from __future__ import division, print_function, absolute_import

import warnings

import numpy as np

from numpy.testing import (
    assert_almost_equal, assert_array_equal, assert_array_almost_equal,
    TestCase, run_module_suite, assert_allclose, assert_equal, assert_,
    assert_raises)

from scipy.interpolate import (
    KroghInterpolator, krogh_interpolate,
    BarycentricInterpolator, barycentric_interpolate,
    approximate_taylor_polynomial, pchip, PchipInterpolator,
    pchip_interpolate, Akima1DInterpolator, CubicSpline, make_interp_spline)

from scipy._lib.six import xrange


def check_shape(interpolator_cls, x_shape, y_shape, deriv_shape=None, axis=0,
                extra_args={}):
    np.random.seed(1234)

    x = [-1, 0, 1, 2, 3, 4]
    s = list(range(1, len(y_shape)+1))
    s.insert(axis % (len(y_shape)+1), 0)
    y = np.random.rand(*((6,) + y_shape)).transpose(s)

    # Cython code chokes on y.shape = (0, 3) etc, skip them
    if y.size == 0:
        return

    xi = np.zeros(x_shape)
    yi = interpolator_cls(x, y, axis=axis, **extra_args)(xi)

    target_shape = ((deriv_shape or ()) + y.shape[:axis]
                    + x_shape + y.shape[axis:][1:])
    assert_equal(yi.shape, target_shape)

    # check it works also with lists
    if x_shape and y.size > 0:
        interpolator_cls(list(x), list(y), axis=axis, **extra_args)(list(xi))

    # check also values
    if xi.size > 0 and deriv_shape is None:
        bs_shape = y.shape[:axis] + (1,)*len(x_shape) + y.shape[axis:][1:]
        yv = y[((slice(None,),)*(axis % y.ndim)) + (1,)]
        yv = yv.reshape(bs_shape)

        yi, y = np.broadcast_arrays(yi, yv)
        assert_allclose(yi, y)

SHAPES = [(), (0,), (1,), (6, 2, 5)]


def test_shapes():

    def spl_interp(x, y, axis):
        return make_interp_spline(x, y, axis=axis)

    for ip in [KroghInterpolator, BarycentricInterpolator, pchip,
               Akima1DInterpolator, CubicSpline, spl_interp]:
        for s1 in SHAPES:
            for s2 in SHAPES:
                for axis in range(-len(s2), len(s2)):
                    if ip != CubicSpline:
                        yield check_shape, ip, s1, s2, None, axis
                    else:
                        for bc in ['natural', 'clamped']:
                            extra = {'bc_type': bc}
                            yield check_shape, ip, s1, s2, None, axis, extra

def test_derivs_shapes():
    def krogh_derivs(x, y, axis=0):
        return KroghInterpolator(x, y, axis).derivatives

    for s1 in SHAPES:
        for s2 in SHAPES:
            for axis in range(-len(s2), len(s2)):
                yield check_shape, krogh_derivs, s1, s2, (6,), axis


def test_deriv_shapes():
    def krogh_deriv(x, y, axis=0):
        return KroghInterpolator(x, y, axis).derivative

    def pchip_deriv(x, y, axis=0):
        return pchip(x, y, axis).derivative()

    def pchip_deriv2(x, y, axis=0):
        return pchip(x, y, axis).derivative(2)

    def pchip_antideriv(x, y, axis=0):
        return pchip(x, y, axis).derivative()

    def pchip_antideriv2(x, y, axis=0):
        return pchip(x, y, axis).derivative(2)

    def pchip_deriv_inplace(x, y, axis=0):
        class P(PchipInterpolator):
            def __call__(self, x):
                return PchipInterpolator.__call__(self, x, 1)
            pass
        return P(x, y, axis)

    def akima_deriv(x, y, axis=0):
        return Akima1DInterpolator(x, y, axis).derivative()

    def akima_antideriv(x, y, axis=0):
        return Akima1DInterpolator(x, y, axis).antiderivative()

