def radar_simple() -> Radar:
c = (
Radar().add_schema(
# 各项的max_值可以不同
schema=[
opts.RadarIndicatorItem(name='计算几何学', max_=100),
opts.RadarIndicatorItem(name='动态规划', max_=100),
opts.RadarIndicatorItem(name='图论', max_=100),
opts.RadarIndicatorItem(name='搜索', max_=100),
opts.RadarIndicatorItem(name='模拟', max_=100),
opts.RadarIndicatorItem(name='数论', max_=100),
]).add(
'旺神',
[[random.randint(10, 101) for _ in range(6)]],
color='red',
areastyle_opts=opts.AreaStyleOpts( #设置填充的属性
opacity=0.5, color='red'),
).add(
'蔡队',
[[random.randint(10, 101) for _ in range(6)]],
color='blue',
areastyle_opts=opts.AreaStyleOpts(
opacity=0.5, #透明度
color='blue'),
).set_series_opts(label_opts=opts.LabelOpts(
is_show=True)).set_global_opts(title_opts=opts.TitleOpts(
title='雷达图示例-ACM集训队队员能力')))
return c
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 memory_line() -> Line:
now = time.strftime('%Y{y}%m{m}%d{d}').format(y='年', m='月', d='日')
mem = memory()
mem_percent_dict = mem[0]
swp_percent_dict = mem[1]
memory_percent_line = (Line().add_xaxis(list(
mem_percent_dict.keys())).add_yaxis(
'内存占用率',
list(mem_percent_dict.values()),
areastyle_opts=opts.AreaStyleOpts(opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
is_smooth=True).add_yaxis(
'交换区暂用率',
list(swp_percent_dict.values()),
areastyle_opts=opts.AreaStyleOpts(opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
is_smooth=True).set_global_opts(
tooltip_opts=opts.TooltipOpts(trigger="axis",
axis_pointer_type="cross"),
title_opts=opts.TitleOpts(title=now + "内存使用率",
pos_left="center"),
yaxis_opts=opts.AxisOpts(min_=0,
max_=100,
split_number=10,
type_="value",
name='%'),
legend_opts=opts.LegendOpts(pos_left="left")))
return 0, 0, 0, 0, 0, 0, memory_percent_line
def load_line() -> Line:
now = time.strftime('%Y{y}%m{m}%d{d}').format(y='年', m='月', d='日')
lod = load()
load_1m_dict = lod[0]
load_5m_dict = lod[1]
load_15m_dict = lod[2]
max_load = lod[3]
load_line = (Line().add_xaxis(list(load_1m_dict.keys())).add_yaxis(
'最近1分钟负载',
list(load_1m_dict.values()),
areastyle_opts=opts.AreaStyleOpts(opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
is_smooth=True).add_yaxis(
'最近5分钟负载',
list(load_5m_dict.values()),
areastyle_opts=opts.AreaStyleOpts(opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
is_smooth=True).add_yaxis(
'最近15分钟负载',
list(load_15m_dict.values()),
areastyle_opts=opts.AreaStyleOpts(opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
is_smooth=True).set_global_opts(
tooltip_opts=opts.TooltipOpts(trigger="axis",
axis_pointer_type="cross"),
title_opts=opts.TitleOpts(title=now + "负载",
pos_left="center"),
yaxis_opts=opts.AxisOpts(min_=0.0,
max_=max_load,
split_number=10,
type_="value",
name=''),
legend_opts=opts.LegendOpts(pos_left="left")))
return load_line
def stacked_area_chart(self, x_data, y1_data, y2_data, flag):
if (flag):
(Line().add_xaxis(xaxis_data=x_data).add_yaxis(
series_name="研究方向数量",
stack="总量",
y_axis=y1_data,
areastyle_opts=opts.AreaStyleOpts(opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
).add_yaxis(
series_name="拟招生人数",
stack="总量",
y_axis=y2_data,
areastyle_opts=opts.AreaStyleOpts(opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
).set_global_opts(
title_opts=opts.TitleOpts(title="高校研究方向折线图"),
tooltip_opts=opts.TooltipOpts(trigger="axis",
axis_pointer_type="cross"),
yaxis_opts=opts.AxisOpts(
type_="value",
axistick_opts=opts.AxisTickOpts(is_show=True),
