def clean_signal(sig, samp_rate):
band_pass_sig = butter_bandpass_filter(sig, 0.1, 210, samp_rate, order=2)
band_stop_sig = butter_bandstop_filter(band_pass_sig, 59, 61, samp_rate, order=2)
clean_sig = butter_bandstop_filter(band_stop_sig, 119, 121, samp_rate, order=2)
return clean_sig
def clean_signal(sig, samp_rate):
band_pass_sig = butter_bandpass_filter(sig, 0.1, 210, samp_rate, order=2)
band_stop_sig = butter_bandstop_filter(band_pass_sig, 59, 61, samp_rate, order=2)
clean_sig = butter_bandstop_filter(band_stop_sig, 119, 121, samp_rate, order=2)
return clean_sig
def transform_file(f, file, f_lo, f_hi, save_file_loc, n_channels):
neural_sig = edfreader.EdfReader(file)
samp_rate = int(neural_sig.samplefrequency(0))
buffer_size = samp_rate*100
window_size = samp_rate*2
size = int(neural_sig.samples_in_file(1))
frequency = np.zeros(shape=(n_channels, (f_hi-f_lo)/3))
cnt=0
if size > buffer_size*3:
for chunk in xrange(0,size-3*buffer_size, buffer_size):
if not os.path.isfile(save_file_loc + str(f)
+ "_" + str((chunk+1)/window_size) + ".p"):
print ("Processing chunk " + str(chunk/(buffer_size)) + " of "
+ str((size-samp_rate)/(buffer_size)) + " in file " + str(f) + "\n")
chan_sig = np.zeros(shape=(n_channels, buffer_size))
for c in xrange(1, 1+n_channels):
sig = np.zeros(buffer_size*4)
neural_sig.readsignal(c, chunk-buffer_size,
buffer_size*3, sig)
band_pass_sig = butter_bandpass_filter(sig, 0.1, 210, samp_rate, order=2)
band_stop_sig = butter_bandstop_filter(band_pass_sig, 59, 61, samp_rate, order=2)
clean_sig = butter_bandstop_filter(band_stop_sig, 119, 121, samp_rate, order=2)
chan_sig[c-1,:] = clean_sig[buffer_size:buffer_size*2]
for sub_chunk in xrange(0,buffer_size-window_size,window_size):
if not os.path.isfile(save_file_loc + str(f) + "_" + str(cnt) + ".p"):
for c in xrange(1, 1+n_channels):
frequency[c-1,:] = (np.abs(np.fft.fft(
chan_sig[c-1,sub_chunk:sub_chunk+window_size]))**2)[f_lo:f_hi:3]
pickle.dump(frequency, open(save_file_loc + str(f)
+ "_" + str(cnt) + ".p", "wb"))
#print (save_file_loc + str(f)
# + "_" + str(cnt) + ".p" + " saved")
# y, x = np.mgrid[slice(0, n_channels, 1),
# slice(0, 100*3, 3)]
# plt.pcolormesh(x,y,frequency, cmap='RdBu', vmin=-1, vmax=1)
# plt.axis([x.min(), x.max(), y.min(), y.max()])
# plt.colorbar()
# #plt.title(track)
# plt.xlabel("Frequency")
# plt.ylabel("Channel")
# plt.show()
else:
print (save_file_loc + str(f)
+ "_" + str(cnt) + ".p" + " skipped")
cnt += 1
else:
cnt += buffer_size/window_size
'-i', fname,
'-vn',
'-f', 'wav',
'-c:a', 'pcm_mulaw',
'-ar', str(samp_rate*2),
'-ac', '1', '-']
pipe = sp.Popen(command, stdout=sp.PIPE, bufsize=10**9)
win_size = samp_rate
f_rate = 30
offset = samp_rate/f_rate
raw_audio = pipe.stdout.read(win_size*2*num_secs)
audio_array = np.fromstring(raw_audio, dtype="int16")
#plt.plot(audio_array[::100])
#plt.show()
#pdb.set_trace()
audio_array = butter_bandpass_filter(audio_array,50,3500, samp_rate*2)
audio_len = len(audio_array)/samp_rate
power_array = np.zeros((len(audio_array)*30)/samp_rate)
print "writing " + str(vid)
for c in range(0,audio_len):
for f in range(0,f_rate):
signal_window = audio_array[c*win_size + offset*f:(c+1)*win_size + offset*f]
power = np.mean((np.abs(np.fft.fft(signal_window*np.hamming(len(signal_window))))[100:3500])**2)
power_array[c*f_rate+f] = power
if len(power_array)>30:
power_array[-30:]=power_array[-31]
print "wrote " + str(vid)
pickle.dump(power_array, open(output_file_loc + sbj_id + "_" + str(day) + "_" + num + ".p", "wb"))
pipe.kill()