    def cspline_deriv(x, y, axis=0):
        return CubicSpline(x, y, axis).derivative()

    def cspline_antideriv(x, y, axis=0):
        return CubicSpline(x, y, axis).antiderivative()

    def bspl_deriv(x, y, axis=0):
        return make_interp_spline(x, y, axis=axis).derivative()

    def bspl_antideriv(x, y, axis=0):
        return make_interp_spline(x, y, axis=axis).antiderivative()

    for ip in [krogh_deriv, pchip_deriv, pchip_deriv2, pchip_deriv_inplace,
               pchip_antideriv, pchip_antideriv2, akima_deriv, akima_antideriv,
               cspline_deriv, cspline_antideriv, bspl_deriv, bspl_antideriv]:
        for s1 in SHAPES:
            for s2 in SHAPES:
                for axis in range(-len(s2), len(s2)):
                    yield check_shape, ip, s1, s2, (), axis


def _check_complex(ip):
    x = [1, 2, 3, 4]
    y = [1, 2, 1j, 3]
    p = ip(x, y)
    assert_allclose(y, p(x))


def test_complex():
    for ip in [KroghInterpolator, BarycentricInterpolator, pchip, CubicSpline]:
        yield _check_complex, ip


class CheckKrogh(TestCase):
    def setUp(self):
        self.true_poly = np.poly1d([-2,3,1,5,-4])
        self.test_xs = np.linspace(-1,1,100)
        self.xs = np.linspace(-1,1,5)
        self.ys = self.true_poly(self.xs)

    def test_lagrange(self):
        P = KroghInterpolator(self.xs,self.ys)
        assert_almost_equal(self.true_poly(self.test_xs),P(self.test_xs))

    def test_scalar(self):
        P = KroghInterpolator(self.xs,self.ys)
        assert_almost_equal(self.true_poly(7),P(7))
        assert_almost_equal(self.true_poly(np.array(7)), P(np.array(7)))

    def test_derivatives(self):
        P = KroghInterpolator(self.xs,self.ys)
        D = P.derivatives(self.test_xs)
        for i in xrange(D.shape[0]):
            assert_almost_equal(self.true_poly.deriv(i)(self.test_xs),
                                D[i])

    def test_low_derivatives(self):
        P = KroghInterpolator(self.xs,self.ys)
        D = P.derivatives(self.test_xs,len(self.xs)+2)
        for i in xrange(D.shape[0]):
            assert_almost_equal(self.true_poly.deriv(i)(self.test_xs),
                                D[i])

    def test_derivative(self):
        P = KroghInterpolator(self.xs,self.ys)
        m = 10
        r = P.derivatives(self.test_xs,m)
        for i in xrange(m):
            assert_almost_equal(P.derivative(self.test_xs,i),r[i])

    def test_high_derivative(self):
        P = KroghInterpolator(self.xs,self.ys)
        for i in xrange(len(self.xs),2*len(self.xs)):
            assert_almost_equal(P.derivative(self.test_xs,i),
                                np.zeros(len(self.test_xs)))

    def test_hermite(self):
        xs = [0,0,0,1,1,1,2]
        ys = [self.true_poly(0),
              self.true_poly.deriv(1)(0),
              self.true_poly.deriv(2)(0),
              self.true_poly(1),
              self.true_poly.deriv(1)(1),
              self.true_poly.deriv(2)(1),
              self.true_poly(2)]
        P = KroghInterpolator(self.xs,self.ys)
        assert_almost_equal(self.true_poly(self.test_xs),P(self.test_xs))

    def test_vector(self):
        xs = [0, 1, 2]
        ys = np.array([[0,1],[1,0],[2,1]])
        P = KroghInterpolator(xs,ys)
        Pi = [KroghInterpolator(xs,ys[:,i]) for i in xrange(ys.shape[1])]
        test_xs = np.linspace(-1,3,100)
        assert_almost_equal(P(test_xs),
                np.rollaxis(np.asarray([p(test_xs) for p in Pi]),-1))
        assert_almost_equal(P.derivatives(test_xs),
                np.transpose(np.asarray([p.derivatives(test_xs) for p in Pi]),
                    (1,2,0)))

