def test_barplot():
"Test plot.uv.barplot"
plot.configure_backend(False, False)
ds = datasets.get_uv()
plot.uv.Barplot('fltvar', 'A%B', match='rm', ds=ds)
plot.uv.Barplot('fltvar', 'A%B', match='rm', test=0, ds=ds)
plt.close('all')
def test_corr():
"Test testnd.corr()"
plot.configure_backend(False, False)
ds = datasets.get_uts(True)
# add correlation
Y = ds['Y']
utsnd = ds['utsnd']
utsnd.x.shape
utsnd.x[:, 3:5, 50:65] += Y.x[:, None, None]
res = testnd.corr('utsnd', 'Y', 'rm', ds=ds)
repr(res)
p = plot.Array(res)
p.close()
res = testnd.corr('utsnd', 'Y', 'rm', ds=ds, samples=10, pmin=0.05)
p = plot.Array(res)
p.close()
# persistence
string = pickle.dumps(res, protocol=pickle.HIGHEST_PROTOCOL)
res_ = pickle.loads(string)
assert_equal(repr(res_), repr(res))
assert_dataobj_equal(res.p_uncorrected, res_.p_uncorrected)
assert_dataobj_equal(res.p, res_.p)
# NaN
r = _testnd._corr(np.arange(10), np.zeros(10))
assert_equal(r, 0)
def test_timeplot():
"Test plot.uv.timeplot"
plot.configure_backend(False, False)
ds = datasets.get_rand()
ds['seq'] = Var(np.arange(2).repeat(30))
plot.uv.Timeplot('Y', 'B', 'seq', match='rm', ds=ds)
plt.close('all')
def test_clusters():
"test plot.uts cluster plotting functions"
plot.configure_backend(False, False)
ds = datasets.get_rand()
A = ds['A']
B = ds['B']
Y = ds['uts']
# fixed effects model
res = testnd.anova(Y, A * B)
p = plot.UTSClusters(res, title="Fixed Effects Model")
p.close()
# random effects model:
subject = Factor(range(15), tile=4, random=True, name='subject')
res = testnd.anova(Y, A * B * subject, samples=2)
p = plot.UTSClusters(res, title="Random Effects Model")
p.close()
# plot UTSStat
p = plot.UTSStat(Y, A % B, match=subject)
p.set_clusters(res.clusters)
p.close()
p = plot.UTSStat(Y, A, Xax=B, match=subject)
p.close()
def test_histogram():
"Test plot.uv.histogram"
plot.configure_backend(False, False)
ds = datasets.get_rand()
plot.uv.Histogram('Y', 'A%B', ds=ds)
plot.uv.Histogram('Y', 'A%B', match='rm', ds=ds)
plt.close('all')
def test_timeplot():
"Test plot.uv.timeplot"
plot.configure_backend(False, False)
ds = datasets.get_rand()
ds['seq'] = Var(np.arange(2).repeat(30))
plot.uv.Timeplot('Y', 'B', 'seq', match='rm', ds=ds)
plt.close('all')
def test_barplot():
"Test plot.uv.barplot"
plot.configure_backend(False, False)
ds = datasets.get_uv()
plot.uv.Barplot('fltvar', 'A%B', match='rm', ds=ds)
plot.uv.Barplot('fltvar', 'A%B', match='rm', test=0, ds=ds)
plt.close('all')
def test_corr():
"Test testnd.corr()"
plot.configure_backend(False, False)
ds = datasets.get_rand(True)
# add correlation
Y = ds['Y']
utsnd = ds['utsnd']
utsnd.x.shape
utsnd.x[:, 3:5, 50:65] += Y.x[:, None, None]
res = testnd.corr('utsnd', 'Y', 'rm', ds=ds)
repr(res)
p = plot.Array(res)
p.close()
res = testnd.corr('utsnd', 'Y', 'rm', ds=ds, samples=10, pmin=0.05)
p = plot.Array(res)
p.close()
# persistence
string = pickle.dumps(res, protocol=pickle.HIGHEST_PROTOCOL)
res_ = pickle.loads(string)
assert_equal(repr(res_), repr(res))
assert_dataobj_equal(res.p_uncorrected, res_.p_uncorrected)
assert_dataobj_equal(res.p, res_.p)
# NaN
r = _testnd._corr(np.arange(10), np.zeros(10))
assert_equal(r, 0)
def test_histogram():
"Test plot.uv.histogram"
