def plot_avg_contra_ipsi_psth(units, axs=None):
units = units.proj()
if axs is None:
fig, axs = plt.subplots(1, 2, figsize=(16, 6))
assert axs.size == 2
period_starts = _get_trial_event_times(['sample', 'delay', 'go'], units,
'good_noearlylick_hit')
hemi = _get_units_hemisphere(units)
good_unit = ephys.Unit & 'unit_quality != "all"'
conds_i = (psth.TrialCondition
& {
'trial_condition_name':
'good_noearlylick_left_hit'
if hemi == 'left' else 'good_noearlylick_right_hit'
}).fetch('KEY')
conds_c = (psth.TrialCondition
& {
'trial_condition_name':
'good_noearlylick_right_hit'
if hemi == 'left' else 'good_noearlylick_left_hit'
}).fetch('KEY')
sel_i = (ephys.Unit * psth.UnitSelectivity
& 'unit_selectivity = "ipsi-selective"' & units)
sel_c = (ephys.Unit * psth.UnitSelectivity
& 'unit_selectivity = "contra-selective"' & units)
psth_is_it = (((psth.UnitPsth & conds_i) * ephys.Unit.proj('unit_posy'))
& good_unit.proj() & sel_i.proj()).fetch(
'unit_psth', order_by='unit_posy desc')
psth_is_ct = (((psth.UnitPsth & conds_c) * ephys.Unit.proj('unit_posy'))
& good_unit.proj() & sel_i.proj()).fetch(
'unit_psth', order_by='unit_posy desc')
psth_cs_ct = (((psth.UnitPsth & conds_c) * ephys.Unit.proj('unit_posy'))
& good_unit.proj() & sel_c.proj()).fetch(
'unit_psth', order_by='unit_posy desc')
psth_cs_it = (((psth.UnitPsth & conds_i) * ephys.Unit.proj('unit_posy'))
& good_unit.proj() & sel_c.proj()).fetch(
'unit_psth', order_by='unit_posy desc')
_plot_avg_psth(psth_cs_it, psth_cs_ct, period_starts, axs[0],
'Contra-selective')
_plot_avg_psth(psth_is_it, psth_is_ct, period_starts, axs[1],
'Ipsi-selective')
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])
def plot_unit_psth(unit_key,
condition_name_kw=['good_noearlylick_', '_hit'],
axs=None,
title='',
xlim=_plt_xlim):
"""
Default raster and PSTH plot for a specified unit - only {good, no early lick, correct trials} selected
condition_name_kw: list of keywords to match for the TrialCondition name
"""
hemi = _get_units_hemisphere(unit_key)
ipsi_cond_name = TrialCondition.get_cond_name_from_keywords(
condition_name_kw + ['left' if hemi == 'left' else 'right'])[0]
contra_cond_name = TrialCondition.get_cond_name_from_keywords(
condition_name_kw + ['right' if hemi == 'left' else 'left'])[0]
ipsi_hit_unit_psth = UnitPsth.get_plotting_data(
unit_key, {'trial_condition_name': ipsi_cond_name})
contra_hit_unit_psth = UnitPsth.get_plotting_data(
unit_key, {'trial_condition_name': contra_cond_name})
# get event start times: sample, delay, response
trial_cond_name = TrialCondition.get_cond_name_from_keywords(
condition_name_kw)[0]
period_starts = _get_trial_event_times(['sample', 'delay', 'go'], unit_key,
trial_cond_name)
# photostim shaded bar (if applicable)
try:
stim_trial_cond_name = TrialCondition.get_cond_name_from_keywords(
condition_name_kw + ['_stim'])[0]
stim_bar = _get_photostim_time_and_duration(
unit_key,
TrialCondition().get_trials(stim_trial_cond_name))
except:
stim_bar = None
if axs is None:
fig, axs = plt.subplots(2, 1)
_plot_spike_raster(ipsi_hit_unit_psth,
contra_hit_unit_psth,
ax=axs[0],
vlines=period_starts,
shade_bar=stim_bar,
title=title if title else f'Unit #: {unit_key["unit"]}',
xlim=xlim)
_plot_psth(ipsi_hit_unit_psth,
contra_hit_unit_psth,
vlines=period_starts,
shade_bar=stim_bar,
ax=axs[1],
xlim=xlim)
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: bilateral 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')
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 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)
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
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_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_stacked_contra_ipsi_psth(units, axs=None):
units = units.proj()
