def getRadar(self, sheet: xlrd.sheet) -> Radar:
namelist = sheet.col_values(0, 1)
maxvaluelist = sheet.col_values(1, 1)
maxlist = [
opts.RadarIndicatorItem(name=namelist[i], max_=maxvaluelist[i])
for i in range(len(namelist))
]
valuelists = dict()
for i in range(2, sheet.ncols):
valuelists[sheet.cell(0, i).value] = sheet.col_values(i, 1)
r = Radar()
r.add_schema(
schema=maxlist,
splitarea_opt=opts.SplitAreaOpts(
is_show=True, areastyle_opts=opts.AreaStyleOpts(opacity=1)),
textstyle_opts=opts.TextStyleOpts(color="#fff"),
)
#设置颜色的列表
colorstr = ["#CD0000", "#5CACEE", "#7B68EE", "#FFFF00"]
colorth = 0
for name, values in valuelists.items():
r.add(
series_name=name,
data=values,
linestyle_opts=opts.LineStyleOpts(color=colorstr[colorth]),
)
colorth = (colorth + 1) % len(colorstr) #颜色循环
r.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
r.set_global_opts(title_opts=opts.TitleOpts(
title=sheet.name,
title_textstyle_opts=opts.TextStyleOpts(color="#fff")),
legend_opts=opts.LegendOpts())
return r
def DrawRadar(city_name):
schema = [('最高气温', 45), ('气压', 1500), ('湿度', 100), ('最低气温', 45), ('PM2.5', 200)]
data = qr.get_data(city_name)
values = [[int(data['Htemperature']), int(data['QY']), int(data['SD'].strip('%')), int(data['Ltemperature']), int(data['pm25'])]]
radar = Radar()
radar.config(schema)
radar.add('%s天气信息' % city_name, values, is_splitline=True, is_axisline_show=True)
radar.render('weatherRadar.html')
def radar_product_rating_base():
"""
此函数用于获取产品多维评价的雷达图的参数。
Returns
-------
tl : TYPE-echarts parameters
return echarts parameters.
"""
# data query
dataX, products, list_dataY = product_rating_query()
c_schema = []
for i in range(len(dataX)):
c_schema.append({"name":dataX[i],"max":5,"min":0})
# Declare objects, render pictures
tl = Timeline()
for i in range(len(products)):
c = (
Radar(init_opts=opts.InitOpts(width="1280px", height="720px"))
.add_schema(schema=c_schema,
splitarea_opt=opts.SplitAreaOpts(is_show=True, areastyle_opts=opts.AreaStyleOpts(opacity=1)),
textstyle_opts=opts.TextStyleOpts(color="#fff"),
)
.add(series_name=products[i], data=[list(list_dataY[i])],
linestyle_opts=opts.LineStyleOpts(color="#CD0000"),
)
.set_series_opts(label_opts=opts.LabelOpts(is_show=True))
.set_global_opts(title_opts=opts.TitleOpts(title="Multi-dimensional analysis of product performance"),
legend_opts=opts.LegendOpts(pos_left="80%", pos_top="50%"))
)
tl.add(c, "{}".format(products[i]))
return tl
def radar_base(data, email_max_number, copy_max_number, copied_max_number, receiver_max_num, mail_server_max_number) -> Radar:
radar = (
Radar(init_opts=opts.InitOpts(width="1280px", height="720px"))
.add_schema(
schema=[
opts.RadarIndicatorItem(name="邮件数量", max_=email_max_number),
opts.RadarIndicatorItem(name="含有抄送的邮件数量", max_=copy_max_number),
opts.RadarIndicatorItem(name="被抄送的邮件数量", max_=copied_max_number),
opts.RadarIndicatorItem(name="联系人数量", max_=receiver_max_num),
opts.RadarIndicatorItem(name="服务器个数", max_=mail_server_max_number),
