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Python 动态抓取 Android 进程内存信息 数据可视化_python实现对手机性能的可视化分析
作者：我家小花儿 | 2024-04-22 02:49:02
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python实现对手机性能的可视化分析
1-简介
- 如果我们想要测试我们的某些应用在安卓系统上的性能使用情况, 我们可以通过adb桥去查询, 但是如果每次都去手动查询, 自己生成报表,那无疑是非常繁重的工作
- 而python就是实现性能自动化测试脚本的脚本语言甚至数据分析、可以方便的实现我们的需求
- 环境依赖: Mac OS、 PyCharm、adb命令1.0.41版本、小米5手机
2-准备
用数据线连接电脑和手机, 并打开手机的开发者usb调试,当在电脑端输入 adb devices
命令能后查看到如下类似输出证明连接成功
[mac@macdeMacBook-Pro ~]$adb devices
List of devices attached
6eafb6ef device
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打开网易云,然后查看网易云后台进程
[mac@macdeMacBook-Pro ~]$adb shell ps | grep music*
u0_a140 13763 763 2220068 318184 futex_wait 0000000000 S com.netease.cloudmusic
u0_a140 13901 763 1955592 148572 SyS_epoll_ 0000000000 S com.netease.cloudmusic:videoplay
u0_a140 13939 763 1945240 152800 SyS_epoll_ 0000000000 S com.netease.cloudmusic:play
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主要是对网易云app的两个进程 com.netease.cloudmusic com.netease.cloudmusic:play 进行内存分析,当然也可以选择其他进程
2- ADB 工具类 的 使用
- 由于要和android设备之间进行交互, 而adb命令就是常用手段之一
- 那么我们就可以创建子进程去执行本地的adb命令
- 创建AdbUtil.py文件编写下面代码
import subprocess
import re
def runShellCmd(cmds: str, input=None, catchResult=None) -> str:
cmds = "shell " + cmds
return runCmd(cmds, input, catchResult)
def runCmd(cmds: str, input=None, catchResult=None) -> str:
"""
:param cmds: adb 命令
:param input: 自定义标准输入
:param catchResult: 自定义去从标准输出中抓取数据返回的函数
:return:
"""
cmds = 'adb ' + cmds
# p = subprocess.Popen(cmds, stdin=subprocess.PIPE, stderr=subprocess.PIPE,
# creationflags=subprocess.CREATE_NEW_PROCESS_GROUP)
sin = subprocess.PIPE
if input is not None and type('') != type(input):
sin = input
input = None
# stdin、stdout、stderr 分别指定标准输入.标注输出、标准错误输出的位置
# 这里设置为管道,就可以用变量接收
p = subprocess.Popen(cmds, stdin=sin, stdout=subprocess.PIPE, stderr=subprocess.PIPE, shell=True)
out_data, err_data = p.communicate(input)
# 生成的结果被b''包裹着， 在mac下
out_data = str(out_data).strip("b'").strip("'")
err_data = str(err_data).strip("b'").strip("'")
ret = p.returncode # 进程执行状态码，0正常
if catchResult is None:
if out_data != '':
return out_data
else:
return err_data
content = ''
if out_data is not None:
content += out_data
if err_data is not None and err_data != '':
content += err_data
msg = ">>>指令{0},执行过程出现错误: {1}".format(cmds, err_data.strip())
print(msg)
return catchResult(content)
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3-抓取内存信息
核心代码:
- 为了方便观察,抓20次即可
- 执行
catch_mem()方法
def getProcessMem(process_name: str = ""):
"""
抓取指定进程名称的内存信息
:param process_name:
:return:
"""
mem_info = AdbUtil.runShellCmd(f"dumpsys meminfo {process_name}").replace(r"\r\n", "\n").replace(r"\n", "\n")
# todo 自定义生成的内存信息文件保存目录:
process_mem_dir = Constant.MEM_ALL
if not os.path.exists(process_mem_dir):
os.makedirs(process_mem_dir)
# 当前抓取时间
now_time = time.strftime('%Y-%m-%d_%H%M%S', time.localtime())