splitline_opts=opts.SplitLineOpts(is_show=True),
),
xaxis_opts=opts.AxisOpts(type_="category",
boundary_gap=False,
name_rotate=60,
name_gap=20),
).render("ReserachDirections.html"))
print("绘制成功，请在同级目录下查看 ReserachDirections.html 文件！")
else:
print("出现错误，停止绘制图表！")
def drawer2(stage_name, data):
c_schema = [
{
"name": "乐",
"max": 0.55,
"min": 0
},
{
"name": "好",
"max": 0.55,
"min": 0
},
{
"name": "哀",
"max": 0.55,
"min": 0
},
{
"name": '恶',
"max": 0.55,
"min": 0
},
{
"name": "惧",
"max": 0.55,
"min": 0
},
]
c = (Radar().set_colors(["#CC3300"]).add_schema(
schema=c_schema,
shape="circle",
center=["50%", "50%"],
radius="80%",
angleaxis_opts=opts.AngleAxisOpts(
min_=0,
max_=360,
is_clockwise=False,
interval=5,
axistick_opts=opts.AxisTickOpts(is_show=False),
axislabel_opts=opts.LabelOpts(is_show=False),
axisline_opts=opts.AxisLineOpts(is_show=False),
splitline_opts=opts.SplitLineOpts(is_show=False),
),
radiusaxis_opts=opts.RadiusAxisOpts(
min_=-4,
max_=4,
interval=2,
splitarea_opts=opts.SplitAreaOpts(
is_show=True, areastyle_opts=opts.AreaStyleOpts(opacity=1)),
),
polar_opts=opts.PolarOpts(),
splitarea_opt=opts.SplitAreaOpts(is_show=False),
splitline_opt=opts.SplitLineOpts(is_show=False),
).add(
series_name=stage_name,
data=data,
areastyle_opts=opts.AreaStyleOpts(opacity=0.1),
linestyle_opts=opts.LineStyleOpts(width=3),
).render("res\\output\\multi-emotion\\image\\" + stage_name + ".html"))
def get_line_ba(self,df, init_account, stock_info):
trade_dates = np.array(df.trade_date).tolist()
balances = np.array((df['balance'] - init_account) / init_account).tolist()
c = (
Line()
.add_xaxis(trade_dates)
.add_yaxis("收益率"
, balances
, is_smooth=True,
is_hover_animation=False,
linestyle_opts=opts.LineStyleOpts(width=2, opacity=0.9),
label_opts=opts.LabelOpts(is_show=False),
)
.set_global_opts(
title_opts=opts.TitleOpts(title=stock_info['name']),
xaxis_opts=opts.AxisOpts(
is_scale=True,
type_="category",
splitarea_opts=opts.SplitAreaOpts(
is_show=True, areastyle_opts=opts.AreaStyleOpts(opacity=1)
),
),
yaxis_opts=opts.AxisOpts(
is_scale=True,
splitarea_opts=opts.SplitAreaOpts(
is_show=True, areastyle_opts=opts.AreaStyleOpts(opacity=1)
),
),
datazoom_opts=[
opts.DataZoomOpts(
is_show=True,
xaxis_index=[0],
type_="slider",
pos_top="90%",
range_start=90,
range_end=100,
),
# opts.DataZoomOpts(
# is_show=True,
# yaxis_index=[0],
# type_="slider",
# orient = 'vertical',
# # pos_top="90%",
# pos_right="0%",
# range_start=0,
# range_end=100,
# ),
],
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"),
),
)
)
return c
def draw_centrality():
degree_centrality = nx.degree_centrality(G)
closeness_centrality = nx.closeness_centrality(G)
betweenness_centrality = nx.betweenness_centrality(G)
degree_centrality_sort = sorted(degree_centrality, key=lambda x: degree_centrality[x], reverse=True)
closeness_centrality_sort = sorted(closeness_centrality, key=lambda x: closeness_centrality[x], reverse=True)
betweenness_centrality_sort = sorted(betweenness_centrality, key=lambda x: betweenness_centrality[x], reverse=True)
top_authors = sorted(
[(x, degree_centrality[x] + closeness_centrality[x] + betweenness_centrality[x]) for x in set.intersection(
set(betweenness_centrality_sort[:30]), set(closeness_centrality_sort[:30]),
set(degree_centrality_sort[:30]))], key=lambda x: x[1], reverse=True)
table = prettytable.PrettyTable(
