def test_nan_flag_equals_noflag(self):
assert_almost_equal(
sum_tc(self.a,
nans=True,
standardize_subs=False,
standardize_out=False),
sum_tc(self.a,
nans=False,
standardize_subs=False,
standardize_out=False))
def test_nan_misflag_returns_nan(self):
ts_nan = self.a.copy()
ts_nan[:, 0] = np.nan
assert any(
np.isnan(
sum_tc(ts_nan,
nans=False,
standardize_subs=False,
standardize_out=False)))
def crosscor_full(A, B=None, nan_thresh=None):
"""From data (dims sub x seg x vox) calculate sub against others pattern cor
Parameters:
A: sub x seg x vox matrix
B: (optional) seg x vox matrix
Returns:
seg x seg correlation matrix
"""
# standardize all along last dim, so don't need to in correlation
A = standardize(A)
all_cors = []
# Within group correlations
if B is None:
others = sum_tc(A)
for sub in A:
# check for nan
to_remove = np.any(np.isnan(sub), axis=0)
if np.any(to_remove):
tmp_sub = sub[..., to_remove]
tmp_others = others[..., to_remove]
else:
tmp_sub, tmp_others = sub, others
# cross correlate (1 x seg x seg)
if nan_thresh is not None and to_remove.mean() > nan_thresh:
cormat = np.empty(sub.shape[0:1] * 2) * np.nan
else:
cormat = crosscor(tmp_sub,
standardize(tmp_others - tmp_sub),
standardized=True)
all_cors.append(cormat)
return np.array(all_cors)
# Between group correlations
else:
B = standardize(B)
for sub in A:
cormat = crosscor(sub, B, standardized=True)
all_cors.append(cormat)
return np.array(all_cors)
def crosscor_full(A, B=None, nan_thresh=None):
"""From data (dims sub x seg x vox) calculate sub against others pattern cor
Parameters:
A: sub x seg x vox matrix
B: (optional) seg x vox matrix
Returns:
seg x seg correlation matrix
"""
# standardize all along last dim, so don't need to in correlation
A = standardize(A)
all_cors = []
# Within group correlations
if B is None:
others = sum_tc(A)
for sub in A:
# check for nan
to_remove = np.any(np.isnan(sub), axis=0)
if np.any(to_remove):
tmp_sub = sub[...,to_remove]
tmp_others = others[..., to_remove]
else:
tmp_sub, tmp_others = sub, others
# cross correlate (1 x seg x seg)
if nan_thresh is not None and to_remove.mean() > nan_thresh:
cormat = np.empty(sub.shape[0:1]*2) * np.nan
else:
cormat = crosscor(tmp_sub, standardize(tmp_others - tmp_sub), standardized=True)
all_cors.append(cormat)
return np.array(all_cors)
# Between group correlations
else:
B = standardize(B)
for sub in A:
cormat = crosscor(sub, B, standardized=True)
all_cors.append(cormat)
return np.array(all_cors)
def test_standardize_in_equal_standardize_func(self):
stand_a = standardize(self.a)
assert_almost_equal(sum_tc(self.a, standardize_subs=True),
sum_tc(stand_a, standardize_subs=False))
def test_standardize_out_equal_standardize_func(self):
summed = sum_tc(self.a, standardize_out=True)
assert_almost_equal(summed, standardize(summed))
def test_standardize_out_var(self):
sd = np.std(sum_tc(self.a, standardize_out=True), axis=-1, ddof=1)
assert_almost_equal(1, sd)
def test_standardize_out_mean(self):
mew = np.mean(sum_tc(self.a, standardize_out=True), axis=-1)
assert_almost_equal(0, mew)
def test_standardize_not_inplace(self):
assert sum_tc(self.a, standardize_subs=True) is not self.a
def test_nan_flag_equals_noflag(self):
assert_almost_equal(sum_tc(self.a, nans = True, standardize_subs=False, standardize_out=False),
sum_tc(self.a, nans=False, standardize_subs=False, standardize_out=False))
def test_nan_misflag_returns_nan(self):
ts_nan = self.a.copy()
ts_nan[:,0] = np.nan
assert any(np.isnan(sum_tc(ts_nan, nans=False, standardize_subs=False, standardize_out=False)))
def test_standardize_in_equal_standardize_func(self):
stand_a = standardize(self.a)
assert_almost_equal(sum_tc(self.a, standardize_subs=True),
sum_tc(stand_a, standardize_subs=False))
def test_standardize_out_equal_standardize_func(self):
summed = sum_tc(self.a, standardize_out=True)
assert_almost_equal(summed, standardize(summed))
def test_standardize_out_var(self):
sd = np.std(sum_tc(self.a, standardize_out=True), axis=-1, ddof=1)
assert_almost_equal(1, sd)
def test_standardize_out_mean(self):
mew = np.mean(sum_tc(self.a, standardize_out=True), axis=-1)
assert_almost_equal(0, mew)
def test_standardize_not_inplace(self):
assert sum_tc(self.a, standardize_subs=True) is not self.a
def test_3d(self):
solution = [[24., 28.], [32., 36.]]
assert_almost_equal(sum_tc(self.ts_3d), solution)
def test_3d(self):
solution= [[ 24., 28.],
[ 32., 36.]]
assert_almost_equal(sum_tc(self.ts_3d), solution)
