def corrcoef(self):
#If there is more than one channel in the seed time-series:
if len(self.seed.shape) > 1:
# Preallocate results
Cxy = np.empty((self.seed.data.shape[0],
self.target.data.shape[0]), dtype=np.float)
for seed_idx, this_seed in enumerate(self.seed.data):
Cxy[seed_idx] = tsa.seed_corrcoef(this_seed, self.target.data)
#In the case where there is only one channel in the seed time-series:
else:
Cxy = tsa.seed_corrcoef(self.seed.data, self.target.data)
return Cxy.squeeze()
def test_seed_correlation():
seed = np.random.rand(10)
targ = np.random.rand(10, 10)
our_coef_array = tsa.seed_corrcoef(seed, targ)
np_coef_array = np.array(map(lambda a: np.corrcoef(seed, a)[0, 1], targ))
npt.assert_array_almost_equal(our_coef_array, np_coef_array)
def test_seed_correlation():
seed = np.random.rand(10)
targ = np.random.rand(10, 10)
our_coef_array = tsa.seed_corrcoef(seed, targ)
np_coef_array = np.array([np.corrcoef(seed, a)[0, 1] for a in targ])
npt.assert_array_almost_equal(our_coef_array, np_coef_array)
def test_seed_correlation():
seed = np.random.rand(10)
targ = np.random.rand(10, 10)
our_coef_array = tsa.seed_corrcoef(seed, targ)
np_coef_array = np.array([np.corrcoef(seed, a)[0, 1] for a in targ])
npt.assert_array_almost_equal(our_coef_array, np_coef_array)
def corrcoef(self):
#If there is more than one channel in the seed time-series:
if len(self.seed.shape) > 1:
# Preallocate results
Cxy = np.empty(
(self.seed.data.shape[0], self.target.data.shape[0]),
dtype=np.float)
for seed_idx, this_seed in enumerate(self.seed.data):
Cxy[seed_idx] = tsa.seed_corrcoef(this_seed, self.target.data)
#In the case where there is only one channel in the seed time-series:
else:
Cxy = tsa.seed_corrcoef(self.seed.data, self.target.data)
return Cxy.squeeze()