['Rank', 'Name', 'Degree centrality', 'Closeness centrality', 'Betweenness centrality', 'Sum centrality'])
[table.add_row(
[index + 1, value[0], round(degree_centrality[value[0]], 3), round(closeness_centrality[value[0]], 3),
round(betweenness_centrality[value[0]], 3), round(value[1], 3)]) for index, value in enumerate(top_authors)]
print(table)
h_authors = set.intersection(
set(betweenness_centrality_sort[:15]),
set(closeness_centrality_sort[:15]),
set(degree_centrality_sort[:15]))
data = [[G.nodes[x]["name"], [betweenness_centrality[x], closeness_centrality[x], degree_centrality[x]]] for x in
h_authors[:10]]
c = (
Radar()
.add_schema(
schema=[
opts.RadarIndicatorItem(name="Betweenness centrality [0,0.15]", max_=0.15, min_=0),
opts.RadarIndicatorItem(name="Closeness centrality [0,0.6]", max_=0.6, min_=0),
opts.RadarIndicatorItem(name="Degree centrality [0,0.35]", max_=0.35, min_=0),
],
shape="circle",
center=["50%", "50%"],
radius="80%",
splitarea_opt=opts.SplitAreaOpts(
is_show=True, areastyle_opts=opts.AreaStyleOpts(opacity=1)
),
textstyle_opts=opts.TextStyleOpts(color="#000"),
).set_series_opts(label_opts=opts.LabelOpts(is_show=False))
)
for x in data:
color = randomcolor()
c.add(
series_name=x[0],
data=[x[1]],
areastyle_opts=opts.AreaStyleOpts(opacity=0.1, color=color),
linestyle_opts=opts.LineStyleOpts(width=1, color=color),
label_opts=opts.LabelOpts(is_show=False)
)
return c
def line_areastyle() -> Line:
c = (Line().add_xaxis(Faker.choose()).add_yaxis(
"商家A", Faker.values(),
areastyle_opts=opts.AreaStyleOpts(opacity=0.5)).add_yaxis(
"商家B",
Faker.values(),
areastyle_opts=opts.AreaStyleOpts(opacity=0.5)).set_global_opts(
title_opts=opts.TitleOpts(title="Line-面积图")))
return c
def draw_centrality():
degree_centrality = nx.degree_centrality(G)
closeness_centrality = nx.closeness_centrality(G)
betweenness_centrality = nx.betweenness_centrality(G)
degree_centrality_sort = sorted(degree_centrality,
key=lambda x: degree_centrality[x],
reverse=True)
closeness_centrality_sort = sorted(closeness_centrality,
key=lambda x: closeness_centrality[x],
reverse=True)
betweenness_centrality_sort = sorted(
betweenness_centrality,
key=lambda x: betweenness_centrality[x],
reverse=True)
h_authors = set.intersection(set(betweenness_centrality_sort[:15]),
set(closeness_centrality_sort[:15]),
set(degree_centrality_sort[:15]))
data = [[
G.nodes[x]["name"],
[
betweenness_centrality[x], closeness_centrality[x],
degree_centrality[x]
]
] for x in h_authors]
c = (Radar().add_schema(
schema=[
opts.RadarIndicatorItem(name="Betweenness centrality [0,1]",
max_=1,
min_=0),
opts.RadarIndicatorItem(name="Closeness centrality [0,0.6]",
max_=0.6,
min_=0),
opts.RadarIndicatorItem(name="Degree centrality [0,0.1]",
max_=0.1,
min_=0),
],
shape="circle",
center=["50%", "50%"],
radius="80%",
splitarea_opt=opts.SplitAreaOpts(
is_show=True, areastyle_opts=opts.AreaStyleOpts(opacity=1)),
textstyle_opts=opts.TextStyleOpts(color="#000"),
).set_series_opts(label_opts=opts.LabelOpts(is_show=False)))
for x in data:
color = randomcolor()
c.add(series_name=x[0],
data=[x[1]],
areastyle_opts=opts.AreaStyleOpts(opacity=0.1, color=color),
linestyle_opts=opts.LineStyleOpts(width=1, color=color),
label_opts=opts.LabelOpts(is_show=False))
c.render("radar_angle_radius_axis.html")
def draw_Kline(self):
"""
画股票的K线图，需要传入四个数据
分别是["open", "close", "high", "low"]
:return: 画出图像
"""
Df_s1 = Read_One_Stock(self.SC).select_col("open", "high", "low",
