def month_loan_income():
'''生成本月放款收入图表'''
# X轴label
attr = dates
# 图表数据
v1 = list(np.random.randint(2000, 6000, len(dates)) / 10)
# 生成图表实例
chart = Line("本月放款收入趋势(单位:万)", background_color=BACKGROUND_COLOR, width="100%")
# 使用暗色主题
chart.use_theme('dark')
# 载入默认设置并差异化更新
chart_kwargs = DEFAULT_KWARGS
chart_kwargs.update({
"xaxis_interval":0,
"mark_line": ["average"],
"is_smooth": True,
"is_fill": True,
"area_opacity": 0.3,
"yaxis_min": "dataMin",
"xaxis_rotate":90,
})
# 添加数据
chart.add("放款收入", attr, v1, **chart_kwargs)
return chart
def monthly_sign():
'''生成每月签约率图表'''
# X轴label
attr = months
# 图表数据
v1 = list(np.random.randint(700, 800, len(months)) / 10)
# 生成图表实例
chart = Line("每月签约率趋势", background_color=BACKGROUND_COLOR, width="100%")
# 使用暗色主题
chart.use_theme('dark')
# 载入默认设置并差异化更新
chart_kwargs = DEFAULT_KWARGS
chart_kwargs.update({
"xaxis_interval":0,
"mark_line": ["average"],
"is_smooth": True,
"is_fill": True,
"area_opacity": 0.3,
"yaxis_min": "dataMin",
"yaxis_formatter": "%",
"label_formatter": "{c}%",
"xaxis_rotate":90,
})
# 添加数据
chart.add("签约率", attr, v1, **chart_kwargs)
return chart
def month_apply_bill():
'''生成本月申请件数图表'''
# X轴label
attr = dates
# 图表数据
v1 = list(np.random.randint(20, 100, len(dates)))
# 生成图表实例
chart = Line("本月申请件数趋势", background_color=BACKGROUND_COLOR, width="100%")
# 使用暗色主题
chart.use_theme('dark')
# 载入默认设置并差异化更新
chart_kwargs = DEFAULT_KWARGS
chart_kwargs.update({
"xaxis_interval":0,
"mark_line": ["average"],
"is_smooth": True,
"is_fill": True,
"area_opacity": 0.3,
"xaxis_rotate":90,
})
# 添加数据
chart.add("申请件数", attr, v1, **chart_kwargs)
return chart
def monthly_loan_amount():
'''生成每月放款金额图表'''
# X轴label
attr = months
# 图表数据
v1 = list(np.random.randint(60000, 300000, len(months)) / 10)
# 生成图表实例
chart = Line("每月放款金额趋势", background_color=BACKGROUND_COLOR, width="100%")
# 使用暗色主题
chart.use_theme('dark')
# 载入默认设置并差异化更新
chart_kwargs = DEFAULT_KWARGS
chart_kwargs.update({
"xaxis_interval":0,
"mark_line": ["average"],
"is_smooth": True,
"is_fill": True,
"area_opacity": 0.3,
"xaxis_rotate":90,
})
# 添加数据
chart.add("放款金额", attr, v1, **chart_kwargs)
return chart
def get_mem_data():
base_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
performance_data = os.path.join(base_dir, 'data')
image_path = performance_data + "\\result.png"
html_path = "%s\\memory_report.html" % (performance_data)
hfile = ('%s\\memory_data.txt' % (performance_data))
fp = open(hfile, "r")
# fp = open(r'D:\moxiu\youyang\data\xin.txt', 'r')
meminfo_data = fp.readlines()
case_name_list = []
for i in range(len(meminfo_data)):
j = meminfo_data[i].replace('\n', '').split(',')
case_name_list.append(j[0:1][0])
heap_size_list = []
for i in range(len(meminfo_data)):
j = meminfo_data[i].replace('\n', '').split(',')
heap_size_list.append(float(j[1:2][0]))
heap_alloc_list = []
for i in range(len(meminfo_data)):
j = meminfo_data[i].replace('\n', '').split(',')
heap_alloc_list.append(float(j[2:3][0]))
heap_free_list = []
for i in range(len(meminfo_data)):
j = meminfo_data[i].replace('\n', '').split(',')
heap_free_list.append(float(j[3:4][0]))
bar = Line("有样__内存数据图形报表", "图表纵轴为数据大小，横轴为case名称，直线为平均值")
bar.add("heap_size",
case_name_list,
heap_size_list,
label_color=['#B22222'],
mark_line=["average"],
mark_point=["max", "min"],
xaxis_interval=0,
xaxis_rotate=90)
