#!/usr/bin/env python
# Created by Pearu Peterson, September 2002
""" Test functions for fftpack.basic module
"""
from __future__ import division, print_function, absolute_import

__usage__ = """
Build fftpack:
  python setup_fftpack.py build
Run tests if scipy is installed:
  python -c 'import scipy;scipy.fftpack.test()'
Run tests if fftpack is not installed:
  python tests/test_basic.py
"""

from numpy.testing import assert_, assert_equal, assert_array_almost_equal, \
        assert_array_almost_equal_nulp, assert_raises, run_module_suite, \
        TestCase, dec
from scipy.fftpack import ifft,fft,fftn,ifftn,rfft,irfft, fft2
from scipy.fftpack import _fftpack as fftpack

from numpy import arange, add, array, asarray, zeros, dot, exp, pi,\
     swapaxes, double, cdouble
import numpy as np
import numpy.fft

# "large" composite numbers supported by FFTPACK
LARGE_COMPOSITE_SIZES = [
    2**13,
    2**5 * 3**5,
    2**3 * 3**3 * 5**2,
]
SMALL_COMPOSITE_SIZES = [
    2,
    2*3*5,
    2*2*3*3,
]
# prime
LARGE_PRIME_SIZES = [
    2011
]
SMALL_PRIME_SIZES = [
    29
]

from numpy.random import rand
def random(size):
    return rand(*size)

def get_mat(n):
    data = arange(n)
    data = add.outer(data,data)
    return data

def direct_dft(x):
    x = asarray(x)
    n = len(x)
    y = zeros(n,dtype=cdouble)
    w = -arange(n)*(2j*pi/n)
    for i in range(n):
        y[i] = dot(exp(i*w),x)
    return y

def direct_idft(x):
    x = asarray(x)
    n = len(x)
    y = zeros(n,dtype=cdouble)
    w = arange(n)*(2j*pi/n)
    for i in range(n):
        y[i] = dot(exp(i*w),x)/n
    return y

def direct_dftn(x):
    x = asarray(x)
    for axis in range(len(x.shape)):
        x = fft(x,axis=axis)
    return x

def direct_idftn(x):
    x = asarray(x)
    for axis in range(len(x.shape)):
        x = ifft(x,axis=axis)
    return x

def direct_rdft(x):
    x = asarray(x)
    n = len(x)
    w = -arange(n)*(2j*pi/n)
    r = zeros(n,dtype=double)
    for i in range(n//2+1):
        y = dot(exp(i*w),x)
        if i:
            r[2*i-1] = y.real
            if 2*i<n:
                r[2*i] = y.imag
        else:
            r[0] = y.real
    return r

def direct_irdft(x):
    x = asarray(x)
    n = len(x)
    x1 = zeros(n,dtype=cdouble)
    for i in range(n//2+1):
        if i:
            if 2*i<n:
                x1[i] = x[2*i-1] + 1j* x[2*i]
                x1[n-i] = x[2*i-1] - 1j* x[2*i]
            else:
                x1[i] = x[2*i-1]
        else:
            x1[0] = x[0]
    return direct_idft(x1).real

class _TestFFTBase(TestCase):
    def setUp(self):
        self.cdt = None
        self.rdt = None
        np.random.seed(1234)

    def test_definition(self):
        x = np.array([1,2,3,4+1j,1,2,3,4+2j], dtype = self.cdt)
        y = fft(x)
        self.assertTrue(y.dtype == self.cdt,
                "Output dtype is %s, expected %s" % (y.dtype, self.cdt))
        y1 = direct_dft(x)
        assert_array_almost_equal(y,y1)
        x = np.array([1,2,3,4+0j,5], dtype = self.cdt)
        assert_array_almost_equal(fft(x),direct_dft(x))

    def test_n_argument_real(self):
        x1 = np.array([1,2,3,4], dtype=self.rdt)
        x2 = np.array([1,2,3,4], dtype=self.rdt)
        y = fft([x1,x2],n=4)
        self.assertTrue(y.dtype == self.cdt,
                "Output dtype is %s, expected %s" % (y.dtype, self.cdt))
        assert_equal(y.shape,(2,4))
        assert_array_almost_equal(y[0],direct_dft(x1))
        assert_array_almost_equal(y[1],direct_dft(x2))

    def _test_n_argument_complex(self):
        x1 = np.array([1,2,3,4+1j], dtype=self.cdt)
        x2 = np.array([1,2,3,4+1j], dtype=self.cdt)
        y = fft([x1,x2],n=4)
        self.assertTrue(y.dtype == self.cdt,
                "Output dtype is %s, expected %s" % (y.dtype, self.cdt))
        assert_equal(y.shape,(2,4))
        assert_array_almost_equal(y[0],direct_dft(x1))
        assert_array_almost_equal(y[1],direct_dft(x2))

    def test_djbfft(self):
        for i in range(2,14):
            n = 2**i
            x = list(range(n))
            y = fftpack.zfft(x)
            y2 = numpy.fft.fft(x)
            assert_array_almost_equal(y,y2)
            y = fftpack.zrfft(x)
            assert_array_almost_equal(y,y2)

class TestDoubleFFT(_TestFFTBase):
    def setUp(self):
        self.cdt = np.cdouble
        self.rdt = np.double

class TestSingleFFT(_TestFFTBase):
    def setUp(self):
        self.cdt = np.complex64
        self.rdt = np.float32

    @dec.knownfailureif(True, "single-precision FFT implementation is partially disabled, until accuracy issues with large prime powers are resolved")
    def test_notice(self):
        pass

class _TestIFFTBase(TestCase):
    def setUp(self):
        np.random.seed(1234)

    def test_definition(self):
        x = np.array([1,2,3,4+1j,1,2,3,4+2j], self.cdt)
        y = ifft(x)
        y1 = direct_idft(x)
        self.assertTrue(y.dtype == self.cdt,
                "Output dtype is %s, expected %s" % (y.dtype, self.cdt))
        assert_array_almost_equal(y,y1)

        x = np.array([1,2,3,4+0j,5], self.cdt)
        assert_array_almost_equal(ifft(x),direct_idft(x))

    def test_definition_real(self):
        x = np.array([1,2,3,4,1,2,3,4], self.rdt)
        y = ifft(x)
        self.assertTrue(y.dtype == self.cdt,
                "Output dtype is %s, expected %s" % (y.dtype, self.cdt))
        y1 = direct_idft(x)
        assert_array_almost_equal(y,y1)

        x = np.array([1,2,3,4,5], dtype=self.rdt)
        self.assertTrue(y.dtype == self.cdt,
                "Output dtype is %s, expected %s" % (y.dtype, self.cdt))
        assert_array_almost_equal(ifft(x),direct_idft(x))

    def test_djbfft(self):
        for i in range(2,14):
            n = 2**i
            x = list(range(n))
            y = fftpack.zfft(x,direction=-1)
            y2 = numpy.fft.ifft(x)
            assert_array_almost_equal(y,y2)
            y = fftpack.zrfft(x,direction=-1)
            assert_array_almost_equal(y,y2)

    def test_random_complex(self):
        for size in [1,51,111,100,200,64,128,256,1024]:
            x = random([size]).astype(self.cdt)
            x = random([size]).astype(self.cdt) +1j*x
            y1 = ifft(fft(x))
            y2 = fft(ifft(x))
            self.assertTrue(y1.dtype == self.cdt,
                    "Output dtype is %s, expected %s" % (y1.dtype, self.cdt))
            self.assertTrue(y2.dtype == self.cdt,
                    "Output dtype is %s, expected %s" % (y2.dtype, self.cdt))
            assert_array_almost_equal (y1, x)
            assert_array_almost_equal (y2, x)

    def test_random_real(self):
        for size in [1,51,111,100,200,64,128,256,1024]:
            x = random([size]).astype(self.rdt)
            y1 = ifft(fft(x))
            y2 = fft(ifft(x))
            self.assertTrue(y1.dtype == self.cdt,
                    "Output dtype is %s, expected %s" % (y1.dtype, self.cdt))
            self.assertTrue(y2.dtype == self.cdt,
                    "Output dtype is %s, expected %s" % (y2.dtype, self.cdt))
            assert_array_almost_equal (y1, x)
            assert_array_almost_equal (y2, x)

    def test_size_accuracy(self):
        # Sanity check for the accuracy for prime and non-prime sized inputs
        if self.rdt == np.float32:
            rtol = 1e-5
        elif self.rdt == np.float64:
            rtol = 1e-10

