def plot_psth_photostim_effect(units, condition_name_kw=['both_alm'], axs=None):
"""
For the specified `units`, plot PSTH comparison between stim vs. no-stim with left/right trial instruction
The stim location (or other appropriate search keywords) can be specified in `condition_name_kw` (default: both ALM)
"""
units = units.proj()
fig = None
if axs is None:
fig, axs = plt.subplots(1, 2, figsize=(16, 6))
assert axs.size == 2
hemi = _get_units_hemisphere(units)
# no photostim:
psth_n_l = psth.TrialCondition.get_cond_name_from_keywords(['_nostim', '_left'])[0]
psth_n_r = psth.TrialCondition.get_cond_name_from_keywords(['_nostim', '_right'])[0]
psth_n_l = (psth.UnitPsth * psth.TrialCondition & units
& {'trial_condition_name': psth_n_l} & 'unit_psth is not NULL').fetch('unit_psth')
psth_n_r = (psth.UnitPsth * psth.TrialCondition & units
& {'trial_condition_name': psth_n_r} & 'unit_psth is not NULL').fetch('unit_psth')
# with photostim
psth_s_l = psth.TrialCondition.get_cond_name_from_keywords(condition_name_kw + ['_stim_left'])[0]
psth_s_r = psth.TrialCondition.get_cond_name_from_keywords(condition_name_kw + ['_stim_right'])[0]
psth_s_l = (psth.UnitPsth * psth.TrialCondition & units
& {'trial_condition_name': psth_s_l} & 'unit_psth is not NULL').fetch('unit_psth')
psth_s_r = (psth.UnitPsth * psth.TrialCondition & units
& {'trial_condition_name': psth_s_r} & 'unit_psth is not NULL').fetch('unit_psth')
# get event start times: sample, delay, response
period_names, period_starts = _get_trial_event_times(['sample', 'delay', 'go'], units, 'good_noearlylick_hit')
# get photostim onset and duration
stim_trial_cond_name = psth.TrialCondition.get_cond_name_from_keywords(condition_name_kw + ['_stim'])[0]
stim_durs = np.unique((experiment.Photostim & experiment.PhotostimEvent
* psth.TrialCondition().get_trials(stim_trial_cond_name)
& units).fetch('duration'))
stim_dur = _extract_one_stim_dur(stim_durs)
stim_time = _get_stim_onset_time(units, stim_trial_cond_name)
if hemi == 'left':
psth_s_i = psth_s_l
psth_n_i = psth_n_l
psth_s_c = psth_s_r
psth_n_c = psth_n_r
else:
psth_s_i = psth_s_r
psth_n_i = psth_n_r
psth_s_c = psth_s_l
psth_n_c = psth_n_l
_plot_avg_psth(psth_n_i, psth_n_c, period_starts, axs[0],
'Control')
_plot_avg_psth(psth_s_i, psth_s_c, period_starts, axs[1],
'Photostim')
# cosmetic
ymax = max([ax.get_ylim()[1] for ax in axs])
for ax in axs:
ax.set_ylim((0, ymax))
ax.set_xlim([_plt_xmin, _plt_xmax])
# add shaded bar for photostim
axs[1].axvspan(stim_time, stim_time + stim_dur, alpha=0.3, color='royalblue')
return fig
def plot_psth_bilateral_photostim_effect(units, axs=None):
units = units.proj()
hemi = _get_units_hemisphere(units)
psth_s_l = (
psth.UnitPsth * psth.TrialCondition & units
& {
'trial_condition_name': 'all_noearlylick_both_alm_stim_left'
}).fetch('unit_psth')
psth_n_l = (
psth.UnitPsth * psth.TrialCondition & units
& {
'trial_condition_name': 'all_noearlylick_both_alm_nostim_left'
}).fetch('unit_psth')
psth_s_r = (
psth.UnitPsth * psth.TrialCondition & units
& {
'trial_condition_name': 'all_noearlylick_both_alm_stim_right'
}).fetch('unit_psth')
psth_n_r = (
psth.UnitPsth * psth.TrialCondition & units
& {
'trial_condition_name': 'all_noearlylick_both_alm_nostim_right'
}).fetch('unit_psth')
# get event start times: sample, delay, response
period_names, period_starts = _get_trial_event_times(
['sample', 'delay', 'go'], units, 'good_noearlylick_hit')
# get photostim duration
stim_durs = np.unique(
(experiment.Photostim & experiment.PhotostimEvent *
psth.TrialCondition().get_trials('all_noearlylick_both_alm_stim')
& units).fetch('duration'))
