def get_input():
output_data_path, tree_input_path = get_data_paths_from_args()
classification_config = parse_classification_config()
trees: List[nx.Graph] = []
for tree_path in Path(tree_input_path).glob("*/tree.graphml"):
trees.append(nx.read_graphml(tree_path))
return output_data_path, trees, classification_config
def get_input():
output_data_path, tree_input_path = get_data_paths_from_args(inputs=1)
classification_config = parse_classification_config()
trees: List[nx.Graph] = []
ignored_patients = get_ignored_patients()
print(ignored_patients)
for tree_path in Path(tree_input_path).glob("*/tree.graphml"):
if tree_path.parent.name not in ignored_patients:
trees.append(nx.read_graphml(tree_path))
return output_data_path, trees, classification_config
def get_input():
output_data_path, tree_input_path = get_data_paths_from_args()
trees: List[List[nx.Graph]] = []
for tree_path in Path(tree_input_path).glob("*"):
pair = []
for name in ["tree.graphml", "tree_gt.graphml"]:
if (tree_path / name).exists():
pair.append(nx.read_graphml(tree_path / name))
trees.append(pair)
classification_config = parse_classification_config()
return output_data_path, trees, classification_config
def get_input():
output_data_path, tree_input_path, render_path = get_data_paths_from_args(
inputs=2)
classification_config = parse_classification_config()
for cc_dict in classification_config.values():
if "clustering_endnode" not in cc_dict:
cc_dict["clustering_endnode"] = False
trees: List[nx.Graph] = []
ignored_patients = get_ignored_patients()
for tree_path in Path(tree_input_path).glob("*/tree.graphml"):
if tree_path.parent.name not in ignored_patients:
trees.append(nx.read_graphml(tree_path))
return output_data_path, trees, classification_config, render_path
for node_id in tree.nodes():
if node_id in allowed:
if condition(tree, node_id):
ids.append(node_id)
else:
allowed.update(bfs_successors.get(node_id, []))
return ids
def is_lobe(tree: nx.Graph, node_id: int) -> bool:
return re.fullmatch(
r"[RL](Lower|Middle|Upper)Lobe",
tree.nodes[node_id]["split_classification"]) is not None
classification_config = parse_classification_config()
def is_segment(tree: nx.Graph, node_id: int) -> bool:
return classification_config.get(
tree.nodes[node_id]["split_classification"],
{}).get("clustering_endnode", False)
def main():
(
output_data_path,
reduced_model_path,
distance_mask_path,
tree_path,
) = get_data_paths_from_args(inputs=3)