        for size in LARGE_COMPOSITE_SIZES + LARGE_PRIME_SIZES:
            np.random.seed(1234)
            x = np.random.rand(size).astype(self.rdt)
            y = ifft(fft(x))
            self.assertTrue(np.linalg.norm(x - y) < rtol*np.linalg.norm(x),
                            (size, self.rdt))
            y = fft(ifft(x))
            self.assertTrue(np.linalg.norm(x - y) < rtol*np.linalg.norm(x),
                            (size, self.rdt))

            x = (x + 1j*np.random.rand(size)).astype(self.cdt)
            y = ifft(fft(x))
            self.assertTrue(np.linalg.norm(x - y) < rtol*np.linalg.norm(x),
                            (size, self.rdt))
            y = fft(ifft(x))
            self.assertTrue(np.linalg.norm(x - y) < rtol*np.linalg.norm(x),
                            (size, self.rdt))

class TestDoubleIFFT(_TestIFFTBase):
    def setUp(self):
        self.cdt = np.cdouble
        self.rdt = np.double

class TestSingleIFFT(_TestIFFTBase):
    def setUp(self):
        self.cdt = np.complex64
        self.rdt = np.float32

class _TestRFFTBase(TestCase):
    def setUp(self):
        np.random.seed(1234)

    def test_definition(self):
        for t in [[1, 2, 3, 4, 1, 2, 3, 4], [1, 2, 3, 4, 1, 2, 3, 4, 5]]:
            x = np.array(t, dtype=self.rdt)
            y = rfft(x)
            y1 = direct_rdft(x)
            assert_array_almost_equal(y,y1)
            self.assertTrue(y.dtype == self.rdt,
                    "Output dtype is %s, expected %s" % (y.dtype, self.rdt))

    def test_djbfft(self):
        from numpy.fft import fft as numpy_fft
        for i in range(2,14):
            n = 2**i
            x = list(range(n))
            y2 = numpy_fft(x)
            y1 = zeros((n,),dtype=double)
            y1[0] = y2[0].real
            y1[-1] = y2[n//2].real
            for k in range(1, n//2):
                y1[2*k-1] = y2[k].real
                y1[2*k] = y2[k].imag
            y = fftpack.drfft(x)
            assert_array_almost_equal(y,y1)

class TestRFFTDouble(_TestRFFTBase):
    def setUp(self):
        self.cdt = np.cdouble
        self.rdt = np.double

class TestRFFTSingle(_TestRFFTBase):
    def setUp(self):
        self.cdt = np.complex64
        self.rdt = np.float32

class _TestIRFFTBase(TestCase):
    def setUp(self):
        np.random.seed(1234)

    def test_definition(self):
        x1 = [1,2,3,4,1,2,3,4]
        x1_1 = [1,2+3j,4+1j,2+3j,4,2-3j,4-1j,2-3j]
        x2= [1,2,3,4,1,2,3,4,5]
        x2_1 = [1,2+3j,4+1j,2+3j,4+5j,4-5j,2-3j,4-1j,2-3j]

        def _test(x, xr):
            y = irfft(np.array(x, dtype=self.rdt))
            y1 = direct_irdft(x)
            self.assertTrue(y.dtype == self.rdt,
                    "Output dtype is %s, expected %s" % (y.dtype, self.rdt))
            assert_array_almost_equal(y,y1, decimal=self.ndec)
            assert_array_almost_equal(y,ifft(xr), decimal=self.ndec)

        _test(x1, x1_1)
        _test(x2, x2_1)

    def test_djbfft(self):
        from numpy.fft import ifft as numpy_ifft
        for i in range(2,14):
            n = 2**i
            x = list(range(n))
            x1 = zeros((n,),dtype=cdouble)
            x1[0] = x[0]
            for k in range(1, n//2):
                x1[k] = x[2*k-1]+1j*x[2*k]
                x1[n-k] = x[2*k-1]-1j*x[2*k]
            x1[n//2] = x[-1]
            y1 = numpy_ifft(x1)
            y = fftpack.drfft(x,direction=-1)
            assert_array_almost_equal(y,y1)

