts.TitleOpts(title="结果mean图"))
# sd折线图
l = Line(init_opts=opts.InitOpts(width='1900px'))
l.add_xaxis(x)
for instru in instrulist:
sd = get_sd(data, instru)
l.add_yaxis(instru, sd, is_connect_nones=True)
l.set_global_opts(title_opts=opts.TitleOpts(title="结果sd图", pos_top='48%'))
grid = Grid(init_opts=opts.InitOpts(width='1900px'))
grid.add(b, grid_opts=opts.GridOpts(pos_bottom='60%'))
grid.add(l, grid_opts=opts.GridOpts(pos_top='60%'))
return grid
def v_techindex(self, end=yesterdayobj(), col=None, rendered=True, vopts=None):
"""
visualization on netvalue curve and specified indicators
:param end: date string or obj, the end date of the figure
:param col: list, list of strings for price col name, eg.['MA5','BBI']
remember generate these indicators before the visualization,
these cols don't automatically generate for visualization
:param vopts: dict, options for pyecharts instead of builtin settings
"""
partprice = self.price[self.price["date"] <= end]
xdata = [d.date() for d in list(partprice.date)]
netvaldata = list(partprice.netvalue)
if vopts is None:
vopts = line_opts
line = Line()
line.add_xaxis(xdata)
line.add_yaxis(series_name="netvalue", y_axis=netvaldata, is_symbol_show=False)
line.set_global_opts(**vopts)
if col is not None:
for ind in col:
inddata = list(partprice[ind])
line.add_yaxis(series_name=ind, y_axis=inddata, is_symbol_show=False)
if rendered:
return line.render_notebook()
else:
return line
def v_tradecost(self, start=None, end=yesterdayobj(), vopts=None):
"""
visualization giving the average cost line together with netvalue line
:param vopts: global option for line in pyecharts
:returns: pyecharts.line
"""
funddata = []
costdata = []
pprice = self.aim.price[self.aim.price["date"] <= end]
if start is not None:
pprice = pprice[pprice["date"] >= start]
for _, row in pprice.iterrows():
date = row["date"]
funddata.append(row["netvalue"])
cost = 0
if (date - self.cftable.iloc[0].date).days >= 0:
cost = self.unitcost(date)
costdata.append(cost)
line = Line()
if vopts is None:
vopts = line_opts
line.add_xaxis([d.date() for d in pprice.date])
line.add_yaxis(series_name="基金净值", y_axis=funddata, is_symbol_show=False)
line.add_yaxis(series_name="持仓成本", y_axis=costdata, is_symbol_show=False)
line.set_global_opts(**vopts)
return line.render_notebook()
def box_render_air(self):
c = Line()
c.add_xaxis(self.x_value)
c.add_yaxis(series_name=self.y_name,
y_axis=self.y_value,
is_symbol_show=self.is_symbol_show,
linestyle_opts=opts.LineStyleOpts(width=self.line_width),
label_opts=opts.LabelOpts(is_show=self.is_show))
c.set_global_opts(
title_opts=opts.TitleOpts(title=self.title,
subtitle=self.subtitle),
tooltip_opts=opts.TooltipOpts(trigger="axis"),
legend_opts=opts.LegendOpts(pos_left="center",
pos_top=self.pos_top,
legend_icon=self.legend_icon),
datazoom_opts=opts.DataZoomOpts(
is_show=self.is_zoom,
range_start=0,
range_end=100,
orient=self.is_orient # 表示横轴可滑动还是纵轴可滑动；
),
yaxis_opts=opts.AxisOpts(
type_="value",
max_=self.y_max,
min_=self.y_min,
boundary_gap=self.boundary_gap, # 封闭坐标轴，左右都有顶上的刻度线；
axislabel_opts=opts.LabelOpts(color="#7D7D7D"),
