test_pyfftw_numpy_interface.py

# Copyright 2015 Knowledge Economy Developments Ltd
#
# Henry Gomersall
# heng@kedevelopments.co.uk
#
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# * Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# * Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
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# * Neither the name of the copyright holder nor the names of its contributors
# may be used to endorse or promote products derived from this software without
# specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
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from pyfftw import interfaces, _supported_types, _all_types_np

from .test_pyfftw_base import run_test_suites
from ._get_default_args import get_default_args

from distutils.version import LooseVersion
import unittest
import numpy
from numpy import fft as np_fft
import warnings
import copy
warnings.filterwarnings('always')

if LooseVersion(numpy.version.version) <= LooseVersion('1.6.2'):
    # We overwrite the broken _cook_nd_args with a fixed version.
    from ._cook_nd_args import _cook_nd_args
    numpy.fft.fftpack._cook_nd_args = _cook_nd_args

complex_dtypes = []
real_dtypes = []
if '32' in _supported_types:
    complex_dtypes.extend([numpy.complex64]*2)
    real_dtypes.extend([numpy.float16, numpy.float32])
if '64' in _supported_types:
    complex_dtypes.append(numpy.complex128)
    real_dtypes.append(numpy.float64)
if 'ld' in _supported_types:
    complex_dtypes.append(numpy.clongdouble)
    real_dtypes.append(numpy.longdouble)

def make_complex_data(shape, dtype):
    ar, ai = dtype(numpy.random.randn(2, *shape))
    return ar + 1j*ai

def make_real_data(shape, dtype):
    return dtype(numpy.random.randn(*shape))

def _numpy_fft_has_norm_kwarg():
    """returns True if numpy's fft supports the norm keyword argument

    This should be true for numpy >= 1.10
    """
    # return LooseVersion(numpy.version.version) >= LooseVersion('1.10')
    try:
        np_fft.fft(numpy.ones(4), norm=None)
        return True
    except TypeError:
        return False

if _numpy_fft_has_norm_kwarg() and numpy.__version__ < '1.13':
    # use version of numpy.fft.rfft* with normalisation bug fixed
    # The patched version here, corresponds to the following bugfix PR:
    #     https://github.com/numpy/numpy/pull/8445
    from numpy.fft import fftpack as fftpk

    def rfft_fix(a, n=None, axis=-1, norm=None):
        # from numpy.fft import fftpack_lite as fftpack
        # from numpy.fft.fftpack import _raw_fft, _unitary, _real_fft_cache
        a = numpy.array(a, copy=True, dtype=float)
        output = fftpk._raw_fft(a, n, axis, fftpk.fftpack.rffti,
                                fftpk.fftpack.rfftf, fftpk._real_fft_cache)
        if fftpk._unitary(norm):
            if n is None:
                n = a.shape[axis]
            output *= 1 / numpy.sqrt(n)
        return output

    def rfftn_fix(a, s=None, axes=None, norm=None):
        a = numpy.array(a, copy=True, dtype=float)
        s, axes = fftpk._cook_nd_args(a, s, axes)
        a = rfft_fix(a, s[-1], axes[-1], norm)
        for ii in range(len(axes)-1):
            a = fftpk.fft(a, s[ii], axes[ii], norm)
        return a

    def rfft2_fix(a, s=None, axes=(-2, -1), norm=None):
        return rfftn_fix(a, s, axes, norm)

    np_fft.rfft = rfft_fix
    np_fft.rfft2 = rfft2_fix
    np_fft.rfftn = rfftn_fix

functions = {
        'fft': 'complex',
        'ifft': 'complex',
        'rfft': 'r2c',
        'irfft': 'c2r',
        'rfftn': 'r2c',
        'hfft': 'c2r',
        'ihfft': 'r2c',
        'irfftn': 'c2r',
        'rfft2': 'r2c',
        'irfft2': 'c2r',
        'fft2': 'complex',
        'ifft2': 'complex',
        'fftn': 'complex',
        'ifftn': 'complex'}

acquired_names = ('fftfreq', 'fftshift', 'ifftshift')

if LooseVersion(numpy.version.version) >= LooseVersion('1.8'):
    acquired_names += ('rfftfreq', )


class InterfacesNumpyFFTTestModule(unittest.TestCase):
    ''' A really simple test suite to check the module works as expected.
    '''

    def test_acquired_names(self):
        for each_name in acquired_names:

            numpy_fft_attr = getattr(numpy.fft, each_name)
            acquired_attr = getattr(interfaces.numpy_fft, each_name)

            self.assertIs(numpy_fft_attr, acquired_attr)


class InterfacesNumpyFFTTestFFT(unittest.TestCase):

    io_dtypes = {
            'complex': (complex_dtypes, make_complex_data),
            'r2c': (real_dtypes, make_real_data),
            'c2r': (complex_dtypes, make_complex_data)}

    validator_module = np_fft
    test_interface = interfaces.numpy_fft
    func = 'fft'
    axes_kw = 'axis'
    overwrite_input_flag = 'overwrite_input'
    default_s_from_shape_slicer = slice(-1, None)

    test_shapes = (
            ((100,), {}),
            ((128, 64), {'axis': 0}),
            ((128, 32), {'axis': -1}),
            ((59, 100), {}),
            ((59, 99), {'axis': -1}),
            ((59, 99), {'axis': 0}),
            ((32, 32, 4), {'axis': 1}),
            ((32, 32, 2), {'axis': 1, 'norm': 'ortho'}),
            ((64, 128, 16), {}),
            )

    # invalid_s_shapes is:
    # (size, invalid_args, error_type, error_string)
    invalid_args = (
            ((100,), ((100, 200),), TypeError, ''),
            ((100, 200), ((100, 200),), TypeError, ''),
            ((100,), (100, (-2, -1)), TypeError, ''),
            ((100,), (100, -20), IndexError, ''))

    realinv = False
    has_norm_kwarg = _numpy_fft_has_norm_kwarg()

    @property
    def test_data(self):
        for test_shape, kwargs in self.test_shapes:
            axes = self.axes_from_kwargs(kwargs)
            s = self.s_from_kwargs(test_shape, kwargs)

            if not self.has_norm_kwarg and 'norm' in kwargs:
                kwargs.pop('norm')

            if self.realinv:
                test_shape = list(test_shape)
                test_shape[axes[-1]] = test_shape[axes[-1]]//2 + 1
                test_shape = tuple(test_shape)

            yield test_shape, s, kwargs

    def __init__(self, *args, **kwargs):

        super(InterfacesNumpyFFTTestFFT, self).__init__(*args, **kwargs)

        # Assume python 3, but keep backwards compatibility
        if not hasattr(self, 'assertRaisesRegex'):
            self.assertRaisesRegex = self.assertRaisesRegexp

    def validate(self, array_type, test_shape, dtype,
                 s, kwargs, copy_func=copy.copy):

        # Do it without the cache

        # without:
        interfaces.cache.disable()
        self._validate(array_type, test_shape, dtype, s, kwargs,
                       copy_func=copy_func)

    def munge_input_array(self, array, kwargs):
        return array

    def _validate(self, array_type, test_shape, dtype,
                  s, kwargs, copy_func=copy.copy):

        input_array = self.munge_input_array(
                array_type(test_shape, dtype), kwargs)

        orig_input_array = copy_func(input_array)

        np_input_array = numpy.asarray(input_array)

        # Why are long double inputs copied to double precision? It's what
        # numpy silently does anyways as of v1.10 but helps with backward
        # compatibility and scipy.
        # https://github.com/pyFFTW/pyFFTW/pull/189#issuecomment-356449731
        if np_input_array.dtype == 'clongdouble':
            np_input_array = numpy.complex128(input_array)

        elif np_input_array.dtype == 'longdouble':
            np_input_array = numpy.float64(input_array)

        with warnings.catch_warnings(record=True) as w:
            # We catch the warnings so as to pick up on when
            # a complex array is turned into a real array

            if 'axes' in kwargs:
                validator_kwargs = {'axes': kwargs['axes']}
            elif 'axis' in kwargs:
                validator_kwargs = {'axis': kwargs['axis']}
            else:
                validator_kwargs = {}

            if self.has_norm_kwarg and 'norm' in kwargs:
                validator_kwargs['norm'] = kwargs['norm']

            try:
                test_out_array = getattr(self.validator_module, self.func)(
                        copy_func(np_input_array), s, **validator_kwargs)

            except Exception as e:
                interface_exception = None
                try:
                    getattr(self.test_interface, self.func)(
                            copy_func(input_array), s, **kwargs)
                except Exception as _interface_exception:
                    # It's necessary to assign the exception to the
                    # already defined variable in Python 3.
                    # See http://www.python.org/dev/peps/pep-3110/#semantic-changes
                    interface_exception = _interface_exception

