def vtradecost(self,
cftable,
unitcost=False,
start=None,
end=yesterdayobj(),
rendered=True):
"""
visualization giving the average cost line together with netvalue line as well as buy and sell points
:returns: pyecharts.line
"""
funddata = []
costdata = []
pprice = self.price[self.price["date"] <= end]
pcftable = cftable
if start is not None:
pprice = pprice[pprice["date"] >= start]
pcftable = pcftable[pcftable["date"] >= start]
for _, row in pprice.iterrows():
date = row["date"]
funddata.append(row["netvalue"])
if unitcost:
cost = 0
if (date - self.cftable.iloc[0].date).days >= 0:
cost = self.unitcost(date)
costdata.append(cost)
coords = []
# pcftable = pcftable[abs(pcftable["cash"]) > threhold]
for i, r in pcftable.iterrows():
coords.append(
[r.date, pprice[pprice["date"] <= r.date].iloc[-1]["netvalue"]])
upper = pcftable.cash.abs().max()
lower = pcftable.cash.abs().min()
if upper == lower:
upper = 2 * lower + 1 # avoid zero in denominator
def marker_factory(x, y):
buy = pcftable[pcftable["date"] <= x].iloc[-1]["cash"]
if buy < 0:
color = "#ff7733"
else:
color = "#3366ff"
size = (abs(buy) - lower) / (upper - lower) * 5 + 5
return opts.MarkPointItem(
coord=[x.date(), y],
itemstyle_opts=opts.ItemStyleOpts(color=color),
# this nested itemstyle_opts within MarkPointItem is only supported for pyechart>1.7.1
symbol="circle",
symbol_size=size,
)
line = Line()
line.add_xaxis([d.date() for d in pprice.date])
if unitcost:
line.add_yaxis(
series_name="持仓成本",
y_axis=costdata,
is_symbol_show=False,
)
line.add_yaxis(
series_name="基金净值",
y_axis=funddata,
is_symbol_show=False,
markpoint_opts=opts.MarkPointOpts(
data=[marker_factory(*c) for c in coords], ),
)
line.set_global_opts(
datazoom_opts=[
opts.DataZoomOpts(is_show=True,
type_="slider",
range_start=50,
range_end=100),
opts.DataZoomOpts(
is_show=True,
type_="slider",
orient="vertical",
range_start=50,
range_end=100,
),
],
tooltip_opts=opts.TooltipOpts(
is_show=True,
trigger="axis",
trigger_on="mousemove",
axis_pointer_type="cross",
),
)
if rendered:
return line.render_notebook()
else:
return line
def plot_7d_weather(cip):
url = "http://www.weather.com.cn/weather/{}.shtml".format(cip)
response = requests.get(url, headers=headers)
response.encoding = "utf-8"
html = response.text
# 解析数据
soup = BeautifulSoup(html, "lxml")
lis = soup.select("div.c7d > ul > li")
date_list = []
weather_list = []
lowest_temp_list = []
highest_temp_list = []
for li in lis:
date_ = re.findall("(.*?)（.*?）", li.select("h1")[0].text)[0]
if len(date_) == 2:
date_ = "0" + date_
which_day = re.findall(".*?（(.*?)）", li.select("h1")[0].text)[0]
weather = li.select("p")[0].text
temp = li.select("p")[1].text.strip().replace("℃", "")
if "/" in temp:
highest_temp = temp.split("/")[0]
lowest_temp = temp.split("/")[1]
else:
highest_temp = temp
lowest_temp = temp
date_list.append(f"{date_}\n({which_day})")
weather_list.append(weather)
lowest_temp_list.append(int(lowest_temp))
highest_temp_list.append(int(highest_temp))
line = Line(init_opts=opts.InitOpts(width="450px", height="170px", theme=ThemeType.MACARONS))
line.add_xaxis(
xaxis_data=date_list
)
line.add_yaxis(
series_name="最高温度",
y_axis=highest_temp_list,
symbol_size=8,
is_hover_animation=False,
label_opts=opts.LabelOpts(font_size=15)
)
line.add_yaxis(
series_name="最低温度",
y_axis=lowest_temp_list,
symbol_size=8,
