def testEncodeScalar(self): array_str = array_to_source.array_to_source('array', 4) expected_str = """array = np.array([ 4, ]).reshape(()) """ self.assertEqual(expected_str, array_str)
def testEncodeArray(self): array_str = array_to_source.array_to_source('array', [1, 2, 3]) expected_str = """array = np.array([ 1, 2, 3, ]).reshape((3,)) """ self.assertEqual(expected_str, array_str)
def save_ground_truth_part(name, tuple_path, mean, sem, std, sestd): """Saves a ground truth part to strings. This is meant to be called with outputs of `nest.flatten_with_tuple_paths(ground_truth_mean)`. Args: name: Python `str`. Name of the sample transformation. tuple_path: Tuple path of the part of the ground truth we're saving. See `nest.flatten_with_tuple_paths`. mean: Ground truth mean, or `None` if it is absent. sem: Ground truth stadard error of the mean, or `None` if it is absent. std: Ground truth standard deviation, or `None` if it is absent. sestd: Ground truth mean, or `None` if it is absent. Returns: array_strs: Python list of strings, representing the encoded arrays (that were present). Typically these would be joined with a newline and written out to a module, which can then be passed to `load_ground_truth_part`. """ array_strs = [] mean_name, sem_name, std_name, sestd_name = _get_global_variable_names( name, tuple_path) if mean is not None: array_strs.append(array_to_source.array_to_source(mean_name, mean)) if sem is not None: array_strs.append(array_to_source.array_to_source(sem_name, sem)) if std is not None: array_strs.append(array_to_source.array_to_source(std_name, std)) if sestd is not None: array_strs.append(array_to_source.array_to_source(sestd_name, sestd)) return array_strs