# get event start times: sample, delay, response
period_names, period_starts = _get_trial_event_times(
['sample', 'delay', 'go'], units, 'good_noearlylick_hit')
hemi = _get_units_hemisphere(units)
conds_i = (psth.TrialCondition
& {
'trial_condition_name':
'good_noearlylick_left_hit'
if hemi == 'left' else 'good_noearlylick_right_hit'
}).fetch1('KEY')
conds_c = (psth.TrialCondition
& {
'trial_condition_name':
'good_noearlylick_right_hit'
if hemi == 'left' else 'good_noearlylick_left_hit'
}).fetch1('KEY')
sel_i = (ephys.Unit * psth.UnitSelectivity
& 'unit_selectivity = "ipsi-selective"' & units)
sel_c = (ephys.Unit * psth.UnitSelectivity
& 'unit_selectivity = "contra-selective"' & units)
# ipsi selective ipsi trials
psth_is_it = (psth.UnitPsth * sel_i.proj('unit_posy')
& conds_i).fetch(order_by='unit_posy desc')
# ipsi selective contra trials
psth_is_ct = (psth.UnitPsth * sel_i.proj('unit_posy')
& conds_c).fetch(order_by='unit_posy desc')
# contra selective contra trials
psth_cs_ct = (psth.UnitPsth * sel_c.proj('unit_posy')
& conds_c).fetch(order_by='unit_posy desc')
# contra selective ipsi trials
psth_cs_it = (psth.UnitPsth * sel_c.proj('unit_posy')
& conds_i).fetch(order_by='unit_posy desc')
fig = None
if axs is None:
fig, axs = plt.subplots(1, 2, figsize=(20, 20))
assert axs.size == 2
_plot_stacked_psth_diff(psth_cs_ct,
psth_cs_it,
ax=axs[0],
vlines=period_starts,
flip=True)
axs[0].set_title('Contra-selective Units')
axs[0].set_ylabel('Unit (by depth)')
axs[0].set_xlabel('Time to go (s)')
_plot_stacked_psth_diff(psth_is_it,
psth_is_ct,
ax=axs[1],
vlines=period_starts)
axs[1].set_title('Ipsi-selective Units')
axs[1].set_ylabel('Unit (by depth)')
axs[1].set_xlabel('Time to go (s)')
return fig
def plot_selectivity_change_photostim_effect(units,
condition_name_kw,
recover_time_window=None,
ax=None):
"""
For each unit in the specified units, extract:
+ control, left-instruct PSTH (ctrl_left)
+ control, right-instruct PSTH (ctrl_right)
+ stim, left-instruct PSTH (stim_left)
+ stim, right-instruct PSTH (stim_right)
Then, control_PSTH and stim_PSTH is defined as
(ctrl_left - ctrl_right) for ipsi-selective unit that locates on the left-hemisphere, and vice versa
(stim_left - stim_right) for ipsi-selective unit that locates on the left-hemisphere, and vice versa
Selectivity change is then defined as: control_PSTH - stim_PSTH
"""
trial_cond_name = psth.TrialCondition.get_cond_name_from_keywords(
['good_noearlylick_', '_hit'])[0]
period_starts = _get_trial_event_times(['sample', 'delay', 'go'], units,
trial_cond_name)
stim_trial_cond_name = psth.TrialCondition.get_cond_name_from_keywords(
condition_name_kw + ['_stim'])[0]
stim_time, stim_dur = _get_photostim_time_and_duration(
units,
psth.TrialCondition().get_trials(stim_trial_cond_name))
ctrl_left_cond_name = 'all_noearlylick_nostim_left'
ctrl_right_cond_name = 'all_noearlylick_nostim_right'
stim_left_cond_name = psth.TrialCondition().get_cond_name_from_keywords(
condition_name_kw + ['noearlylick', 'stim', 'left'])[0]
stim_right_cond_name = psth.TrialCondition().get_cond_name_from_keywords(
condition_name_kw + ['noearlylick', 'stim', 'right'])[0]
delta_sels, ctrl_psths = [], []
for unit in (units * psth.UnitSelectivity
& 'unit_selectivity != "non-selective"'
).proj('unit_selectivity').fetch(as_dict=True):
# ---- trial count criteria ----
# no less than 5 trials for control
if (len(psth.TrialCondition.get_trials(ctrl_left_cond_name) & unit) < 5
or len(
psth.TrialCondition.get_trials(ctrl_right_cond_name)
& unit) < 5):
continue
# no less than 2 trials for stimulation
if (len(psth.TrialCondition.get_trials(stim_left_cond_name) & unit) < 2
or len(
psth.TrialCondition.get_trials(stim_right_cond_name)
& unit) < 2):
continue
hemi = _get_units_hemisphere(unit)
ctrl_left_psth, t_vec = psth.UnitPsth.get_plotting_data(
unit, {'trial_condition_name': ctrl_left_cond_name})['psth']
