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mavfft_pid: allow PID target and error frequencies to be visualized #850

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Aug 23, 2023
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mavfft_pid: allow PID target and error frequencies to be visualized #850

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34 changes: 27 additions & 7 deletions tools/mavfft_pid.py
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Original file line number Diff line number Diff line change
Expand Up @@ -21,16 +21,29 @@
def fft(logfile):
'''display fft for PID data in logfile'''

sample_rate = args.sample_rate

print("Processing log %s" % filename)
mlog = mavutil.mavlink_connection(filename)
sample_rate = args.sample_rate

data = {'PIDR.rate' : 400,
'PIDP.rate' : 400,
'PIDY.rate' : 400, }
while True:
m = mlog.recv_match()
if m is None:
break
type = m.get_type()
if type == "PARM" and m.Name == 'SCHED_LOOP_RATE':
sample_rate = int(m.Value)
break


mlog = mavutil.mavlink_connection(filename)

data = {'PIDR.rate' : sample_rate,
'PIDP.rate' : sample_rate,
'PIDY.rate' : sample_rate, }

for gyr in ['PIDR','PIDP', 'PIDY']:
for ax in ['P', 'I', 'D']:
for ax in ['P', 'I', 'D', 'Tar', 'Err']:
data[gyr+'.'+ax] = []

# now gather all the data
Expand All @@ -43,14 +56,20 @@ def fft(logfile):
data[type+'.P'].append(m.P)
data[type+'.I'].append(m.I)
data[type+'.D'].append(m.D)
data[type+'.Tar'].append(m.Tar)
data[type+'.Err'].append(m.Err)
elif type == "PIDP":
data[type+'.P'].append(m.P)
data[type+'.I'].append(m.I)
data[type+'.D'].append(m.D)
data[type+'.Tar'].append(m.Tar)
data[type+'.Err'].append(m.Err)
elif type == "PIDY":
data[type+'.P'].append(m.P)
data[type+'.I'].append(m.I)
data[type+'.D'].append(m.D)
data[type+'.Tar'].append(m.Tar)
data[type+'.Err'].append(m.Err)

print("Extracted %u data points, sample rate %uHz" % (len(data['PIDR.P']), sample_rate))

Expand All @@ -61,7 +80,7 @@ def fft(logfile):
for msg in ['PIDR', 'PIDP', 'PIDY']:
pylab.figure()

for axis in ['P', 'I', 'D']:
for axis in ['P', 'I', 'D', 'Tar', 'Err']:
field = msg + '.' + axis
d = data[field]
counts = len(d) // fs
Expand All @@ -81,7 +100,8 @@ def fft(logfile):
sum_fft += d_fft
freq = numpy.fft.rfftfreq(fs, 1.0 / fs)
# compute power spectral density
psd = numpy.sqrt((2 * sum_fft / counts) / (fs * S2))
psd = numpy.sqrt((2 * sum_fft / counts) / (fs * S2)) + 0.00001
psd = 10 * numpy.log10 (psd)
pylab.plot(freq, psd, label=field)
pylab.legend(loc='upper right')

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