                # If the test interface raised, so must this.
                self.assertEqual(type(interface_exception), type(e),
                        msg='Interface exception raised. ' +
                        'Testing for: ' + repr(e))
                return
            try:
                output_array = getattr(self.test_interface, self.func)(
                                    copy_func(np_input_array), s, **kwargs)
            except NotImplementedError as e:
                # check if exception due to missing precision
                msg = repr(e)
                if 'Rebuild pyFFTW with support for' in msg:
                    self.skipTest(msg)
                else:
                    raise

            if (functions[self.func] == 'r2c'):
                if numpy.iscomplexobj(input_array):
                    if len(w) > 0:
                        # Make sure a warning is raised
                        self.assertIs(
                                w[-1].category, numpy.ComplexWarning)

        self.assertTrue(
                numpy.allclose(output_array, test_out_array,
                    rtol=1e-2, atol=1e-4))

        if _all_types_np.get(np_input_array.real.dtype, "") in _supported_types:
            # supported precisions should not be converted
            self.assertEqual(np_input_array.real.dtype,
                             output_array.real.dtype)

        if (not self.overwrite_input_flag in kwargs or
                not kwargs[self.overwrite_input_flag]):
            self.assertTrue(numpy.allclose(input_array,
                orig_input_array))

        return output_array

    def axes_from_kwargs(self, kwargs):
        default_args = get_default_args(
            getattr(self.test_interface, self.func))

        if 'axis' in kwargs:
            axes = (kwargs['axis'],)

        elif 'axes' in kwargs:
            axes = kwargs['axes']
            if axes is None:
                axes = default_args['axes']

        else:
            if 'axis' in default_args:
                # default 1D
                axes = (default_args['axis'],)
            else:
                # default nD
                axes = default_args['axes']

        if axes is None:
            axes = (-1,)

        return axes

    def s_from_kwargs(self, test_shape, kwargs):
        ''' Return either a scalar s or a tuple depending on
        whether axis or axes is specified
        '''
        default_args = get_default_args(
            getattr(self.test_interface, self.func))

        if 'axis' in kwargs:
            s = test_shape[kwargs['axis']]

        elif 'axes' in kwargs:
            axes = kwargs['axes']
            if axes is not None:
                s = []
                for each_axis in axes:
                    s.append(test_shape[each_axis])
            else:
                # default nD
                s = []
                try:
                    for each_axis in default_args['axes']:
                        s.append(test_shape[each_axis])
                except TypeError:
                    try:
                        s = list(test_shape[
                            self.default_s_from_shape_slicer])
                    except TypeError:
                        # We had an integer as the default, so force
                        # it to be a list
                        s = [test_shape[self.default_s_from_shape_slicer]]

        else:
            if 'axis' in default_args:
                # default 1D
                s = test_shape[default_args['axis']]
            else:
                # default nD
                s = []
                try:
                    for each_axis in default_args['axes']:
                        s.append(test_shape[each_axis])
                except TypeError:
                    s = None

        return s

    def test_valid(self):
        dtype_tuple = self.io_dtypes[functions[self.func]]

        for dtype in dtype_tuple[0]:
            for test_shape, s, kwargs in self.test_data:
                s = None

                self.validate(dtype_tuple[1],
                        test_shape, dtype, s, kwargs)

    def test_on_non_numpy_array(self):
        dtype_tuple = self.io_dtypes[functions[self.func]]

        array_type = (lambda test_shape, dtype:
                dtype_tuple[1](test_shape, dtype).tolist())

        for dtype in dtype_tuple[0]:
            for test_shape, s, kwargs in self.test_data:
                s = None

                self.validate(array_type,
                        test_shape, dtype, s, kwargs)


    def test_fail_on_invalid_s_or_axes_or_norm(self):
        dtype_tuple = self.io_dtypes[functions[self.func]]

        for dtype in dtype_tuple[0]:

            for test_shape, args, exception, e_str in self.invalid_args:
                input_array = dtype_tuple[1](test_shape, dtype)

                if len(args) > 2 and not self.has_norm_kwarg:
                    # skip tests invovling norm argument if it isn't available
                    continue

                self.assertRaisesRegex(exception, e_str,
                        getattr(self.test_interface, self.func),
                        *((input_array,) + args))


    def test_same_sized_s(self):
        dtype_tuple = self.io_dtypes[functions[self.func]]
        for dtype in dtype_tuple[0]:
            for test_shape, s, kwargs in self.test_data:

                self.validate(dtype_tuple[1],
                        test_shape, dtype, s, kwargs)

    def test_bigger_s(self):
        dtype_tuple = self.io_dtypes[functions[self.func]]
        for dtype in dtype_tuple[0]:
            for test_shape, s, kwargs in self.test_data:

                try:
                    for each_axis, length in enumerate(s):
                        s[each_axis] += 2
                except TypeError:
                    s += 2

                self.validate(dtype_tuple[1],
                        test_shape, dtype, s, kwargs)


    def test_smaller_s(self):
        dtype_tuple = self.io_dtypes[functions[self.func]]
        for dtype in dtype_tuple[0]:
            for test_shape, s, kwargs in self.test_data:

                try:
                    for each_axis, length in enumerate(s):
                        s[each_axis] -= 2
                except TypeError:
                    s -= 2

                self.validate(dtype_tuple[1],
                        test_shape, dtype, s, kwargs)

    def check_arg(self, arg, arg_test_values, array_type, test_shape,
            dtype, s, kwargs):
        '''Check that the correct arg is passed to the builder'''
        # We trust the builders to work as expected when passed
        # the correct arg (the builders have their own unittests).

