def vsource_tfce_result(res, title, desc):
"""Section for volume-source space TFCE result object"""
content = [desc]
if isinstance(res, (MultiEffectNDTest, LMGroup)):
if isinstance(res, LMGroup):
effects = res.column_names
stat_maps = [res.tests[e].masked_parameter_map() for e in effects]
max_stats = [res.tests[e]._max_statistic() for e in effects]
ps = [test.p.min() for test in res.tests]
else:
effects = res.effects
stat_maps = [res.masked_parameter_map(e) for e in effects]
max_stats = [res._max_statistic(e) for e in effects]
ps = [x.min() for x in res.p]
for effect, stat_map, p, max_statistic in zip(effects, stat_maps, ps, max_stats):
fig = vsource_tfce_map(effect.capitalize(), stat_map, p, res._statistic, max_statistic)
content.append(fig)
p = min(ps)
else:
p = res.p.min()
fig = vsource_tfce_map("Increase of z-transformed correlation", res.masked_difference(), p, res._statistic, res._max_statistic())
content.append(fig)
title = FMText([title, ' (', fmtxt.peq(p), fmtxt.Stars.from_p(p, tag=None), ')'])
section = Section(title, content)
return section
def source_tfce_result(res, surfer_kwargs, title, desc, brain=None):
"""Section for TFCE result object"""
content = [desc]
if isinstance(res, (MultiEffectNDTest, LMGroup)):
if isinstance(res, LMGroup):
effects = res.column_names
p_maps = [res.tests[e].p for e in effects]
stat_maps = [res.tests[e]._statistic_map for e in effects]
max_stats = [res.tests[e]._max_statistic() for e in effects]
else:
effects = res.effects
p_maps = res.p
stat_maps = res.f
max_stats = [f.max() for f in res.f]
p = 1.
for effect, stat_map, p_map, max_statistic in zip(effects, stat_maps, p_maps, max_stats):
p = min(p, p_map.min())
fig, brain = source_tfce_pmap(effect.capitalize(), p_map, stat_map, max_statistic, surfer_kwargs, brain)
content.append(fig)
else:
p = res.p.min()
fig, brain = source_tfce_pmap("Increase of z-transformed correlation", res.p, res._statistic_map, res._max_statistic(), surfer_kwargs, brain, res.difference)
content.append(fig)
title = FMText([title, ' (', fmtxt.peq(p), fmtxt.Stars.from_p(p, tag=None), ')'])
section = Section(title, content)
return section, brain
def test_eq():
"Test equation factory"
s = fmtxt.eq('t', 0.1234)
eq_(str(s), "t = 0.12")
eq_(html(s), "t = 0.12")
eq_(tex(s), "$t = 0.12$")
s = fmtxt.eq('t', 0.1234, 18)
eq_(str(s), "t(18) = 0.12")
eq_(html(s), "t<sub>18</sub> = 0.12")
eq_(tex(s), "$t_{18} = 0.12$")
s = fmtxt.peq(0.1299)
eq_(str(s), "p = .130")
eq_(html(s), "p = .130")
eq_(tex(s), "$p = .130$")
s = fmtxt.peq(0.0009)
eq_(str(s), "p < .001")
eq_(html(s), "p < .001")
eq_(tex(s), "$p < .001$")
def test_eq():
"Test equation factory"
s = fmtxt.eq('t', 0.1234)
assert str(s) == "t = 0.12"
assert html(s) == "t = 0.12"
assert tex(s) == "$t = 0.12$"
s = fmtxt.eq('t', 0.1234, 18)
assert str(s) == "t(18) = 0.12"
assert html(s) == "t<sub>18</sub> = 0.12"
assert tex(s) == "$t_{18} = 0.12$"
s = fmtxt.peq(0.1299)
assert str(s) == "p = .130"
assert html(s) == "p = .130"
assert tex(s) == "$p = .130$"
s = fmtxt.peq(0.0009)
assert str(s) == "p < .001"
assert html(s) == "p < .001"
assert tex(s) == "$p < .001$"
def test_eq():
"Test equation factory"
s = fmtxt.eq('t', 0.1234)
eq_(str(s), "t = 0.12")
eq_(html(s), "t = 0.12")
eq_(tex(s), "$t = 0.12$")
s = fmtxt.eq('t', 0.1234, 18)
eq_(str(s), "t(18) = 0.12")
eq_(html(s), "t<sub>18</sub> = 0.12")
eq_(tex(s), "$t_{18} = 0.12$")
s = fmtxt.peq(0.1299)
eq_(str(s), "p = .130")
eq_(html(s), "p = .130")
eq_(tex(s), "$p = .130$")
s = fmtxt.peq(0.0009)
eq_(str(s), "p < .001")
eq_(html(s), "p < .001")
eq_(tex(s), "$p < .001$")
def source_tfce_pmap(effect, pmap, statmap, max_statistic, surfer_kwargs, brain, difference=None):
if brain is None:
brain = plot.brain.brain(pmap.source, **surfer_kwargs)
else:
brain.remove_data()
brain.add_ndvar_p_map(pmap, statmap)
cbar = brain.plot_colorbar(orientation='vertical', show=False, width=0.2, w=1.1, h=2.8)
content = [brain.image(), cbar.image()]
cbar.close()
if difference is not None:
brain.remove_data()
brain.add_ndvar(difference)
cbar = brain.plot_colorbar(orientation='vertical', show=False, width=0.2, w=1.1, h=2.8)
content.append(brain.image())
content.append(cbar.image())
cbar.close()
statistic = statmap.info['meas']
p = pmap.min()
caption = FMText([f"{effect}; ", fmtxt.eq(statistic, max_statistic, 'max'), ', ', fmtxt.peq(p)])
fig = Figure(content, caption)
return fig, brain
def vsource_tfce_map(effect, stat_map, p, statistic, max_statistic):
brain = plot.GlassBrain(stat_map, show=False)
cbar = brain.plot_colorbar(orientation='vertical', show=False, width=0.2, w=1.1, h=2.8)
content = [brain.image(), cbar.image()]
brain.close()
cbar.close()
caption = FMText([f"{effect}; ", fmtxt.eq(statistic, max_statistic, 'max'), ', ', fmtxt.peq(p)])
return Figure(content, caption)