def main(argv):
#create new nidm-experiment document with project
kwargs={Constants.NIDM_PROJECT_NAME:"FBIRN_PhaseII",Constants.NIDM_PROJECT_IDENTIFIER:9610,Constants.NIDM_PROJECT_DESCRIPTION:"Test investigation"}
project = Project(attributes=kwargs)
#test add string attribute with existing namespace
#nidm_doc.addLiteralAttribute("nidm","isFun","ForMe")
project.add_attributes({Constants.NIDM["isFun"]:"ForMe"})
#test adding string attribute with new namespace/term
project.addLiteralAttribute("fred","notFound","in namespaces","www.fred.org/")
#test add float attribute
project.addLiteralAttribute("nidm", "float", float(2.34))
#test adding attributes in bulk with mix of existing and new namespaces
#nidm_doc.addAttributesWithNamespaces(nidm_doc.getProject(),[{"prefix":"nidm", "uri":nidm_doc.namespaces["nidm"], "term":"score", "value":int(15)}, \
# {"prefix":"dave", "uri":"http://www.davidkeator.com/", "term":"isAwesome", "value":"15"}, \
# {"prefix":"nidm", "uri":nidm_doc.namespaces["nidm"], "term":"value", "value":float(2.34)}])
#nidm_doc.addAttributes(nidm_doc.getProject(),{"nidm:test":int(15), "ncit:isTerminology":"15","ncit:joker":float(1)})
#test add PI to investigation
project_PI = project.add_person(role=Constants.NIDM_PI, attributes={Constants.NIDM_FAMILY_NAME:"Keator", Constants.NIDM_GIVEN_NAME:"David"})
#test add session to graph and associate with project
session = Session(project)
project.add_sessions(session)
#test add acquisition activity to graph and associate with session
acq_act = Acquisition(session=session)
#test add acquisition object entity to graph associated with participant role NIDM_PARTICIPANT
acq_entity = MRAcquisitionObject(acquisition=acq_act)
acq_entity.add_person(role=Constants.NIDM_PARTICIPANT,attributes={Constants.NIDM_GIVEN_NAME:"George"})
#save a turtle file
with open("test.ttl",'w') as f:
f.write (project.serializeTurtle())
#save a DOT graph as PDF
project.save_DotGraph("test.png",format="png")
def main(argv):
parser = ArgumentParser(
description=
'This program will convert a BIDS MRI dataset to a NIDM-Experiment \
RDF document. It will parse phenotype information and simply store variables/values \
and link to the associated json data dictionary file.')
parser.add_argument('-d',
dest='directory',
required=True,
help="Path to BIDS dataset directory")
parser.add_argument('-o',
dest='outputfile',
default="nidm.ttl",
help="NIDM output turtle file")
args = parser.parse_args()
directory = args.directory
outputfile = args.outputfile
#importlib.reload(sys)
#sys.setdefaultencoding('utf8')
#Parse dataset_description.json file in BIDS directory
with open(os.path.join(directory,
'dataset_description.json')) as data_file:
dataset = json.load(data_file)
#print(dataset_data)
#create project / nidm-exp doc
project = Project()
#add various attributes if they exist in BIDS dataset
for key in dataset:
#print(key)
#if key from dataset_description file is mapped to term in BIDS_Constants.py then add to NIDM object
if key in BIDS_Constants.dataset_description:
if type(dataset[key]) is list:
project.add_attributes({
BIDS_Constants.dataset_description[key]:
"".join(dataset[key])
})
else:
project.add_attributes(
{BIDS_Constants.dataset_description[key]: dataset[key]})
#create empty dictinary for sessions where key is subject id and used later to link scans to same session as demographics
session = {}
#Parse participants.tsv file in BIDS directory and create study and acquisition objects
with open(os.path.join(directory, 'participants.tsv')) as csvfile:
participants_data = csv.DictReader(csvfile, delimiter='\t')
#print(participants_data.fieldnames)
for row in participants_data:
#create session object for subject to be used for participant metadata and image data
#parse subject id from "sub-XXXX" string
subjid = row['participant_id'].split("-")
session[subjid[1]] = Session(project)
#add acquisition object
acq = Acquisition(session=session[subjid[1]])
acq_entity = DemographicsAcquisitionObject(acquisition=acq)