    def test_empty(self):
        P = KroghInterpolator(self.xs,self.ys)
        assert_array_equal(P([]), [])

    def test_shapes_scalarvalue(self):
        P = KroghInterpolator(self.xs,self.ys)
        assert_array_equal(np.shape(P(0)), ())
        assert_array_equal(np.shape(P(np.array(0))), ())
        assert_array_equal(np.shape(P([0])), (1,))
        assert_array_equal(np.shape(P([0,1])), (2,))

    def test_shapes_scalarvalue_derivative(self):
        P = KroghInterpolator(self.xs,self.ys)
        n = P.n
        assert_array_equal(np.shape(P.derivatives(0)), (n,))
        assert_array_equal(np.shape(P.derivatives(np.array(0))), (n,))
        assert_array_equal(np.shape(P.derivatives([0])), (n,1))
        assert_array_equal(np.shape(P.derivatives([0,1])), (n,2))

    def test_shapes_vectorvalue(self):
        P = KroghInterpolator(self.xs,np.outer(self.ys,np.arange(3)))
        assert_array_equal(np.shape(P(0)), (3,))
        assert_array_equal(np.shape(P([0])), (1,3))
        assert_array_equal(np.shape(P([0,1])), (2,3))

    def test_shapes_1d_vectorvalue(self):
        P = KroghInterpolator(self.xs,np.outer(self.ys,[1]))
        assert_array_equal(np.shape(P(0)), (1,))
        assert_array_equal(np.shape(P([0])), (1,1))
        assert_array_equal(np.shape(P([0,1])), (2,1))

    def test_shapes_vectorvalue_derivative(self):
        P = KroghInterpolator(self.xs,np.outer(self.ys,np.arange(3)))
        n = P.n
        assert_array_equal(np.shape(P.derivatives(0)), (n,3))
        assert_array_equal(np.shape(P.derivatives([0])), (n,1,3))
        assert_array_equal(np.shape(P.derivatives([0,1])), (n,2,3))

    def test_wrapper(self):
        P = KroghInterpolator(self.xs,self.ys)
        assert_almost_equal(P(self.test_xs),krogh_interpolate(self.xs,self.ys,self.test_xs))
        assert_almost_equal(P.derivative(self.test_xs,2),krogh_interpolate(self.xs,self.ys,self.test_xs,der=2))
        assert_almost_equal(P.derivatives(self.test_xs,2),krogh_interpolate(self.xs,self.ys,self.test_xs,der=[0,1]))

    def test_int_inputs(self):
        # Check input args are cast correctly to floats, gh-3669
        x = [0, 234,468,702,936,1170,1404,2340,3744,6084,8424,13104,60000]
        offset_cdf = np.array([-0.95, -0.86114777, -0.8147762, -0.64072425, -0.48002351,
                               -0.34925329, -0.26503107, -0.13148093, -0.12988833, -0.12979296,
                               -0.12973574, -0.08582937, 0.05])
        f = KroghInterpolator(x, offset_cdf)

        assert_allclose(abs((f(x) - offset_cdf) / f.derivative(x, 1)), 0, atol=1e-10)


class CheckTaylor(TestCase):
    def test_exponential(self):
        degree = 5
        p = approximate_taylor_polynomial(np.exp, 0, degree, 1, 15)
        for i in xrange(degree+1):
            assert_almost_equal(p(0),1)
            p = p.deriv()
        assert_almost_equal(p(0),0)


class CheckBarycentric(TestCase):
    def setUp(self):
        self.true_poly = np.poly1d([-2,3,1,5,-4])
        self.test_xs = np.linspace(-1,1,100)
        self.xs = np.linspace(-1,1,5)
        self.ys = self.true_poly(self.xs)