axisline_opts=opts.AxisLineOpts(
linestyle_opts=opts.LineStyleOpts(width=1.5,
color="#A0A7B3")),
axistick_opts=opts.AxisTickOpts(is_show=True, is_inside=True),
splitline_opts=opts.SplitLineOpts(
is_show=True,
linestyle_opts=opts.LineStyleOpts(
color="#E2E2E2")), # 设置网格线；
),
xaxis_opts=opts.AxisOpts(
type_="category",
min_=self.x_min,
boundary_gap=self.boundary_gap,
axislabel_opts=opts.LabelOpts(color="#7D7D7D"),
axisline_opts=opts.AxisLineOpts(
linestyle_opts=opts.LineStyleOpts(width=1.5,
color="#A0A7B3")),
axistick_opts=opts.AxisTickOpts(is_show=True, is_inside=True),
splitline_opts=opts.SplitLineOpts(
is_show=True,
linestyle_opts=opts.LineStyleOpts(color="#E2E2E2")),
),
)
if len(self.args) > 0:
for num in range(len(self.args[0])):
c.add_yaxis(
series_name=self.args[0][num],
y_axis=self.args[1][num],
is_symbol_show=self.is_symbol_show,
linestyle_opts=opts.LineStyleOpts(width=self.line_width),
label_opts=opts.LabelOpts(is_show=self.is_show))
self.c_render = c
return c
def stat_area_trend():
df = load_data()
df['发证月份'] = [i[:7] for i in df['发证日期']]
summary = df.groupby('发证月份').sum()
columns_area = ['预售建筑面积', '总建筑面积', '住宅面积', '办公面积', '其他面积', '车库面积']
columns_other = ['层数', '住宅套数']
chart = Line()
chart.add_xaxis(summary.index.tolist())
for column in columns_area:
chart.add_yaxis(column,
summary[column].tolist(),
areastyle_opts=opts.AreaStyleOpts(opacity=0.1),
is_smooth=True)
chart.set_series_opts(label_opts=opts.LabelOpts(
is_show=False), ).extend_axis(yaxis=opts.AxisOpts(
axislabel_opts=opts.LabelOpts(is_show=False), is_show=False))
bar = Bar()
bar.add_xaxis(summary.index.tolist())
for column in columns_other:
bar.add_yaxis(column, summary[column].tolist(), yaxis_index=1)
chart.overlap(bar)
min_date = min(df['发证日期'])
max_date = max(df['发证日期'])
chart.set_global_opts(title_opts=opts.TitleOpts(
title="长沙预售房屋面积变化趋势",
subtitle="统计日期:{}至{}，数据来源：{}".format(min_date, max_date, DATA_SOURCE)),
legend_opts=opts.LegendOpts(is_show=True),
toolbox_opts=opts.ToolboxOpts(is_show=True))
filename = os.path.join('..', 'assets',
'01_changsha_zhufangyushou_面积趋势.html')
chart.render(filename)
def draw_balance_line(xaxis,
yaxis,
title="消费统计",
markline=None,
width=2000) -> Line:
"""
x = [月_日, 月_日, 月_日, ....]
y = [(title1, [num1, num2, num3, num4, ...]), (title2, [num1, num2, num3, num4, ...])]
:param xaxis: x轴
:param yaxis: y轴
:param title: 标题
:param markline: 标记辅助线
:param width: 宽
:return: Line
"""
line = Line()
line.add_xaxis(xaxis)
for name, axis in yaxis:
line.add_yaxis(name, axis, is_symbol_show=True)
line.set_global_opts(title_opts=opts.TitleOpts(title=title, ),
datazoom_opts=[
opts.DataZoomOpts(range_start=0, range_end=100),
opts.DataZoomOpts(type_="inside")
],
tooltip_opts=opts.TooltipOpts(
trigger='axis', axis_pointer_type='shadow'))
line.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
if markline is not None:
line.set_series_opts(markline_opts=opts.MarkLineOpts(
data=[opts.MarkLineItem(y=markline, name='预算')]))
return line
def line(self, data, xaxis=None, position=0):
if not xaxis: xaxis = self.xaxis
line = Line(init_opts=opts.InitOpts(**self.chart_opt)).add_xaxis(xaxis_data=xaxis)
