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. Note, you must obtain an API key to Interlex by signing up \
for an account at scicrunch.org then going to My Account and API Keys. Then set the environment \
variable INTERLEX_API_KEY with your key.')
parser.add_argument('-csv', dest='csv_file', required=True, help="Full 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="Full path to user-suppled JSON file containing variable-term mappings.")
parser.add_argument('-nidm', dest='nidm_file', required=False, help="Optional full path of NIDM file to add CSV->NIDM converted graph to")
parser.add_argument('-no_concepts', action='store_true', required=False, help='If this flag is set then no concept associations will be'
'asked of the user. This is useful if you already have a -json_map specified without concepts and want to'
'simply run this program to get a NIDM file with user interaction to associate concepts.')
# 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('-log','--log', dest='logfile',required=False, default=None, help="full path to directory to save log file. Log file name is csv2nidm_[arg.csv_file].log")
parser.add_argument('-out', dest='output_file', required=True, help="Full path with filename to save NIDM file")
args = parser.parse_args()
#open CSV file and load into
df = pd.read_csv(args.csv_file)
#temp = csv.reader(args.csv_file)
#df = pd.DataFrame(temp)
#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:
# if user did not specify -no_concepts then associate concepts interactively with user
if not args.no_concepts:
column_to_terms, cde = map_variables_to_terms(df=df, assessment_name=basename(args.csv_file),directory=dirname(args.output_file), output_file=args.output_file, json_file=args.json_map)
# run without concept mappings
else:
column_to_terms, cde = map_variables_to_terms(df=df, assessment_name=basename(args.csv_file),
directory=dirname(args.output_file), output_file=args.output_file,
json_file=args.json_map, associate_concepts=False)
if args.logfile is not None:
logging.basicConfig(filename=join(args.logfile,'csv2nidm_' + os.path.splitext(os.path.basename(args.csv_file))[0] + '.log'), level=logging.DEBUG)
# add some logging info
logging.info("csv2nidm %s" %args)
#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']:
key_tuple = eval(key)
#id_field=key
id_field = key_tuple.variable
#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})
break
#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: ")
# Make sure user selected one of the options. If not present user with selection input again
while (not selection.isdigit()) or (int(selection) > int(option)):
# Wait for user input
selection = input("Please select the subject ID field from the list above: \t" % option)
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(source=StringIO(project.serializeTurtle()),format='turtle')
print("Querying for existing participants in NIDM graph....")
#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.query(query)
for row in qres:
logging.info("found existing participant %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)
# add git-annex info if exists
num_sources = addGitAnnexSources(obj=acq_entity,filepath=args.csv_file,bids_root=dirname(args.csv_file))
# if there aren't any git annex sources then just store the local directory information
if num_sources == 0:
# WIP: add absolute location of BIDS directory on disk for later finding of files
acq_entity.add_attributes({Constants.PROV['Location']:"file:/" + args.csv_file})
# store file to acq_entity
acq_entity.add_attributes({Constants.NIDM_FILENAME:basename(args.csv_file)})
#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
add_attributes_with_cde(acq_entity, cde, row_variable, csv_row[row_variable].values[0])
continue
print ("Adding CDEs to graph....")
# convert to rdflib Graph and add CDEs
rdf_graph = Graph()
rdf_graph.parse(source=StringIO(project.serializeTurtle()),format='turtle')
rdf_graph = rdf_graph + cde
print("Backing up original NIDM file...")
copy2(src=args.nidm_file,dst=args.nidm_file+".bak")
print("Writing NIDM file....")
