def draw_coherency_matrix(
bird,
block,
segment,
hemi,
stim_id,
trial,
syllable_index,
data_dir="/auto/tdrive/mschachter/data",
exp=None,
save=True,
):
# load up the experiment
if exp is None:
bird_dir = os.path.join(data_dir, bird)
exp_file = os.path.join(bird_dir, "%s.h5" % bird)
stim_file = os.path.join(bird_dir, "stims.h5")
exp = Experiment.load(exp_file, stim_file)
seg = exp.get_segment(block, segment)
# get the start and end times of the stimulus
etable = exp.get_epoch_table(seg)
i = etable["id"] == stim_id
stim_times = zip(etable[i]["start_time"].values, etable[i]["end_time"].values)
stim_times.sort(key=operator.itemgetter(0))
start_time, end_time = stim_times[trial]
stim_dur = float(end_time - start_time)
# get a slice of the LFP
lfp_data = exp.get_lfp_slice(seg, start_time, end_time)
electrode_indices, lfps, sample_rate = lfp_data[hemi]
# rescale the LFPs to they are in uV
lfps *= 1e6
# get the log spectrogram of the stimulus
stim_spec_t, stim_spec_freq, stim_spec = exp.get_spectrogram_slice(seg, start_time, end_time)
stim_spec_t = np.linspace(0, stim_dur, len(stim_spec_t))
stim_spec_dt = np.diff(stim_spec_t)[0]
nz = stim_spec > 0
stim_spec[nz] = 20 * np.log10(stim_spec[nz]) + 100
stim_spec[stim_spec < 0] = 0
# get the amplitude envelope
amp_env = stim_spec.std(axis=0, ddof=1)
amp_env -= amp_env.min()
amp_env /= amp_env.max()
# segment the amplitude envelope into syllables
merge_thresh = int(0.002 * sample_rate)
events = break_envelope_into_events(amp_env, threshold=0.05, merge_thresh=merge_thresh)
# translate the event indices into actual times
events *= stim_spec_dt
syllable_start, syllable_end, syllable_max_amp = events[syllable_index]
syllable_si = int(syllable_start * sample_rate)
syllable_ei = int(syllable_end * sample_rate)
# compute all cross and auto-correlations
if hemi == "L":
electrode_order = ROSTRAL_CAUDAL_ELECTRODES_LEFT
else:
electrode_order = ROSTRAL_CAUDAL_ELECTRODES_RIGHT
lags = np.arange(-20, 21)
lags_ms = (lags / sample_rate) * 1e3
window_fraction = 0.35
noise_db = 25.0
nelectrodes = len(electrode_order)
cross_mat = np.zeros([nelectrodes, nelectrodes, len(lags)])
for i in range(nelectrodes):
for j in range(nelectrodes):
lfp1 = lfps[i, syllable_si:syllable_ei]
lfp2 = lfps[j, syllable_si:syllable_ei]
if i != j:
x = coherency(lfp1, lfp2, lags, window_fraction=window_fraction, noise_floor_db=noise_db)
else:
x = correlation_function(lfp1, lfp2, lags)
_e1 = electrode_indices[i]
_e2 = electrode_indices[j]
i1 = electrode_order.index(_e1)
i2 = electrode_order.index(_e2)
# print 'i=%d, j=%d, e1=%d, e2=%d, i1=%d, i2=%d' % (i, j, _e1, _e2, i1, i2)
cross_mat[i1, i2, :] = x
# make a plot
figsize = (24.0, 13.5)
fig = plt.figure(figsize=figsize)