# 直接把shell命令结果写入文件即可
with open(os.path.join(process_mem_dir, r'mem_' + now_time + r'.txt'), 'w') as f:
f.write(mem_info)
f.close()
del f
def catch_mem() -> NoReturn:
count = 20
while count:
count -= 1
print('>>>------------抓取内存数据------------')
getProcessMem() # 抓取总体的内存信息
time.sleep(5)
print('>>>------------抓取内存数据------------ 完成')
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效果最终生成内存信息文件目录如下:
具体内容为:
- 它代表着这一时刻, 安卓系统每个进程的内存占用情况
- 接下来主要对
com.netease.cloudmusic和com.netease.cloudmusic:play两个进程数据的抓取出来
Applications Memory Usage (in Kilobytes):
Uptime: 6668394 Realtime: 189563989
Total PSS by process:
290,590K: com.netease.cloudmusic (pid 13763 / activities)
205,009K: system (pid 1448)
120,151K: com.android.systemui (pid 1774 / activities)
116,014K: com.miui.home (pid 2713 / activities)
86,907K: surfaceflinger (pid 581)
81,680K: com.netease.cloudmusic:play (pid 13939)
60,001K: com.netease.cloudmusic:videoplay (pid 13901)
56,121K: com.android.mms (pid 26214)
55,870K: com.android.quicksearchbox (pid 13349)
50,151K: com.baidu.input_mi (pid 5107)
44,296K: com.kingroot.kinguser:service (pid 13994)
42,446K: clatd-rmnet_data6 (pid 28499)
38,950K: com.android.phone (pid 1939)
35,180K: com.miui.securitycenter.remote (pid 2937)
33,103K: com.miui.yellowpage (pid 26158)
31,965K: com.android.fileexplorer (pid 17523 / activities)
30,751K: com.miui.gallery (pid 16605 / activities)
28,547K: tpsd (pid 15014)
26,680K: com.miui.whetstone (pid 2629)
24,413K: com.miui.analytics (pid 13643)
23,982K: com.android.settings (pid 15352 / activities)
23,568K: com.miui.cleanmaster:cmsdk (pid 16917)
21,652K: android.process.media (pid 13721)
21,092K: app_process (pid 9387)
20,332K: com.miui.powerkeeper:service (pid 3034)
19,673K: com.lbe.security.miui (pid 3310)
19,295K: mm-qcamera-daemon (pid 897)
18,247K: logd (pid 455)
18,142K: kgod (pid 9341)
16,511K: com.xiaomi.metoknlp (pid 2864)
15,993K: com.miui.daemon (pid 2588)
15,938K: com.miui.systemAdSolution (pid 13601)
15,522K: com.android.calendar (pid 18319)
15,204K: com.xiaomi.xmsf (pid 2503)
13,407K: android.process.acore (pid 26129)
13,336K: com.xiaomi.finddevice (pid 2609)
13,177K: .dataservices (pid 2564)
13,000K: com.android.nfc (pid 2603)
12,828K: com.miui.cloudservice (pid 17967)
12,382K: com.miui.personalassistant (pid 26518)
12,229K: com.android.mms (pid 19865)
10,614K: zygote (pid 763)
10,493K: com.miui.sysbase (pid 2999)
10,012K: audioserver (pid 769)
9,913K: com.mipay.wallet (pid 26499)
9,556K: com.miui.weather2:pushservice (pid 15654)
9,307K: com.android.providers.calendar (pid 18339)
8,905K: rild (pid 847)
8,843K: com.android.settings:remote (pid 15602)
8,092K: com.android.deskclock (pid 25934)
6,758K: com.miui.contentcatcher (pid 2558)
6,668K: com.xiaomi.simactivate.service (pid 26243)
6,573K: rild (pid 938)
6,115K: zygote64 (pid 761)
5,654K: com.miui.core (pid 2749)