"close", "vol", "amount")
length = Df_s1.shape[0]
Df_s1.sort_values("trade_date", inplace=True)
Df_s1.index = list(range(length))
price = np.array(Df_s1[["open", "close", "high", "low"]]).tolist()
date = np.array(Df_s1["trade_date"], dtype=np.string_).tolist()
ma_value_5 = calculate_ma_n(list(Df_s1['close']), 5)
ma_value_10 = calculate_ma_n(list(Df_s1['close']), 10)
ma_value = np.array([ma_value_5, ma_value_10]).tolist()
kline = Kline()
kline.set_global_opts(
xaxis_opts=opts.AxisOpts(is_scale=True),
yaxis_opts=opts.AxisOpts(
is_scale=True,
splitarea_opts=opts.SplitAreaOpts(
is_show=True,
areastyle_opts=opts.AreaStyleOpts(opacity=1)),
),
datazoom_opts=[opts.DataZoomOpts()],
title_opts=opts.TitleOpts(title="K-Line of {}".format(self.SC)),
)
kline.add_xaxis(date)
kline.add_yaxis('K-Line', price)
line = Line()
line.add_xaxis(date)
line.add_yaxis(series_name="ma5",
y_axis=ma_value[0],
label_opts=opts.LabelOpts(is_show=False))
line.add_yaxis(series_name="ma10",
y_axis=ma_value[1],
label_opts=opts.LabelOpts(is_show=False))
line.set_global_opts(
xaxis_opts=opts.AxisOpts(is_scale=True),
yaxis_opts=opts.AxisOpts(
is_scale=True,
splitarea_opts=opts.SplitAreaOpts(
is_show=True,
areastyle_opts=opts.AreaStyleOpts(opacity=1)),
),
datazoom_opts=[opts.DataZoomOpts()],
title_opts=opts.TitleOpts(title=""))
kline.overlap(line)
kline.render("./Plots/{} Candle Plot.html".format(self.SC))
def radar_plot(v1, v2, v3, i):
m = max([max(v1[0]), max(v2[0]), max(v3[0])])
c = (Radar(init_opts=opts.InitOpts(theme=ThemeType.LIGHT,width="640px", height="360px",bg_color="#CCCCCC"))
.add_schema(
shape='polygon',
schema=[
opts.RadarIndicatorItem(name="焦虑", max_=m),
opts.RadarIndicatorItem(name="悲伤", max_=m),
opts.RadarIndicatorItem(name="愤怒", max_=m),
opts.RadarIndicatorItem(name="喜悦", max_=m),
opts.RadarIndicatorItem(name="感激", max_=m),
opts.RadarIndicatorItem(name="信任", max_=m),
],
splitarea_opt=opts.SplitAreaOpts(
is_show=True, areastyle_opts=opts.AreaStyleOpts(opacity=1)
),
textstyle_opts=opts.TextStyleOpts(color="#fff"),
)
.add(
symbol = None,
series_name="官方媒体",
data=v1,
linestyle_opts=opts.LineStyleOpts(width=1),
areastyle_opts=opts.AreaStyleOpts(opacity=0.1),
)
# .add("北京", value_bj, color=)
# .add("上海", value_sh, color="#b3e4a1")
# .set_series_opts(label_opts=opts.LabelOpts(is_show=False))
.add(
symbol=None,
series_name="大众心理",
data=v2,
linestyle_opts=opts.LineStyleOpts(color='#61a0a8'),
areastyle_opts=opts.AreaStyleOpts(opacity=0.1,color='#61a0a8'),)
).add(
series_name="自媒体",
symbol=None,
data=v3,
areastyle_opts=opts.AreaStyleOpts(opacity=0.1,color="#fab27b"),
linestyle_opts=opts.LineStyleOpts(color="#fab27b"),
)\
.set_series_opts(label_opts=opts.LabelOpts(is_show=False))\
.set_global_opts(
title_opts=opts.TitleOpts(title="第"+str(i+1)+"阶段心态分析"), legend_opts=opts.LegendOpts()
)
return c
def heat_plot(data):
n, m = data.shape
x, y = np.arange(0, n, 1), np.arange(0, m, 1)
x, y = np.meshgrid(x, y, indexing='ij')
z = data.values.flatten()
x = m - 1 - x
_data = list(zip(y.flatten(), x.flatten(), z))
_data = [[int(d[0]), int(d[1]), np.round(d[2], 4)] for d in _data]
h = (
HeatMap(
init_opts=opts.InitOpts(width="1000px", height="300px")).add_xaxis(
xaxis_data=data.columns.astype(str).tolist()).add_yaxis(
series_name="",
yaxis_data=data.index.astype(str).tolist()[::-1],
value=_data,
label_opts=opts.LabelOpts(position='inside',
is_show=True,
font_size=15),
).set_series_opts().set_global_opts(
