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 draw_figures(bird, block, segment, hemi, e1, e2, stim_id, trial, syllable_index, data_dir='/auto/tdrive/mschachter/data', exp=None): # 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, zscore=True) electrode_indices,lfps,sample_rate = lfp_data[hemi] # 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] amp_env_rs = amp_env*(stim_spec_freq.max() - stim_spec_freq.min()) + stim_spec_freq.min() last_syllable_end = events[-1, 1] + 0.025 i1 = electrode_indices.index(e1) i2 = electrode_indices.index(e2) lfp1 = lfps[i1, :] lfp2 = lfps[i2, :] t = np.arange(len(lfp1)) / sample_rate legend = ['E%d' % e1, 'E%d' % e2] if hemi == 'L': electrode_order = ROSTRAL_CAUDAL_ELECTRODES_LEFT else: electrode_order = ROSTRAL_CAUDAL_ELECTRODES_RIGHT # get the power spectrum stats for this site psd_stats = get_psd_stats(bird, block, segment, hemi) # compute the power spectra and cross coherence for all electrodes lags_ms = get_lags_ms(sample_rate) spectra,cross_mat = compute_spectra_and_coherence_multi_electrode_single_trial(lfps, sample_rate, electrode_indices, electrode_order, psd_stats=psd_stats) # plot the stimulus and raw LFP for two electrodes figsize = (24.0, 10) fig = plt.figure(figsize=figsize) plt.subplots_adjust(top=0.95, bottom=0.05, left=0.03, right=0.99, hspace=0.10) ax = plt.subplot(2, 1, 1) plot_spectrogram(stim_spec_t, stim_spec_freq, stim_spec, ax=ax, ticks=True, fmin=300, fmax=8000, colormap='SpectroColorMap', colorbar=False) # plt.plot(stim_spec_t, amp_env_rs, 'k-', linewidth=2.0, alpha=0.50) plt.axvline(syllable_start, c='k', linestyle='dashed', linewidth=3.0) plt.axvline(syllable_end, c='k', linestyle='dashed', linewidth=3.0) plt.axis('tight') plt.xlim(0, last_syllable_end) ax = plt.subplot(2, 1, 2) plt.plot(t, lfp1, 'b-', linewidth=3.0) plt.plot(t, lfp2, 'r-', linewidth=3.0, alpha=0.7) plt.axvline(syllable_start, c='k', linestyle='dashed', linewidth=3.0) plt.axvline(syllable_end, c='k', linestyle='dashed', linewidth=3.0) plt.xlabel('Time (ms)') plt.ylabel('LFP (z-scored)') plt.legend(legend) plt.axis('tight') plt.xlim(0, last_syllable_end) fname = os.path.join(get_this_dir(), 'raw.svg') plt.savefig(fname, facecolor='w', edgecolor='none') # restrict the lfps to a single syllable syllable_si = int(syllable_start*sample_rate) syllable_ei = int(syllable_end*sample_rate) lfp1 = lfp1[syllable_si:syllable_ei] lfp2 = lfp2[syllable_si:syllable_ei] # plot the two power spectra psd_ub = 6 psd_lb = 0 i1 = electrode_order.index(e1) i2 = electrode_order.index(e2) a1 = spectra[i1, :] a2 = spectra[i2, :] freqs = get_freqs(sample_rate) figsize = (10.0, 4.0) fig = plt.figure(figsize=figsize) plt.subplots_adjust(top=0.90, bottom=0.10, left=0.10, right=0.99, hspace=0.10) ax = plt.subplot(1, 2, 1) plt.plot(freqs, a1, 'b-', linewidth=3.0) plt.plot(freqs, a2, 'r-', linewidth=3.0) plt.xlabel('Frequency (Hz)') plt.ylabel('Power (z-scored)') plt.title('Power Spectrum') handles = custom_legend(['b', 