//  [python]
def __setitem__(self, args, val):
        """ Write to the HDF5 dataset from a Numpy array.

        NumPy's broadcasting rules are honored, for "simple" indexing
        (slices and integers).  For advanced indexing, the shapes must
        match.
        """
        args = args if isinstance(args, tuple) else (args,)

        # Sort field indices from the slicing
        names = tuple(x for x in args if isinstance(x, six.string_types))
        args = tuple(x for x in args if not isinstance(x, six.string_types))
        if not six.PY3:
            names = tuple(x.encode('utf-8') if isinstance(x, six.text_type) else x for x in names)

        # Generally we try to avoid converting the arrays on the Python
        # side.  However, for compound literals this is unavoidable.
        vlen = h5t.check_dtype(vlen=self.dtype)
        if vlen is not None and vlen not in (bytes, six.text_type):
            try:
                val = numpy.asarray(val, dtype=vlen)
            except ValueError:
                try:
                    val = numpy.array([numpy.array(x, dtype=vlen)
                                       for x in val], dtype=self.dtype)
                except ValueError:
                    pass
            if vlen == val.dtype:
                if val.ndim > 1:
                    tmp = numpy.empty(shape=val.shape[:-1], dtype=object)
                    tmp.ravel()[:] = [i for i in val.reshape(
                        (numpy.product(val.shape[:-1]), val.shape[-1]))]
                else:
                    tmp = numpy.array([None], dtype=object)
                    tmp[0] = val
                val = tmp
        elif self.dtype.kind == "O" or \
          (self.dtype.kind == 'V' and \
          (not isinstance(val, numpy.ndarray) or val.dtype.kind != 'V') and \
          (self.dtype.subdtype == None)):
            if len(names) == 1 and self.dtype.fields is not None:
                # Single field selected for write, from a non-array source
                if not names[0] in self.dtype.fields:
                    raise ValueError("No such field for indexing: %s" % names[0])
                dtype = self.dtype.fields[names[0]][0]
                cast_compound = True
            else:
                dtype = self.dtype
                cast_compound = False

            val = numpy.asarray(val, dtype=dtype.base, order='C')
            if cast_compound:
                val = val.view(numpy.dtype([(names[0], dtype)]))
                val = val.reshape(val.shape[:len(val.shape) - len(dtype.shape)])
        else:
            val = numpy.asarray(val, order='C')

        # Check for array dtype compatibility and convert
        if self.dtype.subdtype is not None:
            shp = self.dtype.subdtype[1]
            valshp = val.shape[-len(shp):]
            if valshp != shp:  # Last dimension has to match
                raise TypeError("When writing to array types, last N dimensions have to match (got %s, but should be %s)" % (valshp, shp,))
            mtype = h5t.py_create(numpy.dtype((val.dtype, shp)))
            mshape = val.shape[0:len(val.shape)-len(shp)]

        # Make a compound memory type if field-name slicing is required
        elif len(names) != 0:

            mshape = val.shape

            # Catch common errors
            if self.dtype.fields is None:
                raise TypeError("Illegal slicing argument (not a compound dataset)")
            mismatch = [x for x in names if x not in self.dtype.fields]
            if len(mismatch) != 0:
                mismatch = ", ".join('"%s"'%x for x in mismatch)
                raise ValueError("Illegal slicing argument (fields %s not in dataset type)" % mismatch)
        
            # Write non-compound source into a single dataset field
            if len(names) == 1 and val.dtype.fields is None:
                subtype = h5t.py_create(val.dtype)
                mtype = h5t.create(h5t.COMPOUND, subtype.get_size())
                mtype.insert(self._e(names[0]), 0, subtype)

            # Make a new source type keeping only the requested fields
            else:
                fieldnames = [x for x in val.dtype.names if x in names] # Keep source order
                mtype = h5t.create(h5t.COMPOUND, val.dtype.itemsize)
                for fieldname in fieldnames:
                    subtype = h5t.py_create(val.dtype.fields[fieldname][0])
                    offset = val.dtype.fields[fieldname][1]
                    mtype.insert(self._e(fieldname), offset, subtype)

        # Use mtype derived from array (let DatasetID.write figure it out)
        else:
            mshape = val.shape
            mtype = None

        # Perform the dataspace selection
        selection = sel.select(self.shape, args, dsid=self.id)

        if selection.nselect == 0:
            return

        # Broadcast scalars if necessary.
        if mshape == () and selection.mshape != ():
            if self.dtype.subdtype is not None:
                raise TypeError("Scalar broadcasting is not supported for array dtypes")
            val2 = numpy.empty(selection.mshape[-1], dtype=val.dtype)
            val2[...] = val
            val = val2
            mshape = val.shape

        # Perform the write, with broadcasting
        # Be careful to pad memory shape with ones to avoid HDF5 chunking
        # glitch, which kicks in for mismatched memory/file selections
        if len(mshape) < len(self.shape):
            mshape_pad = (1,)*(len(self.shape)-len(mshape)) + mshape
        else:
            mshape_pad = mshape
        mspace = h5s.create_simple(mshape_pad, (h5s.UNLIMITED,)*len(mshape_pad))
        for fspace in selection.broadcast(mshape):
            self.id.write(mspace, fspace, val, mtype, dxpl=self._dxpl)