spdl.io.load_npy

load_npy(data: bytes | bytearray, *, copy: bool = False) → ndarray[tuple[int, ...], dtype[_ScalarType_co]]

Load NumPy NDArray from bytes.

This function loads NumPy NDArray from memory. It is equivalent to numpy.load(io.BytesIO(data)), but it is more efficient.

Note

This function does not support object dtype, and Fortran order.

Example

>>> ref = np.arange(20)
>>> buffer = BytesIO()
>>> np.save(buffer, ref)
>>> buffer.seek(0)
>>> data = buffer.getvalue()
>>> restore = spdl.io.load_npy(data)
>>> assert np.array_equal(restore, ref)

Parameters:

• data – The data generated by numpy.save() function. (Note that it is different from numpy.ndarray.tobytes(), which does not contain shape and dtype.)

• copy – Whether to copy data. Default: no copy.

Returns:

The restored array data.

See also

• numpy.lib.format: The detail of NPY serialization is found.

• _read_array_header: The function called by numpy.load() to parse the header.

• _read_array: The function called by numpy.load() when loading the data region of NPY file.

Note

There is a branch in _read_array function where the execution can call some faster implementation. Howver, BytesIO does not meet the condition. (isfileobj() function returns False for io.BytesIO. [source]) Even if the execution takes the faster numpy.fromfile() path, it creates a new array.