def plot_weights(ax, Coefs, prds=None, xlim=None, xlab=tlab,
ylab='unit coefficient', title='', ytitle=1.04):
"""Plot decoding weights."""
# Unstack dataframe with results.
lCoefs = pd.DataFrame(Coefs.unstack().unstack(), columns=['coef'])
lCoefs['time'] = lCoefs.index.get_level_values(0)
lCoefs['value'] = lCoefs.index.get_level_values(1)
lCoefs['uid'] = lCoefs.index.get_level_values(2)
lCoefs.index = np.arange(len(lCoefs.index))
# Plot time series.
sns.tsplot(lCoefs, time='time', value='coef', unit='value',
condition='uid', ax=ax)
# Add chance level line and stimulus periods.
putil.add_chance_level(ax=ax, ylevel=0)
putil.plot_periods(prds, ax=ax)
# Set axis limits.
xlim = xlim if xlim is not None else tlim
putil.set_limits(ax, xlim)
# Format plot.
putil.set_labels(ax, xlab, ylab, title, ytitle)
putil.hide_legend(ax)
def plot_mean_rates(mRates,
aa_res_dir,
tasks=None,
task_lbls=None,
baseline=None,
xlim=None,
ylim=None,
ci=68,
ffig=None,
figsize=(6, 4)):
"""Plot mean rates across tasks."""
# Init.
if tasks is None:
tasks = mRates.keys()
# Plot mean activity.
lRates = []
for task in tasks:
lrates = pd.DataFrame(mRates[task].unstack(), columns=['rate'])
lrates['task'] = task
lRates.append(lrates)
lRates = pd.concat(lRates)
lRates['time'] = lRates.index.get_level_values(0)
lRates['unit'] = lRates.index.get_level_values(1)
if task_lbls is not None:
lRates.task.replace(task_lbls, inplace=True)
# Plot as time series.
#putil.set_style('notebook', 'white')
fig = putil.figure(figsize=figsize)
ax = putil.axes()
sns.tsplot(lRates,
time='time',
value='rate',
unit='unit',
condition='task',
ci=ci,
ax=ax)
# Add periods and baseline.
putil.plot_periods(ax=ax)
if baseline is not None:
putil.add_baseline(baseline, ax=ax)
# Format plot.
sns.despine(ax=ax)
putil.set_labels(ax, xlab='time since S1 onset', ylab='rate (sp/s)')
putil.set_limits(ax, xlim, ylim)
putil.hide_legend_title(ax)
# Save plot.
putil.save_fig(ffig, fig)
return fig, ax
def raster(spk_trains,
t_unit=ms,
prds=None,
c='b',
xlim=None,
title=None,
xlab=None,
ylab=None,
ffig=None,
ax=None):
"""Plot rasterplot."""
# Init.
ax = putil.axes(ax)
putil.plot_periods(prds, ax=ax)
putil.set_limits(ax, xlim)
# There's nothing to plot.
if not len(spk_trains):
return ax
# Plot raster.
for i, spk_tr in enumerate(spk_trains):
x = np.array(spk_tr.rescale(t_unit))
y = (i + 1) * np.ones_like(x)
# Spike markers are plotted in absolute size (figure coordinates).
# ax.scatter(x, y, c=c, s=1.8, edgecolor=c, marker='|')
# Spike markers are plotted in relative size (axis coordinates)
patches = [
Rectangle((xi - wsp / 2, yi - hsp / 2), wsp, hsp)
for xi, yi in zip(x, y)
]
collection = PatchCollection(patches, facecolor=c, edgecolor=c)
ax.add_collection(collection)
# Format plot.
ylim = [0.5, len(spk_trains) + 0.5] if len(spk_trains) else [0, 1]
if xlab is not None:
xlab = putil.t_lbl.format(xlab)
putil.format_plot(ax, xlim, ylim, xlab, ylab, title)
putil.hide_axes(ax, show_x=True)
putil.hide_spines(ax)
# Order trials from top to bottom, only after setting axis limits.
ax.invert_yaxis()
# Save and return plot.
putil.save_fig(ffig)
return ax
def plot_ROC_mean(d_faroc,
t1=None,
t2=None,
ylim=None,
colors=None,
ylab='AROC',
ffig=None):
"""Plot mean ROC curves over given period."""
# Import results.
d_aroc = {}
for name, faroc in d_faroc.items():
aroc = util.read_objects(faroc, 'aroc')
d_aroc[name] = aroc.unstack().T
# Format results.
laroc = pd.DataFrame(pd.concat(d_aroc), columns=['aroc'])
laroc['task'] = laroc.index.get_level_values(0)
laroc['time'] = laroc.index.get_level_values(1)
laroc['unit'] = laroc.index.get_level_values(2)
laroc.index = np.arange(len(laroc.index))
# Init figure.
fig = putil.figure(figsize=(6, 6))
ax = sns.tsplot(laroc,
time='time',
value='aroc',
unit='unit',
condition='task',
color=colors)
# Highlight stimulus periods.
putil.plot_periods(ax=ax)
# Plot mean results.
[ax.lines[i].set_linewidth(3) for i in range(len(ax.lines))]
# Add chance level line.
putil.add_chance_level(ax=ax, alpha=0.8, color='k')
ax.lines[-1].set_linewidth(1.5)
# Format plot.
xlab = 'Time since S1 onset (ms)'
putil.set_labels(ax, xlab, ylab)
putil.set_limits(ax, [t1, t2], ylim)
putil.set_spines(ax, bottom=True, left=True, top=False, right=False)
putil.set_legend(ax, loc=0)
# Save plot.
putil.save_fig(ffig, fig, ytitle=1.05, w_pad=15)
def plot_scores(ax, Scores, Perm=None, Psdo=None, nvals=None, prds=None,
col='b', perm_col='grey', psdo_col='g', xlim=None,
ylim=ylim_scr, xlab=tlab, ylab=ylab_scr, title='',
ytitle=1.04):
"""Plot decoding accuracy results."""