participant = acq.add_person(role=Constants.NIDM_PARTICIPANT,
attributes=({
Constants.NIDM_SUBJECTID:
row['participant_id']
}))
for key, value in row.items():
#for now only convert variables in participants.tsv file who have term mappings in BIDS_Constants.py
if key in BIDS_Constants.participants:
acq_entity.add_attributes(
{BIDS_Constants.participants[key]: value})
#get BIDS layout
bids_layout = BIDSLayout(directory)
#create acquisition objects for each scan for each subject
#loop through all subjects in dataset
for subject_id in bids_layout.get_subjects():
#skip .git directories...added to support datalad datasets
if subject_id.startswith("."):
continue
for file_tpl in bids_layout.get(subject=subject_id,
extensions=['.nii', '.nii.gz']):
#create an acquisition activity
acq = Acquisition(session[subject_id])
#print(file_tpl.type)
if file_tpl.modality == 'anat':
#do something with anatomicals
acq_obj = MRAcquisitionObject(acq)
acq_obj.add_attributes(
{PROV_TYPE: BIDS_Constants.scans[file_tpl.modality]})
#add file link
#make relative link to
acq_obj.add_attributes(
{Constants.NIDM_FILENAME: file_tpl.filename})
#get associated JSON file if exists
json_data = bids_layout.get_metadata(file_tpl.filename)
if json_data:
for key in json_data:
if key in BIDS_Constants.json_keys:
if type(json_data[key]) is list:
acq_obj.add_attributes({
BIDS_Constants.json_keys[key]:
''.join(str(e) for e in json_data[key])
})
else:
acq_obj.add_attributes({
BIDS_Constants.json_keys[key]:
json_data[key]
})
elif file_tpl.modality == 'func':
#do something with functionals
acq_obj = MRAcquisitionObject(acq)
acq_obj.add_attributes(
{PROV_TYPE: BIDS_Constants.scans[file_tpl.modality]})
#add file link
acq_obj.add_attributes(
{Constants.NIDM_FILENAME: file_tpl.filename})
if 'run' in file_tpl._fields:
acq_obj.add_attributes(
{BIDS_Constants.json_keys["run"]: file_tpl.run})
#get associated JSON file if exists
json_data = bids_layout.get_metadata(file_tpl.filename)
if json_data:
for key in json_data:
if key in BIDS_Constants.json_keys:
if type(json_data[key]) is list:
acq_obj.add_attributes({
BIDS_Constants.json_keys[key]:
''.join(str(e) for e in json_data[key])
})
else:
acq_obj.add_attributes({
BIDS_Constants.json_keys[key]:
json_data[key]
})
#get associated events TSV file
if 'run' in file_tpl._fields:
events_file = bids_layout.get(subject=subject_id,
extensions=['.tsv'],
modality=file_tpl.modality,
task=file_tpl.task,
run=file_tpl.run)
else:
events_file = bids_layout.get(subject=subject_id,
extensions=['.tsv'],
modality=file_tpl.modality,
task=file_tpl.task)
#if there is an events file then this is task-based so create an acquisition object for the task file and link
if events_file:
#for now create acquisition object and link it to the associated scan
events_obj = AcquisitionObject(acq)
#add prov type, task name as prov:label, and link to filename of events file
events_obj.add_attributes({
PROV_TYPE:
Constants.NIDM_MRI_BOLD_EVENTS,
BIDS_Constants.json_keys["TaskName"]:
json_data["TaskName"],
Constants.NFO["filename"]:
events_file[0].filename
})
#link it to appropriate MR acquisition entity
events_obj.wasAttributedTo(acq_obj)
elif file_tpl.modality == 'dwi':
#do stuff with with dwi scans...
acq_obj = MRAcquisitionObject(acq)
acq_obj.add_attributes(
{PROV_TYPE: BIDS_Constants.scans[file_tpl.modality]})
#add file link
acq_obj.add_attributes(
{Constants.NIDM_FILENAME: file_tpl.filename})
if 'run' in file_tpl._fields:
acq_obj.add_attributes(
{BIDS_Constants.json_keys["run"]: file_tpl.run})
#get associated JSON file if exists
json_data = bids_layout.get_metadata(file_tpl.filename)
if json_data:
for key in json_data:
if key in BIDS_Constants.json_keys:
if type(json_data[key]) is list:
acq_obj.add_attributes({
BIDS_Constants.json_keys[key]:
''.join(str(e) for e in json_data[key])
})
else:
acq_obj.add_attributes({
BIDS_Constants.json_keys[key]:
json_data[key]
})
#for bval and bvec files, what to do with those?
#for now, create new generic acquisition objects, link the files, and associate with the one for the DWI scan?
acq_obj_bval = AcquisitionObject(acq)
acq_obj_bval.add_attributes(
{PROV_TYPE: BIDS_Constants.scans["bval"]})
#add file link to bval files
acq_obj_bval.add_attributes({
Constants.NIDM_FILENAME:
bids_layout.get_bval(file_tpl.filename)
})
acq_obj_bvec = AcquisitionObject(acq)
acq_obj_bvec.add_attributes(
{PROV_TYPE: BIDS_Constants.scans["bvec"]})
#add file link to bvec files
acq_obj_bvec.add_attributes({
Constants.NIDM_FILENAME:
bids_layout.get_bvec(file_tpl.filename)
})
#link bval and bvec acquisition object entities together or is their association with enclosing activity enough?
#Added temporarily to support phenotype files
#for each *.tsv / *.json file pair in the phenotypes directory
for tsv_file in glob.glob(os.path.join(directory, "phenotype",
"*.tsv")):
#for now, open the TSV file, extract the row for this subject, store it in an acquisition object and link to
#the associated JSON data dictionary file
with open(tsv_file) as phenofile:
pheno_data = csv.DictReader(phenofile, delimiter='\t')
for row in pheno_data:
subjid = row['participant_id'].split("-")
if not subjid[1] == subject_id:
continue
else:
#add acquisition object
acq = Acquisition(session=session[subjid[1]])
acq_entity = AssessmentAcquisitionObject(
acquisition=acq)
participant = acq.add_person(
role=Constants.NIDM_PARTICIPANT,
attributes=({
Constants.NIDM_SUBJECTID:
row['participant_id']
}))
for key, value in row.items():
if not key == "participant_id":
#for now we're using a placeholder namespace for BIDS and simply the variable names as the concept IDs..
acq_entity.add_attributes(
{Constants.BIDS[key]: value})
#link TSV file
acq_entity.add_attributes(
{Constants.NIDM_FILENAME: tsv_file})
#link associated JSON file if it exists
data_dict = os.path.join(
directory, "phenotype",
os.path.splitext(os.path.basename(tsv_file))[0] +
".json")
if os.path.isfile(data_dict):
acq_entity.add_attributes(
{Constants.BIDS["data_dictionary"]: data_dict})
#serialize graph
#print(project.graph.get_provn())
with open(outputfile, 'w') as f:
f.write(project.serializeTurtle())
#f.write(project.graph.get_provn())
#save a DOT graph as PNG
project.save_DotGraph(str(outputfile + ".png"), format="png")
def main(argv):
parser = ArgumentParser(description='This program will load in a CSV file and iterate over the header \
variable names performing an elastic search of https://scicrunch.org/ for NIDM-ReproNim \
tagged terms that fuzzy match the variable names. The user will then interactively pick \
a term to associate with the variable name. The resulting annotated CSV data will \
then be written to a NIDM data file.')
parser.add_argument('-csv', dest='csv_file', required=True, help="Path to CSV file to convert")
parser.add_argument('-ilxkey', dest='key', required=True, help="Interlex/SciCrunch API key to use for query")
parser.add_argument('-json_map', dest='json_map',required=False,help="User-suppled JSON file containing variable-term mappings.")
parser.add_argument('-nidm', dest='nidm_file', required=False, help="Optional NIDM file to add CSV->NIDM converted graph to")
#parser.add_argument('-owl', action='store_true', required=False, help='Optionally searches NIDM OWL files...internet connection required')
parser.add_argument('-png', action='store_true', required=False, help='Optional flag, when set a PNG image file of RDF graph will be produced')
parser.add_argument('-jsonld', action='store_true', required=False, help='Optional flag, when set NIDM files are saved as JSON-LD instead of TURTLE')
parser.add_argument('-out', dest='output_file', required=True, help="Filename to save NIDM file")
args = parser.parse_args()
#open CSV file and load into
df = pd.read_csv(args.csv_file)
#maps variables in CSV file to terms
#if args.owl is not False:
# column_to_terms = map_variables_to_terms(df=df, apikey=args.key, directory=dirname(args.output_file), output_file=args.output_file, json_file=args.json_map, owl_file=args.owl)
#else:
column_to_terms = map_variables_to_terms(df=df, apikey=args.key, directory=dirname(args.output_file), output_file=args.output_file, json_file=args.json_map)
#If user has added an existing NIDM file as a command line parameter then add to existing file for subjects who exist in the NIDM file
if args.nidm_file:
print("Adding to NIDM file...")
