def plot_coding_direction(units, time_period=None, label=None, axs=None):
# get event start times: sample, delay, response
period_names, period_starts = _get_trial_event_times(['sample', 'delay', 'go'], units, 'good_noearlylick_hit')
_, proj_contra_trial, proj_ipsi_trial, time_stamps, _ = psth.compute_CD_projected_psth(
units.fetch('KEY'), time_period=time_period)
fig = None
if axs is None:
fig, axs = plt.subplots(1, 1, figsize=(8, 6))
# plot
_plot_with_sem(proj_contra_trial, time_stamps, ax=axs, c='b')
_plot_with_sem(proj_ipsi_trial, time_stamps, ax=axs, c='r')
for x in period_starts:
axs.axvline(x=x, linestyle = '--', color = 'k')
# cosmetic
axs.spines['right'].set_visible(False)
axs.spines['top'].set_visible(False)
axs.set_ylabel('CD projection (a.u.)')
axs.set_xlabel('Time (s)')
if label:
axs.set_title(label)
return fig
def plot_avg_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)
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')
fig = None
if axs is None:
fig, axs = plt.subplots(1, 2, figsize=(16, 6))
assert axs.size == 2
_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))
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_unit_psth(unit_key, 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_hit_unit_psth = psth.UnitPsth.get_plotting_data(
unit_key, {
'trial_condition_name':
f'good_noearlylick_{"left" if hemi == "left" else "right"}_hit'
})
contra_hit_unit_psth = psth.UnitPsth.get_plotting_data(
unit_key, {
'trial_condition_name':
f'good_noearlylick_{"right" if hemi == "left" else "left"}_hit'
})
ipsi_miss_unit_psth = psth.UnitPsth.get_plotting_data(
unit_key, {
'trial_condition_name':
f'good_noearlylick_{"left" if hemi == "left" else "right"}_miss'
})
contra_miss_unit_psth = psth.UnitPsth.get_plotting_data(
unit_key, {
'trial_condition_name':
f'good_noearlylick_{"right" if hemi == "left" else "left"}_miss'
})
# get event start times: sample, delay, response
periods, period_starts = _get_trial_event_times(['sample', 'delay', 'go'],
unit_key,
'good_noearlylick_hit')
fig = None
if axs is None:
fig, axs = plt.subplots(2, 2)
# correct response
_plot_spike_raster(ipsi_hit_unit_psth,
contra_hit_unit_psth,
ax=axs[0, 0],
vlines=period_starts,
title=title if title else
f'Unit #: {unit_key["unit"]}\nCorrect Response',
xlim=xlim)
_plot_psth(ipsi_hit_unit_psth,
contra_hit_unit_psth,
vlines=period_starts,
ax=axs[1, 0],
xlim=xlim)
# incorrect response
_plot_spike_raster(ipsi_miss_unit_psth,
contra_miss_unit_psth,
ax=axs[0, 1],
vlines=period_starts,
title=title if title else
f'Unit #: {unit_key["unit"]}\nIncorrect Response',
xlim=xlim)
_plot_psth(ipsi_miss_unit_psth,
contra_miss_unit_psth,
vlines=period_starts,
ax=axs[1, 1],
xlim=xlim)
return fig
def make(self, key):
water_res_num, sess_date = get_wr_sessdate(key)
sess_dir = store_stage / water_res_num / sess_date
sess_dir.mkdir(parents=True, exist_ok=True)
# ---- Setup ----
time_period = (-0.4, 0)
probe_keys = (ephys.ProbeInsertion & key).fetch(
'KEY', order_by='insertion_number')
fig1, axs = plt.subplots(len(probe_keys),
len(probe_keys),
figsize=(16, 16))
if len(probe_keys) > 1:
[a.axis('off') for a in axs.flatten()]
# ---- Plot Coding Direction per probe ----
probe_proj = {}
for pid, probe in enumerate(probe_keys):
units = ephys.Unit & probe
label = (ephys.ProbeInsertion & probe).aggr(
ephys.ProbeInsertion.RecordableBrainRegion.proj(
brain_region='CONCAT(hemisphere, " ", brain_area)'),
brain_regions='GROUP_CONCAT(brain_region SEPARATOR", ")'
).fetch1('brain_regions')
label = '({}) {}'.format(probe['insertion_number'], label)
_, period_starts = _get_trial_event_times(
['sample', 'delay', 'go'], units, 'good_noearlylick_hit')
# ---- compute CD projected PSTH ----
_, proj_contra_trial, proj_ipsi_trial, time_stamps, hemi = psth.compute_CD_projected_psth(
units.fetch('KEY'), time_period=time_period)
# ---- save projection results ----
probe_proj[pid] = (proj_contra_trial, proj_ipsi_trial,
time_stamps, label, hemi)
# ---- generate fig with CD plot for this probe ----
fig, ax = plt.subplots(1, 1, figsize=(6, 6))
_plot_with_sem(proj_contra_trial, time_stamps, ax=ax, c='b')
_plot_with_sem(proj_ipsi_trial, time_stamps, ax=ax, c='r')
# cosmetic
for x in period_starts:
ax.axvline(x=x, linestyle='--', color='k')
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
ax.set_ylabel('CD projection (a.u.)')
