Introduction of 1 –
-
If we want to test the performance of some of our applications on Android system, we can use adb bridge to query, but if every time to manually query, to generate their own reports, it is undoubtedly very heavy work
-
Python is the scripting language for automated performance test scripts and even data analysis, which can easily meet our needs
-
Environment dependence: Mac OS, PyCharm, ADB command version 1.0.41, Mi 5 phone
2 – prepare
Connect the computer and mobile phone with the data line, and open the developer USB debugging of the mobile phone. When you input ADB Devices command on the computer, you can see the output similar to the following to prove that the connection is successful
[mac@macdeMacBook-Pro ~]$adb devices
List of devices attached
6eafb6ef device
Copy the codeOpen the netease Cloud and view the background process of the netease Cloud
[mac@macdeMacBook-Pro ~]$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
Copy the codeMainly two processes of netease cloud app com.net help ease. Cloudmusic com.net help ease. Cloudmusic: analyzing memory play,, of course, also can choose other processes
2- Use of ADB utility classes
- Because of the interaction with Android devices, ADB commands are a common tool
- We can then create child processes to execute local ADB commands
- Create the adbutil.py file and write the following code
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 command :param input: param catchResult: param input: param catchResult: param input: adb command :param input: param catchResult: param input: param catchResult:
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 specify standard input, respectively. Position of annotation output and standard error output
Set this to a pipe, and you can use variables to receive
p = subprocess.Popen(cmds, stdin=sin, stdout=subprocess.PIPE, stderr=subprocess.PIPE, shell=True)
out_data, err_data = p.communicate(input)
The result generated by # todo is wrapped in b ", under MAC
out_data = str(out_data).strip("b'").strip("'")
err_data = str(err_data).strip("b'").strip("'")
ret = p.returncode The process execution status code is 0
if catchResult is None:
ifout_data ! =' ':
return out_data
else:
return err_data
content = ' '
if out_data is not None:
content += out_data
if err_data is not None anderr_data ! =' ':
content += err_data
msg = ">>> Instruction {0}, execution error: {1}".format(cmds, err_data.strip())
print(msg)
return catchResult(content)
Copy the code3- Capture memory information
Core code:
- For easy observation, just 20 times
- perform
catch_mem()methods
def getProcessMem(process_name: str = "") :
Param process_name: :return: """
mem_info = AdbUtil.runShellCmd(f"dumpsys meminfo {process_name}").replace(r"\r\n"."\n").replace(r"\n"."\n")
# todo custom generated memory information file save directory:
process_mem_dir = Constant.MEM_ALL
if not os.path.exists(process_mem_dir):
os.makedirs(process_mem_dir)
# current fetching time
now_time = time.strftime('%Y-%m-%d_%H%M%S', time.localtime())
Write the shell command to the file
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('>>>------------ Fetch memory data ------------')
getProcessMem() Fetch total memory information
time.sleep(5)
print('>>>------------ Fetch memory data ------------ done ')
Copy the codeThe resulting memory information file directory is as follows:
The specific contents are as follows:
- It represents the memory usage of each android process at this point in time
- The next major pair
com.netease.cloudmusic和com.netease.cloudmusic:playTwo process data fetching out
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)
Copy the code3- Data analysis
- Through all the memory information files, calculate the memory occupied by the two processes of netease Cloud at each moment, and combine the memory occupied by the two processes at each moment. Finally, map the data with PyLab of Matplotlib drawing library
3-1 Data collection
- In order to store the captured data conveniently, a Cache object and DataResult class are defined to store the captured result process
- To write the Cache file, write the following code
from typing import NoReturn
# result set object
dataResult = DataResult()
# List of processes to be counted
ProcessList = ["com.netease.cloudmusic"."com.netease.cloudmusic:play"]
class DataResult:
Com.net ease.cloudmusic: {mem: com.netease.cloudmusic: {mem: com.netease.cloudmusic: X_start :[] # start:[] # time reference point}, "Com.net help ease. Cloudmusic: play" : {mem: [], x_list: [] x_start: []} # other process is same as above... } "" "
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:
Process_dict.process_name. Key contains 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)
Copy the code3-2 Core code:
- The main method is
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
Fetch a regular expression for the process memory data
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) :
""" converts to timestamp :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) :
# Filter out duplicate process data, because duplicate process data may exist in memory information files
visit_set = set(a)with open(file_path, 'r') as f:
Walk through each line of the memory information file
for line in f:
ret = mem_info_cp.search(line)
if not ret:
continue
Get the process name
process_name = ret.group(2)
Get the size of memory used
mem_size = ret.group(1).replace(","."")
If the process has not been accessed and is within the scope of the process we are observing, add the result to the Cache
if process_name not in visit_set and process_name in Cache.ProcessList:
visit_set.add(process_name)
# add the ordinate
Cache.dataResult.put(process_name, "mem".float(mem_size) / 1024)
# add the x-coordinate
# The abscissa time is not used directly, using the first memory acquisition time not reference point as the time difference
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")
# Calculate the time difference
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 is the memory information file directory, which stores the memory information file at each moment
mem_all_file_list = os.listdir(Constant.MEM_ALL)
mem_all_file_list.sort() In case the list of files retrieved is out of order
# 1- Iterate through each memory information file and store the result in the dataResult class in the Cache
for file_path in mem_all_file_list:
The absolute path to the file
file_path = os.path.join(Constant.MEM_ALL, file_path)
computeFormMemAll(file_path)
# 2- Draw a trend graph for each process in the Cache's dataResult class, and merge the data for each process to draw a trend graph
# 2-1 Summarize the data for each process and merge the abscissa and ordinate
# There is no need to deal with the time consistency of the abscissa. The more complex case is the time inconsistency, then they need to be mixed
# calculate the total y-coordinate (total memory size) by iterating through each x-coordinate.
process_name_key = Cache.ProcessList[0]
all_x_list = Cache.dataResult.get(process_name_key, "mem")
all_y_list = []
Walk through the x coordinate of each time
for x in all_x_list:
# Go to each process to find if the x coordinate exists, if there is a merge memory size
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 - drawing
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")
# Calculate the maximum memory size
max_mem_value = max(mem_list)
Calculate the memory average
avg_mem_value = np.mean(mem_list)
if title:
if count % 3= =0 andcount ! =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)
Copy the codeThe following output is generated:
