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(attributes={Constants.NIDM_FAMILY_NAME:"Keator", Constants.NIDM_GIVEN_NAME:"David"})
#add qualified association of project PI to project activity
project.add_qualified_association(person=project_PI,role=Constants.NIDM_PI)
#test add session to graph and associate with project
session = Session(project)
session.add_attributes({Constants.NIDM:"test"})
#project.add_sessions(session)
#test add MR acquisition activity / entity to graph and associate with session
acq_act = MRAcquisition(session=session)
#test add acquisition object entity to graph associated with participant role NIDM_PARTICIPANT
acq_entity = MRObject(acquisition=acq_act)
#add person to graph
person = acq_act.add_person(attributes={Constants.NIDM_GIVEN_NAME:"George"})
#add qualified association of person with role NIDM_PARTICIPANT, and associated with acquistion activity
acq_act.add_qualified_association(person=person, role=Constants.NIDM_PARTICIPANT)
#test add Assessment acquisition activity / entity to graph and associate with session
acq_act = AssessmentAcquisition(session=session)
#test add acquisition object entity to graph associated with participant role NIDM_PARTICIPANT
acq_entity = AssessmentObject(acquisition=acq_act)
acq_entity.add_attributes({Constants.NIDM["Q1"]:"Q1 Answer",Constants.NIDM["Q2"]:"Q2 Answer" })
#associate person as participant
acq_act.add_qualified_association(person=person, role=Constants.NIDM_PARTICIPANT)
#test add DemographicsAssessment acquisition activity / entity to graph and associate with session
acq_act = AssessmentAcquisition(session=session)
#test add acquisition object entity to graph associated with participant role NIDM_PARTICIPANT
acq_entity = DemographicsObject(acquisition=acq_act)
#add new person to graph
person2 = acq_act.add_person(attributes={Constants.NIDM_FAMILY_NAME:"Doe", \
Constants.NIDM_GIVEN_NAME:"John"})
#associate person2 with assessment acquisition
acq_act.add_qualified_association(person=person2, role=Constants.NIDM_PARTICIPANT)
acq_entity.add_attributes({Constants.NIDM_AGE:60,Constants.NIDM_GENDER:"Male" })
#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 bidsmri2project(directory, args):
# initialize empty cde graph...it may get replaced if we're doing variable to term mapping or not
cde=Graph()
# Parse dataset_description.json file in BIDS directory
if (os.path.isdir(os.path.join(directory))):
try:
with open(os.path.join(directory,'dataset_description.json')) as data_file:
dataset = json.load(data_file)
except OSError:
logging.critical("Cannot find dataset_description.json file which is required in the BIDS spec")
exit("-1")
else:
logging.critical("Error: BIDS directory %s does not exist!" %os.path.join(directory))
exit("-1")
# create project / nidm-exp doc
project = Project()
# if there are git annex sources then add them
num_sources=addGitAnnexSources(obj=project.get_uuid(),bids_root=directory)
# else just add the local path to the dataset
if num_sources == 0:
project.add_attributes({Constants.PROV['Location']:"file:/" + directory})
# add various attributes if they exist in BIDS dataset
for key in dataset:
# 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]})
# get BIDS layout
bids_layout = BIDSLayout(directory)
# create empty dictinary for sessions where key is subject id and used later to link scans to same session as demographics
session={}
participant={}
# Parse participants.tsv file in BIDS directory and create study and acquisition objects
if os.path.isfile(os.path.join(directory,'participants.tsv')):
with open(os.path.join(directory,'participants.tsv')) as csvfile:
participants_data = csv.DictReader(csvfile, delimiter='\t')
