def get_syllable_props(agg, stim_id, syllable_order, data_dir):
wave_pad = 20e-3
i = (agg.df.stim_id == stim_id) & (agg.df.syllable_order == syllable_order)
bird = agg.df[i].bird.values[0]
start_time = agg.df[i].start_time.values[0] - wave_pad
end_time = agg.df[i].end_time.values[0] + wave_pad
xindex = agg.df[i].xindex.values[0]
aprops = {aprop: agg.Xraw[xindex, k] for k, aprop in enumerate(USED_ACOUSTIC_PROPS)}
aprops['start_time'] = start_time
aprops['end_time'] = end_time
duration = end_time - start_time
sfile = os.path.join(data_dir, bird, 'stims.h5')
# get the raw sound pressure waveform
sound_manager = SoundManager(HDF5Store, sfile, read_only=True, db_args={'read_only': True})
wave = sound_manager.reconstruct(stim_id)
wave_sr = wave.samplerate
wave = np.array(wave).squeeze()
wave /= np.abs(wave).max()
wave *= aprops['maxAmp']
wave_t = np.arange(len(wave)) / wave_sr
wave_si = np.min(np.where(wave_t >= start_time)[0])
wave_ei = wave_si + int(duration * wave_sr)
amp_env = temporal_envelope(wave, wave_sr, cutoff_freq=200.0)
amp_env /= amp_env.max()
amp_env *= wave.max()
# compute the spectrogram
spec_sr = 1000.
spec_t, spec_freq, spec, spec_rms = gaussian_stft(wave, float(wave_sr), 0.007, 1. / spec_sr, min_freq=300.,
max_freq=8000.)
spec = np.abs(spec) ** 2
log_transform(spec, dbnoise=70)
spec_si = np.min(np.where(spec_t >= start_time)[0])
spec_ei = spec_si + int(duration * spec_sr)
# compute power spectrum
ps_freq, ps = power_spectrum(wave[wave_si:wave_ei], wave_sr, log=False, hanning=False)
return {'wave_t':wave_t, 'wave':wave, 'wave_si':wave_si, 'wave_ei':wave_ei,
'ps_freq':ps_freq, 'ps':ps, 'amp_env':amp_env,
'spec_t':spec_t, 'spec_freq':spec_freq, 'spec':spec, 'spec_si':spec_si, 'spec_ei':spec_ei,
'spec_sample_rate':spec_sr, 'aprops':aprops
}
def plot_psds(psd_file, data_dir='/auto/tdrive/mschachter/data'):
# read PairwiseCF file
pcf_file = os.path.join(data_dir, 'aggregate', 'pairwise_cf.h5')
pcf = AggregatePairwiseCF.load(pcf_file)
# pcf.zscore_within_site()
g = pcf.df.groupby(['bird', 'block', 'segment', 'electrode'])
nsamps_electrodes = len(g)
i = pcf.df.cell_index != -1
g = pcf.df[i].groupby(['bird', 'block', 'segment', 'electrode', 'cell_index'])
nsamps_cells = len(g)
print '# of electrodes: %d' % nsamps_electrodes
print '# of cells: %d' % nsamps_cells
print '# of lfp samples: %d' % (pcf.lfp_psds.shape[0])
print '# of spike psd samples: %d' % (pcf.spike_psds.shape[0])
# compute the LFP mean and std
lfp_psds = deepcopy(pcf.lfp_psds)
print 'lfp_psds_ind: max=%f, q99=%f' % (lfp_psds.max(), np.percentile(lfp_psds.ravel(), 99))
log_transform(lfp_psds)
print 'lfp_psds_ind: max=%f, q99=%f' % (lfp_psds.max(), np.percentile(lfp_psds.ravel(), 99))
nz = lfp_psds.sum(axis=1) > 0
lfp_psds = lfp_psds[nz, :]
lfp_psd_mean = lfp_psds.mean(axis=0)
lfp_psd_std = lfp_psds.std(axis=0, ddof=1)
nsamps_lfp = lfp_psds.shape[0]
# get the spike rate
spike_rate = pcf.df.spike_rate.values
# plt.figure()
# plt.hist(spike_rate, bins=20, color='g', alpha=0.7)
# plt.title('Spike Rate Histogram, q1=%0.3f, q5=%0.3f, q10=%0.3f, q50=%0.3f, q99=%0.3f' %
# (np.percentile(spike_rate, 1), np.percentile(spike_rate, 5), np.percentile(spike_rate, 10),
# np.percentile(spike_rate, 50), np.percentile(spike_rate, 99)))
# plt.show()
# compute the covariance
lfp_psd_z = deepcopy(lfp_psds)
lfp_psd_z -= lfp_psd_mean
lfp_psd_z /= lfp_psd_std
lfp_and_spike_cov_est = LedoitWolf()