rdf_graph.serialize(destination=args.nidm_file,format='turtle')
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():
# using skos:sameAs relationship to associate subject identifier variable from csv with a known term
# for subject IDs
if 'sameAs' in column_to_terms[key]:
if Constants.NIDM_SUBJECTID.uri == column_to_terms[key]['sameAs']:
key_tuple = eval(key)
id_field=key_tuple.variable
#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})
break
#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: ")
# Make sure user selected one of the options. If not present user with selection input again
while (not selection.isdigit()) or (int(selection) > int(option)):
# Wait for user input
selection = input("Please select the subject ID field from the list above: \t" % option)
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})
#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)
#create prov:Agent for subject
#acq.add_person(attributes=({Constants.NIDM_SUBJECTID:row['participant_id']}))
# add git-annex info if exists
num_sources = addGitAnnexSources(obj=acq_entity,filepath=args.csv_file,bids_root=os.path.dirname(args.csv_file))
# if there aren't any git annex sources then just store the local directory information
if num_sources == 0:
# WIP: add absolute location of BIDS directory on disk for later finding of files
acq_entity.add_attributes({Constants.PROV['Location']:"file:/" + args.csv_file})
# store file to acq_entity
acq_entity.add_attributes({Constants.NIDM_FILENAME : basename(args.csv_file)})
#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:
### WIP: Check if agent already exists with the same ID. If so, use it else create a new agent
#add qualified association with person
acq.add_qualified_association(person= acq.add_person(attributes=({Constants.NIDM_SUBJECTID:str(row_data)})),role=Constants.NIDM_PARTICIPANT)
continue
else:
add_attributes_with_cde(acq_entity, cde, row_variable, row_data)
#print(project.serializeTurtle())
# convert to rdflib Graph and add CDEs
rdf_graph = Graph()
rdf_graph.parse(source=StringIO(project.serializeTurtle()),format='turtle')
rdf_graph = rdf_graph + cde
print("Writing NIDM file....")
rdf_graph.serialize(destination=args.output_file,format='turtle')
def bidsmri2project(directory, args):
#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()
#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]})
#add absolute location of BIDS directory on disk for later finding of files which are stored relatively in NIDM document
project.add_attributes({Constants.PROV['Location']: directory})
#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)
#do variable-term mappings
if ((args.json_map != False) or (args.key != None)):
#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)
column_to_terms, cde = map_variables_to_terms(
directory=directory,
assessment_name='participants.tsv',
df=temp,
apikey=args.key,
output_file=os.path.join(directory,
'participants.json'))
else:
#maps variables in CSV file to terms
temp = DataFrame(columns=mapping_list)
column_to_terms, cde = map_variables_to_terms(
directory=directory,
assessment_name='participants.tsv',
df=temp,
apikey=args.key,
output_file=os.path.join(directory,
'participants.json'),
json_file=os.path.join(directory,
'participants.json'))
else:
#maps variables in CSV file to terms
temp = DataFrame(columns=mapping_list)
column_to_terms, cde = map_variables_to_terms(
directory=directory,
assessment_name='participants.tsv',
df=temp,
apikey=args.key,
output_file=os.path.join(directory,
'participants.json'),
json_file=args.json_map)
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 qualified association of participant with acquisition activity
acq.add_qualified_association(
person=participant[subjid]['person'],
role=Constants.NIDM_PARTICIPANT)
print(acq)
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:
#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's a session number. If so, store it in the session activity
session_dirs = bids_layout.get(target='session',
subject=subject_id,
return_type='dir')
#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')
#check whether sessions have been created (i.e. was there a participants.tsv file? If not, create here
if not (subject_id in session):
session[subject_id] = Session(project)
for file_tpl in bids_layout.get(subject=subject_id,
extensions=['.nii', '.nii.gz']):
#create an acquisition activity
acq = MRAcquisition(session[subject_id])