    def test_random_real(self):
        for size in [1,51,111,100,200,64,128,256,1024]:
            x = random([size]).astype(self.rdt)
            y1 = irfft(rfft(x))
            y2 = rfft(irfft(x))
            self.assertTrue(y1.dtype == self.rdt,
                    "Output dtype is %s, expected %s" % (y1.dtype, self.rdt))
            self.assertTrue(y2.dtype == self.rdt,
                    "Output dtype is %s, expected %s" % (y2.dtype, self.rdt))
            assert_array_almost_equal (y1, x, decimal=self.ndec,
                                       err_msg="size=%d" % size)
            assert_array_almost_equal (y2, x, decimal=self.ndec,
                                       err_msg="size=%d" % size)

    def test_size_accuracy(self):
        # Sanity check for the accuracy for prime and non-prime sized inputs
        if self.rdt == np.float32:
            rtol = 1e-5
        elif self.rdt == np.float64:
            rtol = 1e-10

        for size in LARGE_COMPOSITE_SIZES + LARGE_PRIME_SIZES:
            np.random.seed(1234)
            x = np.random.rand(size).astype(self.rdt)
            y = irfft(rfft(x))
            self.assertTrue(np.linalg.norm(x - y) < rtol*np.linalg.norm(x),
                            (size, self.rdt))
            y = rfft(irfft(x))
            self.assertTrue(np.linalg.norm(x - y) < rtol*np.linalg.norm(x),
                            (size, self.rdt))

# self.ndec is bogus; we should have a assert_array_approx_equal for number of
# significant digits
class TestIRFFTDouble(_TestIRFFTBase):
    def setUp(self):
        self.cdt = np.cdouble
        self.rdt = np.double
        self.ndec = 14

class TestIRFFTSingle(_TestIRFFTBase):
    def setUp(self):
        self.cdt = np.complex64
        self.rdt = np.float32
        self.ndec = 5

class Testfft2(TestCase):
    def setUp(self):
        np.random.seed(1234)

    def test_regression_244(self):
        """fft returns wrong result with axes parameter."""
        # fftn (and hence fft2) used to break when both axes and shape were
        # used
        x = numpy.ones((4,4,2))
        y = fft2(x, shape=(8,8), axes=(-3,-2))
        y_r = numpy.fft.fftn(x, s=(8, 8), axes=(-3,  -2))
        assert_array_almost_equal(y, y_r)

class TestFftnSingle(TestCase):
    def setUp(self):
        np.random.seed(1234)

    def test_definition(self):
        x = [[1,2,3],[4,5,6],[7,8,9]]
        y = fftn(np.array(x, np.float32))
        if not y.dtype == np.complex64:
            raise ValueError("double precision output with single precision")

        y_r = np.array(fftn(x), np.complex64)
        assert_array_almost_equal_nulp(y, y_r)

    def test_size_accuracy(self):
        for size in SMALL_COMPOSITE_SIZES + SMALL_PRIME_SIZES:
            np.random.seed(1234)
            x = np.random.rand(size, size) + 1j*np.random.rand(size, size)
            y1 = fftn(x.real.astype(np.float32))
            y2 = fftn(x.real.astype(np.float64)).astype(np.complex64)

            self.assertTrue(y1.dtype == np.complex64)
            assert_array_almost_equal_nulp(y1, y2, 2000)

        for size in LARGE_COMPOSITE_SIZES + LARGE_PRIME_SIZES:
            np.random.seed(1234)
            x = np.random.rand(size, 3) + 1j*np.random.rand(size, 3)
            y1 = fftn(x.real.astype(np.float32))
            y2 = fftn(x.real.astype(np.float64)).astype(np.complex64)

            self.assertTrue(y1.dtype == np.complex64)
            assert_array_almost_equal_nulp(y1, y2, 2000)

class TestFftn(TestCase):
    def setUp(self):
        np.random.seed(1234)

    def test_definition(self):
        x = [[1,2,3],[4,5,6],[7,8,9]]
        y = fftn(x)
        assert_array_almost_equal(y,direct_dftn(x))
        x = random((20,26))
        assert_array_almost_equal(fftn(x),direct_dftn(x))
        x = random((5,4,3,20))
        assert_array_almost_equal(fftn(x),direct_dftn(x))