5,634K: media.extractor (pid 843)
5,367K: cameraserver (pid 792)
5,314K: ims_rtp_daemon (pid 956)
5,262K: cnd (pid 754)
4,766K: com.xiaomi.android.dm.service (pid 25404)
4,577K: imscmservice (pid 957)
4,519K: com.miui.wmsvc (pid 4101)
4,519K: mm-pp-dpps (pid 599)
4,481K: mtd (pid 906)
4,413K: android.ext.services (pid 15250)
4,402K: netmgrd (pid 962)
4,352K: com.qualcomm.qcrilmsgtunnel (pid 1928)
4,202K: com.android.printspooler (pid 14814)
4,199K: shelld (pid 764)
4,191K: mediaserver (pid 845)
4,095K: qseeproxydaemon (pid 896)
4,053K: qseer2fdaemon (pid 911)
4,041K: qpaydaemon (pid 905)
4,015K: qvop-daemon (pid 900)
3,814K: com.miui.audioeffect (pid 25836)
3,734K: com.qualcomm.qti.services.secureui:sui_service (pid 2645)
3,675K: com.qualcomm.timeservice (pid 19888)
3,637K: com.xiaomi.micloud.sdk (pid 26995)
3,595K: media.codec (pid 802)
3,230K: netd (pid 846)
2,858K: thermal-engine (pid 755)
2,816K: vsimd (pid 907)
2,666K: wpa_supplicant (pid 25086)
2,516K: imsdatadaemon (pid 921)
2,063K: audiod (pid 903)
2,043K: usf_proximity (pid 904)
2,036K: slim_daemon (pid 1150)
1,793K: lowi-server (pid 1149)
1,783K: /init (pid 1)
1,559K: mcd (pid 2455)
1,533K: tcpdump (pid 5096)
1,525K: vold (pid 466)
1,496K: drmserver (pid 795)
1,437K: 260 (pid 7108)
1,434K: mediadrmserver (pid 805)
1,225K: keystore (pid 799)
1,148K: cnss_diag (pid 5095)
1,143K: cnss-daemon (pid 889)
1,099K: ipacm (pid 928)
1,074K: loc_launcher (pid 890)
1,052K: qti (pid 951)
1,048K: fingerprintd (pid 899)
1,046K: sensors.qcom (pid 662)
1,023K: time_daemon (pid 902)
999K: dpmd (pid 920)
995K: imsqmidaemon (pid 757)
976K: installd (pid 798)
967K: qcom-system-daemon (pid 752)
948K: ueventd (pid 423)
910K: adbd (pid 10336)
902K: pm-service (pid 575)
853K: qseecomd (pid 618)
829K: gatekeeperd (pid 912)
789K: displayfeature (pid 600)
747K: perfd (pid 475)
746K: seempd (pid 21744)
733K: lmkd (pid 579)
719K: fdpp (pid 766)
719K: mlipayd (pid 908)
715K: pm-proxy (pid 598)
703K: servicemanager (pid 580)
646K: fidoca (pid 909)
635K: dumpsys (pid 28666)
631K: mqsasd (pid 767)
612K: rmt_storage (pid 567)
576K: healthd (pid 564)
569K: adsprpcd (pid 756)
559K: tftp_server (pid 569)
551K: otad (pid 765)
547K: sh (pid 24482)
541K: qseecomd (pid 577)
535K: sh (pid 14103)
534K: dpmd (pid 850)
521K: port-bridge (pid 967)
511K: sh (pid 9365)
503K: sh (pid 14935)
494K: ipacm-diag (pid 917)
488K: subsystem_ramdump (pid 6469)
480K: hvdcp_opti (pid 759)
456K: gpu_monitor (pid 6624)
336K: debuggerd64 (pid 465)
317K: debuggerd64:signaller (pid 468)
309K: debuggerd (pid 464)
307K: 61:0 (pid 7101)
258K: 61:0 (pid 8197)
257K: debuggerd:signaller (pid 469)
243K: 41:10139 (pid 14099)
243K: 41:10139 (pid 14929)
173K: 41:0 (pid 14102)
173K: 41:0 (pid 14934)
153K: 41:0 (pid 7866)
151K: 260 (pid 7115)
104K: watchdogd (pid 14504)
100K: watchdogd (pid 7584)
94K: ku.sud (pid 7576)
92K: ktools (pid 14439)
