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 get_collection_image_metadata(collection=None, data_dir=None, limit=10):
''' Get image metadata associated with collection
Args:
collection: (int) collection id
data_dir: (str) data directory
limit: (int) number of images to increment
Returns:
metadata: (pd.DataFrame) Dataframe with full image metadata from
collection
'''
if os.path.isfile(os.path.join(data_dir, 'metadata.csv')):
dat = pd.read_csv(os.path.join(data_dir, 'metadata.csv'))
else:
offset = 0
api = Client()
i = api.get_collection_images(collection_id=collection,
limit=limit,
offset=offset)
dat = pd.DataFrame(columns=i['results'][0].keys())
while int(offset) < int(i['count']):
for x in i['results']:
dat = dat.append(x, ignore_index=True)
offset = offset + limit
i = api.get_collection_images(collection_id=collection,
limit=limit,
offset=offset)
dat.to_csv(os.path.join(data_dir, 'metadata.csv'), index=False)
return dat
def get_collection_image_metadata(collection=None, data_dir=None, limit=10):
"""
Get image metadata associated with collection
Args:
collection (int, optional): collection id. Defaults to None.
data_dir (str, optional): data directory. Defaults to None.
limit (int, optional): number of images to increment. Defaults to 10.
Returns:
pd.DataFrame: Dataframe with full image metadata from collection
"""
if os.path.isfile(os.path.join(data_dir, "metadata.csv")):
dat = pd.read_csv(os.path.join(data_dir, "metadata.csv"))
else:
offset = 0
api = Client()
i = api.get_collection_images(collection_id=collection,
limit=limit,
offset=offset)
dat = pd.DataFrame(columns=i["results"][0].keys())
while int(offset) < int(i["count"]):
for x in i["results"]:
dat = dat.append(x, ignore_index=True)
offset = offset + limit
i = api.get_collection_images(collection_id=collection,
limit=limit,
offset=offset)
dat.to_csv(os.path.join(data_dir, "metadata.csv"), index=False)
return dat
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')
def upload_neurovault(flask_app, file_id, n_subjects=None):
""" Upload image file to NeuroVault
Args:
file_id (int): NeurovaultFileUpload object id
n_subjects (int): Number of subjects in analysis
"""
api = Client(access_token=flask_app.config['NEUROVAULT_ACCESS_TOKEN'])
file_object = NeurovaultFileUpload.query.filter_by(id=file_id).one()
basename = Path(file_object.path).parts[-1]
contrast_name = re.findall('contrast-(.*)_', str(basename))[0]
map_type = re.findall('stat-(.*)_', str(basename))[0]
map_type = MAP_TYPE_CHOICES[map_type]
if file_object.level == 'GROUP':
analysis_level = 'G'
else:
analysis_level = 'S'
n_subjects = None
try:
api.add_image(file_object.collection.collection_id,
file_object.path,
name=contrast_name,
modality="fMRI-BOLD",
map_type=map_type,
analysis_level=analysis_level,
cognitive_paradigm_cogatlas='None',
number_of_subjects=n_subjects,
is_valid=True)
except Exception as e:
update_record(file_object,
exception=e,
traceback='Error adding image to collection')
raise
return update_record(file_object, status='OK')
"""
create neurovault collection with results
"""
import os
import glob
from pynv import Client
assert 'NEUROVAULT_API_KEY' in os.environ
access_token = os.environ['NEUROVAULT_API_KEY']
assert 'NARPS_BASEDIR' in os.environ
basedir = os.environ['NARPS_BASEDIR']
output_dir = os.path.join(basedir, 'output')
api = Client(access_token=access_token)
# tuple contains directory name and wildcard
imagesets = {
'overlap':
('overlap_binarized_thresh', 'hypo*.nii.gz', 'Other',
'NARPS Study: Overlap maps, showing roportion of teams with significant activity at any voxel (Figure 1)'
), # noqa
'clustermaps':
('cluster_maps', 'hyp*_cluster*_mean.nii.gz', 'Other',
'NARPS Study: Cluster maps, showing mean Z statistic across teams for each hypothesis/cluster (Figure 2, Supplementary Figures 2-7)'
), # noqa
'CBMA':
('ALE', 'hypo*_fdr_oneminusp.nii.gz', 'Other',
'NARPS Study: Coordinate-based meta-analysis results (1 - P after FDR correction) (Supplementary Figure 1)'
), # noqa
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'
)
def client():
return Client(access_token=ACCESS_TOKEN)
def anonymous_client():
return Client()