View source for 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()