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 plot_RF_results(RF_res, stims, fdir, sup_title):
"""Plot receptive field results."""
# Plot distribution of coverage values.
putil.set_style('poster', 'white')
fig = putil.figure()
ax = putil.axes()
sns.distplot(RF_res.S1_cover, bins=np.arange(0, 1.01, 0.1),
kde=False, rug=True, ax=ax)
putil.set_limits(ax, [0, 1])
fst = util.format_to_fname(sup_title)
ffig = fdir + fst + '_S1_coverage.png'
putil.save_fig(ffig, fig, sup_title)
# Plot RF coverage and rate during S1 on regression plot for each
# recording and task.
tasks = RF_res.index.get_level_values(-1).unique()
for vname, ylim in [('mean_rate', [0, None]), ('max_rate', [0, None]),
('mDSI', [0, 1])]:
fig, gs, axes = putil.get_gs_subplots(nrow=len(stims), ncol=len(tasks),
subw=4, subh=4, ax_kws_list=None,
create_axes=True)
colors = sns.color_palette('muted', len(tasks))
for istim, stim in enumerate(stims):
for itask, task in enumerate(tasks):
# Plot regression plot.
ax = axes[istim, itask]
scov, sval = [stim + '_' + name for name in ('cover', vname)]
df = RF_res.xs(task, level=-1)
sns.regplot(scov, sval, df, color=colors[itask], ax=ax)
# Add unit labels.
uids = df.index.droplevel(0)
putil.add_unit_labels(ax, uids, df[scov], df[sval])
# Add stats.
r, p = sp.stats.pearsonr(df[sval], df[scov])
pstr = util.format_pvalue(p)
txt = 'r = {:.2f}, {}'.format(r, pstr)
ax.text(0.02, 0.98, txt, va='top', ha='left',
transform=ax.transAxes)
# Set labels.
title = '{} {}'.format(task, stim)
xlab, ylab = [sn.replace('_', ' ') for sn in (scov, sval)]
putil.set_labels(ax, xlab, ylab, title)
# Set limits.
xlim = [0, 1]
putil.set_limits(ax, xlim, ylim)
# Save plot.
fst = util.format_to_fname(sup_title)
fname = '{}_cover_{}.png'.format(fst, vname)
ffig = util.join([fdir, vname, fname])
putil.save_fig(ffig, fig, sup_title)
def plot_auc_heatmap(aroc_mat,
cmap='viridis',
events=None,
xlbl_freq=500,
ylbl_freq=10,
xlab='time',
ylab='unit index',
title='AROC over time',
ffig=None,
fig=None):
"""Plot ROC AUC of list of units on heatmap."""
fig = putil.figure(fig)
# Plot heatmap.
yticklabels = np.arange(len(aroc_mat.index)) + 1
ax = pplot.heatmap(aroc_mat,
vmin=0,
vmax=1,
cmap=cmap,
xlab=xlab,
ylab=ylab,
title=title,
yticklabels=yticklabels)
# Format labels.
xlbls = pd.Series(aroc_mat.columns.map(str))
xlbls[aroc_mat.columns % xlbl_freq != 0] = ''
putil.set_xtick_labels(ax, lbls=xlbls)
putil.rot_xtick_labels(ax, rot=0, ha='center')
putil.sparsify_tick_labels(fig,
ax,
'y',
istart=ylbl_freq - 1,
freq=ylbl_freq,
reverse=True)
putil.hide_tick_marks(ax)
putil.hide_spines(ax)
# Plot events.
if events is not None:
putil.plot_events(events,
add_names=False,
color='black',
alpha=0.3,
ls='-',
lw=1,
ax=ax)
# Save plot.
putil.save_fig(ffig, dpi=300)
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_slope_diffs_btw_groups(fit_res,
prd,
res_dir,
groups=None,
figsize=None):
"""Plot differences in slopes between group pairs."""
