android 端取 cpu,fps,men,wifi/gprs 流量等值

最近在做性能监控移动端的方面的研究，把遇到的问题总结下:

• 尽量不要用 split 切割的方式取值，因为不同手机取值不一样，还可能会出现数组越界
• 取 cpu 占用情况时，不要用 top 和 dump cpuinfo,因为他们拿到的是几个 cpu 的值，会出现超过 100%

取 men 占用情况

cmd = "adb -s " +  devices +" shell  dumpsys  meminfo %s" % (pkg_name)
print(cmd)
output = subprocess.check_output(cmd).split()
s_men = ".".join([x.decode() for x in output]) # 转换为string
print(s_men)
men2 = int(re.findall("TOTAL.(d+)*", s_men, re.S)[0])
print(men2 )

取电量占用情况

def get_battery(devices):
cmd = "adb -s " + devices + " shell dumpsys battery"
print(cmd)
output = subprocess.check_output(cmd).split()
stderr=subprocess.PIPE).stdout.readlines()
st = ".".join([x.decode() for x in output]) # 转换为string
print(st)
battery2 = int(re.findall("level:.(d+)*", st, re.S)[0])
writeInfo(battery2, PATH("../info/" + devices + "_battery.pickle"))
return battery2

取 wifi,gprs 上下行流量

def get_flow(pid, type, devices):
# pid = get_pid(pkg_name)
_flow1 = [[], []]
if pid is not None:
cmd = "adb -s " + devices + " shell cat /proc/" + pid + "/net/dev"
print(cmd)
_flow = subprocess.Popen(cmd, shell=True, stdout=subprocess.PIPE,
stderr=subprocess.PIPE).stdout.readlines()
for item in _flow:
if type == "wifi" and item.split()[0].decode() == "wlan0:":  # wifi
# 0 上传流量，1 下载流量
_flow1[0].append(int(item.split()[1].decode()))
_flow1[1].append(int(item.split()[9].decode()))
print("------flow---------")
print(_flow1)
break
if type == "gprs" and item.split()[0].decode() == "rmnet0:":  # gprs
print("-----flow---------")
_flow1[0].append(int(item.split()[1].decode()))
_flow1[1].append(int(item.split()[9].decode()))
print(_flow1)
break
else:
_flow1[0].append(0)
_flow1[1].append(0)

得到 cpu 几核

def get_cpu_kel(devices):
cmd = "adb -s " + devices + " shell cat /proc/cpuinfo"
print(cmd)
output = subprocess.check_output(cmd).split()
sitem = ".".join([x.decode() for x in output])  # 转换为string
return len(re.findall("processor", sitem))