],
splitarea_opt=opts.SplitAreaOpts(
is_show=True
),
textstyle_opts=opts.TextStyleOpts(color="#000000"),
)
)
for mailbox in data:
tot = 0
for num in data[mailbox][0]:
tot += num
if num is not 0:
radar.add(
series_name=mailbox,
data=data[mailbox],
linestyle_opts=opts.LineStyleOpts(color=utils.randomColor(), width=1),
)
radar.set_series_opts(label_opts=opts.LabelOpts(is_show=False)).set_global_opts(
legend_opts=opts.LegendOpts()
)
return radar
def user_ability_radar(v1, v2) -> Radar:
radar = (Radar(init_opts=opts.InitOpts()).add_schema(
schema=[
opts.RadarIndicatorItem(name="字符串", max_=100),
opts.RadarIndicatorItem(name="线性表", max_=100),
opts.RadarIndicatorItem(name="数组", max_=100),
opts.RadarIndicatorItem(name="查找算法", max_=100),
opts.RadarIndicatorItem(name="排序算法", max_=100),
opts.RadarIndicatorItem(name="数字操作", max_=100),
opts.RadarIndicatorItem(name="树结构", max_=100),
opts.RadarIndicatorItem(name="图结构", max_=100),
],
splitarea_opt=opts.SplitAreaOpts(
is_show=True, areastyle_opts=opts.AreaStyleOpts(opacity=1)),
textstyle_opts=opts.TextStyleOpts(color="#000"),
).add(
series_name="学生平均得分",
data=v2,
linestyle_opts=opts.LineStyleOpts(color="#5CACEE",
width=3,
opacity=0.7),
).add(
series_name="学生能力评估",
data=v1,
linestyle_opts=opts.LineStyleOpts(color="#CD0000",
width=3,
opacity=0.7),
).set_series_opts(label_opts=opts.LabelOpts(
is_show=False)).set_global_opts(title_opts=opts.TitleOpts(),
legend_opts=opts.LegendOpts()))
return radar
def radar_selected_mode() -> Radar:
DaLuan = list[1:10]
print(list[:10])
random.shuffle(DaLuan)
print(DaLuan)
c = (
Radar()
.add_schema(textstyle_opts={'color':'#c7a252',"fontSize": 16,},#文字样式的颜色
schema=[
opts.RadarIndicatorItem(name=list[0][0], max_=max),
opts.RadarIndicatorItem(name=DaLuan[0][0], max_=max),
opts.RadarIndicatorItem(name=DaLuan[1][0], max_=max ),
opts.RadarIndicatorItem(name=DaLuan[2][0], max_=max ),
opts.RadarIndicatorItem(name=DaLuan[3][0], max_=max),
opts.RadarIndicatorItem(name=DaLuan[4][0], max_=max),
opts.RadarIndicatorItem(name=DaLuan[5][0], max_=max),
opts.RadarIndicatorItem(name=DaLuan[6][0], max_=max),
opts.RadarIndicatorItem(name=DaLuan[7][0], max_=max),
opts.RadarIndicatorItem(name=DaLuan[8][0], max_=max),
],
)
.add("Top10能力要求", [[max]+[DaLuan[i][1] for i in range(0,9)]],
areastyle_opts={"color": '#009ad6'},linestyle_opts={"color": '#009ad6'})
.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
.set_global_opts(
legend_opts=opts.LegendOpts(selected_mode="single"),
title_opts=opts.TitleOpts(title="岗位技能雷达图"),
)
)
return c
def reder_base(v1,v2,v3):
c=(
Radar()
.add_schema(
schema=[
opts.RadarIndicatorItem(name='动画',max_=650000),
opts.RadarIndicatorItem(name='娱乐',max_=650000),
opts.RadarIndicatorItem(name='影视',max_=650000),
opts.RadarIndicatorItem(name='时尚',max_=650000),
opts.RadarIndicatorItem(name='游戏',max_=650000),
opts.RadarIndicatorItem(name='生活',max_=650000),
opts.RadarIndicatorItem(name='科技',max_=650000),
opts.RadarIndicatorItem(name='音乐',max_=650000),