lgn_patches = []
# Plot permuted results (if exist).
if not util.is_null(Perm) and not Perm.isnull().all().all():
x, pval = Perm.columns, Perm.loc['pval']
ymean, ystd = Perm.loc['mean'], Perm.loc['std']
plot_mean_std_sdiff(x, ymean, ystd, pval, pth=0.01, lw=6,
color=perm_col, ax=ax)
lgn_patches.append(putil.get_artist('permuted', perm_col))
# Plot population shuffled results (if exist).
if not util.is_null(Psdo) and not Psdo.isnull().all().all():
x, pval = Psdo.columns, Psdo.loc['pval']
ymean, ystd = Psdo.loc['mean'], Psdo.loc['std']
plot_mean_std_sdiff(x, ymean, ystd, pval, pth=0.01, lw=3,
color=psdo_col, ax=ax)
lgn_patches.append(putil.get_artist('pseudo-population', psdo_col))
# Plot scores.
plot_score_set(Scores, ax, color=col)
lgn_patches.append(putil.get_artist('synchronous', col))
# Add legend.
lgn_patches = lgn_patches[::-1]
putil.set_legend(ax, handles=lgn_patches)
# Add chance level line.
# This currently plots all nvals combined across stimulus period!
if nvals is not None:
chance_lvl = 1.0 / nvals
putil.add_chance_level(ax=ax, ylevel=chance_lvl)
# Add stimulus periods.
if prds is not None:
putil.plot_periods(prds, ax=ax)
# Set axis limits.
xlim = xlim if xlim is not None else tlim
putil.set_limits(ax, xlim, ylim)
# Format plot.
putil.set_labels(ax, xlab, ylab, title, ytitle)
def plot_auc_over_time(auc,
tvec,
prds=None,
evts=None,
xlim=None,
ylim=None,
xlab='time',
ylab='AUC',
title=None,
ax=None):
"""Plot AROC values over time."""
# Init params.
ax = putil.axes(ax)
if xlim is None:
xlim = [min(tvec), max(tvec)]
# Plot periods first.
putil.plot_periods(prds, ax=ax)
# Plot AUC over time.
pplot.lines(tvec, auc, ylim, xlim, xlab, ylab, title, color='green', ax=ax)
# Add chance level line.
putil.add_chance_level(ax=ax)
# # Set minimum y axis scale.
# ymin, ymax = ax.get_ylim()
# ymin, ymax = min(ymin, 0.3), max(ymax, 0.7)
# ax.set_ylim([ymin, ymax])
# Set y tick labels.
if ylim is not None and ylim[0] == 0 and ylim[1] == 1:
tck_marks = np.linspace(0, 1, 5)
tck_lbls = np.array(tck_marks, dtype=str)
tck_lbls[1::2] = ''
putil.set_ytick_labels(ax, tck_marks, tck_lbls)
putil.set_max_n_ticks(ax, 5, 'y')
# Plot event markers.
putil.plot_event_markers(evts, ax=ax)
return ax
def select_units_trials(UA, utids=None, fres=None, ffig=None,
min_n_units=5, min_n_trs_per_unit=5):
"""
Select optimal set of units and trials for population decoding.
min_n_units: minimum number of units to keep (0: off)
min_n_trs_per_unit: minimum number of trials per unit to keep (0: off)
"""
print('Selecting optimal set of units and trials for decoding...')
# Init.
if utids is None:
utids = UA.utids(as_series=True)
u_rt_grpby = utids.groupby(level=['subj', 'date', 'task'])
# Unit info frame.
UInc = pd.Series(False, index=utids.index)
# Included trials by unit.
IncTrs = pd.Series([(UA.get_unit(utid[:-1], utid[-1]).inc_trials())
for utid in utids], index=utids.index)
# Result DF.
rec_task = pd.MultiIndex.from_tuples([rt for rt, _ in u_rt_grpby],
names=['subj', 'date', 'task'])
cols = ['elec', 'units', 'nunits', 'nallunits', '% remaining units',
'trials', 'ntrials', 'nalltrials', '% remaining trials']
RecInfo = pd.DataFrame(index=rec_task, columns=cols)
rt_utids = [utids.xs((s, d, t), level=('subj', 'date', 'task'))
for s, d, t in rec_task]
RecInfo.nallunits = [len(utids) for utids in rt_utids]
rt_ulist = [UA.get_unit(utids[0][:-1], utids[0][-1]) for utids in rt_utids]
RecInfo.nalltrials = [int(u.QualityMetrics['NTrialsTotal'])
for u in rt_ulist]
# Function to plot matrix (DF) included/excluded trials.
def plot_inc_exc_trials(IncTrsMat, ax, title=None, ytitle=None,
xlab='Trial #', ylab=None,):
# Plot on heatmap.
sns.heatmap(IncTrsMat, cmap='RdYlGn', center=0.5, cbar=False, ax=ax)
# Set tick labels.
putil.hide_tick_marks(ax)
tr_ticks = [1] + list(np.arange(25, IncTrsMat.shape[1]+1, 25))
ax.xaxis.set_ticks(tr_ticks)
ax.set_xticklabels(tr_ticks)
putil.rot_xtick_labels(ax, 0)
putil.rot_ytick_labels(ax, 0, va='center')
putil.set_labels(ax, xlab, ylab, title, ytitle)
# Init plotting.
ytitle = 1.40
putil.set_style('notebook', 'whitegrid')
fig, gsp, axs = putil.get_gs_subplots(nrow=len(rec_task), ncol=3,
subw=6, subh=4, create_axes=True)
for i_rt, ((subj, date, task), rt_utids) in enumerate(u_rt_grpby):
print('{} / {}: {} - {} {}'.format(i_rt+1, len(u_rt_grpby), subj,
date, task))
# Init electrode.
elecs = rt_utids.index.get_level_values('elec').unique()
if len(elecs) != 1:
warnings.warn('More than one electrode?')