ax.set_xlabel('Time (s)')
ax.set_title(label)
fig.tight_layout()
# ---- plot this fig into the main figure ----
buf = io.BytesIO()
fig.savefig(buf, format='png')
buf.seek(0)
axs[pid, pid].imshow(Image.open(buf))
buf.close()
plt.close(fig)
# ---- Plot probe-pair correlation ----
for p1, p2 in itertools.combinations(probe_proj.keys(), r=2):
proj_contra_trial_g1, proj_ipsi_trial_g1, time_stamps, label_g1, p1_hemi = probe_proj[
p1]
proj_contra_trial_g2, proj_ipsi_trial_g2, time_stamps, label_g2, p2_hemi = probe_proj[
p2]
labels = [label_g1, label_g2]
# plot trial CD-endpoint correlation
p_start, p_end = time_period
contra_cdend_1 = proj_contra_trial_g1[:,
np.logical_and(
time_stamps >=
p_start, time_stamps
< p_end)].mean(
axis=1)
ipsi_cdend_1 = proj_ipsi_trial_g1[:,
np.logical_and(
time_stamps >= p_start,
time_stamps < p_end
)].mean(axis=1)
if p1_hemi == p2_hemi:
contra_cdend_2 = proj_contra_trial_g2[:,
np.logical_and(
time_stamps >=
p_start,
time_stamps <
p_end)].mean(
axis=1)
ipsi_cdend_2 = proj_ipsi_trial_g2[:,
np.logical_and(
time_stamps >=
p_start, time_stamps
< p_end)].mean(
axis=1)
else:
contra_cdend_2 = proj_ipsi_trial_g2[:,
np.logical_and(
time_stamps >=
p_start,
time_stamps < p_end
)].mean(axis=1)
ipsi_cdend_2 = proj_contra_trial_g2[:,
np.logical_and(
time_stamps >=
p_start,
time_stamps < p_end
)].mean(axis=1)
c_df = pd.DataFrame([contra_cdend_1, contra_cdend_2]).T
c_df.columns = labels
c_df['trial-type'] = 'contra'
i_df = pd.DataFrame([ipsi_cdend_1, ipsi_cdend_2]).T
i_df.columns = labels
i_df['trial-type'] = 'ipsi'
df = c_df.append(i_df)
# remove NaN trial - could be due to some trials having no spikes
non_nan = ~np.logical_or(
np.isnan(df[labels[0]]).values,
np.isnan(df[labels[1]]).values)
df = df[non_nan]
fig = plt.figure(figsize=(6, 6))
jplot = _jointplot_w_hue(data=df,
x=labels[0],
y=labels[1],
hue='trial-type',
colormap=['b', 'r'],
figsize=(8, 6),
fig=fig,
scatter_kws=None)
# ---- plot this fig into the main figure ----
buf = io.BytesIO()
fig.savefig(buf, format='png')
buf.seek(0)
axs[p1, p2].imshow(Image.open(buf))
buf.close()
plt.close(fig)
else:
# ---- Plot Single-Probe Coding Direction ----
probe = probe_keys[0]
units = ephys.Unit & probe
label = (ephys.ProbeInsertion & probe).aggr(
ephys.ProbeInsertion.RecordableBrainRegion.proj(
brain_region='CONCAT(hemisphere, " ", brain_area)'),
brain_regions='GROUP_CONCAT(brain_region SEPARATOR", ")'
).fetch1('brain_regions')
unit_characteristic_plot.plot_coding_direction(
units, time_period=time_period, label=label, axs=axs)
# ---- Save fig and insert ----
fn_prefix = f'{water_res_num}_{sess_date}_'
fig_dict = save_figs((fig1, ), ('coding_direction', ), sess_dir,
fn_prefix)
plt.close('all')
self.insert1({**key, **fig_dict, 'cd_probe_count': len(probe_keys)})
def plot_paired_coding_direction(unit_g1, unit_g2, labels=None, time_period=None):
"""
Plot trial-to-trial CD-endpoint correlation between CD-projected trial-psth from two unit-groups (e.g. two brain regions)