is_hover_animation=False,
label_opts=opts.LabelOpts(
position="bottom",
font_size=15
)
)
line.set_global_opts(
title_opts=opts.TitleOpts(
# subtitle="数据来源于中国天气网",
# pos_top="-5px"
),
legend_opts=opts.LegendOpts(pos_right="30px"),
xaxis_opts=opts.AxisOpts(
type_="category",
axislabel_opts=opts.LabelOpts(font_size=13)
),
yaxis_opts=opts.AxisOpts(
# is_scale=True,
min_=min(lowest_temp_list)-10 if min(lowest_temp_list)-10 > 0 else 0,
max_=max(highest_temp_list)+5,
interval=10,
name="温度(°C)",
name_location="center",
# name_gap=25,
name_gap=5,
name_textstyle_opts=opts.TextStyleOpts(
font_size=13,
color="black"
),
type_="value",
axislabel_opts=opts.LabelOpts(is_show=False),
# axistick_opts=opts.AxisTickOpts(length=5)
axistick_opts=opts.AxisTickOpts(length=0)
)
)
line.set_series_opts(
linestyle_opts=opts.LineStyleOpts(width=2.5),
markpoint_opts=opts.MarkPointOpts(
data=[opts.MarkPointItem(coord=[z[0], z[1]], value=z[2]) for z in zip(date_list, highest_temp_list, weather_list)],
symbol_size=1,
label_opts=opts.LabelOpts(
color="black",
rotate=25,
distance=28,
font_size=11
)
)
)
line.render("time-temp-curve.html")
def plot_1d_weather(cip):
url = "http://www.weather.com.cn/weather1d/{}.shtml#input".format(cip)
response = requests.get(url, headers=headers)
response.encoding = "utf-8"
html = response.text
# print(response.text)
soup = BeautifulSoup(html, "lxml")
script = soup.select("div#today > script")[1].text.strip().strip("var hour3data=")
json_ = json.loads(script)
# print(json_)
data = json_["1d"]
# print(data)
time_list = []
temp_list = []
weather_list = []
for i in range(len(data)):
time_ = data[i].split(",")[0]
wind = data[i].split(",")[2]
temp = data[i].split(",")[3]
time_list.append(time_)
weather_list.append(wind)
temp_list.append(temp)
# time_list = [re.findall("\d+", i)[0] + "-" + re.findall("\d+", i)[1] for i in time_list]
time_list = time_list[:-1]
temp_list = [int(i.strip("℃")) for i in temp_list][:-1]
weather_list = weather_list[:-1]
line = Line(init_opts=opts.InitOpts(width="450px", height="170px", theme=ThemeType.MACARONS))
# line = Line(init_opts=opts.InitOpts(width="1000px", height="500px", theme=ThemeType.MACARONS))
line.add_xaxis(xaxis_data=time_list)
line.add_yaxis(
series_name="",
y_axis=temp_list,
symbol_size=8,
is_hover_animation=False
)
line.set_global_opts(
title_opts=opts.TitleOpts(
subtitle="数据来源于中国天气网",
# pos_left="10px",
pos_left="center",
pos_top="-5px"
),
xaxis_opts=opts.AxisOpts(
type_="category",
boundary_gap=True,
axislabel_opts=opts.LabelOpts(
rotate=30,
font_size=12
),
axistick_opts=opts.AxisTickOpts(is_align_with_label=False)
),
yaxis_opts=opts.AxisOpts(
# is_scale=False,
min_=min(temp_list)-10,
max_=max(temp_list)+2,
interval=5,
name="温度(°C)",
name_location="center",
# name_gap=25,
name_gap=5,
name_textstyle_opts=opts.TextStyleOpts(
font_size=13,
color="black"
),
type_="value",
axislabel_opts=opts.LabelOpts(
font_size=12,
is_show=False
),
# axistick_opts=opts.AxisTickOpts(length=5)
axistick_opts=opts.AxisTickOpts(length=0)
),
)
line.set_series_opts(
label_opts=opts.LabelOpts(
font_size=15,
position="bottom",
color="#f68227"
),
linestyle_opts=opts.LineStyleOpts(width=2.5),
markpoint_opts=opts.MarkPointOpts(
symbol_size=1,
data=[
opts.MarkPointItem(name="天气", coord=[time_list[i], temp_list[i]], value=weather_list[i]) for i in range(len(time_list))
],