bar.add("heap_alloc",
case_name_list,
heap_alloc_list,
label_color=['#008080'],
mark_line=["average"],
mark_point=["max", "min"],
xaxis_interval=0,
xaxis_rotate=90)
bar.add("heap_free",
case_name_list,
heap_free_list,
label_color=['#483D8B'],
mark_line=["average"],
mark_point=["max", "min"],
xaxis_interval=0,
xaxis_rotate=90)
bar.use_theme("vintage")
base_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
performance_data = os.path.join(base_dir, 'data')
bar.render('%s\\memory_report.html' % (performance_data))
make_a_snapshot(html_path, image_path)
im = Image.open(image_path)
out = im.resize((800, 400), Image.ANTIALIAS)
out.save(image_path)
def drawLine(title, data, savepath='./results'):
if not os.path.exists(savepath):
os.mkdir(savepath)
line = Line(title, title_pos='center')
line.use_theme('purple-passion')
attrs = [i for i, j in data.items()]
values = [j for i, j in data.items()]
line.add('', attrs, values, xaxis_rotate=30, yaxis_rotate=30, mark_point=['max', 'min'])
line.render(os.path.join(savepath, '%s.html' % title))
def caculate_mi_list(dir_path):
files = common.get_filelist(dir_path)
res = _caculate_mi_list(files[3], files[0:10])
data_a, data_b = Bar.cast(res)
print data_a
print data_b
bar = Line("甜甜的实验", "二哥出品")
bar.use_theme('vintage')
bar.add("数据", data_a, data_b)
bar.render()
return
def single_line_maker(start, end):
# 子函数：获取数据
# 己函数：把数据放到 Line 中
def lining_single():
records = get_info(start, end)
stamps = []
cpu_percents = []
cpu_temps = []
free_rams = []
free_disks = []
for i in range(start + 60, end, 60):
stamps.append(time.strftime('%H:%M', time.gmtime(i + 28800)))
if i // 60 in records:
cpu_percents.append(records[i // 60].cpu_percent)
cpu_temps.append(records[i // 60].cpu_temp)
free_rams.append(records[i // 60].free_ram)
free_disks.append(records[i // 60].free_disk)
else:
cpu_percents.append('')
cpu_temps.append('')
free_rams.append('')
free_disks.append('')
return stamps, cpu_percents, cpu_temps, free_rams, free_disks
line_cpu = Line(width=width, height=height, title='CPU 监控')
line_space = Line(width=width, height=height, title='DISK 监控')
if time.localtime().tm_hour >= 21 or time.localtime().tm_hour <= 6:
line_cpu.use_theme('dark')
line_space.use_theme('dark')
stamps, cpu_percents, cpu_temps, free_rams, free_disks = lining_single()
line_cpu.add('使用率',
stamps,
cpu_percents,
mark_line=["average"],
line_width=2)
line_cpu.add('温度', stamps, cpu_temps, mark_point=["max"], line_width=2)
line_space.add('剩余内存',
stamps,
free_rams,
mark_line=["average"],
line_width=2)
line_space.add('剩余磁盘', stamps, free_disks, line_width=2)
js_list = list(
set(line_cpu.get_js_dependencies() + line_space.get_js_dependencies()))
return line_cpu, line_space, js_list
def picture(table, time, data):
# print(time)
attr = time
line = Line(str(table + "发展趋势"), background_color='white')
# print("theme shape %s"%(data.shape[0]))
for i in range(0, data.shape[0]):
line.add("第%s主题" % (i),
attr,
data[i].tolist(),
is_random=True,
is_label_show=True,
is_smooth=False)
# print(data[i].tolist())
line.use_theme("vintage")
path = table + ".html"
line.render(path)
def CPUintoChart(cpufile, name):
row = []
row_cpu = 1
for line in cpufile:
if row_cpu%2 == 1:
line = line.split(' ')
cpudata = [line[i] for i in (2, 4, 7)]
row.append(cpudata)
row_cpu += 1
data = []