plot.configure_backend(False, False)
ds = datasets.get_rand()
plot.uv.Histogram('Y', 'A%B', ds=ds)
plot.uv.Histogram('Y', 'A%B', match='rm', ds=ds)
plt.close('all')
def test_clusters():
"test plot.uts cluster plotting functions"
plot.configure_backend(False, False)
ds = datasets.get_rand()
A = ds['A']
B = ds['B']
Y = ds['uts']
# fixed effects model
res = testnd.anova(Y, A * B)
p = plot.UTSClusters(res, title="Fixed Effects Model")
p.close()
# random effects model:
subject = Factor(range(15), tile=4, random=True, name='subject')
res = testnd.anova(Y, A * B * subject, samples=2)
p = plot.UTSClusters(res, title="Random Effects Model")
p.close()
# plot UTSStat
p = plot.UTSStat(Y, A % B, match=subject)
p.set_clusters(res.clusters)
p.close()
p = plot.UTSStat(Y, A, Xax=B, match=subject)
p.close()
def test_2():
"Test fmtxt/table.py"
exa_dir = os.path.join(examples_dir, 'fmtxt')
exa_file = os.path.join(exa_dir, 'table.py')
# find required files
with open(exa_file) as fid:
text = fid.read()
filenames = re.findall("# requires: (\w+.\w+)", text)
text = text.replace("n_samples = 1000", "n_samples = 2")
# copy all files to temporary dir
tempdir = mkdtemp()
dst = os.path.join(tempdir, 'table.py')
with open(dst, 'w') as fid:
fid.write(text)
for filename in filenames:
src = os.path.join(exa_dir, filename)
shutil.copy(src, tempdir)
# execute example
logging.info("executing from %s" % tempdir)
os.chdir(tempdir)
plot.configure_backend(False, False)
execfile('table.py', {})
plt.close('all')
# delete temporary files
shutil.rmtree(tempdir)
def test_2():
"Test fmtxt/table.py"
exa_dir = os.path.join(examples_dir, 'fmtxt')
exa_file = os.path.join(exa_dir, 'table.py')
# find required files
with open(exa_file) as fid:
text = fid.read()
filenames = re.findall("# requires: (\w+.\w+)", text)
text = text.replace("n_samples = 1000", "n_samples = 2")
# copy all files to temporary dir
tempdir = mkdtemp()
dst = os.path.join(tempdir, 'table.py')
with open(dst, 'w') as fid:
fid.write(text)
for filename in filenames:
src = os.path.join(exa_dir, filename)
shutil.copy(src, tempdir)
# execute example
logging.info("executing from %s" % tempdir)
os.chdir(tempdir)
plot.configure_backend(False, False)
execfile('table.py', {})
plt.close('all')
# delete temporary files
shutil.rmtree(tempdir)
def test_plot_array():
"Test plot.Array"
plot.configure_backend(False, False)
ds = datasets.get_rand(utsnd=True)
p = plot.Array('utsnd', 'A%B', ds=ds)
p.close()
p = plot.Array('utsnd', ds=ds)
p.close()
def test_uts():
"test plot.UTS plotting function"
plot.configure_backend(False, False)
ds = datasets.get_rand()
p = plot.UTS('uts', ds=ds)
p.close()
p = plot.UTS('uts', 'A%B', ds=ds)
p.close()
def test_uts():
"test plot.UTS plotting function"
plot.configure_backend(False, False)
ds = datasets.get_rand()
p = plot.UTS('uts', ds=ds)
p.close()
p = plot.UTS('uts', 'A%B', ds=ds)
p.close()
def test_plot_array():
"Test plot.Array"
plot.configure_backend(False, False)
ds = datasets.get_rand(utsnd=True)
p = plot.Array('utsnd', 'A%B', ds=ds)
p.close()
p = plot.Array('utsnd', ds=ds)
p.close()
def test_plot_array():
"Test plot.TopoArray"
plot.configure_backend(False, False)
ds = datasets.get_rand(utsnd=True)
p = plot.TopoArray('utsnd', ds=ds)
