def draw_preds(agg, data_dir='/auto/tdrive/mschachter/data'):
df_best = pd.read_csv('/auto/tdrive/mschachter/data/aggregate/rnn_best.csv')
# print 'min_err=', df_best.err.min()
bird = 'GreBlu9508M'
block = 'Site2'
segment = 'Call2'
hemi = 'L'
best_md5 = 'db5d601c8af2621f341d8e4cbd4108c1'
fname = '%s_%s_%s_%s' % (bird, block, segment, hemi)
preproc_file = os.path.join(data_dir, bird, 'preprocess', 'RNNPreprocess_%s.h5' % fname)
rnn_file = os.path.join(data_dir, bird, 'rnn', 'RNNLFPEncoder_%s_%s.h5' %(fname, best_md5))
pred_file = os.path.join(data_dir, bird, 'rnn', 'RNNLFPEncoderPred_%s_%s.h5' %(fname, best_md5))
linear_file = os.path.join(data_dir, bird, 'rnn', 'LFPEnvelope_%s.h5' % fname)
if not os.path.exists(pred_file):
rpt = RNNPreprocessTransform.load(preproc_file)
rpt.write_pred_file(rnn_file, pred_file, lfp_enc_file=linear_file)
visualize_pred_file(pred_file, 3, 0, 2.4, electrode_order=ROSTRAL_CAUDAL_ELECTRODES_LEFT[::-1], dbnoise=4.0)
leg = custom_legend(['k', 'r', 'b'], ['Real', 'Linear', 'RNN'])
plt.legend(handles=leg)
fname = os.path.join(get_this_dir(), 'rnn_preds.svg')
plt.savefig(fname, facecolor='w', edgecolor='none')
plt.show()
def plot_raw_dists(data_dir='/auto/tdrive/mschachter/data'):
edata = pd.read_csv(os.path.join(data_dir, 'aggregate', 'electrode_data+dist.csv'))
i = edata.bird != 'BlaBro09xxF'
edata = edata[i]
birds = edata.bird.unique()
clrs = ['r', 'g', 'b']
fig = plt.figure()
fig.subplots_adjust(top=0.99, bottom=0.01, right=0.99, left=0.01, hspace=0, wspace=0)
for k,b in enumerate(birds):
i = (edata.bird == b) & ~np.isnan(edata.dist_midline) & ~np.isnan(edata.dist_l2a)
x = edata[i].dist_midline.values
y = edata[i].dist_l2a.values
if b == 'GreBlu9508M':
y *= 4
reg = edata[i].region.values
for xx,yy,r in zip(x, y, reg):
plt.plot(xx, yy, 'o', markersize=8, c=clrs[k], alpha=0.4)
plt.text(xx, yy, r, fontsize=8)
plt.axis('tight')
plt.xlabel('Dist from Midline (mm)')
plt.ylabel('Dist from LH (mm)')
leg = custom_legend(clrs, birds)
plt.legend(handles=leg)
plt.show()
def _plot_freqs(pdata, ax):
plt.sca(ax)
nsamps_lfp = len(pdata['lfp'])
lkrat_lfp_mean = pdata['lfp'].mean(axis=0)
lkrat_lfp_std = pdata['lfp'].std(axis=0, ddof=1) / np.sqrt(nsamps_lfp)
nsamps_spike = len(pdata['spike'])
lkrat_spike_mean = pdata['spike'].mean(axis=0)
lkrat_spike_std = pdata['spike'].std(axis=0, ddof=1) / np.sqrt(nsamps_spike)
if pdata['aprop'] != 'category':
plt.axhline(1.0, c='k', linestyle='dashed', alpha=0.7, linewidth=2.0)
plt.errorbar(pdata['freqs'], lkrat_lfp_mean, yerr=lkrat_lfp_std, c=COLOR_BLUE_LFP, linewidth=7.0, alpha=0.9, ecolor='k', elinewidth=2.0)
plt.errorbar(pdata['freqs']+2., lkrat_spike_mean, yerr=lkrat_spike_std, c=COLOR_YELLOW_SPIKE, linewidth=7.0, alpha=0.9, ecolor='k', elinewidth=2.0)
plt.xlabel('Frequency (Hz)')