lfp_and_spike_cov_est.fit(lfp_psd_z)
lfp_and_spike_cov = lfp_and_spike_cov_est.covariance_
"""
# read CRCNS file
cell_data = dict()
hf = h5py.File(psd_file, 'r')
cnames = hf.attrs['col_names']
for c in cnames:
cell_data[c] = np.array(hf[c])
crcns_psds = np.array(hf['psds'])
freqs = hf.attrs['freqs']
hf.close()
cell_df = pd.DataFrame(cell_data)
print 'regions=',cell_df.superregion.unique()
name_map = {'brainstem':'MLd', 'thalamus':'OV', 'cortex':'Field L+CM'}
"""
# resample the lfp mean and std
freq_rs = np.linspace(pcf.freqs.min(), pcf.freqs.max(), 1000)
lfp_mean_cs = interp1d(pcf.freqs, lfp_psd_mean, kind='cubic')
lfp_mean_rs = lfp_mean_cs(freq_rs)
lfp_std_cs = interp1d(pcf.freqs, lfp_psd_std, kind='cubic')
lfp_std_rs = lfp_std_cs(freq_rs)
# concatenate the lfp psd and log spike rate
lfp_psd_and_spike_rate = list()
for k,(li,si) in enumerate(zip(pcf.df['lfp_index'], pcf.df['spike_index'])):
lpsd = pcf.lfp_psds[li, :]
srate,sstd = pcf.spike_rates[si, :]
if srate > 0:
lfp_psd_and_spike_rate.append(np.hstack([lpsd, np.log(srate)]))
lfp_psd_and_spike_rate = np.array(lfp_psd_and_spike_rate)
nfreqs = len(pcf.freqs)
lfp_rate_cc = np.zeros([nfreqs])
for k in range(nfreqs):
lfp_rate_cc[k] = np.corrcoef(lfp_psd_and_spike_rate[:, k], lfp_psd_and_spike_rate[:, -1])[0, 1]
fig = plt.figure(figsize=(24, 12))
fig.subplots_adjust(left=0.05, right=0.95, wspace=0.30, hspace=0.30)
nrows = 2
ncols = 100
gs = plt.GridSpec(nrows, ncols)
ax = plt.subplot(gs[0, :35])
plt.errorbar(freq_rs, lfp_mean_rs, yerr=lfp_std_rs, c='k', linewidth=9.0, elinewidth=3.0,
ecolor='#D8D8D8', alpha=0.5, capthick=0.)
plt.axis('tight')
plt.xlabel('Frequency (Hz)')
plt.ylabel('Power (dB)')
# plt.ylim(0, 1)
plt.title('Mean LFP PSD')
ax = plt.subplot(gs[1, :35])
plt.plot(pcf.freqs, lfp_rate_cc, '-', c=COLOR_BLUE_LFP, linewidth=9.0, alpha=0.7)
plt.axhline(0, c='k')
plt.axis('tight')
plt.xlabel('Frequency (Hz)')
plt.ylabel('Correlation Coefficient')
plt.ylim(-0.05, 0.25)
plt.title('LFP Power vs log Spike Rate')
"""
fi = freqs < 200
ax = plt.subplot(gs[1, :35])
clrs = ['k', '#d60036', COLOR_YELLOW_SPIKE]
alphas = [0.8, 0.8, 0.6]
for k,reg in enumerate(['brainstem', 'thalamus', 'cortex']):
i = cell_df.superregion == reg
indices = cell_df['index'][i].values
psds = crcns_psds[indices, :]
log_psds = deepcopy(psds)
log_transform(log_psds)
# compute the mean and sd of the power spectra
psd_mean = log_psds.mean(axis=0)
psd_std = log_psds.std(axis=0, ddof=1)
psd_cv = psd_std / psd_mean
# plot the mean power spectrum on the left
plt.plot(freqs[fi], psd_mean[fi], c=clrs[k], linewidth=9.0, alpha=alphas[k])
plt.ylabel('Power (dB)')
plt.xlabel('Frequency (Hz)')
plt.axis('tight')
plt.ylim(0, 1.0)
plt.legend(['MLd', 'OV', 'Field L+CM'], fontsize='x-small', loc='upper right')
plt.title('Mean PSTH PSDs (CRCNS Data)')
"""
ax = plt.subplot(gs[:, 40:])
plt.imshow(lfp_and_spike_cov, aspect='auto', interpolation='nearest', origin='lower', cmap=magma, vmin=0, vmax=1)
plt.colorbar(label='Correlation Coefficient')
xy = np.arange(len(pcf.freqs))
lbls = ['%d' % f for f in pcf.freqs]
plt.xticks(xy, lbls, rotation=0)
plt.yticks(xy, lbls)
plt.axhline(nfreqs-0.5, c='w')
plt.axvline(nfreqs-0.5, c='w')
plt.xlabel('Frequency (Hz)')
plt.ylabel('Frequency (Hz)')
plt.title('LFP PSD Correlation Matrix')
fname = os.path.join(get_this_dir(), 'crcns_data.svg')
plt.savefig(fname, facecolor='w', edgecolor='none')
plt.show()
