def annotations_put_fbs_helper(data_adaptor, annotations, fbs):
"""helper function to write annotations from fbs"""
if annotations is None:
raise DisabledFeatureError("Writable annotations are not enabled")
new_label_df = decode_matrix_fbs(fbs)
if not new_label_df.empty:
data_adaptor.check_new_labels(new_label_df)
annotations.write_labels(new_label_df, data_adaptor)
def test_roundtrip(self):
dfSrc = pd.DataFrame(
data={
"a": np.zeros((10,), dtype=np.float32),
"b": np.ones((10,), dtype=np.int64),
"c": np.array([i for i in range(0, 10)], dtype=np.uint16),
"d": pd.Series(["x", "y", "z", "x", "y", "z", "a", "x", "y", "z"], dtype="category"),
}
)
dfDst = decode_matrix_fbs(encode_matrix_fbs(matrix=dfSrc, col_idx=dfSrc.columns))
self.assertEqual(dfSrc.shape, dfDst.shape)
self.assertEqual(set(dfSrc.columns), set(dfDst.columns))
for c in dfSrc.columns:
self.assertTrue(c in dfDst.columns)
if isinstance(dfSrc[c], pd.Series):
self.assertTrue(np.all(dfSrc[c] == dfDst[c]))
else:
self.assertEqual(dfSrc[c], dfDst[c])
def convert(mat, cols):
return decode_matrix_fbs(encode_matrix_fbs(
mat, col_idx=cols)).to_numpy()