    def test_lagrange(self):
        P = BarycentricInterpolator(self.xs,self.ys)
        assert_almost_equal(self.true_poly(self.test_xs),P(self.test_xs))

    def test_scalar(self):
        P = BarycentricInterpolator(self.xs,self.ys)
        assert_almost_equal(self.true_poly(7),P(7))
        assert_almost_equal(self.true_poly(np.array(7)),P(np.array(7)))

    def test_delayed(self):
        P = BarycentricInterpolator(self.xs)
        P.set_yi(self.ys)
        assert_almost_equal(self.true_poly(self.test_xs),P(self.test_xs))

    def test_append(self):
        P = BarycentricInterpolator(self.xs[:3],self.ys[:3])
        P.add_xi(self.xs[3:],self.ys[3:])
        assert_almost_equal(self.true_poly(self.test_xs),P(self.test_xs))

    def test_vector(self):
        xs = [0, 1, 2]
        ys = np.array([[0,1],[1,0],[2,1]])
        P = BarycentricInterpolator(xs,ys)
        Pi = [BarycentricInterpolator(xs,ys[:,i]) for i in xrange(ys.shape[1])]
        test_xs = np.linspace(-1,3,100)
        assert_almost_equal(P(test_xs),
                np.rollaxis(np.asarray([p(test_xs) for p in Pi]),-1))

    def test_shapes_scalarvalue(self):
        P = BarycentricInterpolator(self.xs,self.ys)
        assert_array_equal(np.shape(P(0)), ())
        assert_array_equal(np.shape(P(np.array(0))), ())
        assert_array_equal(np.shape(P([0])), (1,))
        assert_array_equal(np.shape(P([0,1])), (2,))

    def test_shapes_vectorvalue(self):
        P = BarycentricInterpolator(self.xs,np.outer(self.ys,np.arange(3)))
        assert_array_equal(np.shape(P(0)), (3,))
        assert_array_equal(np.shape(P([0])), (1,3))
        assert_array_equal(np.shape(P([0,1])), (2,3))

    def test_shapes_1d_vectorvalue(self):
        P = BarycentricInterpolator(self.xs,np.outer(self.ys,[1]))
        assert_array_equal(np.shape(P(0)), (1,))
        assert_array_equal(np.shape(P([0])), (1,1))
        assert_array_equal(np.shape(P([0,1])), (2,1))

    def test_wrapper(self):
        P = BarycentricInterpolator(self.xs,self.ys)
        assert_almost_equal(P(self.test_xs),barycentric_interpolate(self.xs,self.ys,self.test_xs))


class TestPCHIP(TestCase):
    def _make_random(self, npts=20):
        np.random.seed(1234)
        xi = np.sort(np.random.random(npts))
        yi = np.random.random(npts)
        return pchip(xi, yi), xi, yi

    def test_overshoot(self):
        # PCHIP should not overshoot
        p, xi, yi = self._make_random()
        for i in range(len(xi)-1):
            x1, x2 = xi[i], xi[i+1]
            y1, y2 = yi[i], yi[i+1]
            if y1 > y2:
                y1, y2 = y2, y1
            xp = np.linspace(x1, x2, 10)
            yp = p(xp)
            assert_(((y1 <= yp) & (yp <= y2)).all())

    def test_monotone(self):
        # PCHIP should preserve monotonicty
        p, xi, yi = self._make_random()
        for i in range(len(xi)-1):
            x1, x2 = xi[i], xi[i+1]
            y1, y2 = yi[i], yi[i+1]
            xp = np.linspace(x1, x2, 10)
            yp = p(xp)
            assert_(((y2-y1) * (yp[1:] - yp[:1]) > 0).all())

    def test_cast(self):
        # regression test for integer input data, see gh-3453
        data = np.array([[0, 4, 12, 27, 47, 60, 79, 87, 99, 100],
                         [-33, -33, -19, -2, 12, 26, 38, 45, 53, 55]])
        xx = np.arange(100)
        curve = pchip(data[0], data[1])(xx)