'r'], legend) plt.legend(handles=handles, fontsize='small') plt.axis('tight') plt.ylim(psd_lb, psd_ub) # plot the coherency cf_lb = -0.1 cf_ub = 0.3 coh = cross_mat[i1, i2, :] ax = plt.subplot(1, 2, 2) plt.axhline(0, c='k') plt.axvline(0, c='k') plt.plot(lags_ms, coh, 'g-', linewidth=3.0) plt.xlabel('Frequency (Hz)') plt.title('Coherency') plt.axis('tight') plt.ylim(cf_lb, cf_ub) fname = os.path.join(get_this_dir(), 'auto+cross.svg') plt.savefig(fname, facecolor='w', edgecolor='none') # compute all cross and auto-correlations nelectrodes = len(electrode_order) # 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] clr = 'k' if i == j: if _e1 == e1: clr = 'b' elif _e1 == e2: clr = 'r' else: if _e1 == e1 and _e2 == e2: clr = 'g' if i == j: plt.plot(freqs, spectra[i, :], '-', c=clr, linewidth=2.0) else: plt.plot(lags_ms, cross_mat[i, j, :], '-', c=clr, linewidth=2.0) plt.xticks([]) plt.yticks([]) plt.axis('tight') if i != j: plt.ylim(cf_lb, cf_ub) else: plt.axhline(0, c='k') plt.ylim(psd_lb, psd_ub) if j == 0: plt.ylabel('E%d' % electrode_order[i]) if i == nelectrodes-1: plt.xlabel('E%d' % electrode_order[j]) fname = os.path.join(get_this_dir(), 'cross_all.svg') plt.savefig(fname, facecolor='w', edgecolor='none')
def draw_figures(bird, block, segment, hemi, e1, e2, stim_id, syllable_index, data_dir='/auto/tdrive/mschachter/data', exp=None): # 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)) stim_times = np.array(stim_times) stim_durs = stim_times[:, 1] - stim_times[:, 0] stim_dur = stim_durs.min() # aggregate the LFPs across trials lfps = list() sample_rate = None electrode_indices = None for start_time,end_time in stim_times: # get a slice of the LFP lfp_data = exp.get_lfp_slice(seg, start_time, end_time) electrode_indices,the_lfps,sample_rate = lfp_data[hemi] stim_dur_i = int(stim_dur*sample_rate) lfps.append(the_lfps[:, :stim_dur_i]) lfps = np.array(lfps) # rescale the LFPs to they are in uV lfps *= 1e6 # get the log spectrogram of the stimulus start_time_0 = stim_times[0][0] end_time_0 = stim_times[0][1] stim_spec_t,stim_spec_freq,stim_spec = exp.get_spectrogram_slice(seg, start_time_0, end_time_0) 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_start -= 0.005 syllable_end += 0.010 amp_env_rs = amp_env*(stim_spec_freq.max() - stim_spec_freq.min()) + stim_spec_freq.min() last_syllable_end = events[-1, 1] + 0.025 i1 = electrode_indices.index(e1) i2 = electrode_indices.index(e2) lfp1 = lfps[:, i1, :] lfp2 = lfps[:, i2, :] # zscore the LFP lfp1 -= lfp1.mean() lfp1 /= lfp1.std(ddof=1) lfp2 -= lfp2.mean() lfp2 /= lfp2.std(ddof=1) ntrials,nelectrodes,nt = lfps.shape ntrials_to_plot = 5 t = np.arange(nt) / sample_rate # plot the stimulus and raw LFP for two electrodes figsize = (24.0, 10) 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(3, 100) ax = plt.subplot(gs[0, :60]) plot_spectrogram(stim_spec_t, stim_spec_freq, stim_spec, ax=ax, ticks=True, fmin=300, fmax=8000, colormap='SpectroColorMap', colorbar=False) # plt.plot(stim_spec_t, amp_env_rs, 