def setrose():
x1 = ['2016年', '2017年', '2018年', '2019年', '2020年']
y1 = ['文学', 350.0, 345.0, 345.0, 355.0, 355.0]
y2 = ['理学', 285.0, 290.0, 280.0, 290.0, 288.0]
c = (Line(init_opts=opts.InitOpts(
width="1600px", height="800px")).add_xaxis(xaxis_data=x1).add_yaxis(
series_name="文学",
y_axis=y1,
markpoint_opts=opts.MarkPointOpts(data=[
opts.MarkPointItem(type_="max", name="最大值"),
opts.MarkPointItem(type_="min", name="最小值"),
]),
markline_opts=opts.MarkLineOpts(
data=[opts.MarkLineItem(type_="average", name="平均值")]),
).add_yaxis(
series_name="理学",
y_axis=y2,
markpoint_opts=opts.MarkPointOpts(data=[
opts.MarkPointItem(type_="max", name="最大值"),
opts.MarkPointItem(type_="min", name="最小值"),
]),
markline_opts=opts.MarkLineOpts(data=[
opts.MarkLineItem(type_="average", name="平均值"),
opts.MarkLineItem(symbol="none", x="90%", y="max"),
opts.MarkLineItem(symbol="circle", type_="max", name="最高点"),
]),
).set_global_opts(
title_opts=opts.TitleOpts(title="近5年理学/文学国家线趋势"),
tooltip_opts=opts.TooltipOpts(trigger="axis"),
toolbox_opts=opts.ToolboxOpts(is_show=True),
xaxis_opts=opts.AxisOpts(type_="category", boundary_gap=False),
))
return c
def enroll():
global wid, hei
data = analysis_data.enroll_rate()
data['true_rate'] = data['true_rate'].apply(lambda x: float(x) * 10)
print(data['year'].values)
c = (Line(init_opts=opts.InitOpts(width=wid, height=hei)).add_xaxis(
[x for x in data['year'].values]).add_yaxis(
'报名人数',
data['num_people'].values,
is_smooth=True,
markpoint_opts=opts.MarkPointOpts(
data=[opts.MarkPointItem(type_="max")])).add_yaxis(
"实际录取人数",
data['true_num'].values,
is_smooth=True,
markpoint_opts=opts.MarkPointOpts(
data=[opts.MarkPointItem(type_="max")]),
).add_yaxis(
"实际录取率",
[round(x, 2) for x in data['true_rate'].values],
is_smooth=True,
markpoint_opts=opts.MarkPointOpts(
data=[opts.MarkPointItem(type_="max")]),
).set_global_opts(title_opts=opts.TitleOpts(title="全国高考走势图")))
return c
def three_lines_base():
c = (
Line().add_xaxis(genre).add_yaxis(
'viewing',
view,
#设置标签指出最高值
markpoint_opts=opts.MarkPointOpts(
data=[opts.MarkPointItem(type_="max")]),
).add_yaxis(
'coins',
coin,
# 设置标签指出最高值
markpoint_opts=opts.MarkPointOpts(
data=[opts.MarkPointItem(type_="max")]),
).add_yaxis(
'favorite',
favorite,
# 设置标签指出最高值
markpoint_opts=opts.MarkPointOpts(
data=[opts.MarkPointItem(type_="max")]),
).set_global_opts(
title_opts=opts.TitleOpts(
title='viewing amount grouped by categories'),
#将分区名旋转45度来保证所有分区都能显示
xaxis_opts=opts.AxisOpts(
axislabel_opts={'rotate': 45})).dump_options_with_quotes())
return c
def line_base(l1, l2) -> Line:
lh_list = [y["total_box"] for y in l2]
lh_list.extend([0 for _ in range(3)]) # 前面三天为0
c = (Line(
init_opts=opts.InitOpts(bg_color="", page_title="总票房")).add_xaxis(
[y["date"] for y in reversed(l1)]).add_yaxis(
"哪吒之魔童降世", [y["total_box"] for y in reversed(l1)],
is_smooth=True,
markpoint_opts=opts.MarkPointOpts(
data=[opts.MarkPointItem(type_="max")])).add_yaxis(
"烈火英雄",
reversed(lh_list),
is_smooth=True,
markpoint_opts=opts.MarkPointOpts(
data=[opts.MarkPointItem(
type_="max")])).set_global_opts(
title_opts=opts.TitleOpts(
title="总票房",
subtitle_textstyle_opts={
"color": "red"
},
subtitle="单位: 万元"),
toolbox_opts=opts.ToolboxOpts()))
return c.render("line.html")
def line_markpoint():
obj_l = Line()
obj_l.add_xaxis(Faker.choose())
obj_l.add_yaxis("商家A",
Faker.values(),
markpoint_opts=opts.MarkPointOpts(
data=[opts.MarkPointItem(type_="min"),opts.MarkPointItem(type_="max")],
)
)
obj_l.add_yaxis("商家B",
Faker.values(),
markpoint_opts=opts.MarkPointOpts(
data=[opts.MarkPointItem(type_="min"),opts.MarkPointItem(type_="max")]
)
)
obj_l.set_series_opts(