        data1 = data * 1.0
        curve1 = pchip(data1[0], data1[1])(xx)

        assert_allclose(curve, curve1, atol=1e-14, rtol=1e-14)

    def test_nag(self):
        # Example from NAG C implementation,
        # http://nag.com/numeric/cl/nagdoc_cl25/html/e01/e01bec.html
        # suggested in gh-5326 as a smoke test for the way the derivatives
        # are computed (see also gh-3453)
        from scipy._lib.six import StringIO
        dataStr = '''
          7.99   0.00000E+0
          8.09   0.27643E-4
          8.19   0.43750E-1
          8.70   0.16918E+0
          9.20   0.46943E+0
         10.00   0.94374E+0
         12.00   0.99864E+0
         15.00   0.99992E+0
         20.00   0.99999E+0
        '''
        data = np.loadtxt(StringIO(dataStr))
        pch = pchip(data[:,0], data[:,1])

        resultStr = '''
           7.9900       0.0000
           9.1910       0.4640
          10.3920       0.9645
          11.5930       0.9965
          12.7940       0.9992
          13.9950       0.9998
          15.1960       0.9999
          16.3970       1.0000
          17.5980       1.0000
          18.7990       1.0000
          20.0000       1.0000
        '''
        result = np.loadtxt(StringIO(resultStr))
        assert_allclose(result[:,1], pch(result[:,0]), rtol=0., atol=5e-5)

    def test_endslopes(self):
        # this is a smoke test for gh-3453: PCHIP interpolator should not
        # set edge slopes to zero if the data do not suggest zero edge derivatives
        x = np.array([0.0, 0.1, 0.25, 0.35])
        y1 = np.array([279.35, 0.5e3, 1.0e3, 2.5e3])
        y2 = np.array([279.35, 2.5e3, 1.50e3, 1.0e3])
        for pp in (pchip(x, y1), pchip(x, y2)):
            for t in (x[0], x[-1]):
                assert_(pp(t, 1) != 0)

    def test_all_zeros(self):
        x = np.arange(10)
        y = np.zeros_like(x)

        # this should work and not generate any warnings
        with warnings.catch_warnings():
            warnings.filterwarnings('error')
            pch = pchip(x, y)

        xx = np.linspace(0, 9, 101)
        assert_equal(pch(xx), 0.)

    def test_two_points(self):
        # regression test for gh-6222: pchip([0, 1], [0, 1]) fails because
        # it tries to use a three-point scheme to estimate edge derivatives,
        # while there are only two points available.
        # Instead, it should construct a linear interpolator.
        x = np.linspace(0, 1, 11)
        p = pchip([0, 1], [0, 2])
        assert_allclose(p(x), 2*x, atol=1e-15)

    def test_pchip_interpolate(self):
        assert_array_almost_equal(
            pchip_interpolate([1,2,3], [4,5,6], [0.5], der=1),
            [1.])

        assert_array_almost_equal(
            pchip_interpolate([1,2,3], [4,5,6], [0.5], der=0),
            [3.5])

        assert_array_almost_equal(
            pchip_interpolate([1,2,3], [4,5,6], [0.5], der=[0, 1]),
            [[3.5], [1]])

    def test_roots(self):
        # regression test for gh-6357: .roots method should work
        p = pchip([0, 1], [-1, 1])
        r = p.roots()
        assert_allclose(r, 0.5)

class TestCubicSpline(object):
    @staticmethod
    def check_correctness(S, bc_start='not-a-knot', bc_end='not-a-knot',
                          tol=1e-14):
        """Check that spline coefficients satisfy the continuity and boundary
        conditions."""
        x = S.x
        c = S.c
        dx = np.diff(x)
        dx = dx.reshape([dx.shape[0]] + [1] * (c.ndim - 2))
        dxi = dx[:-1]