# toolbox_opts=opts.ToolboxOpts(is_show=True),
legend_opts=opts.LegendOpts(is_show=False),
tooltip_opts=opts.TooltipOpts(is_show=True, ),
xaxis_opts=opts.AxisOpts(
type_="category",
splitarea_opts=opts.SplitAreaOpts(
is_show=True,
areastyle_opts=opts.AreaStyleOpts(opacity=1)),
),
yaxis_opts=opts.AxisOpts(
type_="category",
splitarea_opts=opts.SplitAreaOpts(
is_show=True,
areastyle_opts=opts.AreaStyleOpts(opacity=1)),
),
visualmap_opts=opts.VisualMapOpts(min_=z.min(),
max_=z.max(),
is_calculable=True,
orient="vertical",
pos_left="left"),
toolbox_opts=opts.ToolboxOpts(
is_show=True,
feature=opts.ToolBoxFeatureOpts(
save_as_image=opts.ToolBoxFeatureSaveAsImageOpts(
background_color='white',
connected_background_color="white",
pixel_ratio=2,
name="pic",
), )),
))
return h
def image_host_resource(doc_path, hosts_metrics, host):
options = opts.InitOpts(js_host=os.path.join(os.getcwd(), "scripts/javascript/"),
animation_opts=opts.AnimationOpts(animation=False))
line1 = (
Line(options)
.add_xaxis(hosts_metrics[host['ip']]['metrics']['datetime'])
.add_yaxis("CPU", hosts_metrics[host['ip']]['metrics']['cpu'], is_symbol_show=False, is_smooth=True,
areastyle_opts=opts.AreaStyleOpts(color=utils.JsCode(
"new echarts.graphic.LinearGradient(0,0,0,1,[{offset:0,color:'rgb(255,158,68)'},{offset:1,color:'rgb(255,70,131)'}])"),
opacity=0.5))
.add_yaxis("Memory", hosts_metrics[host['ip']]['metrics']['memory'], is_symbol_show=False, is_smooth=True)
.set_series_opts(linestyle_opts=opts.LineStyleOpts(width=2))
.set_global_opts(
title_opts=opts.TitleOpts(title="主机资源使用率 (%s)" % (host['ip']),
title_textstyle_opts=opts.TextStyleOpts(font_size=13),
subtitle=" 数据来源-健康度平台"),
xaxis_opts=opts.AxisOpts(type_="time"),
yaxis_opts=opts.AxisOpts(
axislabel_opts=opts.LabelOpts(formatter=utils.JsCode("function(val){return val + '%';}")))
)
)
line2 = (
Line(options)
.add_xaxis(hosts_metrics[host['ip']]['metrics']['datetime'])
.add_yaxis("Process", hosts_metrics[host['ip']]['metrics']['process'], is_symbol_show=False, is_smooth=True,
areastyle_opts=opts.AreaStyleOpts(color=utils.JsCode(
"new echarts.graphic.LinearGradient(0,0,0,1,[{offset:0,color:'#fff'},{offset:1,color:'#61a0a8'}])"),
opacity=0.5)
)
.set_series_opts(linestyle_opts=opts.LineStyleOpts(width=2))
.set_global_opts(
xaxis_opts=opts.AxisOpts(type_="time", position="top", axislabel_opts=opts.LabelOpts(is_show=False)),
yaxis_opts=opts.AxisOpts(is_inverse=True),
title_opts=opts.TitleOpts(title="应用运行数量(所有)", pos_bottom="0%",
title_textstyle_opts=opts.TextStyleOpts(font_size=13)),
legend_opts=opts.LegendOpts(pos_bottom="0%")
)
)
grid = (
Grid(options)
.add(line1, grid_opts=opts.GridOpts(pos_top=50, pos_left=50, pos_right=50, pos_bottom="50%"))
.add(line2, grid_opts=opts.GridOpts(pos_top="58%", pos_left=50, pos_right=50, pos_bottom=30))
)
make_snapshot(snapshot, grid.render(os.path.join(doc_path, "render_%s.html" % (host['ip'].replace(".", "_")))),
os.path.join(doc_path, "host_res_%s.png" % (host['ip'].replace(".", "_"))), pixel_ratio=1, delay=1,
is_remove_html=True)
def disk_line() -> Line:
total, used, free = disk()
c = (
Line(init_opts=opts.InitOpts(width="1680px", height="800px"))
.add_xaxis(xaxis_data=disk_dict['disk_time'])
.add_yaxis(
series_name="写入数据",
y_axis=disk_dict['write_bytes'],
areastyle_opts=opts.AreaStyleOpts(opacity=0.5),
linestyle_opts=opts.LineStyleOpts(),
label_opts=opts.LabelOpts(is_show=False),
