def collect_data(self, which='scan'):
if which == 'scan':
ripple_rat = scan_ripple_rat
noripple_rat = scan_noripple_rat
elif which == 'pause':
ripple_rat = pause_ripple_rat
noripple_rat = pause_noripple_rat
else:
raise ValueError, 'bad event type: %s' % which
for rat, ax in self.get_plot(rat_list):
cdf = np.cumsum(distro(ripple_rat[rat]))
cdf_null = np.cumsum(distro(noripple_rat[rat]))
ax.plot(centers, cdf, 'b-', centers, cdf_null, 'r-')
ax.set(xlim=(centers[0], centers[-1]), ylim=(0, 1))
ax.tick_params(top=False, right=False)
quicktitle(ax, 'rat%03d' % rat)
if self.firstonpage:
ax.set_ylabel('CDF')
else:
ax.set_yticklabels([])
if self.lastonpage:
ax.legend(['w/ ripples', 'no ripples'],
loc=2,
bbox_to_anchor=(1.1, 1))
ax.set_xlabel('Peak Z(P(Theta))')
else:
ax.set_xticklabels([])
def collect_data(self, sessions):
"""Display forward-running speed vs. tracking speed distributions for
the sessions provided as a list of rds triplets.
"""
for rds, ax in self.get_plot(sessions):
data = SessionData(rds=rds, load_clusters=False)
# Trajectory data
ts = data.trajectory.ts
omega = data.trajectory.forward_velocity
# Data initialization
lower = np.empty((data.N_laps, ), 'd')
upper = np.empty((data.N_laps, ), 'd')
median = np.empty((data.N_laps, ), 'd')
mean = np.empty((data.N_laps, ), 'd')
# Compute the distribution stats
for lap in xrange(data.N_laps):
_t, lap_speed = time_slice_sample(ts,
omega,
start=data.laps[lap],
end=data.laps[lap + 1])
lower[lap], upper[lap] = IQR(lap_speed, factor=0)
median[lap] = np.median(lap_speed)
mean[lap] = np.mean(lap_speed)
# Plot the stats
laps = np.arange(1, data.N_laps + 1)
p = Polygon(np.c_[np.r_[laps, laps[::-1]], np.r_[lower,
upper[::-1]]],
alpha=0.5,
ec='none',
fc='c',
zorder=-1)
ax.add_artist(p)
ax.plot(laps, median, 'k-', lw=2)
ax.plot(laps, mean, 'k--', lw=1)
ax.axhline(0, ls='-', c='k')
ax.set_xticks(laps)
ax.set_xlim(1, data.N_laps)
ax.set_ylim(-50, 100)
ax.set_xticklabels([])
quicktitle(ax, '%d-%02d-m%d' % rds, size='x-small')
if self.firstonpage:
ax.set_ylabel(u'Forward Speed (\u00b0CW/s)')
else:
ax.set_yticklabels([])
if self.lastonpage:
ax.set_xlabel('Laps')
self.out('All done!')
def plot_rat_distros(ax, data, label, xlim=plim):
ax.imshow(data,
interpolation='nearest',
origin='upper',
aspect='auto',
extent=[xlim[0], xlim[1], 0, N])
ax.set(yticks=(np.arange(N) + 0.5),
yticklabels=map(str, res.rat_number[::-1]))
ax.tick_params(axis='y',
right=False,
labelsize='xx-small',
direction='out',
length=3)
quicktitle(ax, '%s' % label)
def _plot_results(self, rds, tc, scan, S, P_xk, P_xk_scan, P_xk_diff, W_x):
f = plt.figure()
density_kwds = dict(cmask='c',
cmap='gray') #, norm=False, cmin=0, cmax=1)
diff_kwds = dict(cmask='w',
cmap='RdBu',
norm=False,
cmin=-0.5,
cmax=0.5)
f.suptitle('rat%03d-%02d-m%d %s scan%02d\nS = %.5f' %
(rds + (tc, scan['number'], S)))
ax = densitymap(P_xk, (0, 360), (0, self.k_edges[-1] + 1),
ax=plt.subplot(221),
**density_kwds)
quicktitle(ax, 'P_xk')
ax = densitymap(P_xk_scan, (0, 360), (0, self.k_edges[-1] + 1),
ax=plt.subplot(222),
**density_kwds)
quicktitle(ax, 'P_xk_scan')
ax = densitymap(P_xk_diff, (0, 360), (0, self.k_edges[-1] + 1),
ax=plt.subplot(223),
**diff_kwds)
quicktitle(ax, 'P_xk_diff')
ax = f.add_subplot(224)
centers = (lambda e: (e[:-1] + e[1:]) / 2)(self.H_bins[0])
ax.plot(centers, W_x.flatten(), 'b-o', mew=0, ms=6, mfc='b', lw=2)
ax.set_xlim(0, 360)
quicktitle(ax, 'W_x')
def plot_rat_spectra(ax, data, label):
ax.imshow(data,
interpolation='nearest',
origin='upper',
aspect='auto',
extent=[F[0], F[-1] + (F[-1] - F[-2]), 0, rats.size])
ax.set(yticks=(np.arange(rats.size) + 0.5),
yticklabels=map(str, rats[::-1]))
ax.set_xlim(flim)
ax.tick_params(axis='y',
right=False,
direction='out',
labelsize='xx-small',
length=3)
quicktitle(ax, '%s spectra' % label, size='x-small')
def _plot_rat_average_correlations():
ax = f.add_subplot(321)
ax.plot(lags, C_rat.T, 'k-', lw=1.5, alpha=0.5)
ax.axhline(y=1.0, **line_fmt)
ax.set(xlim=tlim, ylim=ylim, ylabel='Correlation')
ax.tick_params(top=False, right=False)
quicktitle(ax, 'per rat')
ax = f.add_subplot(322)
C_rat_star = medfilt(C_rat, kernel_size=[1, smoothing])
mu = C_rat_star.mean(axis=0)
err = C_rat_star.std(axis=0) / np.sqrt(C_rat_star.shape[0])
ax.axhline(y=1.0, **line_fmt)
ax.plot(lags, mu, 'k-', lw=2)
shaded_error(lags, mu, err, alpha=0.4, ec='none', fc='slateblue')
ax.tick_params(top=False, right=False)
ax.set(xlim=tlim, ylim=ylim)
quicktitle(ax, r'mean $\pm$ s.e.m.')
def plot_rat_distros(ax, data, label):
rats = res.rat_number
rat_distro = np.empty((rats.size, F.size), 'd')
norm = lambda P: P / P.max()
for i, rat in enumerate(rats):
rat_distro[i] = norm(data[res.rat_number == rat].mean(axis=0))
ax.imshow(rat_distro,
interpolation='nearest',
origin='upper',
aspect='auto',
extent=[F[0], F[-1] + (F[-1] - F[-2]), 0, rats.size])
ax.set(yticks=(np.arange(N) + 0.5),
yticklabels=map(str, rats[::-1]))
ax.tick_params(axis='y',
right=False,
labelsize='xx-small',
direction='out',
length=3)
quicktitle(ax, '%s p[%s]' % (label, res.distro))
def create_xcorr_figure(signal):
f = self.new_figure('%s_xcorrs' % signal,
'Scan x %s Cross-Correlations' % signal,
figsize=figsize)
for i, point in enumerate(self.results['scan_points']):
rat_averages = data_file.getNode(
xcorr_data_group,
self._get_xcorr_array_name(signal, point)).read()
N_rats, N_t = rat_averages.shape
mu = rat_averages.mean(axis=0)
sem = rat_averages.std(axis=0) / np.sqrt(N_rats)
t = np.linspace(-lag, lag, N_t)
ax = f.add_subplot(nrows, ncols, i + 1)
ax.plot(t, mu, '-', c=fc, zorder=1)
shaded_error(t, mu, sem, ax=ax, fc=fc, alpha=0.3, zorder=0)
ax.axhline(0.0, c='k', ls='-', zorder=-2)
ax.set_xlim(-lag, lag)
ax.set_ylim(ylim[signal])
ax.tick_params(top=False, right=False, left=False)
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
quicktitle(ax, point.title())
def plot(self, cmap='jet'):
assert self.I_skaggs1 is not None, 'please run compare_tables_in_files first'
f = plt.figure(figsize=(10, 10))
axlist = AxesList()
axlist.make_grid((4, 4))
f.suptitle('1) %s\n2) %s' % (self.kfilepath1, self.kfilepath2))
I = (self.I_skaggs1, self.I_skaggs2, self.I_olypher1, self.I_olypher2)
name = ('sk1', 'sk2', 'oly1', 'oly2')
I_range = range(len(I))
D = reduce(op.mul, map(np.isfinite, I))
for i, j in outer_pairs(I_range, I_range):
cmp_name = '%s - %s' % (name[j], name[i])
sys.stdout.write('Plotting %s...\n' % cmp_name)
sys.stdout.flush()
ax = axlist[j + len(I) * i]
heatmap(I[j][D], I[i][D], bins=128, ax=ax, cmap=cmap)
quicktitle(ax, cmp_name, size='x-small')
return f
def process_data(self,
disp_max_k=19.2,
max_k=25,
drange=(0, 1, 11),
rrange=(0, 15, 4),
srange=(0, 120, 3),
which='spikes'):
"""Multi-variate likelihood estimation of scan spike counts up to max_k,
for ranges specified in normalized field-traversal distance (drange),
firing rate (rrange), and size (srange). Ranges specified as inclusive
bin-edge tuples (low, high, N). Select spike counts from outbound,
inbound, or whole-scan by setting *which* to 'out_spikes', 'in_spikes',
or 'spikes'.
