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 year_store_order_base():
# 不同门店的年营业额数据查询
dataX, dataY1, dataY2 = year_store_query()
# 对象声明
radar = Radar(init_opts=opts.InitOpts(
width="1280px", height="720px", bg_color="#CCCCCC"))
radar.add_schema(
schema=[
opts.RadarIndicatorItem(name="门店订单金额", max_=34000),
opts.RadarIndicatorItem(name="门店顾客数", max_=10000),
opts.RadarIndicatorItem(name="门店商品类型", max_=10000),
opts.RadarIndicatorItem(name="门店订单量", max_=10000)
],
splitarea_opt=opts.SplitAreaOpts(
is_show=True, areastyle_opts=opts.AreaStyleOpts(opacity=1)),
textstyle_opts=opts.TextStyleOpts(color="#fff"),
)
radar.add(
series_name="Store 1",
data=[list(dataY1)],
linestyle_opts=opts.LineStyleOpts(color="#CD0000"),
)
radar.add(
series_name="Store 2",
data=[list(dataY2)],
linestyle_opts=opts.LineStyleOpts(color="#5CACEE"),
)
radar.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
radar.set_global_opts(title_opts=opts.TitleOpts(title="2005年门店竞争优势多维分析"),
legend_opts=opts.LegendOpts())
return radar
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 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 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 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 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 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 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_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 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 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 drawer():
v1 = [[0.3176, 0.3104, 0.0356, 0.0441, 0.2910]]
v2 = [[0.2953, 0.2881, 0.0552, 0.0422, 0.3188]]
v3 = [[0.2793, 0.3582, 0.0546, 0.0556, 0.2518]]
v4 = [[0.2767, 0.3668, 0.0521, 0.0670, 0.2370]]
(Radar(init_opts=opts.InitOpts(
width="720px", height="480px", bg_color="#ffffff")).add_schema(
schema=[
opts.RadarIndicatorItem(name="乐", max_=0.4),
opts.RadarIndicatorItem(name="好", max_=0.4),
opts.RadarIndicatorItem(name="哀", max_=0.4),
opts.RadarIndicatorItem(name="恶", max_=0.4),
opts.RadarIndicatorItem(name="惧", max_=0.4),
],
splitarea_opt=opts.SplitAreaOpts(
is_show=True, areastyle_opts=opts.AreaStyleOpts(opacity=1)),
textstyle_opts=opts.TextStyleOpts(color="#000"),
).add(
series_name="阶段一(红色)",
data=v1,
linestyle_opts=opts.LineStyleOpts(color="#CD0000", width=3),
).add(
series_name="阶段二(蓝色)",
data=v2,
linestyle_opts=opts.LineStyleOpts(color="#5CACEE", width=3),
).add(
series_name="阶段三(绿色)",
data=v3,
linestyle_opts=opts.LineStyleOpts(color="green",
width=3,
opacity=0.3,
type_='dashed'),
).add(
series_name="阶段四(黄色)",
data=v4,
linestyle_opts=opts.LineStyleOpts(color="yellow",
width=3,
opacity=0.8),
).set_series_opts(label_opts=opts.LabelOpts(
is_show=False)).set_global_opts(
title_opts=opts.TitleOpts(title="多维情感分析"),
legend_opts=opts.LegendOpts()).render(
"res\\output\\multi-emotion\\image\\多维雷达图.html"))
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 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 radar_(x):
# 基础风险，药物滥用主观态度，神经行为学，社会遗传学，药物滥用相关复发病和健康量表
# 基础风险、主观风险、神经行为风险、社会遗传风险、复发病风险
c = (
Radar()
.add_schema(
schema=[
opts.RadarIndicatorItem(name="基础风险", max_=100),
opts.RadarIndicatorItem(name="主观风险", max_=100),
opts.RadarIndicatorItem(name="复发病风险", max_=100),
opts.RadarIndicatorItem(name="神经行为风险", max_=100),
opts.RadarIndicatorItem(name="社会遗传风险", max_=100),
]
)
.add("风险", x)
.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='平均风险：'+str(round(np.mean(x), 2))),
)
# .render("radar_selected_mode.html")
)
return c.render_embed()
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
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 create_radarmap(labels):
values = [[]]
inds = []
d = [
'交通是否便利', '距离商圈远近', '是否容易寻找', '排队等候时间', '服务人员态度', '是否容易停车', '点菜/上菜速度',
'价格水平', '性价比', '折扣力度', '装修情况', '嘈杂情况', '就餐空间', '卫生情况', '分量', '口感',
'外观', '推荐程度', '本次消费感受', '再次消费的意愿'
]
for i in range(len(labels)):
if labels[i] > 0:
values[0].append(int(labels[i]) - 1)
inds.append(d[i])
radar = (Radar().add_schema(
schema=[opts.RadarIndicatorItem(name=ind, max_=2)
for ind in inds]).add('分类指标', values))
make_snapshot(snapshot, radar.render(), "static/images/radar.png")
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 drawRadar(arr, value, valuemax, name):
radar = Radar(init_opts=opts.InitOpts(
width="1280px", height="720px", bg_color="#CCCCCC"))
