def main(outputfilename, outputmetafilename, tempdir):
tempfiles = [f for f in os.listdir(tempdir) if not f.startswith('.')]
numwins = len(tempfiles)
if not os.path.exists(outputmetafilename):
temp0file = os.path.join(tempdir, "temp0.npz")
os.remove(temp0file)
# remove success file
outfile = os.path.basename(outputfilename)
success_flag_name = outfile.replace('fragmodel.npz',
'frag_success.txt')
success_flag_name = os.path.join(walk_up_folder(tempdir, 2),
success_flag_name)
os.remove(success_flag_name)
sys.stdout.write("File Removed {}!".format(success_flag_name))
raise RuntimeError(
"Number of windows is not the same as the number of"
"files in the temp dir. Should be {}".format(numwins))
# run merge
merger = RunMergeModel(tempdir, numwins)
merger.loadmetafile(outputmetafilename)
metadata = merger.metadata
if not metadata['numwins'] == numwins:
# remove success file
outfile = os.path.basename(outputfilename)
success_flag_name = outfile.replace('fragmodel.npz',
'frag_success.txt')
success_flag_name = os.path.join(walk_up_folder(tempdir, 2),
success_flag_name)
os.remove(success_flag_name)
sys.stdout.write("File Removed {}!".format(success_flag_name))
raise RuntimeError(
"Number of windows is not the same as the number of"
"files in the temp dir. Should be {} vs #files: {}".format(
metadata['numwins'], numwins))
merger.loadmetadata(metadata)
merger.mergefragilitydata(outputfilename=outputfilename)
merger.writemetadata_tofile(outputmetafilename=outputmetafilename)
outputjson_filepath = args.outputjson_filepath
modality = args.datatype
excelfilepath = args.exceldatafilepath
# time the program
start = time.time()
outputjson_filepath = outputjson_filepath.replace('_raw.fif', '.json')
print("saving to ", outputjson_filepath)
''' EXTRACT USEFUL INFORMATION FROM THE OUTPUTJSON FILEPATH '''
# extract the clinical center from the path name
clinical_center = os.path.basename(os.path.normpath(rawdatadir))
# extract root directory
pat_id = os.path.basename(walk_up_folder(outputjson_filepath, 4))
dataset_id = ''.join(
os.path.basename(outputjson_filepath.replace(
'_raw', '')).split('.')[0].split('_')[1:])
# load in metadata and clinical df object
metadata = loadmetadata(outputjson_filepath)
clindf = loadclinicaldf(excelfilepath)
if 'la' in pat_id:
dataset_id = dataset_id.replace("scalp", '').replace("sz", '')
print("looking at: ", pat_id, dataset_id)
# merge in data based on patient id and dataset id
metadata = merge_metadata(metadata,
def pair_name_edffiles(config, ignore_interictal=False, ignore_ictal=False):
# hack inserted to make sure all modalities are for ieeg
modality = config['modality']
# if modality == 'ieeg':
# modality = 'seeg'
edf_path = Path(os.path.join(config['rawdatadir'], config['center'])).glob(
"*/{}/edf/*.edf".format(modality))
if not edf_path:
modality = 'ieeg'
edf_path = Path(os.path.join(config['rawdatadir'],
config['center'])).glob(
"*/{}/edf/*.edf".format(modality))
# pair edf name to infile path using a dictionary
edf_infiles_dict = {}
for f in edf_path:
# get the actual dataset name
edf_name = f.name
if edf_name.startswith('.'):
continue
# seeg datasets to ignore in this rule
if modality == 'ieeg' or modality == 'seeg':
if edf_name.startswith('.'):
continue
# create the new filepath we want the output of this pipeline to be
fifdir = os.path.dirname(str(f)).replace('edf', 'fif')
edfdir = fifdir.replace('fif', 'edf')
edf_filepath = rename_file(os.path.join(edfdir, f.name))
edf_name = os.path.basename(edf_filepath)
# use preformatter filename mapping - TRIMMING DATASET FILENAME
fif_name = BaseMeta.map_filename(edf_name)
# if not any(x in fif_name for x in [
# 'nl01'
# # 'la21',
# # 'la22',
# # 'la23',
# # 'la24'
# # 'tvb'
# ]):
# continue
# extract root directory
pat_id = os.path.basename(walk_up_folder(edf_filepath, 4))
if pat_id not in fif_name:
fif_name = pat_id + fif_name
if modality == 'scalp':
fif_name = fif_name.replace('_scalp', '')
elif modality == 'seeg':
fif_name = fif_name.replace("_seeg", "")
if ignore_interictal:
if 'ii' in fif_name:
continue
if ignore_ictal:
if 'sz' in fif_name:
continue
# set the dictionary
edf_infiles_dict[str(os.path.join(fifdir,
fif_name))] = str(edf_filepath)
return edf_infiles_dict
if __name__ == '__main__':
args = parser.parse_args()
rawdatadir = args.rawdatadir
inputedf_filepath = args.inputedf_filepath
outputjson_filepath = args.outputjson_filepath
datatype = args.datatype
# time the program
start = time.time()
''' EXTRACT USEFUL INFORMATION FROM THE OUTPUTJSON FILEPATH '''
# extract the clinical center from the path name
clinical_center = os.path.basename(os.path.normpath(rawdatadir))
# extract root directory
pat_id = os.path.basename(walk_up_folder(outputjson_filepath, 4))
dataset_id = '_'.join(
os.path.basename(outputjson_filepath.replace(
'_raw', '')).split('.')[0].split('_')[1:])
patdatadir = os.path.join(rawdatadir, pat_id)
# extract the actual json filename
outputjson_filename = os.path.basename(outputjson_filepath)
# set the directories according to how we decided
seegdir = os.path.join(walk_up_folder(outputjson_filepath, 3), 'edf')
fifdir = seegdir.replace('edf', 'fif')
if not os.path.exists(fifdir):
os.makedirs(fifdir)
print("seegdir ", seegdir)
def mergefragilitydata(self, outputfilename):
"""
Function to merge fragility computed data (i.e. LTVN and Perturbation model)
Performs a loop over files in the self.tempdir
:param outputfilename: (os.PathLike) output filepath to save the resulting data
as .npz file type.
