check battery plot

check_batt.py

#!/bin/env python
import subprocess
from time import sleep
from datetime import datetime,timedelta
import json
import plotext as plt
from math import ceil
plt.date_form('H:M:S')
plt.theme('pro')
plt.limit_size(False,True)
plt.plotsize(55, 15)
avg_wind=4 # avg. window for dSOC/dt
def step(times,dt,top_vars,past,avg_wind):
    output=json.loads(subprocess.check_output('termux-battery-status').decode())
    top_vars+=[[output['current'],output['voltage'],output['percentage']]]
    plt.clt() # to clear the terminal
    plt.cld() # to clear the data only
    plt.subplots(2,1)
    plt.subplots(1,1).plotsize(plt.tw(),int(ceil(plt.th()*0.9))) # should not work but int(ceil(plt.th()*2/3)) results smaller than height
    plt.subplots(2,1).plotsize(plt.tw(),int(ceil(plt.th()*0.9))) # should not work but int(ceil(plt.th()*1/3)) results smaller than height
    plt.subplot(1,1)
    #times+=[datetime.now().strftime('%H:%M:%S')]
    now=datetime.now()
    dt+=[now-past]
    past=now
    times+=[times[-1]+dt[-1]]
    currents=[x[0] for x in top_vars]
    voltages=[x[1] for x in top_vars]
    soc=[x[2] for x in top_vars]
    dsocdt=min(avg_wind,len(times))*[0]+[(sum(soc[j+1:j+1+avg_wind])-sum(soc[j:j+avg_wind]))/sum([x.total_seconds() for x in dt[j:j+avg_wind]]) for j in range(len(times)-avg_wind)] # moving avg. window (4)
    plt.plot(currents,yside='left',label='current')
    plt.plot(voltages,yside='right',label='voltage')
    plt.title('current (left), voltage/mV (right)')
    plt.subplot(2,1)
    plt.plot(soc,yside='left',label='soc/%')
    plt.plot(dsocdt,yside='right',label='(dsoc/dt)/(%/s)')
    plt.title('SoC/% (left), (dSoC/dt)/(%/s) mv. avg. 20s (right)')
    plt.show()
    if len(times)>250: # show up to 250 points
        times.pop(0)
        top_vars.pop(0)
        dt.pop(0)
    past=datetime.now()
    print('press CTRL+C to quit')
    sleep(1)
    return times,dt,top_vars,past

try:
    # init.
    past=datetime.now()
    times=[past];dt=[timedelta(seconds=1)];top_vars=[]
    output=json.loads(subprocess.check_output('termux-battery-status').decode())
    top_vars+=[[output['current'],output['voltage'],output['percentage']]]
    while True:
        times,dt,top_vars,past=step(times,dt,top_vars,past,avg_wind)
except KeyboardInterrupt:
    pass

check_cpu.ps1

while ($true) {(Get-Counter '\prozess(*)\prozessorzeit (%)').CounterSamples | Select-Object -Property instancename, cookedvalue |  Sort-Object -Property cookedvalue -Descending | Select-Object -First 20 | ft InstanceName,@{L='CPU';E={($_.Cookedvalue/100/$env:NUMBER_OF_PROCESSORS).toString('P')}} -AutoSize | Out-File -FilePath $env:home\cpu_consumption.txt; Get-Content -Path $env:home\cpu_consumption.txt; sleep 1}

check_cpu.py

#!/bin/env python
import subprocess
from os import name,system
from numpy import fromregex,concatenate,unique,bincount,empty
from os.path import sep
from os import environ
from time import sleep
from datetime import datetime
from pandas import DataFrame
import plotext as plt
plt.date_form('H:M:S')
plt.limit_size(False,True)
plt.plotsize(55, 15)
filename=sep.join([environ['HOME'],'cpu_consumption.txt'])
past=datetime.now()
t=0
times=[];top_vars=[]
powershell_path=r'C:\Windows\System32\WindowsPowerShell\v1.0\powershell.exe'
while True:
    subprocess.run([powershell_path,"(Get-Counter '\prozess(*)\prozessorzeit (%)').CounterSamples | Select-Object -Property instancename, cookedvalue |  Sort-Object -Property cookedvalue -Descending | Select-Object -First 20 | ft InstanceName,@{L='CPU';E={($_.Cookedvalue/100/$env:NUMBER_OF_PROCESSORS).toString('P')}} -AutoSize | Out-File -FilePath $env:home\cpu_consumption.txt"]) #; Get-Content -Path $env:home\cpu_consumption.txt"])
    #system('cls' if name == 'nt' else 'clear')
    plt.clt() # to clear the terminal
    plt.cld() # to clear the data only
    times+=[datetime.now().strftime('%H:%M:%S')]
    dt=(datetime.now()-past).total_seconds()
    t+=dt
    dat=fromregex(sep.join([environ['HOME'],'cpu_consumption.txt']),r'(\w+)\s+(\d+\.\d+)',[('key','U25'),('CPU',float)],encoding='utf16')
    cpu_inst=dat['CPU'].copy()/100
    dat['CPU']=cpu_inst*dt    
    names_inst=dat['key'].copy()
    #dat=DataFrame(dat,index=dat['key']).drop(columns='key')
    if  'dat_old' in locals():
        dat=concatenate([dat,dat_old])
    u,idx=unique(dat['key'],return_inverse=True) # get all uniques and indexes of these in array
    s=bincount(idx,weights=dat['CPU']) # get cumulative sum
    dat=empty([len(u)],dtype=[('key','U25'),('CPU',float)]) # clear for merged data
    dat['key']=u # unique
    dat['CPU']=s # result
    dat=dat[dat['CPU'].argsort()[::-1]] # sort highest CPU consumers
    dat=dat[:20] # track only highest consumers
    dat_old=dat.copy()
    cpu_pct=empty(dat['CPU'].shape)
    cpu_pct[dat['key']!='_total']=dat['CPU'][dat['key']!='_total']/dat['CPU'][dat['key']!='_total'].sum()
    cpu_pct[dat['key']=='_total']=1
    printstr='{:30s} {:<11.5s} {:<11.5s} {:<11.5s}\n'.format('proc','CPU/s','CPU/%','CPU_inst/%')
    for j, row in enumerate(dat):
        cpu_inst_row=cpu_inst[names_inst==row['key']].sum()
        if not cpu_inst_row:
            cpu_inst_row=0
        printstr+='{:30s} {:<11.5g} {:<11.5g} {:<11.5g}\n'.format(row['key'],row['CPU'],cpu_pct[j]*100,cpu_inst_row*100)
    print(printstr)
    print('dt={:0.4g} seconds. Total: {:0.4g} seconds'.format(dt,t))
    idx=(dat['key']!='_total')&(dat['key']!='idle')
    top_vars+=[cpu_pct[idx][:5]*100]
    for j in range(5):
        plt.plot([y[j] for y in top_vars])
    plt.show()
    if len(times)>20:
        times.pop(0)
        top_vars.pop(0)
    past=datetime.now()
    sleep(1)