Explorer/RaspberryPi/Log

#!/usr/bin/env python3
import re
import numpy as np
import pandas as pd 
from scipy.interpolate import griddata, interp1d
import matplotlib.pyplot as plt
import seaborn as sns
import matplotlib as mpl

class msgparse():
  def __init__(self, msg_name, columns, drop_columns, nbpat, pat):
    self.msg_name     = msg_name
    self.columns      = columns
    self.drop_columns = drop_columns
    self.nbpat        = nbpat
    self.pat          = pat
    self.list_meas    = []
   
msg = []
msg.append(msgparse('attitude',['time', 'phi','psi','theta'],['time'],4,'ATTITUDE (\S+) (\S+) (\S+)'))
msg.append(msgparse('actuators',['time', 'S0','S1','S2'],['time'],4,'ACTUATORS (\S+),(\S+),(\S+)'))
msg.append(msgparse('commands',['time', 'C0','C1','C2'],['time'],4,'COMMANDS (\S+),(\S+),(\S+)'))
msg.append(msgparse('imuaccel',['time','Ax','Ay','Az'],['time'],4,'IMU_ACCEL (\S+) (\S+) (\S+)'))
msg.append(msgparse('imugyro',['time','Gx','Gy','Gz'],['time'],4,'IMU_GYRO (\S+) (\S+) (\S+)'))
msg.append(msgparse('mode',['time','mode','1','2','3','4','5'],['time','1','2','3','4','5'],7,'PPRZ_MODE (\S+) (\S+) (\S+) (\S+) (\S+) (\S+)'))
msg.append(msgparse('settings',['time','m1','m2','add1','add2'],['time'],5,'SETTINGS (\S+) (\S+) (\S+) (\S+)'))


def read_log(ac_id, filename):
  pattern = []
  for i in range(0,len(msg)):
    pattern.append(re.compile("(\S+) "+ac_id+" "+msg[i].pat))  
  f = open(filename, 'r')

  while True:
    line = f.readline().strip()
    if line == :
      break
    for i in range(0,len(msg)):
      m = re.match(pattern[i], line)
      if m:
        tmp = []
        for j in range(1,msg[i].nbpat+1):
          tmp.append(float(m.group(j)))
        msg[i].list_meas.append(tmp)

def linearize_time(pad, sample_period,df, min_t=None, max_t=None):
  if (min_t or max_t) == None:
    min_t = min(df.index)
    max_t = max(df.index)
  time = np.arange(int(min_t)+pad, int(max_t)-pad, sample_period)
  out = pd.DataFrame()
  out['time'] = time
  for col in df.columns:
    func = interp1d(df.index , df[col])
    out[col] = func(time)
  out.index = out.time
  out.drop(['time'], axis=1, inplace=True)
  return out

if __name__ == "__main__":
  ac_id = '9'
  filename = '/data/logfile.txt'
  read_log(ac_id,filename) 

  df_list = []
  for i in range(0,len(msg)):
    exec('data_values = np.array(msg[i].list_meas)'.format(msg[i].msg_name))
    df = pd.DataFrame(data_values, columns=msg[i].columns)
    df.index = df.time
    df.drop(msg[i].drop_columns, axis=1, inplace=True)
    df_list.append(df) 

  min_t = 1000.;max_t = -1.
  for df in df_list:
    min_t = min(min_t, min(df.index))
    max_t = max(max_t, max(df.index)) 

  pad=10;sample_period=0.01
  frames = [linearize_time(pad,sample_period,df_list[i], min_t, max_t) for i in range(0,4)]
  df_All=pd.concat(frames, axis=1, ignore_index=False, sort=False) 

  sns.set()
  df_All.plot(subplots=True, figsize=(12,10));plt.show()
  #df_list[6].plot(subplots=True, figsize=(12,10));plt.show()