for d in data:
line.add_yaxis(
series_name=d["label"],
y_axis=d["data"],
is_smooth=True,
is_selected=True,
is_symbol_show=False,
linestyle_opts=opts.LineStyleOpts(opacity=1),
label_opts=opts.LabelOpts(is_show=False),
)
line.set_global_opts(
xaxis_opts=opts.AxisOpts(
type_="category",
grid_index=position,
is_scale=True,
axislabel_opts=opts.LabelOpts(is_show=False),
),
yaxis_opts=opts.AxisOpts(
is_scale=True,
axistick_opts=opts.AxisTickOpts(is_show=False),
axislabel_opts=opts.LabelOpts(is_show=False),
splitarea_opts=opts.SplitAreaOpts(
is_show=True, areastyle_opts=opts.AreaStyleOpts(opacity=1)
),
),
)
return line
def line_markpoint_custom():
x, y = Faker.choose(), Faker.values()
obj_l = Line()
obj_l.add_xaxis(x)
obj_l.add_yaxis("商家A",
y,
markpoint_opts=opts.MarkPointOpts(
data=[opts.MarkPointItem(name="自定义标记点", coord=[x[2], y[2]], value=y[2])]
)
)
obj_l.set_series_opts(
# 设置该参数，则x对应的y值会显示出来，该参数默认为True
label_opts=opts.LabelOpts(is_show=False),
)
obj_l.set_global_opts(
title_opts=opts.TitleOpts(title="Line-MarkPoint（自定义）",subtitle="自定义标注"),
xaxis_opts=opts.AxisOpts(
name="x轴",
# 坐标轴两边留白策略，类目轴和非类目轴的设置和表现不一样,可设置x轴刻度顶格
boundary_gap=True,
# 类目轴中在 boundaryGap 为 true 的时候有效，可以保证刻度线和标签对齐。
axistick_opts=opts.AxisTickOpts(is_align_with_label=True)
),
)
return obj_l
def line_markline():
obj_l = Line()
obj_l.add_xaxis(Faker.choose())
obj_l.add_yaxis("商家A",
Faker.values(),
markline_opts=opts.MarkLineOpts(data=[opts.MarkLineItem(type_="average")]),
# 该参数设置在这里只对商家A起作用
label_opts=opts.LabelOpts(is_show=False)
)
obj_l.add_yaxis("商家B",
Faker.values(),
markline_opts=opts.MarkLineOpts(data=[opts.MarkLineItem(type_="average")]),
)
obj_l.set_series_opts(
# 该参数设置在这里对商家A,商家B都起作用
# 设置该参数，则x对应的y值会显示出来，该参数默认为True
label_opts=opts.LabelOpts(is_show=False),
)
obj_l.set_global_opts(
title_opts=opts.TitleOpts(title="Line-MarkLine", subtitle="平均值的线"),
xaxis_opts=opts.AxisOpts(
name="x轴",
# 坐标轴两边留白策略，类目轴和非类目轴的设置和表现不一样,可设置x轴刻度顶格
boundary_gap=True,
# 类目轴中在 boundaryGap 为 true 的时候有效，可以保证刻度线和标签对齐。
axistick_opts=opts.AxisTickOpts(is_align_with_label=True)
),
)
return obj_l
def gen_atr(df1, n):
high_list = df1.apply(lambda record: float(record['high']),
axis=1).tolist()
high_list = np.array(high_list)
low_list = df1.apply(lambda record: float(record['low']), axis=1).tolist()
low_list = np.array(low_list)
close_list = df1.apply(lambda record: float(record['close']),
axis=1).tolist()
close_list = np.array(close_list)
atr_1 = talib.ATR(high_list, low_list, close_list, 1)
atr_n = talib.SMA(atr_1, n)
line = Line()
line.add_xaxis(xaxis_data=list(df1['datetime']))
line.add_yaxis(
'atr_' + str(n),
y_axis=atr_n,
xaxis_index=1,
yaxis_index=1,
label_opts=opts.LabelOpts(is_show=False),
)
line.set_global_opts(xaxis_opts=opts.AxisOpts(is_show=False),
legend_opts=opts.LegendOpts(is_show=True,
pos_right="35%"))
return line
def commentChannel(data, time):
line = Line()
temp = list(arrangeByTime(data).get(time).keys())
line.add_xaxis(temp)
data = arrangeByChannel(data) # 按频道分
news = dict()
for i in data.keys():
news[i] = arrangeByTime(data.get(i)).get(time)