67K: ku.sud (pid 7574)
Total PSS by OOM adjustment:
415,302K: Native
86,907K: surfaceflinger (pid 581)
42,446K: clatd-rmnet_data6 (pid 28499)
28,547K: tpsd (pid 15014)
21,092K: app_process (pid 9387)
19,295K: mm-qcamera-daemon (pid 897)
18,247K: logd (pid 455)
18,142K: kgod (pid 9341)
10,614K: zygote (pid 763)
10,012K: audioserver (pid 769)
8,905K: rild (pid 847)
6,573K: rild (pid 938)
6,115K: zygote64 (pid 761)
5,634K: media.extractor (pid 843)
5,367K: cameraserver (pid 792)
5,314K: ims_rtp_daemon (pid 956)
5,262K: cnd (pid 754)
4,577K: imscmservice (pid 957)
4,519K: mm-pp-dpps (pid 599)
4,481K: mtd (pid 906)
4,402K: netmgrd (pid 962)
4,199K: shelld (pid 764)
4,191K: mediaserver (pid 845)
4,095K: qseeproxydaemon (pid 896)
4,053K: qseer2fdaemon (pid 911)
4,041K: qpaydaemon (pid 905)
4,015K: qvop-daemon (pid 900)
3,595K: media.codec (pid 802)
3,230K: netd (pid 846)
2,858K: thermal-engine (pid 755)
2,816K: vsimd (pid 907)
2,666K: wpa_supplicant (pid 25086)
2,516K: imsdatadaemon (pid 921)
2,063K: audiod (pid 903)
2,043K: usf_proximity (pid 904)
2,036K: slim_daemon (pid 1150)
1,793K: lowi-server (pid 1149)
1,783K: /init (pid 1)
1,559K: mcd (pid 2455)
1,533K: tcpdump (pid 5096)
1,525K: vold (pid 466)
1,496K: drmserver (pid 795)
1,437K: 260 (pid 7108)
1,434K: mediadrmserver (pid 805)
1,225K: keystore (pid 799)
1,148K: cnss_diag (pid 5095)
1,143K: cnss-daemon (pid 889)
1,099K: ipacm (pid 928)
1,074K: loc_launcher (pid 890)
1,052K: qti (pid 951)
1,048K: fingerprintd (pid 899)
1,046K: sensors.qcom (pid 662)
1,023K: time_daemon (pid 902)
999K: dpmd (pid 920)
995K: imsqmidaemon (pid 757)
976K: installd (pid 798)
967K: qcom-system-daemon (pid 752)
948K: ueventd (pid 423)
910K: adbd (pid 10336)
902K: pm-service (pid 575)
853K: qseecomd (pid 618)
829K: gatekeeperd (pid 912)
789K: displayfeature (pid 600)
747K: perfd (pid 475)
746K: seempd (pid 21744)
733K: lmkd (pid 579)
719K: fdpp (pid 766)
719K: mlipayd (pid 908)
715K: pm-proxy (pid 598)
703K: servicemanager (pid 580)
646K: fidoca (pid 909)
635K: dumpsys (pid 28666)
631K: mqsasd (pid 767)
612K: rmt_storage (pid 567)
576K: healthd (pid 564)
569K: adsprpcd (pid 756)
559K: tftp_server (pid 569)
551K: otad (pid 765)
547K: sh (pid 24482)
541K: qseecomd (pid 577)
535K: sh (pid 14103)
534K: dpmd (pid 850)
521K: port-bridge (pid 967)
511K: sh (pid 9365)
503K: sh (pid 14935)
494K: ipacm-diag (pid 917)
488K: subsystem_ramdump (pid 6469)
480K: hvdcp_opti (pid 759)
456K: gpu_monitor (pid 6624)
336K: debuggerd64 (pid 465)
317K: debuggerd64:signaller (pid 468)
309K: debuggerd (pid 464)
307K: 61:0 (pid 7101)
258K: 61:0 (pid 8197)
257K: debuggerd:signaller (pid 469)
243K: 41:10139 (pid 14099)
243K: 41:10139 (pid 14929)
173K: 41:0 (pid 14102)
173K: 41:0 (pid 14934)
153K: 41:0 (pid 7866)
151K: 260 (pid 7115)
104K: watchdogd (pid 14504)
100K: watchdogd (pid 7584)
94K: ku.sud (pid 7576)
92K: ktools (pid 14439)