plt.subplots_adjust(top=0.95, bottom=0.05, left=0.03, right=0.99, hspace=0.10)
gs = plt.GridSpec(nelectrodes, nelectrodes)
for i in range(nelectrodes):
for j in range(i + 1):
ax = plt.subplot(gs[i, j])
plt.axhline(0, c="k")
plt.axvline(0, c="k")
_e1 = electrode_order[i]
_e2 = electrode_order[j]
plt.plot(lags_ms, cross_mat[i, j, :], "k-", linewidth=2.0)
plt.xticks([])
plt.yticks([])
plt.axis("tight")
plt.ylim(-0.5, 1.0)
if j == 0:
plt.ylabel("E%d" % electrode_order[i])
if i == nelectrodes - 1:
plt.xlabel("E%d" % electrode_order[j])
if save:
fname = os.path.join(get_this_dir(), "coherency_matrix.svg")
plt.savefig(fname, facecolor="w", edgecolor="none")
def test_cross_psd(self):
np.random.seed(1234567)
sr = 1000.0
dur = 1.0
nt = int(dur * sr)
t = np.arange(nt) / sr
# create a simple signal
freqs = list()
freqs.extend(np.arange(8, 12))
freqs.extend(np.arange(60, 71))
freqs.extend(np.arange(130, 151))
s1 = np.zeros([nt])
for f in freqs:
s1 += np.sin(2 * np.pi * f * t)
s1 /= s1.max()
# create a noise corrupted, bandpassed filtered version of s1
noise = np.random.randn(nt) * 1e-1
# s2 = convolve1d(s1, filt, mode='mirror') + noise
s2 = bandpass_filter(s1, sample_rate=sr, low_freq=40., high_freq=90.)
s2 /= s2.max()
s2 += noise
# compute the signal's power spectrums
welch_freq1, welch_psd1 = welch(s1, fs=sr)
welch_freq2, welch_psd2 = welch(s2, fs=sr)
welch_psd_max = max(welch_psd1.max(), welch_psd2.max())
welch_psd1 /= welch_psd_max
welch_psd2 /= welch_psd_max
# compute the auto-correlation functions
lags = np.arange(-200, 201)
acf1 = correlation_function(s1, s1, lags, normalize=True)
acf2 = correlation_function(s2, s2, lags, normalize=True)
# compute the cross correlation functions
cf12 = correlation_function(s1, s2, lags, normalize=True)
coh12 = coherency(s1,
s2,
lags,
window_fraction=0.75,
noise_floor_db=100.)
# do an FFT shift to the lags and the window, otherwise the FFT of the ACFs is not equal to the power
# spectrum for some numerical reason
shift_lags = fftshift(lags)
if len(lags) % 2 == 1:
# shift zero from end of shift_lags to beginning
shift_lags = np.roll(shift_lags, 1)
acf1_shift = correlation_function(s1, s1, shift_lags)
acf2_shift = correlation_function(s2, s2, shift_lags)
# compute the power spectra from the auto-spectra
ps1 = fft(acf1_shift)
ps1_freq = fftfreq(len(acf1), d=1.0 / sr)
fi = ps1_freq > 0
ps1 = ps1[fi]
assert np.sum(
np.abs(ps1.imag) > 1e-8
) == 0, "Nonzero imaginary part for fft(acf1) (%d)" % np.sum(
np.abs(ps1.imag) > 1e-8)
ps1_auto = np.abs(ps1.real)
ps1_auto_freq = ps1_freq[fi]
ps2 = fft(acf2_shift)
ps2_freq = fftfreq(len(acf2), d=1.0 / sr)
fi = ps2_freq > 0
ps2 = ps2[fi]
assert np.sum(np.abs(ps2.imag) > 1e-8
) == 0, "Nonzero imaginary part for fft(acf2)"
