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updated spectral.py

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Jan Benda 2025-05-24 00:45:30 +02:00
parent 997d737c40
commit 752837f4ec
1 changed files with 11 additions and 2 deletions
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spectral.py
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@ -157,7 +157,7 @@ def spectra(stimulus, spikes, dt, nfft):
return freq, pss, np.mean(prr, 0), np.mean(prs, 0) return freq, pss, np.mean(prr, 0), np.mean(prs, 0)
def susceptibilities(stimulus, spikes, dt=0.0005, nfft=2**9): def susceptibilities(stimulus, spikes, dt=0.0005, nfft=2**9, nmax=0):
""" Stimulus- and response spectra up to second order. """ Stimulus- and response spectra up to second order.
Compute the complex-valued transfer function (first-order Compute the complex-valued transfer function (first-order
@ -202,6 +202,8 @@ def susceptibilities(stimulus, spikes, dt=0.0005, nfft=2**9):
Sampling interval of stimulus and resolution of the binary spike train. Sampling interval of stimulus and resolution of the binary spike train.
nfft: int nfft: int
Number of samples used for each Fourier transformation. Number of samples used for each Fourier transformation.
nmax: int
Maximum number of FFT segments to be used. If 0, use all segement.
Returns Returns
------- -------
@ -228,7 +230,6 @@ def susceptibilities(stimulus, spikes, dt=0.0005, nfft=2**9):
f0 = len(freqs)//4 f0 = len(freqs)//4
f1 = 3*len(freqs)//4 f1 = 3*len(freqs)//4
segments = range(0, len(stimulus) - nfft, nfft) segments = range(0, len(stimulus) - nfft, nfft)
scale = dt/nfft/n
# stimulus: # stimulus:
p_ss = np.zeros(len(freqs)) p_ss = np.zeros(len(freqs))
fourier_s = np.zeros((len(segments), len(freqs)), complex) fourier_s = np.zeros((len(segments), len(freqs)), complex)
@ -238,6 +239,9 @@ def susceptibilities(stimulus, spikes, dt=0.0005, nfft=2**9):
fourier_s[j] = np.fft.fftshift(fourier_s[j]) fourier_s[j] = np.fft.fftshift(fourier_s[j])
p_ss += np.abs(fourier_s[j]*np.conj(fourier_s[j])) p_ss += np.abs(fourier_s[j]*np.conj(fourier_s[j]))
n += 1 n += 1
if nmax > 0 and n >= nmax:
break
scale = dt/nfft/n
p_ss *= scale p_ss *= scale
# response spectra: # response spectra:
time = np.arange(len(stimulus))*dt time = np.arange(len(stimulus))*dt
@ -260,6 +264,11 @@ def susceptibilities(stimulus, spikes, dt=0.0005, nfft=2**9):
p_rs += np.conj(fourier_s[j])*fourier_r p_rs += np.conj(fourier_s[j])*fourier_r
p_rss += fourier_s12*fourier_r[fsum_idx] p_rss += fourier_s12*fourier_r[fsum_idx]
n += 1 n += 1
if nmax > 0 and n >= nmax:
break
if nmax > 0 and n >= nmax:
break
scale = dt/nfft/n
return freqs[f0:f1], p_ss[f0:f1], p_rr[f0:f1]*scale, p_rs[f0:f1]*scale, p_rss[f0:f1, f0:f1]*dt*scale, n return freqs[f0:f1], p_ss[f0:f1], p_rr[f0:f1]*scale, p_rs[f0:f1]*scale, p_rss[f0:f1, f0:f1]*dt*scale, n