#check whether participant (i.e. agent) for this subject already exists (i.e. if participants.tsv file exists) else create one
if not (subject_id in participant):
participant[subject_id] = {}
participant[subject_id]['person'] = acq.add_person(
attributes=({
Constants.NIDM_SUBJECTID: subject_id
}))
#add qualified association with person
acq.add_qualified_association(
person=participant[subject_id]['person'],
role=Constants.NIDM_PARTICIPANT)
if file_tpl.entities['datatype'] == 'anat':
#do something with anatomicals
acq_obj = MRObject(acq)
#add image contrast type
if file_tpl.entities['suffix'] in BIDS_Constants.scans:
acq_obj.add_attributes({
Constants.NIDM_IMAGE_CONTRAST_TYPE:
BIDS_Constants.scans[file_tpl.entities['suffix']]
})
else:
logging.info(
"WARNING: No matching image contrast type found in BIDS_Constants.py for %s"
% file_tpl.entities['suffix'])
#add image usage type
if file_tpl.entities['datatype'] in BIDS_Constants.scans:
acq_obj.add_attributes({
Constants.NIDM_IMAGE_USAGE_TYPE:
BIDS_Constants.scans[file_tpl.entities['datatype']]
})
else:
logging.info(
"WARNING: No matching image usage type found in BIDS_Constants.py for %s"
% file_tpl.entities['datatype'])
#add file link
#make relative link to
acq_obj.add_attributes({
Constants.NIDM_FILENAME:
getRelPathToBIDS(join(file_tpl.dirname, file_tpl.filename),
directory)
})
#WIP: add absolute location of BIDS directory on disk for later finding of files
acq_obj.add_attributes({Constants.PROV['Location']: directory})
#add sha512 sum
if isfile(join(directory, file_tpl.dirname,
file_tpl.filename)):
acq_obj.add_attributes({
Constants.CRYPTO_SHA512:
getsha512(
join(directory, file_tpl.dirname,
file_tpl.filename))
})
else:
logging.info(
"WARNINGL file %s doesn't exist! No SHA512 sum stored in NIDM files..."
% join(directory, file_tpl.dirname, file_tpl.filename))
#get associated JSON file if exists
#There is T1w.json file with information
json_data = (bids_layout.get(
suffix=file_tpl.entities['suffix'],
subject=subject_id))[0].metadata
if len(json_data.info) > 0:
for key in json_data.info.items():
if key in BIDS_Constants.json_keys:
if type(json_data.info[key]) is list:
acq_obj.add_attributes({
BIDS_Constants.json_keys[key.replace(
" ", "_")]:
''.join(
str(e) for e in json_data.info[key])
})
else:
acq_obj.add_attributes({
BIDS_Constants.json_keys[key.replace(
" ", "_")]:
json_data.info[key]
})
#Parse T1w.json file in BIDS directory to add the attributes contained inside
if (os.path.isdir(os.path.join(directory))):
try:
with open(os.path.join(directory,
'T1w.json')) as data_file:
dataset = json.load(data_file)
except OSError:
logging.critical(
"Cannot find T1w.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")
#add various attributes if they exist in BIDS dataset
for key in dataset:
#if key from T1w.json file is mapped to term in BIDS_Constants.py then add to NIDM object
if key in BIDS_Constants.json_keys:
if type(dataset[key]) is list:
acq_obj.add_attributes({
BIDS_Constants.json_keys[key]:
"".join(dataset[key])
})
else:
acq_obj.add_attributes(
{BIDS_Constants.json_keys[key]: dataset[key]})
elif file_tpl.entities['datatype'] == 'func':
#do something with functionals
acq_obj = MRObject(acq)
#add image contrast type
if file_tpl.entities['suffix'] in BIDS_Constants.scans:
acq_obj.add_attributes({
Constants.NIDM_IMAGE_CONTRAST_TYPE:
BIDS_Constants.scans[file_tpl.entities['suffix']]
})
else:
logging.info(
"WARNING: No matching image contrast type found in BIDS_Constants.py for %s"
% file_tpl.entities['suffix'])
#add image usage type
if file_tpl.entities['datatype'] in BIDS_Constants.scans:
acq_obj.add_attributes({
Constants.NIDM_IMAGE_USAGE_TYPE:
BIDS_Constants.scans[file_tpl.entities['datatype']]
})
else:
logging.info(
"WARNING: No matching image usage type found in BIDS_Constants.py for %s"
% file_tpl.entities['datatype'])
#make relative link to
acq_obj.add_attributes({
Constants.NIDM_FILENAME:
getRelPathToBIDS(join(file_tpl.dirname, file_tpl.filename),
directory)
})
#WIP: add absolute location of BIDS directory on disk for later finding of files
acq_obj.add_attributes({Constants.PROV['Location']: directory})
#add sha512 sum
if isfile(join(directory, file_tpl.dirname,
file_tpl.filename)):
acq_obj.add_attributes({
Constants.CRYPTO_SHA512:
getsha512(
join(directory, file_tpl.dirname,
file_tpl.filename))
})
else:
logging.info(
"WARNINGL file %s doesn't exist! No SHA512 sum stored in NIDM files..."
