def _graphs(self):
"""
volumetric numpy array, shape (n, v, v),
accounting for isolate nodes by unioning the vertices of all component edgelists,
sorted in the same order as `self.files`.
Returns
-------
graphs : np.ndarray, shape (n, v, v), 3D
Volumetric numpy array, n vxv adjacency matrices corresponding to each edgelist.
graphs[0, :, :] corresponds to files[0].D
"""
list_of_arrays = import_edgelist(self.files, delimiter=self.delimiter)
if not isinstance(list_of_arrays, list):
list_of_arrays = [list_of_arrays]
return np.atleast_3d(list_of_arrays)
def load_HNU1(ptr=None, return_subid=False):
path = Path(MODULE_PATH).parents[1] / "data/raw/HNU1/"
f = sorted(path.glob('*.ssv'))
df = pd.read_csv(path / "HNU1.csv")
subid = [int(fname.stem.split('_')[0].split('-')[1][-5:]) for fname in f]
y = np.array([np.unique(df.SEX[df.SUBID == s])[0] - 1 for s in subid])
g = np.array(import_edgelist(f, 'ssv'))
if ptr is not None:
g = np.array([pass_to_ranks(x) for x in g])
if return_subid:
return g, y, subid
else:
return g, y
def load_BNU1(ptr=None):
path = Path(MODULE_PATH).parents[1] / "data/raw/BNU1/"
f = sorted(path.glob('*.ssv'))
df = pd.read_csv(path / "BNU1_phenotypic_data.csv")
subid = [int(fname.stem.split('_')[0].split('-')[1][-5:]) for fname in f]
y = np.array([
np.unique(df.SEX[(df.SUBID == s) & (df.SESSION == 'Baseline')])[0]
for s in subid
]).astype(int)
y -= 1
g = np.array(import_edgelist(f, 'ssv'))
if ptr is not None:
g = np.array([pass_to_ranks(x) for x in g])
return g, y