def test_plot_butterfly():
"Test plot.Butterfly"
ds = datasets.get_uts(utsnd=True)
p = plot.Butterfly('utsnd', ds=ds, show=False)
p.close()
p = plot.Butterfly('utsnd', 'A%B', ds=ds, show=False)
p.close()
def test_plot_butterfly():
"Test plot.Butterfly"
plot.configure_backend(False, False)
ds = datasets.get_rand(utsnd=True)
p = plot.Butterfly('utsnd', ds=ds)
p.close()
p = plot.Butterfly('utsnd', 'A%B', ds=ds)
p.close()
def test_time_slicer():
"Test linked time axes"
ds = datasets.get_uts(True)
p1 = plot.Butterfly(ds['utsnd'], show=False)
p2 = plot.Array('utsnd', 'A', ds=ds, show=False)
p1.link_time_axis(p2)
p1._set_time(.1, True)
eq_(p2._current_time, .1)
eq_(p2._time_fixed, True)
p2._set_time(.2)
eq_(p1._current_time, .2)
eq_(p1._time_fixed, False)
p1 = plot.TopoButterfly(ds['utsnd'], show=False)
p2 = plot.Array('utsnd', 'A', ds=ds, show=False)
p2.link_time_axis(p1)
p1._set_time(.1, True)
eq_(p2._current_time, .1)
eq_(p2._time_fixed, True)
p2._set_time(.2)
eq_(p1._current_time, .2)
eq_(p1._time_fixed, False)
def test_time_slicer():
"Test linked time axes"
ds = datasets.get_uts(True)
p1 = plot.Butterfly(ds['utsnd'])
p2 = plot.Array('utsnd', 'A', ds=ds)
p1.link_time_axis(p2)
p1._set_time(.1, True)
assert p2._current_time == .1
assert p2._time_fixed == True
p2._set_time(.2)
assert p1._current_time == .2
assert p1._time_fixed == False
p1 = plot.TopoButterfly(ds['utsnd'])
p2 = plot.Array('utsnd', 'A', ds=ds)
p2.link_time_axis(p1)
p1._set_time(.1, True)
assert p2._current_time == .1
assert p2._time_fixed == True
# merge another
p3 = plot.TopoButterfly(ds[0, 'utsnd'])
p3.link_time_axis(p2)
p2._set_time(.2)
assert p1._current_time == .2
assert p1._time_fixed == False
def test_plot_butterfly():
"Test plot.Butterfly"
ds = datasets.get_uts(utsnd=True)
p = plot.Butterfly('utsnd', ds=ds)
p.close()
p = plot.Butterfly('utsnd', 'A%B', ds=ds)
p.close()
# other y-dim
stc = datasets.get_mne_stc(True, subject='fsaverage')
p = plot.Butterfly(stc)
p.close()
# _ax_bfly_epoch (used in GUI, not part of a figure)
fig = Figure(1)
ax = _ax_bfly_epoch(fig._axes[0], ds[0, 'utsnd'])
fig.show()
ax.set_data(ds[1, 'utsnd'])
fig.draw()
def test_plot_butterfly():
"Test plot.Butterfly"
ds = datasets.get_uts(utsnd=True)
p = plot.Butterfly('utsnd', ds=ds, show=False)
p.close()
p = plot.Butterfly('utsnd', 'A%B', ds=ds, show=False)
p.close()
# other y-dim
stc = datasets.get_mne_stc(True)
p = plot.Butterfly(stc, show=False)
p.close()
# _ax_bfly_epoch
fig = Figure(1, show=False)
ax = _ax_bfly_epoch(fig._axes[0], ds[0, 'utsnd'])
fig.show()
ax.set_data(ds[1, 'utsnd'])
fig.draw()
raw_AEF.notch_filter(np.arange(60, 181, 60), fir_design='firwin')
# band pass filtering 1-8 Hz
raw_AEF.filter(1.0, 8.0, fir_design='firwin')
# resample to 100 Hz
raw_AEF.resample(100, npad="auto")
### LOAD RELEVANT VARIABLES AS eelbrain.NDVar
# load as epochs for plot only
ds = load.fiff.events(raw=raw_AEF,
proj=True,
stim_channel='UPPT001',
events=event_fname)
epochs = load.fiff.epochs(ds, tmin=-0.1, tmax=0.5, baseline=(None, 0))
plot.Butterfly(epochs)
# pick MEG channels
picks = mne.pick_types(raw_AEF.info,
meg=True,
eeg=False,
stim=False,
eog=False,
ref_meg=False,
exclude='bads')
# Read as a single chunk of data
y, t = raw_AEF.get_data(picks, return_times=True)
sensor_dim = load.fiff.sensor_dim(raw_AEF.info, picks=picks)
time = eelbrain.UTS.from_int(0, t.size - 1, raw_AEF.info['sfreq'])
meg = eelbrain.NDVar(y, dims=(sensor_dim, time))
def source_trfs(
ress: ResultCollection,
heading: FMTextArg = None,
brain_view: Union[str, Sequence[float]] = None,
axw: float = None,
surf: str = 'inflated',
cortex: Any = ((1.00,) * 3, (.4,) * 3),
vmax: float = None,
xlim: Tuple[float, float] = None,
times: Sequence[float] = None,
cmap: str = None,
labels: Dict[str, str] = None,
rasterize: bool = None,
brain_timewindow: float = 0.050
):
"Only used for TRFExperiment model-test"
layout = BrainLayout(brain_view, axw)
dt = brain_timewindow / 2
if heading is not None:
doc = fmtxt.Section(heading)
else:
doc = fmtxt.FMText()
if cmap is None:
cmap = 'lux-a'
if labels is None:
labels = {}
trf_table = fmtxt.Table('ll')
for key, res in ress.items():
trf_resampled = resample(res.masked_difference(), 1000)
label = labels.get(key, key)
if rasterize is None:
rasterize = len(trf_resampled.source) > 500
# times for anatomical plots
if times is None:
trf_tc = abs(trf_resampled).sum('source')
trf_tc_mask = (~trf_resampled.get_mask()).sum('source') >= 10
times_ = find_peak_times(trf_tc, trf_tc_mask)
else:
times_ = times
# butterfly-plot
p = plot.Butterfly(trf_resampled, h=3, w=4, ylabel=False, title=label, vmax=vmax, xlim=xlim, show=False)
for t in times_:
p.add_vline(t, color='k')
trf_table.cell(fmtxt.asfmtext(p, rasterize=rasterize))
p.close()
# peak sources
if not times_:
trf_table.cell()
sp = plot.brain.SequencePlotter()
if layout.brain_view:
sp.set_parallel_view(*layout.brain_view)
sp.set_brain_args(surf=surf, cortex=cortex)
for t in times_:
yt = trf_resampled.mean(time=(t - dt, t + dt + 0.001))
if isinstance(cmap, str):
vmax_ = vmax or max(-yt.min(), yt.max()) or 1
cmap_ = plot.soft_threshold_colormap(cmap, vmax_ / 10, vmax_)
else:
cmap_ = cmap
sp.add_ndvar(yt, cmap=cmap_, label=f'{t * 1000:.0f} ms', smoothing_steps=10)
p = sp.plot_table(view='lateral', orientation='vertical', **layout.table_args)
trf_table.cell(p)
p.close()
doc.append(fmtxt.Figure(trf_table))
return doc