elec = elecs[0]
RecInfo.loc[(subj, date, task), 'elec'] = elec
# Create matrix of included trials of recording & task of units.
ch_idxs = rt_utids.index.droplevel(-1).droplevel(2).droplevel(1).droplevel(0)
n_alltrs = RecInfo.nalltrials[(subj, date, task)]
IncTrsMat = pd.DataFrame(np.zeros((len(ch_idxs), n_alltrs), dtype=int),
index=ch_idxs, columns=np.arange(n_alltrs)+1)
for ch_idx, utid in zip(ch_idxs, rt_utids):
IncTrsMat.loc[ch_idx].iloc[IncTrs[utid]] = 1
# Plot included/excluded trials after preprocessing.
ax = axs[i_rt, 0]
ylab = '{} {} {}'.format(subj, date, task)
title = ('Included (green) and excluded (red) trials'
if i_rt == 0 else None)
plot_inc_exc_trials(IncTrsMat, ax, title, ytitle, ylab=ylab)
# Calculate and plot overlap of trials across units.
# How many trials will remain if we iteratively excluding units
# with the least overlap with the rest of the units?
def n_cov_trs(df): # return number of trials covered in df
return sum(df.all())
def calc_heuristic(df):
return df.shape[0] * n_cov_trs(df)
n_trs = IncTrsMat.sum(1)
n_units = IncTrsMat.shape[0]
# Init results DF.
columns = ('uid', 'ntrs_cov', 'n_rem_u', 'trial x units')
tr_covs = pd.DataFrame(columns=columns, index=range(n_units+1))
tr_covs.loc[0] = ('none', n_cov_trs(IncTrsMat), n_units,
calc_heuristic(IncTrsMat))
# Subset of included units (to be updated in each iteration).
uinc = IncTrsMat.index.to_series()
for iu in range(1, len(uinc)):
# Number of covered trials after removing each unit.
sntrscov = pd.Series([n_cov_trs(IncTrsMat.loc[uinc.drop(uid)])
for uid in uinc], index=uinc.index)
#########################################
# Select and remove unit that #
# (a) yields maximum trial coverage, #
# (b) has minimum number of trials #
#########################################
maxtrscov = sntrscov.max()
worst_us = sntrscov[sntrscov == maxtrscov].index # (a)
utrs = n_trs.loc[worst_us]
uid_remove = utrs[(utrs == min(utrs))].index[0] # (b)
# Update current subset of units and their trial DF.
uinc.drop(uid_remove, inplace=True)
tr_covs.loc[iu] = (uid_remove, maxtrscov, len(uinc),
calc_heuristic(IncTrsMat.loc[uinc]))
# Add last unit.
tr_covs.iloc[-1] = (uinc[0], 0, 0, 0)
# Plot covered trials against each units removed.
ax_trc = axs[i_rt, 1]
sns.tsplot(tr_covs['ntrs_cov'], marker='o', ms=4, color='b',
ax=ax_trc)
title = ('Trial coverage during iterative unit removal'
if i_rt == 0 else None)
xlab, ylab = 'current unit removed', '# trials covered'
putil.set_labels(ax_trc, xlab, ylab, title, ytitle)
ax_trc.xaxis.set_ticks(tr_covs.index)
x_ticklabs = ['none'] + ['{} - {}'.format(ch, ui)
for ch, ui in tr_covs.uid.loc[1:]]
ax_trc.set_xticklabels(x_ticklabs)
putil.rot_xtick_labels(ax_trc, 45)
ax_trc.grid(True)
# Add # of remaining units to top.
ax_remu = ax_trc.twiny()
ax_remu.xaxis.set_ticks(tr_covs.index)
ax_remu.set_xticklabels(list(range(len(x_ticklabs)))[::-1])
ax_remu.set_xlabel('# units remaining')
ax_remu.grid(None)
# Add heuristic index.
ax_heur = ax_trc.twinx()
sns.tsplot(tr_covs['trial x units'], linestyle='--', marker='o',
ms=4, color='m', ax=ax_heur)
putil.set_labels(ax_heur, ylab='remaining units x covered trials')
[tl.set_color('m') for tl in ax_heur.get_yticklabels()]
[tl.set_color('b') for tl in ax_trc.get_yticklabels()]
ax_heur.grid(None)
# Decide on which units to exclude.
min_n_trials = min_n_trs_per_unit * tr_covs['n_rem_u']
sub_tr_covs = tr_covs[(tr_covs['n_rem_u'] >= min_n_units) &
(tr_covs['ntrs_cov'] >= min_n_trials)]
# If any subset of units passed above criteria.
rem_uids, exc_uids = pd.Series(), tr_covs.uid[1:]
n_tr_rem, n_tr_exc = 0, IncTrsMat.shape[1]
if len(sub_tr_covs.index):
hmax_idx = sub_tr_covs['trial x units'].argmax()
rem_uids = tr_covs.uid[(hmax_idx+1):]
exc_uids = tr_covs.uid[1:hmax_idx+1]
n_tr_rem = tr_covs.ntrs_cov[hmax_idx]
n_tr_exc = IncTrsMat.shape[1] - n_tr_rem
# Add to UnitInfo dataframe
rt_utids = [(subj, date, elec, ch, ui, task)
for ch, ui in rem_uids]
UInc[rt_utids] = True
# Highlight selected point in middle plot.
sel_seg = [('selection', exc_uids.shape[0]-0.4,
exc_uids.shape[0]+0.4)]
putil.plot_periods(sel_seg, ax=ax_trc, alpha=0.3)
[ax.set_xlim([-0.5, n_units+0.5]) for ax in (ax_trc, ax_remu)]
# Generate remaining trials dataframe.
RemTrsMat = IncTrsMat.copy().astype(float)
for exc_uid in exc_uids: # Remove all trials from excluded units.
