def plot_LFP_example(self, rds=(95,4,1), scan_number=22, margin=2.0):
"""Plot example LFP traces showing theta power during a head scan
"""
data = SessionData.get(rds)
scan_table = get_node('/behavior', 'scans')
scan = get_unique_row(scan_table,
data.session_query + '&(number==%d)'%scan_number)
t_scan = { k: data.T_(scan[k]) for k in ScanPoints }
window = t_scan['downshift'] - margin, t_scan['upshift'] + margin
plt.ioff()
f = self.new_figure('scan_LFP_example',
'Scan LFP Example: Rat %d, Day %d, M%d @ t=%.2f'%(rds + (window[0],)))
theta_tt = find_theta_tetrode(rds[:2], condn='(EEG==True)&(area=="CA1")')[0]
ts, EEG = get_eeg_timeseries(rds, theta_tt)
t_EEG = data.T_(ts)
data_file = self.get_data_file()
session = get_unique_row(data_file.root.sessions, data.session_query)
t_theta, x_theta = Theta.timeseries(t_EEG, EEG)
ZP_theta = Z(Theta.power(x_theta, filtered=True)) #data_file.getNode('/arrays', session['ZP_theta'])
f_theta = data_file.getNode('/arrays', session['f_theta'])
traj = data.trajectory
t_traj = data.T_(traj.ts)
s_traj = time_slice(t_traj, start=window[0], end=window[1])
s_EEG = time_slice(t_EEG, start=window[0], end=window[1])
s_theta = time_slice(t_theta, start=window[0], end=window[1])
y = 0
dy = -2
norm = lambda x: (x - np.mean(x)) / (1.1 * float(np.max(np.abs((x - np.mean(x))))))
ax = f.add_subplot(111)
ax.axhline(y, c='k', ls='-', zorder=0)
ax.plot(t_traj[s_traj], y + norm(traj.radius[s_traj]), 'b-', lw=2, zorder=1); y += dy
ax.plot(t_EEG[s_EEG], y + norm(EEG[s_EEG]), 'k-', lw=1, zorder=1); y += dy
ax.plot(t_theta[s_theta], y + norm(x_theta[s_theta]), 'k-', lw=1, zorder=1); y += dy
ax.plot(t_theta[s_theta], y + norm(ZP_theta[s_theta]), 'k-', lw=1, zorder=1); y += dy
ax.plot(t_theta[s_theta], y + norm(f_theta[s_theta]), 'k-', lw=1, zorder=1); y += dy
ax.axvspan(t_scan['downshift'], t_scan['start'], lw=0, fc='g', alpha=0.3, zorder=-2)
ax.axvspan(t_scan['start'], t_scan['max'], lw=0, fc='m', alpha=0.3, zorder=-2)
ax.axvspan(t_scan['max'], t_scan['return'], lw=0, fc='y', alpha=0.3, zorder=-2)
ax.axvspan(t_scan['return'], t_scan['end'], lw=0, fc='m', alpha=0.3, zorder=-2)
ax.axvspan(t_scan['end'], t_scan['upshift'], lw=0, fc='g', alpha=0.3, zorder=-2)
ax.set_yticks([0, -2, -4, -6, -8])
ax.set_yticklabels(['r', 'EEG', 'theta', 'ZP_theta', 'f_theta'])
ax.set_ylim(-8.75, 1.25)
ax.set_xlim(*window)
ax.tick_params(top=False, right=False)
plt.ion()
plt.show()
def plot_scan_example(self, rds=(95,4,1), scan_number=22, margin=1.0):
"""Plot example scan with relevant behavior variables and scan phases
"""
data = SessionData.get(rds)
scan_table = get_node('/behavior', 'scans')
scan = get_unique_row(scan_table,
data.session_query + '&(number==%d)'%scan_number)
t_scan = { k: data.T_(scan[k]) for k in ScanPoints }
window = t_scan['downshift'] - margin, t_scan['upshift'] + margin
plt.ioff()
f = self.new_figure('scan_example',
'Scan Example: Rat %d, Day %d, M%d @ t=%.2f'%(rds + (window[0],)))
traj = data.trajectory
t = data.T_(traj.ts)
s = time_slice(t, start=window[0], end=window[1])
scan_slice = time_slice(t, start=t_scan['start'], end=t_scan['end'])
y = 0
dy = -2
norm = lambda x: x / np.max(np.abs(x))
ax = f.add_subplot(111)
ax.axhline(y, c='k', ls='-', zorder=0)
ax.plot(t[s], y + norm(traj.radius[s]), 'b-', lw=2, zorder=1); y += dy
ax.axhline(y, c='k', ls='-', zorder=0)
ax.plot(t[s], y + norm(traj.radial_velocity[s]), 'b-', lw=2, zorder=1); y += dy
ax.axhline(y, c='k', ls='-', zorder=0)
ax.plot(t[s], y + norm(traj.forward_velocity[s]), 'b-', lw=2, zorder=1); y += dy