for i in row:
if i[1][0].isdigit() is False:
pass
else:
data.append(i)
package = []
user = []
kernel = []
for j, k, l in data:
package.append(j[0:-1])
user.append(k[0:-1])
kernel.append(l[0:-1])
package.insert(0, 'package')
user.insert(0, 'user')
kernel.insert(0, 'kernel')
print(package)
print(user)
print(kernel)
id = []
for i in range(len(package) - 1):
id.append(i)
bar = Line(f"{name}__CPU数据图形报表", "图表纵轴为数据大小，横轴为时间节点，直线为平均值")
bar.add("package", id, package[1:], label_color=['#800080'], mark_line=["average"],
mark_point=["max", "min"], xaxis_interval=0, xaxis_rotate=90)
bar.add("user", id, user[1:], label_color=['#0000FF'], mark_line=["average"],
mark_point=["max", "min"], xaxis_interval=0, xaxis_rotate=90)
bar.add("kernel", id, kernel[1:], label_color=['#2E8B57'], mark_line=["average"],
mark_point=["max", "min"], xaxis_interval=0, xaxis_rotate=90)
bar.use_theme("vintage")
t = time.time()
bar.render('%s\\cpu%s.html' % (setting.data, int(t)))
def dashboard_loan_amount():
'''添加放款金额轮播图表'''
# 生成图表实例
timeline = Timeline(is_auto_play=True, timeline_bottom=0, width="100%")
# 使用暗色主题
timeline.use_theme('dark')
# 生成每个月份的图表
for month in months:
# X轴label
n_day_in_month = pd.Period(month+"-01").days_in_month
attr = list(range(1,n_day_in_month+1))
# 图表数据
v1 = list(np.random.randint(2000, 10000, n_day_in_month) / 10)
# 生成图表实例
chart = Line("%s月放款金额走势图" %month,"当月放款总金额为%.2f万" %np.sum(v1), background_color=BACKGROUND_COLOR, width="100%")
# 使用暗色主题
chart.use_theme('dark')
# 载入默认设置并差异化更新
chart_kwargs = DEFAULT_KWARGS
chart_kwargs.update({
"xaxis_interval":0,
"mark_line": ["average"],
"is_smooth": True,
"is_fill": True,
"area_opacity": 0.3,
})
# 添加数据
chart.add("放款金额", attr, v1, **chart_kwargs)
timeline.add(chart,month)
return timeline
def history_chart():
BOTTOM = 1
TOP = 200
XAXIS_COUNT = 10
XAXIS_INTERVAL = ((TOP - BOTTOM) // XAXIS_COUNT) - 1
chart = Line(title="Python History Ratings",
subtitle="Source: www.tiobe.com",
title_color="DarkSlateGray",
background_color="Azure",
width=1000,
height=500,
page_title="Python Ratings History")
chart.use_theme('walden')
df = pd.read_csv(path.join(DATA_PATH, "pythonratehistory.csv"), sep=",")
TOP = len(df.Python)
values = list(df.Python[BOTTOM:TOP])
title = list(df.Date[BOTTOM:TOP])
chart.add(
name="Rating",
x_axis=title,
y_axis=values,
yaxis_name="Rating (%)",
xaxis_name="Date",
# xaxis_interval=XAXIS_INTERVAL,
# is_label_show=True,
# label_formatter="{a}%",
is_legend_show=False,
is_smooth=True,
is_symbol_show=False,
line_width=4,
mark_point=['max'],
mark_point_symbolsize=60,
mark_line=["max", "min"],
is_datazoom_show=True,
is_visualmap=True,
visual_range=[0, 8])
return chart
def MemintoChart(memfile, name):
native = subprocess.getoutput(' grep "Native Heap " %s |awk \'{print $3}\' ' % memfile).split('\n')
native.insert(0, 'Native')
print(native)
dalvik = subprocess.getoutput(' grep "Dalvik Heap " %s |awk \'{print $3}\' '% memfile).split('\n')
dalvik.insert(0, 'Dalvik')
print (dalvik)
total = subprocess.getoutput('grep "TOTAL:" %s |awk \'{print $2}\' ' % memfile).split('\n')
total.insert(0, 'Total')
id = []
for i in range(len(native)-1):
id.append(i)
bar = Line(f"{name}__内存数据图形报表", "图表纵轴为数据大小，横轴为时间节点，直线为平均值")
bar.add("native", id, native[1:], label_color=['#800080'], mark_line=["average"],