:return:
"""
saveflag = True
# self.logger.info("Merging windowed analysis compute together of "
# "{} windows!".format(len(self.alltempfiles)))
# save adjacency matrix
for idx, tempfile in enumerate(self.alltempfiles):
# get the window numbr of this file just to check
buff = tempfile.split('_')
winnum = int(buff[-1].split('.')[0])
if winnum != idx:
raise ValueError("Win num {} should match idx {}".format(
winnum, idx))
tempfilename = os.path.join(self.tempdir, tempfile)
# load result data
try:
data = super(RunMergeModel, self)._loadnpzfile(tempfilename)
except:
saveflag = False
# remove that tempfilename
os.remove(tempfilename)
print("File Removed {}!".format(tempfilename))
# remove success file
outfile = os.path.basename(outputfilename)
success_flag_name = os.path.join(
walk_up_folder(self.tempdir, 2),
outfile.replace('fragmodel.npz', 'frag_success.txt'))
try:
os.remove(success_flag_name)
print("File Removed {}!".format(success_flag_name))
except Exception as e:
print(e)
print("Success flag file already removed!")
# self.logger.error("Success flag file already removed at "
# "{} window!".format(idx))
continue
try:
pertmat = data['pertmat']
# delfreqs = pertmat_data['delfreqs']
delvecs = data['delvecs']
adjmat = data['adjmat']
except:
saveflag = False
# remove that tempfilename
os.remove(tempfilename)
print("File Removed {}!".format(tempfilename))
# remove success file
outfile = os.path.basename(outputfilename)
success_flag_name = os.path.join(
walk_up_folder(self.tempdir, 2),
outfile.replace('fragmodel.npz', 'frag_success.txt'))
try:
os.remove(success_flag_name)
print("File Removed {}!".format(success_flag_name))
except Exception as e:
print(e)
print("Success flag file already removed!")
# self.logger.error("Success flag file already removed at "
# "{} window!".format(idx))
continue
if idx == 0:
numchans, _ = pertmat.shape
# initializ adjacency matrices over time
pertmats = np.zeros((numchans, len(self.alltempfiles)))
delvecs_array = np.zeros(
(numchans, numchans, len(self.alltempfiles)),
dtype='complex')
adjmats = np.zeros(
(len(self.alltempfiles), numchans, numchans))
# delfreqs_array = np.zeros((numchans, len(alltempfiles)))
pertmats[:, idx] = pertmat.ravel()
delvecs_array[:, :, idx] = delvecs
adjmats[idx, :, :] = adjmat
# delfreqs_array[:,idx] = delfreqs
if saveflag:
# save adjmats, pertmats and delvecs array along with metadata
super(RunMergeModel, self)._writenpzfile(outputfilename,
adjmats=adjmats,
pertmats=pertmats,
delvecs=delvecs_array)
def get_patientdir(rawdatasetpath):
patdir = walk_up_folder(rawdatasetpath, 4)
return patdir
reference = args.reference
modality = args.modality
gnuind = args.gnuind
gnusize = args.gnusize
print("\n\nArguments are: {}".format(args), flush=True)
print(root_dir, modality, flush=True)
print(jsonfilepath, flush=True)
for dirpath, dirnames, filenames in os.walk(root_dir):
for filename in [f for f in filenames if f == jsonfilepath]:
jsonfilepath = os.path.join(dirpath, filename)
# reassign root dir
root_dir = walk_up_folder(jsonfilepath, 4)
modality = os.path.basename(walk_up_folder(jsonfilepath, 3))
root_dir = os.path.join(root_dir, modality, "fif")
print("\nInside run frag...\n", flush=True)
print(root_dir, modality, flush=True)
print(jsonfilepath, flush=True)
# load in the data
eegts = load_raw_data(root_dir,
jsonfilepath,
reference,
apply_mask=True,
remove_wm_contacts=True,
modality=modality)