def draw_figures():
pfile = '/auto/tdrive/mschachter/data/aggregate/decoders_pairwise_coherence_multi_freq.h5'
agg = AggregatePairwiseDecoder.load(pfile)
nbands = agg.df['band'].max()
sample_rate = 381.4697265625
freqs = get_freqs(sample_rate)
g = agg.df.groupby(['bird', 'block', 'segment', 'hemi'])
"""
# TODO: compute the average likelihood ratio between intecept-only and full model for all sites!
i = (agg.df['bird'] == 'GreBlu9508M') & (agg.df['block'] == 'Site4') & (agg.df['segment'] == 'Call1') & (agg.df['hemi'] == 'L') & (agg.df['band'] == 0)
assert i.sum() == 1
full_likelihood_for_null = agg.df['likelihood'][i].values[0]
null_likelihood = 1.63 # for GreBlu9508_Site4_Call1_L
null_likelihood_ratio = 2*(null_likelihood - full_likelihood_for_null)
print 'full_likelihood_for_null=',full_likelihood_for_null
print 'null_likelihood=',null_likelihood
print 'null_likelihood_ratio=',null_likelihood_ratio
"""
full_likelihoods = list()
likelihood_ratios = list()
pccs = list()
pcc_thresh = 0.25
single_band_likelihoods = list()
single_band_pccs = list()
for (bird,block,seg,hemi),gdf in g:
# get the likelihood of the fullmodel
i = gdf['band'] == 0
assert i.sum() == 1
num_samps = gdf[i]['num_samps'].values[0]
print 'num_samps=%d' % num_samps
full_likelihood = -gdf[i]['likelihood'].values[0] * num_samps
pcc = gdf[i]['pcc'].values[0]
if pcc < pcc_thresh:
continue
full_likelihoods.append(full_likelihood)
pccs.append(pcc)
# get the likelihood per frequency band
ratio_by_band = np.zeros(nbands)
single_likelihood_by_band = np.zeros(nbands)
single_pcc_band = np.zeros(nbands)
for k in range(nbands):
i = (gdf['band'] == k+1) & (gdf['exfreq'] == True)
assert i.sum() == 1
num_samps2 = gdf[i]['num_samps'].values[0]
assert num_samps2 == num_samps
leftout_likelihood = -gdf[i]['likelihood'].values[0] * num_samps
i = (gdf['band'] == k+1) & (gdf['exfreq'] == False)
assert i.sum() == 1
num_samps3 = gdf[i]['num_samps'].values[0]
assert num_samps3 == num_samps2
single_leftout_likelihood = -gdf[i]['likelihood'].values[0] * num_samps
pcc = gdf[i]['pcc'].values[0]
print '(%s,%s,%s,%s,%d) leftout=%0.6f, full=%0.6f, single=%0.6f, single_pcc=%0.6f, num_samps=%d' % \
(bird, block, seg, hemi, k, leftout_likelihood, full_likelihood, single_leftout_likelihood, pcc, num_samps)
# compute the likelihood ratio
lratio = -2*(leftout_likelihood - full_likelihood)
ratio_by_band[k] = lratio
single_likelihood_by_band[k] = single_leftout_likelihood
single_pcc_band[k] = pcc
likelihood_ratios.append(ratio_by_band)
single_band_likelihoods.append(single_likelihood_by_band)
single_band_pccs.append(single_pcc_band)
pccs = np.array(pccs)
likelihood_ratios = np.array(likelihood_ratios)
full_likelihoods = np.array(full_likelihoods)
single_band_likelihoods = np.array(single_band_likelihoods)
single_band_pccs = np.array(single_band_pccs)
# exclude segments whose likelihood ratio goes below zero
# i = np.array([np.any(lrat < 0) for lrat in likelihood_ratios])
i = np.ones(len(likelihood_ratios), dtype='bool')
print 'i.sum()=%d' % i.sum()
# compute significance threshold
x = np.linspace(1, 150, 1000)
df = 12
p = chi2.pdf(x, df)
sig_thresh = max(x[p > 0.01])
# compute mean and std
lrat_mean = likelihood_ratios[i, :].mean(axis=0)
lrat_std = likelihood_ratios[i, :].std(axis=0, ddof=1)
single_l_mean = single_band_likelihoods[i, :].mean(axis=0)
single_l_std = single_band_likelihoods[i, :].std(axis=0, ddof=1)
single_pcc_mean = single_band_pccs[i, :].mean(axis=0)
single_pcc_std = single_band_pccs[i, :].std(axis=0, ddof=1)
fig = plt.figure(figsize=(24, 16))
plt.subplots_adjust(top=0.95, bottom=0.05, left=0.05, right=0.99, hspace=0.40, wspace=0.20)
ax = plt.subplot(2, 3, 1)
plt.plot(full_likelihoods, pccs, 'go', linewidth=2.0)
plt.xlabel('log Likelihood')
plt.ylabel('PCC')
plt.axis('tight')
ax = plt.subplot(2, 3, 2)
for k,lrat in enumerate(likelihood_ratios[i, :]):
plt.plot(freqs, lrat, '-', linewidth=2.0, alpha=0.7)
plt.xlabel('Frequency (Hz)')
plt.ylabel('Likelihood Ratio')
plt.axis('tight')
ax = plt.subplot(2, 3, 4)
nsamps = len(likelihood_ratios)
plt.errorbar(freqs, single_pcc_mean, yerr=single_pcc_std/np.sqrt(nsamps), ecolor='r', elinewidth=3.0, fmt='k-', linewidth=7.0, alpha=0.75)
plt.xlabel('Frequency (Hz)')
plt.ylabel('PCC')
plt.title('Mean Single Band Decoder PCC')
plt.axis('tight')
ax = plt.subplot(2, 3, 5)
plt.errorbar(freqs, single_l_mean, yerr=single_l_std/np.sqrt(nsamps), ecolor='r', elinewidth=3.0, fmt='k-', linewidth=7.0, alpha=0.75)
plt.xlabel('Frequency (Hz)')
plt.ylabel('log Likelihood')
plt.title('Mean Single Band Decoder Likelihood')
plt.axis('tight')
ax = plt.subplot(2, 3, 6)
plt.errorbar(freqs, lrat_mean, yerr=lrat_std/np.sqrt(nsamps), ecolor='r', elinewidth=3.0, fmt='k-', linewidth=7.0, alpha=0.75)
plt.plot(freqs, np.ones_like(freqs)*sig_thresh, 'k--', linewidth=7.0, alpha=0.75)
plt.xlabel('Frequency (Hz)')
plt.ylabel('Likelihood Ratio')
plt.title('Mean Likelihood Ratio')
plt.axis('tight')
plt.ylim(0, lrat_mean.max())
fname = os.path.join(get_this_dir(), 'figs.svg')
plt.savefig(fname, facecolor='w', edgecolor='none')
plt.show()
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')