    def test_axes_argument(self):
        #plane == ji_plane, x== kji_space
        plane1 = [[1,2,3],[4,5,6],[7,8,9]]
        plane2 = [[10,11,12],[13,14,15],[16,17,18]]
        plane3 = [[19,20,21],[22,23,24],[25,26,27]]
        ki_plane1 = [[1,2,3],[10,11,12],[19,20,21]]
        ki_plane2 = [[4,5,6],[13,14,15],[22,23,24]]
        ki_plane3 = [[7,8,9],[16,17,18],[25,26,27]]
        jk_plane1 = [[1,10,19],[4,13,22],[7,16,25]]
        jk_plane2 = [[2,11,20],[5,14,23],[8,17,26]]
        jk_plane3 = [[3,12,21],[6,15,24],[9,18,27]]
        kj_plane1 = [[1,4,7],[10,13,16],[19,22,25]]
        kj_plane2 = [[2,5,8],[11,14,17],[20,23,26]]
        kj_plane3 = [[3,6,9],[12,15,18],[21,24,27]]
        ij_plane1 = [[1,4,7],[2,5,8],[3,6,9]]
        ij_plane2 = [[10,13,16],[11,14,17],[12,15,18]]
        ij_plane3 = [[19,22,25],[20,23,26],[21,24,27]]
        ik_plane1 = [[1,10,19],[2,11,20],[3,12,21]]
        ik_plane2 = [[4,13,22],[5,14,23],[6,15,24]]
        ik_plane3 = [[7,16,25],[8,17,26],[9,18,27]]
        ijk_space = [jk_plane1,jk_plane2,jk_plane3]
        ikj_space = [kj_plane1,kj_plane2,kj_plane3]
        jik_space = [ik_plane1,ik_plane2,ik_plane3]
        jki_space = [ki_plane1,ki_plane2,ki_plane3]
        kij_space = [ij_plane1,ij_plane2,ij_plane3]
        x = array([plane1,plane2,plane3])

        assert_array_almost_equal(fftn(x),fftn(x,axes=(-3,-2,-1))) # kji_space
        assert_array_almost_equal(fftn(x),fftn(x,axes=(0,1,2)))
        y = fftn(x,axes=(2,1,0)) # ijk_space
        assert_array_almost_equal(swapaxes(y,-1,-3),fftn(ijk_space))
        y = fftn(x,axes=(2,0,1)) # ikj_space
        assert_array_almost_equal(swapaxes(swapaxes(y,-1,-3),
                                                   -1,-2)
                                  ,fftn(ikj_space))
        y = fftn(x,axes=(1,2,0)) # jik_space
        assert_array_almost_equal(swapaxes(swapaxes(y,-1,-3),
                                                   -3,-2)
                                  ,fftn(jik_space))
        y = fftn(x,axes=(1,0,2)) # jki_space
        assert_array_almost_equal(swapaxes(y,-2,-3),fftn(jki_space))
        y = fftn(x,axes=(0,2,1)) # kij_space
        assert_array_almost_equal(swapaxes(y,-2,-1),
                                  fftn(kij_space))

        y = fftn(x,axes=(-2,-1)) # ji_plane
        assert_array_almost_equal(fftn(plane1),y[0])
        assert_array_almost_equal(fftn(plane2),y[1])
        assert_array_almost_equal(fftn(plane3),y[2])
        y = fftn(x,axes=(1,2)) # ji_plane
        assert_array_almost_equal(fftn(plane1),y[0])
        assert_array_almost_equal(fftn(plane2),y[1])
        assert_array_almost_equal(fftn(plane3),y[2])
        y = fftn(x,axes=(-3,-2)) # kj_plane
        assert_array_almost_equal(fftn(x[:,:,0]),y[:,:,0])
        assert_array_almost_equal(fftn(x[:,:,1]),y[:,:,1])
        assert_array_almost_equal(fftn(x[:,:,2]),y[:,:,2])
        y = fftn(x,axes=(-3,-1)) # ki_plane
        assert_array_almost_equal(fftn(x[:,0,:]),y[:,0,:])
        assert_array_almost_equal(fftn(x[:,1,:]),y[:,1,:])
        assert_array_almost_equal(fftn(x[:,2,:]),y[:,2,:])
        y = fftn(x,axes=(-1,-2)) # ij_plane
        assert_array_almost_equal(fftn(ij_plane1),swapaxes(y[0],-2,-1))
        assert_array_almost_equal(fftn(ij_plane2),swapaxes(y[1],-2,-1))
        assert_array_almost_equal(fftn(ij_plane3),swapaxes(y[2],-2,-1))
        y = fftn(x,axes=(-1,-3)) # ik_plane
        assert_array_almost_equal(fftn(ik_plane1),swapaxes(y[:,0,:],-1,-2))
        assert_array_almost_equal(fftn(ik_plane2),swapaxes(y[:,1,:],-1,-2))
        assert_array_almost_equal(fftn(ik_plane3),swapaxes(y[:,2,:],-1,-2))
        y = fftn(x,axes=(-2,-3)) # jk_plane
        assert_array_almost_equal(fftn(jk_plane1),swapaxes(y[:,:,0],-1,-2))
        assert_array_almost_equal(fftn(jk_plane2),swapaxes(y[:,:,1],-1,-2))
        assert_array_almost_equal(fftn(jk_plane3),swapaxes(y[:,:,2],-1,-2))