p.close()
p = plot.TopoArray('utsnd', ds=ds, vmax=0.2, w=2)
p.close()
p = plot.TopoArray('utsnd', 'A%B', ds=ds, axw=4)
p.close()
def test_plot_array():
"Test plot.TopoArray"
plot.configure_backend(False, False)
ds = datasets.get_rand(utsnd=True)
p = plot.TopoArray('utsnd', ds=ds)
p.close()
p = plot.TopoArray('utsnd', ds=ds, vmax=0.2, w=2)
p.close()
p = plot.TopoArray('utsnd', 'A%B', ds=ds, axw=4)
p.close()
def test_boxplot():
"Test plot.uv.boxplot"
plot.configure_backend(False, False)
ds = datasets.get_uv()
plot.uv.Boxplot('fltvar', 'A%B', match='rm', ds=ds)
plt.close('all')
# many pairwise significances
ds['fltvar'][ds.eval("A%B==('a1','b1')")] += 1
ds['fltvar'][ds.eval("A%B==('a2','b2')")] -= 1
ds['C'] = Factor('qw', rep=10, tile=4)
plot.uv.Boxplot('fltvar', 'A%B%C', ds=ds)
def test_plot_topomap():
"Test plot.Topomap"
plot.configure_backend(False, False)
ds = datasets.get_rand(utsnd=True)
topo = ds.eval('utsnd.summary(time=(0.075, 0.125))')
p = plot.Topomap(topo, ds=ds)
p.close()
p = plot.Topomap(topo, ds=ds, vmax=0.2, w=2)
p.close()
p = plot.Topomap(topo, 'A%B', ds=ds, axw=2)
p.close()
def test_plot_topomap():
"Test plot.Topomap"
plot.configure_backend(False, False)
ds = datasets.get_rand(utsnd=True)
topo = ds.eval('utsnd.summary(time=(0.075, 0.125))')
p = plot.Topomap(topo, ds=ds)
p.close()
p = plot.Topomap(topo, ds=ds, vmax=0.2, w=2)
p.close()
p = plot.Topomap(topo, 'A%B', ds=ds, axw=2)
p.close()
def test_boxplot():
"Test plot.uv.boxplot"
plot.configure_backend(False, False)
ds = datasets.get_uv()
plot.uv.Boxplot('fltvar', 'A%B', match='rm', ds=ds)
plt.close('all')
# many pairwise significances
ds['fltvar'][ds.eval("A%B==('a1','b1')")] += 1
ds['fltvar'][ds.eval("A%B==('a2','b2')")] -= 1
ds['C'] = Factor('qw', rep=10, tile=4)
plot.uv.Boxplot('fltvar', 'A%B%C', ds=ds)
def test_scatterplot():
"Test plot.uv.corrplot and lot.uv.regplot"
plot.configure_backend(False, False)
ds = datasets.get_rand()
ds['cov'] = ds['Y'] + np.random.normal(0, 1, (60, ))
plot.uv.Correlation('Y', 'cov', ds=ds)
plot.uv.Correlation('Y', 'cov', 'A%B', ds=ds)
plot.uv.Regression('Y', 'cov', ds=ds)
plot.uv.Regression('Y', 'cov', 'A%B', ds=ds)
plt.close('all')
def test_scatterplot():
"Test plot.uv.corrplot and lot.uv.regplot"
plot.configure_backend(False, False)
ds = datasets.get_rand()
ds['cov'] = ds['Y'] + np.random.normal(0, 1, (60,))
plot.uv.Correlation('Y', 'cov', ds=ds)
plot.uv.Correlation('Y', 'cov', 'A%B', ds=ds)
plot.uv.Regression('Y', 'cov', ds=ds)
plot.uv.Regression('Y', 'cov', 'A%B', ds=ds)
plt.close('all')
def test_plot_butterfly():
"Test plot.TopoButterfly"
plot.configure_backend(False, False)
ds = datasets.get_rand(utsnd=True)
p = plot.TopoButterfly('utsnd', ds=ds)
p.close()
p = plot.TopoButterfly('utsnd', ds=ds, vmax=0.2, w=2)
p.close()
p = plot.TopoButterfly('utsnd', 'A%B', ds=ds, axw=2)
p.close()
p = plot.TopoButterfly('utsnd', mark=[1, 2], ds=ds)
p.close()
p = plot.TopoButterfly('utsnd', mark=['1', '2'], ds=ds)
p.close()
def test_plot_butterfly():
"Test plot.TopoButterfly"
plot.configure_backend(False, False)
ds = datasets.get_rand(utsnd=True)
p = plot.TopoButterfly('utsnd', ds=ds)
p.close()
p = plot.TopoButterfly('utsnd', ds=ds, vmax=0.2, w=2)
p.close()