#read in NIDM file
project = read_nidm(args.nidm_file)
#get list of session objects
session_objs=project.get_sessions()
#look at column_to_terms dictionary for NIDM URL for subject id (Constants.NIDM_SUBJECTID)
id_field=None
for key, value in column_to_terms.items():
if Constants.NIDM_SUBJECTID._str == column_to_terms[key]['label']:
id_field=key
#make sure id_field is a string for zero-padded subject ids
#re-read data file with constraint that key field is read as string
#df = pd.read_csv(args.csv_file,dtype={id_field : str})
#if we couldn't find a subject ID field in column_to_terms, ask user
if id_field is None:
option=1
for column in df.columns:
print("%d: %s" %(option,column))
option=option+1
selection=input("Please select the subject ID field from the list above: ")
id_field=df.columns[int(selection)-1]
#make sure id_field is a string for zero-padded subject ids
#re-read data file with constraint that key field is read as string
#df = pd.read_csv(args.csv_file,dtype={id_field : str})
#use RDFLib here for temporary graph making query easier
rdf_graph = Graph()
rdf_graph_parse = rdf_graph.parse(source=StringIO(project.serializeTurtle()),format='turtle')
#find subject ids and sessions in NIDM document
query = """SELECT DISTINCT ?session ?nidm_subj_id ?agent
WHERE {
?activity prov:wasAssociatedWith ?agent ;
dct:isPartOf ?session .
?agent rdf:type prov:Agent ;
ndar:src_subject_id ?nidm_subj_id .
}"""
#print(query)
qres = rdf_graph_parse.query(query)
for row in qres:
print('%s \t %s' %(row[0],row[1]))
#find row in CSV file with subject id matching agent from NIDM file
#csv_row = df.loc[df[id_field]==type(df[id_field][0])(row[1])]
#find row in CSV file with matching subject id to the agent in the NIDM file
#be carefull about data types...simply type-change dataframe subject id column and query to strings.
#here we're removing the leading 0's from IDs because pandas.read_csv strips those unless you know ahead of
#time which column is the subject id....
csv_row = df.loc[df[id_field].astype('str').str.contains(str(row[1]).lstrip("0"))]
#if there was data about this subject in the NIDM file already (i.e. an agent already exists with this subject id)
#then add this CSV assessment data to NIDM file, else skip it....
if (not (len(csv_row.index)==0)):
#NIDM document sesssion uuid
session_uuid = row[0]
#temporary list of string-based URIs of session objects from API
temp = [o.identifier._uri for o in session_objs]
#get session object from existing NIDM file that is associated with a specific subject id
#nidm_session = (i for i,x in enumerate([o.identifier._uri for o in session_objs]) if x == str(session_uuid))
nidm_session = session_objs[temp.index(str(session_uuid))]
#for nidm_session in session_objs:
# if nidm_session.identifier._uri == str(session_uuid):
#add an assessment acquisition for the phenotype data to session and associate with agent
acq=AssessmentAcquisition(session=nidm_session)
#add acquisition entity for assessment
acq_entity = AssessmentObject(acquisition=acq)
#add qualified association with existing agent
acq.add_qualified_association(person=row[2],role=Constants.NIDM_PARTICIPANT)
#store other data from row with columns_to_term mappings
for row_variable in csv_row:
#check if row_variable is subject id, if so skip it
if row_variable==id_field:
continue
else:
if not csv_row[row_variable].values[0]:
continue
#get column_to_term mapping uri and add as namespace in NIDM document
#provNamespace(Core.safe_string(None,string=str(row_variable)), column_to_terms[row_variable]["url"])
acq_entity.add_attributes({QualifiedName(provNamespace(Core.safe_string(None,string=str(row_variable)), column_to_terms[row_variable]["url"]), ""):csv_row[row_variable].values[0]})
continue
#serialize NIDM file
with open(args.nidm_file,'w') as f:
print("Writing NIDM file...")
if args.jsonld:
f.write(project.serializeJSONLD())
else:
f.write(project.serializeTurtle())
project.save_DotGraph(str(args.nidm_file + ".png"), format="png")
else:
print("Creating NIDM file...")