% join(directory, file_tpl.dirname, file_tpl.filename))
if 'run' in file_tpl.entities:
acq_obj.add_attributes({
BIDS_Constants.json_keys["run"]:
file_tpl.entities['run']
})
#get associated JSON file if exists
json_data = (bids_layout.get(
suffix=file_tpl.entities['suffix'],
subject=subject_id))[0].metadata
if len(json_data.info) > 0:
for key in json_data.info.items():
if key in BIDS_Constants.json_keys:
if type(json_data.info[key]) is list:
acq_obj.add_attributes({
BIDS_Constants.json_keys[key.replace(
" ", "_")]:
''.join(
str(e) for e in json_data.info[key])
})
else:
acq_obj.add_attributes({
BIDS_Constants.json_keys[key.replace(
" ", "_")]:
json_data.info[key]
})
#get associated events TSV file
if 'run' in file_tpl.entities:
events_file = bids_layout.get(
subject=subject_id,
extensions=['.tsv'],
modality=file_tpl.entities['datatype'],
task=file_tpl.entities['task'],
run=file_tpl.entities['run'])
else:
events_file = bids_layout.get(
subject=subject_id,
extensions=['.tsv'],
modality=file_tpl.entities['datatype'],
task=file_tpl.entities['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.NIDM_FILENAME:
getRelPathToBIDS(events_file[0].filename, directory)
})
#link it to appropriate MR acquisition entity
events_obj.wasAttributedTo(acq_obj)
#Parse task-rest_bold.json file in BIDS directory to add the attributes contained inside
if (os.path.isdir(os.path.join(directory))):
try:
with open(
os.path.join(
directory,
'task-rest_bold.json')) as data_file:
dataset = json.load(data_file)
except OSError:
logging.critical(
"Cannot find task-rest_bold.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")
#add various attributes if they exist in BIDS dataset
for key in dataset:
#if key from task-rest_bold.json file is mapped to term in BIDS_Constants.py then add to NIDM object
if key in BIDS_Constants.json_keys:
if type(dataset[key]) is list:
acq_obj.add_attributes({
BIDS_Constants.json_keys[key]:
",".join(map(str, dataset[key]))
})
else:
acq_obj.add_attributes(
{BIDS_Constants.json_keys[key]: dataset[key]})
elif file_tpl.entities['datatype'] == 'dwi':
#do stuff with with dwi scans...
acq_obj = MRObject(acq)
#add image contrast type
if file_tpl.entities['suffix'] in BIDS_Constants.scans:
acq_obj.add_attributes({
Constants.NIDM_IMAGE_CONTRAST_TYPE:
BIDS_Constants.scans[file_tpl.entities['suffix']]
})
else:
logging.info(
"WARNING: No matching image contrast type found in BIDS_Constants.py for %s"
% file_tpl.entities['suffix'])
#add image usage type
if file_tpl.entities['datatype'] in BIDS_Constants.scans:
acq_obj.add_attributes({
Constants.NIDM_IMAGE_USAGE_TYPE:
BIDS_Constants.scans["dti"]
})
else:
logging.info(
"WARNING: No matching image usage type found in BIDS_Constants.py for %s"
% file_tpl.entities['datatype'])
#make relative link to
acq_obj.add_attributes({
Constants.NIDM_FILENAME:
getRelPathToBIDS(join(file_tpl.dirname, file_tpl.filename),
directory)
})
#add sha512 sum
if isfile(join(directory, file_tpl.dirname,
file_tpl.filename)):
acq_obj.add_attributes({
Constants.CRYPTO_SHA512:
getsha512(
join(directory, file_tpl.dirname,
file_tpl.filename))
})
else:
logging.info(
"WARNINGL file %s doesn't exist! No SHA512 sum stored in NIDM files..."
