def test_legendre_val():
"""Test Legendre polynomial (derivative) equivalence
"""
rng = np.random.RandomState(0)
# check table equiv
xs = np.linspace(-1., 1., 1000)
n_terms = 100
# True, numpy
vals_np = legendre.legvander(xs, n_terms - 1)
# Table approximation
for fun, nc in zip([_get_legen_lut_fast, _get_legen_lut_accurate],
[100, 50]):
lut, n_fact = _get_legen_table('eeg', n_coeff=nc, force_calc=True)
vals_i = fun(xs, lut)
# Need a "1:" here because we omit the first coefficient in our table!
assert_allclose(vals_np[:, 1:vals_i.shape[1] + 1],
vals_i,
rtol=1e-2,
atol=5e-3)
# Now let's look at our sums
ctheta = rng.rand(20, 30) * 2.0 - 1.0
beta = rng.rand(20, 30) * 0.8
lut_fun = partial(fun, lut=lut)
c1 = _comp_sum_eeg(beta.flatten(), ctheta.flatten(), lut_fun, n_fact)
c1.shape = beta.shape
# compare to numpy
n = np.arange(1, n_terms, dtype=float)[:, np.newaxis, np.newaxis]
coeffs = np.zeros((n_terms, ) + beta.shape)
coeffs[1:] = (np.cumprod([beta] * (n_terms - 1), axis=0) *
(2.0 * n + 1.0) * (2.0 * n + 1.0) / n)
# can't use tensor=False here b/c it isn't in old numpy
c2 = np.empty((20, 30))
for ci1 in range(20):
for ci2 in range(30):
c2[ci1, ci2] = legendre.legval(ctheta[ci1, ci2], coeffs[:, ci1,
ci2])
assert_allclose(c1, c2, 1e-2, 1e-3) # close enough...
# compare fast and slow for MEG
ctheta = rng.rand(20 * 30) * 2.0 - 1.0
beta = rng.rand(20 * 30) * 0.8
lut, n_fact = _get_legen_table('meg', n_coeff=10, force_calc=True)
fun = partial(_get_legen_lut_fast, lut=lut)
coeffs = _comp_sums_meg(beta, ctheta, fun, n_fact, False)
lut, n_fact = _get_legen_table('meg', n_coeff=20, force_calc=True)
fun = partial(_get_legen_lut_accurate, lut=lut)
coeffs = _comp_sums_meg(beta, ctheta, fun, n_fact, False)
def test_legendre_val():
"""Test Legendre polynomial (derivative) equivalence
"""
rng = np.random.RandomState(0)
# check table equiv
xs = np.linspace(-1., 1., 1000)
n_terms = 100
# True, numpy
vals_np = legendre.legvander(xs, n_terms - 1)
# Table approximation
for fun, nc in zip([_get_legen_lut_fast, _get_legen_lut_accurate],
[100, 50]):
lut, n_fact = _get_legen_table('eeg', n_coeff=nc, force_calc=True)
vals_i = fun(xs, lut)
# Need a "1:" here because we omit the first coefficient in our table!
assert_allclose(vals_np[:, 1:vals_i.shape[1] + 1], vals_i,
rtol=1e-2, atol=5e-3)
# Now let's look at our sums
ctheta = rng.rand(20, 30) * 2.0 - 1.0
beta = rng.rand(20, 30) * 0.8
lut_fun = partial(fun, lut=lut)
c1 = _comp_sum_eeg(beta.flatten(), ctheta.flatten(), lut_fun, n_fact)
c1.shape = beta.shape
# compare to numpy
n = np.arange(1, n_terms, dtype=float)[:, np.newaxis, np.newaxis]
coeffs = np.zeros((n_terms,) + beta.shape)
coeffs[1:] = (np.cumprod([beta] * (n_terms - 1), axis=0) *
(2.0 * n + 1.0) * (2.0 * n + 1.0) / n)
# can't use tensor=False here b/c it isn't in old numpy
c2 = np.empty((20, 30))
for ci1 in range(20):
for ci2 in range(30):
c2[ci1, ci2] = legendre.legval(ctheta[ci1, ci2],
coeffs[:, ci1, ci2])
assert_allclose(c1, c2, 1e-2, 1e-3) # close enough...