)
.add_yaxis(
series_name="读取数据",
y_axis=disk_dict['read_bytes'],
yaxis_index=1,
areastyle_opts=opts.AreaStyleOpts(opacity=0.5),
linestyle_opts=opts.LineStyleOpts(),
label_opts=opts.LabelOpts(is_show=False),
)
.extend_axis(
yaxis=opts.AxisOpts(
name_location="start",
type_="value",
is_inverse=True,
axistick_opts=opts.AxisTickOpts(is_show=True),
splitline_opts=opts.SplitLineOpts(is_show=True),
name='KB/2S'
)
)
.set_global_opts(
title_opts=opts.TitleOpts(
title="磁盘IO",
pos_left="center",
pos_top="top",
),
tooltip_opts=opts.TooltipOpts(trigger="axis", axis_pointer_type="cross"),
legend_opts=opts.LegendOpts(pos_left="left"),
xaxis_opts=opts.AxisOpts(type_="category", boundary_gap=False),
yaxis_opts=opts.AxisOpts(type_="value", name='KB/2S'),
)
.set_series_opts(
axisline_opts=opts.AxisLineOpts(),
)
)
return total, used, free, c
def line_connect_null() -> Line:
url = 'https://view.inews.qq.com/g2/getOnsInfo?name=disease_h5'
r_data = json.loads(requests.get(url).text)
data = json.loads(r_data['data']) #初始化json数据，为dict ['chinaTotal']
#每日确诊增加数
Dailyincrease = []
a = [x['confirm'] for x in data['chinaDayList']]
for i in range(len(a)):
if i == 0:
Dailyincrease.append(0)
else:
Dailyincrease.append(int(a[i]) - int(a[i-1]))
#每日疑似增加数
Dailysuspect = []
a = [x['suspect'] for x in data['chinaDayList']]
for i in range(len(a)):
if i == 0:
Dailysuspect.append(0)
else:
Dailysuspect.append(int(a[i]) - int(a[i-1]))
c = (
Line()
.add_xaxis([x['date'] for x in data['chinaDayList']]) #直接列表
.add_yaxis('确诊',[x['confirm'] for x in data['chinaDayList']]) #‘列表名，[]’
.add_yaxis('疑似',[x['suspect'] for x in data['chinaDayList']])
.add_yaxis('治愈',[x['heal'] for x in data['chinaDayList']])
.add_yaxis('死亡',[x['dead'] for x in data['chinaDayList']])
.add_yaxis('每日确诊增加数',Dailyincrease,areastyle_opts=opts.AreaStyleOpts(opacity=0.5)) #areastyle_opts=opts.AreaStyleOpts(opacity=0.5) 投射面积
.add_yaxis('每日疑似增加数',Dailysuspect,is_smooth=True) #is_smooth=True 代表平滑曲线
.set_global_opts(
#title_opts=opts.TitleOpts(title="2019-nCov"),
datazoom_opts=opts.DataZoomOpts(range_end=100),
)
)
return c
def line_base(self, street=None) -> Line:
info = {}
properties = Property.objects.all()
for pro in properties:
info.update({pro.name: [0.6, 0.5, 1, 0.8, 1.2, 1.5, 0.9]})
date_list = get_recent_date(7)
if street:
info = self.get_line_data(date_list, street, info)
else:
info = self.get_line_all(date_list, info)
c = (Line().add_xaxis(date_list))
for key in info.keys():
c.add_yaxis(key, info[key], is_smooth=True)
c.set_series_opts(
# areastyle_opts=opts.AreaStyleOpts(opacity=0.5),
# label_opts=opts.LabelOpts(is_show=False),
# linestyle_opts=opts.LineStyleOpts(width=2)
areastyle_opts=opts.AreaStyleOpts(opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
)
c.set_global_opts(
# xaxis_opts=opts.AxisOpts(
# axistick_opts=opts.AxisTickOpts(is_align_with_label=True),
# is_scale=False,
# boundary_gap=False,
# ),
# title_opts=opts.TitleOpts(title="Line-面积图（紧贴 Y 轴）"),
xaxis_opts=opts.AxisOpts(
axistick_opts=opts.AxisTickOpts(is_align_with_label=True),
is_scale=False,
boundary_gap=False,
), )
c = c.dump_options_with_quotes()
return c
def tail_northup():
"""
北上资金跟踪
:return:
"""
df = db.read("tail_northup")
result_df = df.set_index(['day', 'name']).unstack('name')['share_ratio']
rs_df = ((result_df - result_df.shift()).fillna(0) * 100).applymap(lambda x: round(x))