% join(directory, file_tpl.dirname, 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(
suffix=file_tpl.entities['suffix'],
subject=subject_id))[0].metadata
if len(json_data.info) > 0:
for key in json_data.info.items():
if key in BIDS_Constants.json_keys:
if type(json_data.info[key]) is list:
acq_obj.add_attributes({
BIDS_Constants.json_keys[key.replace(
" ", "_")]:
''.join(
str(e) for e in json_data.info[key])
})
else:
acq_obj.add_attributes({
BIDS_Constants.json_keys[key.replace(
" ", "_")]:
json_data.info[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:
getRelPathToBIDS(
join(file_tpl.dirname,
bids_layout.get_bval(file_tpl.filename)),
directory)
})
#WIP: add absolute location of BIDS directory on disk for later finding of files
acq_obj_bval.add_attributes(
{Constants.PROV['Location']: directory})
#add sha512 sum
if isfile(join(directory, file_tpl.dirname,
file_tpl.filename)):
acq_obj_bval.add_attributes({
Constants.CRYPTO_SHA512:
getsha512(
join(directory, file_tpl.dirname,
file_tpl.filename))
})
else:
logging.info(
"WARNINGL file %s doesn't exist! No SHA512 sum stored in NIDM files..."
% join(directory, file_tpl.dirname, 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:
getRelPathToBIDS(
join(file_tpl.dirname,
bids_layout.get_bvec(file_tpl.filename)),
directory)
})
#WIP: add absolute location of BIDS directory on disk for later finding of files
acq_obj_bvec.add_attributes(
{Constants.PROV['Location']: directory})
if isfile(join(directory, file_tpl.dirname,
file_tpl.filename)):
#add sha512 sum
acq_obj_bvec.add_attributes({
Constants.CRYPTO_SHA512:
getsha512(
join(directory, file_tpl.dirname,
file_tpl.filename))
})
else:
logging.info(
"WARNINGL file %s doesn't exist! No SHA512 sum stored in NIDM files..."
% join(directory, file_tpl.dirname, file_tpl.filename))
#link bval and bvec acquisition object entities together or is their association with DWI scan...
#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)
})
#WIP: add absolute location of BIDS directory on disk for later finding of files
acq_entity.add_attributes(
{Constants.PROV['Location']: directory})
#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"]:
getRelPathToBIDS(data_dict, directory)
})
return project, cde
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
def main(argv):
parser = ArgumentParser(
description="""This program will load in a CSV file made during simple-2
brain volumes experiment which has the following organization:
source FSL FSL FSL
participant_id left nucleus accumbens volume left amygdala volume
sub-0050002 796.4723293 1255.574283 4449.579039
sub-0050003 268.9688215 878.7860634 3838.602449
sub-0050004 539.0969914 1195.288168 3561.518188
If will use the first row to determine the software used for the segmentations and the
second row for the variable names. Then it does a simple NIDM conversion using
example model in: https://docs.google.com/document/d/1PyBoM7J0TuzTC1TIIFPDqd05nomcCM5Pvst8yCoqLng/edit"""
)
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('-out',
dest='output_file',
required=True,
help="Filename to save NIDM file")
args = parser.parse_args()
#open CSV file and read first line which is the source of the segmentations
source_row = pd.read_csv(args.csv_file, nrows=0)
#open CSV file and load into
df = pd.read_csv(args.csv_file, skiprows=0, header=1)
#account for duplicate column names
# df.columns = df.iloc[0]
df = df.reindex(df.index.drop(0)).reset_index(drop=True)
#get unique variable names from CSV data file
