def test_precalc_psd():
nfft = 256
E,V = libtfr.dpss(200, 3, 5)
D = libtfr.mfft_precalc(nfft, E, V)
assert_array_equal(E, D.tapers)
Z = D.mtpsd(sig)
assert_tuple_equal(Z.shape, (nfft//2 + 1,))
assert_equal(Z.dtype, libtfr.DTYPE)
def test_precalc_psd():
nfft = 256
E, V = libtfr.dpss(200, 3, 5)
D = libtfr.mfft_precalc(nfft, E, V)
assert_array_equal(E, D.tapers)
Z = D.mtpsd(sig)
assert_tuple_equal(Z.shape, (nfft // 2 + 1, ))
assert_equal(Z.dtype, libtfr.DTYPE)
def test_hanning_mtstft():
from numpy import hanning
nfft = 256
shift = 10
window = hanning(nfft - 50)
nframes = (sig.size - window.size) // shift + 1
D = libtfr.mfft_precalc(nfft, window)
Z = D.mtstft(sig, shift)
assert_tuple_equal(Z.shape, (nfft//2 + 1, nframes, 1))
assert_equal(Z.dtype, libtfr.CTYPE)
def test_hanning_mtstft():
from numpy import hanning
nfft = 256
shift = 10
window = hanning(nfft - 50)
nframes = (sig.size - window.size) // shift + 1
D = libtfr.mfft_precalc(nfft, window)
Z = D.mtstft(sig, shift)
assert_tuple_equal(Z.shape, (nfft // 2 + 1, nframes, 1))
assert_equal(Z.dtype, libtfr.CTYPE)
def load_stimulus(path,
window,
step,
f_min=0.5,
f_max=8.0,
f_count=30,
compress=1,
gammatone=False,
**kwargs):
"""Load sound stimulus and calculate spectrotemporal representation.
Parameters:
path: location of wave file
window: duration of window (in ms)
step: window step (in ms)
f_min: minimum frequency (in kHz)
f_max: maximum frequency (in kHz)
f_count: number of frequency bins
gammatone: if True, use gammatone filterbank
Returns spectrogram, duration (ms)
"""
import ewave
fp = ewave.open(path, "r")
Fs = fp.sampling_rate / 1000.
osc = ewave.rescale(fp.read(), 'h')
if gammatone:
import gammatone.gtgram as gg
Pxx = gg.gtgram(osc, Fs * 1000, window / 1000, step / 1000, f_count,
f_min * 1000)
else:
import libtfr
# nfft based on desired number of channels btw f_min and f_max
nfft = int(f_count / (f_max - f_min) * Fs)
npoints = int(Fs * window)
if nfft < npoints:
raise ValueError(
"window size {} ({} points) too large for desired freq resolution {}. "
"Decrease to {} ms or increase f_count.".format(
window, f_count, npoints, nfft / Fs))
nstep = int(Fs * step)
taper = np.hanning(npoints)
mfft = libtfr.mfft_precalc(nfft, taper)
Pxx = mfft.mtspec(osc, nstep)
freqs, ind = libtfr.fgrid(Fs, nfft, [f_min, f_max])
Pxx = Pxx[ind, :]
if compress is not None:
Pxx = np.log10(Pxx + compress) - np.log10(compress)
return Pxx, Pxx.shape[1] * step