p = plot.TopoButterfly('utsnd', 'A%B', ds=ds, axw=2)
p.close()
p = plot.TopoButterfly('utsnd', mark=[1, 2], ds=ds)
p.close()
p = plot.TopoButterfly('utsnd', mark=['1', '2'], ds=ds)
p.close()
def test_multi():
"Test plot.SensorMaps"
plot.configure_backend(False, False)
ds = datasets.get_rand(utsnd=True)
Y = ds['utsnd']
p = plot.SensorMaps(Y)
roi = [1, 2]
p.set_selection(roi)
roi2 = p.get_selection()
test_range = np.arange(3)
assert_array_equal(test_range[roi2], test_range[roi], "ROI changed after "
"set/get")
p.close()
def test_map2d():
"Test plot.SensorMap2d"
plot.configure_backend(False, False)
ds = datasets.get_rand(utsnd=True)
Y = ds['utsnd']
p = plot.SensorMap2d(Y)
# plot attributes
p.set_label_color('g')
p.set_label_text('idx')
# connectivity
p.show_connectivity()
p.show_connectivity(None)
# mark sensors
p.mark_sensors([1, 2])
p.mark_sensors([0])
p.remove_markers()
p.close()
def test_stat():
"test plot.UTSStat plotting function"
plot.configure_backend(False, False)
ds = datasets.get_rand()
p = plot.UTSStat('uts', ds=ds)
p.close()
p = plot.UTSStat('uts', 'A%B', ds=ds)
p.close()
p = plot.UTSStat('uts', 'A', Xax='B', ds=ds)
p.close()
p = plot.UTSStat('uts', 'A%B', 'rm', sub="rm.isin(('R00', 'R01'))", ds=ds)
p.close()
# clusters
sds = ds.sub("B == 'b0'")
res = testnd.ttest_rel('uts', 'A', 'a1', 'a0', match='rm', ds=sds,
samples=0, pmin=0.05, mintime=0.02)
p = plot.UTSStat('uts', 'A', clusters=res.clusters, ds=ds)
p.close()
res = testnd.ttest_rel('uts', 'A', 'a1', 'a0', match='rm', ds=sds,
samples=100, pmin=0.05, mintime=0.02)
p = plot.UTSStat('uts', 'A', clusters=res.clusters, ds=ds)
p.close()
def test_stat():
"test plot.UTSStat plotting function"
plot.configure_backend(False, False)
ds = datasets.get_rand()
p = plot.UTSStat('uts', ds=ds)
p.close()
p = plot.UTSStat('uts', 'A%B', ds=ds)
p.close()
p = plot.UTSStat('uts', 'A', Xax='B', ds=ds)
p.close()
p = plot.UTSStat('uts', 'A%B', 'rm', sub="rm.isin(('R00', 'R01'))", ds=ds)
p.close()
# clusters
sds = ds.sub("B == 'b0'")
res = testnd.ttest_rel('uts',
'A',
'a1',
'a0',
match='rm',
ds=sds,
samples=0,
pmin=0.05,
mintime=0.02)
p = plot.UTSStat('uts', 'A', clusters=res.clusters, ds=ds)
p.close()
res = testnd.ttest_rel('uts',
'A',
'a1',
'a0',
match='rm',
ds=sds,
samples=100,
pmin=0.05,
mintime=0.02)
p = plot.UTSStat('uts', 'A', clusters=res.clusters, ds=ds)
p.close()
def test_anova():
"Test testnd.anova()"
plot.configure_backend(False, False)
ds = datasets.get_rand(True)
testnd.anova('utsnd', 'A*B', ds=ds)
for samples in (0, 2):
logger.info("TEST: samples=%r" % samples)
testnd.anova('utsnd', 'A*B', ds=ds, samples=samples)
testnd.anova('utsnd', 'A*B', ds=ds, samples=samples, pmin=0.05)
testnd.anova('utsnd', 'A*B', ds=ds, samples=samples, tfce=True)
res = testnd.anova('utsnd', 'A*B*rm', ds=ds, samples=0, pmin=0.05)
repr(res)
p = plot.Array(res)
p.close()
res = testnd.anova('utsnd', 'A*B*rm', ds=ds, samples=2, pmin=0.05)
repr(res)
p = plot.Array(res)
p.close()
# persistence
string = pickle.dumps(res, protocol=pickle.HIGHEST_PROTOCOL)
res_ = pickle.loads(string)
assert_equal(repr(res_), repr(res))
# threshold-free
res = testnd.anova('utsnd', 'A*B*rm', ds=ds, samples=10)
repr(res)
assert_in('A clusters', res.clusters.info)