comment_channel = dict()
for i in news.keys():
comment_channel[i] = dict()
for j in news[i].keys():
comment_channel[i][j] = divideComment(news[i].get(j))
for i in comment_channel.keys():
y = []
for j in temp:
if j in comment_channel.get(i).keys():
y.append(len(comment_channel.get(i).get(j)))
else:
y.append(None)
if i not in ['js', 'jilin', 'video', 'live']:
line.add_yaxis(tran(i),
y,
is_connect_nones=True,
is_symbol_show=(time == 'monthly'))
# line.render("NewPage/无权每月评论.html")
line.set_global_opts(title_opts=options.TitleOpts(
title=titleTransform("新浪新闻评论" + str(time) + "评论数量"), pos_top='55'))
return line
def gen_er(df1, n):
dt_list = list(df1['datetime'])
close_list = df1.apply(lambda record: float(record['close']),
axis=1).tolist()
close_list = np.array(close_list)
r = [np.nan] * (n - 1)
size = len(close_list)
for i in range(n, size + 1):
c_list = close_list[:i]
r.append(er(c_list, n))
line = Line()
line.add_xaxis(xaxis_data=dt_list)
line.add_yaxis(
'er_' + str(n),
y_axis=r,
xaxis_index=2,
yaxis_index=2,
label_opts=opts.LabelOpts(is_show=False),
)
line.set_global_opts(xaxis_opts=opts.AxisOpts(is_show=False),
legend_opts=opts.LegendOpts(is_show=True,
pos_right="30%"))
return line
def Line_charts(users, xaxis_data, yaxis_data, date) -> Line:
"""定义一个Line_charts函数"""
c = Line()
c.add_xaxis(xaxis_data=xaxis_data)
# 设置图例信息
c.add_yaxis(series_name=str(users) + "users" + " " + "date: " + date,
y_axis=yaxis_data,
is_smooth=True)
# c.add_yaxis(series_name='400users', y_axis=y2)
data_zoom = {
"show": False,
"title": {
"zoom": "data zoom",
"back": "data zoom restore"
}
}
# 数据项设置,全局只设置一次
c.set_global_opts(
# 设置标题
title_opts=opts.TitleOpts(title="不同users对应的TPM值"),
# 设置图例is_show=False是 不显示图例
legend_opts=opts.LegendOpts(is_show=True),
# 设置提示项
tooltip_opts=opts.TooltipOpts(trigger='axis',
axis_pointer_type='cross'),
# 工具箱的设置
toolbox_opts=opts.ToolboxOpts(
is_show=False,
feature=opts.ToolBoxFeatureOpts(data_zoom=data_zoom)))
return c
def plot_multiple_line(x_axis,
y_axis_dict,
title,
double_ylabel_str,
double_ylabel=False,
ylabel_smooth=False,
is_show=True):
# 由于全部展示图例时数字太多影响效果，is_show用于控制是否展示或者都不展示
line = Line()
line.add_xaxis(x_axis)
for _name in y_axis_dict.keys():
if _name != double_ylabel_str:
line.add_yaxis(_name,
y_axis_dict[_name],
label_opts=opts.LabelOpts(is_show=is_show))
line.set_global_opts(title_opts=opts.TitleOpts(title=title, pos_top=20),
toolbox_opts=opts.ToolboxOpts(pos_top=30),
legend_opts=opts.LegendOpts(pos_bottom=0,
orient="horizontal"))
if double_ylabel:
line.extend_axis(yaxis=opts.AxisOpts(
name=double_ylabel_str, axislabel_opts=opts.LabelOpts(interval=5)))
line.add_yaxis(double_ylabel_str,
y_axis_dict[double_ylabel_str],
yaxis_index=1,
is_smooth=ylabel_smooth)
return line
def drawline(arrt, value, name):
# 图表初始化配置
init_opts = opts.InitOpts(page_title=name)
line = Line(init_opts=init_opts)
# 标题配置
title = opts.TitleOpts(title=name, pos_left="10%")
# 图例配置
legend_opts = opts.LegendOpts(orient="horizontal",
pos_top="5%",
pos_right="15%")
# 工具箱配置
# feature = opts.ToolBoxFeatureOpts(save_as_image=True, restore=True, data_view=True, data_zoom=True)
# 工具箱配置
toolbox_opts = opts.ToolboxOpts(