# Test pair-wise difference from each other.
if groups is None:
groups = fit_res['group'].unique()
empty_df = pd.DataFrame(np.nan, index=groups, columns=groups)
pw_diff_v = empty_df.copy()
pw_diff_p = empty_df.copy()
pw_diff_a = empty_df.copy()
for grp1, grp2 in combinations(groups, 2):
# Get slopes in each task.
slp_t1 = fit_res.slope[fit_res.group == grp1]
slp_t2 = fit_res.slope[fit_res.group == grp2]
# Mean difference value.
diff = slp_t1.mean() - slp_t2.mean()
pw_diff_v.loc[grp1, grp2] = diff
# Do test for statistical difference.
stat, pval = stats.mann_whithney_u_test(slp_t1, slp_t2)
pw_diff_p.loc[grp1, grp2] = pval
# Annotation DF plot.
a1, a2 = [
'{:.2f}{}'.format(v, util.star_pvalue(pval)) for v in (diff, -diff)
]
pw_diff_a.loc[grp1, grp2] = a1
# Plot and save figure of mean pair-wise difference.
fig = putil.figure(figsize=figsize)
sns.heatmap(pw_diff_v, annot=pw_diff_a, fmt='', linewidths=0.5, cbar=False)
title = 'Mean difference in {} slopes (sp/s / s)'.format(prd)
putil.set_labels(title=title)
ffig = res_dir + '{}_anticipatory_slope_pairwise_diff.png'.format(prd)
putil.save_fig(ffig, fig)
return pw_diff_v
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 plot_group_violin(res,
x,
y,
groups=None,
npval=None,
pth=0.01,
color='grey',
ylim=None,
ylab=None,
ffig=None):
"""Plot group-wise results on violin plots."""
if groups is None:
groups = res['group'].unique()
# Test difference from zero in each groups.
ttest_res = {
group: sp.stats.ttest_1samp(gres[y], 0)
for group, gres in res.groupby(x)
}
ttest_res = pd.DataFrame.from_dict(ttest_res, 'index')
# Binarize significance test.
res['is_sign'] = res[npval] < pth if npval is not None else True
res['direction'] = np.sign(res[y])
# Set up figure and plot data.
fig = putil.figure()
ax = putil.axes()
putil.add_baseline(ax=ax)
sns.violinplot(x=x, y=y, data=res, inner=None, order=groups, ax=ax)
sns.swarmplot(x=x,
y=y,
hue='is_sign',
data=res,
color=color,
order=groups,
hue_order=[True, False],
ax=ax)
putil.set_labels(ax, xlab='', ylab=ylab)
putil.set_limits(ax, ylim=ylim)
putil.hide_legend(ax)
# Add annotations.
ymin, ymax = ax.get_ylim()
ylvl = ymax
for i, group in enumerate(groups):
gres = res.loc[res.group == group]
# Mean.
mean_str = 'Mean:\n' if i == 0 else '\n'
mean_str += '{:.2f}'.format(gres[y].mean())
# Non-zero test of distribution.
str_pval = util.format_pvalue(ttest_res.loc[group, 'pvalue'])
mean_str += '\n({})'.format(str_pval)
# Stats on difference from baseline.
nnonsign, ntot = (~gres.is_sign).sum(), len(gres)
npos, nneg = [
sum(gres.is_sign & (gres.direction == d)) for d in (1, -1)
]
sign_to_report = [('+', npos), ('=', nnonsign), ('-', nneg)]
nsign_str = ''
for symb, n in sign_to_report:
prc = str(int(round(100 * n / ntot)))
nsign_str += '{} {:>3} / {} ({:>2}%)\n'.format(
symb, int(n), ntot, prc)
lbl = '{}\n\n{}'.format(mean_str, nsign_str)
ax.text(i, ylvl, lbl, fontsize='smaller', va='bottom', ha='center')
# Save plot.
putil.save_fig(ffig, fig)
return fig, ax
def cat_mean(df,
x,
y,
add_stats=True,
fstats=None,
bar_ylvl=None,
ci=68,
add_mean=True,
mean_ylvl=None,
ylbl=None,
fig=None,
ax=None,
ffig=None):
"""Plot mean of two categorical dataset."""