label_opts=opts.LabelOpts(
color="black",
font_size=14,
distance=10
)
)
)
line.render("time-temp-curve.html")
def kline_pro(kline: List[dict],
fx: List[dict] = None,
bi: List[dict] = None,
xd: List[dict] = None,
bs: List[dict] = None,
title: str = "缠中说禅K线分析",
width: str = "1400px",
height: str = '580px') -> Grid:
"""绘制缠中说禅K线分析结果
:param kline: K线
:param fx: 分型识别结果
:param bi: 笔识别结果
{'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 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 = 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 pyecharts(request):
"""dashboard view"""
# 工单数量统计
chart_dao = ChartDao()
data = chart_dao.workflow_by_date(30)
today = date.today()
one_month_before = today - relativedelta(days=+30)
attr = chart_dao.get_date_list(one_month_before, today)
_dict = {}
for row in data['rows']:
_dict[row[0]] = row[1]
value = [_dict.get(day) if _dict.get(day) else 0 for day in attr]
bar1 = Bar(init_opts=opts.InitOpts(width='600', height='380px'))
bar1.add_xaxis(attr)
bar1.add_yaxis("", value)
# 工单按组统计
data = chart_dao.workflow_by_group(30)
attr = [row[0] for row in data['rows']]
value = [row[1] for row in data['rows']]
pie1 = Pie(init_opts=opts.InitOpts(width='600', height='380px'))
pie1.set_global_opts(title_opts=opts.TitleOpts(title=''),
legend_opts=opts.LegendOpts(orient="vertical",
pos_top="15%",
pos_left="2%",
is_show=False))
pie1.set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {c}"))
pie1.add("", [list(z) for z in zip(attr, value)])
# 工单按人统计
data = chart_dao.workflow_by_user(30)
attr = [row[0] for row in data['rows']]
value = [row[1] for row in data['rows']]
bar2 = Bar(init_opts=opts.InitOpts(width='600', height='380px'))
bar2.add_xaxis(attr)
bar2.add_yaxis("", value)
# SQL语句类型统计
data = chart_dao.syntax_type()
attr = [row[0] for row in data['rows']]
value = [row[1] for row in data['rows']]
pie2 = Pie()
pie2.set_global_opts(title_opts=opts.TitleOpts(title='SQL上线工单统计(类型)'),
legend_opts=opts.LegendOpts(orient="vertical",
pos_top="15%",
pos_left="2%"))
pie2.set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {c}"))
pie2.add("", [list(z) for z in zip(attr, value)])
# SQL查询统计(每日检索行数)
attr = chart_dao.get_date_list(one_month_before, today)
effect_data = chart_dao.querylog_effect_row_by_date(30)
effect_dict = {}
for row in effect_data['rows']:
effect_dict[row[0]] = int(row[1])
effect_value = [
effect_dict.get(day) if effect_dict.get(day) else 0 for day in attr
]
count_data = chart_dao.querylog_count_by_date(30)
count_dict = {}
for row in count_data['rows']:
count_dict[row[0]] = int(row[1])
count_value = [
count_dict.get(day) if count_dict.get(day) else 0 for day in attr
]
line1 = Line(init_opts=opts.InitOpts(width='600', height='380px'))
line1.set_global_opts(title_opts=opts.TitleOpts(title=''),
legend_opts=opts.LegendOpts(selected_mode='single'))
line1.add_xaxis(attr)
line1.add_yaxis("检索行数",
effect_value,
is_smooth=True,
markpoint_opts=opts.MarkPointOpts(
data=[opts.MarkPointItem(type_="average")]))
line1.add_yaxis("检索次数",
count_value,
is_smooth=True,
markline_opts=opts.MarkLineOpts(data=[
opts.MarkLineItem(type_="max"),
opts.MarkLineItem(type_="average")
]))
# SQL查询统计(用户检索行数)
data = chart_dao.querylog_effect_row_by_user(30)
attr = [row[0] for row in data['rows']]
value = [int(row[1]) for row in data['rows']]
pie4 = Pie(init_opts=opts.InitOpts(width='600', height='380px'))