orient="vertical",
pos_bottom="15%",
pos_left="90%",
)
line.set_global_opts(
title_opts=title,
legend_opts=legend_opts,
toolbox_opts=toolbox_opts,
datazoom_opts=opts.DataZoomOpts(orient="vertical"),
)
line.add_xaxis(arrt, )
line.add_yaxis(name,
value,
is_smooth=True,
linestyle_opts=opts.LineStyleOpts(color="#E83132",
width="4"))
line.render('{0}.html'.format(name))
def line_areastyle():
obj_l = Line()
obj_l.add_xaxis(Faker.choose())
obj_l.add_yaxis("商家A", Faker.values(), areastyle_opts=opts.AreaStyleOpts(opacity=0.5))
obj_l.add_yaxis("商家B", Faker.values(), areastyle_opts=opts.AreaStyleOpts(opacity=0.5))
obj_l.set_global_opts(title_opts=opts.TitleOpts(title="Line-面积图", subtitle="副标题"))
return obj_l
def print_bar(self):
bar = Bar(init_opts=opts.InitOpts(width='1080px', height='480px'))
bar.add_xaxis(self.timedata)
# category 是同系列直接的距离，gap是不同系列之间的距离
bar.add_yaxis('治愈人数',
self.heal,
gap='100%',
label_opts=opts.LabelOpts(is_show=False),
itemstyle_opts=opts.ItemStyleOpts(color='#90EE90'))
if self.name == '湖北':
bar.add_yaxis('死亡人数',
self.dead,
gap='100%',
label_opts=opts.LabelOpts(is_show=False),
itemstyle_opts=opts.ItemStyleOpts(color='#696969'))
# 设置全局变量：x轴标签倾斜度，html主标题
bar.set_global_opts(datazoom_opts=[
opts.DataZoomOpts(),
opts.DataZoomOpts(type_='inside')
],
toolbox_opts=opts.ToolboxOpts(),
xaxis_opts=opts.AxisOpts(name_rotate=-15),
title_opts=opts.TitleOpts(self.name,
subtitle='数据来自inews'))
line = Line()
line.add_xaxis(self.timedata)
line.add_yaxis('确诊人数',
self.confirm,
label_opts=opts.LabelOpts(is_show=False),
itemstyle_opts=opts.ItemStyleOpts(color='#F08080'))
print(self.name + '.html', '已成功生成到', os.getcwd())
# 在bar的基础上画line
bar.overlap(line).render(self.name + '.html')
def line_power():
obj_l = Line()
obj_l.add_xaxis(xaxis_data=["0","1", "2", "3", "4", "5", "6", "7", "8"])
obj_l.add_yaxis("2的指数图像", y_axis=[1, 2, 4, 8, 16, 32, 64, 128, 256],linestyle_opts=opts.LineStyleOpts(width=2), is_smooth=True)
obj_l.set_series_opts(
# 设置该参数，则x对应的y值会显示出来，该参数默认为True
label_opts=opts.LabelOpts(is_show=True),
)
obj_l.set_global_opts(
title_opts=opts.TitleOpts(title="Line-2的指数图像",subtitle="副标题"),
xaxis_opts=opts.AxisOpts(
name="x轴",
# 坐标轴两边留白策略，类目轴和非类目轴的设置和表现不一样,可设置x轴刻度顶格
boundary_gap=False
# 类目轴中在 boundaryGap 为 true 的时候有效，可以保证刻度线和标签对齐。
# axistick_opts=opts.AxisTickOpts(is_align_with_label=True)
),
yaxis_opts=opts.AxisOpts(
# y轴名字
name="y轴",
# 根据y轴刻度的横线
splitline_opts=opts.SplitLineOpts(is_show=True),
# 只在数值轴中（type: 'value'）有效
# is_scale = False
)
)
return obj_l
def gen_rsi(df1):
close_list = df1.apply(lambda record: float(record['close']),
axis=1).tolist()
close_list = np.array(close_list)
rsi_5 = talib.RSI(close_list, 5)
rsi_20 = talib.RSI(close_list, 20)
line = Line()
line.add_xaxis(xaxis_data=list(df1['datetime']))
line.add_yaxis(
'rsi_5',
y_axis=rsi_5,
xaxis_index=1,
yaxis_index=1,
label_opts=opts.LabelOpts(is_show=False),
)
line.add_yaxis(
'rsi_20',
y_axis=rsi_20,
xaxis_index=1,
yaxis_index=1,
label_opts=opts.LabelOpts(is_show=False),
)
line.set_global_opts(