"""
assert which in ('out_spikes', 'in_spikes', 'spikes'), \
"invalid scan phase spikes argument"
self.out.outfd = file(os.path.join(self.datadir, 'likelihood.log'),
'w')
self.out.timestamp = False
# Load results data
data_file = self.get_data_file()
scan_data = data_file.root.field_scans
# 'field_distance'
# 'traversal_distance'
# 'spikes'
# 'field_size'
# 'traversal_size'
# 'strength'
# Compute empirical likelihood functions of scan spikes based on field
# spiking data
dbins = np.linspace(*drange)
rbins = np.r_[np.linspace(*rrange),
np.inf] # no upper bound on final bins
sbins = np.r_[np.linspace(*srange),
np.inf] # no upper bound on final bins
edges = (dbins, rbins, sbins)
N = (dbins.size - 1, rbins.size - 1, sbins.size - 1, max_k + 1)
H = np.zeros(N, 'd')
P = np.empty(N[:-1], object)
def make_query(name, low, high):
q = '(%s>=%f)' % (name, low)
if np.isfinite(high):
q += '&(%s<%f)' % (name, high)
return q
# Compute the multi-variate conditional probability distributions
for d in xrange(N[0]):
dquery = make_query('traversal_distance', dbins[d], dbins[d + 1])
self.out('%d: %s' % (d, dquery))
for r in xrange(N[1]):
rquery = make_query('strength', rbins[r], rbins[r + 1])
self.out('%d: %s' % (r, rquery))
for s in xrange(N[2]):
squery = make_query('traversal_size', sbins[s],
sbins[s + 1])
self.out('%d: %s' % (s, squery))
query = '&'.join([dquery, rquery, squery])
spikes = [rec[which] for rec in scan_data.where(query)]
k, histo = integer_hist(spikes,
int_range=(0, max_k),
open_range=True,
relative=False)
if not np.any(histo):
histo[0] = 1 # set 0-mode for unsampled bins
H[d, r, s] = histo
P[d, r, s] = SampleDistribution(histo, k)
self.out('-' * 3)
self.out('-' * 3)
self.out.outfd.close()
self.close_data_file()
# Save the likelihood data
self.out('Saving edges...')
edges_fd = file(os.path.join(self.datadir, 'edges.pickle'), 'w')
cPickle.dump(edges, edges_fd)
edges_fd.close()
self.out('Saving histograms...')
H_fd = file(os.path.join(self.datadir, 'H.pickle'), 'w')
cPickle.dump(H, H_fd)
H_fd.close()
self.out('Saving distributions...')
P_fd = file(os.path.join(self.datadir, 'P.pickle'), 'w')
cPickle.dump(P, P_fd)
P_fd.close()
# Create distance x firing-rate figure
kvals = np.arange(max_k + 1)
self.figure = {}
self.out('Bringing up distance x rate figure...')
self.figure['bias_distance_rate'] = f = plt.figure(figsize=(10, 8))
axlist = AxesList()
axlist.make_grid(N[2])
f.suptitle(
'Distribution Means for Scan-%s: Traversal Distance x Firing Rate'
% which.replace('_', '-').title())
for s, ax in enumerate(axlist):
M = np.empty((N[0], N[1]), 'd')
for d in xrange(N[0]):
for r in xrange(N[1]):
M[d, r] = (kvals * (H[d, r, s] / H[d, r, s].sum())).sum()
lrbt = [dbins[0], dbins[-1], rbins[0], 2 * rbins[-2] - rbins[-3]]
ax.imshow(M.T,
aspect='auto',
origin='lower',
vmin=(0, disp_max_k),
interpolation='nearest',
extent=lrbt)
ax.set(xlim=lrbt[:2], ylim=lrbt[2:])
if s == 0:
ax.set_ylabel('Traversal Rate')
else:
ax.set_yticklabels([])
if s == N[2] - 1:
ax.set_xlabel('Traversal Distance')
else:
ax.set_xticklabels([])
textlabel(ax, '%.1f' % M.max())
quicktitle(ax, 'size > %d' % int(sbins[s]))
# Create distance x size figure
self.out('Bringing up distance x size figure...')
self.figure['bias_distance_size'] = f = plt.figure(figsize=(10, 8))
axlist = AxesList()
axlist.make_grid(N[1])
f.suptitle(
'Distribution Means for Scan-%s: Traversal Distance x Size' %
which.replace('_', '-').title())
for r, ax in enumerate(axlist):
M = np.empty((N[0], N[2]), 'd')
for d in xrange(N[0]):
for s in xrange(N[2]):
M[d, s] = (kvals * (H[d, r, s] / H[d, r, s].sum())).sum()
lrbt = [dbins[0], dbins[-1], sbins[0], 2 * sbins[-2] - sbins[-3]]
ax.imshow(M.T,
aspect='auto',
origin='lower',
vmin=(0, disp_max_k),
interpolation='nearest',
extent=lrbt)
ax.set(xlim=lrbt[:2], ylim=lrbt[2:])
if r == 0:
ax.set_ylabel('Traversal Size')
else:
ax.set_yticklabels([])
if r == N[1] - 1:
ax.set_xlabel('Traversal Distance')
else:
ax.set_xticklabels([])
textlabel(ax, '%.1f' % M.max())
quicktitle(ax, 'rate > %.1f' % rbins[r])
# Create distance x size figure
self.out('Bringing up rate x size figure...')
