def test_trial(self, trial):
# present instruction trial
self.image.image = self.stimuli_folder + trial['Picture']
self.image.draw()
self.win.callOnFlip(self.clock.reset)
self.isi.complete()
self.win.flip()
if trial['trialAudio'] != '':
audio.play(self.instructions[trial['trialAudio']], wait=True)
if trial['answer_type'] == 'spoken':
audio.write(self.log_prefix + '_' + trial['Picture'][:-4] + '.wav',
audio.record(25, wait=True))
else:
keys = event.waitKeys(keyList=['escape'] +
trial['keyboard'].split(' '),
timeStamped=self.clock)
trial['keypress'], trial['RT'] = keys[0]
if trial['keypress'] == 'escape':
core.quit()
if trial['keypress'] == trial['key']:
trial['ACC'] = 1
else:
trial['ACC'] = 0
self.win.callOnFlip(self.isi.start,
float(trial['ITI']) / 1000 - self.frame_dur)
# flip buffer again and start ISI timer
self.win.flip()
return trial
def run(self):
# set up presentation window color, and size
bgcolor = 'black'
txtcolor = 'white'
self.win = visual.Window(fullscr=True, color=bgcolor)
#self.win = visual.Window((1200, 900), color=bgcolor) # temporary presentation window setup, exchange for line above when running actual experiment
self.text = visual.TextStim(self.win, color=txtcolor)
for i in range(5, 0, -1):
self.text.text = '+' * (2 * i - 1)
self.text.draw()
self.win.flip()
core.wait(1)
self.text.text = '-'
self.text.draw()
self.win.flip()
# record
samples = audio.record(60, wait=True)
self.text.text = '+'
self.text.draw()
self.win.flip()
audio.write(self.log_fname, samples)
core.wait(3)
self.win.close()
def run(self):
# set up presentation window color, and size
bgcolor = 'black'
txtcolor = 'white'
self.win = visual.Window(fullscr=True, color=bgcolor)
#self.win = visual.Window((1200, 900), color=bgcolor) # temporary presentation window setup, exchange for line above when running actual experiment
self.text = visual.TextStim(self.win, color=txtcolor)
words = [
audio.read(self.stimuli_prefix + str(i + 1) + '.wav')
for i in range(24)
]
recordings = []
self.text.text = '||'
self.text.draw()
self.win.flip()
audio.play(audio.read(self.instructions_folder + self.mode + '.wav'),
wait=True)
key = event.waitKeys(keyList=['return'])
for word in words:
self.text.text = '+'
self.text.draw()
self.win.flip()
audio.play(word, wait=True)
self.text.text = '-'
self.text.draw()
self.win.flip()
recordings += [audio.record(((len(word) / 44100) + 1), wait=True)]
for i in range(len(words)):
audio.write(self.log_prefix + str(i + 1) + '.wav', recordings[i])
self.win.close()
def playback(a):
p = a.back
gain = int(255.0 * p.outputgain.val)
a.val = 1.0
a.fixact()
panel.drawpanel()
audio.setoutgain(gain)
audio.write(p.sample)
audio.setoutgain(0)
a.val = 0.0
a.fixact()
def writeSamples(midiPath, audioPath, outputPath):
midi = midiProxy.loadMidiDrums(midiPath)
audioLoad = audio.load(audioPath)
wave = audioLoad[1]
rate = audioLoad[0]
#check correctness
spectrogram, samplingRate = audio.performFFTs(audioLoad)
audio.visualizeSpectrogram(wave=None,
spectrogram=spectrogram,
midi=midi,
name=midiPath)
#lowest frequency = 10Hz = 0.1s per wave
#time between 16th notes : 200bpm = 300 b/ms = 0.3 b/s = 0.075 16th/s
step = 0.5
samples = int(step * rate)
preDelay = 0.05
for midiEvent in midi:
#get the name and the time of the midi event
eventName = midiEvent[
'notes'] #midiProxy.getVectorToNote(midiEvent['notes']) #from [0,0,1,0...] to [40, 36]
time = midiEvent["startTime"]
#if the event is not in the wave
min = int(rate * (time - preDelay))
max = int(rate * (time - preDelay)) + samples
if min < 0 or max > len(wave):
continue
#create folder for the samples
directory = outputPath + "/" + str(eventName)
if not os.path.exists(directory):
os.makedirs(directory)