# xaxis_opts=opts.AxisOpts(is_show=False),
xaxis_opts=opts.AxisOpts(
axislabel_opts=opts.LabelOpts(is_show=False), ),
legend_opts=opts.LegendOpts(is_show=True, pos_right="50%"),
)
line.set_series_opts(label_opts=opts.LabelOpts(is_show=False), )
return line
def index():
date_ = db.session.query(
func.distinct(CourseCount.update_at).label('update_at')).order_by(
CourseCount.update_at.asc() # noqa
).all()
name_ = db.session.query(Course.name).order_by(Course.id.asc() # noqa
).all()
course_count_ = db.session.query(
CourseCount.count, CourseCount.update_at,
Course.name).join(Course, CourseCount.course_id == Course.id).order_by(
CourseCount.update_at.asc(), # noqa
CourseCount.course_id.asc() # noqa
).all()
x_date = [_.update_at for _ in date_]
y_name = [_.name for _ in name_]
data = []
for name in y_name:
d = list()
for course_count in course_count_:
if course_count.name == name:
d.append(course_count.count)
data.append((name, d))
c = Line().add_xaxis(x_date).set_global_opts(title_opts=opts.TitleOpts(
title="腾讯课堂-课程数量走势"))
for d in data:
c.add_yaxis(d[0], d[1], is_connect_nones=True)
return Markup(c.render_embed())
def v_totvalue(self, end=yesterdayobj(), rendered=True, vopts=None):
"""
visualization on the total values daily change of the aim
"""
partp = self.price[self.price["date"] >= self.cftable.iloc[0].date]
# 多基金账单时起点可能非该基金持有起点
partp = partp[partp["date"] <= end]
date = [d.date() for d in partp.date]
valuedata = [
self.briefdailyreport(d).get("currentvalue", 0) for d in partp.date
]
line = Line()
if vopts is None:
vopts = line_opts
line.add_xaxis(date)
line.add_yaxis(series_name="持仓总值",
y_axis=valuedata,
is_symbol_show=False)
line.set_global_opts(**vopts)
if rendered:
return line.render_notebook()
else:
return line
def line():
cursor = connection.cursor()
cursor.execute("""SELECT
DATE_FORMAT( rev.create_time, '%Y-%m-%d' ) create_time,
revt.`name`,
count( * )
FROM
resource_event rev
LEFT JOIN resource_eventtype revt ON rev.event_type_id = revt.id
GROUP BY
create_time,
event_type_id""")
rows = cursor.fetchall()
name_list = []
event_type_list = []
for row in rows:
name = row[0]
if name not in name_list:
name_list.append(name)
event_type = row[1]
if event_type not in event_type_list:
event_type_list.append(event_type)
line = Line(init_opts=opts.InitOpts(height="350px"))
line.add_xaxis(name_list)
for event_type in event_type_list:
tmp_list = [0 for i in range(len(name_list))]
for row in rows:
if event_type == row[1]:
index = name_list.index(row[0])
tmp_list[index] = row[2]
line.add_yaxis(event_type, tmp_list, is_smooth=True)
line.set_global_opts(title_opts=opts.TitleOpts(title="数量变化统计"))
return line.render_embed()
def report(request):
"""返回慢SQL历史趋势"""
checksum = request.GET.get('checksum')
cnt_data = ChartDao().slow_query_review_history_by_cnt(checksum)
pct_data = ChartDao().slow_query_review_history_by_pct_95_time(checksum)
cnt_x_data = [row[1] for row in cnt_data['rows']]
cnt_y_data = [int(row[0]) for row in cnt_data['rows']]
pct_y_data = [str(row[0]) for row in pct_data['rows']]
line = Line(init_opts=opts.InitOpts(width='800', height='380px'))
line.add_xaxis(cnt_x_data)