# compare fast and slow for MEG
ctheta = rng.rand(20 * 30) * 2.0 - 1.0
beta = rng.rand(20 * 30) * 0.8
lut, n_fact = _get_legen_table('meg', n_coeff=10, force_calc=True)
fun = partial(_get_legen_lut_fast, lut=lut)
coeffs = _comp_sums_meg(beta, ctheta, fun, n_fact, False)
lut, n_fact = _get_legen_table('meg', n_coeff=20, force_calc=True)
fun = partial(_get_legen_lut_accurate, lut=lut)
coeffs = _comp_sums_meg(beta, ctheta, fun, n_fact, False)
def test_cache_dir():
"""Test use of cache dir
"""
tempdir = _TempDir()
orig_dir = os.getenv("MNE_CACHE_DIR", None)
orig_size = os.getenv("MNE_MEMMAP_MIN_SIZE", None)
rng = np.random.RandomState(0)
X = rng.randn(9, 2, 10)
log_file = op.join(tempdir, "log.txt")
try:
os.environ["MNE_MEMMAP_MIN_SIZE"] = "1K"
os.environ["MNE_CACHE_DIR"] = tempdir
# Fix error for #1507: in-place when memmapping
permutation_cluster_1samp_test(
X, buffer_size=None, n_jobs=2, n_permutations=1, seed=0, stat_fun=ttest_1samp_no_p, verbose=False
)
# ensure that non-independence yields warning
stat_fun = partial(ttest_1samp_no_p, sigma=1e-3)
set_log_file(log_file)
permutation_cluster_1samp_test(
X, buffer_size=10, n_jobs=2, n_permutations=1, seed=0, stat_fun=stat_fun, verbose=False
)
with open(log_file, "r") as fid:
assert_true("independently" in "".join(fid.readlines()))
finally:
if orig_dir is not None:
os.environ["MNE_CACHE_DIR"] = orig_dir
else:
del os.environ["MNE_CACHE_DIR"]
if orig_size is not None:
os.environ["MNE_MEMMAP_MIN_SIZE"] = orig_size
else:
del os.environ["MNE_MEMMAP_MIN_SIZE"]
set_log_file(None)
def test_read_ch_connectivity():
"Test reading channel connectivity templates"
tempdir = _TempDir()
a = partial(np.array, dtype='<U7')
# no pep8
nbh = np.array([[(['MEG0111'], [[a(['MEG0131'])]]),
(['MEG0121'], [[a(['MEG0111'])], [a(['MEG0131'])]]),
(['MEG0131'], [[a(['MEG0111'])], [a(['MEG0121'])]])]],
dtype=[('label', 'O'), ('neighblabel', 'O')])
mat = dict(neighbours=nbh)
mat_fname = op.join(tempdir, 'test_mat.mat')
savemat(mat_fname, mat)
ch_connectivity = read_ch_connectivity(mat_fname)
x = ch_connectivity
assert_equal(x.shape, (3, 3))
assert_equal(x[0, 1], False)
assert_equal(x[0, 2], True)
assert_true(np.all(x.diagonal()))
assert_raises(ValueError, read_ch_connectivity, mat_fname, [0, 3])
ch_connectivity = read_ch_connectivity(mat_fname, picks=[0, 2])
assert_equal(ch_connectivity.shape[0], 2)
ch_names = ['EEG01', 'EEG02', 'EEG03']
neighbors = [['EEG02'], ['EEG04'], ['EEG02']]
assert_raises(ValueError, ch_neighbor_connectivity, ch_names, neighbors)
neighbors = [['EEG02'], ['EEG01', 'EEG03'], ['EEG 02']]
assert_raises(ValueError, ch_neighbor_connectivity, ch_names[:2],
neighbors)
neighbors = [['EEG02'], 'EEG01', ['EEG 02']]
assert_raises(ValueError, ch_neighbor_connectivity, ch_names, neighbors)
def test_read_ch_connectivity():
"Test reading channel connectivity templates"
a = partial(np.array, dtype='<U7')
# no pep8
nbh = np.array([[(['MEG0111'], [[a(['MEG0131'])]]),
(['MEG0121'], [[a(['MEG0111'])],
[a(['MEG0131'])]]),
