def _create_collection(analysis, force=False):
collection_name = analysis.name
if force is True:
timestamp = datetime.datetime.utcnow().strftime('%Y-%m-%d_%H:%M')
collection_name += f"_{timestamp}"
url = f"https://{current_app.config['SERVER_NAME']}"\
f"/builder/{analysis.hash_id}"
try:
api = Client(
access_token=current_app.config['NEUROVAULT_ACCESS_TOKEN'])
collection = api.create_collection(collection_name,
description=analysis.description,
full_dataset_url=url)
except Exception:
abort(
422, "Error creating collection, "
"perhaps one with that name already exists?")
# Create new NV collection
upload = NeurovaultCollection(
analysis_id=analysis.hash_id,
collection_id=collection['id'],
)
db.session.add(upload)
db.session.commit()
upload_dir = Path(current_app.config['FILE_DIR']) / 'uploads' / str(
collection['id'])
upload_dir.mkdir(exist_ok=True)
return upload
def upload_neurovault(flask_app,
img_tarball,
hash_id,
upload_id,
timestamp=None,
n_subjects=None):
""" Upload results to NeuroVault
Args:
img_tarball (str): tarball path containg images
hash_id (str): Analysis hash_id
upload_id (int): NeurovaultCollection object id
timestamp (str): Current server timestamp
n_subjects (int): Number of subjects in analysis
"""
upload_object = NeurovaultCollection.query.filter_by(id=upload_id).one()
timestamp = "_" + timestamp if timestamp is not None else ''
api = Client(access_token=flask_app.config['NEUROVAULT_ACCESS_TOKEN'])
try:
# Untar:
tmp_dir = Path(mkdtemp())
with tarfile.open(img_tarball, mode="r:gz") as tf:
tf.extractall(tmp_dir)
except Exception as e:
update_record(upload_object,
exception=e,
traceback='Error decompressing image bundle')
raise
try:
collection = api.create_collection('{}{}'.format(hash_id, timestamp))
for img_path in tmp_dir.glob('*stat-t_statmap.nii.gz'):
contrast_name = re.findall('contrast-(.*)_', str(img_path))[0]
api.add_image(collection['id'],
img_path,
name=contrast_name,
modality="fMRI-BOLD",
map_type='T',
analysis_level='G',
cognitive_paradigm_cogatlas='None',
number_of_subjects=n_subjects,
is_valid=True)
except Exception as e:
update_record(upload_object,
exception=e,
traceback='Error uploading, perhaps a \
collection with the same name exists?')
raise
return update_record(upload_object,
collection_id=collection['id'],
status='OK')
for c in my_collections:
if k in c:
if c[k] == kwargs[k]:
matches.append(c['id'])
return (matches)
collection = {}
for imgset in imagesets:
if upload and imgset not in collection:
cname = 'NARPS_%s' % imgset
c = find_collection(api, name=cname)
if len(c) > 0:
collection[imgset] = api.get_collection(c[0])
else:
collection[imgset] = api.create_collection(
cname, description=imagesets[imgset][3])
imgs = glob.glob(
os.path.join(output_dir, imagesets[imgset][0], imagesets[imgset][1]))
imgs.sort()
for img in imgs:
# get hypothesis number
hypnum = int([
i for i in os.path.basename(img).split('_') if i.find('hyp') > -1
][0].split('.')[0].replace('hypo', 'hyp').replace('hyp', ''))
print(imgset, hypnum)
if upload:
image = api.add_image(collection[imgset]['id'],
img,
name='%s_Hyp%d' % (imgset, hypnum),
modality='fMRI-BOLD',
from pynv import Client
api=Client(access_token='uctb6zhxK6BUY4JgPZZzGZK22tk2zxbX8U5QLNSl')
collection=api.create_collection('Gender')
image_file_path='D:/FaceData/func_img/wrarun1.nii'
image=api.add_image(
collection['9850'],
image_file_path,
name='Gender',
modality='fMRI_BOLD',
map_type='Other'
)