opts.RadarIndicatorItem(name='鬼畜',max_=650000),
]
)
#添加指标，设置线条颜色
.add('投币',v1,color='#49010F')
.add('点赞',v2,color='#C16200')
.add('收藏',v3,color='#881600')
.set_series_opts(
#不显示指标数值
label_opts=opts.LabelOpts(is_show=False),
# 设置线条格式，宽度为3，样式为虚线
linestyle_opts=opts.LineStyleOpts(width=3,type_='dotted'),
)
.set_global_opts(title_opts=opts.TitleOpts(title='top100各分区三连情况'))
)
return c
def radar(schema, radar_data: list[dict]) -> Radar:
"""
绘制【不同星级商户在"菜价菜量", "口味", "环境", "服务", "交通"方面综合评估】的雷达图需求
8.1 设置主题"purple-passion"；
8.2 标题”星级商铺评分“
8.3 返回c
:param schema: 雷达图的坐标系配置项
:param radar_data: 雷达图的数据系列配置项
:return: 绘制的雷达图对象
"""
c = (Radar(init_opts=opts.InitOpts(
theme=ThemeType.PURPLE_PASSION)).set_global_opts(
title_opts=opts.TitleOpts("星级商铺评分"),
legend_opts=opts.LegendOpts(
orient="horizontal", align="left")).add_schema(schema=schema))
# 添加各种配置数据
for data_item in radar_data:
c.add(series_name=data_item["series_name"],
data=data_item["data"],
color=data_item["color"],
label_opts=data_item["label_opts"],
areastyle_opts=data_item["areastyle_opts"])
return c
def radar_base():
c = (
Radar().add_schema(schema=[
opts.RadarIndicatorItem(name='动画', max_=650000),
opts.RadarIndicatorItem(name='娱乐', max_=650000),
opts.RadarIndicatorItem(name='影视', max_=650000),
opts.RadarIndicatorItem(name='时尚', max_=650000),
opts.RadarIndicatorItem(name='游戏', max_=650000),
opts.RadarIndicatorItem(name='生活', max_=650000),
opts.RadarIndicatorItem(name='科技', max_=650000),
opts.RadarIndicatorItem(name='音乐', max_=650000),
opts.RadarIndicatorItem(name='鬼畜', max_=650000),
])
#添加指标，设置线条颜色
.add('投币', [top_100['coin'].tolist()], color='#49010F').add(
'点赞', [top_100['like'].tolist()], color='#C16200').add(
'收藏', [top_100['favorite'].tolist()],
color='#881600').set_series_opts(
#不显示指标数值
label_opts=opts.LabelOpts(is_show=False),
# 设置线条格式，宽度为3，样式为虚线
linestyle_opts=opts.LineStyleOpts(width=3, type_='dotted'),
).set_global_opts(title_opts=opts.TitleOpts(
title='Operations on top100')).dump_options_with_quotes())
return c
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 radar_base() -> Radar:
rl = []
with open("Ex3.6.3.csv", "r") as f:
rL = f.read().split("\n")
data = []
for i in range(1, len(rL) - 1):
data.append(rL[i].split(","))
c = (Radar().add_schema(schema=[
opts.RadarIndicatorItem(name="Violent", max_=1000),
opts.RadarIndicatorItem(name="Property", max_=5000),
opts.RadarIndicatorItem(name="Murder", max_=10),
opts.RadarIndicatorItem(name="Rape", max_=100),
opts.RadarIndicatorItem(name="Robbery", max_=500),
opts.RadarIndicatorItem(name="Assault", max_=1000),
opts.RadarIndicatorItem(name="Burglary", max_=1000),
opts.RadarIndicatorItem(name="Larceny", max_=3000),
opts.RadarIndicatorItem(name="Auto", max_=1000),
opts.RadarIndicatorItem(name="Inmates", max_=1000),
]).set_series_opts(label_opts=opts.LabelOpts(
is_show=False)).set_global_opts(
legend_opts=opts.LegendOpts(selected_mode="single"),
title_opts=opts.TitleOpts(title="2014年美国各州犯罪率")))
