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(stim_event, stim_ids, syllable_indices, e1=5, e2=2): assert isinstance(stim_event, StimEventTransform) sample_rate = stim_event.lfp_sample_rate lags_ms = get_lags_ms(sample_rate) cross_functions_total = dict() cross_functions_locked = dict() cross_functions_nonlocked = dict() specs = dict() syllable_times = dict() stim_end_time = dict() hemi = ','.join(stim_event.rcg_names) # compute all cross and auto-correlations if hemi == 'L': electrode_order = ROSTRAL_CAUDAL_ELECTRODES_LEFT else: electrode_order = ROSTRAL_CAUDAL_ELECTRODES_RIGHT freqs = None nelectrodes = None index2electrode = stim_event.index2electrode seg_uname = stim_event.seg_uname bird,block,seg = seg_uname.split('_') psd_stats = get_psd_stats(bird, stim_event.block_name, stim_event.segment_name, hemi) for stim_id,syllable_index in zip(stim_ids, syllable_indices): lfp = stim_event.lfp_reps_by_stim['raw'][stim_id] ntrials,nelectrodes,nt = lfp.shape # get the start and end time of the syllable i = (stim_event.segment_df['stim_id'] == stim_id) & (stim_event.segment_df['order'] == syllable_index) assert i.sum() > 0, "No syllable for stim_id=%d, order=%d" % (stim_id, syllable_index) assert i.sum() == 1, "More than one syllable for stim_id=%d, order=%d" % (stim_id, syllable_index) start_time = stim_event.segment_df[i]['start_time'].values[0] end_time = stim_event.segment_df[i]['end_time'].values[0] syllable_times[stim_id] = (start_time, end_time) # get the end time of the last syllable i = (stim_event.segment_df['stim_id'] == stim_id) stim_end_time[stim_id] = stim_event.segment_df[i]['end_time'].max() si = int((stim_event.pre_stim_time + start_time)*sample_rate) ei = int((stim_event.pre_stim_time + end_time)*sample_rate) # restrict the lfp to just the syllable time lfp = lfp[:, :, si:ei] specs[stim_id] = stim_event.spec_by_stim[stim_id] i1 = index2electrode.index(e1) lfp1 = lfp[:, i1, :] i2 = index2electrode.index(e2) lfp2 = lfp[:, i2, :] # compute the covariance functions pfreq, 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) cross_functions_total[stim_id] = (psd1, psd2, c12_total) cross_functions_locked[stim_id] = (psd1, psd2, c12) cross_functions_nonlocked[stim_id] = (psd1, psd2, c12_nonlocked) # plot the cross covariance functions to overlap for each stim plot_cross_pair(stim_ids, cross_functions_total, electrode_order, freqs, lags_ms) plt.suptitle('Total Covariance') fname = os.path.join(get_this_dir(), 'cross_total.svg') plt.savefig(fname, facecolor='w', edgecolor='none') # plot_cross_mat(stim_ids, cross_functions_locked, electrode_order, freqs) plot_cross_pair(stim_ids, cross_functions_locked, electrode_order, freqs, lags_ms) plt.suptitle('Stim-locked Covariance') fname = os.path.join(get_this_dir(), 'cross_locked.svg') plt.savefig(fname, facecolor='w', edgecolor='none') # plot_cross_mat(stim_ids, cross_functions_nonlocked, electrode_order, freqs) plot_cross_pair(stim_ids, cross_functions_nonlocked, electrode_order, freqs, lags_ms) plt.suptitle('Non-locked Covariance') fname = os.path.join(get_this_dir(), 'cross_nonlocked.svg') plt.savefig(fname, facecolor='w', edgecolor='none') # plot the spectrograms fig_height = 2 fig_max_width = 6 spec_lens = np.array([stim_end_time[stim_id] for stim_id in stim_ids]) spec_ratios = spec_lens / spec_lens.max() spec_sample_rate = sample_rate for k,stim_id in enumerate(stim_ids): spec = specs[stim_id] syllable_start,syllable_end = syllable_times[stim_id] print 'stim_id=%d, syllable_start=%0.3f, syllable_end=%0.3f' % (stim_id, syllable_start, syllable_end) spec_t = np.arange(spec.shape[1]) / spec_sample_rate stim_end = stim_end_time[stim_id] figsize = (spec_ratios[k]*fig_max_width, fig_height) fig = plt.figure(figsize=figsize) ax = plt.gca() plot_spectrogram(spec_t, stim_event.spec_freq, spec, ax=ax, ticks=True, fmin=300., fmax=8000., colormap='SpectroColorMap', colorbar=False) plt.axvline(syllable_start, c='k', linestyle='dashed', linewidth=3.0) plt.axvline(syllable_end, c='k', linestyle='dashed', linewidth=3.0) plt.xlim(0, stim_end+0.005) fname = os.path.join(get_this_dir(), 'stim_spec_%d.svg' % stim_id)