RemTrsMat.loc[exc_uid] = 0.5
# Remove uncovered trials in remaining units.
exc_trs = np.where(~RemTrsMat.loc[list(rem_uids)].all())[0]
if exc_trs.size:
RemTrsMat.iloc[:, exc_trs] = 0.5
# Overwrite by trials excluded during preprocessing.
RemTrsMat[IncTrsMat == False] = 0.0
# Plot remaining trials.
ax = axs[i_rt, 2]
n_u_rem, n_u_exc = len(rem_uids), len(exc_uids)
title = ('# units remaining: {}, excluded: {}'.format(n_u_rem,
n_u_exc) +
'\n# trials remaining: {}, excluded: {}'.format(n_tr_rem,
n_tr_exc))
plot_inc_exc_trials(RemTrsMat, ax, title=title, ylab='')
# Add remaining units and trials to RecInfo.
rt = (subj, date, task)
RecInfo.loc[rt, ('units', 'nunits')] = list(rem_uids), len(rem_uids)
cov_trs = RemTrsMat.loc[list(rem_uids)].all()
inc_trs = pd.Int64Index(np.where(cov_trs)[0])
RecInfo.loc[rt, ('trials', 'ntrials')] = inc_trs, sum(cov_trs)
RecInfo['% remaining units'] = 100 * RecInfo.nunits / RecInfo.nallunits
RecInfo['% remaining trials'] = 100 * RecInfo.ntrials / RecInfo.nalltrials
# Save results.
if fres is not None:
results = {'RecInfo': RecInfo, 'UInc': UInc}
util.write_objects(results, fres)
# Save plot.
title = 'Trial & unit selection prior decoding'
putil.save_fig(ffig, fig, title, w_pad=3, h_pad=3)
return RecInfo, UInc
def plot_qm(u,
bs_stats,
stab_prd_res,
prd_inc,
tr_inc,
spk_inc,
add_lbls=False,
ftempl=None,
fig=None,
sps=None):
"""Plot quality metrics related figures."""
# Init values.
waveforms = np.array(u.Waveforms)
wavetime = u.Waveforms.columns * us
spk_times = np.array(u.SpikeParams['time'], dtype=float)
base_rate = u.QualityMetrics['baseline']
# Minimum and maximum gain.
gmin = u.SessParams['minV']
gmax = u.SessParams['maxV']
# %% Init plots.
# Disable inline plotting to prevent memory leak.
putil.inline_off()
# Init figure and gridspec.
fig = putil.figure(fig)
if sps is None:
sps = putil.gridspec(1, 1)[0]
ogsp = putil.embed_gsp(sps, 2, 1, height_ratios=[0.02, 1])
info_sps, qm_sps = ogsp[0], ogsp[1]
# Info header.
info_ax = fig.add_subplot(info_sps)
putil.hide_axes(info_ax)
title = putil.get_unit_info_title(u)
putil.set_labels(ax=info_ax, title=title, ytitle=0.80)
# Create axes.
gsp = putil.embed_gsp(qm_sps, 3, 2, wspace=0.3, hspace=0.4)
ax_wf_inc, ax_wf_exc = [fig.add_subplot(gsp[0, i]) for i in (0, 1)]
ax_wf_amp, ax_wf_dur = [fig.add_subplot(gsp[1, i]) for i in (0, 1)]
ax_amp_dur, ax_rate = [fig.add_subplot(gsp[2, i]) for i in (0, 1)]
# Trial markers.
trial_starts, trial_stops = u.TrData.TrialStart, u.TrData.TrialStop
tr_markers = pd.DataFrame({'time': trial_starts[9::10]})
tr_markers['label'] = [
str(itr + 1) if i % 2 else '' for i, itr in enumerate(tr_markers.index)
]
# Common variables, limits and labels.
WF_T_START = test_sorting.WF_T_START
spk_t = u.SessParams.sampl_prd * (np.arange(waveforms.shape[1]) -
WF_T_START)
ses_t_lim = test_sorting.get_start_stop_times(spk_times, trial_starts,
trial_stops)
ss, sa = 1.0, 0.8 # marker size and alpha on scatter plot
# Color spikes by their occurance over session time.
my_cmap = putil.get_cmap('jet')
spk_cols = np.tile(np.array([.25, .25, .25, .25]), (len(spk_times), 1))
if np.any(spk_inc): # check if there is any spike included
spk_t_inc = np.array(spk_times[spk_inc])
tmin, tmax = float(spk_times.min()), float(spk_times.max())
spk_cols[spk_inc, :] = my_cmap((spk_t_inc - tmin) / (tmax - tmin))
# Put excluded trials to the front, and randomise order of included trials
# so later spikes don't systematically cover earlier ones.
spk_order = np.hstack((np.where(np.invert(spk_inc))[0],
np.random.permutation(np.where(spk_inc)[0])))
# Common labels for plots
ses_t_lab = 'Recording time (s)'
# %% Waveform shape analysis.
# Plot included and excluded waveforms on different axes.
# Color included by occurance in session time to help detect drifts.
s_waveforms, s_spk_cols = waveforms[spk_order, :], spk_cols[spk_order]
wf_t_lim, glim = [min(spk_t), max(spk_t)], [gmin, gmax]
wf_t_lab, volt_lab = 'WF time ($\mu$s)', 'Voltage'
for st in ('Included', 'Excluded'):
ax = ax_wf_inc if st == 'Included' else ax_wf_exc
spk_idx = spk_inc if st == 'Included' else np.invert(spk_inc)
tr_idx = tr_inc if st == 'Included' else np.invert(tr_inc)
nspsk, ntrs = sum(spk_idx), sum(tr_idx)
title = '{} WFs, {} spikes, {} trials'.format(st, nspsk, ntrs)
# Select waveforms and colors.
rand_spk_idx = spk_idx[spk_order]
wfs = s_waveforms[rand_spk_idx, :]
cols = s_spk_cols[rand_spk_idx]
# Plot waveforms.
xlab, ylab = (wf_t_lab, volt_lab) if add_lbls else (None, None)
pwaveform.plot_wfs(wfs,
spk_t,
cols=cols,
lw=0.1,
alpha=0.05,
xlim=wf_t_lim,
ylim=glim,
title=title,
xlab=xlab,
ylab=ylab,
ax=ax)