        y = fftn(x,axes=(-1,)) # i_line
        for i in range(3):
            for j in range(3):
                assert_array_almost_equal(fft(x[i,j,:]),y[i,j,:])
        y = fftn(x,axes=(-2,)) # j_line
        for i in range(3):
            for j in range(3):
                assert_array_almost_equal(fft(x[i,:,j]),y[i,:,j])
        y = fftn(x,axes=(0,)) # k_line
        for i in range(3):
            for j in range(3):
                assert_array_almost_equal(fft(x[:,i,j]),y[:,i,j])

        y = fftn(x,axes=()) # point
        assert_array_almost_equal(y,x)

    def test_shape_argument(self):
        small_x = [[1,2,3],[4,5,6]]
        large_x1 = [[1,2,3,0],[4,5,6,0],[0,0,0,0],[0,0,0,0]]
        y = fftn(small_x,shape=(4,4))
        assert_array_almost_equal (y,fftn(large_x1))
        y = fftn(small_x,shape=(3,4))
        assert_array_almost_equal (y,fftn(large_x1[:-1]))

    def test_shape_axes_argument(self):
        small_x = [[1,2,3],[4,5,6],[7,8,9]]
        large_x1 = array([[1,2,3,0],
                                  [4,5,6,0],
                                  [7,8,9,0],
                                  [0,0,0,0]])
        # Disable tests with shape and axes of different lengths
        #y = fftn(small_x,shape=(4,4),axes=(-1,))
        #for i in range(4):
        #    assert_array_almost_equal (y[i],fft(large_x1[i]))
        #y = fftn(small_x,shape=(4,4),axes=(-2,))
        #for i in range(4):
        #    assert_array_almost_equal (y[:,i],fft(large_x1[:,i]))
        y = fftn(small_x,shape=(4,4),axes=(-2,-1))
        assert_array_almost_equal (y,fftn(large_x1))
        y = fftn(small_x,shape=(4,4),axes=(-1,-2))
        assert_array_almost_equal (y,swapaxes(\
            fftn(swapaxes(large_x1,-1,-2)),-1,-2))

    def test_shape_axes_argument2(self):
        # Change shape of the last axis
        x = numpy.random.random((10, 5, 3, 7))
        y = fftn(x, axes=(-1,), shape=(8,))
        assert_array_almost_equal(y, fft(x, axis=-1, n=8))

        # Change shape of an arbitrary axis which is not the last one
        x = numpy.random.random((10, 5, 3, 7))
        y = fftn(x, axes=(-2,), shape=(8,))
        assert_array_almost_equal(y, fft(x, axis=-2, n=8))