pie4.set_global_opts(title_opts=opts.TitleOpts(title=''),
legend_opts=opts.LegendOpts(orient="vertical",
pos_top="15%",
pos_left="2%",
is_show=False))
pie4.set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {c}"))
pie4.add("", [list(z) for z in zip(attr, value)])
# SQL查询统计(DB检索行数)
data = chart_dao.querylog_effect_row_by_db(30)
attr = [row[0] for row in data['rows']]
value = [int(row[1]) for row in data['rows']]
pie5 = Pie(init_opts=opts.InitOpts(width='600', height='380px'))
pie5.set_global_opts(title_opts=opts.TitleOpts(title=''),
legend_opts=opts.LegendOpts(orient="vertical",
pos_top="15%",
pos_left="2%",
is_show=False))
pie5.set_series_opts(
label_opts=opts.LabelOpts(formatter="{b}: {c}", position="left"))
pie5.add("", [list(z) for z in zip(attr, value)])
# 可视化展示页面
chart = {
"bar1": bar1.render_embed(),
"pie1": pie1.render_embed(),
"bar2": bar2.render_embed(),
"pie2": pie2.render_embed(),
"line1": line1.render_embed(),
"pie4": pie4.render_embed(),
"pie5": pie5.render_embed(),
}
# 获取统计数据
try:
dashboard_count_stats = cache.get('dashboard_count_stats')
except Exception:
dashboard_count_stats = {
"sql_wf_cnt": SqlWorkflow.objects.count(),
"query_wf_cnt": QueryPrivilegesApply.objects.count(),
"user_cnt": Users.objects.count(),
"ins_cnt": Instance.objects.count()
}
else:
dashboard_count_stats = dashboard_count_stats or {
"sql_wf_cnt": SqlWorkflow.objects.count(),
"query_wf_cnt": QueryPrivilegesApply.objects.count(),
"user_cnt": Users.objects.count(),
"ins_cnt": Instance.objects.count()
}
cache.set("dashboard_count_stats", dashboard_count_stats, 600)
return render(request, "dashboard.html", {
"chart": chart,
"count_stats": dashboard_count_stats
})
def generate_html(folder_path):
"""
info_dict = dict(
time=time.strftime("%H:%M:%S", time.localtime(timestamp)),
device=device_name,
cpu=cpu,
mem=mem,
fps=fps,
)
"""
pickle_folder_path = conf.info_path + os.sep + folder_path
if os.listdir(pickle_folder_path):
pass
else:
print("该目录下没有任何性能数据")
exit(1)
total_data = {}
page = Page(page_title="移动端性能图表")
cpu_line = Line(init_opts=opts.InitOpts(width="1200px", height="600px", theme=ThemeType.DARK))
mem_line = Line(init_opts=opts.InitOpts(width="1200px", height="600px", theme=ThemeType.DARK))
fps_line = Line(init_opts=opts.InitOpts(width="1200px", height="600px", theme=ThemeType.DARK))
# 给所有折线图增加title和工具栏
cpu_line.set_global_opts(title_opts=opts.TitleOpts(title="CPU", subtitle="所有设备CPU数据(%)"),
toolbox_opts=opts.ToolboxOpts())
mem_line.set_global_opts(title_opts=opts.TitleOpts(title="内存", subtitle="所有设备内存数据(Mb)"),
toolbox_opts=opts.ToolboxOpts())
fps_line.set_global_opts(title_opts=opts.TitleOpts(title="FPS", subtitle="所有设备FPS数据(帧)"),
toolbox_opts=opts.ToolboxOpts())
for file in os.listdir(pickle_folder_path):
device_name = file.split(".")[0]
total_data[device_name] = op.read(pickle_folder_path + os.sep + file)
longest_result = sorted([(len(total_data.get(k)), k) for k in total_data.keys()])[-1][1] # get the longest result
x_coordinate = [i.get("time") for i in total_data.get(longest_result)]
cpu_line.add_xaxis(x_coordinate)
mem_line.add_xaxis(x_coordinate)
fps_line.add_xaxis(x_coordinate)
for k in total_data.keys(): # 按设备加折线数据
data = total_data.get(k)
cpu_line.add_yaxis(data[0].get("device"), [i.get("cpu") for i in data], )
mem_line.add_yaxis(data[0].get("device"), [i.get("mem") for i in data], )