ps2_auto = np.abs(ps2.real)
ps2_auto_freq = ps2_freq[fi]
assert np.sum(ps1_auto < 0) == 0, "negatives in ps1_auto"
assert np.sum(ps2_auto < 0) == 0, "negatives in ps2_auto"
# compute the cross spectral density from the correlation function
cf12_shift = correlation_function(s1, s2, shift_lags, normalize=True)
psd12 = fft(cf12_shift)
psd12_freq = fftfreq(len(cf12_shift), d=1.0 / sr)
fi = psd12_freq > 0
psd12 = np.abs(psd12[fi])
psd12_freq = psd12_freq[fi]
# compute the cross spectral density from the power spectra
psd12_welch = welch_psd1 * welch_psd2
psd12_welch /= psd12_welch.max()
# compute the coherence from the cross spectral density
cfreq,coherence,coherence_var,phase_coherence,phase_coherence_var,coh12_freqspace,coh12_freqspace_t = \
coherence_jn(s1, s2, sample_rate=sr, window_length=0.100, increment=0.050, return_coherency=True)
coh12_freqspace /= np.abs(coh12_freqspace).max()
# weight the coherence by one minus the normalized standard deviation
coherence_std = np.sqrt(coherence_var)
# cweight = coherence_std / coherence_std.sum()
# coherence_weighted = (1.0 - cweight)*coherence
coherence_weighted = coherence - coherence_std
coherence_weighted[coherence_weighted < 0] = 0
# compute the coherence from the fft of the coherency
coherence2 = fft(fftshift(coh12))
coherence2_freq = fftfreq(len(coherence2), d=1.0 / sr)
fi = coherence2_freq > 0
coherence2 = np.abs(coherence2[fi])
coherence2_freq = coherence2_freq[fi]
"""
plt.figure()
ax = plt.subplot(2, 1, 1)
plt.plot(ps1_auto_freq, ps1_auto*ps2_auto, 'c-', linewidth=2.0, alpha=0.75)
plt.plot(psd12_freq, psd12, 'g-', linewidth=2.0, alpha=0.9)
plt.plot(ps1_auto_freq, ps1_auto, 'k-', linewidth=2.0, alpha=0.75)
plt.plot(ps2_auto_freq, ps2_auto, 'r-', linewidth=2.0, alpha=0.75)
plt.axis('tight')
plt.legend(['denom', '12', '1', '2'])
ax = plt.subplot(2, 1, 2)
plt.plot(psd12_freq, coherence, 'b-')
plt.axis('tight')
plt.show()
"""
# normalize the cross-spectral density and power spectra
psd12 /= psd12.max()
ps_auto_max = max(ps1_auto.max(), ps2_auto.max())
ps1_auto /= ps_auto_max
ps2_auto /= ps_auto_max
# make some plots
plt.figure()
nrows = 2
ncols = 2
# plot the signals
ax = plt.subplot(nrows, ncols, 1)
plt.plot(t, s1, 'k-', linewidth=2.0)
plt.plot(t, s2, 'r-', alpha=0.75, linewidth=2.0)
plt.xlabel('Time (s)')
plt.ylabel('Signal')
plt.axis('tight')
# plot the spectra
ax = plt.subplot(nrows, ncols, 2)
plt.plot(welch_freq1, welch_psd1, 'k-', linewidth=2.0, alpha=0.85)
plt.plot(ps1_auto_freq, ps1_auto, 'k--', linewidth=2.0, alpha=0.85)
plt.plot(welch_freq2, welch_psd2, 'r-', alpha=0.75, linewidth=2.0)
plt.plot(ps2_auto_freq, ps2_auto, 'r--', linewidth=2.0, alpha=0.75)
plt.axis('tight')
plt.xlabel('Frequency (Hz)')
plt.ylabel('Power')