取 cpu 使用情况

'''
每一个cpu快照均
'''
def totalCpuTime(devices):
user=nice=system=idle=iowait=irq=softirq= 0
'''
user:从系统启动开始累计到当前时刻，处于用户态的运行时间，不包含 nice值为负进程。
nice:从系统启动开始累计到当前时刻，nice值为负的进程所占用的CPU时间
system 从系统启动开始累计到当前时刻，处于核心态的运行时间
idle 从系统启动开始累计到当前时刻，除IO等待时间以外的其它等待时间
iowait 从系统启动开始累计到当前时刻，IO等待时间(since 2.5.41)
irq 从系统启动开始累计到当前时刻，硬中断时间(since 2.6.0-test4)
softirq 从系统启动开始累计到当前时刻，软中断时间(since 2.6.0-test4)
stealstolen  这是时间花在其他的操作系统在虚拟环境中运行时（since 2.6.11）
guest 这是运行时间guest 用户Linux内核的操作系统的控制下的一个虚拟CPU（since 2.6.24）
'''
cmd = "adb -s " + devices +" shell cat /proc/stat"
print(cmd)
p = subprocess.Popen(cmd, stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
stdin=subprocess.PIPE, shell=True)
(output, err) = p.communicate()
res = output.split()
for info in res:
if info.decode() == "cpu":
user = res[1].decode()
nice = res[2].decode()
system = res[3].decode()
idle = res[4].decode()
iowait = res[5].decode()
irq = res[6].decode()
softirq = res[7].decode()
print("user=" + user)
print("nice=" + nice)
print("system=" + system)
print("idle=" + idle)
print("iowait=" + iowait)
print("irq=" + irq)
print("softirq=" + softirq)
result = int(user) + int(nice) + int(system) + int(idle) + int(iowait) + int(irq) + int(softirq)
print("totalCpuTime"+str(result))
return result
'''
每一个进程快照
'''
def processCpuTime(pid, devices):
'''
pid     进程号
utime   该任务在用户态运行的时间，单位为jiffies
stime   该任务在核心态运行的时间，单位为jiffies
cutime  所有已死线程在用户态运行的时间，单位为jiffies
cstime  所有已死在核心态运行的时间，单位为jiffies
'''
utime=stime=cutime=cstime = 0
cmd = "adb -s "+ devices + " shell cat /proc/" + pid +"/stat"
print(cmd)
p = subprocess.Popen(cmd, stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
stdin=subprocess.PIPE, shell=True)
(output, err) = p.communicate()
res = output.split()
utime = res[13].decode()
stime = res[14].decode()
cutime = res[15].decode()
cstime = res[16].decode()
print("utime="+utime)
print("stime="+stime)
print("cutime="+cutime)
print("cstime="+cstime)
result = int(utime) + int(stime) + int(cutime) + int(cstime)
print("processCpuTime="+str(result))
return result
'''
计算某进程的cpu使用率
100*( processCpuTime2 – processCpuTime1) / (totalCpuTime2 – totalCpuTime1) (按100%计算，如果是多核情况下还需乘以cpu的个数);
cpukel cpu几核
pid 进程id
'''
def cpu_rate(pid, cpukel, devices):
# pid = get_pid(pkg_name)
processCpuTime1 = processCpuTime(pid, devices)
time.sleep(1)
processCpuTime2 = processCpuTime(pid, devices)
processCpuTime3 = processCpuTime2 - processCpuTime1
totalCpuTime1 = totalCpuTime(devices)
time.sleep(1)
totalCpuTime2 = totalCpuTime(devices)
totalCpuTime3 = (totalCpuTime2 - totalCpuTime1)*cpukel
cpu = 100 * (processCpuTime3) / (totalCpuTime3)
print(cpu)

得到 fps

'''
@author fenfenzhong
'''
def get_fps(pkg_name, devices):
_adb = "adb -s " + devices +" shell dumpsys gfxinfo %s" % pkg_name
print(_adb)
results = os.popen(_adb).read().strip()
frames = [x for x in results.split('n') if validator(x)]
frame_count = len(frames)
jank_count = 0
vsync_overtime = 0
render_time = 0
for frame in frames:
time_block = re.split(r's+', frame.strip())
if len(time_block) == 3:
try:
render_time = float(time_block[0]) + float(time_block[1]) + float(time_block[2])
except Exception as e:
render_time = 0
'''
当渲染时间大于16.67，按照垂直同步机制，该帧就已经渲染超时
那么，如果它正好是16.67的整数倍，比如66.68，则它花费了4个垂直同步脉冲，减去本身需要一个，则超时3个
如果它不是16.67的整数倍，比如67，那么它花费的垂直同步脉冲应向上取整，即5个，减去本身需要一个，即超时4个，可直接算向下取整
最后的计算方法思路：
执行一次命令，总共收集到了m帧（理想情况下m=128），但是这m帧里面有些帧渲染超过了16.67毫秒，算一次jank，一旦jank，
需要用掉额外的垂直同步脉冲。其他的就算没有超过16.67，也按一个脉冲时间来算（理想情况下，一个脉冲就可以渲染完一帧）
所以FPS的算法可以变为：
m / （m + 额外的垂直同步脉冲） * 60
'''
if render_time > 16.67:
jank_count += 1
if render_time % 16.67 == 0:
vsync_overtime += int(render_time / 16.67) - 1
else:
vsync_overtime += int(render_time / 16.67)
_fps = int(frame_count * 60 / (frame_count + vsync_overtime))
# return (frame_count, jank_count, fps)
print("-----fps------")
print(_fps)

取值 cpu 参考资料

• How do I get the total CPU usage of an application from /proc/pid/stat
• Why is the CPU usage reported by top in Linux over 100%
• PROC 系列之---/proc/pid/stat 详解

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