v_max = [list(z) for z in zip(arr, valuemax)]
radar.add_schema(
schema=[opts.RadarIndicatorItem(name=k, max_=v) for k, v in v_max],
splitarea_opt=opts.SplitAreaOpts(
is_show=True, areastyle_opts=opts.AreaStyleOpts(opacity=1)),
textstyle_opts=opts.TextStyleOpts(color="#fff"),
)
radar.add(
series_name=name,
data=value,
linestyle_opts=opts.LineStyleOpts(color="#CD0000"),
)
radar.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
radar.set_global_opts(title_opts=opts.TitleOpts(title=name),
legend_opts=opts.LegendOpts())
radar.render("{0}.html".format(name))
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 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(request):
v1 = [[4300, 10000, 28000, 35000, 50000, 19000]]
v2 = [[5000, 14000, 28000, 31000, 42000, 21000]]
c = (Radar().add_schema(schema=[
opts.RadarIndicatorItem(name="养老保险", max_=6500),
opts.RadarIndicatorItem(name="医疗保险", max_=16000),
opts.RadarIndicatorItem(name="失业保险", max_=30000),
opts.RadarIndicatorItem(name="工伤保险", max_=38000),
opts.RadarIndicatorItem(name="生育保险", max_=52000),
opts.RadarIndicatorItem(name="公积金", 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 HttpResponse(c.render_embed())
def radar_picture(self, seriesname: list, values: list, schemanames):
#
# maxvalue = 0
# for value in value1+value2:
# if value>maxvalue:
# maxvalue=value
'''
v1 = [[4300, 10000, 28000, 35000, 50000, 19000]]
v2 = [[5000, 14000, 28000, 31000, 42000, 21000]]
'''
schema = []
print("schenames:", schemanames)
for index, name in enumerate(schemanames):
maxvalue = -1
all = []
# 第一个循环遍历所有series
for i in range(0, len(seriesname)):
if values[i][index] > maxvalue:
maxvalue = values[i][index]
all.append(values[i][index])
schema.append(opts.RadarIndicatorItem(name=name, max_=maxvalue))
print(all)
r = (Radar(init_opts=self.init_opts)
.add_schema(
schema=schema
)
.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
.set_global_opts(title_opts=opts.TitleOpts(title=self.title, subtitle=self.subtitle),
# 添加logo
graphic_opts=self.logo
))
for name, value in zip(seriesname, values):
r.add(name, [value])
return r
def test_radar_options(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),
],
radiusaxis_opts=opts.RadiusAxisOpts(),
angleaxis_opts=opts.AngleAxisOpts(),
polar_opts=opts.PolarOpts(),
).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_in("radiusAxis", content)
assert_in("angleAxis", content)
assert_in("polar", content)
def radar_base(data) -> Radar:
c = (
Radar(opts.InitOpts(width = "1200px", height = "800px"))
.add_schema (
schema = [
opts.RadarIndicatorItem(name="Murder", max_ = 30),
opts.RadarIndicatorItem(name="Forcible rape", max_= 50),
opts.RadarIndicatorItem(name="Robbery", max_= 300),
opts.RadarIndicatorItem(name="Aggravated assault", max_= 5000),
opts.RadarIndicatorItem(name="Burglary", max_= 1000),
opts.RadarIndicatorItem(name="Larceny thief", max_= 3000),
opts.RadarIndicatorItem(name="Motor Vehicle theft", max_= 1000),
]
)
.set_series_opts(label_opts = opts.LabelOpts(is_show = False))
.set_global_opts (
title_opts = opts.TitleOpts(title = "美国各州犯罪率"),
legend_opts = opts.LegendOpts(type_ = 'scroll', selected_mode = 'single', pos_top = "5%"),
toolbox_opts = opts.ToolboxOpts()
)
)
for i in range(len(data)):
c.add(data[i][0], [list(map(float, data[i][1:]))])
return c
def drawRadar(self):
df = pd.read_csv('energySpider/homec(1).csv')
df_luzi1 = df.loc[0, "炉子 1 [kW]"]
df_luzi2 = df.loc[0, "炉子 2 [kW]"]
df_jiatingbangongshi = df.loc[0, "家庭办公室 [kW]"]
df_bingxiang = df.loc[0, "冰箱 [kW]"]
df_jiujiao = df.loc[0, "酒窖 [kW]"]
df_chekumen = df.loc[0, "车库门 [kW]"]
v1 = [[
df_luzi1, df_luzi2, df_jiujiao, df_bingxiang, df_jiatingbangongshi,
df_chekumen
]]
v2 = [[8.1, 120000, 16000, 7000, 15000, 7000]]
radar = (
Radar()
# 设置边属性
.add_schema(schema=[
opts.RadarIndicatorItem(name='炉子 1', max_=0.45),
opts.RadarIndicatorItem(name='炉子 2', max_=0.45),
opts.RadarIndicatorItem(name='酒窖', max_=0.45),
opts.RadarIndicatorItem(name='冰箱', max_=0.45),
opts.RadarIndicatorItem(name='家庭办公室', max_=0.45),
opts.RadarIndicatorItem(name='车库门', max_=0.45),
]).add(
'能耗',
v1,
color='green',
# 通过颜色属性 将其填充
areastyle_opts=opts.AreaStyleOpts(
opacity=0.5, # 透明度设置
color='green'),
).set_series_opts(label_opts=opts.LabelOpts(is_show=False))
# 全局的属性设置 不要一个一个的写出
.set_global_opts(title_opts=opts.TitleOpts(title='家电能耗对比')))
return radar