# 该参数设置在这里对商家A,商家B都起作用
# 设置该参数，则x对应的y值会显示出来，该参数默认为True
label_opts=opts.LabelOpts(is_show=False)
)
obj_l.set_global_opts(
title_opts=opts.TitleOpts(title="Line-MarkPoint（最大最小值）", subtitle="最大最小值标注"),
xaxis_opts=opts.AxisOpts(
name="x轴",
# 坐标轴两边留白策略，类目轴和非类目轴的设置和表现不一样,可设置x轴刻度顶格
boundary_gap=True,
# 类目轴中在 boundaryGap 为 true 的时候有效，可以保证刻度线和标签对齐。
axistick_opts=opts.AxisTickOpts(is_align_with_label=True)
),
)
return obj_l
def line_markpoint(data) -> Line:
level = data[2]
city = data[0]
# s=data[1]
# print(s)
title = "北成上广深折线图-个数"
c = (Line().add_xaxis(level).add_yaxis(
city[0],
data[1][0],
markpoint_opts=opts.MarkPointOpts(
data=[opts.MarkPointItem(type_="max")])).add_yaxis(
city[1],
data[1][1],
markpoint_opts=opts.MarkPointOpts(
data=[opts.MarkPointItem(type_="max")])).add_yaxis(
city[2],
data[1][2],
markpoint_opts=opts.MarkPointOpts(
data=[opts.MarkPointItem(type_="min")])).add_yaxis(
city[3],
data[1][3],
markpoint_opts=opts.MarkPointOpts(
data=[opts.MarkPointItem(type_="max")])).
add_yaxis(
city[4],
data[1][4],
markpoint_opts=opts.MarkPointOpts(
data=[opts.MarkPointItem(type_="max")])).set_global_opts(
title_opts=opts.TitleOpts(title="北成上广深-折线图")))
return c
def sentiment_analysis(self):
with open(r'./用户影评相关数据/' + self.filmname + '用户影评相关信息.json',
'r',
encoding='UTF-8') as f:
t1 = json.load(f, strict=False)
self.textBrowser.append("开始生成" + self.filmname + "的情感分析图......")
QApplication.processEvents()
# 取出里面的数据
comment_list = []
for each in t1:
comment_list.append(each['用户评论'])
# 存储情感数据
sentimentslist = []
for i in comment_list:
s = round(SnowNLP(i).sentiments, 2)
sentimentslist.append(s)
# 对数据进行处理,计算出各个得分的个数
result = {}
for i in set(sentimentslist):
result[i] = sentimentslist.count(i)
info = sorted(result.items(), key=lambda x: x[0],
reverse=False) # dict的排序方法
attr, val = [], []
for each in info[:-1]:
attr.append(str(each[0]))
val.append(each[1])
e = (Line(init_opts=opts.InitOpts(
width="665px", height="500px")).add_xaxis(attr).add_yaxis(
"评论情感分析折线图",
val,
markpoint_opts=opts.MarkPointOpts(data=[opts.MarkPointItem()]),
is_smooth=True,
).set_global_opts(
tooltip_opts=opts.TooltipOpts(is_show=True),
toolbox_opts=opts.ToolboxOpts(
is_show=True,
pos_right="30%",
),
title_opts=opts.TitleOpts(title="评论情感分析折线图")).render(
"./爬虫数据关联可视化/" + self.filmname +
"影评可视化数据/line_markpoint_custom.html"))
QApplication.processEvents()
self.emotion_pic = (Line(init_opts=opts.InitOpts(
width="665px", height="500px")).add_xaxis(attr).add_yaxis(
"评论情感分析折线图",
val,
markpoint_opts=opts.MarkPointOpts(data=[opts.MarkPointItem()]),
is_smooth=True,
).set_global_opts(tooltip_opts=opts.TooltipOpts(is_show=True),
toolbox_opts=opts.ToolboxOpts(
is_show=True,
pos_right="30%",
),
title_opts=opts.TitleOpts(title="评论情感分析折线图")))
self.saveflag = '3'
self.textBrowser.append(self.filmname + "的情感分析图生成完毕！")
QApplication.processEvents()
self.show_sentiment_analysis()
QApplication.processEvents()
def render_lines(country_name):
#-------------------------------------------------------------------------------------
# 第一步：读取数据
#-------------------------------------------------------------------------------------
n = "dataSets\\countrydata.csv"
data = pd.read_csv(n)
data = data[data['countryName'] == country_name]
date_list = list(data['dateId'])
date_list = list(map(lambda x:str(x),date_list))
confirm_list = list(data['confirmedCount'])
current_list = list(data['currentConfirmedCount'])
dead_list = list(data['deadCount'])
heal_list = list(data['curedCount'])
print(len(date_list))
#print(date_list) # 日期
#print(confirm_list) # 确诊数据
#print(current_list) # 疑似数据
#print(dead_list) # 死亡数据
#print(heal_list) # 治愈数据
#-------------------------------------------------------------------------------------
