numpy.bool is deprecated

The usage of numpy.bool has been deprecated as of Numpy 1.20. Instead, it's recommended to either use the built in python bool or use the numpy scalar numpy.bool_.

~/.local/src/miniconda3/lib/python3.9/site-packages/vtkmodules/util/numpy_support.py in get_vtk_to_numpy_typemap()
     72 def get_vtk_to_numpy_typemap():
     73     """Returns the VTK array type to numpy array type mapping."""
---> 74     _vtk_np = {vtkConstants.VTK_BIT:numpy.bool,
     75                 vtkConstants.VTK_CHAR:numpy.int8,
     76                 vtkConstants.VTK_SIGNED_CHAR:numpy.int8,

~/.local/src/miniconda3/lib/python3.9/site-packages/numpy/__init__.py in __getattr__(attr)
    285                 pass
    286             else:
--> 287                 warnings.warn(msg, DeprecationWarning, stacklevel=2)
    288                 return val
    289 

DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_
` here.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations

Not sure which is preferable in this context though. bool would be the direct replacement and prevent any compatibility issues with previous versions of numpy (I'm not sure when np.bool_ was introduced, maybe it's always been there).

Supposedly, bool is the same size as int (so normally 4 bytes), while the numpy.bool_ is only one byte long. So using the lower memory footprint may be desirable. But I can't seem to "prove" that there is any difference in the memory allocation, as np.ones(100, dtype=bool).nbytes and np.ones(100, dtype=np.bool_).nbytes both return with 100 bytes.