self.figure['bias_rate_size'] = f = plt.figure(figsize=(10, 8))
axlist = AxesList()
axlist.make_grid(N[0])
f.suptitle(
'Distribution Means for Scan-%s: Traversal Firing-rate x Size' %
which.replace('_', '-').title())
for d, ax in enumerate(axlist):
M = np.empty((N[1], N[2]), 'd')
for r in xrange(N[1]):
for s in xrange(N[2]):
M[r, s] = (kvals * (H[d, r, s] / H[d, r, s].sum())).sum()
lrbt = [
rbins[0], 2 * rbins[-2] - rbins[-3], sbins[0],
2 * sbins[-2] - sbins[-3]
]
ax.imshow(M.T,
aspect='auto',
origin='lower',
vmin=(0, disp_max_k),
interpolation='nearest',
extent=lrbt)
ax.set(xlim=lrbt[:2], ylim=lrbt[2:])
if d == 0:
ax.set_ylabel('Traversal Size')
else:
ax.set_yticklabels([])
if d == N[0] - 1:
ax.set_xlabel('Traversal Firing-Rate')
else:
ax.set_xticklabels([])
textlabel(ax, '%.1f' % M.max())
quicktitle(ax, 'distance > %.2f' % dbins[d])
# View flattened distributions
CDF_flat = np.empty((H.shape[0] * H.shape[1] * H.shape[2], H.shape[3]),
'd')
PDF_flat = np.empty((H.shape[0] * H.shape[1] * H.shape[2], H.shape[3]),
'd')
i = 0
for d in xrange(N[0]):
for r in xrange(N[1]):
for s in xrange(N[2]):
CDF_flat[i] = np.cumsum(H[d, r, s]) / np.sum(H[d, r, s])
PDF_flat[i] = H[d, r, s] / np.max(H[d, r, s])
i += 1
self.figure['all_cdfs'] = f = plt.figure(figsize=(10, 8))
f.suptitle('Empirical CDFs for All %s Distributions' %
which.replace('_', '-').title())
ax = f.add_subplot(111)
ax.imshow(CDF_flat[:, :-1],
origin='upper',
interpolation='nearest',
aspect='auto',
vmin=(0, 1))
ax.set_xlabel('Spike Count')
ax.set_ylabel('(distance, rate, size)')
quicktitle(ax, 'Scan-Spike Bias ECDFs')
self.figure['all_pdfs'] = f = plt.figure(figsize=(10, 8))
f.suptitle('Empirical PDFs for All %s Distributions' %
which.replace('_', '-').title())
ax = f.add_subplot(111)
ax.imshow(PDF_flat,
origin='upper',
interpolation='nearest',
aspect='auto',
vmin=(0, 1))
ax.set_xlabel('Spike Count')
ax.set_ylabel('(distance, rate, size)')
quicktitle(ax, 'Scan-Spike Bias EPDFs')
self.out('All done!')
def process_data(self,
use_max=False,
zlim=(-3, 3),
numbins=64,
siglevel=0.05):
"""Compute scan-ripple event distributions
use_max -- use the max z-power theta across event instead of average
zlim -- z-power limits for distributions
numbins -- number of histogram bins use to compute distributions
"""
self.out.outfd = file(os.path.join(self.datadir, 'figure.log'), 'w')
# Load results data
data_file = self.get_data_file()
root = data_file.root
# Overall and rat-specific scan/pause distributions contingent on ripples
theta = use_max and 'theta_max' or 'theta_avg'
pause_noripple = np.array(
[rec[theta] for rec in root.pause_data.where('ripples==0')])
pause_ripple = np.array(
[rec[theta] for rec in root.pause_data.where('ripples>0')])
scan_noripple = np.array(
[rec[theta] for rec in root.scan_data.where('ripples==0')])
scan_ripple = np.array(
[rec[theta] for rec in root.scan_data.where('ripples>0')])
rat_list = np.unique(root.scan_data.cols.rat[:])
pause_noripple_rat = {}
pause_ripple_rat = {}
scan_noripple_rat = {}
scan_ripple_rat = {}
for rat in rat_list:
pause_noripple_rat[rat] = np.array([
rec[theta]
for rec in root.pause_data.where('(ripples==0)&(rat==%d)' %
rat)
])
pause_ripple_rat[rat] = np.array([
rec[theta]
for rec in root.pause_data.where('(ripples>0)&(rat==%d)' % rat)
])
scan_noripple_rat[rat] = np.array([
rec[theta]
for rec in root.scan_data.where('(ripples==0)&(rat==%d)' % rat)
])
scan_ripple_rat[rat] = np.array([
rec[theta]
for rec in root.scan_data.where('(ripples>0)&(rat==%d)' % rat)
])
# Ripple distributions
ripple_theta = root.ripple_data.cols.theta[:]
ripple_running = np.array(
[rec['theta'] for rec in root.ripple_data.where('running==True')])
ripple_pause = np.array(
[rec['theta'] for rec in root.ripple_data.where('pause==True')])
ripple_scan = np.array(
[rec['theta'] for rec in root.ripple_data.where('scan==True')])
ripple_theta_rat = {}
ripple_running_rat = {}
ripple_pause_rat = {}
ripple_scan_rat = {}
for rat in rat_list:
ripple_theta_rat[rat] = np.array([
rec['theta'] for rec in root.ripple_data.where('rat==%d' % rat)
])
ripple_running_rat[rat] = np.array([
rec['theta']
for rec in root.ripple_data.where('(running==True)&(rat==%d)' %
rat)
])
ripple_pause_rat[rat] = np.array([
rec['theta']
for rec in root.ripple_data.where('(pause==True)&(rat==%d)' %
rat)
])
ripple_scan_rat[rat] = np.array([
rec['theta']
for rec in root.ripple_data.where('(scan==True)&(rat==%d)' %
rat)
])
# Output statistics
stat_msgs = []
stat_msgs += [
'Pauses with ripples: %d / %d' %
(len(pause_ripple), root.pause_data.nrows)
]
stat_msgs += [
'Scans with ripples: %d / %d' %
(len(scan_ripple), root.scan_data.nrows)
]
stat_msgs += ['--']
stat_msgs += [
'Pause, ripple vs. no ripple: T = %.4f, p < %.5f' %
t_welch(pause_ripple, pause_noripple)
]
stat_msgs += [
'Scan, ripple vs. no ripple: T = %.4f, p < %.5f' %
t_welch(scan_ripple, scan_noripple)
]
stat_msgs += ['--']
stat_msgs += [
'Ripples during running: %d / %d' %
(len(ripple_running), root.ripple_data.nrows)
]
stat_msgs += [
'Ripples during pause: %d / %d' %
(len(ripple_pause), root.ripple_data.nrows)
]
stat_msgs += [
'Ripples during scan: %d / %d' %
(len(ripple_scan), root.ripple_data.nrows)
]
stat_msgs += ['--']
stat_msgs += [
'Ripple, overall: %0.3f +/- %0.3f; T = %.4f, p < %.5f' %
((ripple_theta.mean(), ripple_theta.std()) +
t_one_tailed(ripple_theta, 0))
]
stat_msgs += [
'Ripple, running: %0.3f +/- %0.3f; T = %.4f, p < %.5f' %
((np.mean(ripple_running), np.std(ripple_running)) +
t_one_tailed(ripple_running, 0))
]
stat_msgs += [
'Ripple, pause: %0.3f +/- %0.3f; T = %.4f, p < %.5f' %
((np.mean(ripple_pause), np.std(ripple_pause)) +
t_one_tailed(ripple_pause, 0))
]
stat_msgs += [
'Ripple, scan: %0.3f +/- %0.3f; T = %.4f, p < %.5f' %
((np.mean(ripple_scan), np.std(ripple_scan)) +
t_one_tailed(ripple_scan, 0))
]
stat_msgs += ['--']
stat_msgs += [
'Ripple, scan vs. running: T = %.4f, p < %.5f' %