ax.axvspan(t_scan['downshift'], t_scan['start'], lw=0, fc='g', alpha=0.3, zorder=-2)
ax.axvspan(t_scan['start'], t_scan['max'], lw=0, fc='m', alpha=0.3, zorder=-2)
ax.axvspan(t_scan['max'], t_scan['return'], lw=0, fc='y', alpha=0.3, zorder=-2)
ax.axvspan(t_scan['return'], t_scan['end'], lw=0, fc='m', alpha=0.3, zorder=-2)
ax.axvspan(t_scan['end'], t_scan['upshift'], lw=0, fc='g', alpha=0.3, zorder=-2)
for k in ScanPoints:
ax.axvline(t_scan[k], c='k', ls='-', zorder=-1)
ax.set_yticks([0, -2, -4])
ax.set_yticklabels(['r', 'rs', 'fwd'])
ax.set_ylim(-4.75, 1.25)
ax.set_xlim(*window)
ax.tick_params(top=False, right=False)
f = self.new_figure('scan_example_space', 'Scan Example', (4,4))
ax = f.add_subplot(111)
ax.plot(traj.x[s], traj.y[s], 'k-', alpha=0.6)
ax.plot(traj.x[scan_slice], traj.y[scan_slice], 'r-', lw=2)
ax.axis('equal')
ax.set_axis_off()
plot_track_underlay(ax=ax, ls='dotted')
plt.ion()
plt.show()
def collect_data(self, datasets=None):
"""Get head scan table and print out reports
Keyword arguments:
datasets -- restrict report to particular datasets specified as a list
of (rat, day) tuples
"""
pause_table = get_node('/behavior', 'pauses')
self.out('Reporting pauses found in %s...' % pause_table._v_pathname)
fmt = dict(lw=SCAN_LW, solid_capstyle='round', alpha=0.7)
for rds, lap, ax in self.get_plot(datasets=datasets,
timing_issue=True,
missing_HD=True):
ts, x, y, hd = get_tracking(*rds)
lapslice = time_slice(ts, start=lap[0], end=lap[1])
ax.plot(x[lapslice], y[lapslice], '-', c='0.4', lw=TRAJ_LW)
plot_track_underlay(ax, lw=0.5, ls='dotted')
for pause in pause_table.where(
'(rat==%d)&(day==%d)&(session==%d)' % rds):
if not (lap[0] <= pause['start'] <= lap[1]):
continue
tslice = slice(*pause['slice'])
ax.plot(x[tslice], y[tslice], 'r-', zorder=5, **fmt)
ax.axis([-50, 50, -48, 48])
# ax.axis('equal')
ax.axis('off')
class HeadScanLapReport(BaseLapReport):
"""
Print a complete by-lap report of head scan events
"""
label = 'head scan laps'
def collect_data(self, datasets=None, phasing=True):
"""Get head scan table and print out reports
Keyword arguments:
datasets -- restrict report to particular datasets specified as a list
of (rat, day) tuples
"""
kfile = get_kdata_file()
bpath = get_group_path(tree='behavior')
table_name = CfgData['scan_table']
try:
scan_table = kfile.getNode(bpath, table_name)
except NodeError, e:
raise e, 'could not find valid scan table'
self.out('Reporting scans found in %s...' % scan_table._v_pathname)
colors = dict(prefix=('g', 0),
outbound=('r', 2),
dwell=('y', 0),
inbound=('m', 1),
postfix=('g', 0))
phase_names = colors.keys()
fmt = dict(lw=SCAN_LW, solid_capstyle='round', alpha=0.7)
for rds, lap, ax in self.get_plot(datasets=datasets, missing_HD=True):
ts, x, y, hd = get_tracking(*rds)
lapslice = time_slice(ts, start=lap[0], end=lap[1])
ax.plot(x[lapslice],
y[lapslice],
'-',
c='0.4',
lw=TRAJ_LW,
zorder=-1)
plot_track_underlay(ax, lw=0.5, ls='dotted')
for scan in scan_table.where('(rat==%d)&(day==%d)&(session==%d)' %
rds):
if not (lap[0] <= scan['start'] <= lap[1]):
continue
# Plot the scan on top of trajectory
if phasing:
for phase in phase_names:
start, end = ScanPhasePoints[phase]
tslice = time_slice(ts,
start=scan[start],
end=scan[end])
col, zord = colors[phase]
ax.plot(x[tslice],
y[tslice],
c=col,
zorder=zord,
**fmt)
else:
tslice = slice(*scan['slice'])
ax.plot(x[tslice], y[tslice], 'r-', zorder=5, **fmt)
ax.axis([-50, 50, -48, 48])
# ax.axis('equal')
ax.axis('off')
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')