selected = sorted(list(rs_df.iloc[-1].items()), key=lambda x: x[1], reverse=True)[0][0]
c = Line()
c = c.add_xaxis(rs_df.index.tolist())
for key in rs_df:
is_selected = key == selected
c = c.add_yaxis(key, rs_df[key].values.tolist(), is_smooth=True, is_selected=is_selected)
c = c.set_series_opts(
areastyle_opts=opts.AreaStyleOpts(opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
)
c = c.set_global_opts(
title_opts=opts.TitleOpts(title="主力跟踪-北上资金", subtitle=datetime.now().strftime("%Y%m%d")),
xaxis_opts=opts.AxisOpts(
axistick_opts=opts.AxisTickOpts(is_align_with_label=True),
is_scale=False,
boundary_gap=False,
),
legend_opts=opts.LegendOpts(pos_left=100, pos_top=50)
)
return c
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 get_line(x, y, y_name, x_name):
print(y_name)
new_bar = (Bar().add_xaxis(x))
for y_label in y:
new_bar.add_yaxis(
y_label,
y[y_label],
color="#409EFF",
label_opts=opt.LabelOpts(),
# is_smooth=True,
markline_opts=opt.MarkLineOpts(
data=[opt.MarkLineItem(type_="average")]),
markpoint_opts=opt.MarkPointOpts(
data=[opt.MarkPointItem(type_='max', name="最大值")]))
new_bar.set_series_opts(areastyle_opts=opt.AreaStyleOpts(opacity=0.5),
label_opts=opt.LabelOpts(is_show=False))
new_bar.set_global_opts(
datazoom_opts=opt.DataZoomOpts(type_='inside'),
legend_opts=opt.LegendOpts(is_show=True),
yaxis_opts=opt.AxisOpts(
name=y_name, name_textstyle_opts=opt.TextStyleOpts(color="black")),
xaxis_opts=opt.AxisOpts(
name=x_name,
name_textstyle_opts=opt.TextStyleOpts(color="black"),
type_="category",
)),
return new_bar.dump_options_with_quotes()
def line_pf() -> Line:
c = (Line(opts.InitOpts(
bg_color='#FFFFFF', width='800px',
height='500px')).add_xaxis(arr).add_yaxis(
"功率因数",
val[13],
color='LightSkyBlue',
is_symbol_show=False,
markline_opts=opts.MarkLineOpts(
data=[opts.MarkLineItem(type_="average")]),
areastyle_opts=opts.AreaStyleOpts(opacity=0.4),
markpoint_opts=opts.MarkPointOpts(data=[
opts.MarkPointItem(type_="max"),
opts.MarkPointItem(type_="min")
])).add_yaxis(
"下限(0.9)",
paras[5],
linestyle_opts=opts.LineStyleOpts(width=1.5, type_='dashed'),
is_symbol_show=False).set_series_opts(
label_opts=opts.LabelOpts(is_show=False)).set_global_opts(
title_opts=opts.TitleOpts(
title='功率因数趋势图',
subtitle='日期：' + df.Date.iloc[0] + ' to ' +
df.Date.iloc[-1],
)))
return c
def dispose_line() -> Line:
x_data = ["Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun"]
y_data = [82, 932, 901, 934, 1290, 1330, 1320]
c = (
Line()
.add_xaxis(x_data)
.add_yaxis(
series_name="",
y_axis=y_data,
is_symbol_show=False,
label_opts=opts.LabelOpts(is_show=False),
areastyle_opts=opts.AreaStyleOpts(opacity=1, color="#40a0c0")
)
# .set_global_opts(
# yaxis_opts=opts.AxisOpts(is_show=False),
# xaxis_opts=opts.AxisOpts(is_show=False, type_="category", boundary_gap=False)
# )
.set_global_opts(
tooltip_opts=opts.TooltipOpts(is_show=False),
xaxis_opts=opts.AxisOpts(type_="category"),
yaxis_opts=opts.AxisOpts(
type_="value",
axistick_opts=opts.AxisTickOpts(is_show=True),
splitline_opts=opts.SplitLineOpts(is_show=True),
),
)
.set_series_opts(label_opts=opts.LabelOpts(formatter="{b}"))
.dump_options_with_quotes()
)
return c
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 bar_base() -> Kline:
exchane = ccxt.okex()
kline_BTC = exchane.fetch_ohlcv('BTC/USDT', timeframe=time_change)
df_BTC = pd.DataFrame(kline_BTC, dtype='float')
df_BTC[0] = pd.to_datetime(df_BTC[0], unit='ms') # 转换为想要的时间