'k-', linewidth=2.0, alpha=0.50) plt.axvline(syllable_start, c='k', linestyle='dashed', linewidth=3.0) plt.axvline(syllable_end, c='k', linestyle='dashed', linewidth=3.0) plt.axis('tight') plt.xlim(0, last_syllable_end) # plot the first LFP (all trials) ax = plt.subplot(gs[1, :60]) for k in range(ntrials_to_plot): plt.plot(t, lfp1[k, :], '-', linewidth=2.0, alpha=0.75) plt.plot(t, lfp1.mean(axis=0), 'k-', linewidth=3.0) plt.axvline(syllable_start, c='k', linestyle='dashed', linewidth=3.0) plt.axvline(syllable_end, c='k', linestyle='dashed', linewidth=3.0) plt.xlabel('Time (ms)') plt.ylabel('E%d (z-scored)' % e1) plt.axis('tight') plt.xlim(0, last_syllable_end) ax = plt.subplot(gs[2, :60]) for k in range(ntrials_to_plot): plt.plot(t, lfp2[k, :], '-', linewidth=2.0, alpha=0.75) plt.plot(t, lfp2.mean(axis=0), 'k-', linewidth=3.0) plt.axvline(syllable_start, c='k', linestyle='dashed', linewidth=3.0) plt.axvline(syllable_end, c='k', linestyle='dashed', linewidth=3.0) plt.xlabel('Time (ms)') plt.ylabel('E%d (z-scored)' % e2) plt.axis('tight') plt.xlim(0, last_syllable_end) # restrict the lfps to a single syllable print 'syllable_start=%f, syllable_end=%f' % (syllable_start, syllable_end) syllable_si = int(syllable_start*sample_rate) syllable_ei = int(syllable_end*sample_rate) lfp1 = lfp1[:, syllable_si:syllable_ei] lfp2 = lfp2[:, syllable_si:syllable_ei] # compute the trial averaged and mean subtracted lfps lfp1_mean,lfp1_ms = compute_avg_and_ms(lfp1) lfp2_mean,lfp2_ms = compute_avg_and_ms(lfp2) psd_stats = get_psd_stats(bird, block, segment, hemi) freqs, psd1, psd2, psd1_ms, psd2_ms, c12, c12_nonlocked, c12_total = compute_spectra_and_coherence_single_electrode(lfp1, lfp2, sample_rate, e1, e2, psd_stats=psd_stats) lags_ms = get_lags_ms(sample_rate) lfp_absmax = max(np.abs(lfp1_mean).max(), np.abs(lfp2_mean).max()) lfp_ms_absmax = max(np.abs(lfp1_ms).max(), np.abs(lfp2_ms).max()) ax = plt.subplot(gs[0, 65:80]) plt.axhline(0, c='k') plt.plot(t[syllable_si:syllable_ei], lfp1_mean, 'k-', linewidth=3.0, alpha=1.) plt.plot(t[syllable_si:syllable_ei], lfp2_mean, '-', c='#c0c0c0', linewidth=3.0) # plt.xlabel('Time (s)') plt.ylabel('Trial-avg LFP') leg = custom_legend(['k', '#c0c0c0'], ['E%d' % e1, 'E%d' % e2]) plt.legend(handles=leg, fontsize='x-small') plt.axis('tight') plt.ylim(-lfp_absmax, lfp_absmax) ax = plt.subplot(gs[0, 85:]) plt.axhline(0, c='k') plt.plot(t[syllable_si:syllable_ei], lfp1_ms, 'k-', linewidth=3.0, alpha=1.) plt.plot(t[syllable_si:syllable_ei], lfp2_ms, '-', c='#c0c0c0', linewidth=3.0) # plt.xlabel('Time (s)') plt.ylabel('Mean-sub LFP') plt.legend(handles=leg, fontsize='x-small') plt.axis('tight') plt.ylim(-lfp_ms_absmax, lfp_ms_absmax) psd_max = max(psd1.max(), psd2.max(), psd1_ms.max(), psd2_ms.max()) ax = plt.subplot(gs[1, 65:80]) plt.axhline(0, c='k') plt.plot(freqs, psd1, 'k-', linewidth=3.0) plt.plot(freqs, psd2, '-', c='#c0c0c0', linewidth=3.0) plt.xlabel('Time (s)') plt.ylabel('Trial-avg