# plot the correlation functions
ax = plt.subplot(nrows, ncols, 3)
plt.axhline(0, c='k')
plt.plot(lags, acf1, 'k-', linewidth=2.0)
plt.plot(lags, acf2, 'r-', alpha=0.75, linewidth=2.0)
plt.plot(lags, cf12, 'g-', alpha=0.75, linewidth=2.0)
plt.plot(lags, coh12, 'b-', linewidth=2.0, alpha=0.75)
plt.plot(coh12_freqspace_t * 1e3,
coh12_freqspace,
'm-',
linewidth=1.0,
alpha=0.95)
plt.xlabel('Lag (ms)')
plt.ylabel('Correlation Function')
plt.axis('tight')
plt.ylim(-0.5, 1.0)
handles = custom_legend(['k', 'r', 'g', 'b', 'c'],
['acf1', 'acf2', 'cf12', 'coh12', 'coh12_f'])
plt.legend(handles=handles)
# plot the cross spectral density
ax = plt.subplot(nrows, ncols, 4)
handles = custom_legend(['g', 'k', 'b'],
['CSD', 'Coherence', 'Weighted'])
plt.axhline(0, c='k')
plt.axhline(1, c='k')
plt.plot(psd12_freq, psd12, 'g-', linewidth=3.0)
plt.errorbar(cfreq,
coherence,
yerr=np.sqrt(coherence_var),
fmt='k-',
ecolor='r',
linewidth=3.0,
elinewidth=5.0,
alpha=0.8)
plt.plot(cfreq, coherence_weighted, 'b-', linewidth=3.0, alpha=0.75)
plt.xlabel('Frequency (Hz)')
plt.ylabel('Cross-spectral Density/Coherence')
plt.legend(handles=handles)
"""
plt.figure()
plt.axhline(0, c='k')
plt.plot(lags, cf12, 'k-', alpha=1, linewidth=2.0)
plt.plot(lags, coh12, 'b-', linewidth=3.0, alpha=0.75)
plt.plot(coh12_freqspace_t*1e3, coh12_freqspace, 'r-', linewidth=2.0, alpha=0.95)
plt.xlabel('Lag (ms)')
plt.ylabel('Correlation Function')
plt.axis('tight')
plt.ylim(-0.5, 1.0)
handles = custom_legend(['k', 'b', 'r'], ['cf12', 'coh12', 'coh12_f'])
plt.legend(handles=handles)
"""
plt.show()
def test_cross_psd(self):
np.random.seed(1234567)
sr = 1000.0
dur = 1.0
nt = int(dur*sr)
t = np.arange(nt) / sr
# create a simple signal
freqs = list()
freqs.extend(np.arange(8, 12))
freqs.extend(np.arange(60, 71))
freqs.extend(np.arange(130, 151))
s1 = np.zeros([nt])
for f in freqs:
s1 += np.sin(2*np.pi*f*t)
s1 /= s1.max()
# create a noise corrupted, bandpassed filtered version of s1
noise = np.random.randn(nt)*1e-1
# s2 = convolve1d(s1, filt, mode='mirror') + noise
s2 = bandpass_filter(s1, sample_rate=sr, low_freq=40., high_freq=90.)
s2 /= s2.max()
s2 += noise
# compute the signal's power spectrums
welch_freq1,welch_psd1 = welch(s1, fs=sr)
welch_freq2,welch_psd2 = welch(s2, fs=sr)
welch_psd_max = max(welch_psd1.max(), welch_psd2.max())
welch_psd1 /= welch_psd_max
welch_psd2 /= welch_psd_max
# compute the auto-correlation functions
lags = np.arange(-200, 201)
acf1 = correlation_function(s1, s1, lags, normalize=True)
acf2 = correlation_function(s2, s2, lags, normalize=True)
# compute the cross correlation functions
cf12 = correlation_function(s1, s2, lags, normalize=True)
coh12 = coherency(s1, s2, lags, window_fraction=0.75, noise_floor_db=100.)