(['MEG0131'], [[a(['MEG0111'])],
[a(['MEG0121'])]])]],
dtype=[('label', 'O'), ('neighblabel', 'O')])
mat = dict(neighbours=nbh)
mat_fname = op.join(tempdir, 'test_mat.mat')
savemat(mat_fname, mat)
ch_connectivity = read_ch_connectivity(mat_fname)
x = ch_connectivity
assert_equal(x.shape, (3, 3))
assert_equal(x[0, 1], False)
assert_equal(x[0, 2], True)
assert_true(np.all(x.diagonal()))
assert_raises(ValueError, read_ch_connectivity, mat_fname, [0, 3])
ch_connectivity = read_ch_connectivity(mat_fname, picks=[0, 2])
assert_equal(ch_connectivity.shape[0], 2)
ch_names = ['EEG01', 'EEG02', 'EEG03']
neighbors = [['EEG02'], ['EEG04'], ['EEG02']]
assert_raises(ValueError, ch_neighbor_connectivity, ch_names, neighbors)
neighbors = [['EEG02'], ['EEG01', 'EEG03'], ['EEG 02']]
assert_raises(ValueError, ch_neighbor_connectivity, ch_names[:2],
neighbors)
neighbors = [['EEG02'], 'EEG01', ['EEG 02']]
assert_raises(ValueError, ch_neighbor_connectivity, ch_names, neighbors)
def test_cache_dir():
"""Test use of cache dir
"""
tempdir = _TempDir()
orig_dir = os.getenv('MNE_CACHE_DIR', None)
orig_size = os.getenv('MNE_MEMMAP_MIN_SIZE', None)
rng = np.random.RandomState(0)
X = rng.randn(9, 2, 10)
try:
os.environ['MNE_MEMMAP_MIN_SIZE'] = '1K'
os.environ['MNE_CACHE_DIR'] = tempdir
# Fix error for #1507: in-place when memmapping
with catch_logging() as log_file:
permutation_cluster_1samp_test(
X, buffer_size=None, n_jobs=2, n_permutations=1,
seed=0, stat_fun=ttest_1samp_no_p, verbose=False)
# ensure that non-independence yields warning
stat_fun = partial(ttest_1samp_no_p, sigma=1e-3)
assert_true('independently' not in log_file.getvalue())
with warnings.catch_warnings(record=True): # independently
permutation_cluster_1samp_test(
X, buffer_size=10, n_jobs=2, n_permutations=1,
seed=0, stat_fun=stat_fun, verbose=False)
assert_true('independently' in log_file.getvalue())
finally:
if orig_dir is not None:
os.environ['MNE_CACHE_DIR'] = orig_dir
else:
del os.environ['MNE_CACHE_DIR']
if orig_size is not None:
os.environ['MNE_MEMMAP_MIN_SIZE'] = orig_size
else:
del os.environ['MNE_MEMMAP_MIN_SIZE']
def test_no_conversion():
""" Test bti no-conversion option """
get_info = partial(_get_bti_info,
rotation_x=0.0,
translation=(0.0, 0.02, 0.11),
convert=False,
ecg_ch='E31',
eog_ch=('E63', 'E64'),
rename_channels=False,
sort_by_ch_name=False)
for pdf, config, hs in zip(pdf_fnames, config_fnames, hs_fnames):
raw_info, _ = get_info(pdf, config, hs, convert=False)
raw_info_con = read_raw_bti(pdf_fname=pdf,
config_fname=config,
head_shape_fname=hs,
convert=True,
preload=False).info
pick_info(raw_info_con,
pick_types(raw_info_con, meg=True, ref_meg=True),
copy=False)
pick_info(raw_info,
pick_types(raw_info, meg=True, ref_meg=True),
copy=False)
bti_info = _read_bti_header(pdf, config)
dev_ctf_t = _correct_trans(bti_info['bti_transform'][0])
assert_array_equal(dev_ctf_t, raw_info['dev_ctf_t']['trans'])
assert_array_equal(raw_info['dev_head_t']['trans'], np.eye(4))
assert_array_equal(raw_info['ctf_head_t']['trans'], np.eye(4))
