def test_fmtext():
"Tet FMText base class"
t = fmtxt.FMText('test')
print(t)
tuc = fmtxt.FMText(u'FMText with unicode: \x80abc')
print(str(tuc))
ts = fmtxt.FMText((t, tuc, u'unicode: \x80abc'))
print(str(ts))
print(html(ts))
print(tex(ts))
def sensor_results(
ress: ResultCollection,
heading: FMTextArg = None,
axw: float = None,
vmax: float = None,
cmap: str = None,
):
"Only used for TRFExperiment model-test"
if heading is not None:
doc = fmtxt.Section(heading)
else:
doc = fmtxt.FMText()
# table
doc.append(fmtxt.Figure(ress.table(title='Model test')))
# plots tests
topographies = [res.masked_difference() for res in ress.values()]
if vmax is None:
vlims = plot._base.find_fig_vlims([topographies], vmax)
_, vmax = vlims.pop(None)
panels = []
for (x, res), y in zip(ress.items(), topographies):
title = [x, fmtxt.Stars.from_p(res.p.min())]
p = plot.Topomap(y, cmap=cmap, vmax=vmax, title=title, axw=axw, show=False, clip='circle')
panels.append(p.image())
p.close()
doc.append(fmtxt.Figure(fmtxt.FloatingLayout(panels)))
return doc
def source_results(
ress: ResultCollection,
ress_hemi: ResultCollection = None,
heading: FMTextArg = None,
brain_view: Union[str, Sequence[float]] = None,
axw: float = None,
surf: str = 'inflated',
cortex: Any = ((1.00,) * 3, (.4,) * 3),
sig: bool = True,
vmax: float = None,
cmap: str = None,
alpha: float = 1.,
):
"Only used for TRFExperiment model-test"
layout = BrainLayout(brain_view, axw)
if heading is not None:
doc = fmtxt.Section(heading)
else:
doc = fmtxt.FMText()
tables = [ress.table(title='Model test')]
if ress_hemi is not None:
tables.append(ress_hemi.table(title="Lateralization"))
doc.append(fmtxt.Figure(fmtxt.FloatingLayout(tables)))
if sig and all(res.p.min() > 0.05 for res in ress.values()):
return doc
# plots tests
panels = []
all_ress = (ress,) if ress_hemi is None else (ress, ress_hemi)
for ress_i in all_ress:
sp = plot.brain.SequencePlotter()
if layout.brain_view:
sp.set_parallel_view(*layout.brain_view)
sp.set_brain_args(surf=surf, cortex=cortex)
for x, res in ress_i.items():
y = res.masked_difference() if sig else res.difference
sp.add_ndvar(y, label=x, cmap=cmap, vmax=vmax, alpha=alpha)
panel = sp.plot_table(view='lateral', orientation='vertical', **layout.table_args)
panels.append(panel)
doc.append(fmtxt.Figure(panels))
for panel in panels:
panel.close()
return doc
def table(self, title=None, caption=None):
"""Table with effects and smallest p-value"""
if self.test_type is LMGroup:
cols = sorted(
{col
for res in self.values() for col in res.column_names})
table = fmtxt.Table('l' * (1 + len(cols)),
title=title,
caption=caption)
table.cell('')
table.cells(*cols)
table.midrule()
for key, lmg in self.items():
table.cell(key)
for res in (lmg.tests[c] for c in cols):
pmin = res.p.min()
table.cell(fmtxt.FMText([fmtxt.p(pmin), star(pmin)]))
elif self.test_type is anova:
table = fmtxt.Table('lllll', title=title, caption=caption)
table.cells('Test', 'Effect',
fmtxt.symbol(self.test_type._statistic, 'max'),
fmtxt.symbol('p'), 'sig')
table.midrule()
for key, res in self.items():
for i, effect in enumerate(res.effects):
table.cells(key, effect)
pmin = res.p[i].min()
table.cell(fmtxt.stat(res._max_statistic(i)))
table.cell(fmtxt.p(pmin))
table.cell(star(pmin))
key = ''
else:
table = fmtxt.Table('llll', title=title, caption=caption)
table.cells('Effect',
fmtxt.symbol(self.test_type._statistic, 'max'),
fmtxt.symbol('p'), 'sig')
table.midrule()
for key, res in self.items():
table.cell(key)
pmin = res.p.min()
table.cell(fmtxt.stat(res._max_statistic()))
table.cell(fmtxt.p(pmin))
table.cell(star(pmin))
return table
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
