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_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')