mark_point=["max", "min"], xaxis_interval=0, xaxis_rotate=90)
bar.add("dalvik", id, dalvik[1:], label_color=['#0000FF'], mark_line=["average"],
mark_point=["max", "min"], xaxis_interval=0, xaxis_rotate=90)
# bar.add("total", id, total[1:], label_color=['#2E8B57'], mark_line=["average"],
# mark_point=["max", "min"], xaxis_interval=0, xaxis_rotate=90)
bar.use_theme("vintage")
t = time.time()
bar.render('%s\\memory%s.html' % (setting.data, int(t)))
['一星', '二星', '三星', '四星', '五星'],
scores.values,
is_random=True,
is_legend_show=False, #不显示图例
is_label_show=True #显示标签（各个属性的数据信息）
)
pie1.render('评分.html')
#评论数
datas['dates'] = datas['date'].apply(
lambda x: pd.Timestamp(x).date()) #注意，此处为dates，区别原date
datas['hour'] = datas['date'].apply(lambda x: pd.Timestamp(x).time().hour)
num_of_date = datas.author.groupby(datas.dates).count() #每天有多少作者评论
#评论数时间分布
chart = Line("评论数时间分布")
chart.use_theme('dark')
chart.add(
'评论时间分布',
num_of_date.index,
num_of_date.values,
is_fill=True, #填充曲线所绘制面积
line_opacity=0.2, #折线透明度
area_opacity=0.4, #区域透明度
symbol=None,
)
chart.render('评论时间分布.html')
#时间分布
num_of_hour = datas.author.groupby(datas.hour).count() #各个小时段有多少作者评论
chart = Line("评论日内时间分布")
chart.use_theme('dark')
chart.add('评论日内时间分布',
# is_random=True,
# # radius=[30, 75],
# is_legend_show=False,
# is_label_show=True
# )
# pie1.render('scores.html')
###########################################################################
# 评论时间分布
data['dates'] = data.date.apply(lambda x: pd.Timestamp(x).date())
data['time'] = data.date.apply(lambda x: pd.Timestamp(x).time().hour)
# print(data.author)
num_date = data.author.groupby(data['dates']).count()
chart = Line("评论数时间分布")
chart.use_theme('dark')
chart.add('评论时间',
num_date.index,
num_date.values,
is_fill=True,
line_opacity=0.2,
area_opacity=0.4,
symbol=None)
chart.render('comment_time_stamp.html')
# 好评字数分析
datalikes = ['num', 'likes']
datalikes = data.loc[data.likes > 5]
datalikes['num'] = datalikes.content.apply(lambda x: len(x))
chart = Scatter("likes")
for i in tokens:
d[i] = d.get(i,0) + 1
count_list = sorted(d.items(), key=lambda x:x[0], reverse=False)
del count_list[-2]
keyword_list = [k[0] for k in count_list]
k2 = []
for i in keyword_list:
k2.append(i[0])
#k_list = sorted(k, key=lambda x:x[0], reverse=False)
value_list = [k[1] for k in count_list]
v2 = value_list
line = Line(background_color='#404a59')
line.use_theme('dark')
# is_datazoom_show=True,
line.add('2018',k1,v1,mark_point=['average'], is_smooth=True)
line.add('2019',k2,v2,mark_point=['average'], is_smooth=True)
#line.add('',attr,v1)
#line.render(r"D:\BI大屏\工作经验折线.html")
page.add_chart(line,name="line")
#=============================================================================
# 漏斗图
from pyecharts import Funnel
import os as os
from bs4 import BeautifulSoup
#=============================================================================
def draw_chan(stock, stock_df): #
stock_df_original = stock_df[['open', 'close', 'low', 'high']]
stock_df_bi = stock_df[(stock_df['tb'] == TopBotType.top) |
(stock_df['tb'] == TopBotType.bot)][['chan_price']]
stock_df_xd = stock_df[(stock_df['xd_tb'] == TopBotType.top) |
(stock_df['xd_tb'] == TopBotType.bot)][[
'chan_price'
]]
crp = CentralRegionProcess(stock_df, isdebug=False)
crp.define_central_region()
stock_zs_x, stock_zs_y = crp.convert_to_graph_data()