dig, t = _process_bti_headshape(hs, convert=False, use_hpi=False)
assert_array_equal(t['trans'], np.eye(4))
for ii, (old, new, con) in enumerate(
zip(dig, raw_info['dig'], raw_info_con['dig'])):
assert_equal(old['ident'], new['ident'])
assert_array_equal(old['r'], new['r'])
assert_true(not np.allclose(old['r'], con['r']))
if ii > 10:
break
ch_map = dict((ch['chan_label'], ch['loc']) for ch in bti_info['chs'])
for ii, ch_label in enumerate(raw_info['ch_names']):
if not ch_label.startswith('A'):
continue
t1 = ch_map[ch_label] # correction already performed in bti_info
t2 = raw_info['chs'][ii]['loc']
t3 = raw_info_con['chs'][ii]['loc']
assert_allclose(t1, t2, atol=1e-15)
assert_true(not np.allclose(t1, t3))
idx_a = raw_info_con['ch_names'].index('MEG 001')
idx_b = raw_info['ch_names'].index('A22')
assert_equal(raw_info_con['chs'][idx_a]['coord_frame'],
FIFF.FIFFV_COORD_DEVICE)
assert_equal(raw_info['chs'][idx_b]['coord_frame'],
FIFF.FIFFV_MNE_COORD_4D_HEAD)
def test_no_conversion():
""" Test bti no-conversion option """
get_info = partial(
_get_bti_info,
rotation_x=0.0, translation=(0.0, 0.02, 0.11), convert=False,
ecg_ch='E31', eog_ch=('E63', 'E64'),
rename_channels=False, sort_by_ch_name=False)
for pdf, config, hs in zip(pdf_fnames, config_fnames, hs_fnames):
with warnings.catch_warnings(record=True): # weight tables
raw_info, _ = get_info(pdf, config, hs, convert=False)
with warnings.catch_warnings(record=True): # weight tables
raw_info_con = read_raw_bti(
pdf_fname=pdf, config_fname=config, head_shape_fname=hs,
convert=True, preload=False).info
pick_info(raw_info_con,
pick_types(raw_info_con, meg=True, ref_meg=True),
copy=False)
pick_info(raw_info,
pick_types(raw_info, meg=True, ref_meg=True), copy=False)
bti_info = _read_bti_header(pdf, config)
dev_ctf_t = _correct_trans(bti_info['bti_transform'][0])
assert_array_equal(dev_ctf_t, raw_info['dev_ctf_t']['trans'])
assert_array_equal(raw_info['dev_head_t']['trans'], np.eye(4))
assert_array_equal(raw_info['ctf_head_t']['trans'], np.eye(4))
dig, t = _process_bti_headshape(hs, convert=False, use_hpi=False)
assert_array_equal(t['trans'], np.eye(4))
for ii, (old, new, con) in enumerate(zip(
dig, raw_info['dig'], raw_info_con['dig'])):
assert_equal(old['ident'], new['ident'])
assert_array_equal(old['r'], new['r'])
assert_true(not np.allclose(old['r'], con['r']))
if ii > 10:
break
ch_map = dict((ch['chan_label'],
ch['loc']) for ch in bti_info['chs'])
for ii, ch_label in enumerate(raw_info['ch_names']):
if not ch_label.startswith('A'):
continue
t1 = ch_map[ch_label] # correction already performed in bti_info
t2 = raw_info['chs'][ii]['loc']
t3 = raw_info_con['chs'][ii]['loc']
assert_allclose(t1, t2, atol=1e-15)
assert_true(not np.allclose(t1, t3))
idx_a = raw_info_con['ch_names'].index('MEG 001')
idx_b = raw_info['ch_names'].index('A22')
assert_equal(
raw_info_con['chs'][idx_a]['coord_frame'],
FIFF.FIFFV_COORD_DEVICE)
assert_equal(
raw_info['chs'][idx_b]['coord_frame'],
FIFF.FIFFV_MNE_COORD_4D_HEAD)