plt.ylabel('Normalized LR')
leg = custom_legend([COLOR_BLUE_LFP, COLOR_YELLOW_SPIKE], ['LFP', 'Spike'])
plt.legend(handles=leg, fontsize='x-small')
plt.title(pdata['aprop'])
plt.axis('tight')
if pdata['aprop'] != 'category':
plt.ylim(0, 5)
else:
plt.axis('tight')
def draw_pairwise_weights_vs_dist(agg):
wdf = export_pairwise_decoder_weights(agg)
r2_thresh = 0.20
# aprops = ALL_ACOUSTIC_PROPS
aprops = ['meanspect', 'stdspect', 'sal', 'maxAmp']
aprop_clrs = {'meanspect':'#FF8000', 'stdspect':'#FFBF00', 'sal':'#088A08', 'maxAmp':'k'}
print wdf.decomp.unique()
# get scatter data for weights vs distance
scatter_data = dict()
for decomp in ['full_psds+full_cfs', 'spike_rate+spike_sync']:
i = wdf.decomp == decomp
assert i.sum() > 0
df = wdf[i]
for n,aprop in enumerate(aprops):
ii = (df.r2 > r2_thresh) & (df.aprop == aprop) & ~np.isnan(df.dist) & ~np.isinf(df.dist)
d = df.dist[ii].values
w = df.w[ii].values
scatter_data[(decomp, aprop)] = (d, w)
decomp_labels = {'full_psds+full_cfs':'LFP Pairwise Correlations', 'spike_rate+spike_sync':'Spike Synchrony'}
figsize = (14, 5)
fig = plt.figure(figsize=figsize)
fig.subplots_adjust(left=0.10, right=0.98)
for k,decomp in enumerate(['full_psds+full_cfs', 'spike_rate+spike_sync']):
ax = plt.subplot(1, 2, k+1)
for aprop in aprops:
d,w = scatter_data[(decomp, aprop)]
wz = w**2
wz /= wz.std(ddof=1)
xcenter, ymean, yerr, ymean_cs = compute_mean_from_scatter(d, wz, bins=5, num_smooth_points=300)
# plt.plot(xcenter, ymean, '-', c=aprop_clrs[aprop], linewidth=7.0, alpha=0.7)
plt.errorbar(xcenter, ymean, linestyle='-', yerr=yerr, c=aprop_clrs[aprop], linewidth=9.0, alpha=0.7,
elinewidth=8., ecolor='#d8d8d8', capsize=0.)
plt.axis('tight')
plt.ylabel('Decoder Weight Effect')
plt.xlabel('Pairwise Distance (mm)')
plt.title(decomp_labels[decomp])
aprop_lbls = [ACOUSTIC_PROP_NAMES[aprop] for aprop in aprops]
aclrs = [aprop_clrs[aprop] for aprop in aprops]
leg = custom_legend(aclrs, aprop_lbls)
plt.legend(handles=leg, loc='lower left')
fname = os.path.join(get_this_dir(), 'pairwise_decoder_effect_vs_dist.svg')
plt.savefig(fname, facecolor='w', edgecolor='none')
plt.show()
def draw_spike_rate_vs_power(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)
# concatenate the lfp and spike psds
nfreqs = len(pcf.freqs)
lfp_and_spike_psds = np.zeros([len(pcf.df), nfreqs*2 + 1])
nz = np.zeros(len(pcf.df), dtype='bool')
for k,(lfp_index,spike_index) in enumerate(zip(pcf.df['lfp_index'], pcf.df['spike_index'])):
lpsd = pcf.lfp_psds[lfp_index, :]
spsd = pcf.spike_psds[spike_index, :]
srate,sstd = pcf.spike_rates[spike_index, :]
nz[k] = np.abs(lpsd).sum() > 0 and np.abs(spsd).sum() > 0
lfp_and_spike_psds[k, :nfreqs] = lpsd
lfp_and_spike_psds[k, nfreqs:-1] = spsd
lfp_and_spike_psds[k, -1] = np.log(srate)