# do an FFT shift to the lags and the window, otherwise the FFT of the ACFs is not equal to the power
# spectrum for some numerical reason
shift_lags = fftshift(lags)
if len(lags) % 2 == 1:
# shift zero from end of shift_lags to beginning
shift_lags = np.roll(shift_lags, 1)
acf1_shift = correlation_function(s1, s1, shift_lags)
acf2_shift = correlation_function(s2, s2, shift_lags)
# compute the power spectra from the auto-spectra
ps1 = fft(acf1_shift)
ps1_freq = fftfreq(len(acf1), d=1.0/sr)
fi = ps1_freq > 0
ps1 = ps1[fi]
assert np.sum(np.abs(ps1.imag) > 1e-8) == 0, "Nonzero imaginary part for fft(acf1) (%d)" % np.sum(np.abs(ps1.imag) > 1e-8)
ps1_auto = np.abs(ps1.real)
ps1_auto_freq = ps1_freq[fi]
ps2 = fft(acf2_shift)
ps2_freq = fftfreq(len(acf2), d=1.0/sr)
fi = ps2_freq > 0
ps2 = ps2[fi]
assert np.sum(np.abs(ps2.imag) > 1e-8) == 0, "Nonzero imaginary part for fft(acf2)"
ps2_auto = np.abs(ps2.real)
ps2_auto_freq = ps2_freq[fi]
assert np.sum(ps1_auto < 0) == 0, "negatives in ps1_auto"
assert np.sum(ps2_auto < 0) == 0, "negatives in ps2_auto"
# compute the cross spectral density from the correlation function
cf12_shift = correlation_function(s1, s2, shift_lags, normalize=True)
psd12 = fft(cf12_shift)
psd12_freq = fftfreq(len(cf12_shift), d=1.0/sr)
fi = psd12_freq > 0
psd12 = np.abs(psd12[fi])
psd12_freq = psd12_freq[fi]
# compute the cross spectral density from the power spectra
psd12_welch = welch_psd1*welch_psd2
psd12_welch /= psd12_welch.max()
# compute the coherence from the cross spectral density
cfreq,coherence,coherence_var,phase_coherence,phase_coherence_var,coh12_freqspace,coh12_freqspace_t = \
coherence_jn(s1, s2, sample_rate=sr, window_length=0.100, increment=0.050, return_coherency=True)
coh12_freqspace /= np.abs(coh12_freqspace).max()
# weight the coherence by one minus the normalized standard deviation
coherence_std = np.sqrt(coherence_var)
# cweight = coherence_std / coherence_std.sum()
# coherence_weighted = (1.0 - cweight)*coherence
coherence_weighted = coherence - coherence_std
coherence_weighted[coherence_weighted < 0] = 0
# compute the coherence from the fft of the coherency
coherence2 = fft(fftshift(coh12))
coherence2_freq = fftfreq(len(coherence2), d=1.0/sr)
fi = coherence2_freq > 0
coherence2 = np.abs(coherence2[fi])
coherence2_freq = coherence2_freq[fi]
"""
plt.figure()
ax = plt.subplot(2, 1, 1)
plt.plot(ps1_auto_freq, ps1_auto*ps2_auto, 'c-', linewidth=2.0, alpha=0.75)
plt.plot(psd12_freq, psd12, 'g-', linewidth=2.0, alpha=0.9)
plt.plot(ps1_auto_freq, ps1_auto, 'k-', linewidth=2.0, alpha=0.75)
plt.plot(ps2_auto_freq, ps2_auto, 'r-', linewidth=2.0, alpha=0.75)
plt.axis('tight')
plt.legend(['denom', '12', '1', '2'])
ax = plt.subplot(2, 1, 2)
plt.plot(psd12_freq, coherence, 'b-')
plt.axis('tight')
plt.show()
"""
# normalize the cross-spectral density and power spectra
psd12 /= psd12.max()
ps_auto_max = max(ps1_auto.max(), ps2_auto.max())
ps1_auto /= ps_auto_max