stim_dur = _extract_one_stim_dur(stim_durs)
if hemi == 'left':
psth_s_i = psth_s_l
psth_n_i = psth_n_l
psth_s_c = psth_s_r
psth_n_c = psth_n_r
else:
psth_s_i = psth_s_r
psth_n_i = psth_n_r
psth_s_c = psth_s_l
psth_n_c = psth_n_l
fig = None
if axs is None:
fig, axs = plt.subplots(1, 2, figsize=(16, 6))
assert axs.size == 2
_plot_avg_psth(psth_n_i, psth_n_c, period_starts, axs[0], 'Control')
_plot_avg_psth(psth_s_i, psth_s_c, period_starts, axs[1],
'Bilateral ALM photostim')
# cosmetic
ymax = max([ax.get_ylim()[1] for ax in axs])
for ax in axs:
ax.set_ylim((0, ymax))
# add shaded bar for photostim
stim_time = period_starts[np.where(period_names == 'delay')[0][0]]
axs[1].axvspan(stim_time,
stim_time + stim_dur,
alpha=0.3,
color='royalblue')
return fig
def plot_unit_bilateral_photostim_effect(probe_insertion, clustering_method=None, axs=None):
probe_insertion = probe_insertion.proj()
if not (psth.TrialCondition().get_trials('all_noearlylick_both_alm_stim') & probe_insertion):
raise PhotostimError('No Bilateral ALM Photo-stimulation present')
if clustering_method is None:
try:
clustering_method = _get_clustering_method(probe_insertion)
except ValueError as e:
raise ValueError(str(e) + '\nPlease specify one with the kwarg "clustering_method"')
dv_loc = (ephys.ProbeInsertion.InsertionLocation & probe_insertion).fetch1('depth')
no_stim_cond = (psth.TrialCondition
& {'trial_condition_name':
'all_noearlylick_nostim'}).fetch1('KEY')
bi_stim_cond = (psth.TrialCondition
& {'trial_condition_name':
'all_noearlylick_both_alm_stim'}).fetch1('KEY')
units = ephys.Unit & probe_insertion & {'clustering_method': clustering_method} & 'unit_quality != "all"'
metrics = pd.DataFrame(columns=['unit', 'x', 'y', 'frate_change'])
# get photostim onset and duration
stim_durs = np.unique((experiment.Photostim & experiment.PhotostimEvent
* psth.TrialCondition().get_trials('all_noearlylick_both_alm_stim')
& probe_insertion).fetch('duration'))
stim_dur = _extract_one_stim_dur(stim_durs)
stim_time = _get_stim_onset_time(units, 'all_noearlylick_both_alm_stim')
# XXX: could be done with 1x fetch+join
for u_idx, unit in enumerate(units.fetch('KEY', order_by='unit')):
if clustering_method in ('kilosort2'):
x, y = (ephys.Unit * lab.ElectrodeConfig.Electrode.proj()
* lab.ProbeType.Electrode.proj('x_coord', 'y_coord') & unit).fetch1('x_coord', 'y_coord')
else:
x, y = (ephys.Unit & unit).fetch1('unit_posx', 'unit_posy')
# obtain unit psth per trial, for all nostim and bistim trials
nostim_trials = ephys.Unit.TrialSpikes & unit & psth.TrialCondition.get_trials(no_stim_cond['trial_condition_name'])
bistim_trials = ephys.Unit.TrialSpikes & unit & psth.TrialCondition.get_trials(bi_stim_cond['trial_condition_name'])
nostim_psths, nostim_edge = psth.compute_unit_psth(unit, nostim_trials.fetch('KEY'), per_trial=True)
bistim_psths, bistim_edge = psth.compute_unit_psth(unit, bistim_trials.fetch('KEY'), per_trial=True)
# compute the firing rate difference between contra vs. ipsi within the stimulation time window
ctrl_frate = np.array([nostim_psth[np.logical_and(nostim_edge >= stim_time,
nostim_edge <= stim_time + stim_dur)].mean()
for nostim_psth in nostim_psths])
stim_frate = np.array([bistim_psth[np.logical_and(bistim_edge >= stim_time,
bistim_edge <= stim_time + stim_dur)].mean()
for bistim_psth in bistim_psths])
frate_change = (stim_frate.mean() - ctrl_frate.mean()) / ctrl_frate.mean()
frate_change = abs(frate_change) if frate_change < 0 else 0.0001
metrics.loc[u_idx] = (int(unit['unit']), x, float(dv_loc) + y, frate_change)
metrics.frate_change = metrics.frate_change / metrics.frate_change.max()