        # Check C2 continuity.
        assert_allclose(c[3, 1:], c[0, :-1] * dxi**3 + c[1, :-1] * dxi**2 +
                        c[2, :-1] * dxi + c[3, :-1], rtol=tol, atol=tol)
        assert_allclose(c[2, 1:], 3 * c[0, :-1] * dxi**2 +
                        2 * c[1, :-1] * dxi + c[2, :-1], rtol=tol, atol=tol)
        assert_allclose(c[1, 1:], 3 * c[0, :-1] * dxi + c[1, :-1],
                        rtol=tol, atol=tol)

        # Check that we found a parabola, the third derivative is 0.
        if x.size == 3 and bc_start == 'not-a-knot' and bc_end == 'not-a-knot':
            assert_allclose(c[0], 0, rtol=tol, atol=tol)
            return

        # Check periodic boundary conditions.
        if bc_start == 'periodic':
            assert_allclose(S(x[0], 0), S(x[-1], 0), rtol=tol, atol=tol)
            assert_allclose(S(x[0], 1), S(x[-1], 1), rtol=tol, atol=tol)
            assert_allclose(S(x[0], 2), S(x[-1], 2), rtol=tol, atol=tol)
            return

        # Check other boundary conditions.
        if bc_start == 'not-a-knot':
            if x.size == 2:
                slope = (S(x[1]) - S(x[0])) / dx[0]
                assert_allclose(S(x[0], 1), slope, rtol=tol, atol=tol)
            else:
                assert_allclose(c[0, 0], c[0, 1], rtol=tol, atol=tol)
        elif bc_start == 'clamped':
            assert_allclose(S(x[0], 1), 0, rtol=tol, atol=tol)
        elif bc_start == 'natural':
            assert_allclose(S(x[0], 2), 0, rtol=tol, atol=tol)
        else:
            order, value = bc_start
            assert_allclose(S(x[0], order), value, rtol=tol, atol=tol)

        if bc_end == 'not-a-knot':
            if x.size == 2:
                slope = (S(x[1]) - S(x[0])) / dx[0]
                assert_allclose(S(x[1], 1), slope, rtol=tol, atol=tol)
            else:
                assert_allclose(c[0, -1], c[0, -2], rtol=tol, atol=tol)
        elif bc_end == 'clamped':
            assert_allclose(S(x[-1], 1), 0, rtol=tol, atol=tol)
        elif bc_end == 'natural':
            assert_allclose(S(x[-1], 2), 0, rtol=tol, atol=tol)
        else:
            order, value = bc_end
            assert_allclose(S(x[-1], order), value, rtol=tol, atol=tol)

    def check_all_bc(self, x, y, axis):
        deriv_shape = list(y.shape)
        del deriv_shape[axis]
        first_deriv = np.empty(deriv_shape)
        first_deriv.fill(2)
        second_deriv = np.empty(deriv_shape)
        second_deriv.fill(-1)
        bc_all = [
            'not-a-knot',
            'natural',
            'clamped',
            (1, first_deriv),
            (2, second_deriv)
        ]
        for bc in bc_all[:3]:
            S = CubicSpline(x, y, axis=axis, bc_type=bc)
            self.check_correctness(S, bc, bc)

        for bc_start in bc_all:
            for bc_end in bc_all:
                S = CubicSpline(x, y, axis=axis, bc_type=(bc_start, bc_end))
                self.check_correctness(S, bc_start, bc_end, tol=2e-14)

    def test_general(self):
        x = np.array([-1, 0, 0.5, 2, 4, 4.5, 5.5, 9])
        y = np.array([0, -0.5, 2, 3, 2.5, 1, 1, 0.5])
        for n in [2, 3, x.size]:
            self.check_all_bc(x[:n], y[:n], 0)