# throw some bad data points out
lfp_sum = lfp_and_spike_psds[:, :nfreqs].sum(axis=1)
spike_sum = lfp_and_spike_psds[:, nfreqs:-1].sum(axis=1)
nz = ~np.isinf(lfp_and_spike_psds[:, -1]) & (lfp_sum > 0) & (spike_sum > 0) & ~np.isnan(spike_sum) & ~np.isnan(lfp_sum)
print '# of good data points: %d out of %d' % (nz.sum(), lfp_and_spike_psds.shape[0])
# zscore the concatenated matrix
lfp_and_spike_psds = lfp_and_spike_psds[nz, :]
lfp_and_spike_psds -= lfp_and_spike_psds.mean(axis=0)
lfp_and_spike_psds /= lfp_and_spike_psds.std(axis=0, ddof=1)
# compute CC between spike rate and power
lfp_spike_rate_cc = np.zeros(len(pcf.freqs))
spike_spike_rate_cc = np.zeros(len(pcf.freqs))
for k,f in enumerate(pcf.freqs):
lfp_spike_rate_cc[k] = np.corrcoef(lfp_and_spike_psds[:, k], lfp_and_spike_psds[:, -1])[0, 1]
spike_spike_rate_cc[k] = np.corrcoef(lfp_and_spike_psds[:, k+len(pcf.freqs)], lfp_and_spike_psds[:, -1])[0, 1]
fig = plt.figure(figsize=(12, 7))
plt.axhline(0, c='k')
plt.plot(pcf.freqs, lfp_spike_rate_cc, '-', linewidth=7.0, alpha=0.7, c=COLOR_BLUE_LFP)
plt.plot(pcf.freqs, spike_spike_rate_cc, '-', linewidth=7.0, alpha=0.7, c=COLOR_YELLOW_SPIKE)
plt.xlabel('Frequency (Hz)')
plt.ylabel('Correlation Coefficient')
plt.title('CC Between Log Spike Rate and Spectral Power')
plt.axis('tight')
plt.ylim(-0.1, 0.6)
leg = custom_legend([COLOR_BLUE_LFP, COLOR_YELLOW_SPIKE], ['LFP PSD', 'Spike PSD'])
plt.legend(handles=leg, fontsize='x-small')
fname = os.path.join(get_this_dir(), 'power_vs_rate.svg')
plt.savefig(fname, facecolor='w', edgecolor='none')
plt.show()
def draw_rate_weight_by_dist(agg):
wdf = get_encoder_weight_data_for_psd(agg, include_sync=False, write_to_file=False)
def exp_func(_x, _a, _b, _c):
return _a * np.exp(-_b * _x) + _c
# plot the average encoder weight as a function of distance from predicted electrode
freqs = [15, 55, 135]
band_labels = ['0-30Hz', '30-80Hz', '80-190Hz']
clrs = {15:'k', 55:'r', 135:'b'}
for f in freqs:
i = ~np.isnan(wdf.dist_from_electrode.values) & (wdf.r2 > 0.05) & (wdf.dist_from_electrode > 0) & (wdf.f == f)
x = wdf.dist_from_electrode[i].values
y = (wdf.w[i].values)**2
popt, pcov = curve_fit(exp_func, x, y)
ypred = exp_func(x, *popt)
ysqerr = (y - ypred)**2
sstot = np.sum((y - y.mean())**2)
sserr = np.sum(ysqerr)
r2 = 1. - (sserr / sstot)
print 'f=%dHz, a=%0.6f, space_const=%0.6f, bias=%0.6f, r2=%0.2f: ' % (f, popt[0], 1. / popt[1], popt[2], r2)
npts = 100
xreg = np.linspace(x.min()+1e-1, x.max()-1e-1, npts)
yreg = exp_func(xreg, *popt)
# approximate sqrt(err) with a cubic spline for plotting
err_xcenter, err_ymean, err_yerr, err_ymean_cs = compute_mean_from_scatter(x, np.sqrt(ysqerr), bins=4,
num_smooth_points=npts)
# yerr = err_ymean_cs(xreg)
# plt.plot(x, y, 'ko', alpha=0.7)
plt.plot(xreg, yreg, clrs[f], alpha=0.7, linewidth=5.0)
