def stim_conditions(angles, onebeep_nb, twobeep_nb, onebeep_tc, twobeep_tc):
""" Takes completed stimuli from above functions and makes all of the
conditions for the flash beep with three possible locations and azmuith
angles.
"""
##### make single auditory stim################################################
#conditions_1A = [-30_1A, 0_1A, 30_1A, -30_2A, 0_2A, 30_2A]
spatials = ('-30', '0', '30')
beep_combos_1a = ('onebeep_nb', 'twobeep_nb', 'onebeep_tc', 'twobeep_tc')
##### make competing auditory stim#############################################
#conditions_2A = []
spatials = ('-30x0', '0x30', '-30x30')
beep_combos_2a = ('onebeep_nbxonebeep_tc', 'twobeep_nbxonebeep_tc',
'onebeep_nbxtc2', 'twobeep_nbxtwobeep_tc')
all_spatials = [s.split('x') for s in spatials]
for s in all_spatials[1:]:
all_spatials[0] += s
all_spatials = all_spatials[0]
all_spatials = list(np.unique([float(s) for s in all_spatials]))
all_combos = [ss.split('x') for ss in beep_combos_2a]
for ss in all_combos[1:]:
all_combos[0] += ss
all_combos = all_combos[0]
all_combos = list(np.unique([float(ss) for ss in all_combos]))
##### convolve with HRTF at appropriate angles ################################
move_sig = np.concatenate([convolve_hrtf(stim, fs, ang)
for ang in range(-30, 30)], axis=1)
return move_sig
def test_hrtf_convolution():
"""Test HRTF convolution
"""
data = np.random.randn(2, 10000)
assert_raises(ValueError, convolve_hrtf, data, 44100, 0)
data = data[0]
assert_raises(ValueError, convolve_hrtf, data, 44100, 0.5) # invalid angle
out = convolve_hrtf(data, 44100, 0)
out_2 = convolve_hrtf(data, 24414, 0)
assert_equal(out.ndim, 2)
assert_equal(out.shape[0], 2)
assert_true(out.shape[1] > data.size)
assert_true(out_2.shape[1] < out.shape[1])
# ensure that, at least for zero degrees, it's close
out = convolve_hrtf(data, 44100, 0)[:, 1024:-1024]
assert_allclose(np.mean(rms(out)), rms(data), rtol=1e-1)
out = convolve_hrtf(data, 44100, -90)
rmss = rms(out)
assert_true(rmss[0] > 4 * rmss[1])
def test_hrtf_convolution():
"""Test HRTF convolution
"""
data = np.random.randn(2, 10000)
assert_raises(ValueError, convolve_hrtf, data, 44100, 0)
data = data[0]
assert_raises(ValueError, convolve_hrtf, data, 44100, 0.5) # invalid angle
for source in ['barb', 'cipic']:
out = convolve_hrtf(data, 44100, 0, source=source)
out_2 = convolve_hrtf(data, 24414, 0, source=source)
assert_equal(out.ndim, 2)
assert_equal(out.shape[0], 2)
assert_true(out.shape[1] > data.size)
assert_true(out_2.shape[1] < out.shape[1])
# ensure that, at least for zero degrees, it's close
out = convolve_hrtf(data, 44100, 0, source=source)[:, 1024:-1024]
assert_allclose(np.mean(rms(out)), rms(data), rtol=1e-1)
out = convolve_hrtf(data, 44100, -90, source=source)
rmss = rms(out)
assert_true(rmss[0] > 4 * rmss[1])
def test_hrtf_convolution():
"""Test HRTF convolution."""