# %% Waveform summary metrics.
# Init data.
wf_amp_all = u.SpikeParams['amplitude']
wf_amp_inc = wf_amp_all[spk_inc]
wf_dur_all = u.SpikeParams['duration']
wf_dur_inc = wf_dur_all[spk_inc]
# Set common limits and labels.
dur_lim = [0, wavetime[-2] - wavetime[WF_T_START]] # same across units
glim = max(wf_amp_all.max(), gmax - gmin)
amp_lim = [0, glim]
amp_lab = 'Amplitude'
dur_lab = 'Duration ($\mu$s)'
# Waveform amplitude across session time.
m_amp, sd_amp = wf_amp_inc.mean(), wf_amp_inc.std()
title = 'WF amplitude: {:.1f} $\pm$ {:.1f}'.format(m_amp, sd_amp)
xlab, ylab = (ses_t_lab, amp_lab) if add_lbls else (None, None)
pplot.scatter(spk_times,
wf_amp_all,
spk_inc,
c='m',
bc='grey',
s=ss,
xlab=xlab,
ylab=ylab,
xlim=ses_t_lim,
ylim=amp_lim,
edgecolors='',
alpha=sa,
id_line=False,
title=title,
ax=ax_wf_amp)
# Waveform duration across session time.
mdur, sdur = wf_dur_inc.mean(), wf_dur_inc.std()
title = 'WF duration: {:.1f} $\pm$ {:.1f} $\mu$s'.format(mdur, sdur)
xlab, ylab = (ses_t_lab, dur_lab) if add_lbls else (None, None)
pplot.scatter(spk_times,
wf_dur_all,
spk_inc,
c='c',
bc='grey',
s=ss,
xlab=xlab,
ylab=ylab,
xlim=ses_t_lim,
ylim=dur_lim,
edgecolors='',
alpha=sa,
id_line=False,
title=title,
ax=ax_wf_dur)
# Waveform duration against amplitude.
title = 'WF duration - amplitude'
xlab, ylab = (dur_lab, amp_lab) if add_lbls else (None, None)
pplot.scatter(wf_dur_all[spk_order],
wf_amp_all[spk_order],
c=spk_cols[spk_order],
s=ss,
xlab=xlab,
ylab=ylab,
xlim=dur_lim,
ylim=amp_lim,
edgecolors='',
alpha=sa,
id_line=False,
title=title,
ax=ax_amp_dur)
# %% Firing rate.
tmean = np.array(bs_stats['tmean'])
rmean = util.remove_dim_from_series(bs_stats['rate'])
prd_tstart, prd_tstop = stab_prd_res['tstart'], stab_prd_res['tstop']
# Color segments depending on whether they are included / excluded.
def plot_periods(v, color, ax):
# Plot line segments.
for i in range(len(prd_inc[:-1])):
col = color if prd_inc[i] and prd_inc[i + 1] else 'grey'
x, y = [(tmean[i], tmean[i + 1]), (v[i], v[i + 1])]
ax.plot(x, y, color=col)
# Plot line points.
for i in range(len(prd_inc)):
col = color if prd_inc[i] else 'grey'
x, y = [tmean[i], v[i]]
ax.plot(x,
y,
color=col,
marker='o',
markersize=3,
markeredgecolor=col)
# Firing rate over session time.
title = 'Baseline rate: {:.1f} spike/s'.format(float(base_rate))
xlab, ylab = (ses_t_lab, putil.FR_lbl) if add_lbls else (None, None)
ylim = [0, 1.25 * np.max(rmean)]
plot_periods(rmean, 'b', ax_rate)
pplot.lines([], [],
c='b',
xlim=ses_t_lim,
ylim=ylim,
title=title,
xlab=xlab,
ylab=ylab,
ax=ax_rate)
# Trial markers.
putil.plot_events(tr_markers,
lw=0.5,
ls='--',
alpha=0.35,
y_lbl=0.92,
ax=ax_rate)
# Excluded periods.
excl_prds = []
tstart, tstop = ses_t_lim
if tstart != prd_tstart:
excl_prds.append(('beg', tstart, prd_tstart))
if tstop != prd_tstop:
excl_prds.append(('end', prd_tstop, tstop))
putil.plot_periods(excl_prds, ymax=0.92, ax=ax_rate)
# %% Post-formatting.
# Maximize number of ticks on recording time axes to prevent covering.
for ax in (ax_wf_amp, ax_wf_dur, ax_rate):
putil.set_max_n_ticks(ax, 6, 'x')
# %% Save figure.
if ftempl is not None:
fname = ftempl.format(u.name_to_fname())
putil.save_fig(fname, fig, title, rect_height=0.92)
putil.inline_on()
return [ax_wf_inc, ax_wf_exc], ax_wf_amp, ax_wf_dur, ax_amp_dur, ax_rate
def rate(rate_list,
names=None,
prds=None,
evts=None,
cols=None,
baseline=None,
pval=0.05,
test='mann_whitney_u',
test_kws=None,
xlim=None,
ylim=None,
title=None,
xlab=None,
ylab=putil.FR_lbl,
add_lgn=True,
lgn_lbl='trs',
ffig=None,
ax=None):
"""Plot firing rate."""
# Init.
ax = putil.axes(ax)
if test_kws is None:
test_kws = dict()
# Plot periods and baseline first.
putil.plot_periods(prds, ax=ax)
if baseline is not None:
putil.add_baseline(baseline, ax=ax)
putil.set_limits(ax, xlim)
if not len(rate_list):
return ax
if cols is None:
cols = putil.get_colors(as_cycle=False)
if names is None:
names = len(rate_list) * ['']
# Iterate through list of rate arrays
xmin, xmax, ymax = None, None, None
for i, rts in enumerate(rate_list):
# Init.
name = names[i]
col = cols[i]
# Skip empty array (no trials).
if not rts.shape[0]:
continue
# Set line label. Convert to Numpy array to format floats nicely.
lbl = str(np.array(name)) if util.is_iterable(name) else str(name)
if lgn_lbl is not None:
lbl += ' ({} {})'.format(rts.shape[0], lgn_lbl)
# Plot mean +- SEM of rate vectors.
tvec, meanr, semr = rts.columns, rts.mean(), rts.sem()
ax.plot(tvec, meanr, label=lbl, color=col)
ax.fill_between(tvec,
meanr - semr,
meanr + semr,
alpha=0.2,
facecolor=col,
edgecolor=col)
# Update limits.
tmin, tmax, rmax = tvec.min(), tvec.max(), (meanr + semr).max()