overlap = Overlap(width=1500, height=600)
overlap.use_theme("dark")
kline = Kline("����")
kline.use_theme("dark")
kline.add(stock, [str(d) for d in stock_df_original.index],
stock_df_original.values,
is_datazoom_show=True,
datazoom_range=[80, 100],
xaxis_interval=10,
xaxis_rotate=30,
background_color='black')
overlap.add(kline)
line_1 = Line()
line_1.use_theme("dark")
line_1.add("�ֱ�", [str(d) for d in stock_df_bi.index],
stock_df_bi.values,
line_color='yellow',
is_datazoom_show=True,
xaxis_interval=10,
xaxis_rotate=30)
overlap.add(line_1)
if stock_df_xd is not None:
line_2 = Line()
line_2.use_theme("dark")
line_2.add("�ֶ�", [str(d) for d in stock_df_xd.index],
stock_df_xd.values,
line_color='blue',
mark_point_symbol="arrow",
is_datazoom_show=True,
xaxis_interval=10,
xaxis_rotate=30)
overlap.add(line_2)
# print(stock_zs_x)
# print(stock_zs_y)
for i in range(0, len(stock_zs_x), 2):
line_a = Line()
line_a.use_theme("dark")
line_a.add("����", [str(d) for d in stock_zs_x[i:i + 2]],
stock_zs_y[i:i + 2],
line_color='red',
line_width=3,
is_step=True,
is_label_show=True,
is_datazoom_show=True,
xaxis_interval=10,
xaxis_rotate=30)
overlap.add(line_a)
overlap.render('stock_chan.html')
scores = datas.rating.groupby(datas['rating']).count()
# print(scores.index.values)
pie = Pie('《工作细胞》用户评分')
pie.add(
'',
['1星', '2星', '3星', '4星', '5星'],# scores.index.values,
scores.values,
center=[50,60],
is_label_show=True,
)
pie.render('评分.html')
datas['dates'] = datas.ctime.apply(lambda x:pd.Timestamp(datetime.fromtimestamp(x)).date())
num_date = datas.mid.groupby(datas['dates']).count()
line1 = Line('评论数时间分布')
line1.use_theme('dark')
line1.add(
'',
num_date.index.values,
num_date.values,
is_fill=True,
)
line1.render('评论数时间分布.html')
datas['times'] = datas.ctime.apply(lambda x:pd.Timestamp(datetime.fromtimestamp(x)).time().hour)
num_time = datas.mid.groupby(datas['times']).count()
line2 = Line('评论数日内分布')
line2.use_theme('dark')
line2.add(
'',
num_time.index.values,
def group_line_maker(start, end, group_by):
# 子函数：获取数据
# 己函数：把数据放到 Line 中
def lining_grouped():
records = get_info(start, end)
stamps = []
top_list = [[], [], [],
[]] # cpu_percents, cpu_temps, free_rams, free_disks
average_list = [[], [], [],
[]] # cpu_percents, cpu_temps, free_rams, free_disks
bottom_list = [[], [], [],
[]] # cpu_percents, cpu_temps, free_rams, free_disks
tops = [-1000, -1000, -1000, -1000]
averages = [[], [], [], []]
bottoms = [1000, 1000, 1000, 1000]
for i in range(start + 60, end, 60):
flag = get_stamp(i, group_by)
if not stamps:
stamps.append(flag)
if i // 60 in records:
new_info = [
records[i // 60].cpu_percent,
records[i // 60].cpu_temp, records[i // 60].free_ram,
records[i // 60].free_disk
]
for ind in range(4):
tops[ind] = max(tops[ind], new_info[ind])
averages[ind].append(new_info[ind])
bottoms[ind] = min(bottoms[ind], new_info[ind])
else:
tops = ['', '', '', '']
averages = ['', '', '', '']
bottoms = ['', '', '', '']
elif flag != stamps[-1]:
stamps.append(flag)
for ind in range(4):
top_list[ind].append(
tops[ind] if tops[ind] != -1000 else 0)
ind_tmp = sum(averages[ind]) / len(
averages[ind]) if averages[ind] else 0
average_list[ind].append(round(ind_tmp, 1))
bottom_list[ind].append(
bottoms[ind] if bottoms[ind] != 1000 else 0)
tops = [-1000, -1000, -1000, -1000]