# Init.
if fig is None and ax is None:
fig = putil.figure(figsize=(3, 3))
if ax is None:
ax = putil.axes()
if fstats is None:
fstats = stats.mann_whithney_u_test
# Plot means as bars.
sns.barplot(x=x,
y=y,
data=df,
ci=ci,
ax=ax,
palette=palette,
errwidth=errwidth,
**kwargs)
# Get plotted vectors.
ngrps = [t.get_text() for t in ax.get_xticklabels()]
v1, v2 = [df.loc[df[x] == ngrp, y] for ngrp in ngrps]
# Add significance bar.
if add_stats:
_, pval = fstats(v1, v2)
pval_str = util.format_pvalue(pval)
if bar_ylvl is None:
bar_ylvl = 1.1 * max(v1.mean() + stats.sem(v1),
v2.mean() + stats.sem(v2))
lines([0.1, 0.9], [bar_ylvl, bar_ylvl], color='grey', ax=ax)
ax.text(0.5,
1.01 * bar_ylvl,
pval_str,
fontsize='medium',
fontstyle='italic',
va='bottom',
ha='center')
# Add mean values.
for vec, xpos in [(v1, 0.2), (v2, 1.2)]:
mstr = '{:.2f}'.format(vec.mean())
ypos = 1.005 * vec.mean()
ax.text(xpos,
ypos,
mstr,
fontstyle='italic',
fontsize='smaller',
va='bottom',
ha='center')
# Format plot.
sns.despine()
putil.hide_legend_title(ax)
putil.set_labels(ax, '', ylbl)
putil.sparsify_tick_labels(fig, ax, 'y', freq=2)
# Save plot.
putil.save_fig(ffig, fig)
return ax
def plot_up_down_raster(Spikes, task, rec, itrs):
"""Plot spike raster for up-down dynamics analysis."""
# Set params for plotting.
uids, trs = Spikes.index, Spikes.columns
plot_trs = trs[itrs]
ntrs = len(plot_trs)
nunits = len(uids)
tr_gap = nunits / 2
# Init figure.
putil.set_style('notebook', 'ticks')
fig = putil.figure(figsize=(10, ntrs))
ax = fig.add_subplot(111)
# Per trial, per unit.
for itr, tr in enumerate(plot_trs):
for iu, uid in enumerate(uids):
# Init y level and spike times.
i = (tr_gap + nunits) * itr + iu
spk_tr = Spikes.loc[uid, tr]
# Plot (spike time, y-level) pairs.
x = np.array(spk_tr.rescale('ms'))
y = (i+1) * np.ones_like(x)
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)
# Add stimulus lines.
for stim in constants.stim_dur.index:
t_start, t_stop = constants.fixed_tr_prds.loc[stim]
events = pd.DataFrame([(t_start, 't_start'), (t_stop, 't_stop')],
index=['start', 'stop'],
columns=['time', 'label'])
putil.plot_events(events, add_names=False, color='grey',
alpha=0.5, ls='-', lw=0.5, ax=ax)
# Add inter-trial shading.
for itr in range(ntrs+1):
ymin = itr * (tr_gap + nunits) - tr_gap + 0.5
ax.axhspan(ymin, ymin+tr_gap, alpha=.05, color='grey')
# Set tick labels.
pos = np.arange(ntrs) * (tr_gap + nunits) + nunits/2
lbls = plot_trs + 1
putil.set_ytick_labels(ax, pos, lbls)
# putil.sparsify_tick_labels(ax, 'y', freq=2, istart=1)
putil.hide_tick_marks(ax, show_x_tick_mrks=True)
# Format plot.
xlim = constants.fixed_tr_prds.loc['whole trial']
ylim = [-tr_gap/2, ntrs * (nunits+tr_gap)-tr_gap/2]
xlab = 'Time since S1 onset (ms)'
ylab = 'Trial number'
title = '{} {}'.format(rec, task)
putil.format_plot(ax, xlim, ylim, xlab, ylab, title)
putil.set_spines(ax, True, False, False, False)
# Save figure.
fname = 'UpDown_dynamics_{}_{}.pdf'.format(rec, task)
ffig = util.join(['results', 'UpDown', fname])
putil.save_fig(ffig, fig, dpi=600)