assert_in('B clusters', res.clusters.info)
assert_in('A x B clusters', res.clusters.info)
# no clusters
res = testnd.anova('uts',
'B',
sub="A=='a1'",
ds=ds,
samples=5,
pmin=0.05,
mintime=0.02)
repr(res)
assert_in('v', res.clusters)
assert_in('p', res.clusters)
# all effects with clusters
res = testnd.anova('uts',
'A*B*rm',
ds=ds,
samples=5,
pmin=0.05,
tstart=0.1,
mintime=0.02)
assert_equal(set(res.clusters['effect'].cells), set(res.effects))
# some effects with clusters, some without
res = testnd.anova('uts',
'A*B*rm',
ds=ds,
samples=5,
pmin=0.05,
tstart=0.37,
mintime=0.02)
string = pickle.dumps(res, pickle.HIGHEST_PROTOCOL)
res_ = pickle.loads(string)
assert_dataobj_equal(res.clusters, res_.clusters)
# test multi-effect results (with persistence)
# UTS
res = testnd.anova('uts', 'A*B*rm', ds=ds, samples=5)
repr(res)
string = pickle.dumps(res, pickle.HIGHEST_PROTOCOL)
res = pickle.loads(string)
tfce_clusters = res._clusters(pmin=0.05)
peaks = res.find_peaks()
assert_equal(tfce_clusters.eval("p.min()"), peaks.eval("p.min()"))
unmasked = res.f[0]
masked = res.masked_parameter_map(effect=0, pmin=0.05)
assert_array_equal(masked.x <= unmasked.x, True)
def test_anova():
"Test testnd.anova()"
plot.configure_backend(False, False)
ds = datasets.get_uts(True)
testnd.anova('utsnd', 'A*B', ds=ds)
for samples in (0, 2):
logger.info("TEST: samples=%r" % samples)
testnd.anova('utsnd', 'A*B', ds=ds, samples=samples)
testnd.anova('utsnd', 'A*B', ds=ds, samples=samples, pmin=0.05)
testnd.anova('utsnd', 'A*B', ds=ds, samples=samples, tfce=True)
res = testnd.anova('utsnd', 'A*B*rm', ds=ds, samples=0, pmin=0.05)
repr(res)
p = plot.Array(res)
p.close()
res = testnd.anova('utsnd', 'A*B*rm', ds=ds, samples=2, pmin=0.05)
repr(res)
p = plot.Array(res)
p.close()
# persistence
string = pickle.dumps(res, protocol=pickle.HIGHEST_PROTOCOL)
res_ = pickle.loads(string)
assert_equal(repr(res_), repr(res))
# threshold-free
res = testnd.anova('utsnd', 'A*B*rm', ds=ds, samples=10)
repr(res)
assert_in('A clusters', res.clusters.info)
assert_in('B clusters', res.clusters.info)
assert_in('A x B clusters', res.clusters.info)
# no clusters
res = testnd.anova('uts', 'B', sub="A=='a1'", ds=ds, samples=5, pmin=0.05,
mintime=0.02)
repr(res)
assert_in('v', res.clusters)
assert_in('p', res.clusters)
# all effects with clusters
res = testnd.anova('uts', 'A*B*rm', ds=ds, samples=5, pmin=0.05,
tstart=0.1, mintime=0.02)
assert_equal(set(res.clusters['effect'].cells), set(res.effects))
# some effects with clusters, some without
res = testnd.anova('uts', 'A*B*rm', ds=ds, samples=5, pmin=0.05,
tstart=0.37, mintime=0.02)
string = pickle.dumps(res, pickle.HIGHEST_PROTOCOL)
res_ = pickle.loads(string)
assert_dataobj_equal(res.clusters, res_.clusters)
# test multi-effect results (with persistence)
# UTS
res = testnd.anova('uts', 'A*B*rm', ds=ds, samples=5)
repr(res)
string = pickle.dumps(res, pickle.HIGHEST_PROTOCOL)
res = pickle.loads(string)
tfce_clusters = res.find_clusters(pmin=0.05)
peaks = res.find_peaks()
assert_equal(tfce_clusters.eval("p.min()"), peaks.eval("p.min()"))
unmasked = res.f[0]
masked = res.masked_parameter_map(effect=0, pmin=0.05)
assert_array_equal(masked.x <= unmasked.x, True)