        # Change shape of axes: cf #244, where shape and axes were mixed up
        x = numpy.random.random((4,4,2))
        y = fftn(x, axes=(-3,-2), shape=(8,8))
        assert_array_almost_equal(y, numpy.fft.fftn(x, axes=(-3, -2), s=(8, 8)))

    def test_shape_argument_more(self):
        """Test that fftn raises ValueError when s.shape is longer than x.shape"""
        x = zeros((4, 4, 2))
        assert_raises(ValueError, fftn, x, shape=(8, 8, 2, 1))


class _TestIfftn(TestCase):
    dtype = None
    cdtype = None

    def setUp(self):
        np.random.seed(1234)

    def test_definition(self):
        x = np.array([[1,2,3],[4,5,6],[7,8,9]], dtype=self.dtype)
        y = ifftn(x)
        assert_(y.dtype == self.cdtype)
        assert_array_almost_equal_nulp(y,direct_idftn(x),self.maxnlp)
        x = random((20,26))
        assert_array_almost_equal_nulp(ifftn(x),direct_idftn(x),self.maxnlp)
        x = random((5,4,3,20))
        assert_array_almost_equal_nulp(ifftn(x),direct_idftn(x),self.maxnlp)

    def test_random_complex(self):
        for size in [1,2,51,32,64,92]:
            x = random([size,size]) + 1j*random([size,size])
            assert_array_almost_equal_nulp(ifftn(fftn(x)),x,self.maxnlp)
            assert_array_almost_equal_nulp(fftn(ifftn(x)),x,self.maxnlp)

class TestIfftnDouble(_TestIfftn):
    dtype = np.float64
    cdtype = np.complex128
    maxnlp = 2000

class TestIfftnSingle(_TestIfftn):
    dtype = np.float32
    cdtype = np.complex64
    maxnlp = 3500

class TestLongDoubleFailure(TestCase):
    def setUp(self):
        np.random.seed(1234)

    def test_complex(self):
        if np.dtype(np.longcomplex).itemsize == np.dtype(np.complex).itemsize:
            # longdouble == double; so fft is supported
            return

        x = np.random.randn(10).astype(np.longdouble) + \
                1j * np.random.randn(10).astype(np.longdouble)

        for f in [fft, ifft]:
            try:
                f(x)
                raise AssertionError("Type %r not supported but does not fail" % \
                                     np.longcomplex)
            except ValueError:
                pass

    def test_real(self):
        if np.dtype(np.longdouble).itemsize == np.dtype(np.double).itemsize:
            # longdouble == double; so fft is supported
            return

        x = np.random.randn(10).astype(np.longcomplex)

        for f in [fft, ifft]:
            try:
                f(x)
                raise AssertionError("Type %r not supported but does not fail" % \
                                     np.longcomplex)
            except ValueError:
                pass



class FakeArray(object):
    def __init__(self, data):
        self._data = data
        self.__array_interface__ = data.__array_interface__

class FakeArray2(object):
    def __init__(self, data):
        self._data = data
    def __array__(self):
        return self._data

class TestOverwrite(object):
    """
    Check input overwrite behavior of the FFT functions
    """

    real_dtypes = [np.float32, np.float64]
    dtypes = real_dtypes + [np.complex64, np.complex128]

    def _check(self, x, routine, fftsize, axis, overwrite_x, should_overwrite):
        x2 = x.copy()
        for fake in [lambda x: x, FakeArray, FakeArray2]:
            y = routine(fake(x2), fftsize, axis, overwrite_x=overwrite_x)

            sig = "%s(%s%r, %r, axis=%r, overwrite_x=%r)" % (
                routine.__name__, x.dtype, x.shape, fftsize, axis, overwrite_x)
            if not should_overwrite:
                assert_equal(x2, x, err_msg="spurious overwrite in %s" % sig)
            else:
                if (x2 == x).all():
                    raise AssertionError("no overwrite in %s" % sig)

    def _check_1d(self, routine, dtype, shape, axis, overwritable_dtypes):
        np.random.seed(1234)
        if np.issubdtype(dtype, np.complexfloating):
            data = np.random.randn(*shape) + 1j*np.random.randn(*shape)
        else:
            data = np.random.randn(*shape)
        data = data.astype(dtype)

        for fftsize in [8, 16, 32]:
            for overwrite_x in [True, False]:
                should_overwrite = (overwrite_x
                                    and dtype in overwritable_dtypes
                                    and fftsize <= shape[axis]
                                    and (len(shape) == 1 or
                                         (axis % len(shape) == len(shape)-1
                                          and fftsize == shape[axis])))
                self._check(data, routine, fftsize, axis,
                            overwrite_x=overwrite_x,
                            should_overwrite=should_overwrite)

