ax[1].plot(r_active_el[0, :], r_active_el[1, :], color='blue')
ax[1].plot(miss_el[0, :], miss_el[1, :], color='k')
ax_min = np.min(ax[0].get_xlim() + ax[0].get_ylim())
ax_max = np.max(ax[0].get_xlim() + ax[0].get_ylim())
ax[0].set_xlim((ax_min, ax_max))
ax[0].set_ylim((ax_min, ax_max))
ax[1].set_xlim((ax_min, ax_max))
ax[1].set_ylim((ax_min, ax_max))
#ax[0].set_xlim((-5, 7))
#ax[0].set_ylim((4, 14))
#ax[1].set_xlim((-5, 7))
#ax[1].set_ylim((4, 14))
ax[0].set_xlabel('Decoding Axis')
ax[1].set_xlabel('Decoding Axis')
ax[0].set_ylabel('Correlated Variability Axis')
ax[1].set_ylabel('Correlated Variability Axis')
ax[0].set_title("Passive")
ax[1].set_title("Active")
ax[0].set_aspect(cplt.get_square_asp(ax[0]))
ax[1].set_aspect(cplt.get_square_asp(ax[1]))
f.tight_layout()
f.savefig(fn)
plt.show()
rsc_df = ld.load_rsc('tar_rsc_0_0,2')
rsc_df_site = rsc_df.groupby(by='site').mean()
f, ax = plt.subplots(1, 1, figsize=(8, 6))
ax.bar([0, 1, 2], [
rsc_df_site['act_rsc'].mean(), rsc_df_site['passBig_rsc'].mean(),
rsc_df_site['passSmall_rsc'].mean()
],
edgecolor='k',
color='lightgrey',
lw=2)
for s in rsc_df_site.index:
vals = rsc_df_site.loc[s][['act_rsc', 'passBig_rsc', 'passSmall_rsc']]
ax.plot([0, 1, 2], vals, 'o-', color='k')
ax.set_xticks([0, 1, 2])
ax.set_xticklabels(
['Active', 'Pupil-matched \n passive', 'Small pupil \n passive'],
rotation=45)
ax.set_ylabel('Noise Correlation')
ax.set_aspect(cplt.get_square_asp(ax))
f.tight_layout()
f.savefig(fn)
plt.show()
& (ap_df.index.isin(rr_idx))].groupby(by='site').mean()
ax[0, 0].scatter(rt_pb_bysite[metric],
rt_active_bysite[metric],
color='k',
edgecolor='white')
ax[0, 0].scatter(rt_pb_bysite[metric].loc['TAR010c'],
rt_active_bysite[metric].loc['TAR010c'],
color='r',
edgecolor='white')
ax[0, 0].plot([vmin, vmax], [vmin, vmax], 'k--')
ax[0, 0].set_xlabel('Big Passive')
ax[0, 0].set_ylabel('Active')
ax[0, 0].set_ylim((0, ylim))
ax[0, 0].set_xlim((0, xlim))
ax[0, 0].set_aspect(cplt.get_square_asp(ax[0, 0]))
ax[1, 0].scatter(rr_pb_bysite[metric],
rr_active_bysite[metric],
color='k',
edgecolor='white')
ax[1, 0].scatter(rr_pb_bysite[metric].loc['TAR010c'],
rr_active_bysite[metric].loc['TAR010c'],
color='r',
edgecolor='white')
ax[1, 0].plot([vmin, vmax], [vmin, vmax], 'k--')
ax[1, 0].set_xlabel('Big Passive')
ax[1, 0].set_ylabel('Active')
ax[1, 0].set_ylim((0, ylim))
ax[1, 0].set_xlim((0, xlim))
ax[1, 0].set_aspect(cplt.get_square_asp(ax[1, 0]))
delta.spines['bottom'].set_color(color.SIGNAL)
delta.xaxis.label.set_color(color.SIGNAL)
delta.tick_params(axis='x', colors=color.SIGNAL)
delta.spines['left'].set_color(color.COSTHETA)
delta.yaxis.label.set_color(color.COSTHETA)
delta.tick_params(axis='y', colors=color.COSTHETA)
delta.set_title(r"$\Delta d'^2$")
# plot schematic
sch.plot(Ael[0], Ael[1], color='tab:blue', lw=2)
sch.plot(Bel[0], Bel[1], color='tab:orange', lw=2)
sch.axvline(0, linestyle='--', color='lightgrey', zorder=-1)
sch.axhline(0, linestyle='--', color='lightgrey', zorder=-1)
sch.set_xlabel(r"$dDR_1 (\Delta \mathbf{\mu})$")
sch.set_ylabel(r"$dDR_2$")
sch.set_aspect(cplt.get_square_asp(sch))
# plot regression
palette = {
'full': 'lightgrey',
'upc1_mean': 'r',
'upc1_diff': 'r',
'udU_mag_test': color.SIGNAL,
'unoiseAlign': color.COSTHETA
}
r2a = r2_all[[k for k in r2_all if (k == 'full') | (k.startswith('u'))]]
sns.boxplot(x='variable',
y='value',
data=r2a.melt(),
palette=palette,
width=0.3,