ctrl_right_psth, _ = psth.UnitPsth.get_plotting_data(
unit, {'trial_condition_name': ctrl_right_cond_name})['psth']
try:
stim_left_psth, _ = psth.UnitPsth.get_plotting_data(
unit, {'trial_condition_name': stim_left_cond_name})['psth']
stim_right_psth, _ = psth.UnitPsth.get_plotting_data(
unit, {'trial_condition_name': stim_right_cond_name})['psth']
except:
continue
if unit['unit_selectivity'] == 'ipsi-selective':
ctrl_psth_diff = ctrl_left_psth - ctrl_right_psth if hemi == 'left' else ctrl_right_psth - ctrl_left_psth
stim_psth_diff = stim_left_psth - stim_right_psth if hemi == 'left' else stim_right_psth - stim_left_psth
elif unit['unit_selectivity'] == 'contra-selective':
ctrl_psth_diff = ctrl_left_psth - ctrl_right_psth if hemi == 'right' else ctrl_right_psth - ctrl_left_psth
stim_psth_diff = stim_left_psth - stim_right_psth if hemi == 'right' else stim_right_psth - stim_left_psth
ctrl_psths.append(ctrl_psth_diff)
delta_sels.append(ctrl_psth_diff - stim_psth_diff)
ctrl_psths = np.vstack(ctrl_psths)
delta_sels = np.vstack(delta_sels)
if ax is None:
fig, ax = plt.subplots(1, 1, figsize=(4, 6))
_plot_with_sem(delta_sels, t_vec, ax)
if recover_time_window:
recovery_times = []
for i in range(1000):
i_sample = np.random.choice(delta_sels.shape[0],
delta_sels.shape[0],
replace=True)
btstrp_diff = np.nanmean(delta_sels[i_sample, :],
axis=0) / np.nanmean(
ctrl_psths[i_sample, :], axis=0)
t_recovered = t_vec[(btstrp_diff < 0.2)
& (t_vec > recover_time_window[0]) &
(t_vec < recover_time_window[1])]
if len(t_recovered) > 0:
recovery_times.append(t_recovered[0])
ax.axvline(x=np.mean(recovery_times), linestyle='--', color='g')
ax.axvspan(np.mean(recovery_times) - np.std(recovery_times),
np.mean(recovery_times) + np.std(recovery_times),
alpha=0.2,
color='g')
ax.axhline(y=0, color='k')
for x in period_starts:
ax.axvline(x=x, linestyle='--', color='k')
# add shaded bar for photostim
ax.axvspan(stim_time,
stim_time + stim_dur,
0.95,
1,
alpha=0.3,
color='royalblue')
ax.set_ylabel('Selectivity change (spike/s)')
ax.set_xlabel('Time (s)')
def plot_selectivity_sorted_stacked_contra_ipsi_psth(units, axs=None):
units = units.proj()
if axs is None:
fig, axs = plt.subplots(1, 2, figsize=(20, 20))
assert axs.size == 2
trial_cond_name = psth.TrialCondition.get_cond_name_from_keywords(
['good_noearlylick_', '_hit'])[0]
period_starts = _get_trial_event_times(['sample', 'delay', 'go'], units,
trial_cond_name)
hemi = _get_units_hemisphere(units)
conds_i = (psth.TrialCondition
& {
'trial_condition_name':
'good_noearlylick_left_hit'
if hemi == 'left' else 'good_noearlylick_right_hit'
}).fetch1('KEY')
conds_c = (psth.TrialCondition
& {
'trial_condition_name':
'good_noearlylick_right_hit'
if hemi == 'left' else 'good_noearlylick_left_hit'
}).fetch1('KEY')
# ---- separate units to:
# i) sample or delay not response:
sample_delay_units = units & (psth.PeriodSelectivity
& 'period in ("sample", "delay")'
& 'period_selectivity != "non-selective"')
sample_delay_units = sample_delay_units & (
psth.PeriodSelectivity & units
& 'period = "response"'
& 'period_selectivity = "non-selective"')
# ii) sample or delay and response:
sample_delay_response_units = units & (
psth.PeriodSelectivity
& 'period in ("sample", "delay")'
& 'period_selectivity != "non-selective"')
sample_delay_response_units = sample_delay_response_units & (
psth.PeriodSelectivity & units
& 'period = "response"'
& 'period_selectivity != "non-selective"')
# iii) not sample nor delay and response:
response_units = (units & (psth.PeriodSelectivity & 'period in ("sample")'
& 'period_selectivity = "non-selective"')
& (psth.PeriodSelectivity & 'period in ("delay")'
& 'period_selectivity = "non-selective"'))
response_units = response_units & (
psth.PeriodSelectivity & units
& 'period = "response"'