averages = [[], [], [], []]
bottoms = [1000, 1000, 1000, 1000]
elif i // 60 in records:
new_info = [
records[i // 60].cpu_percent, records[i // 60].cpu_temp,
records[i // 60].free_ram, records[i // 60].free_disk
]
for ind in range(4):
tops[ind] = max(tops[ind], new_info[ind])
averages[ind].append(new_info[ind])
bottoms[ind] = min(bottoms[ind], new_info[ind])
return stamps[1:], top_list, average_list, bottom_list
line_cpu = Line(width=width, height=height, title='CPU 监控')
line_space = Line(width=width, height=height, title='DISK 监控')
if time.localtime().tm_hour >= 21 or time.localtime().tm_hour <= 6:
line_cpu.use_theme('dark')
line_space.use_theme('dark')
stamps, [cpu_percents_top, cpu_temps_top, _, _], [
cpu_percents_average, cpu_temps_average, free_rams_average, _
], [_, _, free_rams_bottom, free_disks_bottom] = lining_grouped()
line_cpu.add('使用率峰值',
stamps,
cpu_percents_top,
is_smooth=True,
line_width=2)
line_cpu.add('平均使用率',
stamps,
cpu_percents_average,
is_smooth=True,
line_width=2)
line_cpu.add('温度峰值',
stamps,
cpu_temps_top,
is_smooth=True,
mark_line=['max'],
line_width=2)
line_cpu.add('平均温度',
stamps,
cpu_temps_average,
is_smooth=True,
line_width=2,
legend_top='bottom')
line_space.add('剩余内存谷值',
stamps,
free_rams_bottom,
is_smooth=True,
line_width=2)
line_space.add('剩余内存均值',
stamps,
free_rams_average,
is_smooth=True,
line_width=2)
line_space.add('剩余磁盘',
stamps,
free_disks_bottom,
is_smooth=True,
line_width=2,
legend_top='bottom')
js_list = list(
set(line_cpu.get_js_dependencies() + line_space.get_js_dependencies()))
return line_cpu, line_space, js_list
import pyecharts
import numpy as np
import pandas as pd
from pyecharts import Line
if __name__ == '__main__':
data = np.genfromtxt("1.txt", delimiter="\t")
shijian = data[:, 0]
renkou = data[:, 1]
qiyegeshu = data[:, 2]
lirun = data[:, -2]
print(shijian)
print(renkou)
print(qiyegeshu)
line = Line("人口变化趋势与企业活力")
line.use_theme('roma')
l = range(9)
line.add("人口(万人)", l, renkou, is_smooth=True, line_width=3)
line.add("规模以上工业企业单位数(个)",
l,
qiyegeshu,
is_smooth=True,
line_color="blue",
line_width=3)
line.add("规模以上工业企业利润总额",
l,
lirun,
is_smooth=True,
line_color="green",
line_width=3)
line.add("规模以上工业企业利润总额", shijian, lirun, is_smooth=True)
page.add(pie)
"""评论时间分布"""
# 保留当天时间
datas['dates'] = datas.date.apply(lambda x: pd.Timestamp(x).date())
sum = datas.score.groupby(datas['dates']).sum()
count = datas.score.groupby(datas['dates']).count()
index_per_day = list(sum.index)
values_per_day = list(sum / count)
values_per_day = [int(x) for x in values_per_day]
chart = Line("评分时间分布")
chart.use_theme("dark")
chart.add(
"评分",
index_per_day,
values_per_day,
)
page.add(chart)
"""每日评论数目"""
chart2 = Line("评论数时间分布")
chart2.use_theme("dark")
chart2.add("评论数",
index_per_day,
list(count.values),
# 每日平均评分
# 筛选出不为0的行
datas = datas[[int(i) > 0 for i in datas.star]]
datas.star = datas.star.astype(int)
datas["timeT"] = datas.time.apply(lambda x: pd.Timestamp(x).date())
sum = datas.star.groupby(datas['timeT']).sum()
count = datas.star.groupby(datas['timeT']).count()
per_day = sum / count
per_day_list = [int(i) for i in per_day.values]
date_list = sum.index.tolist()
line = Line("每日平均评分")
line.use_theme("dark")
line.add(
"评分",
date_list,
per_day_list,
)
page.add(line)
"每日评论数目"
line2 = Line('每日评论数目')
