[sym_adj],
transform="binarize",
height=20,
sizes=(10, 20),
inner_hier_labels=side_labels,
sort_nodes=False,
palette="deep",
legend=False,
)
stashfig("sym-adj")
mg = MetaGraph(sym_adj, mg.meta)
# %% [markdown]
# # Preprocess graph
n_verts = mg.n_verts
mg.make_lcc()
print(f"Removed {n_verts - mg.n_verts} when finding the LCC")
# old_n_verts = sym_adj.shape[0]
# sym_adj, class_labels, side_labels = preprocess_graph(
# sym_adj, class_labels, side_labels
# )
# n_verts = sym_adj.shape[0]
# print(f"Removed {old_n_verts - n_verts} nodes")
# %% [markdown]
# # Embedding
n_verts = mg.n_verts
sym_adj = mg.adj
side_labels = mg["Hemisphere"]
class_labels = mg["Merge Class"]
latent, laplacian = lse(sym_adj, N_COMPONENTS, regularizer=None, ptr=PTR)
threshs = [0.01, 0.02]
else:
threshs = [1, 2, 3]
for thresh in threshs:
for r in [0.3, 0.5, 0.7, 1]:
mg = load_metagraph(graph_type, version=BRAIN_VERSION)
edgelist = mg.to_edgelist()
edgelist = edgelist[edgelist["weight"] > thresh]
nodelist = list(mg.g.nodes())
thresh_g = nx.from_pandas_edgelist(edgelist,
edge_attr=True,
create_using=nx.DiGraph)
nx.set_node_attributes(thresh_g, mg.meta.to_dict(orient="index"))
mg = MetaGraph(thresh_g)
print(len(mg.meta))
mg = mg.make_lcc()
print(len(mg.meta))
not_pdiff = np.where(~mg["is_pdiff"])[0]
mg = mg.reindex(not_pdiff)
print(len(mg.meta))
g_sym = nx.to_undirected(mg.g)
skeleton_labels = np.array(list(g_sym.nodes()))
out_dict = cm.best_partition(g_sym, resolution=r)
partition = np.array(itemgetter(*skeleton_labels)(out_dict))
adj = nx.to_numpy_array(g_sym, nodelist=skeleton_labels)
part_unique, part_count = np.unique(partition, return_counts=True)
for uni, count in zip(part_unique, part_count):
if count < 3:
inds = np.where(partition == uni)[0]
partition[inds] = -1
# sym_contra_adj = (left_right_adj + right_left_adj) / 2
# max
sym_ipsi_adj = np.maximum(left_left_adj, right_right_adj)
sym_contra_adj = np.maximum(left_right_adj, right_left_adj)
sym_adj = adj.copy()
sym_adj[:n_pairs, :n_pairs] = sym_ipsi_adj
sym_adj[n_pairs : 2 * n_pairs, n_pairs : 2 * n_pairs] = sym_ipsi_adj
sym_adj[:n_pairs, n_pairs : 2 * n_pairs] = sym_contra_adj
sym_adj[n_pairs : 2 * n_pairs, :n_pairs] = sym_contra_adj
full_sym_mg = MetaGraph(sym_adj, mg.meta)
n_verts = full_sym_mg.n_verts
full_sym_mg.make_lcc()
print(f"Removed {n_verts - full_sym_mg.n_verts} when finding the LCC")
adj = full_sym_mg.adj
side_labels = full_sym_mg["Hemisphere"]
class_labels = full_sym_mg["Merge Class"]
latent, laplacian = lse(adj, N_COMPONENTS, regularizer=None, ptr=PTR)
latent_dim = latent.shape[1] // 2
screeplot(
laplacian, title=f"Laplacian scree plot, R-DAD (ZG2 = {latent_dim} + {latent_dim})"
)
quick_gridmap(adj, side_labels)
stashfig(preface + "adj")
n_components = latent.shape[1]
for dim1 in range(n_components):