t_welch(ripple_scan, ripple_running)
]
stat_msgs += [
'Ripple, scan vs. pause: T = %.4f, p < %.5f' %
t_welch(ripple_scan, ripple_pause)
]
self.out('\n'.join(stat_msgs))
# Histogram / distro
bins = np.linspace(zlim[0], zlim[1], numbins + 1)
centers = (bins[:-1] + bins[1:]) / 2
def distro(values):
H = np.histogram(values, bins=bins)[0]
return H.astype('d') / H.sum()
# Create the event|ripple figure
self.figure = {}
self.figure['ripple_effect'] = f = plt.figure(figsize=(6, 8))
f.suptitle('Event Distributions of Theta Power Conditioned on Ripples')
# Pauses
ax = f.add_subplot(211)
ax.plot(centers, distro(pause_ripple), 'b-', centers,
distro(pause_noripple), 'r-')
ax.legend(['w/ ripples', 'no ripples'])
ax.set_xlim(*zlim)
quicktitle(ax, 'Pauses')
# Scans
ax = f.add_subplot(212)
ax.plot(centers, distro(scan_ripple), 'b-', centers,
distro(scan_noripple), 'r-')
ax.legend(['w/ ripples', 'no ripples'])
ax.set_xlim(*zlim)
quicktitle(ax, 'Scans')
# Create the ripple|behavior figure
self.figure['behavior'] = f = plt.figure(figsize=(9, 4))
f.suptitle(
'Ripple Distributions of Theta Power at Peak Across Behaviors')
ax = f.add_subplot(111)
ax.plot(centers, distro(ripple_theta), 'k--', label='overall')
ax.plot(centers, distro(ripple_running), 'k-', label='running')
ax.plot(centers, distro(ripple_pause), 'r-', label='pauses')
ax.plot(centers, distro(ripple_scan), 'b-', label='scans')
ax.legend()
ax.set_xlim(*zlim)
quicktitle(ax, 'Ripples')
# Create per-rat plots of event distribution mean differences
self.figure['across_rats'] = f = plt.figure(figsize=(11, 8))
f.suptitle('Mean Theta Power Difference Across Rats')
pause_diff = np.zeros(len(rat_list), 'd')
scan_diff = np.zeros(len(rat_list), 'd')
pause_pval = np.ones(len(rat_list), 'd')
scan_pval = np.ones(len(rat_list), 'd')
for i, rat in enumerate(rat_list):
if len(pause_ripple_rat[rat]) > 1 and len(
pause_noripple_rat[rat]) > 1:
pause_diff[i] = pause_ripple_rat[rat].mean(
) - pause_noripple_rat[rat].mean()
pause_pval[i] = t_welch(pause_ripple_rat[rat],
pause_noripple_rat[rat])[1]
if len(scan_ripple_rat[rat]) > 1 and len(
scan_noripple_rat[rat]) > 1:
scan_diff[i] = scan_ripple_rat[rat].mean(
) - scan_noripple_rat[rat].mean()
scan_pval[i] = t_welch(scan_ripple_rat[rat],
scan_noripple_rat[rat])[1]
pause_sig = pause_pval <= siglevel
scan_sig = scan_pval <= siglevel
N = len(rat_list)
x = np.arange(N)
for subp, sig, diff, label in [(221, pause_sig, pause_diff, 'pause'),
(222, scan_sig, scan_diff, 'scan')]:
ax = f.add_subplot(subp)
h1 = ax.stem(x[sig],
diff[sig],
linefmt='k-',
basefmt='k-',
markerfmt='bo')
h2 = ax.stem(x[True - sig],
diff[True - sig],
linefmt='k-',
basefmt='k-',
markerfmt='ro')
h1[0].set_zorder(100)
h2[0].set_zorder(100)
ax.set(xticks=[], xlim=(-1, N))
ax.tick_params(top=False, right=False)
ax.tick_params(axis='x', direction='out', labelsize='small')
if label == "pause":
ax.set_ylabel(r'$\Delta$[ripples, no ripples]')
quicktitle(ax, '%ss' % label.title())
# Create per-rat plots of ripple distribution scan mean differences
running_diff = np.zeros(len(rat_list), 'd')
scanpause_diff = np.zeros(len(rat_list), 'd')
running_pval = np.ones(len(rat_list), 'd')
scanpause_pval = np.ones(len(rat_list), 'd')
for i, rat in enumerate(rat_list):
if len(ripple_scan_rat[rat]) > 1 and len(
ripple_running_rat[rat]) > 1:
running_diff[i] = ripple_scan_rat[rat].mean(
) - ripple_running_rat[rat].mean()
running_pval[i] = t_welch(ripple_scan_rat[rat],
ripple_running_rat[rat])[1]
if len(ripple_scan_rat[rat]) > 1 and len(
ripple_pause_rat[rat]) > 1:
scanpause_diff[i] = ripple_scan_rat[rat].mean(
) - ripple_pause_rat[rat].mean()
scanpause_pval[i] = t_welch(ripple_scan_rat[rat],
ripple_pause_rat[rat])[1]
running_sig = running_pval <= siglevel
scanpause_sig = scanpause_pval <= siglevel
for subp, sig, diff, label in [
(223, running_sig, running_diff, 'running'),
(224, scanpause_sig, scanpause_diff, 'pause')
]:
ax = f.add_subplot(subp)
h1 = ax.stem(x[sig],
diff[sig],
linefmt='k-',
basefmt='k-',
markerfmt='bo')
h2 = ax.stem(x[True - sig],
diff[True - sig],
linefmt='k-',
basefmt='k-',
markerfmt='ro')
h1[0].set_zorder(100)
h2[0].set_zorder(100)
ax.set(xticks=x[::3],
xticklabels=map(str, rat_list)[::3],
xlim=(-1, N))
ax.tick_params(top=False, right=False)
ax.tick_params(axis='x', direction='out', labelsize='small')
if label == "running":
ax.set_ylabel(r'$\Delta$[scan, non-scan]')
quicktitle(ax, '%ss' % label.title())
# Create pdf reports of individual rat distributions of ripple vs
# no-ripple peak z-power theta for both scan and pause events
class ThetaRatReport(BaseReport):
label = 'rat report'
def collect_data(self, which='scan'):
if which == 'scan':
ripple_rat = scan_ripple_rat
noripple_rat = scan_noripple_rat
elif which == 'pause':
ripple_rat = pause_ripple_rat
noripple_rat = pause_noripple_rat
else:
raise ValueError, 'bad event type: %s' % which
for rat, ax in self.get_plot(rat_list):
cdf = np.cumsum(distro(ripple_rat[rat]))
cdf_null = np.cumsum(distro(noripple_rat[rat]))
ax.plot(centers, cdf, 'b-', centers, cdf_null, 'r-')
ax.set(xlim=(centers[0], centers[-1]), ylim=(0, 1))
ax.tick_params(top=False, right=False)
quicktitle(ax, 'rat%03d' % rat)
if self.firstonpage:
ax.set_ylabel('CDF')
else:
ax.set_yticklabels([])
if self.lastonpage:
ax.legend(['w/ ripples', 'no ripples'],
loc=2,
bbox_to_anchor=(1.1, 1))
ax.set_xlabel('Peak Z(P(Theta))')
else:
ax.set_xticklabels([])
for which in 'scan', 'pause':
reportdir = os.path.join(self.datadir, 'report')
if not os.path.isdir(reportdir):
os.makedirs(reportdir)
report = ThetaRatReport(desc='%s events' % which,
datadir=reportdir)
report(which=which)
report.open()
os.rename(report.results['report'],
os.path.join(self.datadir, 'rat_%s_cdfs.pdf' % which))
os.system('rm -rf %s' % reportdir)
data_file.close()
def process_data(self,
modelim=(0.0, 0.55),
ymax=0.3,
flim=(4, 12),