for i in range(len(data)):
c.add(data[i][0], [list(map(float, data[i][1:]))])
return c
def test_radar_item_base(fake_writer):
series_names = ["预算分配", "实际开销"]
series_data = [
[4300, 10000, 28000, 35000, 50000, 19000],
[5000, 14000, 28000, 31000, 42000, 21000],
]
radar_item = [
opts.RadarItem(name=d[0], value=d[1])
for d in list(zip(series_names, series_data))
]
c = (Radar().add_schema(schema=[
opts.RadarIndicatorItem(name="销售", max_=6500),
opts.RadarIndicatorItem(name="管理", max_=16000),
opts.RadarIndicatorItem(name="信息技术", max_=30000),
opts.RadarIndicatorItem(name="客服", max_=38000),
opts.RadarIndicatorItem(name="研发", max_=52000),
opts.RadarIndicatorItem(name="市场", max_=25000),
]).add("", radar_item).set_series_opts(label_opts=opts.LabelOpts(
is_show=False)).set_global_opts(title_opts=opts.TitleOpts(
title="Radar-基本示例")))
c.render()
_, content = fake_writer.call_args[0]
assert_equal(c.theme, "white")
assert_equal(c.renderer, "canvas")
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 radar():
r = (Radar().add_schema(schema=(
opts.RadarIndicatorItem(v1[0], max_=v2[0]),
opts.RadarIndicatorItem(v1[1], max_=v2[1]),
opts.RadarIndicatorItem(v1[2], max_=v2[2]),
opts.RadarIndicatorItem(v1[3], max_=v2[3]),
)).add('物联网可视化', [v2]).set_global_opts(opts.TitleOpts(title='大数据比赛可视化项目')))
return r
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 tograph(self, arrayVal, name) -> Radar:
value_bj = [arrayVal]
c_schema = [
{
"name": "speed",
"max": 100
},
{
"name": "dribbling",
"max": 100
},
{
"name": "shooting",
"max": 100
},
{
"name": "passing",
"max": 100
},
{
"name": "defence",
"max": 100
},
{
"name": "physical",
"max": 100
},
{
"name": "gk_diving",
"max": 100
},
{
"name": "gk_positioning",
"max": 100
},
{
"name": "gk_reflexes",
"max": 100
},
{
"name": "gk_gk_handling",
"max": 100
},
{
"name": "gk_kicking",
"max": 100
},
]
c = (Radar().add_schema(schema=c_schema, shape="circle").add(
name, value_bj, color="#f9713c").set_series_opts(
label_opts=opts.LabelOpts(is_show=False)).set_global_opts(
title_opts=opts.TitleOpts(title="Radar-Player")))
hname = str(time.time()) + '.html'
c.render('./front-end/graph/' + hname)
return hname
def RadarChart(BookNumsOfPubs: list, NamesOfPubs: list, AvgPoints: list, AvgPrice: list):
'''
出版物数量前10的出版社的数量和评分\n
:param BookNumsOfPubs: 详见RankOfPublisher()函数
:param NamesOfPubs: 同上
:param AvgPoints: 同上
:param AvgPrice: 同上
:return: 画图函数，不知道能返回什么
'''
global dir_to_save
AvgPrice = [[x * 80 for x in AvgPrice]] # 价格放大到原来的80倍
AvgPoints = [[math.exp(x) for x in tqdm(AvgPoints)]] # 平均分取指数，放大差异
BookNumsOfPubs = [[5 * x for x in BookNumsOfPubs]] # 书籍数量放大5倍,与放大后的评分在同一数量级，便于可视化
# 以上数据处理方式没有什么理论依据，仅仅是调整到了一个数量级。有关的统计学知识后续学习
(
Radar(init_opts=opts.InitOpts(width="1000px", height="500px", bg_color="#CCCCCC"))
.add_schema(
schema=[
opts.RadarIndicatorItem(name=NamesOfPubs[0], max_=5500),
opts.RadarIndicatorItem(name=NamesOfPubs[1], max_=5500),
opts.RadarIndicatorItem(name=NamesOfPubs[2], max_=5500),
