def test_copy_of_view(self):
G = nx.OrderedMultiGraph(self.MGv)
assert G.__class__.__name__ == 'OrderedMultiGraph'
G = G.copy(as_view=True)
assert G.__class__.__name__ == 'OrderedMultiGraph'
def test_multigraph(self):
G = nx.OrderedMultiGraph()
def matching(
paths,
atlas,
namer_dir,
):
import glob
import networkx as nx
import numpy as np
from pynets.core import thresholding
from pynets.statistics.utils import parse_closest_ixs
from graspologic.utils import remove_loops, symmetrize, \
multigraph_lcc_intersection
[dwi_graph_path, func_graph_path] = paths
dwi_mat = np.load(dwi_graph_path)
func_mat = np.load(func_graph_path)
dwi_mat = thresholding.autofix(symmetrize(remove_loops(dwi_mat)))
func_mat = thresholding.autofix(symmetrize(remove_loops(func_mat)))
dwi_mat = thresholding.standardize(dwi_mat)
func_mat = thresholding.standardize(func_mat)
node_dict_dwi = parse_closest_ixs(
glob.glob(f"{str(Path(dwi_graph_path).parent.parent)}"
f"/nodes/*.json"), dwi_mat.shape[0])[1]
node_dict_func = parse_closest_ixs(
glob.glob(f"{str(Path(func_graph_path).parent.parent)}"
f"/nodes/*.json"), func_mat.shape[0])[1]
G_dwi = nx.from_numpy_array(dwi_mat)
nx.set_edge_attributes(G_dwi, 'structural',
nx.get_edge_attributes(G_dwi, 'weight').values())
nx.set_node_attributes(G_dwi, dict(node_dict_dwi), name='dwi')
#G_dwi.nodes(data=True)
G_func = nx.from_numpy_array(func_mat)
nx.set_edge_attributes(G_func, 'functional',
nx.get_edge_attributes(G_func, 'weight').values())
nx.set_node_attributes(G_func, dict(node_dict_func), name='func')
#G_func.nodes(data=True)
R = G_dwi.copy()
R.remove_nodes_from(n for n in G_dwi if n not in G_func)
R.remove_edges_from(e for e in G_dwi.edges if e not in G_func.edges)
G_dwi = R.copy()
R = G_func.copy()
R.remove_nodes_from(n for n in G_func if n not in G_dwi)
R.remove_edges_from(e for e in G_func.edges if e not in G_dwi.edges)
G_func = R.copy()
[G_dwi, G_func] = multigraph_lcc_intersection([G_dwi, G_func])
def writeJSON(metadata_str, outputdir):
import json
import uuid
modality = metadata_str.split('modality-')[1].split('_')[0]
metadata_list = [
i for i in metadata_str.split('modality-')[1].split('_')
if '-' in i
]
hash = str(uuid.uuid4())
filename = f"{outputdir}/sidecar_modality-{modality}_{hash}.json"
metadata_dict = {}
for meta in metadata_list:
k, v = meta.split('-')
metadata_dict[k] = v
with open(filename, 'w+') as jsonfile:
json.dump(metadata_dict, jsonfile, indent=4)
jsonfile.close()
return hash
dwi_name = dwi_graph_path.split("/")[-1].split(".npy")[0]
func_name = func_graph_path.split("/")[-1].split(".npy")[0]
dwi_hash = writeJSON(dwi_name, namer_dir)
func_hash = writeJSON(func_name, namer_dir)
name = f"{atlas}_mplx_layer1-dwi_ensemble-{dwi_hash}_" \
f"layer2-func_ensemble-{func_hash}"
dwi_opt, func_opt, best_mi = optimize_mutual_info(
nx.to_numpy_array(G_dwi), nx.to_numpy_array(G_func), bins=50)
func_mat_final = list(func_opt.values())[0]
dwi_mat_final = list(dwi_opt.values())[0]
G_dwi_final = nx.from_numpy_array(dwi_mat_final)
G_func_final = nx.from_numpy_array(func_mat_final)
G_multi = nx.OrderedMultiGraph(nx.compose(G_dwi_final, G_func_final))
out_name = f"{name}_matchthr-{list(dwi_opt.keys())[0]}_" \
f"{list(func_opt.keys())[0]}"
mG = build_mx_multigraph(nx.to_numpy_array(G_func_final),
nx.to_numpy_array(G_dwi_final), out_name,
namer_dir)
mG_nx = f"{namer_dir}/{out_name}.gpickle"
nx.write_gpickle(G_multi, mG_nx)
dwi_file_out = f"{namer_dir}/{dwi_name}.npy"
func_file_out = f"{namer_dir}/{func_name}.npy"
np.save(dwi_file_out, dwi_mat_final)
np.save(func_file_out, func_mat_final)
return mG_nx, mG, dwi_file_out, func_file_out
