)
sns.set(font_scale=0.4)
fig_dir = Path('~/duguidlab/visuomotor_control/figures/srf_grant')
if rates_not_rasters:
firing_rates = exp.align_trials(
ActionLabels.rewarded_push,
Events.back_sensor_open,
'spike_rate',
duration=duration,
)
# IPN
ses = exp[ses_ipn_gpi].name
spike_rate.per_unit_spike_rate(firing_rates[ses_ipn_gpi][ipn], ci='sd')
plt.suptitle(f'IPN - per-unit across-trials firing rate (aligned to push)')
utils.save(fig_dir / f'IPN_unit_spike_rate_{duration}s_{ses}.png')
# GPi
spike_rate.per_unit_spike_rate(firing_rates[ses_ipn_gpi][gpi], ci='sd')
plt.suptitle(f'GPi - per-unit across-trials firing rate (aligned to push)')
utils.save(fig_dir / f'GPi_unit_spike_rate_{duration}s_{ses}.png')
# MTh
ses = exp[ses_m1_mth].name
spike_rate.per_unit_spike_rate(firing_rates[ses_m1_mth][mth], ci='sd')
plt.suptitle(f'Motor thalamus - per-unit across-trials firing rate (aligned to push)')
utils.save(fig_dir / f'MTh_unit_spike_rate_{duration}s_{ses}.png')
else:
# utils.save(fig_dir / f'unit_spike_rate_cued_reach_{duration}s_{area}_{name}')
m2 = stim_all[session][0]
ppc = stim_all[session][1]
for unit in m2:
firing_rate = unit.mean()
firing_rate = firing_rate.iloc[0: 1000]
baseline = firing_rate.iloc[-500: -100]
firing_rate = firing_rate - baseline
if firing_rate.max() < abs(firing_rate.min()):
np.where(firing_rate == firing_rate.min())
else:
np.where(firing_rate == firing_rate.max())
fig = spike_rate.per_unit_spike_rate(stim_all[session][rec_num], ci=ci)
name = exp[session].name
plt.suptitle(f'Session {name} - per-unit across-trials firing rate (aligned to all visual stimulations)')
utils.save(fig_dir / f'unit_spike_rate_all_visual_stim_{duration}s_{area}_{name}')
#fig = spike_rate.per_unit_spike_rate(stim_short[session][rec_num], ci=ci)
#name = exp[session].name
#plt.suptitle(f'Session {name} - per-unit across-trials firing rate (aligned to short visual stimulations)')
#utils.save(fig_dir / f'unit_spike_rate_short_visual_stim_{duration}s_{area}_{name}')
#fig = spike_rate.per_unit_spike_rate(stim_long[session][rec_num], ci=ci)
#name = exp[session].name
#plt.suptitle(f'Session {name} - per-unit across-trials firing rate (aligned to long visual stimulations)')
#utils.save(fig_dir / f'unit_spike_rate_long_visual_stim_{duration}s_{area}_{name}')
#fig = spike_rate.per_unit_spike_rate(stim_left[session][rec_num], ci=ci)
)
pulls = exp.align_trials(
ActionLabels.rewarded_pull_good_mi,
Events.motion_index_onset,
'spike_rate',
duration=duration,
units=units,
)
# Only one recording
rec_num = 0
# I wanted to really check that the same units list is seen in all variables
all_unit_ids = [u for s in units for r in s for u in r]
ps_units = pushes.columns.get_level_values('unit').unique().values.copy()
pl_units = pulls.columns.get_level_values('unit').unique().values.copy()
ps_units.sort()
pl_units.sort()
assert all(ps_units == np.unique(all_unit_ids))
assert all(pl_units == np.unique(all_unit_ids))
# Plot
for i, session in enumerate(exp):
name = session.name
subplots = spike_rate.per_unit_spike_rate(pushes[i][rec_num], ci='sd')
spike_rate.per_unit_spike_rate(pulls[i][rec_num], ci='sd', subplots=subplots)
plt.suptitle(f'Pushes + pulls - per-unit firing rate (aligned to MI onset)')
utils.save(fig_dir / f'pushpulls_unit_spike_rate_{duration}s_{name}.pdf')
'spike_rate',
duration=duration,
uncurated=True,
)
pulls = exp.align_trials(
ActionLabels.rewarded_pull,
Events.front_sensor_open,
'spike_rate',
duration=duration,
uncurated=True,
)
for session in range(len(exp)):
# per unit
fig = spike_rate.per_unit_spike_rate(pushes[session][rec_num], ci='sd')
name = exp[session].name
plt.suptitle(f'Session {name} - per-unit across-trials firing rate (aligned to cued push)')
utils.save(fig_dir / f'unit_spike_rate_cued_push_{duration}s_{name}_sd')
fig = spike_rate.per_unit_spike_rate(pulls[session][rec_num], ci='sd')
name = exp[session].name
plt.suptitle(f'Session {name} - per-unit across-trials firing rate (aligned to cued pull)')
utils.save(fig_dir / f'unit_spike_rate_cued_pull_{duration}s_{name}_sd')
# per trial
fig = spike_rate.per_trial_spike_rate(pushes[session][rec_num], ci='sd')
name = exp[session].name
plt.suptitle(f'Session {name} - per-trial across-units firing rate (aligned to cued push)')
utils.save(fig_dir / f'trial_spike_rate_cued_push_{duration}s_{name}_sd')