def test_cluster_permutation_t_test():
"""Test cluster level permutations T-test
"""
condition1_1d, condition2_1d, condition1_2d, condition2_2d = \
_get_conditions()
# use a very large sigma to make sure Ts are not independent
stat_funs = [ttest_1samp_no_p,
partial(ttest_1samp_no_p, sigma=1e-1)]
for stat_fun in stat_funs:
for condition1 in (condition1_1d, condition1_2d):
# these are so significant we can get away with fewer perms
T_obs, clusters, cluster_p_values, hist =\
permutation_cluster_1samp_test(condition1, n_permutations=100,
tail=0, seed=1,
buffer_size=None)
assert_equal(np.sum(cluster_p_values < 0.05), 1)
T_obs_pos, c_1, cluster_p_values_pos, _ =\
permutation_cluster_1samp_test(condition1, n_permutations=100,
tail=1, threshold=1.67, seed=1,
stat_fun=stat_fun,
buffer_size=None)
T_obs_neg, _, cluster_p_values_neg, _ =\
permutation_cluster_1samp_test(-condition1, n_permutations=100,
tail=-1, threshold=-1.67,
seed=1, stat_fun=stat_fun,
buffer_size=None)
assert_array_equal(T_obs_pos, -T_obs_neg)
assert_array_equal(cluster_p_values_pos < 0.05,
cluster_p_values_neg < 0.05)
# test with 2 jobs and buffer_size enabled
buffer_size = condition1.shape[1] // 10
T_obs_neg_buff, _, cluster_p_values_neg_buff, _ = \
permutation_cluster_1samp_test(-condition1, n_permutations=100,
tail=-1, threshold=-1.67,
seed=1, n_jobs=2,
stat_fun=stat_fun,
buffer_size=buffer_size)
assert_array_equal(T_obs_neg, T_obs_neg_buff)
assert_array_equal(cluster_p_values_neg, cluster_p_values_neg_buff)
def test_cluster_permutation_t_test():
"""Test cluster level permutations T-test
"""
condition1_1d, condition2_1d, condition1_2d, condition2_2d = \
_get_conditions()
# use a very large sigma to make sure Ts are not independent
stat_funs = [ttest_1samp_no_p,
partial(ttest_1samp_no_p, sigma=1e-1)]
for stat_fun in stat_funs:
for condition1 in (condition1_1d, condition1_2d):
# these are so significant we can get away with fewer perms
T_obs, clusters, cluster_p_values, hist =\
permutation_cluster_1samp_test(condition1, n_permutations=100,
tail=0, seed=1,
buffer_size=None)
assert_equal(np.sum(cluster_p_values < 0.05), 1)
T_obs_pos, c_1, cluster_p_values_pos, _ =\
permutation_cluster_1samp_test(condition1, n_permutations=100,
tail=1, threshold=1.67, seed=1,
stat_fun=stat_fun,
buffer_size=None)
T_obs_neg, _, cluster_p_values_neg, _ =\
permutation_cluster_1samp_test(-condition1, n_permutations=100,
tail=-1, threshold=-1.67,
seed=1, stat_fun=stat_fun,
buffer_size=None)
assert_array_equal(T_obs_pos, -T_obs_neg)
assert_array_equal(cluster_p_values_pos < 0.05,
cluster_p_values_neg < 0.05)
# test with 2 jobs and buffer_size enabled
buffer_size = condition1.shape[1] // 10
T_obs_neg_buff, _, cluster_p_values_neg_buff, _ = \
permutation_cluster_1samp_test(-condition1, n_permutations=100,
tail=-1, threshold=-1.67,
seed=1, n_jobs=2,
stat_fun=stat_fun,
buffer_size=buffer_size)
assert_array_equal(T_obs_neg, T_obs_neg_buff)
assert_array_equal(cluster_p_values_neg, cluster_p_values_neg_buff)
