def stockwell_fe(x):
ci = x.shape[0]
cj = x.shape[-1]
res = []
for i in range(ci):
cur_trial = []
for j in range(cj):
coef = sw.st(x[i, :, j], 8, 30)
cur_trial.append(coef)
res.append(np.array(cur_trial).T)
return np.array(res)
def stspecgram(x, fs, lofreq=None, hifreq=None, t0=None, t1=None):
"""
plot out the stockwell spectrum abs(st(x))
given frequency sampling fs in Hz
lofreq and hifreq are the frequency limits expressed in terms of the nyquist frequency(?)
"""
n = x.shape[0]
if t0 == None:
t0 = 0.0
if t1 == None:
t1 = n / float(fs) + t0
if lofreq == None and hifreq == None:
sx = stockwell.st(x)
return plotspec(abs(sx), fs, t0=t0, t1=t1)
lorow = stockwell.stfreq(lofreq, n, fs)
hirow = stockwell.stfreq(hifreq, n, fs)
sx = stockwell.st(x, lorow, hirow)
return plotspec(abs(sx), fs, lofreq=lofreq, hifreq=hifreq, t0=t0, t1=t1)
def timefreqplot(x, fs, lo=0, hi=0, title=None):
"""
plot a signal and its spectrogram
defaultis to find the entire frequency band (lo->0.0, hi-> n/2)
lo (Hz) gets transformed to the sample-based lo_n for use with stockwell.st
hi (Hz)
"""
n = len(x)
if not hi:
hi_n = int(n / 2) # float(fs/2)
hi = fs / 2.0
else:
hi_n = stockwell.stfreq(hi, n, fs)
print("stfreq(hi,n,fs):", hi)
print("setting hi", hi, hi_n)
if not lo:
lo = 0.0
lo_n = 0
else:
lo_n = stockwell.stfreq(lo, n, fs)
print("stfreq(lo,n,fs):", lo)
print("setting low", lo, lo_n)
fig, ax = plt.subplots(nrows=2, ncols=1, sharex=True)
if title: ax.set_title(title)
psx = abs(stockwell.st(x, lo_n, hi_n))
print("psx.shape:", psx.shape)
si = 1.0 / fs
ax[0].plot(np.arange(0, n) * si, x)
# extents = _get_specgram_plot_extents(psx, fs=fs, lofreq=lo, hifreq=hi,t0=0, t1=n/float(fs))
extents = _get_specgram_plot_extents(psx,
fs=fs,
lofreq=lo,
hifreq=hi,
t0=0,
t1=n / float(fs))
print(extents)
ax[1].set_ylabel('Hz')
ax[1].imshow(psx, extent=extents, aspect='auto', origin='lower')
return fig, ax, psx