# 第二步：绘制折线面积图
#-------------------------------------------------------------------------------------
line = (
Line()
.add_xaxis(date_list)
# 平均线 最大值 最小值
.add_yaxis('确诊数据', confirm_list, is_smooth=True,
markpoint_opts=opts.MarkPointOpts(data=[opts.MarkPointItem(type_="max"),
opts.MarkPointItem(type_="min")]))
.add_yaxis('现存确诊数据', current_list, is_smooth=True,
markpoint_opts=opts.MarkPointOpts(data=[opts.MarkPointItem(type_="max"),
opts.MarkPointItem(type_="min")]))
.add_yaxis('死亡数据', dead_list, is_smooth=True,
markpoint_opts=opts.MarkPointOpts(data=[opts.MarkPointItem(type_="max"),
opts.MarkPointItem(type_="min")]))
.add_yaxis('治愈数据', heal_list, is_smooth=True,
markpoint_opts=opts.MarkPointOpts(data=[opts.MarkPointItem(type_="max"),
opts.MarkPointItem(type_="min")]))
# 隐藏数字 设置面积
.set_series_opts(
areastyle_opts=opts.AreaStyleOpts(opacity=0.5),
label_opts=opts.LabelOpts(is_show=False))
# 设置x轴标签旋转角度
.set_global_opts(xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(rotate=-30)),
yaxis_opts=opts.AxisOpts(name='人数', min_=3),
title_opts=opts.TitleOpts(title='2019-nCoV'+country_name+'疫情数据曲线图'))
)
return line
def line_markpoint() -> Line:
c = (Line().add_xaxis(Faker.choose()).add_yaxis(
"商家A",
Faker.values(),
markpoint_opts=opts.MarkPointOpts(
data=[opts.MarkPointItem(type_="min")]),
).add_yaxis(
"商家B",
Faker.values(),
markpoint_opts=opts.MarkPointOpts(
data=[opts.MarkPointItem(type_="max")]),
).set_global_opts(title_opts=opts.TitleOpts(title="Line-MarkPoint")))
return c
def line_three_html(self, title, key, val_min, val_max, val_avg):
line = Line(init_opts=opts.InitOpts(width="100%", height="300px"))
line.set_global_opts(
title_opts=opts.TitleOpts(
title=title,
title_textstyle_opts=opts.TextStyleOpts(color="black", font_size=14),
pos_left="left",
),
# 增添x，y轴以及内部的缩放
datazoom_opts=[opts.DataZoomOpts(range_start=0, range_end=100)],
yaxis_opts=opts.AxisOpts(splitline_opts=opts.SplitLineOpts(is_show=True))
)
line.add_xaxis(key)
line.add_yaxis(
"最小值",
val_min,
is_smooth=True,
markpoint_opts=opts.MarkPointOpts(
data=[
opts.MarkPointItem(type_="min", name="最小值"),
opts.MarkPointItem(type_="max", name="最大值"),
opts.MarkPointItem(type_="average", name="平均值"),
]
),
)
line.add_yaxis(
"最大值",
val_max,
is_smooth=True,
markpoint_opts=opts.MarkPointOpts(
data=[
opts.MarkPointItem(type_="min", name="最小值"),
opts.MarkPointItem(type_="max", name="最大值"),
opts.MarkPointItem(type_="average", name="平均值"),
]
),
)
line.add_yaxis(
"平均值",
val_avg,
is_smooth=True,
markpoint_opts=opts.MarkPointOpts(
data=[
opts.MarkPointItem(type_="min", name="最小值"),
opts.MarkPointItem(type_="max", name="最大值"),
opts.MarkPointItem(type_="average", name="平均值"),
]
),
)
return line.render_embed()
def bar_border_radius():
c = (
Bar(init_opts=opts.InitOpts(
animation_opts=opts.AnimationOpts( # 柱状图显示效果动画控制代码
animation_delay=500,
animation_easing="cubicOut"),
theme=ThemeType.MACARONS, # 柱状图显示主题
page_title="Bar Learn", # 设置html页面标题
))
# .reversal_axis() # 翻转XY轴
.add_xaxis(["草莓", "芒果", "葡萄", "雪梨", "西瓜", "柠檬", "车厘子"]).add_yaxis(
"A",
Faker.values(),
category_gap="50%", # 柱间距对应的控制代码
markpoint_opts=opts.MarkPointOpts(),
is_selected=True # A系列柱子是否显示对应的控制代码
).set_global_opts(
title_opts=opts.TitleOpts( # 标题
title="Bar-参数使用例子", subtitle="副标题"),
toolbox_opts=opts.ToolboxOpts(), # toolbox 工具箱配置
yaxis_opts=opts.AxisOpts(position="right", name="Y轴"), # Y轴右侧控制
datazoom_opts=opts.DataZoomOpts(), # 数据区域放大缩小设置
).set_series_opts(
itemstyle_opts={ # A系列柱子颜色渐变对应的控制代码
"normal": {
"color":
JsCode("""
new echarts.graphic.LinearGradient(
0, 0, 0, 1, [{
offset: 0,
color: 'rgba(0, 244, 255, 1)'}, {
offset: 1,
color: 'rgba(0, 77, 167, 1)'}], false)"""),
"barBorderRadius": [6, 6, 6, 6],
"shadowColor":