# --- prepare for plotting
shank_count = (ephys.ProbeInsertion & probe_insertion).aggr(lab.ElectrodeConfig.Electrode * lab.ProbeType.Electrode,
shank_count='count(distinct shank)').fetch1('shank_count')
m_scale = get_m_scale(shank_count)
fig = None
if axs is None:
fig, axs = plt.subplots(1, 1, figsize=(4, 8))
xmax = 1.3 * metrics.x.max()
xmin = -1/6*xmax
cosmetic = {'legend': None,
'linewidth': 1.75,
'alpha': 0.9,
'facecolor': 'none', 'edgecolor': 'k'}
sns.scatterplot(data=metrics, x='x', y='y', s=metrics.frate_change*m_scale,
ax=axs, **cosmetic)
axs.spines['right'].set_visible(False)
axs.spines['top'].set_visible(False)
axs.set_title('% change')
axs.set_xlim((xmin, xmax))
return fig
def plot_unit_bilateral_photostim_effect(probe_insertion,
clustering_method=None,
axs=None):
probe_insertion = probe_insertion.proj()
if clustering_method is None:
try:
clustering_method = _get_clustering_method(probe_insertion)
except ValueError as e:
raise ValueError(
str(e) +
'\nPlease specify one with the kwarg "clustering_method"')
dv_loc = (ephys.ProbeInsertion.InsertionLocation
& probe_insertion).fetch1('dv_location')
no_stim_cond = (
psth.TrialCondition
& {
'trial_condition_name': 'all_noearlylick_both_alm_nostim'
}).fetch1('KEY')
bi_stim_cond = (psth.TrialCondition
& {
'trial_condition_name': 'all_noearlylick_both_alm_stim'
}).fetch1('KEY')
# get photostim duration
stim_durs = np.unique(
(experiment.Photostim & experiment.PhotostimEvent *
psth.TrialCondition().get_trials('all_noearlylick_both_alm_stim')
& probe_insertion).fetch('duration'))
stim_dur = _extract_one_stim_dur(stim_durs)
units = ephys.Unit & probe_insertion & {
'clustering_method': clustering_method
} & 'unit_quality != "all"'
metrics = pd.DataFrame(columns=['unit', 'x', 'y', 'frate_change'])
_, cue_onset = _get_trial_event_times(['delay'], units,
'all_noearlylick_both_alm_nostim')
cue_onset = cue_onset[0]
# XXX: could be done with 1x fetch+join
for u_idx, unit in enumerate(units.fetch('KEY', order_by='unit')):
x, y = (ephys.Unit & unit).fetch1('unit_posx', 'unit_posy')
# obtain unit psth per trial, for all nostim and bistim trials
nostim_trials = ephys.Unit.TrialSpikes & unit & psth.TrialCondition.get_trials(
no_stim_cond['trial_condition_name'])
bistim_trials = ephys.Unit.TrialSpikes & unit & psth.TrialCondition.get_trials(
bi_stim_cond['trial_condition_name'])
nostim_psths, nostim_edge = psth.compute_unit_psth(
unit, nostim_trials.fetch('KEY'), per_trial=True)
bistim_psths, bistim_edge = psth.compute_unit_psth(
unit, bistim_trials.fetch('KEY'), per_trial=True)
# compute the firing rate difference between contra vs. ipsi within the stimulation duration
ctrl_frate = np.array([
nostim_psth[np.logical_and(
nostim_edge >= cue_onset,
nostim_edge <= cue_onset + stim_dur)].mean()
for nostim_psth in nostim_psths
])
stim_frate = np.array([
bistim_psth[np.logical_and(
bistim_edge >= cue_onset,
bistim_edge <= cue_onset + stim_dur)].mean()
for bistim_psth in bistim_psths
])
frate_change = (stim_frate.mean() -
ctrl_frate.mean()) / ctrl_frate.mean()
frate_change = abs(frate_change) if frate_change < 0 else 0.0001
metrics.loc[u_idx] = (int(unit['unit']), x, y - dv_loc, frate_change)
metrics.frate_change = metrics.frate_change / metrics.frate_change.max()
fig = None
if axs is None:
fig, axs = plt.subplots(1, 1, figsize=(4, 8))
cosmetic = {
'legend': None,
'linewidth': 1.75,
'alpha': 0.9,
'facecolor': 'none',
'edgecolor': 'k'
}
sns.scatterplot(data=metrics,
x='x',
y='y',
s=metrics.frate_change * m_scale,
ax=axs,
**cosmetic)
axs.spines['right'].set_visible(False)
axs.spines['top'].set_visible(False)
axs.set_title('% change')
axs.set_xlim((-10, 60))
return fig