67K: ku.sud (pid 7574)
476,344K: Persistent
205,009K: system (pid 1448)
120,151K: com.android.systemui (pid 1774 / activities)
38,950K: com.android.phone (pid 1939)
26,680K: com.miui.whetstone (pid 2629)
15,993K: com.miui.daemon (pid 2588)
15,204K: com.xiaomi.xmsf (pid 2503)
13,336K: com.xiaomi.finddevice (pid 2609)
13,177K: .dataservices (pid 2564)
13,000K: com.android.nfc (pid 2603)
6,758K: com.miui.contentcatcher (pid 2558)
4,352K: com.qualcomm.qcrilmsgtunnel (pid 1928)
3,734K: com.qualcomm.qti.services.secureui:sui_service (pid 2645)
325,770K: Foreground
290,590K: com.netease.cloudmusic (pid 13763 / activities)
35,180K: com.miui.securitycenter.remote (pid 2937)
332,747K: Visible
81,680K: com.netease.cloudmusic:play (pid 13939)
60,001K: com.netease.cloudmusic:videoplay (pid 13901)
50,151K: com.baidu.input_mi (pid 5107)
44,296K: com.kingroot.kinguser:service (pid 13994)
24,413K: com.miui.analytics (pid 13643)
20,332K: com.miui.powerkeeper:service (pid 3034)
16,511K: com.xiaomi.metoknlp (pid 2864)
15,938K: com.miui.systemAdSolution (pid 13601)
10,493K: com.miui.sysbase (pid 2999)
4,519K: com.miui.wmsvc (pid 4101)
4,413K: android.ext.services (pid 15250)
135,687K: Perceptible
116,014K: com.miui.home (pid 2713 / activities)
19,673K: com.lbe.security.miui (pid 3310)
8,843K: A Services
8,843K: com.android.settings:remote (pid 15602)
41,628K: B Services
21,652K: android.process.media (pid 13721)
9,556K: com.miui.weather2:pushservice (pid 15654)
5,654K: com.miui.core (pid 2749)
4,766K: com.xiaomi.android.dm.service (pid 25404)
371,036K: Cached
56,121K: com.android.mms (pid 26214)
55,870K: com.android.quicksearchbox (pid 13349)
33,103K: com.miui.yellowpage (pid 26158)
31,965K: com.android.fileexplorer (pid 17523 / activities)
30,751K: com.miui.gallery (pid 16605 / activities)
23,982K: com.android.settings (pid 15352 / activities)
23,568K: com.miui.cleanmaster:cmsdk (pid 16917)
15,522K: com.android.calendar (pid 18319)
13,407K: android.process.acore (pid 26129)
12,828K: com.miui.cloudservice (pid 17967)
12,382K: com.miui.personalassistant (pid 26518)
12,229K: com.android.mms (pid 19865)
9,913K: com.mipay.wallet (pid 26499)
9,307K: com.android.providers.calendar (pid 18339)
8,092K: com.android.deskclock (pid 25934)
6,668K: com.xiaomi.simactivate.service (pid 26243)
4,202K: com.android.printspooler (pid 14814)
3,814K: com.miui.audioeffect (pid 25836)
3,675K: com.qualcomm.timeservice (pid 19888)
3,637K: com.xiaomi.micloud.sdk (pid 26995)
Total PSS by category:
472,527K: Dalvik
436,483K: Native
192,622K: .dex mmap
151,645K: .so mmap
129,297K: .oat mmap
117,536K: EGL mtrack
92,916K: GL mtrack
91,727K: .art mmap
69,264K: Unknown
64,578K: Dalvik Other
57,817K: .apk mmap
57,442K: Gfx dev
54,476K: Other mmap
32,094K: Stack
13,882K: Ashmem
2,783K: .ttf mmap
2,323K: Other dev
125K: .jar mmap
0K: Cursor
0K: Other mtrack