'rgb(0, 160, 221)',
}
},
# A系列柱子最大和最小值标记点对应的控制代码
markpoint_opts=opts.MarkPointOpts(data=[
opts.MarkPointItem(type_="max", name="最大值"),
opts.MarkPointItem(type_="min", name="最小值"),
]),
# A系列柱子最大和最小值标记线对应的控制代码
markline_opts=opts.MarkLineOpts(data=[
opts.MarkLineItem(type_="min", name="最小值"),
opts.MarkLineItem(type_="max", name="最大值")
])))
return c
def line_u() -> Line:
c = (
Line(opts.InitOpts(
bg_color='#FFFFFF', width='800px',
height='500px')).add_xaxis(arr).add_yaxis(
"Ua(V)",
val[0],
color='orangered',
is_symbol_show=False,
markpoint_opts=opts.MarkPointOpts(data=[
opts.MarkPointItem(type_="max"),
opts.MarkPointItem(type_="min")
])).add_yaxis(
"Ub(V)",
val[1],
color='mediumseagreen',
is_symbol_show=False,
markpoint_opts=opts.MarkPointOpts(data=[
opts.MarkPointItem(type_="max"),
opts.MarkPointItem(type_="min")
])).add_yaxis(
"Uc(V)",
val[2],
color='orange',
is_symbol_show=False,
markpoint_opts=opts.MarkPointOpts(data=[
opts.MarkPointItem(type_="max"),
opts.MarkPointItem(type_="min")
])).add_yaxis(
"上限",
paras[0],
linestyle_opts=opts.LineStyleOpts(width=1.5,
type_='dashed'),
is_symbol_show=False).add_yaxis(
"下限",
paras[1],
linestyle_opts=opts.LineStyleOpts(
width=1.5, type_='dashed'),
is_symbol_show=False).set_global_opts(
title_opts=opts.TitleOpts(
title='电压趋势图',
subtitle='日期：' + df.Date.iloc[0] +
' to ' + df.Date.iloc[-1],
),
yaxis_opts=opts.AxisOpts(min_=190,
max_=250))
# title_textstyle_opts=opts.series_options.TextStyleOpts(color='#fff'),
# subtitle_textstyle_opts=opts.series_options.TextStyleOpts(color='#fff')
)
return c
def foregin_history_all():
path = readPath + '/各国历史疫情信息'
path2 = savaPath + '/各国历史疫情信息'
dwf.createFile(path2)
Filelist = [] # 将当前文件夹内的所有表名存放此列表
for home, dirs, files in os.walk(path):
for filename in files:
Filelist.append(filename)
for i in Filelist:
data = pd.read_excel(readPath + '/各国历史疫情信息/%s' % (i), index=False)
n = i[:-5] # 只提取国家名，不要后缀（.xlsx）
y1_confirm = data['confirm']
y2_confirm_add = data['confirm_add']
y3_heal = data['heal']
y4_dead = data['dead']
date_list = []
for j in data['date']:
date_list.append(j)
x = date_list
lines = (
Line()
.add_xaxis(xaxis_data=x)
.add_yaxis(series_name='确诊人数', y_axis=y1_confirm, is_symbol_show=True,
label_opts=opts.LabelOpts(is_show=False),
markpoint_opts=opts.MarkPointOpts(data=[opts.MarkPointItem(type_="max"), ]))
.add_yaxis(series_name='增加确诊人数', y_axis=y2_confirm_add, is_symbol_show=True,
label_opts=opts.LabelOpts(is_show=False),
markpoint_opts=opts.MarkPointOpts(data=[opts.MarkPointItem(type_="max"), ]))
.add_yaxis(series_name='治愈人数', y_axis=y3_heal, is_symbol_show=True,
label_opts=opts.LabelOpts(is_show=False),
markpoint_opts=opts.MarkPointOpts(data=[opts.MarkPointItem(type_="max"), ]))
.add_yaxis(series_name='死亡人数', y_axis=y4_dead, is_symbol_show=True,
label_opts=opts.LabelOpts(is_show=False),
markpoint_opts=opts.MarkPointOpts(data=[opts.MarkPointItem(type_="max"), ]))
.set_global_opts(title_opts=opts.TitleOpts(title="%s" % (n) + "疫情走势", subtitle="数据来源：腾讯新闻"),
yaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(font_size=10, interval=3)))
)
# lines.render(savaPath + '\\daliy_changes_view\\各国历史疫情信息\\%s.html' % (n), index=False)
if flag:
dwf.write_to_file(savaPath + '/各国历史疫情信息/%s.txt' % (n),str(lines.dump_options_with_quotes()))
page.add(lines)
def line_base(self, date: list, price: list) -> Line:
line = (
Line()
.add_xaxis(xaxis_data=date)
.add_yaxis(
series_name="价格",
y_axis=price,
markpoint_opts=opts.MarkPointOpts(
data=[
opts.MarkPointItem(type_="max", name="最大值"),
opts.MarkPointItem(type_="min", name="最小值"),
]
),
markline_opts=opts.MarkLineOpts(
data=[opts.MarkLineItem(type_="average", name="平均值")]
),
)
.set_global_opts(
