Xp31 at 17:43, 26 July 2020

2020-07-26T17:43:46Z

← Older revision		Revision as of 11:43, 26 July 2020
Line 85:		Line 85:
	df_All.plot(subplots=True, figsize=(12,10));plt.show()		df_All.plot(subplots=True, figsize=(12,10));plt.show()
	#df_list[6].plot(subplots=True, figsize=(12,10));plt.show()		#df_list[6].plot(subplots=True, figsize=(12,10));plt.show()

			[[Explorer/RaspberryPi]]

Xp31 at 17:41, 26 July 2020

2020-07-26T17:41:31Z

← Older revision		Revision as of 11:41, 26 July 2020
Line 61:		Line 61:

	if __name__ == "__main__":		if __name__ == "__main__":
	ac_id = '9'		ac_id = '214'
	filename = '/data/logfile.txt'		filename = '/data/logfile.txt'
	read_log(ac_id,filename)		read_log(ac_id,filename)

Xp31 at 17:40, 26 July 2020

2020-07-26T17:40:46Z

← Older revision		Revision as of 11:40, 26 July 2020
Line 8:		Line 8:
	import seaborn as sns		import seaborn as sns
	import matplotlib as mpl		import matplotlib as mpl

	class msgparse():		class msgparse():
	def __init__(self, msg_name, columns, drop_columns, nbpat, pat):		def __init__(self, msg_name, columns, drop_columns, nbpat, pat):
Line 45:		Line 45:
	tmp.append(float(m.group(j)))		tmp.append(float(m.group(j)))
	msg[i].list_meas.append(tmp)		msg[i].list_meas.append(tmp)

	def linearize_time(pad, sample_period,df, min_t=None, max_t=None):		def linearize_time(pad, sample_period,df, min_t=None, max_t=None):
	if (min_t or max_t) == None:		if (min_t or max_t) == None:

Xp31: Created page with " #!/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 seabor..."

2020-07-26T17:40:15Z

Created page with " #!/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 seabor..."

New page

#!/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()

Explorer/RaspberryPi/Log - Revision history

Xp31 at 17:43, 26 July 2020

Xp31 at 17:41, 26 July 2020

Xp31 at 17:40, 26 July 2020

Xp31: Created page with " #!/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 seabor..."