line.add_yaxis("慢查次数",
cnt_y_data,
is_smooth=True,
markline_opts=opts.MarkLineOpts(data=[
opts.MarkLineItem(type_="max", name='最大值'),
opts.MarkLineItem(type_="average", name='平均值')
]))
line.add_yaxis("慢查时长(95%)",
pct_y_data,
is_smooth=True,
is_symbol_show=False)
line.set_series_opts(areastyle_opts=opts.AreaStyleOpts(opacity=0.5, ))
line.set_global_opts(
title_opts=opts.TitleOpts(title='SQL历史趋势'),
legend_opts=opts.LegendOpts(selected_mode='single'),
xaxis_opts=opts.AxisOpts(
axistick_opts=opts.AxisTickOpts(is_align_with_label=True),
is_scale=False,
boundary_gap=False,
),
)
result = {"status": 0, "msg": '', "data": line.render_embed()}
return HttpResponse(json.dumps(result), content_type='application/json')
def createPlotLine(df, xAxisTitle, yAxisTitle, title=None):
line = Line()
line.add_xaxis(df[xAxisTitle].tolist())
line.add_yaxis(yAxisTitle, df[yAxisTitle].tolist())
if not title:
title = ""
line.set_global_opts(
tooltip_opts=opts.TooltipOpts(trigger='axis', axis_pointer_type='cross'),
title_opts=opts.TitleOpts(title=title)
)
ptg75 = df[yAxisTitle].quantile(.75)
ptg50 = df[yAxisTitle].quantile(.5)
ptg25 = df[yAxisTitle].quantile(.25)
line.set_series_opts(
markline_opts=opts.MarkLineOpts(
data=[
opts.MarkLineItem(y=ptg75, name="75%"),
opts.MarkLineItem(y=ptg50, name="50%"),
opts.MarkLineItem(y=ptg25, name="25%")
]
),
)
return line
def v_netvalue(self, end=yesterdayobj(), benchmark=True, rendered=True, vopts=None):
"""
visulaization on netvalue curve
:param end: dateobject for indicating the end date in the figure, default to yesterday
:param benchmark: bool, whether include benchmark's netvalue curve, default true
:param vopts: dict, options for pyecharts instead of builtin settings
"""
if getattr(self, "bmprice", None) is None:
benchmark = False
if benchmark:
a, b = self.comparison(end)
else:
a = self.price
if vopts is None:
vopts = line_opts
line = Line()
line.add_xaxis([d.date() for d in list(a.date)])
line.add_yaxis(
y_axis=list(a.netvalue), series_name=self.name, is_symbol_show=False
)
line.set_global_opts(**vopts)
if benchmark:
line.add_yaxis(
series_name=self.benchmark.name,
y_axis=list(b.netvalue),
is_symbol_show=False,
)
if rendered:
return line.render_notebook()
else:
return line
def gen_cci(self, df1, n):
high_list = df1.apply(lambda record: float(record['high']),
axis=1).tolist()
high_list = np.array(high_list)
low_list = df1.apply(lambda record: float(record['low']),
axis=1).tolist()
low_list = np.array(low_list)
close_list = df1.apply(lambda record: float(record['close']),
axis=1).tolist()
close_list = np.array(close_list)
rsi_n = talib.CCI(high_list, low_list, close_list, n)
line = Line()
line.add_xaxis(xaxis_data=list(df1['datetime']))
line.add_yaxis(
'cci_' + str(n),
y_axis=rsi_n,
xaxis_index=1,
yaxis_index=1,
label_opts=opts.LabelOpts(is_show=False),
)
line.set_global_opts(
yaxis_opts=opts.AxisOpts(min_=-150, max_=150),
# xaxis_opts=opts.AxisOpts(is_show=False),
xaxis_opts=opts.AxisOpts(
axislabel_opts=opts.LabelOpts(is_show=False), ),
legend_opts=opts.LegendOpts(is_show=True, pos_right="38%"),
)