xmin = np.min([xmin, tmin]) if xmin is not None else tmin
xmax = np.max([xmax, tmax]) if xmax is not None else tmax
ymax = np.max([ymax, rmax]) if ymax is not None else rmax
# Set ticks, labels and axis limits.
if xlim is None:
if xmin == xmax: # avoid setting identical limits
xmax = None
xlim = (xmin, xmax)
if ylim is None:
ymax = 1.02 * ymax if (ymax is not None) and (ymax > 0) else None
ylim = (0, ymax)
if xlab is not None:
xlab = putil.t_lbl.format(xlab)
putil.format_plot(ax, xlim, ylim, xlab, ylab, title)
t1, t2 = ax.get_xlim() # in case it was set to None
tmarks, tlbls = putil.get_tick_marks_and_labels(t1, t2)
putil.set_xtick_labels(ax, tmarks, tlbls)
putil.set_max_n_ticks(ax, 7, 'y')
# Add legend.
if add_lgn and len(rate_list):
putil.set_legend(ax,
loc=1,
borderaxespad=0.0,
handletextpad=0.4,
handlelength=0.6)
# Add significance line to top of axes.
if (pval is not None) and (len(rate_list) == 2):
rates1, rates2 = rate_list
sign_prds = stats.sign_periods(rates1, rates2, pval, test, **test_kws)
putil.plot_signif_prds(sign_prds, color='m', linewidth=4.0, ax=ax)
# Plot event markers.
putil.plot_event_markers(evts, ax=ax)
# Save and return plot.
putil.save_fig(ffig)
return ax
def plot_combined_rec_mean(recs, stims, res_dir, par_kws,
list_n_most_DS, list_min_nunits,
n_boot=1e4, ci=95,
tasks=None, task_labels=None, add_title=True,
fig=None):
"""Test and plot results combined across sessions."""
# Init.
# putil.set_style('notebook', 'ticks')
vkey = 'all'
# This should be made more explicit!
prds = [[stim] + list(constants.fixed_tr_prds.loc[stim])
for stim in stims]
# Load all results to plot.
dict_rt_res = decutil.load_res(res_dir, list_n_most_DS, **par_kws)
# Create figures.
fig_scr, _, axs_scr = putil.get_gs_subplots(nrow=len(dict_rt_res),
ncol=len(list_min_nunits),
subw=8, subh=6, fig=fig,
create_axes=True)
# Query data.
allScores = {}
allnunits = {}
for n_most_DS, rt_res in dict_rt_res.items():
# Get accuracy scores.
dScores = {(rec, task): res[vkey]['Scores'].mean()
for (rec, task), res in rt_res.items()
if (vkey in res) and (res[vkey] is not None)}
allScores[n_most_DS] = pd.concat(dScores, axis=1).T
# Get number of units.
allnunits[n_most_DS] = {(rec, task): res[vkey]['nunits'].iloc[0]
for (rec, task), res in rt_res.items()
if (vkey in res) and (res[vkey] is not None)}
# Get # values (for baseline plotting.)
all_nvals = pd.Series({(rec, task): res[vkey]['nclasses'].iloc[0]
for (rec, task), res in rt_res.items()
if (vkey in res) and (res[vkey] is not None)})
un_nvals = all_nvals.unique()
if len(un_nvals) > 1 and verbose:
print('Found multiple # of classes to decode: {}'.format(un_nvals))
nvals = un_nvals[0]
allnunits = pd.DataFrame(allnunits)
# Plot mean performance across recordings and
# test significance by bootstrapping.
for inmost, n_most_DS in enumerate(list_n_most_DS):
Scores = allScores[n_most_DS]
nunits = allnunits[n_most_DS]
for iminu, min_nunits in enumerate(list_min_nunits):
ax_scr = axs_scr[inmost, iminu]
# Select only recordings with minimum number of units.
sel_rt = nunits.index[nunits >= min_nunits]
nScores = Scores.loc[sel_rt].copy()
# Nothing to plot.
if nScores.empty:
ax_scr.axis('off')
continue
# Prepare data.
if tasks is None:
tasks = nScores.index.get_level_values(1).unique() # in data
if task_labels is None:
task_labels = {task: task for task in tasks}
dScores = {task: pd.DataFrame(nScores.xs(task, level=1).unstack(),
columns=['accuracy'])
for task in tasks}
lScores = pd.concat(dScores, axis=0)
lScores['time'] = lScores.index.get_level_values(1)
lScores['task'] = lScores.index.get_level_values(0)
lScores['rec'] = lScores.index.get_level_values(2)
lScores.index = np.arange(len(lScores.index))
lScores.task.replace(task_labels, inplace=True)
# Add altered task names for legend plotting.
nrecs = {task_labels[task]: len(nScores.xs(task, level=1))
for task in tasks}
my_format = lambda x: '{} (n={})'.format(x, nrecs[x])
lScores['task_nrecs'] = lScores['task'].apply(my_format)
# Plot as time series.
sns.tsplot(lScores, time='time', value='accuracy', unit='rec',
condition='task_nrecs', ci=ci, n_boot=n_boot, ax=ax_scr)
# Add chance level line.
chance_lvl = 1.0 / nvals
putil.add_chance_level(ax=ax_scr, ylevel=chance_lvl)
# Add stimulus periods.
putil.plot_periods(prds, ax=ax_scr)
# Set axis limits.
putil.set_limits(ax_scr, tlim)
# Format plot.
title = ('{} most DS units'.format(n_most_DS)
if n_most_DS != 0 else 'all units')
title += (', recordings with at least {} units'.format(min_nunits)
if (min_nunits > 1 and len(list_min_nunits) > 1) else '')
ytitle = 1.0
putil.set_labels(ax_scr, tlab, ylab_scr, title, ytitle)
putil.hide_legend_title(ax_scr)
# Match axes across decoding plots.
[putil.sync_axes(axs_scr[inmost, :], sync_y=True)
for inmost in range(axs_scr.shape[0])]
# Save plots.
list_n_most_DS_str = [str(i) if i != 0 else 'all' for i in list_n_most_DS]
par_kws['n_most_DS'] = ', '.join(list_n_most_DS_str)
title = ''
if add_title:
title = decutil.fig_title(res_dir, **par_kws)
title += '\n{}% CE with {} bootstrapped subsamples'.format(ci,
int(n_boot))
fs_title = 'large'
w_pad, h_pad = 3, 3
par_kws['n_most_DS'] = '_'.join(list_n_most_DS_str)
ffig = decutil.fig_fname(res_dir, 'combined_score', fformat, **par_kws)
putil.save_fig(ffig, fig_scr, title, fs_title, w_pad=w_pad, h_pad=h_pad)
return fig_scr, axs_scr, ffig
def plot_scores_across_nunits(recs, stims, res_dir, list_n_most_DS, par_kws):
"""
Plot prediction score results across different number of units included.