ps2_auto /= ps_auto_max
# make some plots
plt.figure()
nrows = 2
ncols = 2
# plot the signals
ax = plt.subplot(nrows, ncols, 1)
plt.plot(t, s1, 'k-', linewidth=2.0)
plt.plot(t, s2, 'r-', alpha=0.75, linewidth=2.0)
plt.xlabel('Time (s)')
plt.ylabel('Signal')
plt.axis('tight')
# plot the spectra
ax = plt.subplot(nrows, ncols, 2)
plt.plot(welch_freq1, welch_psd1, 'k-', linewidth=2.0, alpha=0.85)
plt.plot(ps1_auto_freq, ps1_auto, 'k--', linewidth=2.0, alpha=0.85)
plt.plot(welch_freq2, welch_psd2, 'r-', alpha=0.75, linewidth=2.0)
plt.plot(ps2_auto_freq, ps2_auto, 'r--', linewidth=2.0, alpha=0.75)
plt.axis('tight')
plt.xlabel('Frequency (Hz)')
plt.ylabel('Power')
# plot the correlation functions
ax = plt.subplot(nrows, ncols, 3)
plt.axhline(0, c='k')
plt.plot(lags, acf1, 'k-', linewidth=2.0)
plt.plot(lags, acf2, 'r-', alpha=0.75, linewidth=2.0)
plt.plot(lags, cf12, 'g-', alpha=0.75, linewidth=2.0)
plt.plot(lags, coh12, 'b-', linewidth=2.0, alpha=0.75)
plt.plot(coh12_freqspace_t*1e3, coh12_freqspace, 'm-', linewidth=1.0, alpha=0.95)
plt.xlabel('Lag (ms)')
plt.ylabel('Correlation Function')
plt.axis('tight')
plt.ylim(-0.5, 1.0)
handles = custom_legend(['k', 'r', 'g', 'b', 'c'], ['acf1', 'acf2', 'cf12', 'coh12', 'coh12_f'])
plt.legend(handles=handles)
# plot the cross spectral density
ax = plt.subplot(nrows, ncols, 4)
handles = custom_legend(['g', 'k', 'b'], ['CSD', 'Coherence', 'Weighted'])
plt.axhline(0, c='k')
plt.axhline(1, c='k')
plt.plot(psd12_freq, psd12, 'g-', linewidth=3.0)
plt.errorbar(cfreq, coherence, yerr=np.sqrt(coherence_var), fmt='k-', ecolor='r', linewidth=3.0, elinewidth=5.0, alpha=0.8)
plt.plot(cfreq, coherence_weighted, 'b-', linewidth=3.0, alpha=0.75)
plt.xlabel('Frequency (Hz)')
plt.ylabel('Cross-spectral Density/Coherence')
plt.legend(handles=handles)
"""
plt.figure()
plt.axhline(0, c='k')
plt.plot(lags, cf12, 'k-', alpha=1, linewidth=2.0)
plt.plot(lags, coh12, 'b-', linewidth=3.0, alpha=0.75)
plt.plot(coh12_freqspace_t*1e3, coh12_freqspace, 'r-', linewidth=2.0, alpha=0.95)
plt.xlabel('Lag (ms)')
plt.ylabel('Correlation Function')
plt.axis('tight')
plt.ylim(-0.5, 1.0)
handles = custom_legend(['k', 'b', 'r'], ['cf12', 'coh12', 'coh12_f'])
plt.legend(handles=handles)
"""
plt.show()
def draw_figures():
d = get_full_data('GreBlu9508M', 'Site4', 'Call1', 'L', 287)
syllable_index = 1
syllable_start = d['syllable_props'][syllable_index]['start_time'] - 0.020
syllable_end = d['syllable_props'][syllable_index]['end_time'] + 0.030
sr = d['lfp_sample_rate']
lfp_mean = d['lfp'].mean(axis=0)
lfp_t = np.arange(lfp_mean.shape[1]) / sr
nelectrodes,nt = lfp_mean.shape
lfp_i = (lfp_t >= syllable_start) & (lfp_t <= syllable_end)
electrode_order = d['electrode_order']
# compute the cross coherency between each pair of electrodes
nelectrodes = 16
lags = np.arange(-20, 21)
lags_ms = (lags / sr)*1e3
nlags = len(lags)
window_fraction = 0.60
noise_floor_db = 25,