& 'period_selectivity != "non-selective"')
ipsi_selective_psth, contra_selective_psth = [], []
for units in (sample_delay_units, sample_delay_response_units,
response_units):
sel_i = (ephys.Unit * psth.UnitSelectivity
& 'unit_selectivity = "ipsi-selective"' & units)
sel_c = (ephys.Unit * psth.UnitSelectivity
& 'unit_selectivity = "contra-selective"' & units)
# ipsi selective ipsi trials
psth_is_it = (psth.UnitPsth * sel_i & conds_i).fetch()
# ipsi selective contra trials
psth_is_ct = (psth.UnitPsth * sel_i & conds_c).fetch()
# contra selective contra trials
psth_cs_ct = (psth.UnitPsth * sel_c & conds_c).fetch()
# contra selective ipsi trials
psth_cs_it = (psth.UnitPsth * sel_c & conds_i).fetch()
contra_selective_psth.append(
_plot_stacked_psth_diff(psth_cs_ct,
psth_cs_it,
ax=axs[0],
flip=True,
plot=False))
ipsi_selective_psth.append(
_plot_stacked_psth_diff(psth_is_it,
psth_is_ct,
ax=axs[1],
plot=False))
contra_boundaries = np.cumsum(
[len(k) for k in contra_selective_psth[::-1]])
ipsi_boundaries = np.cumsum([len(k) for k in ipsi_selective_psth[::-1]])
contra_selective_psth = np.vstack(contra_selective_psth)
ipsi_selective_psth = np.vstack(ipsi_selective_psth)
xlim = -3, 2
im = axs[0].imshow(contra_selective_psth,
cmap=plt.cm.bwr,
aspect=4.5 / contra_selective_psth.shape[0],
extent=[-3, 3, 0, contra_selective_psth.shape[0]])
im.set_clim((-1, 1))
im = axs[1].imshow(ipsi_selective_psth,
cmap=plt.cm.bwr,
aspect=4.5 / ipsi_selective_psth.shape[0],
extent=[-3, 3, 0, ipsi_selective_psth.shape[0]])
im.set_clim((-1, 1))
# cosmetic
for ax, title, hspans in zip(
axs, ('Contra-selective Units', 'Ipsi-selective Units'),
(contra_boundaries, ipsi_boundaries)):
for x in period_starts:
ax.axvline(x=x, linestyle='--', color='k')
ax.set_title(title)
ax.set_ylabel('Unit')
ax.set_xlabel('Time to go-cue (s)')
ax.set_xlim(xlim)
for ystart, ystop, color in zip([0] + list(hspans[:-1]), hspans,
('k', 'grey', 'w')):
ax.axhspan(ystart, ystop, 0.98, 1, alpha=1, color=color)
def plot_stacked_contra_ipsi_psth(units, axs=None):
units = units.proj()
if axs is None:
fig, axs = plt.subplots(1, 2, figsize=(20, 20))
assert axs.size == 2
trial_cond_name = psth.TrialCondition.get_cond_name_from_keywords(
['good_noearlylick_', '_hit'])[0]
period_starts = _get_trial_event_times(['sample', 'delay', 'go'], units,
trial_cond_name)
hemi = _get_units_hemisphere(units)
conds_i = (psth.TrialCondition
& {
'trial_condition_name':
'good_noearlylick_left_hit'
if hemi == 'left' else 'good_noearlylick_right_hit'
}).fetch1('KEY')
conds_c = (psth.TrialCondition
& {
'trial_condition_name':
'good_noearlylick_right_hit'
if hemi == 'left' else 'good_noearlylick_left_hit'
}).fetch1('KEY')
sel_i = (ephys.Unit * psth.UnitSelectivity
& 'unit_selectivity = "ipsi-selective"' & units)
sel_c = (ephys.Unit * psth.UnitSelectivity
& 'unit_selectivity = "contra-selective"' & units)
# ipsi selective ipsi trials
psth_is_it = (psth.UnitPsth * sel_i.proj('unit_posy')
& conds_i).fetch(order_by='unit_posy desc')
# ipsi selective contra trials
psth_is_ct = (psth.UnitPsth * sel_i.proj('unit_posy')
& conds_c).fetch(order_by='unit_posy desc')
# contra selective contra trials
psth_cs_ct = (psth.UnitPsth * sel_c.proj('unit_posy')
& conds_c).fetch(order_by='unit_posy desc')
# contra selective ipsi trials
psth_cs_it = (psth.UnitPsth * sel_c.proj('unit_posy')
& conds_i).fetch(order_by='unit_posy desc')
_plot_stacked_psth_diff(psth_cs_ct,
psth_cs_it,
ax=axs[0],
vlines=period_starts,
flip=True)
_plot_stacked_psth_diff(psth_is_it,
psth_is_ct,
ax=axs[1],
vlines=period_starts)
# cosmetic
for ax, title in zip(axs,
('Contra-selective Units', 'Ipsi-selective Units')):
ax.set_title(title)
ax.set_ylabel('Unit')
ax.set_xlabel('Time to go-cue (s)')
ax.set_xlim([_plt_xmin, _plt_xmax])
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)