Method eval

tables/expression.py:604–706

Evaluate the expression and return the outcome. Because of performance reasons, the computation order tries to go along the common main dimension of all inputs. If not such a common main dimension is found, the iteration will go along the leading dimension instead.

(self)

            return (i_nrows, slice_pos, start, stop, step, nrowsinbuf)

    def eval(self) -> ContainerType:  # noqa: A003
        """Evaluate the expression and return the outcome.

        Because of performance reasons, the computation order tries to go along
        the common main dimension of all inputs.  If not such a common main
        dimension is found, the iteration will go along the leading dimension
        instead.

        For non-consistent shapes in inputs (i.e. shapes having a different
        number of dimensions), the regular NumPy broadcast rules applies.
        There is one exception to this rule though: when the dimensions
        orthogonal to the main dimension of the expression are consistent, but
        the main dimension itself differs among the inputs, then the shortest
        one is chosen for doing the computations.  This is so because trying to
        expand very large on-disk arrays could be too expensive or simply not
        possible.

        Also, the regular Numexpr casting rules (which are similar to those of
        NumPy, although you should check the Numexpr manual for the exceptions)
        are applied to determine the output type.

        Finally, if the set_output() method specifying a user container has
        already been called, the output is sent to this user-provided
        container.  If not, a fresh NumPy container is returned instead.

        .. warning::

            When dealing with large on-disk inputs, failing to specify an
            on-disk container may consume all your available memory.

        """
        values, shape, maindim = self.values, self.shape, self.maindim

        # Get different info we need for the main computation loop
        (
            i_nrows,
            slice_pos,
            start,
            stop,
            step,
            nrowsinbuf,
            out,
            o_maindim,
            o_start,
            o_stop,
            o_step,
        ) = self._get_info(shape, maindim)

        if i_nrows == 0:
            # No elements to compute
            if start >= stop and self.start is not None:
                return out
            else:
                return self._single_row_out

        # Create a key that selects every element in inputs and output
        # (including the main dimension)
        i_slices = [slice(None)] * (maindim + 1)