def go_fish():
f,d = download_data()
features = pandas.read_csv(f,sep="\t")
database = pandas.read_csv(d,sep="\t")
pmids = database.id.unique().tolist()
print "NeuroSynth database has %s unique PMIDs" %(len(pmids))
# Generate brain maps to extract relationships with
terms = features.columns.tolist()
terms.pop(0) #pmid
maps_dir = "%s/terms/neurosynth/maps" %(home)
if not os.path.exists(maps_dir):
os.mkdir(maps_dir)
# jobs to download abstract texts
generate_job(func="generate_maps",inputs={"terms":terms},category="terms",batch_num=100)
generate_job(func="extract_text",category="corpus",inputs={"pmids":pmids},batch_num=100)
generate_job(func="extract_terms",category="terms")
generate_job(func="extract_relations",inputs={"terms":terms,"maps_dir":maps_dir},category="relations",batch_num=100)
def go_fish():
generate_job(func="extract_terms",category="terms")
def go_fish():
generate_job(func="extract_text", category="corpus")
def go_fish():
# jobs to download abstract texts
generate_job(func="extract_text",category="corpus",inputs={"uids",uids},batch_num=100)
generate_job(func="extract_terms",category="terms")
generate_job(func="extract_relations",category="relations")
def go_fish():
generate_job(func="extract_terms",category="terms")
generate_job(func="extract_relationships",category="terms")