fps_line.add_yaxis(data[0].get("device"), [i.get("fps") for i in data], )
for line in (cpu_line, mem_line, fps_line): # 给所有折线图添加最大，最小，平均值
(
line.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")
], symbol_size=40),
markline_opts=opts.MarkLineOpts(
data=[
opts.MarkLineItem(type_="average", name="平均值", )
],
symbol="none"
))
)
page.add(cpu_line, mem_line, fps_line)
page.render(conf.report_path + os.sep + folder_path + ".html")
print("html文件生成在report目录下")
# -*- coding: utf-8 -*-
# @Time : 2020/1/21 13:47
# @Author : Mqz
# @FileName: tuxing_show.py
from pyecharts.charts import Bar
from pyecharts.charts import Line
from pyecharts import options as opts
# 不习惯链式调用的开发者依旧可以单独调用方法
# bar = Bar()
# bar.add_xaxis(["测试A", "测试B", "测试C", "测试D", "测试E", "测试F"])
# bar.add_yaxis("隧道ip", [5993, 5976, 6448, 6361, 6257, 90])
# bar.add_yaxis("直连ip", [7128, 6450, 5664, 6915, 6979, 10])
# bar.set_global_opts(title_opts=opts.TitleOpts(title="IP测试", subtitle="每个ip10分钟测试"))
# bar.render()
line = Line()
line.add_xaxis(["测试A", "测试B", "测试C", "测试D", "测试E", "测试F"])
line.add_yaxis("隧道ip", [5993, 5976, 5664, 6361, 6257, 5312])
line.add_yaxis("直连ip", [7128, 6450, 6448, 6915, 6979, 7101])
line.set_global_opts(
title_opts=opts.TitleOpts(title="IP测试", subtitle="每个ip10分钟采集数量测试"))
line.render()
def analysis_total():
# 处理数据
x_data = pdata['年份'].tolist()
# 将人口单位转换为亿
y_data1 = pdata['年末总人口(万人)'].map(lambda x: "%.2f" % (x / 10000)).tolist()
y_data2 = pdata['人口自然增长率(‰)'].tolist()
y_data3 = pdata['人口出生率(‰)'].tolist()
y_data4 = pdata['人口死亡率(‰)'].tolist()
# 总人口柱状图
bar = Bar(init_opts=opts.InitOpts(width="1200px", height="500px"))
bar.add_xaxis(x_data)
bar.add_yaxis("年末总人口（亿）",
y_data1,
category_gap="10%",
label_opts=opts.LabelOpts(rotate=90, position="inside"))
bar.set_global_opts(
title_opts=opts.TitleOpts(title="年末总人口变化情况",
pos_bottom="bottom",
pos_left="center"),
xaxis_opts=opts.AxisOpts(
type_="category",
name='年份',
# 坐标轴名称显示位置
name_location='end',
# x轴数值与坐标点的偏移量
# boundary_gap=False,
axislabel_opts=opts.LabelOpts(is_show=True,
margin=10,
color="#000",
interval=1,
rotate=90),
# axisline_opts=opts.AxisLineOpts(is_show=True, symbol="arrow"),
axistick_opts=opts.AxisTickOpts(is_show=True,
is_align_with_label=True),
axispointer_opts=opts.AxisPointerOpts(
type_="line", label=opts.LabelOpts(is_show=True))),
# y轴相关选项设置
yaxis_opts=opts.AxisOpts(
type_="value",
position="left",
),
legend_opts=opts.LegendOpts(is_show=True))
# bar.render('bartest.html')
# 自然增长率、出生率、死亡率折线图
line = Line(init_opts=opts.InitOpts(width="1400px", height="500px"))
line.add_xaxis(x_data)
line.add_yaxis(series_name="自然增长率(‰)",
y_axis=y_data2,
label_opts=opts.LabelOpts(is_show=False))
line.add_yaxis('出生率(‰)', y_data3, label_opts=opts.LabelOpts(is_show=False))
line.add_yaxis('死亡率(‰)', y_data4, label_opts=opts.LabelOpts(is_show=False))
line.set_global_opts(
title_opts=opts.TitleOpts(title="人口自然增长率、出生率、死亡率",
pos_bottom="bottom",
pos_left="center"),
xaxis_opts=opts.AxisOpts(
name='年份',
name_location='end',
type_="value",
min_="1949",
max_interval=1,
# 设置x轴不必与y轴的0对齐
axisline_opts=opts.AxisLineOpts(is_on_zero=False),
axislabel_opts=opts.LabelOpts(is_show=True,
color="#000",
interval=0,
rotate=90),
axistick_opts=opts.AxisTickOpts(is_show=True,
is_align_with_label=True),
axispointer_opts=opts.AxisPointerOpts(
