def build_asetomes(est_path_iterlist, ID):
"""
Embeds single graphs using the ASE algorithm.
Parameters
----------
est_path_iterlist : list
List of file paths to .npy files, each containing a graph.
ID : str
A subject id or other unique identifier.
"""
from pynets.core.utils import prune_suffices
from pynets.stats.embeddings import _ase_embed
out_paths = []
for file_ in list(flatten(est_path_iterlist)):
mat = np.load(file_)
atlas = prune_suffices(file_.split('/')[-3])
res = prune_suffices('_'.join(
file_.split('/')[-1].split('modality')[1].split('_')[1:]).split(
'_est')[0])
if 'rsn' in res:
subgraph = res.split('rsn-')[1]
else:
subgraph = 'whole_brain'
out_path = _ase_embed(mat, atlas, file_, ID, subgraph_name=subgraph)
out_paths.append(out_path)
return out_paths
def build_asetomes(est_path_iterlist, ID):
"""
Embeds single graphs using the ASE algorithm.
Parameters
----------
est_path_iterlist : list
List of file paths to .npy files, each containing a graph.
ID : str
A subject id or other unique identifier.
"""
import numpy as np
from pynets.core.utils import prune_suffices, flatten
from pynets.stats.embeddings import _ase_embed
import yaml
import pkg_resources
# Available functional and structural connectivity models
with open(pkg_resources.resource_filename("pynets", "runconfig.yaml"),
"r") as stream:
hardcoded_params = yaml.load(stream)
try:
n_components = hardcoded_params["gradients"]["n_components"][0]
except KeyError:
import sys
print("ERROR: available gradient dimensionality presets not "
"sucessfully extracted from runconfig.yaml")
sys.exit(1)
stream.close()
if isinstance(est_path_iterlist, list):
est_path_iterlist = list(flatten(est_path_iterlist))
else:
est_path_iterlist = [est_path_iterlist]
out_paths = []
for file_ in est_path_iterlist:
mat = np.load(file_)
atlas = prune_suffices(file_.split("/")[-3])
res = prune_suffices("_".join(
file_.split("/")[-1].split("modality")[1].split("_")[1:]).split(
"_est")[0])
if "rsn" in res:
subgraph = res.split("rsn-")[1]
else:
subgraph = "all_nodes"
out_path = _ase_embed(mat,
atlas,
file_,
ID,
subgraph_name=subgraph,
n_components=n_components)
out_paths.append(out_path)
return out_paths
def build_asetomes(est_path_iterlist, ID):
"""
Embeds single graphs using the ASE algorithm.
Parameters
----------
est_path_iterlist : list
List of file paths to .npy files, each containing a graph.
ID : str
A subject id or other unique identifier.
"""
import numpy as np
from pynets.core.utils import prune_suffices, flatten
from pynets.stats.embeddings import _ase_embed
if isinstance(est_path_iterlist, list):
est_path_iterlist = list(flatten(est_path_iterlist))
else:
est_path_iterlist = [est_path_iterlist]
out_paths = []
for file_ in est_path_iterlist:
mat = np.load(file_)
atlas = prune_suffices(file_.split("/")[-3])
res = prune_suffices("_".join(
file_.split("/")[-1].split("modality")[1].split("_")[1:]).split(
"_est")[0])
if "rsn" in res:
subgraph = res.split("rsn-")[1]
else:
subgraph = "whole_brain"
out_path = _ase_embed(mat, atlas, file_, ID, subgraph_name=subgraph)
out_paths.append(out_path)
return out_paths
def build_asetomes(est_path_iterlist, ID):
"""
Embeds single graphs using the ASE algorithm.
Parameters
----------
est_path_iterlist : list
List of file paths to .npy files, each containing a graph.
ID : str
A subject id or other unique identifier.
"""
from pathlib import Path
import os
import numpy as np
from pynets.core.utils import prune_suffices, flatten
from pynets.stats.embeddings import _ase_embed
from pynets.core.utils import load_runconfig
# Available functional and structural connectivity models
hardcoded_params = load_runconfig()
try:
n_components = hardcoded_params["gradients"][
"n_components"][0]
except KeyError:
import sys
print(
"ERROR: available gradient dimensionality presets not "
"sucessfully extracted from runconfig.yaml"
)
sys.exit(1)
if isinstance(est_path_iterlist, list):
est_path_iterlist = list(flatten(est_path_iterlist))
else:
est_path_iterlist = [est_path_iterlist]
out_paths = []
for file_ in est_path_iterlist:
mat = np.load(file_)
if np.isfinite(mat).all() == False:
continue
atlas = prune_suffices(file_.split("/")[-3])
res = prune_suffices("_".join(file_.split(
"/")[-1].split("modality")[1].split("_")[1:]).split("_est")[0])
if "rsn" in res:
subgraph = res.split("rsn-")[1].split('_')[0]
else:
subgraph = "all_nodes"
out_path = _ase_embed(mat, atlas, file_, ID, subgraph_name=subgraph,
n_components=n_components)
if out_path is not None:
out_paths.append(out_path)
else:
# Add a null tmp file to prevent pool from breaking
dir_path = str(Path(os.path.dirname(file_)).parent)
namer_dir = f"{dir_path}/embeddings"
if os.path.isdir(namer_dir) is False:
os.makedirs(namer_dir, exist_ok=True)
out_path = f"{namer_dir}/gradient-ASE" \
f"_{atlas}_{subgraph}_{os.path.basename(file_)}_NULL"
if not os.path.exists(out_path):
os.mknod(out_path)
out_paths.append(out_path)
return out_paths