fcrop=(2, 14)):
"""Display PSDs and statistics for scan/pause events
"""
self.out.outfd = file(os.path.join(self.datadir, 'figure.log'), 'w')
self.out.timestamp = False
plt.ioff()
res = Reify(self.results)
F = res.freqs
rats = res.rat_list
crop = (F >= fcrop[0]) * (F <= fcrop[1])
F_crop = F[crop]
P_scan = res.scan_psd[:, crop]
P_pause = res.pause_psd[:, crop]
P_running = res.running_psd[:, crop]
self.out('Found data for %d rats:\n%s' % (len(rats), str(rats)))
c_scan = 'r'
c_pause = 'b'
c_running = 'k'
def CI(P):
return 1.96 * P.std(axis=0) / np.sqrt(P.shape[0])
def freq_mean(P):
return F_crop, P.mean(axis=0)
def freq_mean_error(P):
return freq_mean(P) + (CI(P), )
if type(self.figure) is not dict:
self.figure = {}
self.figure['theta_power'] = f = plt.figure(num=25, figsize=(8, 10))
plt.clf()
f.suptitle('Power Spectra During Scanning and Non-Scanning Behaviors')
ax = f.add_subplot(311)
ax.plot(*freq_mean(P_scan), c=c_scan, lw=2, label='Scan', zorder=10)
shaded_error(*freq_mean_error(P_scan),
ax=ax,
fc=c_scan,
alpha=0.3,
zorder=10)
ax.plot(*freq_mean(P_pause), c=c_pause, lw=2, label='Pause', zorder=5)
shaded_error(*freq_mean_error(P_pause),
ax=ax,
fc=c_pause,
alpha=0.3,
zorder=5)
ax.plot(*freq_mean(P_running),
c=c_running,
lw=2,
label='Running',
zorder=1)
shaded_error(*freq_mean_error(P_running),
ax=ax,
fc=c_running,
alpha=0.3,
zorder=1)
ax.set_xlim(flim)
ax.set_ylim(0, ymax)
ax.tick_params(top=False, right=False, direction='out')
ax.legend(loc='upper right')
theta = (F > 5) * (F < 12)
scan_mode = np.max(res.scan_psd[:, theta], axis=1)
pause_mode = np.max(res.pause_psd[:, theta], axis=1)
running_mode = np.max(res.running_psd[:, theta], axis=1)
self.out('Scan - running mode: T = %.3f, p < %e' %
t_paired(scan_mode, running_mode))
self.out('Scan < running #: %d/%d rats' %
(np.sum(scan_mode - running_mode < 0), len(rats)))
self.out('Pause - running mode: T = %.3f, p < %e' %
t_paired(pause_mode, running_mode))
self.out('Pause < running #: %d/%d rats' %
(np.sum(pause_mode - running_mode < 0), len(rats)))
self.out('Scan - pause mode: T = %.3f, p < %e' %
t_paired(scan_mode, pause_mode))
self.out('Scan > pause #: %d/%d rats' %
(np.sum(scan_mode - pause_mode > 0), len(rats)))
ax = f.add_subplot(323)
ax.scatter(running_mode,
scan_mode,
c=c_scan,
s=24,
marker='o',
edgecolor='k',
linewidths=0.5,
zorder=5)
for i in xrange(len(rats)):
ax.plot([running_mode[i]] * 2, [scan_mode[i], pause_mode[i]],
'-',
c=c_pause,
lw=0.75,
zorder=-1)
ax.scatter(running_mode,
pause_mode,
c=c_pause,
s=18,
marker='v',
linewidths=0,
zorder=0)
ax.plot(modelim, modelim, '-', c='0.3', zorder=-2)
ax.set_xlabel(r'running $\theta$ peak')
# ax.tick_params(direction='out')
ax.axis(modelim * 2)
quicktitle(ax, r'scan/pause $\theta$ peak')
self.figure['power_spectra_rats'] = f = plt.figure(num=26,
figsize=(9, 8))
plt.clf()
f.suptitle('Power Spectra For Individual Rats (N = %d)' % len(rats))
def plot_rat_spectra(ax, data, label):
ax.imshow(data,
interpolation='nearest',
origin='upper',
aspect='auto',
extent=[F[0], F[-1] + (F[-1] - F[-2]), 0, rats.size])
ax.set(yticks=(np.arange(rats.size) + 0.5),
yticklabels=map(str, rats[::-1]))
ax.set_xlim(flim)
ax.tick_params(axis='y',
right=False,
direction='out',
labelsize='xx-small',
length=3)
quicktitle(ax, '%s spectra' % label, size='x-small')
plot_rat_spectra(f.add_subplot(221), res.scan_psd, 'scan')
plot_rat_spectra(f.add_subplot(222), res.pause_psd, 'pause')
plot_rat_spectra(f.add_subplot(223), res.running_psd, 'running')
plt.draw()
plt.show()
self.out.outfd.close()
def collect_data(self, dataset=(57, 1), lag=0.25):
"""Detect all ripples in dataset and plot EEG, ripple-band, and power
signals along with detected event boundaries
"""
ripple_table = get_node('/physiology', 'ripples')
tetrode_query = '(area=="CA1")&(EEG==True)'
dataset_query = '(rat==%d)&(day==%d)' % dataset
pyr_tetrodes = find_pyramidale_tetrodes(dataset, verbose=False)
rat, day = dataset
# Initialize accumulators
time_slices = []
EEG_slices = []
power_slices = []
events = []
timestamps = []
Ripple = RippleFilter()
# Loop through sessions, detecting and storing ripple slices
for rds in unique_sessions(ripple_table, condn=dataset_query):
data = SessionData.get(rds)
self.out('Loading data for rat%03d-%02d-m%d...' % rds)
ts = None
EEG = None
P = None
for tt in pyr_tetrodes:
X = get_eeg_timeseries(rds, tt)
if X is None:
continue
if ts is None:
ts = X[0]
if EEG is None:
EEG = X[1]
else:
EEG = np.vstack((EEG, X[1]))
if P is None:
P = Ripple.power(X[1])
else:
P = np.vstack((P, Ripple.power(X[1])))
if P.ndim == 2:
P = np.mean(P, axis=0)
ts_ripples = [(rec['start'], rec['peak'], rec['end'])
for rec in ripple_table.where(data.session_query)]
t = data.T_(ts)
for timing in ts_ripples:
start, peak, end = data.T_(timing)
chunk = time_slice(t, peak - lag, peak + lag)
time_slices.append(t[chunk] - peak)
EEG_slices.append(EEG[..., chunk])
power_slices.append(P[chunk])
events.append((start - peak, end - peak))
timestamps.append(timing[1])
self.out('Plotting EEG traces of ripple events...')
LW = 0.4
norm = lambda x: x.astype('d') / float(CLIP)
for i, ax in self.get_plot(range(len(time_slices))):
t_chunk = time_slices[i]
traces = EEG_slices[i]
if traces.ndim == 1:
ax.plot(t_chunk,
norm(traces),
'k-',
lw=1.5 * LW,
alpha=1,
zorder=0)
else:
ax.plot(t_chunk,
norm(traces).T,
'k-',
lw=LW,
alpha=0.5,
zorder=-1)
ax.plot(t_chunk,
norm(np.mean(traces, axis=0)),
'k-',
lw=LW,
alpha=1,
zorder=0)
ax.plot(t_chunk,
power_slices[i] / power_slices[i].max(),
'b-',
lw=1.5 * LW,
alpha=1,
zorder=1)
ax.axhline(0, ls='-', c='k', lw=LW, zorder=0)
ax.axvline(events[i][0], ls='-', c='k', lw=LW, zorder=2)
ax.axvline(0, ls=':', c='k', lw=LW, zorder=2, alpha=0.5)
ax.axvline(events[i][1], ls='-', c='k', lw=LW, zorder=2)
ax.set_xlim(-lag, lag)
ax.set_ylim(-1, 1)
ax.set_axis_off()
quicktitle(ax, '%d' % timestamps[i], size='xx-small')
self.out.printf('.')