def test_cache_dir():
"""Test use of cache dir
"""
tempdir = _TempDir()
orig_dir = os.getenv('MNE_CACHE_DIR', None)
orig_size = os.getenv('MNE_MEMMAP_MIN_SIZE', None)
rng = np.random.RandomState(0)
X = rng.randn(9, 2, 10)
log_file = op.join(tempdir, 'log.txt')
try:
os.environ['MNE_MEMMAP_MIN_SIZE'] = '1K'
os.environ['MNE_CACHE_DIR'] = tempdir
# Fix error for #1507: in-place when memmapping
permutation_cluster_1samp_test(X,
buffer_size=None,
n_jobs=2,
n_permutations=1,
seed=0,
stat_fun=ttest_1samp_no_p,
verbose=False)
# ensure that non-independence yields warning
stat_fun = partial(ttest_1samp_no_p, sigma=1e-3)
set_log_file(log_file)
permutation_cluster_1samp_test(X,
buffer_size=10,
n_jobs=2,
n_permutations=1,
seed=0,
stat_fun=stat_fun,
verbose=False)
with open(log_file, 'r') as fid:
assert_true('independently' in ''.join(fid.readlines()))
finally:
if orig_dir is not None:
os.environ['MNE_CACHE_DIR'] = orig_dir
else:
del os.environ['MNE_CACHE_DIR']
if orig_size is not None:
os.environ['MNE_MEMMAP_MIN_SIZE'] = orig_size
else:
del os.environ['MNE_MEMMAP_MIN_SIZE']
set_log_file(None)
def test_cache_dir():
"""Test use of cache dir
"""
tempdir = _TempDir()
orig_dir = os.getenv('MNE_CACHE_DIR', None)
orig_size = os.getenv('MNE_MEMMAP_MIN_SIZE', None)
rng = np.random.RandomState(0)
X = rng.randn(9, 2, 10)
try:
os.environ['MNE_MEMMAP_MIN_SIZE'] = '1K'
os.environ['MNE_CACHE_DIR'] = tempdir
# Fix error for #1507: in-place when memmapping
with catch_logging() as log_file:
permutation_cluster_1samp_test(X,
buffer_size=None,
n_jobs=2,
n_permutations=1,
seed=0,
stat_fun=ttest_1samp_no_p,
verbose=False)
# ensure that non-independence yields warning
stat_fun = partial(ttest_1samp_no_p, sigma=1e-3)
assert_true('independently' not in log_file.getvalue())
with warnings.catch_warnings(record=True): # independently
permutation_cluster_1samp_test(X,
buffer_size=10,
n_jobs=2,
n_permutations=1,
seed=0,
stat_fun=stat_fun,
verbose=False)
assert_true('independently' in log_file.getvalue())
finally:
if orig_dir is not None:
os.environ['MNE_CACHE_DIR'] = orig_dir
else:
del os.environ['MNE_CACHE_DIR']
if orig_size is not None:
os.environ['MNE_MEMMAP_MIN_SIZE'] = orig_size
else:
del os.environ['MNE_MEMMAP_MIN_SIZE']
def test_permutation_connectivity_equiv():
"""Test cluster level permutations with and without connectivity
"""
try:
try:
from sklearn.feature_extraction.image import grid_to_graph
except ImportError:
from scikits.learn.feature_extraction.image import grid_to_graph
except ImportError:
return
rng = np.random.RandomState(0)
# subjects, time points, spatial points
n_time = 2
n_space = 4
X = rng.randn(6, n_time, n_space)
# add some significant points
X[:, :, 0:2] += 10 # span two time points and two spatial points
X[:, 1, 3] += 20 # span one time point
max_steps = [1, 1, 1, 2]
# This will run full algorithm in two ways, then the ST-algorithm in 2 ways
# All of these should give the same results
conns = [
None,
grid_to_graph(n_time, n_space),
grid_to_graph(1, n_space),