opts.RadarIndicatorItem(name=NamesOfPubs[3], max_=5500),
opts.RadarIndicatorItem(name=NamesOfPubs[4], max_=5500),
opts.RadarIndicatorItem(name=NamesOfPubs[5], max_=5500),
opts.RadarIndicatorItem(name=NamesOfPubs[6], max_=5500),
opts.RadarIndicatorItem(name=NamesOfPubs[7], max_=5500),
opts.RadarIndicatorItem(name=NamesOfPubs[8], max_=5500),
opts.RadarIndicatorItem(name=NamesOfPubs[9], max_=5500),
],
splitarea_opt=opts.SplitAreaOpts(
is_show=True, areastyle_opts=opts.AreaStyleOpts(opacity=1)
),
textstyle_opts=opts.TextStyleOpts(color="#228B22"),
)
.add(
series_name="平均分(取指数后)",
data=AvgPoints,
linestyle_opts=opts.LineStyleOpts(color="#D9173B"),
)
.add(
series_name="出版物数量(扩大5倍后)",
data=BookNumsOfPubs,
linestyle_opts=opts.LineStyleOpts(color="#0000CD"),
)
.add(
series_name="出版物均价(扩大80倍后)",
data=AvgPrice,
linestyle_opts=opts.LineStyleOpts(color="#FFD700"),
)
.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
.set_global_opts(
title_opts=opts.TitleOpts(title="出版物数量前10的出版社"), legend_opts=opts.LegendOpts()
)
.render(dir_to_save + "Top10PubsInBookNums_radar.html")
)
print("Successfully drew Top10PubsInBookNums_radar")
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 radar_base(v1):
c = (Radar().add_schema(schema=[
opts.RadarIndicatorItem(name="力荐", max_=100),
opts.RadarIndicatorItem(name="推荐", max_=100),
opts.RadarIndicatorItem(name="还行", max_=1000),
opts.RadarIndicatorItem(name="较差", max_=100),
opts.RadarIndicatorItem(name="很差", max_=100),
]).add("评价情感", [v1]).set_series_opts(label_opts=opts.LabelOpts(
is_show=False)).set_global_opts(title_opts=opts.TitleOpts(
title="评论情感雷达图")).dump_options_with_quotes())
return c
def radar_base() -> Radar:
c = (Radar().add_schema(schema=[
opts.RadarIndicatorItem(name="销售", max_=6500),
opts.RadarIndicatorItem(name="管理", max_=16000),
opts.RadarIndicatorItem(name="信息技术", max_=30000),
opts.RadarIndicatorItem(name="客服", max_=38000),
opts.RadarIndicatorItem(name="研发", max_=52000),
opts.RadarIndicatorItem(name="市场", max_=25000),
]).add("预算分配", v1).add("实际开销", v2).set_series_opts(
label_opts=opts.LabelOpts(is_show=False)).set_global_opts(
title_opts=opts.TitleOpts(title="Radar-基本示例")))
return c
def draw_radar_for_observation_result() -> Radar:
value = [[43, 58, 78, 65, 86, 63]]
min_val = 0
max_val = 100
# 用于调整雷达各维度的范围大小
c_schema = [{"name": "交往能力", "max": max_val, "min": min_val, },
{"name": "感知能力", "max": max_val, "min": min_val, "color": "#ff0000"},
{"name": "运动能力", "max": max_val, "min": min_val, "color": "#000000"},
{"name": "注意力", "max": max_val, "min": min_val, "color": "#00ffff"},
{"name": "语言能力", "max": max_val, "min": min_val, "color": "#ff00ff"},
{"name": "自我照顾能力", "max": max_val, "min": min_val, "color": "#ff00ff"}
]
# 画图
radar = Radar(init_opts=opts.InitOpts(width="750px", height="500px"))
# radar = Radar()
radar.add_schema(c_schema, textstyle_opts=opts.TextStyleOpts(color="#3344ff"))
radar.add(None, value, color="#f9713c",
symbol='circle', label_opts=opts.LabelOpts(is_show=True),
