def test_sym_mat_to_vector():
"""Test converting between vectors and symmetric matrices."""
mat = np.array([[0, 1, 2, 3],
[1, 4, 5, 6],
[2, 5, 7, 8],
[3, 6, 8, 9]])
assert_array_equal(_sym_mat_to_vector(mat),
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
vec = np.arange(10)
assert_array_equal(_vector_to_sym_mat(vec),
[[0, 1, 2, 3],
[1, 4, 5, 6],
[2, 5, 7, 8],
[3, 6, 8, 9]])
# Test complex values: diagonals should be complex conjugates
comp_vec = np.arange(3) + 1j
assert_array_equal(_vector_to_sym_mat(comp_vec),
[[0. + 0.j, 1. + 1.j],
[1. - 1.j, 2. + 0.j]])
# Test preservation of data type
assert _sym_mat_to_vector(mat.astype(np.int8)).dtype == np.int8
assert _vector_to_sym_mat(vec.astype(np.int8)).dtype == np.int8
assert _sym_mat_to_vector(mat.astype(np.float16)).dtype == np.float16
assert _vector_to_sym_mat(vec.astype(np.float16)).dtype == np.float16
def test_sym_mat_to_vector():
"""Test converting between vectors and symmetric matrices."""
mat = np.array([[0, 1, 2, 3],
[1, 4, 5, 6],
[2, 5, 7, 8],
[3, 6, 8, 9]])
assert_array_equal(_sym_mat_to_vector(mat),
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
vec = np.arange(10)
assert_array_equal(_vector_to_sym_mat(vec),
[[0, 1, 2, 3],
[1, 4, 5, 6],
[2, 5, 7, 8],
[3, 6, 8, 9]])
# Test complex values: diagonals should be complex conjugates
comp_vec = np.arange(3) + 1j
assert_array_equal(_vector_to_sym_mat(comp_vec),
[[0. + 0.j, 1. + 1.j],
[1. - 1.j, 2. + 0.j]])
# Test preservation of data type
assert _sym_mat_to_vector(mat.astype(np.int8)).dtype == np.int8
assert _vector_to_sym_mat(vec.astype(np.int8)).dtype == np.int8
assert _sym_mat_to_vector(mat.astype(np.float16)).dtype == np.float16
assert _vector_to_sym_mat(vec.astype(np.float16)).dtype == np.float16
def __getitem__(self, sel): # noqa: D105
"""Subselect frequencies.
Parameters
----------
sel : ndarray
Array of frequency indices to subselect.
Returns
-------
csd : instance of CrossSpectralDensity
A new CSD instance with the subset of frequencies.
"""
return ResultConnectivity(
data=_vector_to_sym_mat(self._data[..., sel]),
info=self.info,
metadata=self.metadata,
fmin=self.fmin,
fmax=self.fmax,
)
def pick_channels_connectivity(csd,
include=[],
exclude=[],
ordered=False,
copy=True):
"""Pick channels from connectivity matrix.
Parameters
----------
csd : instance of ResultConnectivity
The Result object to select the channels from.
include : list of str
List of channels to include (if empty, include all available).
exclude : list of str
Channels to exclude (if empty, do not exclude any).
ordered : bool
If True (default False), ensure that the order of the channels in the
modified instance matches the order of ``include``.
.. versionadded:: 0.20.0
copy : bool
If True (the default), return a copy of the CSD matrix with the
modified channels. If False, channels are modified in-place.
.. versionadded:: 0.20.0
Returns
-------
res : instance of CrossSpectralDensity
Cross-spectral density restricted to selected channels.
"""
if copy:
csd = csd.copy()
sel = pick_channels(csd.ch_names,
include=include,
exclude=exclude,
ordered=ordered)
data = []
for vec in csd._data.T:
mat = _vector_to_sym_mat(vec)
mat = mat[sel, :][:, sel]
data.append(_sym_mat_to_vector(mat))
ch_names = [csd.ch_names[i] for i in sel]
csd._data = np.array(data).T
csd.ch_names = ch_names
return csd
def get_data(self, start=0, stop=None):
"""Get the CSD matrix for a given frequency as NumPy array.
If there is only one matrix defined in the CSD object, calling this
method without any parameters will return it. If multiple matrices are
defined, use either the ``frequency`` or ``index`` parameter to select
one.
Parameters
----------
start : int | 0
Return the CSD matrix for the time point
stop : int | 0
Return the CSD matrix for the time point
Returns
-------
csd : ndarray, shape (n_channels, n_channels, n_times)
The CSD matrix corresponding to the requested frequency.
"""
if stop is None:
stop = self.n_times
return _vector_to_sym_mat(self._data[..., start:stop])