Total RAM: 3,801,352K (status normal)
Free RAM: 2,166,428K ( 371,036K cached pss + 1,707,492K cached kernel + 87,900K free)
Used RAM: 2,173,785K (1,736,321K used pss + 437,464K kernel)
Lost RAM: -555,544K
ZRAM: 17,996K physical used for 73,556K in swap (2,306,044K total swap)
Tuning: 256 (large 512), oom 322,560K, restore limit 107,520K (high-end-gfx)
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3-数据分析
- 遍历所有内存信息文件,统计出每一时刻网易云的两个进程占用内存的大小, 同时也要合并两个进程在每一时刻进程共同占用的内存大小, 最终用matplotlib画图库的pylab进行绘图, 将数据进行可视化展示
3-1 数据收集
- 为了方便将抓取出的数据进行存储,定义一个Cache对象 和 DataResult类对抓取的结果进程存储
- 编写Cache文件, 编写下面代码
from typing import NoReturn
# 结果集对象
dataResult = DataResult()
# 需要统计的进程列表
ProcessList = ["com.netease.cloudmusic", "com.netease.cloudmusic:play"]
class DataResult:
""" 其实是一个多级字典,存放每个进程的内存趋势变化
结构大致如下:
比如
{
"com.netease.cloudmusic": {
mem: [], # 绘图所需的 y 坐标集合 (内存大小)
x_list:[] # 绘图所需的 x 坐标集合 (时间)
x_start:[] # 时间基准点(这个里面就存一个时,但是为了统一操作还是定义成集合)
},
"com.netease.cloudmusic:play": {
mem: [],
x_list:[]
x_start:[]
}
# 其他进程同上
....
}
"""
process_dict: dict = {}
def __init__(self) -> None:
pass
def isContainProcessKey(self, process_name, key) -> bool:
process: dict = self.process_dict.get(process_name)
if process is None or process.get(key) is None:
return False
value_list: list = process.get(key)
return len(value_list) != 0
def put(self, process_name, key, value) -> NoReturn:
if self.process_dict.get(process_name) is None:
self.process_dict[process_name] = {}
if self.process_dict[process_name].get(key) is None:
self.process_dict[process_name][key] = []
self.process_dict[process_name][key].append(value)
def isContaionProcessValue(self, process_name, key, value) -> bool:
"""
判断process_dict.process_name.key 是否包含 value
:param process_name:
:param key:
:param value:
:return:
"""
process: dict = self.process_dict.get(process_name)
if process is None or process.get(key) is None:
return False
if value in self.process_dict[process_name][key]:
return True
return False
def get(self, process_name, key, default=None) -> list:
process: dict = self.process_dict.get(process_name)
if process is None or process.get(key) is None:
return default
return process[key]
def __str__(self) -> str:
return str(self.process_dict)
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3-2 核心代码:
- 主方法是
mem_all_trend()
import project.auto_test.entity.Cache as Cache
import project.auto_test.utils.Constant as Constant
import os
import re
import time
from matplotlib import pylab as pl
import numpy as np
# 抓取进程内存数据的正则表达式
# 示例: 290,590K: com.netease.cloudmusic (pid 13763 / activities)
mem_info_cp = re.compile(r"\s*([\d,]*)\s*[Kk][Bb]?:\s*(\S*)\s*\(pid\s*\d+(\s*/\s*\S*)?\)")
file_name_time_cp = re.compile(r"\d{4}-\d{1,2}-\d{1,2}_\d+")
def convertFileName2Time(file_name: str):
"""
转成时间戳
:param file_name:
:return:
"""
logTime = file_name_time_cp.search(file_name).group()