Note: coding direction is calculated on selective units, contra vs. ipsi, within the specified time_period
"""
_, proj_contra_trial_g1, proj_ipsi_trial_g1, time_stamps, unit_g1_hemi = psth.compute_CD_projected_psth(
unit_g1.fetch('KEY'), time_period=time_period)
_, proj_contra_trial_g2, proj_ipsi_trial_g2, time_stamps, unit_g2_hemi = psth.compute_CD_projected_psth(
unit_g2.fetch('KEY'), time_period=time_period)
# get event start times: sample, delay, response
period_names, period_starts = _get_trial_event_times(['sample', 'delay', 'go'], unit_g1, 'good_noearlylick_hit')
if labels:
assert len(labels) == 2
else:
labels = ('unit group 1', 'unit group 2')
# plot projected trial-psth
fig, axs = plt.subplots(1, 2, figsize=(16, 6))
_plot_with_sem(proj_contra_trial_g1, time_stamps, ax=axs[0], c='b')
_plot_with_sem(proj_ipsi_trial_g1, time_stamps, ax=axs[0], c='r')
_plot_with_sem(proj_contra_trial_g2, time_stamps, ax=axs[1], c='b')
_plot_with_sem(proj_ipsi_trial_g2, time_stamps, ax=axs[1], c='r')
# cosmetic
for ax, label in zip(axs, labels):
for x in period_starts:
ax.axvline(x=x, linestyle = '--', color = 'k')
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
ax.set_ylabel('CD projection (a.u.)')
ax.set_xlabel('Time (s)')
ax.set_title(label)
# plot trial CD-endpoint correlation - if 2 unit-groups are from 2 hemispheres,
# then contra-ipsi definition is based on the first group
p_start, p_end = time_period
contra_cdend_1 = proj_contra_trial_g1[:, np.logical_and(time_stamps >= p_start, time_stamps < p_end)].mean(axis=1)
ipsi_cdend_1 = proj_ipsi_trial_g1[:, np.logical_and(time_stamps >= p_start, time_stamps < p_end)].mean(axis=1)
if unit_g1_hemi == unit_g1_hemi:
contra_cdend_2 = proj_contra_trial_g2[:, np.logical_and(time_stamps >= p_start, time_stamps < p_end)].mean(axis=1)
ipsi_cdend_2 = proj_ipsi_trial_g2[:, np.logical_and(time_stamps >= p_start, time_stamps < p_end)].mean(axis=1)
else:
contra_cdend_2 = proj_ipsi_trial_g2[:, np.logical_and(time_stamps >= p_start, time_stamps < p_end)].mean(axis=1)
ipsi_cdend_2 = proj_contra_trial_g2[:, np.logical_and(time_stamps >= p_start, time_stamps < p_end)].mean(axis=1)
c_df = pd.DataFrame([contra_cdend_1, contra_cdend_2]).T
c_df.columns = labels
c_df['trial-type'] = 'contra'
i_df = pd.DataFrame([ipsi_cdend_1, ipsi_cdend_2]).T
i_df.columns = labels
i_df['trial-type'] = 'ipsi'
df = c_df.append(i_df)
jplot = _jointplot_w_hue(data=df, x=labels[0], y=labels[1], hue= 'trial-type', colormap=['b', 'r'],
figsize=(8, 6), fig=None, scatter_kws=None)
jplot['fig'].show()
return fig
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_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('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')
# 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_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')):
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 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, 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