#note, duplicate variable names will be appended with a ".X" where X is the number of duplicates
unique_vars = []
for variable in list(df):
temp = variable.split(".")[0]
if temp not in unique_vars:
unique_vars.append(temp)
#do same as above for unique software agents
unique_software = []
for variable in list(source_row):
temp = variable.split(".")[0]
if temp not in unique_software:
unique_software.append(temp)
#maps variables in CSV file to terms
if args.owl:
column_to_terms = map_variables_to_terms(
df=pd.DataFrame(columns=unique_vars),
apikey=args.key,
directory=dirname(args.output_file),
output_file=join(dirname(args.output_file), "json_map.json"),
json_file=args.json_map,
owl_file=args.owl)
else:
column_to_terms = map_variables_to_terms(
df=pd.DataFrame(columns=unique_vars),
apikey=args.key,
directory=dirname(args.output_file),
output_file=join(dirname(args.output_file), "json_map.json"),
json_file=args.json_map)
#get subjectID field from CSV
id_field = getSubjIDColumn(column_to_terms, df)
# WIP!!!#########################################################################################
#go line by line through CSV file creating NIDM structures
#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 is not None:
print("Adding to NIDM file...")
#read in NIDM file
project = read_nidm(args.nidm_file)
root_act = project.graph.activity(
QualifiedName(provNamespace("niiri", Constants.NIIRI), getUUID()),
other_attributes={
Constants.NIDM_PROJECT_DESCRIPTION:
"Brain volumes provenance document"
})
#this function sucks...more thought needed for version that works with adding to existing NIDM file versus creating a new NIDM file....
add_brainvolume_data(nidmdoc=project,
df=df,
id_field=id_field,
root_act=root_act,
column_to_terms=column_to_terms,
png_file=args.png,
output_file=args.output_file,
source_row=source_row,
nidm_graph=True)
#serialize NIDM file
with open(args.output_file, 'w') as f:
print("Writing NIDM file...")
f.write(project.serializeTurtle())
#if args.png:
# nidmdoc.save_DotGraph(str(args.output_file + ".png"), format="png")
# #find subject ids and sessions in NIDM document
# query = """SELECT DISTINCT ?session ?nidm_subj_id ?agent ?entity
# WHERE {
# ?activity prov:wasAssociatedWith ?agent ;
# dct:isPartOf ?session .
# ?entity prov:wasGeneratedBy ?activity ;
# nidm:hasImageUsageType nidm:Anatomical .
# ?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 brain volumes data to NIDM file, else skip it....
# if (not (len(csv_row.index)==0)):
#Here we're sure we have an agent in the NIDM graph that corresponds to the participant in the
#brain volumes data. We don't know which AcquisitionObject (entity) describes the T1-weighted scans
#used for the project. Since we don't have the SHA512 sums in the brain volumes data (YET) we can't
#really verify that it's a particular T1-weighted scan that was used for the brain volumes but we're
#simply, for the moment, going to assume it's the activity/session returned by the above query
#where we've specifically asked for the entity which has a nidm:hasImageUsageType nidm:Anatomical
#NIDM document entity uuid which has a nidm:hasImageUsageType nidm:Anatomical
#this is the entity that is associated with the brain volume report for this participant
# entity_uuid = row[3]
#Now we need to set up the entities/activities, etc. to add the brain volume data for this row of the
#CSV file and link it to the above entity and the agent for this participant which is row[0]
#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:
#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...")