areastyle_opts=opts.AreaStyleOpts(opacity=0.5, color="#f9713c"))
# radar.set_global_opts(title_opts=opts.TitleOpts(title="Radar-基本示例"))
# radar.render("observation_result.html")
return radar
def radar_base(schema, value, company_name) -> Radar:
c = (
Radar()
.add_schema(
schema=schema,
shape='circle',
textstyle_opts=opts.TextStyleOpts(font_size=20),
)
.add(company_name, value, areastyle_opts=opts.AreaStyleOpts(color='red', opacity=0.5))
.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
.set_global_opts(title_opts=opts.TitleOpts(title="雷达预警图"))
)
return c
def plot_radar(series_radiant, series_dire):
c_schema = [{
"name": "控制",
"max": 15,
"min": 0
}, {
"name": "核心",
"max": 15,
"min": 0
}, {
"name": "辅助",
"max": 15,
"min": 0
}, {
"name": "逃生",
"max": 15,
"min": 0
}, {
"name": "爆发",
"max": 15,
"min": 0
}, {
"name": "先手",
"max": 15,
"min": 0
}, {
"name": "耐久",
"max": 15,
"min": 0
}, {
"name": "推进",
"max": 15,
"min": 0
}]
radar = Radar()
radar.add_schema(schema=c_schema)
radar.add("radiant", series_radiant, color="#f9713c")
radar.add("dire", series_dire, color="#b3e4a1")
radar.render("Rader.html")
def test_radar_base():
c = (Radar().add_schema(schema=[
opts.RadarIndicatorItem(name="销售", max_=6500),
opts.RadarIndicatorItem(name="管理", max_=16000),
opts.RadarIndicatorItem(name="信息技术", max_=30000),
opts.RadarIndicatorItem(name="客服", max_=38000),
opts.RadarIndicatorItem(name="研发", max_=52000),
opts.RadarIndicatorItem(name="市场", max_=25000),
]).add("预算分配", v1).add(
"实际开销", v2).set_series_opts(label_opts=opts.LabelOpts(is_show=False)))
eq_(c.theme, "white")
eq_(c.renderer, "canvas")
c.render()
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 test_radar_base(fake_writer):
c = (Radar().add_schema(schema=[
opts.RadarIndicatorItem(name="销售", max_=6500),
opts.RadarIndicatorItem(name="管理", max_=16000),
opts.RadarIndicatorItem(name="信息技术", max_=30000),
opts.RadarIndicatorItem(name="客服", max_=38000),
opts.RadarIndicatorItem(name="研发", max_=52000),
opts.RadarIndicatorItem(name="市场", max_=25000),
]).add("预算分配", v1).add(
"实际开销", v2).set_series_opts(label_opts=opts.LabelOpts(is_show=False)))
c.render()
_, content = fake_writer.call_args[0]
assert_equal(c.theme, "white")
assert_equal(c.renderer, "canvas")
def radar_selected_mode() -> Radar:
c = (Radar().add_schema(schema=[
opts.RadarIndicatorItem(name="销售", max_=6500),
opts.RadarIndicatorItem(name="管理", max_=16000),
opts.RadarIndicatorItem(name="信息技术", max_=30000),
opts.RadarIndicatorItem(name="客服", max_=38000),
opts.RadarIndicatorItem(name="研发", max_=52000),
opts.RadarIndicatorItem(name="市场", max_=25000),
]).add("预算分配", v1).add("实际开销", v2).set_series_opts(
label_opts=opts.LabelOpts(is_show=False)).set_global_opts(
legend_opts=opts.LegendOpts(selected_mode="single"),
title_opts=opts.TitleOpts(title="Radar-单例模式"),
))
return c
def tographt(self, arrayVal, name) -> Radar:
value_bj = [arrayVal]
c_schema = [
{
"name": "buildUpPlaySpeed",
"max": 100
},