cross_coherency = np.zeros([nelectrodes, nelectrodes, nlags])
for i in range(nelectrodes):
for j in range(nelectrodes):
if i == j:
continue
lfp1 = lfp_mean[i, lfp_i]
lfp2 = lfp_mean[j, lfp_i]
cross_coherency[i, j, :] = coherency(lfp1, lfp2, lags, window_fraction=window_fraction, noise_floor_db=noise_floor_db)
figsize = (24, 13)
fig = plt.figure(figsize=figsize)
fig.subplots_adjust(top=0.95, bottom=0.02, right=0.97, left=0.03, hspace=0.20, wspace=0.20)
gs = plt.GridSpec(nelectrodes, nelectrodes)
for k in range(nelectrodes):
for j in range(k):
ax = plt.subplot(gs[k, j])
plt.axhline(0, c='k')
plt.plot(lags_ms, cross_coherency[k, j], 'k-', linewidth=2.0, alpha=0.8)
plt.axis('tight')
plt.ylim(-.25, 0.5)
plt.yticks([])
plt.xticks([])
if k == nelectrodes-1:
plt.xlabel('E%d' % electrode_order[j])
xtks = [-40, 0, 40]
plt.xticks(xtks, ['%d' % x for x in xtks])
if j == 0:
plt.ylabel('E%d' % electrode_order[k])
ytks = [-0.2, 0.4]
plt.yticks(ytks, ['%0.1f' % x for x in ytks])
ax = plt.subplot(gs[:7, (nelectrodes-8):])
voffset = 5
for n in range(nelectrodes):
plt.plot(lfp_t, lfp_mean[nelectrodes-n-1, :] + voffset*n, 'k-', linewidth=3.0, alpha=0.75)
plt.axis('tight')
ytick_locs = np.arange(nelectrodes) * voffset
plt.yticks(ytick_locs, list(reversed(d['electrode_order'])))
plt.ylabel('Electrode')
plt.axvline(syllable_start, c='k', linestyle='--', linewidth=3.0, alpha=0.7)
plt.axvline(syllable_end, c='k', linestyle='--', linewidth=3.0, alpha=0.7)
plt.xlabel('Time (s)')
fname = os.path.join(get_this_dir(), 'figure.svg')
plt.savefig(fname, facecolor='w', edgecolor='none')
plt.show()
def compute_spectra_and_coherence_single_electrode(lfp1, lfp2, sample_rate, e1, e2,
window_length=0.060, increment=None, log=True,
window_fraction=0.60, noise_floor_db=25,
lags=np.arange(-20, 21, 1), psd_stats=None):
"""
:param lfp1: An array of shape (ntrials, nt)
:param lfp2: An array of shape (ntrials, nt)
:return:
"""
# compute the mean (locked) spectra
lfp1_mean = lfp1.mean(axis=0)
lfp2_mean = lfp2.mean(axis=0)
if increment is None:
increment = 2.0 / sample_rate
pfreq,psd1,ps_var,phase = power_spectrum_jn(lfp1_mean, sample_rate, window_length, increment)
pfreq,psd2,ps_var,phase = power_spectrum_jn(lfp2_mean, sample_rate, window_length, increment)
if log:
log_transform(psd1)
log_transform(psd2)
c12 = coherency(lfp1_mean, lfp2_mean, lags, window_fraction=window_fraction, noise_floor_db=noise_floor_db)
# compute the nonlocked spectra coherence
c12_pertrial = list()
ntrials,nt = lfp1.shape
psd1_ms_all = list()
psd2_ms_all = list()
for k in range(ntrials):
i = np.ones([ntrials], dtype='bool')
i[k] = False
lfp1_jn_mean = lfp1[i, :].mean(axis=0)
lfp2_jn_mean = lfp2[i, :].mean(axis=0)
lfp1_ms = lfp1[k, :] - lfp1_jn_mean
lfp2_ms = lfp2[k, :] - lfp2_jn_mean
pfreq,psd1_ms,ps_var_ms,phase_ms = power_spectrum_jn(lfp1_ms, sample_rate, window_length, increment)
pfreq,psd2_ms,ps_var_ms,phase_ms = power_spectrum_jn(lfp2_ms, sample_rate, window_length, increment)