title_opts=opts.TitleOpts(title="历史价格记录"),
tooltip_opts=opts.TooltipOpts(trigger="axis"),
toolbox_opts=opts.ToolboxOpts(is_show=False),
xaxis_opts=opts.AxisOpts(type_="category", boundary_gap=False),
)
)
return line
def get_chart_of_date(date):
# 一个只标注出对应年份，其他是空字符串的list
date_mark = [0] * len(date_list)
date_mark[date_list.index(date)] = dayly_count_list[date_list.index(date)]
# print(date_mark)
line_chart = (Line().add_xaxis(date_list).add_yaxis(
"", dayly_count_list,
linestyle_opts=opts.LineStyleOpts(width=3)).add_yaxis(
"",
date_mark,
markpoint_opts=opts.MarkPointOpts(
data=[opts.MarkPointItem(type_='max')])).set_series_opts(
label_opts=opts.LabelOpts(is_show=False)).set_global_opts(
title_opts=opts.TitleOpts(
title='2017年海口市5至10月正常天气滴滴每日出行总量', pos_top='50%')))
bar_chart = (Bar().add_xaxis(xaxis_data=hour_24_list).add_yaxis(
series_name='',
yaxis_data=hourly_count_list[date_list.index(date)],
label_opts=opts.LabelOpts(is_show=True,
position='top',
formatter='{c}'),
).set_global_opts(
xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(is_show=True)),
yaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(is_show=True)),
title_opts=opts.TitleOpts(title=date + '各时段出行量 天气：' +
weather_list[date_list.index(date)] + ', ' +
weekday_list[date_list.index(date)])))
grid = (Grid().add(bar_chart,
grid_opts=opts.GridOpts(pos_bottom="60%")).add(
line_chart, grid_opts=opts.GridOpts(pos_top="60%")))
return grid
def base_select() -> 'html':
the_region = request.form["the_region_selected"]
dfs_base = df_base.query("region=='{}'".format(the_region))
c = (
Bar(init_opts=opts.InitOpts(theme=ThemeType.ESSOS))
.add_xaxis(dfs_base['地区'].tolist())
.add_yaxis(the_region, dfs_base['count'].tolist())
.set_global_opts(title_opts=opts.TitleOpts(title=the_region, subtitle=""),
xaxis_opts=opts.AxisOpts(name="地区", axislabel_opts={"rotate": 45}),
datazoom_opts=[opts.DataZoomOpts()])
.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"), ])
)
)
c.render("./static/tmp/echarts_2018bar.html")
with open("./static/tmp/echarts_2018bar.html", encoding="utf8", mode="r") as f:
plot_all = "".join(f.readlines())
data_str = dfs_base.to_html()
return render_template('pyecharts.html',
myechart=plot_all,
the_res=data_str,
the_select_region=regions_available_base,
bottom_title="分析:柱状图",
mark="base",
)
def plot_one_y(df, title: str):
df.index = pd.to_datetime(df['date'])
df.sort_index(inplace=True)
df = df.drop(['date'], axis=1)
return (Line(
init_opts=opts.InitOpts(width="1200px", height="400px")).add_xaxis(
xaxis_data=df.index.strftime(
'%Y-%m-%d').values.tolist()).add_yaxis(
series_name=title.upper(),
y_axis=np.round(df['r2'].values, 2).tolist(),
is_smooth=True,
label_opts=opts.LabelOpts(is_show=False),
linestyle_opts=opts.LineStyleOpts(width=2),
markline_opts=opts.MarkLineOpts(
data=[opts.MarkLineItem(type_="average")]),
).set_global_opts(
datazoom_opts=opts.DataZoomOpts(),
legend_opts=opts.LegendOpts(pos_bottom="0%",
pos_right='45%'),
title_opts=opts.TitleOpts(
title=title.upper(),
pos_left='0%',
),
tooltip_opts=opts.TooltipOpts(trigger="axis",
axis_pointer_type="cross"),
toolbox_opts=opts.ToolboxOpts(is_show=True),
xaxis_opts=opts.AxisOpts(boundary_gap=False),
yaxis_opts=opts.AxisOpts(
axislabel_opts=opts.LabelOpts(formatter="{value}"),
splitline_opts=opts.SplitLineOpts(is_show=True),
),
).set_series_opts(markpoint_opts=opts.MarkPointOpts(data=[
opts.MarkPointItem(type_='max', name='Max'),
opts.MarkPointItem(type_='min', name='Min')
]), ))
def barSpeed(data) -> Bar:
"""
-- 按列车类型统计运行速度
:param data:list:[[X轴-类型名称], [Y轴-速度]]
:return: Bar
"""
speed_bar = (
Bar(init_opts=opts.InitOpts(width='1000px',
height='500px')).add_xaxis(data[0]).