line2.add(
"数目",
date_list,
sum.values.tolist(),
area_opacity=0.4,
def view_info(range=False):
if not range:
start = time.time() - 1800
gap = 60 - 5
stampType = '%H:%M'
elif range == 'day':
start = time.time() - 86400
gap = 3600 - 60
stampType = '%d %H'
elif range == 'week':
start = time.time() - 86400 * 7
gap = 86400 / 4 - 60
stampType = '%d %H'
elif range == 'month':
start = time.time() - 86400 * 30
gap = 86400 - 60
stampType = '%m-%d'
else:
print('Range Error')
start = time.time() - 1800
gap = 60
stampType = '%H:%M'
flag, rdb = connect()
assert flag
line_cpu = Line(width=width, height=height, title='CPU 监控')
line_space = Line(width=width, height=height, title='DISK 监控')
stamps = []
cpuP_b = []
cpuT_b = []
cpuT_z = []
ram_b = []
ram_z = []
disk_b = []
disk_z = []
tmp = [], [], [], [], [], [], []
intKeys = [int(key) for key in rdb.keys() if int(key) > start]
gapper = start
if not intKeys:
return ('<h2>sth wrong')
if gap == 60 - 5:
for key in sorted(intKeys):
stamps.append(time.strftime(stampType, time.gmtime(key + 28800)))
tmpD = rdb.hgetall(str(key))
cpuP_b.append(tmpD['cpu_percent'])
cpuT_b.append(tmpD['cpu_temp'])
cpuT_z.append(tmpD['zero_cpu_temp'])
ram_b.append(tmpD['free_ram'])
ram_z.append(tmpD['zero_free_ram'])
disk_b.append(tmpD['free_disk'])
disk_z.append(tmpD['zero_free_disk'])
else:
for key in sorted(intKeys):
if key - gapper >= gap:
gapper = key
if tmp[0]:
stamps.append(
time.strftime(stampType, time.gmtime(key + 28800)))
cpuP_b.append(round(sum(tmp[0]) / len(tmp[0]), 0))
cpuT_b.append(round(sum(tmp[1]) / len(tmp[0]), 0))
cpuT_z.append(round(sum(tmp[2]) / len(tmp[0]), 0))
ram_b.append(round(sum(tmp[3]) / len(tmp[0]), 0))
ram_z.append(round(sum(tmp[4]) / len(tmp[0]), 0))
disk_b.append(round(sum(tmp[5]) / len(tmp[0]), 0))
disk_z.append(round(sum(tmp[6]) / len(tmp[0]), 0))
tmp = [], [], [], [], [], [], []
else:
tmpD = rdb.hgetall(str(key))
tmp[0].append(float(tmpD['cpu_percent']))
tmp[1].append(float(tmpD['cpu_temp']))
tmp[2].append(float(tmpD['zero_cpu_temp']))
tmp[3].append(float(tmpD['free_ram']))
tmp[4].append(float(tmpD['zero_free_ram']))
tmp[5].append(float(tmpD['free_disk']))
tmp[6].append(float(tmpD['zero_free_disk']))
if tmp[0]:
stamps.append(time.strftime(stampType, time.gmtime(key + 28800)))
cpuP_b.append(sum(tmp[0]) / len(tmp[0]))
cpuT_b.append(sum(tmp[1]) / len(tmp[0]))
cpuT_z.append(sum(tmp[2]) / len(tmp[0]))
ram_b.append(sum(tmp[3]) / len(tmp[0]))
ram_z.append(sum(tmp[4]) / len(tmp[0]))
disk_b.append(sum(tmp[5]) / len(tmp[0]))
disk_z.append(sum(tmp[6]) / len(tmp[0]))
tmp = [], [], [], [], [], [], []
line_cpu.add('使用率', stamps, cpuP_b, line_width=2, is_smooth=True)
line_cpu.add('温度_3B', stamps, cpuT_b, line_width=2, is_smooth=True)
line_cpu.add('温度_Z', stamps, cpuT_z, line_width=2, is_smooth=True)
line_space.add('内存_3B', stamps, ram_b, line_width=2, is_smooth=True)
line_space.add('内存_Z', stamps, ram_z, line_width=2, is_smooth=True)
if time.localtime().tm_hour >= 21 or time.localtime().tm_hour <= 6:
line_cpu.use_theme('dark')
line_space.use_theme('dark')
js_list = list(
set(line_cpu.get_js_dependencies() + line_space.get_js_dependencies()))
return render_template(
"pyecharts.html",
cpu=line_cpu.render_embed(),
space=line_space.render_embed(),
host='https://pyecharts.github.io/assets/js',
script_list=js_list,
)