grid_to_graph(1, n_space)
]
stat_map = None
thresholds = [2, dict(start=1.5, step=1.0)]
sig_counts = [2, 5]
sdps = [0, 0.05, 0.05]
ots = ['mask', 'mask', 'indices']
stat_fun = partial(ttest_1samp_no_p, sigma=1e-3)
for thresh, count in zip(thresholds, sig_counts):
cs = None
ps = None
for max_step, conn in zip(max_steps, conns):
for sdp, ot in zip(sdps, ots):
t, clusters, p, H0 = \
permutation_cluster_1samp_test(
X, threshold=thresh, connectivity=conn, n_jobs=2,
max_step=max_step, stat_fun=stat_fun,
step_down_p=sdp, out_type=ot)
# make sure our output datatype is correct
if ot == 'mask':
assert_true(isinstance(clusters[0], np.ndarray))
assert_true(clusters[0].dtype == bool)
assert_array_equal(clusters[0].shape, X.shape[1:])
else: # ot == 'indices'
assert_true(isinstance(clusters[0], tuple))
# make sure all comparisons were done; for TFCE, no perm
# should come up empty
if count == 8:
assert_true(not np.any(H0 == 0))
inds = np.where(p < 0.05)[0]
assert_true(len(inds) == count)
this_cs = [clusters[ii] for ii in inds]
this_ps = p[inds]
this_stat_map = np.zeros((n_time, n_space), dtype=bool)
for ci, c in enumerate(this_cs):
if isinstance(c, tuple):
this_c = np.zeros((n_time, n_space), bool)
for x, y in zip(c[0], c[1]):
this_stat_map[x, y] = True
this_c[x, y] = True
this_cs[ci] = this_c
c = this_c
this_stat_map[c] = True
if cs is None:
ps = this_ps
cs = this_cs
if stat_map is None:
stat_map = this_stat_map
assert_array_equal(ps, this_ps)
assert_true(len(cs) == len(this_cs))
for c1, c2 in zip(cs, this_cs):
assert_array_equal(c1, c2)
assert_array_equal(stat_map, this_stat_map)
from mne.io import Raw
from mne.utils import _TempDir, slow_test, requires_module
from mne.io.proc_history import _get_sss_rank
from mne.io.pick import channel_type, _picks_by_type
from mne.fixes import partial
_recent_sklearn_call = """
required_version = '0.15'
import sklearn
version = LooseVersion(sklearn.__version__)
if version < required_version:
raise ImportError
"""
requires_sklearn_0_15 = partial(requires_module,
name='sklearn',
call=_recent_sklearn_call)
warnings.simplefilter('always') # enable b/c these tests throw warnings
base_dir = op.join(op.dirname(__file__), '..', 'io', 'tests', 'data')
cov_fname = op.join(base_dir, 'test-cov.fif')
cov_gz_fname = op.join(base_dir, 'test-cov.fif.gz')
cov_km_fname = op.join(base_dir, 'test-km-cov.fif')
raw_fname = op.join(base_dir, 'test_raw.fif')
ave_fname = op.join(base_dir, 'test-ave.fif')
erm_cov_fname = op.join(base_dir, 'test_erm-cov.fif')
hp_fif_fname = op.join(base_dir, 'test_chpi_raw_sss.fif')
def test_io_cov():
"""Test IO for noise covariance matrices
def test_permutation_connectivity_equiv():
"""Test cluster level permutations with and without connectivity
"""
try:
try:
from sklearn.feature_extraction.image import grid_to_graph
except ImportError:
from scikits.learn.feature_extraction.image import grid_to_graph
except ImportError:
return
rng = np.random.RandomState(0)
# subjects, time points, spatial points
X = rng.randn(7, 2, 10)