{
"name": "buildUpPlayDribbling",
"max": 100
},
{
"name": "buildUpPlayPassing",
"max": 100
},
{
"name": "chanceCreationCrossing",
"max": 100
},
{
"name": "chanceCreationPassing",
"max": 100
},
{
"name": "chanceCreationShooting",
"max": 100
},
{
"name": "defenceAggression",
"max": 100
},
{
"name": "defencePressure",
"max": 100
},
{
"name": "defenceTeamWidth",
"max": 100
},
]
c = (Radar().add_schema(schema=c_schema, shape="circle").add(
name, value_bj, color="#f9713c").set_series_opts(
label_opts=opts.LabelOpts(is_show=False)).set_global_opts(
title_opts=opts.TitleOpts(title="Radar-Player")))
hname = str(time.time()) + '.html'
c.render('./front-end/graph/' + hname)
return hname
def radar_demo():
radar = Radar()
# data1 = [[12, 0, 2, 18, 0, 1]]
radar_data1 = [2.0, 4.9, 7.0, 23.2, 25.6, 76.7, 135.6, 162.2, 32.6, 20.0, 6.4, 3.3]#降水量
# schema = [
# ("执行成功", 20), ("执行失败", 5), ("执行异常", 5),
# ("回滚成功", 20), ("回滚失败", 5), ("执行中断", 5)
# ]
schema = [
("Jan", 5), ("Feb", 10), ("Mar", 10),
("Apr", 50), ("May", 50), ("Jun", 200),
("Jul", 200), ("Aug", 200), ("Sep", 50),
("Oct", 50), ("Nov", 10), ("Dec", 5)
]
radar.add_schema(schema)
radar.add("执行状态", radar_data1)
radar.render("/aa.html")
def student_grade_radar(student_grade: StudentGrade) -> Radar:
"""
生成该考生本次考试的排名雷达图。
:param student_grade: 本次考试成绩StudentGrade对象
:return: radar: Radar图表
"""
subjects = Subject.subjects_by_grade(student_grade)
data = {'student_ranking': [], 'average_ranking': [], 'high_ranking': []}
for subject in subjects:
data['student_ranking'].append(
GradeRanking.grade2ranking(
student_grade.test,
student_grade.grade_dict()[subject], subject, student_grade.subject))
data['average_ranking'].append(TestAverageGrade.query.filter_by(
subject='理科', test_time=student_grade.test_time).first()
.__dict__[subject + '_ranking'])
data['high_ranking'].append(TestHighGrade.query.filter_by(
subject='理科', test_time=student_grade.test_time).first()
.__dict__[subject + '_ranking'])
radar = (
Radar(init_opts=opts.InitOpts(bg_color='#fef8ef'))
.add_schema(
schema=[
opts.RadarIndicatorItem(
name=subject,
color='#778899',
max_=max(
data['student_ranking'] + data['average_ranking'] + data['high_ranking']) + 50
) for subject in subjects
],
textstyle_opts=opts.TextStyleOpts(font_size=18)
)
.set_global_opts(title_opts=opts.TitleOpts('名次雷达图'), )
.set_series_opts()
.add('本次排名', [data['student_ranking']], color='#d7ab82',
linestyle_opts=opts.LineStyleOpts(width=2.5),
label_opts=opts.LabelOpts(is_show=True, font_size=14),
tooltip_opts=opts.TooltipOpts(is_show=False))
.add('平均排名', [data['average_ranking']], color='#919e8b',
linestyle_opts=opts.LineStyleOpts(width=2.5),
label_opts=opts.LabelOpts(is_show=True, font_size=14),
tooltip_opts=opts.TooltipOpts(is_show=False))
.add('高分线排名', [data['high_ranking']], color='#d87c7c',
linestyle_opts=opts.LineStyleOpts(width=2.5),
label_opts=opts.LabelOpts(is_show=True, font_size=14),
tooltip_opts=opts.TooltipOpts(is_show=False))
)
return radar