data = np.random.randn(2, 10000)
assert_raises(ValueError, convolve_hrtf, data, 44100, 0, interp=False)
data = data[0]
assert_raises(ValueError, convolve_hrtf, data, 44100, 0.5, interp=False)
assert_raises(ValueError, convolve_hrtf, data, 44100, 0,
source='foo', interp=False)
assert_raises(ValueError, convolve_hrtf, data, 44100, 90.5, interp=True)
assert_raises(ValueError, convolve_hrtf, data, 44100, 0, interp='foo')
# invalid angle when interp=False
for interp in [True, False]:
for source in ['barb', 'cipic']:
if interp and source == 'barb':
# raise an error when trying to interp with 'barb'
assert_raises(ValueError, convolve_hrtf, data, 44100, 2.5,
source=source, interp=interp)
else:
out = convolve_hrtf(data, 44100, 0, source=source,
interp=interp)
out_2 = convolve_hrtf(data, 24414, 0, source=source,
interp=interp)
assert_equal(out.ndim, 2)
assert_equal(out.shape[0], 2)
assert_true(out.shape[1] > data.size)
assert_true(out_2.shape[1] < out.shape[1])
if interp:
out_3 = convolve_hrtf(data, 44100, 2.5, source=source,
interp=interp)
out_4 = convolve_hrtf(data, 44100, -2.5, source=source,
interp=interp)
assert_equal(out_3.ndim, 2)
assert_equal(out_4.ndim, 2)
# ensure that, at least for zero degrees, it's close
out = convolve_hrtf(data, 44100, 0, source=source,
interp=interp)[:, 1024:-1024]
assert_allclose(np.mean(rms(out)), rms(data), rtol=1e-1)
out = convolve_hrtf(data, 44100, -90, source=source,
interp=interp)
rmss = rms(out)
assert_true(rmss[0] > 4 * rmss[1])
def test_hrtf_convolution():
"""Test HRTF convolution."""
data = np.random.randn(2, 10000)
pytest.raises(ValueError, convolve_hrtf, data, 44100, 0, interp=False)
data = data[0]
pytest.raises(ValueError, convolve_hrtf, data, 44100, 0.5, interp=False)
pytest.raises(ValueError, convolve_hrtf, data, 44100, 0,
source='foo', interp=False)
pytest.raises(ValueError, convolve_hrtf, data, 44100, 90.5, interp=True)
pytest.raises(ValueError, convolve_hrtf, data, 44100, 0, interp='foo')
# invalid angle when interp=False
for interp in [True, False]:
for source in ['barb', 'cipic']:
if interp and source == 'barb':
# raise an error when trying to interp with 'barb'
pytest.raises(ValueError, convolve_hrtf, data, 44100, 2.5,
source=source, interp=interp)
else:
out = convolve_hrtf(data, 44100, 0, source=source,
interp=interp)
out_2 = convolve_hrtf(data, 24414, 0, source=source,
interp=interp)
assert_equal(out.ndim, 2)
assert_equal(out.shape[0], 2)
assert (out.shape[1] > data.size)
assert (out_2.shape[1] < out.shape[1])
if interp:
out_3 = convolve_hrtf(data, 44100, 2.5, source=source,
interp=interp)
out_4 = convolve_hrtf(data, 44100, -2.5, source=source,
interp=interp)
assert_equal(out_3.ndim, 2)
assert_equal(out_4.ndim, 2)
# ensure that, at least for zero degrees, it's close
out = convolve_hrtf(data, 44100, 0, source=source,
interp=interp)[:, 1024:-1024]
assert_allclose(np.mean(rms(out)), rms(data), rtol=1e-1)
out = convolve_hrtf(data, 44100, -90, source=source,
interp=interp)
rmss = rms(out)
assert (rmss[0] > 4 * rmss[1])
Generate more advanced auditory stimuli
=======================================
This shows the methods that we provide that facilitate generation
of more advanced stimuli.
"""
import numpy as np
from expyfun.stimuli import convolve_hrtf, play_sound, window_edges
fs = 44100
dur = 0.5
freq = 500.
# let's make a square wave
sig = np.sin(freq * 2 * np.pi * np.arange(dur * fs, dtype=float) / fs)
sig = ((sig > 0) - 0.5) / 5. # make it reasonably quiet for play_sound
sig = window_edges(sig, fs)
play_sound(sig, norm=False, wait=True)
move_sig = np.concatenate([convolve_hrtf(sig, fs, ang)
for ang in range(-90, 91, 15)], axis=1)
play_sound(move_sig, norm=False, wait=True)
import matplotlib.pyplot as mpl
mpl.ion()
t = np.arange(move_sig.shape[1]) / float(fs)
mpl.plot(t, move_sig.T)
mpl.xlabel('Time (sec)')
longest_word_samples = np.max([x.shape[-1] for x in word_wavs.values()])
stream_len = np.max(tr_onset_samp) + longest_word_samples
tr_mono = np.zeros((trials, streams, stream_len), dtype=float)
for tnum in range(trials):
for snum in range(streams):
for wnum in range(waves):
word = tr_words[tnum, snum, wnum]
samps = word_wavs[word][0]
onset = tr_onset_samp[tnum, snum, wnum]
offset = onset + len(samps)
tr_mono[tnum, snum, onset:offset] += samps
del word, samps, onset, offset
# HRTF CONVOLUTION
print('Convolving with HRTFs')
stream_len = stim.convolve_hrtf(np.zeros(stream_len), output_fs, 0).shape[-1]
tr_hrtf = np.zeros((trials, streams, 2, stream_len), dtype=float)
for tnum in range(trials):
for snum in range(streams):
tr_hrtf[tnum, snum] = stim.convolve_hrtf(tr_mono[tnum, snum],
output_fs, angles[snum])
# RENORMALIZE
print('Renormalizing')
tr_original_rms = stim.rms(tr_mono)
tr_convolved_rms = np.mean(stim.rms(tr_hrtf), axis=-1)
multiplier = tr_original_rms / tr_convolved_rms
tr_norm = (tr_hrtf.T * multiplier.T).T # broadcasting
tr_norm_rms = np.mean(stim.rms(tr_norm), axis=-1) # TODO: test RMS
# COMBINE L & R CHANNELS ACROSS STREAMS
tr_stim = np.sum(tr_norm, axis=1)
This shows the methods that we provide that facilitate generation
of more advanced stimuli.
"""
import numpy as np
import matplotlib.pyplot as plt
from expyfun import building_doc
from expyfun.stimuli import convolve_hrtf, play_sound, window_edges
fs = 24414
dur = 0.5
freq = 500.
# let's make a square wave
sig = np.sin(freq * 2 * np.pi * np.arange(dur * fs, dtype=float) / fs)
sig = ((sig > 0) - 0.5) / 5. # make it reasonably quiet for play_sound
sig = window_edges(sig, fs)
play_sound(sig, fs, norm=False, wait=True)
move_sig = np.concatenate(
[convolve_hrtf(sig, fs, ang) for ang in range(-90, 91, 15)], axis=1)
if not building_doc:
play_sound(move_sig, fs, norm=False, wait=True)
t = np.arange(move_sig.shape[1]) / float(fs)
plt.plot(t, move_sig.T)
plt.xlabel('Time (sec)')
plt.show()
all_spatials = [s.split('x') for s in spatials]
for s in all_spatials[1:]:
all_spatials[0] += s
all_spatials = all_spatials[0]
all_spatials = list(np.unique([float(s) for s in all_spatials]))
letter_dir = op.join(work_dir, 'letters')
wavs = np.zeros((len(talkers), len(letters), len(all_spatials), 2, letter_ns))
for li, letter in enumerate(letters):
for ti, talker in enumerate(talkers):
data, fs_in = read_wav(op.join(letter_dir, talker, '%s.wav' % letter),
verbose=False)
data = resample(data[0], fs, fs_in)
for si, angle in enumerate(all_spatials):
dd = convolve_hrtf(data, fs, angle)
dd *= 0.01 / np.mean(rms(data))
idx = min(dd.shape[1], letter_ns)
wavs[ti, li, si, :, :idx] = dd[:, :idx]
##############################################################################
# Randomization
n_trials = n_tpc * len(attns) * run_matrix.sum() * len(gap_durs)
trial_dur = (letter_dur * (n_cue_let + n_targ_let) + cue_targ_gap +
np.mean(gap_durs) + inter_trial_dur)
exp_dur = trial_dur * n_trials
print('Experiment duration: %s min (%s blocks)' %
(round(exp_dur / 60., 1), round((exp_dur / 60. / n_blocks), 1)))
# figure out what positions work
finalstim_tc = window_edges(tonecomp, fs, ramp_tone, -1, 'hamming') #finalstim_tc *= 0.01 * np.sqrt(2) # Set RMS to 0.01 #finalstim_tc = finalstim_tc*toneamp # check the rms: #tc_rms = rms(finalstim_tc) # First: HRTF tonebeep and noisebeep at each angle and store # ############################################################################ # add 50ms delay between beeps: two_beep_delay = np.zeros(24414. * delay_beeps, float) finalstim_tc_delay = np.append(finalstim_tc, two_beep_delay, axis=1) finalstim_nb_delay = np.append(finalstim_nb, two_beep_delay, axis=1) # with delay one_1_noise_delay = convolve_hrtf(finalstim_nb_delay, fs, -30.0) one_2_noise_delay = convolve_hrtf(finalstim_nb_delay, fs, 0.0) one_3_noise_delay = convolve_hrtf(finalstim_nb_delay, fs, 30.0) one_1_tone_delay = convolve_hrtf(finalstim_tc_delay, fs, -30.0) one_2_tone_delay = convolve_hrtf(finalstim_tc_delay, fs, 0.0) one_3_tone_delay = convolve_hrtf(finalstim_tc_delay, fs, 30.0) #without delay one_1_noise = convolve_hrtf(finalstim_nb, fs, -30.0) one_2_noise = convolve_hrtf(finalstim_nb, fs, 0.0) one_3_noise = convolve_hrtf(finalstim_nb, fs, 30.0) one_1_tone = convolve_hrtf(finalstim_tc, fs, -30.0) one_2_tone = convolve_hrtf(finalstim_tc, fs, 0.0) one_3_tone = convolve_hrtf(finalstim_tc, fs, 30.0)
Generate more advanced auditory stimuli
=======================================
This shows the methods that we provide that facilitate generation
of more advanced stimuli.
"""
import numpy as np
import matplotlib.pyplot as mpl
from expyfun.stimuli import convolve_hrtf, play_sound, window_edges
fs = 24414
dur = 0.5
freq = 500.
# let's make a square wave
sig = np.sin(freq * 2 * np.pi * np.arange(dur * fs, dtype=float) / fs)
sig = ((sig > 0) - 0.5) / 5. # make it reasonably quiet for play_sound
sig = window_edges(sig, fs)
play_sound(sig, fs, norm=False, wait=True)
move_sig = np.concatenate([convolve_hrtf(sig, fs, ang)
for ang in range(-90, 91, 15)], axis=1)
play_sound(move_sig, fs, norm=False, wait=True)
mpl.ion()
t = np.arange(move_sig.shape[1]) / float(fs)
mpl.plot(t, move_sig.T)
mpl.xlabel('Time (sec)')
genders = ['M', 'F'] # male in left channel, female in right
regions = ['NW', 'CH']
talkers = [str(i) for i in range(5)]
loc = ['C', 'S']
az = [0, -60]
n_talkers = 3
wavs = [[np.zeros((2, 0)) for _ in range(2)] for _ in range(2)]
for ti, t in enumerate(talkers[:n_talkers]):
#for ri, r in enumerate(regions):
ri = 0
r = regions[ri]
wav, fs = stim.read_wav(join(opath, r + t + '.wav'))
for gi, g in enumerate(genders):
for li, (l, a) in enumerate(zip(loc, az)):
wav_loc = stim.convolve_hrtf(wav[gi], fs, a)
lens = [wavs[gi][li].shape[-1], wav_loc.shape[-1]]
dl = lens[0] - lens[1]
if dl < 0:
wavs[gi][li] = np.concatenate((wavs[gi][li],
np.zeros((2, -dl))), -1)
if dl > 0:
wav_loc = np.concatenate((wav_loc, np.zeros((2, dl))), -1)
wavs[gi][li] += wav_loc
for gi, g in enumerate(genders):
for li, (l, a) in enumerate(zip(loc, az)):
fn = join(spath, '%s_%s_%s.wav' % (r, g, l))
data = wavs[gi][li] / np.sqrt(n_talkers)
data = np.minimum(1, np.maximum(-1, data))
stim.write_wav(fn, data, fs, overwrite=True)