timeArray = time.strptime(logTime, "%Y-%m-%d_%H%M%S")
return time.mktime(timeArray)
def computeFormMemAll(file_path):
# 过滤掉重复的进程数据,因为内存信息文件里可能存在重复的进程数据
visit_set = set()
with open(file_path, 'r') as f:
# 遍历该内存信息文件的每一行
for line in f:
ret = mem_info_cp.search(line)
if not ret:
continue
# 获得进程名
process_name = ret.group(2)
# 获得占用的内存大小
mem_size = ret.group(1).replace(",", "")
# 如果该进程未被访问过, 并且是在我们要观察的进程范围内,则把结果加入Cache中
if process_name not in visit_set and process_name in Cache.ProcessList:
visit_set.add(process_name)
# 添加纵坐标
Cache.dataResult.put(process_name, "mem", float(mem_size) / 1024)
# 添加横坐标
# 横坐标的时间不直接使用,以第一个内存采集的时间未基准点作时间差
if Cache.dataResult.get(process_name, "x_start") is None:
Cache.dataResult.put(process_name, "x_start", convertFileName2Time(file_path))
Cache.dataResult.put(process_name, "x_list", 0)
else:
value_lit = Cache.dataResult.get(process_name, "x_start")
# 计算时间差
del_time = convertFileName2Time(file_path) - value_lit[0]
Cache.dataResult.put(process_name, "x_list", del_time)
f.close()
def mem_all_trend():
# todo Constant.MEM_ALL就是上图表示的内存信息文件目录,里面存放每一时刻的内存信息文件
mem_all_file_list = os.listdir(Constant.MEM_ALL)
mem_all_file_list.sort() # 以防获取的文件列表是乱序的
# 1-遍历每一个内存信息文件,将抓取出的结果存放到 Cache的dataResult类中
for file_path in mem_all_file_list:
# 文件绝对路径
file_path = os.path.join(Constant.MEM_ALL, file_path)
computeFormMemAll(file_path)
# 2- 遍历 Cache的dataResult类的每个进程数据画一条趋势变化图,而且还要合并每个进程的数据再画一条趋势变化图
# 2-1 汇总每个进程的数据,合并出横坐标和纵坐标
# 横坐标的时间一致不需额外处理, 更复杂的情况是时间不一致这时需要将他们进行混合
# 遍历每个横坐标的时间,计算出总的y坐标(内存总大小)
process_name_key = Cache.ProcessList[0]
all_x_list = Cache.dataResult.get(process_name_key, "mem")
all_y_list = []
# 遍历每个 时间x坐标
for x in all_x_list:
# 去每一个进程找是否存在该x坐标,如果存在合并内存大小
y = 0
for process_name_key in Cache.dataResult.process_dict:
mem_list = Cache.dataResult.get(process_name_key, "mem")
x_list = Cache.dataResult.get(process_name_key, "x_list")
index = x_list.index(x)
if index >= 0:
y += mem_list[index]
all_y_list.append(y)
# 3-绘图
line_color = ['b', 'g', 'r', 'c', 'm', 'y', 'k']
count = 0
title = ""
result_dict = Cache.dataResult.process_dict.copy()
result_dict["all"] = {}
result_dict["all"]["mem"] = all_y_list
result_dict["all"]["x_list"] = all_x_list
for key in result_dict:
mem_list = result_dict.get(key).get("mem")
x_list = result_dict.get(key).get("x_list")
# 计算内存最大值
max_mem_value = max(mem_list)
# 计算内存平均值
avg_mem_value = np.mean(mem_list)
if title:
if count % 3 == 0 and count != 0:
title += '\n'
else:
title += ' | '
title += '{0}_mean={1:.2f}{3},{0}_max={2:.2f}{3}\n'.format(key, avg_mem_value, max_mem_value, "MB")
pl.plot(x_list, mem_list, 'o{0}-'.format(line_color[count]), label=key)
count += 1
if count >= 7:
count = 0
pl.title(title)
pl.legend(loc='best')
pl.xlabel('Time(s)')
pl.ylabel('Size({0})'.format("MB"))
pl.show(block=False)
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执行后生成的结果如下:
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