# plt.errorbar(xreg, yreg, yerr=err_ymean, c=clrs[f], alpha=0.7, linewidth=5.0, ecolor='#b5b5b5')
# plt.show()
# plot_mean_from_scatter(x, y, bins=4, num_smooth_points=200, alpha=0.7, color=clrs[f], ecolor='#b5b5b5', bin_by_quantile=False)
plt.xlabel('Distance From Predicted Electrode (mm)')
plt.ylabel('Encoder Weight^2')
plt.axis('tight')
freq_clrs = [clrs[f] for f in freqs]
leg = custom_legend(colors=freq_clrs, labels=band_labels)
plt.legend(handles=leg, loc='lower right')
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(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(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 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_joint_relationships(data_dir='/auto/tdrive/mschachter/data'):
big3_file = os.path.join(data_dir, 'aggregate', 'acoustic_big3.csv')
if not os.path.exists(big3_file):
bs_file = os.path.join(data_dir, 'aggregate', 'biosound.h5')
bs_agg = AggregateBiosounds.load(bs_file)
aprops = ['maxAmp', 'sal', 'meanspect']
Xz, good_indices = bs_agg.remove_duplicates(aprops=aprops, thresh=np.inf)
stim_types = bs_agg.df.stim_type[good_indices].values
pdata = {'maxAmp': Xz[:, 0], 'sal': Xz[:, 1], 'meanspect': Xz[:, 2], 'stim_type': stim_types}
df = pd.DataFrame(pdata)
df.to_csv(big3_file, header=True, index=False)
else:
df = pd.read_csv(big3_file)
print '# of syllables used: %d' % len(df)
plt.figure()
ax = plt.subplot(111, projection='3d')
maxAmp_rescaled = df.maxAmp
maxAmp_rescaled -= maxAmp_rescaled.min()
maxAmp_rescaled = np.log(maxAmp_rescaled + 1e-6)
maxAmp_rescaled -= maxAmp_rescaled.mean()
maxAmp_rescaled /= maxAmp_rescaled.std(ddof=1)
for ct in DECODER_CALL_TYPES:
i = df.stim_type == ct
if i.sum() == 0:
continue
x = maxAmp_rescaled[i]
y = df.sal[i].values
z = df.meanspect[i].values
c = CALL_TYPE_COLORS[ct]
ax.scatter(x, y, z, s=49, c=c)
ax.set_xlabel('Amplitude')
ax.set_ylabel('Saliency')
ax.set_zlabel('Mean Frequency')
leg = custom_legend([CALL_TYPE_COLORS[ct] for ct in DECODER_CALL_TYPES], DECODER_CALL_TYPES)
plt.legend(handles=leg)
plt.figure()
nrows = 1
ncols = 3
call_type_order = ['song', 'Te', 'DC', 'Be', 'LT', 'Ne', 'Ag', 'Di', 'Th']
# plot amplitude vs saliency
ax = plt.subplot(nrows, ncols, 1)
for ct in call_type_order:
i = df.stim_type == ct
if i.sum() == 0:
continue
x = maxAmp_rescaled[i]
y = df.sal[i].values
c = CALL_TYPE_COLORS[ct]
ax.scatter(x, y, s=49, c=c, alpha=0.7)
plt.xlabel('Maximum Amplitude')
plt.ylabel('Saliency')
plt.xlim(-3, 2)
# plot amplitude vs meanspect
ax = plt.subplot(nrows, ncols, 2)
for ct in call_type_order:
i = df.stim_type == ct
if i.sum() == 0:
continue
x = maxAmp_rescaled[i]
y = df.meanspect[i].values
c = CALL_TYPE_COLORS[ct]
ax.scatter(x, y, s=49, c=c, alpha=0.7)
plt.xlabel('Maximum Amplitude')
plt.ylabel('Mean Spectral Freq')
plt.xlim(-3, 2)