self.out.printf('\n')
def process_data(self, cdf=False, sig_thresh=0.05):
"""Display LFP distributions and statistics for scan/pause events
"""
plt.ioff()
# Load results data
res = Reify(self.results)
N = len(res.rat_number)
F = res.centers
self.out('Found data for %d rats.' % N)
# Choose distribution function
if cdf:
running_df = res.running_cdf
scan_df = res.scan_cdf
pause_df = res.pause_cdf
else:
running_df = res.running_pdf
scan_df = res.scan_pdf
pause_df = res.pause_pdf
def CI(P):
return P.mean(axis=0), 1.96 * P.std(axis=0) / np.sqrt(P.shape[0])
def plot_rat_distros(ax, data, label):
rats = res.rat_number
rat_distro = np.empty((rats.size, F.size), 'd')
norm = lambda P: P / P.max()
for i, rat in enumerate(rats):
rat_distro[i] = norm(data[res.rat_number == rat].mean(axis=0))
ax.imshow(rat_distro,
interpolation='nearest',
origin='upper',
aspect='auto',
extent=[F[0], F[-1] + (F[-1] - F[-2]), 0, rats.size])
ax.set(yticks=(np.arange(N) + 0.5),
yticklabels=map(str, rats[::-1]))
ax.tick_params(axis='y',
right=False,
labelsize='xx-small',
direction='out',
length=3)
quicktitle(ax, '%s p[%s]' % (label, res.distro))
# Create the frequency distributions figure
if type(self.figure) is not dict:
self.figure = {}
self.figure['%s_pdfs' % res.distro] = f = plt.figure(figsize=(9, 8))
f.suptitle('%s Distributions Across Rats During Behaviors' %
res.distro.title())
running_label = 'Running'
ax = f.add_subplot(221)
ax.plot(F, running_df.mean(axis=0), 'k-', lw=1, label=running_label)
ax.plot(F, pause_df.mean(axis=0), 'r-', lw=1, label='Pause')
ax.plot(F, scan_df.mean(axis=0), 'b-', lw=1, label='Scan')
shaded_error(*((F, ) + CI(running_df)), ax=ax, fc='k', alpha=0.4)
shaded_error(*((F, ) + CI(pause_df)), ax=ax, fc='r', alpha=0.4)
shaded_error(*((F, ) + CI(scan_df)), ax=ax, fc='b', alpha=0.4)
ax.legend(loc=4)
quicktitle(ax, 'all sessions')
plot_rat_distros(f.add_subplot(222), scan_df, 'scan')
plot_rat_distros(f.add_subplot(223), pause_df, 'pause')
plot_rat_distros(f.add_subplot(224), running_df, 'running')
# Create the statistical summary figure
self.figure['%s_rats' % res.distro] = f = plt.figure(figsize=(10, 8))
f.suptitle('Statistical Summary of %s %s Differences' %
(res.band.title(), res.distro.title()))
all_labels = ['scan-running', 'pause-running', 'scan-pause']
all_df = [(res.scan_mu, res.running_mu),
(res.pause_mu, res.running_mu), (res.scan_mu, res.pause_mu)]
ax = f.add_subplot(221)
for i, d in enumerate(zip(all_labels, all_df)):
label, mu = d
delta = mu[0] - mu[1]
t, p = t_paired(mu[0], mu[1], 0)
c = (p <= sig_thresh) and 'b' or 'r'
ax.errorbar([i], [delta.mean()],
yerr=CI(delta)[1],
fmt=c + 's',
ecolor='k',
ms=6,
mew=0)
self.out('Rat means, %s differences: %s = %.4f, p < %.6f' %
(res.distro, label, delta.mean(), p))
ax.axhline(0, c='k', ls='-')
ax.set_xlim(-0.5, 2.5)
ax.set(ylabel=r'$\Delta$f', xticks=[0, 1, 2], xticklabels=all_labels)
ax.tick_params(axis='x', labelsize='x-small')
quicktitle(ax, 'across rats')
for i, d in enumerate(zip(all_labels, all_df)):
label, mu = d
df = mu[0] - mu[1]
ax = f.add_subplot(222 + i)
sig_ix = res.scan_p <= sig_thresh
rats = np.arange(N)
if np.any(sig_ix):
h = ax.stem(rats[sig_ix],
df[sig_ix],
linefmt='k-',
basefmt='k-',
markerfmt='bo')
h[0].set_zorder(100)
if not np.all(sig_ix):
h = ax.stem(rats[True - sig_ix],
df[True - sig_ix],
linefmt='k-',
basefmt='k-',
markerfmt='ro')
h[0].set_zorder(100)
ax.set(xticks=rats[::3],
xticklabels=map(str, res.rat_number)[::3],
xlim=(-1, N))
ax.tick_params(top=False, right=False)
ax.tick_params(axis='x', direction='out', labelsize='small')
if i == 1:
ax.set_ylabel(r'$\Delta$f')
quicktitle(ax, label)
plt.draw()
plt.show()
def run_signal_xcorrs(self, event='scans', ripple_nonscan=False,
signal='ZP_theta', lag=3, fc='b', ylim=None):
"""Cross-correlations of behaviors with continuous signals
"""
data_file = self.get_data_file()
session_table = data_file.root.sessions
if event == 'scans':
event_points = ScanPoints
elif event == 'pauses':
event_points = ('start', 'end')
elif event == 'ripples':
event_points = ('start', 'peak', 'end')
else:
raise ValueError, 'unknown event type "%s"'%event
rat_averages = {}
rat_slices = { k: {} for k in event_points }
lag_samples = 2 * lag * Theta.fs
for session in session_table.iterrows():
self.out.printf('.')
t_theta = data_file.getNode('/arrays', session['t_theta'])[:]
x_theta = data_file.getNode('/arrays', session[signal])[:]
events = data_file.getNode('/arrays', session[event])[:]
rat = session['rat']
# If analyzing ripples, need scans to restrict to scan-ripples
if event == 'ripples':
scans = data_file.getNode('/arrays', session['scans'])[:]
for i, phase in enumerate(event_points):
if events.size == 0:
continue
t_event_list = events[:,i]
# Restrict events list if analyzing ripples to just scan-ripples
if event == 'ripples':
if ripple_nonscan:
t_event_list = t_event_list[
exclude_from(t_event_list, scans[:,ScanPhases['related']])]
else:
t_event_list = t_event_list[
select_from(t_event_list, scans[:,ScanPhases['related']])]
for t_scan in t_event_list:
scan_ix = np.argmin(np.abs(t_theta - t_scan))
win_ix = scan_ix - lag_samples, scan_ix + lag_samples
if win_ix[0] < 0 or win_ix[1] > x_theta.size:
continue
signal_slice = x_theta[win_ix[0]:win_ix[1]]
if rat in rat_slices[phase]:
if signal_slice.size != rat_slices[phase][rat].shape[1]:
self.out('Bad signal size: rat%d @ t = %.2f'%(rat, t_scan))
continue
rat_slices[phase][rat] = np.vstack(
(rat_slices[phase][rat], signal_slice))
else:
rat_slices[phase][rat] = signal_slice[np.newaxis]
self.out.printf('\n')
self.out('Averaging rat signal slices...')
for phase in event_points:
N_rats = len(rat_slices[phase].keys())
averages = []
for rat in rat_slices[phase].keys():
averages.append(rat_slices[phase][rat].mean(axis=0))
rat_averages[phase] = np.array(averages)
plt.ioff()
f = self.new_figure('%s_%s_xcorrs'%(signal, event[:-1]),
'%s x %s Phase Cross-Correlations'%(signal, event[:-1].title()),
size=(16,10))
nrows, ncols = 2, 3
for i, phase in enumerate(event_points):
self.out('Plotting xcorrs for "%s" phase'%phase)
N_rats, N_t = rat_averages[phase].shape
mu = rat_averages[phase].mean(axis=0) - 0.2
sem = rat_averages[phase].std(axis=0) / np.sqrt(N_rats)
t = np.linspace(-lag, lag, N_t)
ax = f.add_subplot(nrows, ncols, i+1)
ax.plot(t, mu, '-', c=fc, zorder=1)
shaded_error(t, mu, sem, ax=ax, fc=fc, alpha=0.3, zorder=0)
ax.axhline(0.0, c='k', ls='-', zorder=-2)
ax.set_xlim(-lag, lag)
if ylim is not None:
ax.set_ylim(ylim)
elif signal == 'ZP_theta':
ax.set_ylim(-0.4, 0.4)
elif signal == 'f_theta':
ax.set_ylim(7.2, 7.7)
ax.tick_params(top=False, right=False, left=False)
quicktitle(ax, phase.title())
plt.ion()
plt.show()
def process_data(self, bins=16):
self.out.outfd = file(
os.path.join(self.datadir, 'stats_%d.log' % bins), 'w')
self.out.timestamp = False
data_file = self.get_data_file()
results = data_file.root.theta_velocity
rat_list = unique_rats(results)
velocity_moments = self.results['velocity_moments']
def find_velocity_bins():
vbins = {}
for moment in velocity_moments:
data = results.col(moment)
lo, hi = CI(data, alpha=0.02)
self.out('%s: range %f to %f' % (moment, lo, hi))
vbins[moment] = np.linspace(lo, hi, bins + 1)
return vbins
velocity_bins = find_velocity_bins()
def generate_velocity_modulation_curves():
self.out('Generating velocity modulation curves...')