"""
# Init.
putil.set_style('notebook', 'ticks')
tasks = par_kws['tasks']
# Remove Passive if plotting Saccade or Correct.
if par_kws['feat'] in ['saccade', 'correct']:
tasks = tasks[~tasks.str.contains('Pas')]
# Load all results to plot.
dict_rt_res = decutil.load_res(res_dir, list_n_most_DS, **par_kws)
# Create figures.
fig_scr, _, axs_scr = putil.get_gs_subplots(nrow=len(recs),
ncol=len(tasks),
subw=8, subh=6,
create_axes=True)
# Do plotting per recording and task.
for irec, rec in enumerate(recs):
if verbose:
print('\n' + rec)
for itask, task in enumerate(tasks):
if verbose:
print(' ' + task)
ax_scr = axs_scr[irec, itask]
# Init data.
dict_lScores = {}
cols = sns.color_palette('hls', len(dict_rt_res.keys()))
lncls = []
for (n_most_DS, rt_res), col in zip(dict_rt_res.items(), cols):
# Check if results exist for rec-task combination.
if (((rec, task) not in rt_res.keys()) or
(not len(rt_res[(rec, task)].keys()))):
continue
res = rt_res[(rec, task)]
for v, col in zip(res.keys(), cols):
vres = res[v]
Scores = vres['Scores']
lncls.append(vres['nclasses'])
# Unstack dataframe with results.
lScores = pd.DataFrame(Scores.unstack(), columns=['score'])
lScores['time'] = lScores.index.get_level_values(0)
lScores['fold'] = lScores.index.get_level_values(1)
lScores.index = np.arange(len(lScores.index))
# Get number of units tested.
nunits = vres['nunits']
uni_nunits = nunits.unique()
if len(uni_nunits) > 1 and verbose:
print('Different number of units found.')
nunits = uni_nunits[0]
# Collect results.
dict_lScores[(nunits, v)] = lScores
# Skip rest if no data is available.
# Check if any result exists for rec-task combination.
if not len(dict_lScores):
ax_scr.axis('off')
continue
# Concatenate accuracy scores from every recording.
all_lScores = pd.concat(dict_lScores)
all_lScores['n_most_DS'] = all_lScores.index.get_level_values(0)
all_lScores.index = np.arange(len(all_lScores.index))
# Plot decoding results.
nnunits = len(all_lScores['n_most_DS'].unique())
title = '{} {}, {} sets of units'.format(' '.join(rec), task,
nnunits)
ytitle = 1.0
prds = [[stim] + list(constants.fixed_tr_prds.loc[stim])
for stim in stims]
# Plot time series.
palette = sns.color_palette('muted')
sns.tsplot(all_lScores, time='time', value='score', unit='fold',
condition='n_most_DS', color=palette, ax=ax_scr)
# Add chance level line.
# This currently plots a chance level line for every nvals,
# combined across stimulus period!
uni_ncls = np.unique(np.array(lncls).flatten())
if len(uni_ncls) > 1 and verbose:
print('Different number of classes found.')
for nvals in uni_ncls:
chance_lvl = 1.0 / nvals
putil.add_chance_level(ax=ax_scr, ylevel=chance_lvl)
# Add stimulus periods.
if prds is not None:
putil.plot_periods(prds, ax=ax_scr)
# Set axis limits.
putil.set_limits(ax_scr, tlim, ylim_scr)
# Format plot.
putil.set_labels(ax_scr, tlab, ylab_scr, title, ytitle)
# Match axes across decoding plots.
# [putil.sync_axes(axs_scr[:, itask], sync_y=True)
# for itask in range(axs_scr.shape[1])]
# Save plots.
list_n_most_DS_str = [str(i) if i != 0 else 'all' for i in list_n_most_DS]
par_kws['n_most_DS'] = ', '.join(list_n_most_DS_str)
title = decutil.fig_title(res_dir, **par_kws)
fs_title = 'large'
w_pad, h_pad = 3, 3
par_kws['n_most_DS'] = '_'.join(list_n_most_DS_str)
ffig = decutil.fig_fname(res_dir, 'score_nunits', fformat, **par_kws)
putil.save_fig(ffig, fig_scr, title, fs_title, w_pad=w_pad, h_pad=h_pad)
def plot_score_multi_rec(recs, stims, res_dir, par_kws):
"""Plot prediction scores for multiple recordings."""
# Init.
putil.set_style('notebook', 'ticks')
n_most_DS = par_kws['n_most_DS']
tasks = par_kws['tasks']
# Remove Passive if plotting Saccade or Correct.
if par_kws['feat'] in ['saccade', 'correct']:
tasks = tasks[~tasks.str.contains('Pas')]
# Load results.
rt_res = decutil.load_res(res_dir, **par_kws)[n_most_DS]
# Create figure.
ret = putil.get_gs_subplots(nrow=1, ncol=len(tasks),
subw=8, subh=6, create_axes=True)
fig_scr, _, axs_scr = ret
print('\nPlotting multi-recording results...')