# add some significant points
X[:, 0:2, 0:2] += 10 # span two time points and two spatial points
X[:, 1, 5:9] += 10 # span four time points
max_steps = [1, 1, 1, 2]
# This will run full algorithm in two ways, then the ST-algorithm in 2 ways
# All of these should give the same results
conns = [None, grid_to_graph(2, 10),
grid_to_graph(1, 10), grid_to_graph(1, 10)]
stat_map = None
thresholds = [2, dict(start=0.5, step=0.5)]
sig_counts = [2, 8]
sdps = [0, 0.05, 0.05]
ots = ['mask', 'mask', 'indices']
for thresh, count in zip(thresholds, sig_counts):
cs = None
ps = None
for max_step, conn in zip(max_steps, conns):
for stat_fun in [ttest_1samp_no_p,
partial(ttest_1samp_no_p, sigma=1e-3)]:
for sdp, ot in zip(sdps, ots):
t, clusters, p, H0 = \
permutation_cluster_1samp_test(X,
threshold=thresh,
connectivity=conn,
n_jobs=2,
max_step=max_step,
stat_fun=stat_fun,
step_down_p=sdp,
out_type=ot)
# make sure our output datatype is correct
if ot == 'mask':
assert_true(isinstance(clusters[0], np.ndarray))
assert_true(clusters[0].dtype == bool)
assert_array_equal(clusters[0].shape, X.shape[1:])
else: # ot == 'indices'
assert_true(isinstance(clusters[0], tuple))
# make sure all comparisons were done; for TFCE, no perm
# should come up empty
if count == 8:
assert_true(not np.any(H0 == 0))
inds = np.where(p < 0.05)[0]
assert_true(len(inds) == count)
this_cs = [clusters[ii] for ii in inds]
this_ps = p[inds]
this_stat_map = np.zeros((2, 10), dtype=bool)
for ci, c in enumerate(this_cs):
if isinstance(c, tuple):
this_c = np.zeros((2, 10), bool)
for x, y in zip(c[0], c[1]):
this_stat_map[x, y] = True
this_c[x, y] = True
this_cs[ci] = this_c
c = this_c
this_stat_map[c] = True
if cs is None:
ps = this_ps
cs = this_cs
if stat_map is None:
stat_map = this_stat_map
assert_array_equal(ps, this_ps)
assert_true(len(cs) == len(this_cs))
for c1, c2 in zip(cs, this_cs):
assert_array_equal(c1, c2)
assert_array_equal(stat_map, this_stat_map)
make_ad_hoc_cov)
from mne.io import Raw
from mne.utils import _TempDir, slow_test, requires_module
from mne.io.proc_history import _get_sss_rank
from mne.io.pick import channel_type, _picks_by_type
from mne.fixes import partial
_recent_sklearn_call = """
required_version = '0.15'
import sklearn
version = LooseVersion(sklearn.__version__)
if version < required_version:
raise ImportError
"""
requires_sklearn_0_15 = partial(requires_module, name='sklearn',
call=_recent_sklearn_call)
warnings.simplefilter('always') # enable b/c these tests throw warnings
base_dir = op.join(op.dirname(__file__), '..', 'io', 'tests', 'data')
cov_fname = op.join(base_dir, 'test-cov.fif')
cov_gz_fname = op.join(base_dir, 'test-cov.fif.gz')
cov_km_fname = op.join(base_dir, 'test-km-cov.fif')
raw_fname = op.join(base_dir, 'test_raw.fif')
ave_fname = op.join(base_dir, 'test-ave.fif')
erm_cov_fname = op.join(base_dir, 'test_erm-cov.fif')
hp_fif_fname = op.join(base_dir, 'test_chpi_raw_sss.fif')
def test_ad_hoc_cov():
"""Test ad hoc cov creation and I/O"""
tempdir = _TempDir()