add_yaxis("车次数量", data[1], color='#8c4356').set_global_opts(
legend_opts=opts.LegendOpts(is_show=False), # 不显示图例
xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(
rotate=0)), # 横坐标标签倾斜
yaxis_opts=opts.AxisOpts(name='公里/小时'),
title_opts=opts.TitleOpts(
title='按列车类型统计运行速度',
title_textstyle_opts=opts.TextStyleOpts(
font_size=20))).set_series_opts(
label_opts=opts.LabelOpts(is_show=False),
markline_opts=opts.MarkLineOpts(
symbol='circle',
precision=0,
data=[
opts.MarkLineItem(name='平均',
type_='average')
]), # 平均值线
markpoint_opts=opts.MarkPointOpts(data=[
opts.MarkPointItem(type_="max", name="Max"),
opts.MarkPointItem(type_="min", name="Min")
]),
))
return speed_bar
def line_base():
chinaLine = (
Line()
.add_xaxis(date_list)
.add_yaxis('确诊', china_confirm, is_symbol_show=True, label_opts=opts.LabelOpts(is_show=False),
markpoint_opts=opts.MarkPointOpts(data=[opts.MarkPointItem(type_="max"), ]))
.add_yaxis('死亡', china_dead, is_symbol_show=True, label_opts=opts.LabelOpts(is_show=False),
markpoint_opts=opts.MarkPointOpts(data=[opts.MarkPointItem(type_="max")]))
.add_yaxis('治愈', china_heal, is_symbol_show=True, label_opts=opts.LabelOpts(is_show=False),
markpoint_opts=opts.MarkPointOpts(data=[opts.MarkPointItem(type_="max")]))
.add_yaxis('输入病例', china_importedCase, is_symbol_show=True, label_opts=opts.LabelOpts(is_show=False),
markpoint_opts=opts.MarkPointOpts(data=[opts.MarkPointItem(type_="max")]))
.set_global_opts(title_opts=opts.TitleOpts(title="国内疫情走势", subtitle="数据来源：腾讯新闻"),
yaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(font_size=10, interval=3)))
)
return chinaLine
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 other_line() -> Line:
url = 'https://services1.arcgis.com/0MSEUqKaxRlEPj5g/arcgis/rest/services/cases_time/FeatureServer/0/query?f=json&where=Report_Date%3C%3D%272020-02-01%2015%3A59%3A59%27&returnGeometry=false&spatialRel=esriSpatialRelIntersects&outFields=*&orderByFields=Report_Date%20asc&resultOffset=0&resultRecordCount=2000&cacheHint=true'
r_data = json.loads(requests.get(url).text)
data = r_data['features'] #初始化json数据，为dict ['chinaTotal']
dates = []
numbers = []
for i in data:
date = time.strftime(
"%m.%d", time.localtime(i['attributes']['Report_Date'] / 1000))
dates.append(date)
numbers.append(i['attributes']['Other_Locations'])
c = (
Line(init_opts=opts.InitOpts(theme=ThemeType.VINTAGE)) #设置主题
.add_xaxis(dates) #直接列表
.add_yaxis('确诊', numbers).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), #
).set_series_opts(
# 设置系列配置
markpoint_opts=opts.MarkPointOpts(data=[
opts.MarkPointItem(type_="max", name="最大值"),
opts.MarkPointItem(type_="min", name="最小值"),
]),
areastyle_opts=opts.AreaStyleOpts(opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
))
return c
def Getline(dataX, dataY, n=[0], T=""):
chinaLine = (
Line(init_opts=opts.InitOpts(theme=ThemeType.INFOGRAPHIC))
.add_xaxis(dataX)
.set_global_opts(
title_opts=opts.TitleOpts(title="{}月情感变化{}".format(n[0], T)),
toolbox_opts=opts.ToolboxOpts(is_show=True),
)
.set_series_opts(
label_opts=opts.LabelOpts(is_show=False),
)
)
for i in range(len(dataY)):
chinaLine.add_yaxis('{}月情感变化'.format(n[i]), dataY[i], is_symbol_show=False, label_opts=opts.LabelOpts(is_show=True),
markline_opts=opts.MarkLineOpts(
data=[
opts.MarkLineItem(type_="average", name="平均值"),
opts.MarkLineItem(symbol="none", x="90%", y="max"),
# opts.MarkLineItem(symbol="circle", type_="max", name="最高点"),
]
),
markpoint_opts=opts.MarkPointOpts(data=[opts.MarkPointItem(type_="max"), ]), is_smooth=True)
#
chinaLine.render()
return chinaLine.render_embed()
def barCompanyOut(data) -> Bar:
# 全国民航机场进出港航班数量(TOP 15)
company_out_bar = (
Bar(init_opts=opts.InitOpts(width='1000px',
height='500px')).add_xaxis(data[0]).