# fadein and fadeout to prevent aliasing in the fft ?
# fadedWave = np.array([[int(wave[min+i][0] * fadeMask[i]), int(wave[min+i][1] * fadeMask[i])] for i in xrange(samples)], dtype = wave.dtype)
#write the isolated wave from the sample
audio.write(
directory + "/" + audioPath.split("/")[-1] + str(time) + ".wav",
rate, wave[min:max])
# audio.write(directory + "/" + str(time) + "f.wav", rate, fadedWave)
print directory + "/" + audioPath.split("/")[-1] + str(time) + ".wav"
def dtmfout(dtmf, fname, ftype):
# aliases for the non-numeric keys
STAR = 10
HASH = 11
# DTMF frequencies, indexed by the digit
hi_freq = [1336, 1209, 1336, 1477, 1209, 1336, 1477, 1209, 1336, 1477, 1209, 1477]
lo_freq = [941, 697, 697, 697, 770, 770, 770, 852, 852, 852, 941, 941]
# power levels: lo_freq group has -3dB relative to hi_freq group
# going to use sqrt() from math which is probably overkill
# could have approximated with 0.707
hi_pwr = 1
lo_pwr = 1 / sqrt(2)
# sampling rate and period
Fs = 8000
dt = 1 / float(Fs)
# define the number of samples for silence (100ms) and dtmf digit (200ms)
# silence will be added before and after each dtmf digit ie. total 200ms
n_silence = int(0.1 * Fs)
n_dtmf = int(0.2 * Fs)
silence = [0] * n_silence
# signal will be the final output
signal = []
for n in range(len(dtmf)):
ch = ord(dtmf[n])
if ch >= ord('0') and ch <= ord('9'):
# numeric keys
fhi = hi_freq[ch - ord('0')]
flo = lo_freq[ch - ord('0')]
elif dtmf[n] == '*':
fhi = hi_freq[STAR]
flo = lo_freq[STAR]
elif dtmf[n] == '#':
fhi = hi_freq[HASH]
flo = lo_freq[HASH]
else:
# silently drop invalid characters
continue
dig = [0] * n_dtmf
for m in range(n_dtmf):
dig[m] = int((hi_pwr * cos(2 * pi * fhi * m * dt) +
lo_pwr * cos(2 * pi * flo * m * dt)) * 64)
# wav samples need to be unsigned values so add a DC component
if ftype == audio.TYPE_WAV:
dig[m] = dig[m] + 127
signal = signal + silence + dig + silence
return audio.write(fname, ftype, signal, Fs)
def run(self):
# set up presentation window color, and size
bgcolor = 'black'
txtcolor = 'white'
self.win = visual.Window(fullscr=True, color=bgcolor)
#self.win = visual.Window((1200, 900), color=bgcolor) # temporary presentation window setup, exchange for line above when running actual experiment
self.text = visual.TextStim(self.win, color=txtcolor)
with open(self.trials_fname, 'rU') as trial_file:
# read trial structure
trials = list(csv.DictReader(trial_file, delimiter='\t'))
# preload stimuli
stimuli = [audio.read(self.stimuli_folder + trial['stimulus']) for trial in trials]
recordings = []
self.text.text = '||'
self.text.draw()
self.win.flip()
audio.play(audio.read(self.instructions_folder + self.mode + '.wav'), wait=True)
key = event.waitKeys(keyList=['return'])
self.win.flip()
for stimulus in stimuli:
self.text.text = '+'
self.text.draw()
self.win.flip()
audio.play(stimulus, wait=True)
self.text.text = '-'
self.text.draw()
self.win.flip()
recordings += [audio.record((len(stimulus) / 44100.0) + 1, wait=True)]
keys = event.getKeys(['escape'])
if 'escape' in keys:
break
for i in range(len(recordings)):
audio.write(self.log_prefix + trials[i]['stimulus'], recordings[i])
self.win.close()
def writeSamples(midiPath, audioPath, outputPath):
midi = midiProxy.loadMidiDrums(midiPath)
audioLoad = audio.load(audioPath)
wave = audioLoad[1]
rate = audioLoad[0]
#check correctness
# spectrogram, samplingRate = audio.performFFTs(audioLoad)
# audio.visualizeSpectrogram(wave=None, spectrogram=spectrogram, midi=midi, name=midiPath)
#lowest frequency = 10Hz = 0.1s per wave
#time between 16th notes : 200bpm = 300 b/ms = 0.3 b/s = 0.075 16th/s
step = 0.525 #window of the sound saved in seconds
samples = int(step * rate) #number of samples to save
preDelay = 0.05
for midiEvent in midi:
#get the name and the time of the midi event
eventName = midiEvent['notes'] #midiProxy.getVectorToNote(midiEvent['notes']) #from [0,0,1,0...] to [40, 36]
time = midiEvent["startTime"]
#if the event is not in the wave
min = int(rate * (time - preDelay))
max = int(rate * (time - preDelay)) + samples
if min < 0 or max > len(wave):
continue
#create folder for the samples
directory = outputPath + "/" + str(eventName)
if not os.path.exists(directory):
os.makedirs(directory)
# fadein and fadeout to prevent aliasing in the fft ?
# fadedWave = np.array([[int(wave[min+i][0] * fadeMask[i]), int(wave[min+i][1] * fadeMask[i])] for i in xrange(samples)], dtype = wave.dtype)
#write the isolated wave from the sample
audio.write(directory + "/" + audioPath.split("/")[-1] + str(time) + ".wav", rate, wave[min:max])
# audio.write(directory + "/" + str(time) + "f.wav", rate, fadedWave)
print directory + "/" + audioPath.split("/")[-1] + str(time) + ".wav"
del fp else: del fp data = readfile(tempname) if G.debug: print len(data), 'bytes read from', tempname if G.busy: G.busy = 0 dummy = audio.stop_playing() # # Completely reset the audio device audio.setrate(G.rate) audio.setduration(0) audio.setoutgain(G.gain) # if G.synchronous: audio.write(data) audio.setoutgain(0) else: try: audio.start_playing(data) G.busy = 1 except: stdwin.fleep() del data def readfile(filename): return readfp(open(filename, 'r')) def readfp(fp): data = '' while 1:
del fp else: del fp data = readfile(tempname) if G.debug: print len(data), 'bytes read from', tempname if G.busy: G.busy = 0 dummy = audio.stop_playing() # # Completely reset the audio device audio.setrate(G.rate) audio.setduration(0) audio.setoutgain(G.gain) # if G.synchronous: audio.write(data) audio.setoutgain(0) else: try: audio.start_playing(data) G.busy = 1 except: stdwin.fleep() del data def readfile(filename): return readfp(open(filename, 'r')) def readfp(fp): data = '' while 1: