def test_play_sound():
"""Test playing a sound
"""
data = np.zeros((2, 100))
play_sound(data).stop()
play_sound(data[0], norm=False, wait=True)
assert_raises(ValueError, play_sound, data[:, :, np.newaxis])
def test_play_sound():
"""Test playing a sound."""
data = np.zeros((2, 100))
play_sound(data).stop()
play_sound(data[0], norm=False, wait=True)
assert_raises(ValueError, play_sound, data[:, :, np.newaxis])
# Make sure Pyglet can handle a lot of sounds
for _ in range(100):
snd = play_sound(data)
# we manually stop and delete here, because we don't want to
# have to wait for our Timer instances to get around to doing
# it... this also checks to make sure calling `delete()` more
# than once is okay (it is).
snd.stop()
snd.delete()
def test_play_sound():
"""Test playing a sound."""
data = np.zeros((2, 100))
play_sound(data).stop()
play_sound(data[0], norm=False, wait=True)
assert_raises(ValueError, play_sound, data[:, :, np.newaxis])
# Make sure Pyglet can handle a lot of sounds
for _ in range(100):
snd = play_sound(data)
# we manually stop and delete here, because we don't want to
# have to wait for our Timer instances to get around to doing
# it... this also checks to make sure calling `delete()` more
# than once is okay (it is).
snd.stop()
snd.delete()
def test_play_sound(backend, hide_window): # only works if windowing works
"""Test playing a sound."""
_check_skip_backend(backend)
data = np.zeros((2, 100))
play_sound(data).stop()
play_sound(data[0], norm=False, wait=True)
pytest.raises(ValueError, play_sound, data[:, :, np.newaxis])
# Make sure each backend can handle a lot of sounds
for _ in range(10):
snd = play_sound(data)
# we manually stop and delete here, because we don't want to
# have to wait for our Timer instances to get around to doing
# it... this also checks to make sure calling `delete()` more
# than once is okay (it is).
snd.stop()
snd.delete()
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)')
# generate sinewaves & RMS normalize
wavs = [np.sin(2 * np.pi * f * t) for f in freqs]
wavs = [rms / np.sqrt(np.mean(w**2)) * w for w in wavs]
# collect into dictionary & save
wav_dict = {n: w for (n, w) in zip(names, wavs)}
if save_as == 'hdf5':
num_reps = num_trials // num_freqs + 1
trials = np.tile(range(num_freqs), num_reps)
trial_order = rng.permutation(trials[0:num_trials])
wav_dict.update({
'trial_order': trial_order,
'freqs': freqs,
'fs': fs,
'rms': rms
})
write_hdf5(op.join(output_dir, 'equally_spaced_sinewaves.hdf5'),
wav_dict,
overwrite=True)
elif save_as == 'wav':
for n in names:
write_wav(op.join(output_dir, n + '.wav'), wav_dict[n], int(fs))
return wav_dict
if __name__ == '__main__':
wav_dict = generate_stimuli()
plt.plot(wav_dict['stim_0_500'][:1000])
play_sound(wav_dict['stim_0_500'])
plt.show()
# noise vocoder
data_noise = vocode(data, fs, mode='noise')
data_noise = data_noise * 0.01 / rms(data_noise)
# sinewave vocoder
data_tone = vocode(data, fs, mode='tone')
data_tone = data_tone * 0.01 / rms(data_tone)
# poisson vocoder
data_click = vocode(data, fs, mode='poisson', rate=400)
data_click = data_click * 0.01 / rms(data_click)
# combine all three
cutoff = data.shape[-1] // 3
data_allthree = data_noise.copy()
data_allthree[cutoff:2 * cutoff] = data_tone[cutoff:2 * cutoff]
data_allthree[2 * cutoff:] = data_click[2 * cutoff:]
snd = play_sound(data_allthree, fs, norm=False, wait=False)
# Uncomment this to play the original, too:
# snd = play_sound(data, fs, norm=False, wait=False)
mpl.ion()
ax1 = mpl.subplot(3, 1, 1)
ax1.plot(t, data)
ax1.set_title('Original')
ax1.set_ylabel('Amplitude')
ax2 = mpl.subplot(3, 1, 2, sharex=ax1, sharey=ax1)
ax2.plot(t, data_noise)
ax2.set_title('Vocoded')
ax3 = mpl.subplot(3, 1, 3, sharex=ax1)
ax2.set_title('Spectrogram')
ax2.set_ylabel('Amplitude')
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()
# strings for the filenames / dictionary keys
freq_names = [str(int(f)) for f in freqs]
names = ['stim_%s_%s' % (n, f) for n, f in enumerate(freq_names)]
# generate sinewaves & RMS normalize
wavs = [np.sin(2 * np.pi * f * t) for f in freqs]
wavs = [rms / np.sqrt(np.mean(w ** 2)) * w for w in wavs]
# collect into dictionary & save
wav_dict = {n: w for (n, w) in zip(names, wavs)}
if save_as == 'hdf5':
num_reps = num_trials // num_freqs + 1
trials = np.tile(range(num_freqs), num_reps)
trial_order = rng.permutation(trials[0:num_trials])
wav_dict.update({'trial_order': trial_order, 'freqs': freqs, 'fs': fs,
'rms': rms})
write_hdf5(op.join(output_dir, 'equally_spaced_sinewaves.hdf5'),
wav_dict, overwrite=True)
elif save_as == 'wav':
for n in names:
write_wav(op.join(output_dir, n + '.wav'), wav_dict[n], int(fs))
return wav_dict
if __name__ == '__main__':
wav_dict = generate_stimuli(save_as=None)
plt.plot(wav_dict['stim_0_500'][:1000])
play_sound(wav_dict['stim_0_500'])
plt.show()
# noise vocoder
data_noise = vocode(data, fs, mode='noise')
data_noise = data_noise * 0.01 / rms(data_noise)
# sinewave vocoder
data_tone = vocode(data, fs, mode='tone')
data_tone = data_tone * 0.01 / rms(data_tone)
# poisson vocoder
data_click = vocode(data, fs, mode='poisson', rate=400)
data_click = data_click * 0.01 / rms(data_click)
# combine all three
cutoff = data.shape[-1] // 3
data_allthree = data_noise.copy()
data_allthree[cutoff:2 * cutoff] = data_tone[cutoff:2 * cutoff]
data_allthree[2 * cutoff:] = data_click[2 * cutoff:]
snd = play_sound(data_allthree, fs, norm=False, wait=False)
# Uncomment this to play the original, too:
# snd = play_sound(data, fs, norm=False, wait=False)
ax1 = plt.subplot(3, 1, 1)
ax1.plot(t, data)
ax1.set_title('Original')
ax1.set_ylabel('Amplitude')
ax2 = plt.subplot(3, 1, 2, sharex=ax1, sharey=ax1)
ax2.plot(t, data_noise)
ax2.set_title('Vocoded')
ax3 = plt.subplot(3, 1, 3, sharex=ax1)
ax2.set_title('Spectrogram')
ax2.set_ylabel('Amplitude')
ax3.specgram(data_noise, Fs=fs)
This shows how to generate texture coherence stimuli.
"""
import numpy as np
import matplotlib.pyplot as plt
from expyfun.stimuli import texture_ERB, play_sound
fs = 24414
n_freqs = 20
n_coh = 18 # very coherent example
# let's make a textured stimilus and play it
sig = texture_ERB(n_freqs, n_coh, fs=fs, seq=('inc', 'nb', 'sam'))
play_sound(sig, fs, norm=True, wait=True)
###############################################################################
# Let's look at the time course
t = np.arange(len(sig)) / float(fs)
fig, ax = plt.subplots(1)
ax.plot(t, sig.T, color='k')
ax.set(xlabel='Time (sec)', ylabel='Amplitude (normalized)', xlim=t[[0, -1]])
fig.tight_layout()
###############################################################################
# And now the spectrogram:
fig, ax = plt.subplots(1, figsize=(8, 2))
img = ax.specgram(sig, NFFT=1024, Fs=fs, noverlap=800)[3]
img.set_clim([img.get_clim()[1] - 50, img.get_clim()[1]])
ax.set(xlim=t[[0, -1]], ylim=[0, 10000], xlabel='Time (sec)',
========================
This shows how to make simple vocoded stimuli.
@author: larsoner
"""
import numpy as np
from expyfun.stimuli import vocode_ci, play_sound, window_edges, read_wav
from expyfun import fetch_data_file
data, fs = read_wav(fetch_data_file('audio/dream.wav'))
data = window_edges(data[0], fs)
t = np.arange(data.size) / float(fs)
data_ci = vocode_ci(data, fs, mode='noise', order=4, verbose=True)
# Uncomment this to play the original, too:
#snd = play_sound(data, fs, norm=False, wait=False)
snd = play_sound(data_ci, fs, norm=False, wait=False)
import matplotlib.pyplot as mpl
mpl.ion()
ax1 = mpl.subplot(2, 1, 1)
ax1.plot(t, data)
ax1.set_title('Original')
ax2 = mpl.subplot(2, 1, 2, sharex=ax1, sharey=ax1)
ax2.plot(t, data_ci)
ax2.set_title('Vocoded')
ax2.set_xlabel('Time (sec)')
This shows how to generate texture coherence stimuli.
"""
import numpy as np
import matplotlib.pyplot as plt
from expyfun.stimuli import texture_ERB, play_sound
fs = 24414
n_freqs = 20
n_coh = 18 # very coherent example
# let's make a textured stimilus and play it
sig = texture_ERB(n_freqs, n_coh, fs=fs, seq=('inc', 'nb', 'sam'))
play_sound(sig, fs, norm=True, wait=True)
###############################################################################
# Let's look at the time course
t = np.arange(len(sig)) / float(fs)
fig, ax = plt.subplots(1)
ax.plot(t, sig.T, color='k')
ax.set(xlabel='Time (sec)', ylabel='Amplitude (normalized)', xlim=t[[0, -1]])
fig.tight_layout()
###############################################################################
# And now the spectrogram:
fig, ax = plt.subplots(1, figsize=(8, 2))
img = ax.specgram(sig, NFFT=1024, Fs=fs, noverlap=800)[3]
img.set_clim([img.get_clim()[1] - 50, img.get_clim()[1]])
ax.set(xlim=t[[0, -1]],