date = df_BTC[0].tolist()
list_BTC_USDT = np.array(df_BTC[[1, 4, 3, 2]]).tolist()
c = (
Kline()
.add_xaxis(date)
.add_yaxis('BTC', list_BTC_USDT)
.set_global_opts(
yaxis_opts=opts.AxisOpts(
is_scale=True,
splitarea_opts=opts.SplitAreaOpts(
is_show=True, areastyle_opts=opts.AreaStyleOpts(opacity=1)
),
),
xaxis_opts=opts.AxisOpts(is_scale=True),
datazoom_opts=[opts.DataZoomOpts(type_="inside")],
title_opts=opts.TitleOpts(title=""))
.dump_options()
)
return c
def list_2line_areastyle(res_list, opts_title_name)->Line:
"""
根据传入的list数据，利用pyecharts绘制Line面积图
:param res_list:
:param opts_title_name:
:return:
"""
c = (
Line(init_opts=opts.InitOpts(width="1920px", height="1080px", page_title=opts_title_name, theme=ThemeType.ROMA))
.add_xaxis(res_list[0])
.add_yaxis("A1课题", res_list[1], is_smooth=True)
.add_yaxis("重点滚动课题", res_list[2], is_smooth=True)
.add_yaxis("A2课题", res_list[3], is_smooth=True)
.add_yaxis("B类课题", res_list[4], is_smooth=True)
.add_yaxis("部软科学", res_list[5], is_smooth=True)
.add_yaxis("院软科学", res_list[6], is_smooth=True)
.add_yaxis("团队课题", res_list[7], is_smooth=True)
.add_yaxis("支撑类", res_list[8], is_smooth=True)
.set_series_opts(
areastyle_opts=opts.AreaStyleOpts(opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
)
.set_global_opts(title_opts=opts.TitleOpts(title=opts_title_name),
xaxis_opts=opts.AxisOpts(
axistick_opts=opts.AxisTickOpts(is_align_with_label=True),
is_scale=False,
boundary_gap=False,
),
)
)
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 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 heliu() -> Bar:
a = [1,10,100,150,180,200] #模拟确诊
b = [0,2,20,50,100,102] #模拟治愈
c = [0,1,10,15,18,18] #模拟死亡
v = list(map(lambda x: x[0]-x[1]-x[2], zip(a, b, c))) #等待治愈
x = [0,1,2,3,4,5] #模拟日
c = (
Bar(init_opts=opts.InitOpts(theme=ThemeType.VINTAGE)) #设置主题
.add_xaxis(x) #直接列表
#.add_yaxis('确诊',v,stack="stack1")
.add_yaxis('死亡',c,stack="stack3")
.add_yaxis('治愈',b,stack="stack3")
.add_yaxis('确诊',v,stack="stack3")
.set_global_opts(
title_opts=opts.TitleOpts(title="2019-nCov 海外国家疫情分析", subtitle=""),
toolbox_opts = opts.ToolboxOpts(is_show = True), #
datazoom_opts=opts.DataZoomOpts(),
)
.set_series_opts(
# 设置系列配置
areastyle_opts=opts.AreaStyleOpts(opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
singleaxis_opts=opts.SingleAxisOpts(max_=80)
)
)
return c
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 line_connect_null() -> Line:
chinaDayList,worlddata = get_chinaDayList()
#每日确诊增加数
Dailyincrease = []
a = [x['confirm'] for x in chinaDayList]
for i in range(len(a)):
if i == 0:
Dailyincrease.append(0)
else:
Dailyincrease.append(int(a[i]) - int(a[i-1]))
#每日疑似增加数
Dailysuspect = []
a = [x['suspect'] for x in chinaDayList]
for i in range(len(a)):
if i == 0:
Dailysuspect.append(0)
else:
Dailysuspect.append(int(a[i]) - int(a[i-1]))
c = (
Line()
.add_xaxis([x['date'] for x in chinaDayList]) #直接列表
.add_yaxis('确诊',[x['confirm'] for x in chinaDayList]) #‘列表名，[]’
.add_yaxis('疑似',[x['suspect'] for x in chinaDayList])
.add_yaxis('治愈',[x['heal'] for x in chinaDayList])
.add_yaxis('死亡',[x['dead'] for x in chinaDayList])
.add_yaxis('每日确诊增加数',Dailyincrease,areastyle_opts=opts.AreaStyleOpts(opacity=0.5)) #areastyle_opts=opts.AreaStyleOpts(opacity=0.5) 投射面积
.add_yaxis('每日疑似增加数',Dailysuspect,is_smooth=True) #is_smooth=True 代表平滑曲线
.set_global_opts(
title_opts=opts.TitleOpts(title="2019-nCov"),
datazoom_opts=opts.DataZoomOpts(),
)
)
return c