Leverage scipy's quadrature rules

The proposed changes add support for quadrature rules from 'scipy'.

Known issues:

• Using 'scipy' backend by default will result in a test to fail

======================================================================= test session starts =======================================================================
platform linux -- Python 3.6.7, pytest-4.1.1, py-1.7.0, pluggy-0.8.1
rootdir: /home/xywei/Projects/inteq/src/modepy, inifile:
collected 74 items                                                                                                                                                

test_modes.py ........................                                                                                                                      [ 32%]
test_nodes.py ...............                                                                                                                               [ 52%]
test_quadrature.py ........                                                                                                                                 [ 63%]
test_tools.py F..........................                                                                                                                   [100%]

============================================================================ FAILURES =============================================================================
__________________________________________________________ test_modal_decay[jump-1d-test_func0-1-10--1] ___________________________________________________________

case_name = 'jump-1d', test_func = functools.partial(<function jump at 0x7f648f8e26a8>, 0), dims = 1, n = 10, expected_expn = -1

    @pytest.mark.parametrize(("case_name", "test_func", "dims", "n", "expected_expn"), [
        ("jump-1d", partial(jump, 0), 1, 10, -1),
        ("kink-1d", partial(kink, 0), 1, 10, -1.7),  # Slightly off from -2 (same in paper)  # noqa
        ("c1-1d", partial(c1, 0), 1, 10, -3),
    
        # Offset 1D tests
        ("offset-jump-1d", partial(jump, 0.8), 1, 20, -0.7),
        ("offset-kink-1d", partial(kink, 0.8), 1, 20, -0.7),  # boo
        ("offset-c1-1d", partial(c1, 0.8), 1, 20, -0.8),  # boo
    
        # 2D tests
        # A harsh jump introduces high-frequency wiggles transverse to the jump edge.
        # Therefore, use a smooth one.
        ("jump-2d", partial(smooth_jump, -0.1), 2, 15, -1.1),
        ("kink-2d", partial(kink, 0), 2, 15, -1.6),
        ("c1-2d", partial(c1, -0.1), 2, 15, -2.3),
        ])
    def test_modal_decay(case_name, test_func, dims, n, expected_expn):
        nodes = mp.warp_and_blend_nodes(dims, n)
        basis = mp.simplex_onb(dims, n)
        vdm = mp.vandermonde(basis, nodes)
    
        f = test_func(nodes[0])
        coeffs = la.solve(vdm, f)
    
        if 0:
            from modepy.tools import plot_element_values
            plot_element_values(n, nodes, f, resample_n=70,
                    show_nodes=True)
    
        from modepy.modal_decay import fit_modal_decay
        expn, _ = fit_modal_decay(coeffs.reshape(1, -1), dims, n)
        expn = expn[0]
    
        print(("%s: computed: %g, expected: %g" % (case_name, expn, expected_expn)))
>       assert abs(expn-expected_expn) < 0.1
E       assert 0.12639562678232985 < 0.1
E        +  where 0.12639562678232985 = abs((-1.1263956267823299 - -1))

test_tools.py:107: AssertionError
---------------------------------------------------------------------- Captured stdout call -----------------------------------------------------------------------
jump-1d: computed: -1.1264, expected: -1