            Y = np.empty((2, n, 2))
            Y[0, :, 0] = y[:n]
            Y[0, :, 1] = y[:n] - 1
            Y[1, :, 0] = y[:n] + 2
            Y[1, :, 1] = y[:n] + 3
            self.check_all_bc(x[:n], Y, 1)

    def test_periodic(self):
        for n in [2, 3, 5]:
            x = np.linspace(0, 2 * np.pi, n)
            y = np.cos(x)
            S = CubicSpline(x, y, bc_type='periodic')
            self.check_correctness(S, 'periodic', 'periodic')

            Y = np.empty((2, n, 2))
            Y[0, :, 0] = y
            Y[0, :, 1] = y + 2
            Y[1, :, 0] = y - 1
            Y[1, :, 1] = y + 5
            S = CubicSpline(x, Y, axis=1, bc_type='periodic')
            self.check_correctness(S, 'periodic', 'periodic')

    def test_periodic_eval(self):
        x = np.linspace(0, 2 * np.pi, 10)
        y = np.cos(x)
        S = CubicSpline(x, y, bc_type='periodic')
        assert_almost_equal(S(1), S(1 + 2 * np.pi), decimal=15)

    def test_dtypes(self):
        x = np.array([0, 1, 2, 3], dtype=int)
        y = np.array([-5, 2, 3, 1], dtype=int)
        S = CubicSpline(x, y)
        self.check_correctness(S)

        y = np.array([-1+1j, 0.0, 1-1j, 0.5-1.5j])
        S = CubicSpline(x, y)
        self.check_correctness(S)

        S = CubicSpline(x, x ** 3, bc_type=("natural", (1, 2j)))
        self.check_correctness(S, "natural", (1, 2j))

        y = np.array([-5, 2, 3, 1])
        S = CubicSpline(x, y, bc_type=[(1, 2 + 0.5j), (2, 0.5 - 1j)])
        self.check_correctness(S, (1, 2 + 0.5j), (2, 0.5 - 1j))

    def test_small_dx(self):
        rng = np.random.RandomState(0)
        x = np.sort(rng.uniform(size=100))
        y = 1e4 + rng.uniform(size=100)
        S = CubicSpline(x, y)
        self.check_correctness(S, tol=1e-13)

    def test_incorrect_inputs(self):
        x = np.array([1, 2, 3, 4])
        y = np.array([1, 2, 3, 4])
        xc = np.array([1 + 1j, 2, 3, 4])
        xn = np.array([np.nan, 2, 3, 4])
        xo = np.array([2, 1, 3, 4])
        yn = np.array([np.nan, 2, 3, 4])
        y3 = [1, 2, 3]
        x1 = [1]
        y1 = [1]

        assert_raises(ValueError, CubicSpline, xc, y)
        assert_raises(ValueError, CubicSpline, xn, y)
        assert_raises(ValueError, CubicSpline, x, yn)
        assert_raises(ValueError, CubicSpline, xo, y)
        assert_raises(ValueError, CubicSpline, x, y3)
        assert_raises(ValueError, CubicSpline, x[:, np.newaxis], y)
        assert_raises(ValueError, CubicSpline, x1, y1)

        wrong_bc = [('periodic', 'clamped'),
                    ((2, 0), (3, 10)),
                    ((1, 0), ),
                    (0., 0.),
                    'not-a-typo']

        for bc_type in wrong_bc:
            assert_raises(ValueError, CubicSpline, x, y, 0, bc_type, True)

        # Shapes mismatch when giving arbitrary derivative values:
        Y = np.c_[y, y]
        bc1 = ('clamped', (1, 0))
        bc2 = ('clamped', (1, [0, 0, 0]))
        bc3 = ('clamped', (1, [[0, 0]]))
        assert_raises(ValueError, CubicSpline, x, Y, 0, bc1, True)
        assert_raises(ValueError, CubicSpline, x, Y, 0, bc2, True)
        assert_raises(ValueError, CubicSpline, x, Y, 0, bc3, True)

        # periodic condition, y[-1] must be equal to y[0]:
        assert_raises(ValueError, CubicSpline, x, y, 0, 'periodic', True)


if __name__ == '__main__':
    run_module_suite()
