@classmethod
    def from_dtype(class_, dtype, dflt=None):
        basedtype = dtype.base
        if basedtype.names:
            raise ValueError("compound data types are not supported: %r"
                             % dtype)
        if basedtype.shape != ():
            raise ValueError("nested data types are not supported: %r"
                             % dtype)
        if basedtype.kind == 'S':  # can not reuse something like 'string80'
            itemsize = basedtype.itemsize
            return class_.from_kind('string', itemsize, dtype.shape, dflt)
        elif basedtype.kind == 'U':
            # workaround for unicode type (standard string type in Python 3)
            warnings.warn("support for unicode type is very limited, "
                          "and only works for strings that can be cast as ascii", FlavorWarning)
            itemsize = basedtype.itemsize // 4
            assert str(itemsize) in basedtype.str, "something went wrong in handling unicode."
            return class_.from_kind('string', itemsize, dtype.shape, dflt)
        # Most NumPy types have direct correspondence with PyTables types.
        return class_.from_type(basedtype.name, dtype.shape, dflt)
Invoke a remote method and get back an iterator.
dataset/ETHPy150Open PyTables/PyTables/tables/atom.py/Atom.from_dtype--crossover with--dataset/ETHPy150Open Havate/havate-openstack/proto-build/gui/horizon/Horizon_GUI/openstack_dashboard/openstack/common/rpc/__init__.py/multicall