# 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 an empty NIDM graph
nidmdoc = Core()
root_act = nidmdoc.graph.activity(
QualifiedName(provNamespace("niiri", Constants.NIIRI), getUUID()),
other_attributes={
Constants.NIDM_PROJECT_DESCRIPTION:
"Brain volumes provenance document"
})
#this function sucks...more thought needed for version that works with adding to existing NIDM file versus creating a new NIDM file....
add_brainvolume_data(nidmdoc=nidmdoc,
df=df,
id_field=id_field,
root_act=root_act,
column_to_terms=column_to_terms,
png_file=args.png,
output_file=args.output_file,
source_row=source_row)
#serialize NIDM file
with open(args.output_file, 'w') as f:
print("Writing NIDM file...")
f.write(nidmdoc.serializeTurtle())
if args.png:
# nidmdoc.save_DotGraph(str(args.output_file + ".png"), format="png")
nidmdoc.save_DotGraph(str(args.output_file + ".pdf"),
format="pdf")
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 test_map_vars_to_terms_reproschema():
'''
This function will test the Utils.py "map_vars_to_terms" function with a reproschema-formatted
JSON sidecar file
'''
global DATA, REPROSCHEMA_JSON_MAP
column_to_terms, cde = map_variables_to_terms(df=DATA, json_source=REPROSCHEMA_JSON_MAP,
directory=tempfile.gettempdir(), assessment_name="test")
# check whether JSON mapping structure returned from map_variables_to_terms matches the
# reproshema structure
assert "DD(source='test', variable='age')" in column_to_terms.keys()
assert "DD(source='test', variable='sex')" in column_to_terms.keys()
assert "isAbout" in column_to_terms["DD(source='test', variable='age')"].keys()
assert "http://uri.interlex.org/ilx_0100400" == column_to_terms["DD(source='test', variable='age')"] \
['isAbout'][0]['@id']
assert "http://uri.interlex.org/ilx_0738439" == column_to_terms["DD(source='test', variable='sex')"] \
['isAbout'][0]['@id']
assert "responseOptions" in column_to_terms["DD(source='test', variable='sex')"].keys()
assert "choices" in column_to_terms["DD(source='test', variable='sex')"]['responseOptions'].keys()
assert "Male" in column_to_terms["DD(source='test', variable='sex')"]['responseOptions']['choices'].keys()
assert "m" == column_to_terms["DD(source='test', variable='sex')"]['responseOptions']['choices']['Male']
assert "Male" in column_to_terms["DD(source='test', variable='sex')"]['responseOptions']['choices'].keys()
assert "m" == column_to_terms["DD(source='test', variable='sex')"]['responseOptions']['choices']['Male']
# now check the JSON mapping file created by map_variables_to_terms which should match Reproschema format
with open(join(tempfile.gettempdir(), "nidm_annotations.json")) as fp:
reproschema_json = json.load(fp)
assert "DD(source='test', variable='age')" in column_to_terms.keys()
assert "DD(source='test', variable='sex')" in column_to_terms.keys()
assert "isAbout" in column_to_terms["DD(source='test', variable='age')"].keys()
assert "http://uri.interlex.org/ilx_0100400" == column_to_terms["DD(source='test', variable='age')"] \
['isAbout'][0]['@id']
assert "http://uri.interlex.org/ilx_0738439" == column_to_terms["DD(source='test', variable='sex')"] \
['isAbout'][0]['@id']
assert "responseOptions" in column_to_terms["DD(source='test', variable='sex')"].keys()
assert "choices" in column_to_terms["DD(source='test', variable='sex')"]['responseOptions'].keys()
assert "Male" in column_to_terms["DD(source='test', variable='sex')"]['responseOptions']['choices'].keys()
assert "m" == column_to_terms["DD(source='test', variable='sex')"]['responseOptions']['choices']['Male']
assert "Male" in column_to_terms["DD(source='test', variable='sex')"]['responseOptions']['choices'].keys()
assert "m" == column_to_terms["DD(source='test', variable='sex')"]['responseOptions']['choices']['Male']
# check the CDE dataelement graph for correct information
query = '''
prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#>
select distinct ?uuid ?DataElements ?property ?value
where {
?uuid a/rdfs:subClassOf* nidm:DataElement ;
?property ?value .
}'''
qres = cde.query(query)
results = []
for row in qres:
results.append(list(row))
assert len(results) == 20