if log:
log_transform(psd1_ms)
log_transform(psd2_ms)
psd1_ms_all.append(psd1_ms)
psd2_ms_all.append(psd2_ms)
c12_ms = coherency(lfp1_ms, lfp2_ms, lags, window_fraction=window_fraction, noise_floor_db=noise_floor_db)
c12_pertrial.append(c12_ms)
psd1_ms_all = np.array(psd1_ms_all)
psd2_ms_all = np.array(psd2_ms_all)
psd1_ms = psd1_ms_all.mean(axis=0)
psd2_ms = psd2_ms_all.mean(axis=0)
if psd_stats is not None:
psd_mean1,psd_std1 = psd_stats[e1]
psd_mean2,psd_std2 = psd_stats[e2]
psd1 -= psd_mean1
psd1 /= psd_std1
psd2 -= psd_mean2
psd2 /= psd_std2
psd1_ms -= psd_mean1
psd1_ms /= psd_std1
psd2_ms -= psd_mean2
psd2_ms /= psd_std2
c12_pertrial = np.array(c12_pertrial)
c12_nonlocked = c12_pertrial.mean(axis=0)
# compute the coherence per trial then take the average
c12_totals = list()
for k in range(ntrials):
c12 = coherency(lfp1[k, :], lfp2[k, :], lags, window_fraction=window_fraction, noise_floor_db=noise_floor_db)
c12_totals.append(c12)
c12_totals = np.array(c12_totals)
c12_total = c12_totals.mean(axis=0)
return pfreq, psd1, psd2, psd1_ms, psd2_ms, c12, c12_nonlocked, c12_total
def compute_spectra_and_coherence_multi_electrode_single_trial(lfps, sample_rate, electrode_indices, electrode_order,
window_length=0.060, increment=None, log=True,
window_fraction=0.60, noise_floor_db=25,
lags=np.arange(-20, 21, 1),
psd_stats=None):
"""
:param lfps: an array of shape (ntrials, nelectrodes, nt)
:return:
"""
if increment is None:
increment = 2.0 / sample_rate
nelectrodes,nt = lfps.shape
freqs = get_freqs(sample_rate, window_length, increment)
lags_ms = get_lags_ms(sample_rate, lags)
spectra = np.zeros([nelectrodes, len(freqs)])
cross_mat = np.zeros([nelectrodes, nelectrodes, len(lags_ms)])
for k in range(nelectrodes):
_e1 = electrode_indices[k]
i1 = electrode_order.index(_e1)
lfp1 = lfps[k, :]
freqs,psd1,ps_var,phase = power_spectrum_jn(lfp1, sample_rate, window_length, increment)
if log:
log_transform(psd1)
if psd_stats is not None:
psd_mean,psd_std = psd_stats[_e1]
"""
plt.figure()
plt.subplot(2, 2, 1)
plt.plot(freqs, psd1, 'k-')
plt.title('PSD (%d)' % _e1)
plt.axis('tight')
plt.subplot(2, 2, 3)
plt.plot(freqs, psd_mean, 'g-')
plt.title('Mean')
plt.axis('tight')
plt.subplot(2, 2, 4)
plt.plot(freqs, psd_std, 'c-')
plt.title('STD')
plt.axis('tight')
plt.subplot(2, 2, 2)
psd1_z = deepcopy(psd1)
psd1_z -= psd_mean
psd1_z /= psd_std
plt.plot(freqs, psd1_z, 'r-')
plt.title('Zscored')
plt.axis('tight')
"""
psd1 -= psd_mean
psd1 /= psd_std
spectra[i1, :] = psd1
for j in range(k):
_e2 = electrode_indices[j]
i2 = electrode_order.index(_e2)
lfp2 = lfps[j, :]
cf = coherency(lfp1, lfp2, lags, window_fraction=window_fraction, noise_floor_db=noise_floor_db)
"""
freqs,c12,c_var_amp,c_phase,c_phase_var,coherency,coherency_t = coherence_jn(lfp1, lfp2, sample_rate,
window_length, increment,
return_coherency=True)
"""
cross_mat[i1, i2] = cf
cross_mat[i2, i1] = cf[::-1]
return spectra, cross_mat
