def extract_relations(output_dir):
cattell = get_cattell()
terms = get_terms(cattell)
tuples = []
for term in cattell:
unique_id = term["id"]
opposite = (unique_id,term["opposite_id"],"opposite_of")
tuples.append(opposite)
save_relations(terms,output_dir=output_dir,relationships=tuples)
def extract_relations(uids,output_dir):
# This function will be called by a job, and must call save_relations
# ** ALL USER FUNCTIONS MUST HAVE output_dir as an input
# You should provide a list of tuples, with the
# first and second items for each tuple corresponding
# to term ids you specified in extract_terms
tuples = [(source1,target1,value)]
# Value can be a string or int
# The terms variable is equivalent to the one needed for extract_terms
save_relations(relations=tuples,output_dir=output_dir)
def extract_relations(output_dir):
if has_internet_connectivity():
tuples = []
terms = get_terms()
relations = get_nif_json()
for relation in relations["edges"]:
tup = (relation["obj"],relation["sub"],relation["pred"])
tuples.append(tup)
save_relations(terms,output_dir=output_dir,relationships=tuples)
else:
print "Cannot define fma-nif relationships, no internet connectivity."
def extract_relations(output_dir):
tuples = []
terms = get_terms()
concepts = get_concepts()
for concept in concepts:
if "relationships" in concept:
for relation in concept["relationships"]:
relationship = "%s,%s" %(relation["direction"],relation["relationship"])
tup = (concept["id"],relation["id"],relationship)
tuples.append(tup)
save_relations(terms,output_dir=output_dir,relationships=tuples)
def extract_relations(terms,maps_dir,output_dir):
if isinstance(terms,str):
terms = [terms]
f,d = download_data()
features = pandas.read_csv(f,sep="\t")
database = pandas.read_csv(d,sep="\t")
allterms = features.columns.tolist()
allterms.pop(0) #pmid
dataset = Dataset(d)
dataset.add_features(f)
image_matrix = pandas.DataFrame(columns=range(228453))
for t in range(len(allterms)):
term = allterms[t]
term_name = term.replace(" ","_")
pickled_map = "%s/%s_pFgA_z.pkl" %(maps_dir,term_name)
if not os.path.exists(pickled_map):
print "Generating P(term|activation) for term %s" %(term)
ids = dataset.get_ids_by_features(term)
maps = meta.MetaAnalysis(dataset,ids)
pickle.dump(maps.images["pFgA_z"],open(pickled_map,"wb"))
map_data = pickle.load(open(pickled_map,"rb"))
image_matrix.loc[term] = map_data
sims = pandas.DataFrame(columns=image_matrix.index)
tuples = []
for t1 in range(len(terms)):
term1 = terms[t1]
print "Extracting NeuroSynth relationships for term %s..." %(term1)
for t2 in range(len(terms)):
term2 = terms[t2]
if t1<t2:
score = pearsonr(image_matrix.loc[term1],image_matrix.loc[term2])[0]
tuples.append((term1,term2,score))
save_relations(output_dir=output_dir,relations=tuples)