#If user did not choose to add this data to an existing NIDM file then create a new one for the CSV data
#create empty project
project=Project()
#simply add name of file to project since we don't know anything about it
project.add_attributes({Constants.NIDM_FILENAME:args.csv_file})
#look at column_to_terms dictionary for NIDM URL for subject id (Constants.NIDM_SUBJECTID)
id_field=None
for key, value in column_to_terms.items():
if Constants.NIDM_SUBJECTID._str == column_to_terms[key]['label']:
id_field=key
#make sure id_field is a string for zero-padded subject ids
#re-read data file with constraint that key field is read as string
#df = pd.read_csv(args.csv_file,dtype={id_field : str})
#if we couldn't find a subject ID field in column_to_terms, ask user
if id_field is None:
option=1
for column in df.columns:
print("%d: %s" %(option,column))
option=option+1
selection=input("Please select the subject ID field from the list above: ")
id_field=df.columns[int(selection)-1]
#iterate over rows and store in NIDM file
for csv_index, csv_row in df.iterrows():
#create a session object
session=Session(project)
#create and acquisition activity and entity
acq=AssessmentAcquisition(session)
acq_entity=AssessmentObject(acq)
#store other data from row with columns_to_term mappings
for row_variable,row_data in csv_row.iteritems():
if not row_data:
continue
#check if row_variable is subject id, if so skip it
if row_variable==id_field:
#add qualified association with person
acq.add_qualified_association(person= acq.add_person(attributes=({Constants.NIDM_SUBJECTID:row_data})),role=Constants.NIDM_PARTICIPANT)
continue
else:
#get column_to_term mapping uri and add as namespace in NIDM document
acq_entity.add_attributes({QualifiedName(provNamespace(Core.safe_string(None,string=str(row_variable)), column_to_terms[row_variable]["url"]),""):row_data})
#print(project.serializeTurtle())
#serialize NIDM file
with open(args.output_file,'w') as f:
print("Writing NIDM file...")
if args.jsonld:
f.write(project.serializeJSONLD())
else:
f.write(project.serializeTurtle())
if args.png:
project.save_DotGraph(str(args.output_file + ".png"), format="png")
def main(argv):
parser = ArgumentParser()
parser.add_argument('-d',
dest='directory',
required=True,
help="Path to BIDS dataset directory")
parser.add_argument('-o',
dest='outputfile',
default="nidm.ttl",
help="NIDM output turtle file")
args = parser.parse_args()
directory = args.directory
outputfile = args.outputfile
#Parse dataset_description.json file in BIDS directory
with open(directory + '/' + 'dataset_description.json') as data_file:
dataset = json.load(data_file)
#print(dataset_data)
#create project / nidm-exp doc
project = Project()
#add various attributes if they exist in BIDS dataset
for key in dataset:
#print(key)
#if key from dataset_description file is mapped to term in BIDS_Constants.py then add to NIDM object
if key in BIDS_Constants.dataset_description:
if type(dataset[key]) is list:
project.add_attributes({
BIDS_Constants.dataset_description[key]:
"".join(dataset[key])
})
else:
project.add_attributes(
{BIDS_Constants.dataset_description[key]: dataset[key]})
#create empty dictinary for sessions where key is subject id and used later to link scans to same session as demographics
session = {}
#Parse participants.tsv file in BIDS directory and create study and acquisition objects
with open(directory + '/' + 'participants.tsv') as csvfile:
participants_data = csv.DictReader(csvfile, delimiter='\t')
#print(participants_data.fieldnames)
for row in participants_data:
#create session object for subject to be used for participant metadata and image data
#parse subject id from "sub-XXXX" string
subjid = row['participant_id'].split("-")
session[subjid[1]] = Session(project)
#add acquisition object
acq = Acquisition(session=session[subjid[1]])
acq_entity = DemographicsAcquisitionObject(acquisition=acq)
participant = acq.add_person(role=Constants.NIDM_PARTICIPANT,
attributes=({
Constants.NIDM_SUBJECTID:
row['participant_id']
}))
for key, value in row.items():
#for now only convert variables in participants.tsv file who have term mappings in BIDS_Constants.py