type_="shadow", label=opts.LabelOpts(is_show=True))),
# y轴相关选项设置
yaxis_opts=opts.AxisOpts(name='比例',
type_="value",
position="left",
min_=-10,
axislabel_opts=opts.LabelOpts(is_show=True)),
legend_opts=opts.LegendOpts(is_show=True))
# 渲染图像，将多个图像显示在一个html中
# DraggablePageLayout表示可拖拽
page = Page(layout=Page.DraggablePageLayout)
page.add(bar)
page.add(line)
page.render('population_total.html')
def analysis_sex():
x_data = pdata['年份'].tolist()
# 历年男性人口数
y_data_man = pdata['男性人口(万人)']
# 历年女性人口数
y_data_woman = pdata['女性人口(万人)']
# 2019年男女比饼图
sex_2019 = pdata[pdata['年份'] == 2019][['男性人口(万人)', '女性人口(万人)']]
# 两列相减，获得新列
y_data_man_woman = pdata['男性人口(万人)'] - pdata['女性人口(万人)']
pie = Pie()
pie.add("", [list(z) for z in zip(['男', '女'], np.ravel(sex_2019.values))])
pie.set_global_opts(title_opts=opts.TitleOpts(
title="2019中国男女比", pos_bottom="bottom", pos_left="center"))
pie.set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {d}%"))
pie.render('nvpie.html')
line = Line(init_opts=opts.InitOpts(width="1400px", height="500px"))
line.add_xaxis(x_data)
line.add_yaxis(
series_name="男女差值",
y_axis=y_data_man_woman.values,
# 标出关键点的数据
markpoint_opts=opts.MarkPointOpts(data=[
opts.MarkPointItem(type_="min"),
opts.MarkPointItem(type_="max"),
opts.MarkPointItem(type_="average")
]),
label_opts=opts.LabelOpts(is_show=False),
markline_opts=opts.MarkLineOpts(
data=[opts.MarkLineItem(type_="average")]))
line.set_global_opts(
title_opts=opts.TitleOpts(title="中国70年(1949-2019)男女差值（万人）",
pos_left="center",
pos_top="bottom"),
legend_opts=opts.LegendOpts(is_show=False),
xaxis_opts=opts.AxisOpts(
name='年份',
name_location='end',
type_="value",
min_="1949",
max_interval=1,
# 设置x轴不必与y轴的0对齐
axisline_opts=opts.AxisLineOpts(is_on_zero=False),
axislabel_opts=opts.LabelOpts(is_show=True,
color="#000",
interval=0,
rotate=90),
axistick_opts=opts.AxisTickOpts(is_show=True,
is_align_with_label=True),
axispointer_opts=opts.AxisPointerOpts(
type_="shadow", label=opts.LabelOpts(is_show=True))),
yaxis_opts=opts.AxisOpts(name='差值（万人）',
type_="value",
position="left",
axislabel_opts=opts.LabelOpts(is_show=True)),
)
# 5、渲染图像，将多个图像显示在一个html中
page = Page(layout=Page.DraggablePageLayout)
page.add(pie)
page.add(line)
page.render('population_sex.html')
def draw_roc(fpr: list,
tpr: list,
area_color: list = [],
is_show_ks: bool = True,
title: str = "ROC") -> Line:
"""
Description:
1. Draw roc
Params:
fpr
tpr
area_color: [area_under_yx, area_under_curve]
is_show_ks
title
Return:
None
"""
area_color = area_color or [
LIGHT_COLORS["COLD"][3], LIGHT_COLORS["WARM"][5]
]
# Draw roc
line = Line().add_xaxis(fpr)
line.add_yaxis(
"",
tpr,
is_smooth=True,
areastyle_opts=opts.AreaStyleOpts(
opacity=0.5,
color=area_color[1],
),
label_opts=opts.LabelOpts(is_show=None),
color=area_color[1],
symbol_size=0,
)
line.add_yaxis(
"",
fpr,
is_smooth=True,
areastyle_opts=opts.AreaStyleOpts(
opacity=0.5,
color=area_color[0],
),
label_opts=opts.LabelOpts(is_show=None),
color=area_color[1],
symbol_size=0,
)
line.set_global_opts(title_opts=opts.TitleOpts(title=title,
pos_left="center"),
xaxis_opts=opts.AxisOpts(type_="value", name="FPR"),
yaxis_opts=opts.AxisOpts(type_="value", name="TPR"),
toolbox_opts=opts.ToolboxOpts())
if is_show_ks:
line.set_series_opts(markline_opts=opts.MarkLineOpts(data=[
opts.MarkLineItem(
name="KS",
type_="value",
x=max(zip(tpr - fpr, fpr))[1],
)
]))
return line