add_yaxis("航空公司", data[1], color='#574266').set_global_opts(
legend_opts=opts.LegendOpts(is_show=False), # 不显示图例
datazoom_opts=[
opts.DataZoomOpts(),
opts.DataZoomOpts(type_="inside")
], # DataZoom类型
title_opts=opts.TitleOpts(
title='本周航空公司出港航班数量统计',
title_textstyle_opts=opts.TextStyleOpts(
font_size=20), # 设置标题大小
subtitle='{}-{}，本周发送航班最多的航空公司是：{}，{}，{}。[数据来源：ctrip.com]'.
format(
data[2], data[3], data[4], data[5],
data[6]))).set_series_opts(
label_opts=opts.LabelOpts(is_show=False), # 不显示标签值
markpoint_opts=opts.MarkPointOpts(data=[
opts.MarkPointItem(type_="max", name="Max"),
opts.MarkPointItem(type_="min", name="Min"),
]), # 最大值，最小值
markline_opts=opts.MarkLineOpts(
symbol='circle',
precision=0,
data=[
opts.MarkLineItem(name='平均',
type_='average')
]) # 平均值线
))
return company_out_bar
def set_process() -> Bar:
"""
进程调度比较图
:return:
"""
index = ['时间片轮转调度', '短进程优先调度', '非剥夺优先级调度']
data = [217, 459, 324]
bar = (
Bar()
.add_xaxis(index)
.add_yaxis(series_name='三大调度算法', yaxis_data=data)
.set_global_opts(title_opts=opts.TitleOpts(title='三大算法调度性能分析'),
xaxis_opts=opts.AxisOpts(type_='category', name='算法名称'),
yaxis_opts=opts.AxisOpts(type_='value', name='获胜次数',
axislabel_opts=opts.LabelOpts(formatter='{value}/次')),
toolbox_opts=opts.ToolboxOpts(),
tooltip_opts=opts.TooltipOpts(axis_pointer_type='cross')
)
.set_series_opts(markpoint_opts=opts.MarkPointOpts(data=[opts.MarkPointItem(name='最大值', type_='min'),
opts.MarkPointItem(name='最大值', type_='max')]),
itemstyle_opts={
"normal": {
"color": JsCode("""new echarts.graphic.LinearGradient(0, 0, 0, 1, [{
offset: 0,
color: 'rgba(0, 244, 255, 1)'
}, {
offset: 1,
color: 'rgba(0, 77, 167, 1)'
}], false)"""),
"barBorderRadius": [30, 30, 30, 30],
"shadowColor": 'rgb(0, 160, 221)',
}}
)
)
return bar
def bar_datazoom_inside() -> Timeline:
tl = Timeline()
for i in range(2014, 2020):
c = (
Bar(
init_opts=opts.InitOpts(
animation_opts=opts.AnimationOpts(
animation_delay=1000, animation_easing="elasticOut"
)
)
)
.add_xaxis(list(zip(list(data总.set_index('类别').index))))
.add_yaxis("显示",list(data总["{}".format(i)]))
.set_global_opts(
title_opts=opts.TitleOpts(title="纵横小说月票榜"),
datazoom_opts=opts.DataZoomOpts(type_="inside"),
visualmap_opts=opts.VisualMapOpts(type_="color", max_=250000, min_=200,pos_right='20',pos_top='middle'),
toolbox_opts=opts.ToolboxOpts(),
)
.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"),
]
),
)
)
tl.add(c, "{}年".format(i))
return tl
def barDistance(data) -> Bar:
"""
-- 按运行里程区间统计车次数量
:param data:list:[[X轴-里程区间], [Y轴-车次数量]]
:return: Bar
"""
distance_bar = (
Bar(init_opts=opts.InitOpts(width='1000px',
height='500px')).add_xaxis(data[0]).