Power') plt.legend(handles=leg, fontsize='x-small', loc=2) plt.axis('tight') # plt.ylim(0, psd_max) ax = plt.subplot(gs[1, 85:]) plt.axhline(0, c='k') plt.plot(freqs, psd1_ms, 'k-', linewidth=3.0) plt.plot(freqs, psd2_ms, '-', c='#c0c0c0', linewidth=3.0) plt.xlabel('Time (s)') plt.ylabel('Mean-sub Power') plt.legend(handles=leg, fontsize='x-small', loc=2) plt.axis('tight') # plt.ylim(0, psd_max) ax = plt.subplot(gs[2, 65:80]) plt.axhline(0, c='k') plt.axvline(0, c='k') plt.plot(lags_ms, c12_total, 'k-', linewidth=3.0, alpha=0.75) plt.plot(lags_ms, c12, '-', c='r', linewidth=3.0, alpha=0.75) plt.plot(lags_ms, c12_nonlocked, '-', c='b', linewidth=3.0, alpha=0.75) plt.xlabel('Lags (ms)') plt.ylabel('Coherency') leg = custom_legend(['k', 'r', 'b'], ['Raw', 'Trial-avg', 'Mean-sub']) plt.legend(handles=leg, fontsize='x-small') plt.axis('tight') plt.ylim(-0.2, 0.3) fname = os.path.join(get_this_dir(), 'raw+coherency.svg') plt.savefig(fname, facecolor='w', edgecolor='none')
def draw_figures(data_dir='/auto/tdrive/mschachter/data', bird='GreBlu9508M', output_dir='/auto/tdrive/mschachter/data/sounds'): spec_colormap() exp_dir = os.path.join(data_dir, bird) exp_file = os.path.join(exp_dir, '%s.h5' % bird) stim_file = os.path.join(exp_dir, 'stims.h5') exp = Experiment.load(exp_file, stim_file) bird = exp.bird_name all_stim_ids = list() # iterate through the segments and get the stim ids from each epoch table for seg in exp.get_all_segments(): etable = exp.get_epoch_table(seg) stim_ids = etable['id'].unique() all_stim_ids.extend(stim_ids) stim_ids = np.unique(all_stim_ids) stim_info = list() for stim_id in stim_ids: si = exp.stim_table['id'] == stim_id assert si.sum() == 1, "More than one stimulus defined for id=%d" % stim_id stype = exp.stim_table['type'][si].values[0] if stype == 'call': stype = exp.stim_table['callid'][si].values[0] # get sound pressure waveform sound = exp.sound_manager.reconstruct(stim_id) waveform = np.array(sound.squeeze()) sample_rate = float(sound.samplerate) stim_dur = len(waveform) / sample_rate stim_info.append( (stim_id, stype, sample_rate, waveform, stim_dur)) durations = np.array([x[-1] for x in stim_info]) max_dur = durations.max() min_dur = durations.min() max_fig_size = 15. min_fig_size = 5. for stim_id,stype,sample_rate,waveform,stim_dur in stim_info: fname = os.path.join(output_dir, '%s_stim_%d.wav' % (stype, stim_id)) print 'Writing %s...' % fname wavfile.write(fname, sample_rate, waveform) dfrac = (stim_dur - min_dur) / (max_dur - min_dur) fig_width = dfrac*(max_fig_size - min_fig_size) + min_fig_size spec_t,spec_freq,spec,rms = spectrogram(waveform, sample_rate, sample_rate, 136., min_freq=300, max_freq=8000, log=True, noise_level_db=80, rectify=True, cmplx=False) figsize = (fig_width, 5) fig = plt.figure(figsize=figsize) plot_spectrogram(spec_t, spec_freq, spec, colormap='SpectroColorMap', colorbar=False) plt.title('Stim %d: %s' % (stim_id, stype)) fname = os.path.join(output_dir, '%s_stim_%d.png' % (stype, stim_id)) plt.savefig(fname, facecolor='w') plt.close('all')