def __new__(cls, data, dtype=None, force2d="as_row", *args, **kwargs):
assert force2d in ("as_row", "as_col")
if isinstance(data, cls):
matrix_data = data
cls._preprocess_if_data_is_cls(matrix=matrix_data, *args, **kwargs)
return matrix_data
else:
if isinstance(data, (unicode, str)):
# in case of matrix_data = np.array(unicode-type instance)
# the shape of matrix_data would be () and len(matrix_data.shape) == 0
matrix_data = np.array([data], dtype=dtype)
else:
matrix_data = np.array(data, dtype=dtype)
if len(matrix_data.shape) > 2:
matrix_data = np_tools.clean_no_data_tensors(matrix_data)
if force2d == "as_row":
if len(matrix_data.shape) < 2:
matrix_data = np.array([matrix_data])
elif force2d == "as_col":
if len(matrix_data.shape) < 2:
matrix_data = np.array([matrix_data]).T
assert len(matrix_data.shape) == 2
matrix_data = matrix_data.view(cls)
cls._preprocess_before_init_return(matrix=matrix_data,
*args, **kwargs)
return matrix_data
def __new__(cls, data, dtype=None, *args, **kwargs):
if isinstance(data, cls):
values_vector = data
cls._preprocess_if_data_is_cls(vector=values_vector,*args, **kwargs)
return values_vector
else:
vector_data = np.array(data, dtype=dtype)
if len(vector_data.shape) > 1:
vector_data = np_tools.clean_no_data_tensors(vector_data)
assert len(vector_data.shape) == 1
vector_data = vector_data.view(cls)
cls._preprocess_before_init_return(vector=vector_data,*args, **kwargs)
return vector_data
def _as_1d_array(self):
assert self._is_1d
return np_tools.clean_no_data_tensors(self)
def _is_1d(self):
return len(np_tools.clean_no_data_tensors(self).shape) == 1