add_yaxis("车次数量", data[1], color='#425066').set_global_opts(
legend_opts=opts.LegendOpts(is_show=False), # 不显示图例
xaxis_opts=opts.AxisOpts(
axislabel_opts=opts.LabelOpts(rotate=30),
name='公里'), # 横坐标标签倾斜
yaxis_opts=opts.AxisOpts(name='次'),
title_opts=opts.TitleOpts(
title='按运行里程区间统计车次数量',
title_textstyle_opts=opts.TextStyleOpts(
font_size=20))).set_series_opts(
label_opts=opts.LabelOpts(is_show=False),
markline_opts=opts.MarkLineOpts(
symbol='circle',
precision=0,
data=[
opts.MarkLineItem(name='平均',
type_='average')
]), # 平均值线
markpoint_opts=opts.MarkPointOpts(data=[
opts.MarkPointItem(type_="max", name="Max"),
opts.MarkPointItem(type_="min", name="Min")
]),
))
return distance_bar
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 line_markpoint_custom():
x, y = Faker.choose(), Faker.values()
obj_l = Line()
obj_l.add_xaxis(x)
obj_l.add_yaxis("商家A",
y,
markpoint_opts=opts.MarkPointOpts(
data=[opts.MarkPointItem(name="自定义标记点", coord=[x[2], y[2]], value=y[2])]
)
)
obj_l.set_series_opts(
# 设置该参数，则x对应的y值会显示出来，该参数默认为True
label_opts=opts.LabelOpts(is_show=False),
)
obj_l.set_global_opts(
title_opts=opts.TitleOpts(title="Line-MarkPoint（自定义）",subtitle="自定义标注"),
xaxis_opts=opts.AxisOpts(
name="x轴",
# 坐标轴两边留白策略，类目轴和非类目轴的设置和表现不一样,可设置x轴刻度顶格
boundary_gap=True,
# 类目轴中在 boundaryGap 为 true 的时候有效，可以保证刻度线和标签对齐。
axistick_opts=opts.AxisTickOpts(is_align_with_label=True)
),
)
return obj_l
def barTime(self, data) -> Bar:
# 进出港航班数量按小时统计
time_bar = (
Bar(init_opts=opts.InitOpts(width='1000px', height='500px')).
add_xaxis(data[0][0]).add_yaxis(
"{}航班数量".format(data[2]), data[0][1],
color='#1685a9').set_series_opts(
label_opts=opts.LabelOpts(is_show=False),
markpoint_opts=opts.MarkPointOpts(data=[
opts.MarkPointItem(type_="max", name="Max"),
opts.MarkPointItem(type_="min", name="Min"),
]),
markline_opts=opts.MarkLineOpts(
symbol='circle',
precision=0,
data=[opts.MarkLineItem(name='平均', type_='average')])).
set_global_opts(
legend_opts=opts.LegendOpts(is_show=False), # 不显示图例
xaxis_opts=opts.AxisOpts(name_rotate=30), # 【【【X坐标轴标签倾斜，不好使】】】
title_opts=opts.TitleOpts(
title='{}航班数量按小时统计'.format(data[2]),
title_textstyle_opts=opts.TextStyleOpts(
font_size=20), # 设置标题大小
subtitle='{}，全国共{}航班{}架次。[数据来源：ctrip.com]'.format(
self.today_ymd, data[2], data[1]))))
return time_bar
def draw_charts(product_name, case_name, total_secs, qps, latency):
secs = range(0, int(total_secs), int(total_secs / 100))
secs.append(int(total_secs))
c = (Line(
init_opts=opts.InitOpts(width="1600px", height="800px")
).add_xaxis(xaxis_data=secs).add_yaxis(
series_name="QPS",
y_axis=qps,
markpoint_opts=opts.MarkPointOpts(data=[
opts.MarkPointItem(type_="max", name="Max Value"),
opts.MarkPointItem(type_="min", name="Min Value"),
]),
markline_opts=opts.MarkLineOpts(
data=[opts.MarkLineItem(type_="average", name="Average QPS")]),
).add_yaxis(
series_name="Latency",
y_axis=latency,
markline_opts=opts.MarkLineOpts(
data=[opts.MarkLineItem(type_="average", name="Average Latency")]),
).set_global_opts(
title_opts=opts.TitleOpts(title="QPS", subtitle="DML only"),
tooltip_opts=opts.TooltipOpts(trigger="axis"),
toolbox_opts=opts.ToolboxOpts(is_show=True),
xaxis_opts=opts.AxisOpts(type_="category", boundary_gap=False),
).render("sysbench-" + product_name + '-' + case_name + ".html"))