if key in BIDS_Constants.participants:
acq_entity.add_attributes(
{BIDS_Constants.participants[key]: value})
#get BIDS layout
bids_layout = BIDSLayout(directory)
#create acquisition objects for each scan for each subject
#loop through all subjects in dataset
for subject_id in bids_layout.get_subjects():
for file_tpl in bids_layout.get(subject=subject_id,
extensions=['.nii', '.nii.gz']):
#create an acquisition activity
acq = Acquisition(session[subject_id])
#print(file_tpl.type)
if file_tpl.modality == 'anat':
#do something with anatomicals
acq_obj = MRAcquisitionObject(acq)
acq_obj.add_attributes(
{PROV_TYPE: BIDS_Constants.scans[file_tpl.modality]})
#add file link
acq_obj.add_attributes(
{Constants.NFO["filename"]: file_tpl.filename})
#get associated JSON file if exists
for json_file in bids_layout.get(subject=subject_id,
extensions=['.json'],
modality=file_tpl.modality):
#open json file, grab key-value pairs, map them to terms and add to acquisition object
with open(json_file[0]) as data_file:
json_data = json.load(data_file)
for key in json_data:
if key in BIDS_Constants.json_keys:
if type(json_data[key]) is list:
project.add_attributes({
BIDS_Constants.json_keys[key]:
"".join(json_data[key])
})
else:
project.add_attributes({
BIDS_Constants.json_keys[key]:
json_data[key]
})
#if we want to do something further if T1w or t2, etc
#if file_tpl.type == 'T1w':
#elif file_tpl.type == 'inplaneT2':
elif file_tpl.modality == 'func':
#do something with functionals
acq_obj = MRAcquisitionObject(acq)
acq_obj.add_attributes(
{PROV_TYPE: BIDS_Constants.scans[file_tpl.modality]})
#add file link
acq_obj.add_attributes({
Constants.NFO["filename"]:
file_tpl.filename,
BIDS_Constants.json_keys["run"]:
file_tpl.run
})
#add attributes for task description keys from task JSON file
for task_desc in bids_layout.get(extensions=['.json'],
task=file_tpl.task):
with open(task_desc[0]) as data_file:
json_data = json.load(data_file)
for key in json_data:
if key in BIDS_Constants.json_keys:
acq_obj.add_attributes({
BIDS_Constants.json_keys[key]:
json_data[key]
})
#get associated events TSV file
events_file = bids_layout.get(subject=subject_id,
extensions=['.tsv'],
modality=file_tpl.modality,
task=file_tpl.task,
run=file_tpl.run)
#for now create acquisition object and link it to the associated scan
events_obj = AcquisitionObject(acq)
#add prov type, task name as prov:label, and link to filename of events file
events_obj.add_attributes({
PROV_TYPE:
Constants.NIDM_MRI_BOLD_EVENTS,
BIDS_Constants.json_keys["TaskName"]:
json_data["TaskName"],
Constants.NFO["filename"]:
events_file[0].filename
})
#link it to appropriate MR acquisition entity
events_obj.wasAttributedTo(acq_obj)
elif file_tpl.modality == 'dwi':
#do stuff with with dwi scans...
acq_obj = MRAcquisitionObject(acq)
acq_obj.add_attributes(
{PROV_TYPE: BIDS_Constants.scans[file_tpl.modality]})
#add file link
acq_obj.add_attributes({
Constants.NFO["filename"]:
file_tpl.filename,
BIDS_Constants.json_keys["run"]:
file_tpl.run
})
#add attributes for task description keys from task JSON file
for task_desc in bids_layout.get(extensions=['.json'],
task=file_tpl.task):
with open(task_desc[0]) as data_file:
json_data = json.load(data_file)
for key in json_data:
if key in BIDS_Constants.json_keys:
acq_obj.add_attributes({
BIDS_Constants.json_keys[key]:
json_data[key]
})
#for bval and bvec files, what to do with those?
#for now, create new generic acquisition objects, link the files, and associate with the one for the DWI scan?
acq_obj = AcquisitionObject(acq)
acq_obj.add_attributes(
{PROV_TYPE: BIDS_Constants.scans["bval"]})
for bval in bids_layout.get(extensions=['.bval'],
task=file_tpl.task):
#add file link
acq_obj.add_attributes(
{Constants.NFO["filename"]: bval.filename})
for bvec in bids_layout.get(extensions=['.bvec'],
task=file_tpl.task):
#add file link
acq_obj.add_attributes(
{Constants.NFO["filename"]: bvec.filename})
#serialize graph
#print(project.graph.get_provn())
with open(outputfile, 'w') as f:
f.write(project.serializeTurtle())
#f.write(project.graph.get_provn())
#save a DOT graph as PNG
project.save_DotGraph(str(outputfile + ".png"), format="png")
