#### JP

##### Major Contributor

This script creates polar plots from wow and flutter test tracks. I'll provide more background information on it later. The current "released" code is below.

Python installation instructions are in this thread.

Python:

```
#Version 5C
from scipy import signal
from scipy.io.wavfile import read
import matplotlib.pyplot as plt
import numpy as np
import datetime
import os
#edit here to add a HOME directory, etc.
_FILE = 'wavefile.wav'
info_line = 'PLOT TITLE'
Hz_per_tick = 3
sec_per_rev = 1.8 # 1.8 for 33, 1.33 for 45, .766 for 78.26
stereo_channel = 0 #0 = Left, 1 = Right
filter_freq = 60
#end edit
def instfreq(sig,Fs,filter_freq):
z = signal.hilbert(sig)
rawfreq = Fs/(2*np.pi)*np.diff(np.unwrap(np.angle(z)))
rawfreq = np.append(rawfreq,rawfreq[len(rawfreq)-1]) #np.diff drops one end point
b, a = signal.iirfilter(1,filter_freq/(Fs/2), btype='lowpass')
zi = signal.lfilter_zi(b, a) #Initialize the filter to the mean of the leading edge of the data
rawfreq,_ = signal.lfilter(b,a,rawfreq,zi=zi*np.mean(rawfreq[0:2000])) #reduces glitch, first pass
b, a = signal.iirfilter(3,filter_freq/(Fs/2), btype='lowpass')
instfreq = signal.filtfilt(b,a,rawfreq) #back and forth linear phase IIR filter (6 pole)
return (instfreq)
y = read(_FILE)
Fs = float(y[0])
if np.size(y[1][0]) == 2:
sig = y[1][:,stereo_channel][0:int(Fs*(sec_per_rev*3))] #Grab 3*sec_per_rev of audio from the specified channel
else:
sig = y[1][0:int(Fs*(sec_per_rev*3))] #mono file
t = np.arange(sec_per_rev,0,-1/Fs) #Reverse time (theta axis)
theta = t*2*np.pi/sec_per_rev #Time becomes degrees (1 rev = 2pi radians)
theta = np.roll(theta,int((sec_per_rev*Fs/4))) #Rotate 90 deg to put 0 on top (sec_per_rev*Fs/4)
freq1 = instfreq(sig,Fs,filter_freq)
freq1 = np.roll(freq1,-int(Fs*.2))#Throw away the first .2sec to guarantee the IIR transient settles
if1 = freq1[0:int(Fs*sec_per_rev)]
if2 = freq1[int(Fs*sec_per_rev):int(2*Fs*sec_per_rev)]
maxf = (max(max(if1),max(if2))+.2) #This shuld be changed to make the maximum frequency an even 10th of a Hz.
r1 = 20.-(maxf-if1)/Hz_per_tick #20 radial ticks at Hz_per_tick is fixed, adaptive scaling
r2 = 20.-(maxf-if2)/Hz_per_tick #is an exercise for later
#plt.figure(1)
plt.figure(figsize=(11,11))
ax = plt.subplot(111, projection='polar')
ax.plot(theta,r1)
ax.plot(theta,r2)
dgr = (2*np.pi)/360.
mod_date = datetime.datetime.fromtimestamp(os.path.getmtime(_FILE))
ax.text(226.*dgr, 28.5, 'Mean Rev1 {:4.3f}Hz'.format(np.mean(if1)) + "\n" + \
'Mean Rev2 {:4.3f}Hz'.format(np.mean(if2)) + "\n" + \
_FILE + "\n" + \
mod_date.strftime("%b %d, %Y %H:%M:%S"), fontsize=9)
ax.set_rmax(20)
#Set up the ticks y is radial x is theta, it turns out x and y
#methods still work in polar projection but sometimes do funny things
tick_loc = np.arange(1,21,1)
myticks = []
for x in range(0,20,1):
myticks.append('{:4.2f}Hz'.format(maxf-(19*Hz_per_tick)+x*Hz_per_tick))
ax.set_rgrids(tick_loc, labels = myticks, angle = 90, fontsize = 8)
ax.set_xticklabels(['90'+u'\N{DEGREE SIGN}','45'+u'\N{DEGREE SIGN}','0'+u'\N{DEGREE SIGN}',\
'315'+u'\N{DEGREE SIGN}','270'+u'\N{DEGREE SIGN}','225'+u'\N{DEGREE SIGN}',\
'180'+u'\N{DEGREE SIGN}','135'+u'\N{DEGREE SIGN}'])
ax.grid(True)
ax.set_title(info_line, va='bottom', fontsize=16)
plt.savefig(info_line.replace(' / ', '_') +'.png', bbox_inches='tight', pad_inches=.5)
plt.show()
```

Python installation instructions are in this thread.

Last edited: