How to use the numexpr.set_vml_num_threads function in numexpr

compare_times(expression, 1)
        sys.exit(0)
    nexpr = 0
    for expr in expressions:
        nexpr += 1
        compare_times(expr, nexpr)
    print

if __name__ == '__main__':
    import numexpr
    numexpr.print_versions()

    numpy.seterr(all='ignore')

    numexpr.set_vml_accuracy_mode('low')
    numexpr.set_vml_num_threads(2)

    if len(sys.argv) > 1:
        expression = sys.argv[1]
        print "expression-->", expression
        compare(expression)
    else:
        compare()

    tratios = numpy.array(numpy_ttime) / numpy.array(numexpr_ttime)
    stratios = numpy.array(numpy_sttime) / numpy.array(numexpr_sttime)
    ntratios = numpy.array(numpy_nttime) / numpy.array(numexpr_nttime)


    print "eval method: %s" % eval_method
    print "*************** Numexpr vs NumPy speed-ups *******************"
#     print "numpy total:", sum(numpy_ttime)/iterations

logger.debug("Finding blocks...")

    # This is where the computation happens. Following Scargle et al. 2012.
    # This loop has been optimized for speed:
    # * the expression for the fitness function has been rewritten to
    #  avoid multiple log computations, and to avoid power computations
    # * the use of scipy.weave and numexpr has been evaluated. The latter
    #  gives a big gain (~40%) if used for the fitness function. No other
    #  gain is obtained by using it anywhere else

    # Set numexpr precision to low (more than enough for us), which is
    # faster than high
    oldaccuracy = numexpr.set_vml_accuracy_mode("low")
    numexpr.set_num_threads(1)
    numexpr.set_vml_num_threads(1)

    with progress_bar(N) as progress:

        for R in range(N):
            br = block_length[R + 1]
            T_k = block_length[: R + 1] - br

            # N_k: number of elements in each block
            # This expression has been simplified for the case of
            # unbinned events (i.e., one element in each block)
            # It was:
            N_k = cumsum(x[: R + 1][::-1])[::-1]
            # Now it is:
            # N_k = arange(R + 1, 0, -1)

            # Evaluate fitness function

logger.debug("Finding blocks...")

    # This is where the computation happens. Following Scargle et al. 2012.
    # This loop has been optimized for speed:
    # * the expression for the fitness function has been rewritten to
    #  avoid multiple log computations, and to avoid power computations
    # * the use of scipy.weave and numexpr has been evaluated. The latter
    #  gives a big gain (~40%) if used for the fitness function. No other
    #  gain is obtained by using it anywhere else

    # Set numexpr precision to low (more than enough for us), which is
    # faster than high
    oldaccuracy = numexpr.set_vml_accuracy_mode("low")
    numexpr.set_num_threads(1)
    numexpr.set_vml_num_threads(1)

    # Speed tricks: resolve once for all the functions which will be used
    # in the loop
    numexpr_evaluate = numexpr.evaluate
    numexpr_re_evaluate = numexpr.re_evaluate

    # Pre-compute this

    aranges = np.arange(N + 1, 0, -1)

    for R in range(N):
        br = block_length[R + 1]
        T_k = (
            block_length[: R + 1] - br
        )  # this looks like it is not used, but it actually is,
        # inside the numexpr expression

self._node_manager.node_factory = self.root._g_load_child

        # Save the PyTables format version for this file.
        if new:
            if params['PYTABLES_SYS_ATTRS']:
                root._v_attrs._g__setattr(
                    'PYTABLES_FORMAT_VERSION', format_version)

        # If the file is old, and not opened in "read-only" mode,
        # check if it has a transaction log
        if not new and self.mode != "r" and _trans_group_path in self:
            # It does. Enable the undo.
            self.enable_undo()

        # Set the maximum number of threads for Numexpr
        numexpr.set_vml_num_threads(params['MAX_NUMEXPR_THREADS'])

root._g_postInitHook()

        # Save the PyTables format version for this file.
        if new:
            if params['PYTABLES_SYS_ATTRS']:
                root._v_attrs._g__setattr(
                    'PYTABLES_FORMAT_VERSION', format_version)

        # If the file is old, and not opened in "read-only" mode,
        # check if it has a transaction log
        if not new and self.mode != "r" and _transGroupPath in self:
            # It does. Enable the undo.
            self.enableUndo()

        # Set the maximum number of threads for Numexpr
        numexpr.set_vml_num_threads(params['MAX_NUMEXPR_THREADS'])