P_v = np.empty((len(velocity_moments), len(rat_list), bins), 'd')
f_v = np.empty_like(P_v)
power = results.col('power')
frequency = results.col('frequency')
for i, moment in enumerate(velocity_moments):
self.out('Computing %s...' % moment)
for j, rat in enumerate(rat_list):
self.out.printf('[%d] ' % rat, color='lightgreen')
for k in xrange(bins):
v_lo, v_hi = velocity_bins[moment][k:k + 2]
query = '(rat==%d)&(%s>=%f)&(%s<%f)' % (
rat, moment, v_lo, moment, v_hi)
ix = results.getWhereList(query)
power_sample = power[ix]
frequency_sample = frequency[ix]
P_v[i, j, k] = np.median(power_sample)
f_v[i, j, k] = np.median(frequency_sample)
self.out.printf('\n')
return P_v, f_v
P_v_fn = os.path.join(self.datadir, 'P_v_%d.npy' % bins)
f_v_fn = os.path.join(self.datadir, 'f_v_%d.npy' % bins)
if os.path.exists(P_v_fn) and os.path.exists(f_v_fn):
self.out('Loading previous velocity modulation data...')
P_v, f_v = map(np.load, (P_v_fn, f_v_fn))
else:
P_v, f_v = generate_velocity_modulation_curves()
np.save(P_v_fn, P_v)
np.save(f_v_fn, f_v)
plt.ioff()
if type(self.figure) is not dict:
self.figure = {}
self.figure['velocity_modulation_curves'] = f = plt.figure(
num=20, figsize=(9, 10))
plt.clf()
f.suptitle('Theta (Z-)Power, Frequency - Velocity Modulation')
N_moments = len(velocity_moments)
line_fmt = dict(ls='-', c='k', lw=2)
shade_fmt = dict(ec='none', alpha=0.3, fill=True, fc='k', zorder=-1)
def compute_error(M):
return 1.96 * M.std(axis=0) / np.sqrt(M.shape[0])
def get_errlim(mu, err, factor=0.1):
errmin, errmax = (mu - sem).min(), (mu + sem).max()
derr = errmax - errmin
return errmin - (factor / 2) * derr, errmax + (factor / 2) * derr
labels = ('Z-Power', 'Frequency')
for i, moment in enumerate(velocity_moments[:2]):
centers = (lambda b: (b[1:] + b[:-1]) / 2)(velocity_bins[moment])
for j, X_v in enumerate([P_v, f_v]):
ax = f.add_subplot(N_moments, 2, i * 2 + j + 1)
mu = X_v[i].mean(axis=0)
sem = compute_error(X_v[i])
ax.plot(centers, mu, **line_fmt)
shaded_error(centers, mu, sem, ax=ax, **shade_fmt)
self.out('%s: %s %s' %
(moment.title(), labels[j], friedman_str(X_v[i])))
if i == 0:
quicktitle(ax, labels[j], size='x-small')
ax.set_xlabel(snake2title(moment))
ax.set_xlim(centers[0], centers[-1])
ax.set_ylim(get_errlim(mu, sem))
plt.ion()
plt.show()
self.close_data_file()
self.out.outfd.close()
def run_ripple_xcorrs(self, lag=4, numbins=71, ripple_lock='peak'):
"""Compute scan-ripple cross-correlograms
"""
# Load results data
data_file = self.get_data_file()
sessions = data_file.root.sessions
scan_table = get_node('/behavior', 'scans')
# Correlogram bins
edges = np.linspace(-lag, lag, numbins+1)
centers = (edges[:-1] + edges[1:]) / 2
# Overall scanning xcorrs
self.out("Computing ripple-scan cross-correlations...")
r_ix = dict(start=0, peak=1, end=2)[ripple_lock]
C = { k: np.zeros(numbins, 'd') for k in ScanPoints }
C['pstart'] = np.zeros(numbins, 'd')
C['pend'] = np.zeros(numbins, 'd')
for session in sessions.iterrows():
scans = data_file.getNode('/arrays', session['scans'])
pauses = data_file.getNode('/arrays', session['pauses'])
ripples = data_file.getNode('/arrays', session['ripples'])
if (len(scans) and len(ripples)):
for i, pt in enumerate(ScanPoints):
C[pt] += xcorr(scans[:,i], ripples[:,r_ix], maxlag=lag, bins=numbins)[0]
if (len(pauses) and len(ripples)):
C['pstart'] += xcorr(pauses[:,0], ripples[:,1], maxlag=lag, bins=numbins)[0]
C['pend'] += xcorr(pauses[:,1], ripples[:,1], maxlag=lag, bins=numbins)[0]
f = self.new_figure('xcorrs', 'Scan-Ripple Correlations', (11,12))
ax = f.add_subplot(321)
ax.plot(centers, C['downshift'], drawstyle='steps-mid', label='down')
ax.plot(centers, C['upshift'], drawstyle='steps-mid', label='up')
ax.legend(loc=0)
ax.set(xlim=(-lag, lag), xticks=[-lag, -lag/2., 0, lag/2., lag],
yticks=[])
ax.set_ylim(bottom=0)
ax.tick_params(top=False)
quicktitle(ax, 'Gear Shifting x Ripples')
ax = f.add_subplot(322)
ax.plot(centers, C['start'], drawstyle='steps-mid', label='start')
ax.plot(centers, C['end'], drawstyle='steps-mid', label='end')
ax.legend(loc=0)
ax.set(xlim=(-lag, lag), xticks=[-lag, -lag/2., 0, lag/2., lag],
yticks=[])
ax.set_ylim(bottom=0)
ax.tick_params(top=False)
quicktitle(ax, 'Scans x Ripples')
ax = f.add_subplot(323)
ax.plot(centers, C['max'], drawstyle='steps-mid', label='max')
ax.plot(centers, C['return'], drawstyle='steps-mid', label='return')
ax.legend(loc=0)
ax.set(xlim=(-lag, lag), xticks=[-lag, -lag/2., 0, lag/2., lag],
yticks=[])
ax.set_ylim(bottom=0)
ax.tick_params(top=False)
quicktitle(ax, 'Dwells x Ripples')
ax = f.add_subplot(324)
ax.plot(centers, C['pstart'], drawstyle='steps-mid', label='pause start')
ax.plot(centers, C['pend'], drawstyle='steps-mid', label='pause end')
ax.legend(loc=0)
ax.set(xlim=(-lag, lag), xticks=[-lag, -lag/2., 0, lag/2., lag],
yticks=[])
ax.set_ylim(bottom=0)
ax.tick_params(top=False)
quicktitle(ax, 'Pauses x Ripples')
# Ripple-event fractions across event and experiment types
self.out("Computing ripple-event fractions...")
event_types = ('gearshifts', 'scans', 'pauses')
expt_types = ('DR', 'NOV')
frac = np.empty((len(event_types)*len(expt_types),), 'd')
frac_rat_mu = np.empty_like(frac)
frac_rat_sem = np.empty_like(frac)
labels = []
i = 0
for expt in expt_types:
for event in event_types:
hits = N = 0
hits_rat = {}
N_rat = {}
for session in sessions.where('type=="%s"'%expt):
if event in ('gearshifts', 'scans'):
events = data_file.getNode('/arrays', session['scans'])
if event == 'scans':
events = events[:,ScanPhases['scan']] # start -> end
elif event == 'gearshifts':
events = events[:,ScanPhases['related']] # down -> upshift
else:
events = data_file.getNode('/arrays', session[event])
ripples = data_file.getNode('/arrays', session['ripples'])
N_events = events.shape[0]
if N_events == 0:
continue
# Count hits based on ripple peaks and 1) pauses: start->end,
# and 2) ripples: down->upshift and start->end
H = 0
for v in events:
H += int(np.any(np.logical_and(
ripples[:,r_ix] >= v[0], ripples[:,r_ix] < v[-1])))
N += N_events
hits += H
if session['rat'] in N_rat:
hits_rat[session['rat']] += H
N_rat[session['rat']] += N_events
else:
hits_rat[session['rat']] = H
N_rat[session['rat']] = N_events
labels.append('%s %s'%(expt, event))
frac[i] = hits / float(N)
frac_rat = [hits_rat[rat] / float(N_rat[rat])
for rat in N_rat]
frac_rat_mu[i] = np.mean(frac_rat)
frac_rat_sem[i] = np.std(frac_rat) / np.sqrt(len(N_rat))
i += 1
ax = f.add_subplot(325)
x = np.array([0, 0.5, 1.0, 2, 2.5, 3.0])
fmt = dict(mfc='w', mec='k', mew=1, ms=6)
ax.plot(x + 0.075, frac, 'o', label='overall', **fmt)
ax.errorbar(x - 0.075, frac_rat_mu, yerr=frac_rat_sem, fmt='s',
ecolor='k', elinewidth=1.5, capsize=5, label='across rats', **fmt)
ax.set_xticks(x)
ax.set_xticklabels(labels, size='small', rotation=45)
ax.set_xlim(-0.5, 3.5)
ax.set_ylim(bottom=0)
ax.set_xlabel('Events')
ax.set_ylabel('Fraction')
ax.tick_params(top=False, right=False)
quicktitle(ax, 'Fraction of Events with Ripples', size='small')
# Fraction of behavior events across session type containing ripples
self.out("Computing ripple-phase distributions...")