# logic to map variables to terms.
# first iterate over variables in dataframe and check which ones are already mapped as BIDS constants and which are not. For those that are not
# we want to use the variable-term mapping functions to help the user do the mapping
# iterate over columns
mapping_list=[]
column_to_terms={}
for field in participants_data.fieldnames:
# column is not in BIDS_Constants
if not (field in BIDS_Constants.participants):
# add column to list for column_to_terms mapping
mapping_list.append(field)
#if user didn't supply a json mapping file but we're doing some variable-term mapping create an empty one for column_to_terms to use
if args.json_map == False:
#defaults to participants.json because here we're mapping the participants.tsv file variables to terms
# if participants.json file doesn't exist then run without json mapping file
if not os.path.isfile(os.path.join(directory,'participants.json')):
#maps variables in CSV file to terms
temp=DataFrame(columns=mapping_list)
if args.no_concepts:
column_to_terms,cde = map_variables_to_terms(directory=directory,assessment_name='participants.tsv',
df=temp,output_file=os.path.join(directory,'participants.json'),bids=True,associate_concepts=False)
else:
column_to_terms,cde = map_variables_to_terms(directory=directory,assessment_name='participants.tsv',
df=temp,output_file=os.path.join(directory,'participants.json'),bids=True)
else:
#maps variables in CSV file to terms
temp=DataFrame(columns=mapping_list)
if args.no_concepts:
column_to_terms,cde = map_variables_to_terms(directory=directory, assessment_name='participants.tsv', df=temp,
output_file=os.path.join(directory,'participants.json'),json_file=os.path.join(directory,'participants.json'),bids=True,associate_concepts=False)
else:
column_to_terms,cde = map_variables_to_terms(directory=directory, assessment_name='participants.tsv', df=temp,
output_file=os.path.join(directory,'participants.json'),json_file=os.path.join(directory,'participants.json'),bids=True)
else:
#maps variables in CSV file to terms
temp=DataFrame(columns=mapping_list)
if args.no_concepts:
column_to_terms, cde = map_variables_to_terms(directory=directory, assessment_name='participants.tsv', df=temp,
output_file=os.path.join(directory,'participants.json'),json_file=args.json_map,bids=True,associate_concepts=False)
else:
column_to_terms, cde = map_variables_to_terms(directory=directory, assessment_name='participants.tsv', df=temp,
output_file=os.path.join(directory,'participants.json'),json_file=args.json_map,bids=True)
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
temp = row['participant_id'].split("-")
#for ambiguity in BIDS datasets. Sometimes participant_id is sub-XXXX and othertimes it's just XXXX
if len(temp) > 1:
subjid = temp[1]
else:
subjid = temp[0]
logging.info(subjid)
session[subjid] = Session(project)
#add acquisition object
acq = AssessmentAcquisition(session=session[subjid])
acq_entity = AssessmentObject(acquisition=acq)
participant[subjid] = {}
participant[subjid]['person'] = acq.add_person(attributes=({Constants.NIDM_SUBJECTID:row['participant_id']}))
# add nfo:filename entry to assessment entity to reflect provenance of where this data came from
acq_entity.add_attributes({Constants.NIDM_FILENAME:getRelPathToBIDS(os.path.join(directory,'participants.tsv'),directory)})
#acq_entity.add_attributes({Constants.NIDM_FILENAME:os.path.join(directory,'participants.tsv')})
#add qualified association of participant with acquisition activity
acq.add_qualified_association(person=participant[subjid]['person'],role=Constants.NIDM_PARTICIPANT)
# print(acq)
# if there are git annex sources for participants.tsv file then add them
num_sources=addGitAnnexSources(obj=acq_entity.get_uuid(),bids_root=directory)
# else just add the local path to the dataset
if num_sources == 0:
acq_entity.add_attributes({Constants.PROV['Location']:"file:/" + os.path.join(directory,'participants.tsv')})
# if there's a JSON sidecar file then create an entity and associate it with all the assessment entities
if os.path.isfile(os.path.join(directory,'participants.json')):
json_sidecar = AssessmentObject(acquisition=acq)
json_sidecar.add_attributes({PROV_TYPE:QualifiedName(Namespace("bids",Constants.BIDS),"sidecar_file"), Constants.NIDM_FILENAME:
getRelPathToBIDS(os.path.join(directory,'participants.json'),directory)})
# add Git Annex Sources
# if there are git annex sources for participants.tsv file then add them
num_sources=addGitAnnexSources(obj=json_sidecar.get_uuid(),filepath=os.path.join(directory,'participants.json'),bids_root=directory)
# else just add the local path to the dataset
if num_sources == 0:
json_sidecar.add_attributes({Constants.PROV['Location']:"file:/" + os.path.join(directory,'participants.json')})
# check if json_sidecar entity exists and if so associate assessment entity with it
if 'json_sidecar' in locals():
#connect json_entity with acq_entity
acq_entity.add_attributes({Constants.PROV["wasInfluencedBy"]:json_sidecar})
for key,value in row.items():
if not value:
continue
#for variables in participants.tsv file who have term mappings in BIDS_Constants.py use those, add to json_map so we don't have to map these if user
#supplied arguments to map variables
if key in BIDS_Constants.participants:
# WIP
# Here we are adding to CDE graph data elements for BIDS Constants that remain fixed for each BIDS-compliant dataset
if not (BIDS_Constants.participants[key] == Constants.NIDM_SUBJECTID):
# create a namespace with the URL for fixed BIDS_Constants term
# item_ns = Namespace(str(Constants.BIDS.namespace.uri))
# add prefix to namespace which is the BIDS fixed variable name
# cde.bind(prefix="bids", namespace=item_ns)
# ID for BIDS variables is always the same bids:[bids variable]
cde_id = Constants.BIDS[key]
# add the data element to the CDE graph
cde.add((cde_id,RDF.type, Constants.NIDM['DataElement']))
cde.add((cde_id,RDF.type, Constants.PROV['Entity']))
# add some basic information about this data element
cde.add((cde_id,Constants.RDFS['label'],Literal(BIDS_Constants.participants[key].localpart)))
cde.add((cde_id,Constants.NIDM['isAbout'],URIRef(BIDS_Constants.participants[key].uri)))
cde.add((cde_id,Constants.NIDM['source_variable'],Literal(key)))
cde.add((cde_id,Constants.NIDM['description'],Literal("participant/subject identifier")))
cde.add((cde_id,Constants.RDFS['comment'],Literal("BIDS participants_id variable fixed in specification")))
cde.add((cde_id,Constants.RDFS['valueType'],URIRef(Constants.XSD["string"])))
acq_entity.add_attributes({cde_id:Literal(value)})
# if this was the participant_id, we already handled it above creating agent / qualified association
# if not (BIDS_Constants.participants[key] == Constants.NIDM_SUBJECTID):
# acq_entity.add_attributes({BIDS_Constants.participants[key]:value})
# else if user added -mapvars flag to command line then we'll use the variable-> term mapping procedures to help user map variables to terms (also used
# in CSV2NIDM.py)
else:
