def extract_terms(output_dir):
if has_internet_connectivity():
terms = dict()
atlases = get_json("http://neurovault.org/api/atlases/?format=json")
for atlas in atlases:
label_description_file = "http://neurovault.org/media/images/291/Talairach-labels-2mm.xml"
print "Parsing %s" %(label_description_file)
xml_dict = read_xml_url(label_description_file)
atlas_name = xml_dict["atlas"]["header"]["name"]
atlas_name_label = atlas_name.replace(" ","_").lower()
atlas_labels = xml_dict["atlas"]["data"]["label"]
for l in range(len(atlas_labels)):
label = atlas_labels[l]
# We will use coordinate for unique ID
unique_id = "%s_%s" %(atlas_name_label,l)
terms[unique_id] = {"name":label["#text"],
"x":label["@x"],
"y":label["@y"],
"z":label["@z"]}
save_terms(terms,output_dir=output_dir)
else:
print "Cannot define fsl atlas terms, no internet connectivity."
def extract_terms(output_dir):
if has_internet_connectivity():
terms = get_terms()
save_terms(terms,output_dir=output_dir)
else:
print "Cannot define fma-nif region terms, no internet connectivity."
def extract_terms(output_dir):
# You can provide a dictionary if your terms have meta data
# the key should be some unique ID for your term, and use numbers
# if you do not have any. The "name" variable in the meta data should
# correspoind to the term name
terms = {"term_unique_id1":{"meta1":"meta_value1",
"meta2":"meta_value2",
"name":"term1"}}
# Or a list if not
terms = ["term1"]
save_terms(terms,output_dir=home)
def extract_terms(output_dir):
f,d = download_data()
features = pandas.read_csv(f,sep="\t")
terms = features.columns.tolist()
terms.pop(0) #pmid
save_terms(terms,output_dir)
def extract_terms(output_dir):
terms = get_terms()
save_terms(terms,output_dir=output_dir)
def extract_terms(output_dir):
cattell = get_cattell()
terms = get_terms(cattell)
save_terms(terms,output_dir=output_dir)