line.set_series_opts(label_opts=opts.LabelOpts(is_show=False), )
return line
def gen_line(df1, symbol, price_min, price_max):
#df1['datetime'] = df1['date'] + ' ' + df1['time']
df1['datetime'] = df1['date']
dt_list = list(df1['datetime'])
close_list = df1.apply(lambda record: float(record['close']),
axis=1).tolist()
close_list = np.array(close_list)
line1 = Line(init_opts=opts.InitOpts(height='700px', width='1300px'))
line1.set_global_opts(
yaxis_opts=opts.AxisOpts(
min_=price_min,
#min_=999,
max_=price_max,
splitarea_opts=opts.SplitAreaOpts(
is_show=True, areastyle_opts=opts.AreaStyleOpts(opacity=1)),
),
datazoom_opts=[
opts.DataZoomOpts(
is_show=True,
type_="slider",
range_start=0,
range_end=100,
),
],
)
line1.add_xaxis(xaxis_data=dt_list)
line1.add_yaxis(
symbol,
y_axis=close_list,
)
line1.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
return line1
def gen_line_two(self, df1):
df1['datetime'] = df1['date'] + ' ' + df1['time']
dt_list1 = list(df1['datetime'])
# print( len(dt_list1) )
# dt_list1 = [s[5:10] for s in dt_list1]
close_list1 = df1.apply(lambda record: float(record['close']),
axis=1).tolist()
close_list1 = np.array(close_list1)
kline = Line()
kline.add_xaxis(dt_list1)
kline.add_yaxis('resid',
close_list1,
xaxis_index=1,
yaxis_index=1,
label_opts=opts.LabelOpts(is_show=False))
kline.set_global_opts(yaxis_opts=opts.AxisOpts(
is_scale=True,
splitarea_opts=opts.SplitAreaOpts(
is_show=True, areastyle_opts=opts.AreaStyleOpts(opacity=1))),
legend_opts=opts.LegendOpts(is_show=True,
pos_right="40%")
#xaxis_opts=opts.AxisOpts(type_='time'))
)
return kline
def gen_boll(df1, n, dev):
close_list = df1.apply(lambda record: float(record['close']),
axis=1).tolist()
close_list = np.array(close_list)
dt_list = df1['datetime'].tolist()
up, down = boll(close_list, n, dev)
line = Line()
line.add_xaxis(xaxis_data=dt_list)
line.add_yaxis(
'boll_up',
y_axis=up,
label_opts=opts.LabelOpts(is_show=False),
)
line.add_yaxis(
'boll_down',
y_axis=down,
label_opts=opts.LabelOpts(is_show=False),
)
line.set_global_opts(xaxis_opts=opts.AxisOpts(is_show=False),
legend_opts=opts.LegendOpts(is_show=True,
pos_right="40%"))
return line
def generate_line_chart_with_slider(title: str, xaxis_caption: str,
groups: List[DateCounterGroup]) -> Line:
chart = Line()
chart.set_global_opts(
title_opts=opts.TitleOpts(title=title),
datazoom_opts=opts.DataZoomOpts(range_start=0, range_end=100),
yaxis_opts=opts.AxisOpts(
name='Samples',
name_textstyle_opts=opts.global_options.TextStyleOpts(
font_size=16)),
xaxis_opts=opts.AxisOpts(
name=xaxis_caption,
name_textstyle_opts=opts.global_options.TextStyleOpts(
font_size=16)))
# X axis
chart.add_xaxis(groups[0].captions)
# Y axis
for group in groups:
# Samples per day
chart.add_yaxis(group.file_type,
group.counts,
linestyle_opts=opts.LineStyleOpts(width=2),
label_opts=opts.LabelOpts(is_show=False))
# Simple moving average (SMA)
chart.add_yaxis(group.simple_moving_average_legend,
group.simple_moving_average,
linestyle_opts=opts.LineStyleOpts(width=2,
type_='dashed'),
label_opts=opts.LabelOpts(color='black',
background_color='white'))
return chart