# WIP: trying to add new support for CDEs...
add_attributes_with_cde(prov_object=acq_entity,cde=cde,row_variable=key,value=value)
# if key in column_to_terms:
# acq_entity.add_attributes({QualifiedName(provNamespace(Core.safe_string(None,string=str(key)), column_to_terms[key]["url"]), ""):value})
# else:
# acq_entity.add_attributes({Constants.BIDS[key.replace(" ", "_")]:value})
# create acquisition objects for each scan for each subject
# loop through all subjects in dataset
for subject_id in bids_layout.get_subjects():
logging.info("Converting subject: %s" %subject_id)
# skip .git directories...added to support datalad datasets
if subject_id.startswith("."):
continue
# check if there are a session numbers. If so, store it in the session activity and create a new
# sessions for these imaging acquisitions. Because we don't know which imaging session the root
# participants.tsv file data may be associated with we simply link the imaging acquisitions to different
# sessions (i.e. the participants.tsv file goes into an AssessmentAcquisition and linked to a unique
# sessions and the imaging acquisitions go into MRAcquisitions and has a unique session)
imaging_sessions = bids_layout.get_sessions(subject=subject_id)
# if session_dirs has entries then get any metadata about session and store in session activity
# bids_layout.get(subject=subject_id,type='session',extensions='.tsv')
# bids_layout.get(subject=subject_id,type='scans',extensions='.tsv')
# bids_layout.get(extensions='.tsv',return_type='obj')
# loop through each session if there is a sessions directory
if len(imaging_sessions) > 0:
for img_session in imaging_sessions:
# create a new session
ses = Session(project)
# add session number as metadata
ses.add_attributes({Constants.BIDS['session_number']:img_session})
addimagingsessions(bids_layout=bids_layout,subject_id=subject_id,session=ses,participant=participant, directory=directory,img_session=img_session)
# else we have no ses-* directories in the BIDS layout
addimagingsessions(bids_layout=bids_layout,subject_id=subject_id,session=Session(project),participant=participant, directory=directory)
# Added temporarily to support phenotype files
# for each *.tsv / *.json file pair in the phenotypes directory
# WIP: ADD VARIABLE -> TERM MAPPING HERE
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 = AssessmentAcquisition(session=session[subjid[1]])
# add qualified association with person
acq.add_qualified_association(person=participant[subject_id]['person'],role=Constants.NIDM_PARTICIPANT)
acq_entity = AssessmentObject(acquisition=acq)
for key,value in row.items():
if not value:
continue
# we're using participant_id in NIDM in agent so don't add to assessment as a triple.
# BIDS phenotype files seem to have an index column with no column header variable name so skip those
if ((not key == "participant_id") and (key != "")):
# 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:getRelPathToBIDS(tsv_file,directory)})
#acq_entity.add_attributes({Constants.NIDM_FILENAME:tsv_file})
# if there are git annex sources for participants.tsv file then add them
num_sources=addGitAnnexSources(obj=acq_entity.get_uuid(),bids_root=directory)
# else just add the local path to the dataset
if num_sources == 0:
acq_entity.add_attributes({Constants.PROV['Location']:"file:/" + 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):
# if file exists, create a new entity and associate it with the appropriate activity and a used relationship
# with the TSV-related entity
json_entity = AssessmentObject(acquisition=acq)
json_entity.add_attributes({PROV_TYPE:Constants.BIDS["sidecar_file"], Constants.NIDM_FILENAME:
getRelPathToBIDS(data_dict,directory)})
# add Git Annex Sources
# if there are git annex sources for participants.tsv file then add them
num_sources=addGitAnnexSources(obj=json_entity.get_uuid(),filepath=data_dict,bids_root=directory)
# else just add the local path to the dataset
if num_sources == 0:
json_entity.add_attributes({Constants.PROV['Location']:"file:/" + data_dict})
#connect json_entity with acq_entity
acq_entity.add_attributes({Constants.PROV["wasInfluencedBy"]:json_entity.get_uuid()})
return project, cde