# plot saliency vs meanspect
ax = plt.subplot(nrows, ncols, 3)
for ct in call_type_order:
i = df.stim_type == ct
if i.sum() == 0:
continue
x = df.sal[i].values
y = df.meanspect[i].values
c = CALL_TYPE_COLORS[ct]
ax.scatter(x, y, s=49, c=c, alpha=0.7)
plt.xlabel('Saliency')
plt.ylabel('Mean Spectral Freq')
plt.show()
def draw_decoder_perf_barplots(data_dir='/auto/tdrive/mschachter/data', show_all=True):
aprops_to_display = list(USED_ACOUSTIC_PROPS)
aprops_to_display.remove('stdtime')
if not show_all:
decomps = ['spike_rate', 'full_psds']
sub_names = ['Spike Rate', 'LFP PSD']
sub_clrs = [COLOR_RED_SPIKE_RATE, COLOR_BLUE_LFP]
else:
decomps = ['spike_rate', 'full_psds', 'spike_rate+spike_sync']
sub_names = ['Spike Rate', 'LFP PSD', 'Spike Rate + Sync']
sub_clrs = [COLOR_RED_SPIKE_RATE, COLOR_BLUE_LFP, COLOR_CRIMSON_SPIKE_SYNC]
df_me = pd.read_csv(os.path.join(data_dir, 'aggregate', 'decoder_perfs_for_glm.csv'))
bprop_data = list()
for aprop in aprops_to_display:
bd = dict()
for decomp in decomps:
i = (df_me.decomp == decomp) & (df_me.aprop == aprop)
perfs = df_me.r2[i].values
bd[decomp] = perfs
bprop_data.append({'bd':bd, 'lfp_mean':bd['full_psds'].mean(), 'aprop':aprop})
bprop_data.sort(key=operator.itemgetter('lfp_mean'), reverse=True)
lfp_r2 = [bdict['bd']['full_psds'].mean() for bdict in bprop_data]
lfp_r2_std = [bdict['bd']['full_psds'].std(ddof=1) for bdict in bprop_data]
spike_r2 = [bdict['bd']['spike_rate'].mean() for bdict in bprop_data]
spike_r2_std = [bdict['bd']['spike_rate'].std(ddof=1) for bdict in bprop_data]
if show_all:
spike_sync_r2 = [bdict['bd']['spike_rate+spike_sync'].mean() for bdict in bprop_data]
spike_sync_r2_std = [bdict['bd']['spike_rate+spike_sync'].std(ddof=1) for bdict in bprop_data]
aprops_xticks = [ACOUSTIC_PROP_NAMES[bdict['aprop']] for bdict in bprop_data]
figsize = (23, 7.)
fig = plt.figure(figsize=figsize)
plt.subplots_adjust(top=0.95, bottom=0.15, left=0.05, right=0.99, hspace=0.20, wspace=0.20)
bar_width = 0.4
if show_all:
bar_width = 0.2
bar_data = [(spike_r2, spike_r2_std), (lfp_r2, lfp_r2_std)]
if len(decomps) == 3:
bar_data.append( (spike_sync_r2, spike_sync_r2_std) )
bar_x = np.arange(len(lfp_r2))
for k,(br2,bstd) in enumerate(bar_data):
bx = bar_x + bar_width*k
plt.bar(bx, br2, yerr=bstd, width=bar_width, color=sub_clrs[k], alpha=0.9, ecolor='k')
plt.ylabel('Decoder R2')
plt.xticks(bar_x+0.45, aprops_xticks, rotation=90, fontsize=12)
leg = custom_legend(sub_clrs, sub_names)
plt.legend(handles=leg, loc='upper right')
plt.axis('tight')
plt.xlim(-0.5, bar_x.max() + 1)
plt.ylim(0, 0.6)
fname = os.path.join(get_this_dir(), 'decoder_perf_barplots.svg')
if show_all:
fname = os.path.join(get_this_dir(), 'decoder_perf_barplots_all.svg')
plt.savefig(fname, facecolor='w', edgecolor='none')
plt.show()