def test_neurosynth_words_vectorized():
n_im = 5
with tempfile.TemporaryDirectory() as temp_dir:
words_files = [
os.path.join(temp_dir, 'words_for_image_{0}.json'.format(i))
for i in range(n_im)
]
words = [str(i) for i in range(n_im)]
for i, file_name in enumerate(words_files):
word_weights = np.zeros(n_im)
word_weights[i] = 1
words_dict = {
'data': {
'values':
dict([(k, v) for k, v in zip(words, word_weights)])
}
}
with open(file_name, 'wb') as words_file:
words_file.write(json.dumps(words_dict).encode('utf-8'))
freq, voc = neurovault.neurosynth_words_vectorized(words_files)
assert freq.shape == (n_im, n_im)
assert ((freq.sum(axis=0) == np.ones(n_im)).all())
with pytest.warns(UserWarning):
neurovault.neurosynth_words_vectorized(
(os.path.join(temp_dir, 'no_words_here.json'), ))
def test_neurosynth_words_vectorized():
n_im = 5
with _TestTemporaryDirectory() as temp_dir:
words_files = [
os.path.join(temp_dir, 'words_for_image_{0}.json'.format(i)) for
i in range(n_im)]
words = [str(i) for i in range(n_im)]
for i, file_name in enumerate(words_files):
word_weights = np.zeros(n_im)
word_weights[i] = 1
words_dict = {'data':
{'values':
dict([(k, v) for
k, v in zip(words, word_weights)])}}
with open(file_name, 'wb') as words_file:
words_file.write(json.dumps(words_dict).encode('utf-8'))
freq, voc = neurovault.neurosynth_words_vectorized(words_files)
assert_equal(freq.shape, (n_im, n_im))
assert((freq.sum(axis=0) == np.ones(n_im)).all())
assert_warns(UserWarning, neurovault.neurosynth_words_vectorized,
(os.path.join(temp_dir, 'no_words_here.json'),))