    def test_fft(self):
        overwritable = (np.complex128, np.complex64)
        for dtype in self.dtypes:
            self._check_1d(fft, dtype, (16,), -1, overwritable)
            self._check_1d(fft, dtype, (16, 2), 0, overwritable)
            self._check_1d(fft, dtype, (2, 16), 1, overwritable)

    def test_ifft(self):
        overwritable = (np.complex128, np.complex64)
        for dtype in self.dtypes:
            self._check_1d(ifft, dtype, (16,), -1, overwritable)
            self._check_1d(ifft, dtype, (16, 2), 0, overwritable)
            self._check_1d(ifft, dtype, (2, 16), 1, overwritable)

    def test_rfft(self):
        overwritable = self.real_dtypes
        for dtype in self.real_dtypes:
            self._check_1d(rfft, dtype, (16,), -1, overwritable)
            self._check_1d(rfft, dtype, (16, 2), 0, overwritable)
            self._check_1d(rfft, dtype, (2, 16), 1, overwritable)

    def test_irfft(self):
        overwritable = self.real_dtypes
        for dtype in self.real_dtypes:
            self._check_1d(irfft, dtype, (16,), -1, overwritable)
            self._check_1d(irfft, dtype, (16, 2), 0, overwritable)
            self._check_1d(irfft, dtype, (2, 16), 1, overwritable)

    def _check_nd_one(self, routine, dtype, shape, axes, overwritable_dtypes):
        np.random.seed(1234)
        if np.issubdtype(dtype, np.complexfloating):
            data = np.random.randn(*shape) + 1j*np.random.randn(*shape)
        else:
            data = np.random.randn(*shape)
        data = data.astype(dtype)

        def fftshape_iter(shp):
            if len(shp) <= 0:
                yield ()
            else:
                for j in (shp[0]//2, shp[0], shp[0]*2):
                    for rest in fftshape_iter(shp[1:]):
                        yield (j,) + rest

        if axes is None:
            part_shape = shape
        else:
            part_shape = tuple(np.take(shape, axes))

        for overwrite_x in [True, False]:
            for fftshape in fftshape_iter(part_shape):
                should_overwrite = (overwrite_x
                                    and data.ndim == 1
                                    and np.all([x < y for x, y in zip(fftshape, part_shape)])
                                    and dtype in overwritable_dtypes)
                self._check(data, routine, fftshape, axes,
                            overwrite_x=overwrite_x,
                            should_overwrite=should_overwrite)
                if data.ndim > 1:
                    # check fortran order: it never overwrites
                    self._check(data.T, routine, fftshape, axes,
                                overwrite_x=overwrite_x,
                                should_overwrite=False)

    def _check_nd(self, routine, dtype, overwritable):
        self._check_nd_one(routine, dtype, (16,), None, overwritable)
        self._check_nd_one(routine, dtype, (16,), (0,), overwritable)
        self._check_nd_one(routine, dtype, (16, 2), (0,), overwritable)
        self._check_nd_one(routine, dtype, (2, 16), (1,), overwritable)
        self._check_nd_one(routine, dtype, (8, 16), None, overwritable)
        self._check_nd_one(routine, dtype, (8, 16), (0, 1), overwritable)
        self._check_nd_one(routine, dtype, (8, 16, 2), (0, 1), overwritable)
        self._check_nd_one(routine, dtype, (8, 16, 2), (1, 2), overwritable)
        self._check_nd_one(routine, dtype, (8, 16, 2), (0,), overwritable)
        self._check_nd_one(routine, dtype, (8, 16, 2), (1,), overwritable)
        self._check_nd_one(routine, dtype, (8, 16, 2), (2,), overwritable)
        self._check_nd_one(routine, dtype, (8, 16, 2), None, overwritable)
        self._check_nd_one(routine, dtype, (8, 16, 2), (0,1,2), overwritable)

    def test_fftn(self):
        overwritable = (np.complex128, np.complex64)
        for dtype in self.dtypes:
            self._check_nd(fftn, dtype, overwritable)

    def test_ifftn(self):
        overwritable = (np.complex128, np.complex64)
        for dtype in self.dtypes:
            self._check_nd(ifftn, dtype, overwritable)


if __name__ == "__main__":
    run_module_suite()