phase_partition = ('downshift', 'out', 'dwell', 'inb', 'upshift')
counts = np.zeros(len(phase_partition))
counts_rat = {}
for i, phase in enumerate(phase_partition):
for session in sessions.iterrows():
ripples = data_file.getNode('/arrays', session['ripples'])
scans = data_file.getNode('/arrays', session['scans'])
if not (len(ripples) and len(scans)):
continue
phase_events = scans[:,ScanPhases[phase]]
hits = np.sum(select_from(ripples[:,r_ix], phase_events))
counts[i] += hits
rat = session['rat']
if rat not in counts_rat:
counts_rat[rat] = np.zeros(len(phase_partition))
counts_rat[rat][i] += hits
N_rats = len(counts_rat)
p_phase = np.empty((N_rats, len(phase_partition)), 'd')
for i, rat in enumerate(counts_rat.keys()):
p_phase[i] = counts_rat[rat] / counts_rat[rat].sum()
p_phase_mu = p_phase.mean(axis=0)
p_phase_sem = p_phase.std(axis=0) / np.sqrt(N_rats)
ax = f.add_subplot(326)
x = np.arange(len(phase_partition))
ax.plot(x + 0.1, counts / counts.sum(), 'o', label='overall', **fmt)
ax.errorbar(x - 0.1, p_phase_mu, yerr=p_phase_sem, fmt='s',
ecolor='k', elinewidth=1.5, capsize=5, label='across rats', **fmt)
ax.set_xticks(x)
ax.set_xticklabels(('Downshift', 'Outbound', 'Dwell', 'Inbound', 'Upshift'),
size='small', rotation=45)
ax.set_xlim(-0.5, len(phase_partition) - 0.5)
ax.set_ylim(bottom=0)
ax.set_xlabel('Head-Scan Phase')
ax.set_ylabel('p[Phase]')
ax.tick_params(top=False, right=False)
ax.legend(loc=0)
quicktitle(ax, 'P[phase|ripple]', size='small')
plt.ion()
plt.show()
def process_data(self):
"""Display frequency distributions and statistics for scan/pause events
"""
plt.ioff()
# Load results data
res = Reify(self.results)
N = len(res.rat_number)
F = res.phase_bins
plim = F[0], F[-1] + (F[-1] - F[-2])
def CI(P):
return P.mean(axis=0), 1.96 * P.std(axis=0) / np.sqrt(P.shape[0])
def plot_rat_distros(ax, data, label, xlim=plim):
ax.imshow(data,
interpolation='nearest',
origin='upper',
aspect='auto',
extent=[xlim[0], xlim[1], 0, N])
ax.set(yticks=(np.arange(N) + 0.5),
yticklabels=map(str, res.rat_number[::-1]))
ax.tick_params(axis='y',
right=False,
labelsize='xx-small',
direction='out',
length=3)
quicktitle(ax, '%s' % label)
# Create the summary comparison figure
self.figure = {}
band_label = '%s-%s' % (res.phase_band.title(), res.amp_band.title())
self.figure['phase_modulations'] = f = plt.figure(figsize=(11, 8.5))
f.suptitle('%s Modulation During Running and Non-running Behaviors' %
band_label)
running_label = 'RUN'
ax = f.add_subplot(221)
ax.plot(F,
res.running_distro.mean(axis=0),
'k-',
lw=1,
label=running_label)
ax.plot(F, res.pause_distro.mean(axis=0), 'r-', lw=1, label='PAU')
ax.plot(F, res.scan_distro.mean(axis=0), 'b-', lw=1, label='SCN')
shaded_error(*((F, ) + CI(res.running_distro)),
ax=ax,
fc='k',
alpha=0.4)
shaded_error(*((F, ) + CI(res.pause_distro)), ax=ax, fc='r', alpha=0.4)
shaded_error(*((F, ) + CI(res.scan_distro)), ax=ax, fc='b', alpha=0.4)
ax.legend(loc=4)
ax.set_xlim(plim)
quicktitle(ax, 'all sessions')
plot_rat_distros(f.add_subplot(222), res.scan_distro,
'scan phase distro')
plot_rat_distros(f.add_subplot(223), res.pause_distro,
'pause phase distro')
plot_rat_distros(f.add_subplot(224), res.running_distro,
'running phase distro')
# Create the modulation index bar-plot figure
self.figure['modulation_index'] = f = plt.figure(figsize=(11, 8.5))
f.suptitle('%s Modulation Index across Behaviors' % band_label)
index_data = np.c_[res.running_index, res.scan_index, res.pause_index]
index_labels = ['RUN', 'SCN', 'PAU']
width = 0.8
lefts = np.array([0, 1, 2]) - width / 2
bar_fmt = dict(ecolor='k',
capsize=0,
color=['k', 'b', 'r'],
bottom=0,
width=width)
ax = f.add_subplot(221)
ax.bar(lefts,
index_data.mean(axis=0),
yerr=CI(index_data)[1],
**bar_fmt)
ax.set(xticks=[0, 1, 2], xticklabels=index_labels, xlim=(-0.5, 2.5))
ax.tick_params(labelsize='x-small', top=False)
quicktitle(ax, 'rats')
ax = f.add_subplot(222)
ax.plot([0, 1, 2], index_data.T, 'k-', lw=0.75, alpha=0.6)
ax.set(xticks=[0, 1, 2], xticklabels=index_labels, xlim=(-0.5, 2.5))
ax.tick_params(labelsize='x-small', top=False)
quicktitle(
ax, 'rats [SCN>RUN: %d/%d]' %
((index_data[:, 1] > index_data[:, 0]).sum(), index_data.shape[0]))
# Create the rat cross-correlations figure
self.figure['correlations'] = f = plt.figure(figsize=(11, 8.5))
f.suptitle('Behavioral Cross-correlations of %s Modulation' %
band_label)
clim = (-plim[1] / 2, plim[1] / 2)
running_acorr = np.empty_like(res.running_distro)
scan_xcorrs = np.empty_like(res.running_distro)
pause_xcorrs = np.empty_like(res.running_distro)
scan_pause_xcorrs = np.empty_like(res.running_distro)
for i in xrange(N):
running_acorr[i] = np.correlate(res.running_distro[i],
res.running_distro[i],
mode='same')
scan_xcorrs[i] = np.correlate(res.running_distro[i],
res.scan_distro[i],
mode='same')
pause_xcorrs[i] = np.correlate(res.running_distro[i],
res.pause_distro[i],
mode='same')
scan_pause_xcorrs[i] = np.correlate(res.pause_distro[i],
res.scan_distro[i],
mode='same')
plot_rat_distros(f.add_subplot(221),
scan_xcorrs,
'C[running]',
xlim=clim)
plot_rat_distros(f.add_subplot(222),
scan_xcorrs,
'C[scan, running]',
xlim=clim)
plot_rat_distros(f.add_subplot(223),
pause_xcorrs,
'C[pause, running]',
xlim=clim)
plot_rat_distros(f.add_subplot(224),
scan_pause_xcorrs,
'C[scan, pause]',
xlim=clim)
plt.draw()
plt.show()