for itask, task in enumerate(tasks):
if verbose:
print(' ' + task)
ax_scr = axs_scr[0, itask]
dict_lScores = {}
for irec, rec in enumerate(recs):
# Check if results exist for rec-task combination.
if (((rec, task) not in rt_res.keys()) or
(not len(rt_res[(rec, task)].keys()))):
continue
# Init data.
res = rt_res[(rec, task)]
cols = sns.color_palette('hls', len(res.keys()))
lncls = []
for v, col in zip(res.keys(), cols):
vres = res[v]
if vres is None:
continue
Scores = vres['Scores']
lncls.append(vres['nclasses'])
# Unstack dataframe with results.
lScores = pd.DataFrame(Scores.unstack(), columns=['score'])
lScores['time'] = lScores.index.get_level_values(0)
lScores['fold'] = lScores.index.get_level_values(1)
lScores.index = np.arange(len(lScores.index))
dict_lScores[(rec, v)] = lScores
if not len(dict_lScores):
ax_scr.axis('off')
continue
# Concatenate accuracy scores from every recording.
all_lScores = pd.concat(dict_lScores)
all_lScores['rec'] = all_lScores.index.get_level_values(0)
all_lScores['rec'] = all_lScores['rec'].str.join(' ') # format label
all_lScores.index = np.arange(len(all_lScores.index))
# Plot decoding results.
nrec = len(all_lScores['rec'].unique())
title = '{}, {} recordings'.format(task, nrec)
ytitle = 1.0
prds = [[stim] + list(constants.fixed_tr_prds.loc[stim])
for stim in stims]
# Plot time series.
palette = sns.color_palette('muted')
sns.tsplot(all_lScores, time='time', value='score', unit='fold',
condition='rec', color=palette, ax=ax_scr)
# Add chance level line.
# This currently plots a chance level line for every nvals,
# combined across stimulus period!
uni_ncls = np.unique(np.array(lncls).flatten())
if len(uni_ncls) > 1 and verbose:
print('Different number of classes found.')
for nvals in uni_ncls:
chance_lvl = 1.0 / nvals
putil.add_chance_level(ax=ax_scr, ylevel=chance_lvl)
# Add stimulus periods.
if prds is not None:
putil.plot_periods(prds, ax=ax_scr)
# Set axis limits.
putil.set_limits(ax_scr, tlim, ylim_scr)
# Format plot.
putil.set_labels(ax_scr, tlab, ylab_scr, title, ytitle)
# Save figure.
title = decutil.fig_title(res_dir, **par_kws)
fs_title = 'large'
w_pad, h_pad = 3, 3
ffig = decutil.fig_fname(res_dir, 'all_scores', fformat, **par_kws)
putil.save_fig(ffig, fig_scr, title, fs_title, w_pad=w_pad, h_pad=h_pad)
def plot_scores_weights(recs, stims, res_dir, par_kws):
"""
Plot prediction scores and model weights for given recording and analysis.
"""
# Init.
putil.set_style('notebook', 'ticks')
n_most_DS = par_kws['n_most_DS']
tasks = par_kws['tasks']
# Remove Passive if plotting Saccade or Correct.
if par_kws['feat'] in ['saccade', 'correct']:
tasks = tasks[~tasks.str.contains('Pas')]
# Load results.
rt_res = decutil.load_res(res_dir, **par_kws)[n_most_DS]
# Create figures.
# For prediction scores.
fig_scr, _, axs_scr = putil.get_gs_subplots(nrow=len(recs),
ncol=len(tasks),
subw=8, subh=6,
create_axes=True)
# For unit weights (coefficients).
fig_wgt, _, axs_wgt = putil.get_gs_subplots(nrow=len(recs),
ncol=len(tasks),
subw=8, subh=6,
create_axes=True)
for irec, rec in enumerate(recs):
if verbose:
print('\n' + rec)
for itask, task in enumerate(tasks):
if verbose:
print(' ' + task)
# Init figures.
ax_scr = axs_scr[irec, itask]
ax_wgt = axs_wgt[irec, itask]
# Check if any result exists for rec-task combination.
if (((rec, task) not in rt_res.keys()) or
(not len(rt_res[(rec, task)].keys()))):
ax_scr.axis('off')
ax_wgt.axis('off')
continue
# Init data.
res = rt_res[(rec, task)]
vals = [v for v in res.keys() if not util.is_null(res[v])]
cols = sns.color_palette('hls', len(vals))
lnunits, lntrs, lncls, = [], [], []
for v, col in zip(vals, cols):
# Basic results.
vres = res[v]
Scores = vres['Scores']
Coefs = vres['Coefs']
Perm = vres['Perm']
Psdo = vres['Psdo']
# Decoding params.
lnunits.append(vres['nunits'])
lntrs.append(vres['ntrials'])
lncls.append(vres['nclasses'])
# Plot decoding accuracy.
plot_scores(ax_scr, Scores, Perm, Psdo, col=col)
# Add labels.
uni_lnunits = np.unique(np.array(lnunits).flatten())
if len(uni_lnunits) > 1 and verbose:
print('Different number of units found.')
nunits = uni_lnunits[0]
title = '{} {}, {} units'.format(' '.join(rec), task, nunits)
putil.set_labels(ax_scr, tlab, ylab_scr, title, ytitle=1.04)
# Add chance level line.
uni_ncls = np.unique(np.array(lncls).flatten())
if len(uni_ncls) > 1 and verbose:
print('Different number of classes found.')
for nvals in uni_ncls:
chance_lvl = 1.0 / nvals
putil.add_chance_level(ax=ax_scr, ylevel=chance_lvl)
# Plot stimulus periods.
prds = [[stim] + list(constants.fixed_tr_prds.loc[stim])
for stim in stims]
putil.plot_periods(prds, ax=ax_scr)
# Plot unit weights over time.
plot_weights(ax_wgt, Coefs, prds, tlim, tlab, title=title)
# Match axes across decoding plots.
# [putil.sync_axes(axs_scr[:, itask], sync_y=True)
# for itask in range(axs_scr.shape[1])]
# Save plots.
title = decutil.fig_title(res_dir, **par_kws)
fs_title = 'large'
w_pad, h_pad = 3, 3
# Performance.
ffig = decutil.fig_fname(res_dir, 'score', 'pdf', **par_kws)
putil.save_fig(ffig, fig_scr, title, fs_title, w_pad=w_pad, h_pad=h_pad)
# Weights.
ffig = decutil.fig_fname(res_dir, 'weight', 'pdf', **par_kws)
putil.save_fig(ffig, fig_wgt, title, fs_title, w_pad=w_pad, h_pad=h_pad)