        return_values = []
        input_array = array_type(test_shape, dtype)

        def fake_fft(*args, **kwargs):
            return_values.append((args, kwargs))
            return (args, kwargs)

        try:

            # Replace the function that is to be used
            real_fft = getattr(self.test_interface, self.func)
            setattr(self.test_interface, self.func, fake_fft)

            _kwargs = kwargs.copy()

            for each_value in arg_test_values:
                _kwargs[arg] = each_value
                builder_args = getattr(self.test_interface, self.func)(
                input_array.copy(), s, **_kwargs)

                self.assertTrue(builder_args[1][arg] == each_value)

            # make sure it was called
            self.assertTrue(len(return_values) > 0)
        except:
            raise

        finally:
            # Make sure we set it back
            setattr(self.test_interface, self.func, real_fft)

        # Validate it aswell
        for each_value in arg_test_values:
            _kwargs[arg] = each_value
            builder_args = getattr(self.test_interface, self.func)(
            input_array.copy(), s, **_kwargs)

            self.validate(array_type, test_shape, dtype, s, _kwargs)

    def test_auto_align_input(self):
        dtype_tuple = self.io_dtypes[functions[self.func]]

        for dtype in dtype_tuple[0]:
            for test_shape, s, kwargs in self.test_data:
                self.check_arg('auto_align_input', (True, False),
                        dtype_tuple[1], test_shape, dtype, s, kwargs)

    def test_auto_contiguous_input(self):
        dtype_tuple = self.io_dtypes[functions[self.func]]

        for dtype in dtype_tuple[0]:
            for test_shape, s, kwargs in self.test_data:
                self.check_arg('auto_contiguous', (True, False),
                        dtype_tuple[1], test_shape, dtype, s, kwargs)

    def test_bigger_and_smaller_s(self):
        dtype_tuple = self.io_dtypes[functions[self.func]]
        for dtype in dtype_tuple[0]:
            i = -1
            for test_shape, s, kwargs in self.test_data:

                try:
                    for each_axis, length in enumerate(s):
                        s[each_axis] += i * 2
                        i *= i
                except TypeError:
                    s += i * 2
                    i *= i

                self.validate(dtype_tuple[1],
                        test_shape, dtype, s, kwargs)


    def test_dtype_coercian(self):
        # Make sure we input a dtype that needs to be coerced
        if functions[self.func] == 'r2c':
            dtype_tuple = self.io_dtypes['complex']
        else:
            dtype_tuple = self.io_dtypes['r2c']

        for dtype in dtype_tuple[0]:
            for test_shape, s, kwargs in self.test_data:
                s = None

                self.validate(dtype_tuple[1],
                        test_shape, dtype, s, kwargs)


    def test_planner_effort(self):
        '''Test the planner effort arg
        '''
        dtype_tuple = self.io_dtypes[functions[self.func]]
        test_shape = (16,)

        for dtype in dtype_tuple[0]:
            s = None
            if self.axes_kw == 'axis':
                kwargs = {'axis': -1}
            else:
                kwargs = {'axes': (-1,)}

            for each_effort in ('FFTW_ESTIMATE', 'FFTW_MEASURE',
                    'FFTW_PATIENT', 'FFTW_EXHAUSTIVE'):

                kwargs['planner_effort'] = each_effort

                self.validate(
                        dtype_tuple[1], test_shape, dtype, s, kwargs)

            kwargs['planner_effort'] = 'garbage'

            self.assertRaisesRegex(ValueError, 'Invalid planner effort',
                    self.validate,
                    *(dtype_tuple[1], test_shape, dtype, s, kwargs))

    def test_threads_arg(self):
        '''Test the threads argument
        '''
        dtype_tuple = self.io_dtypes[functions[self.func]]
        test_shape = (16,)

        for dtype in dtype_tuple[0]:
            s = None
            if self.axes_kw == 'axis':
                kwargs = {'axis': -1}
            else:
                kwargs = {'axes': (-1,)}

            self.check_arg('threads', (1, 2, 5, 10),
                        dtype_tuple[1], test_shape, dtype, s, kwargs)

            kwargs['threads'] = 'bleh'

            # Should not work
            self.assertRaises(TypeError,
                    self.validate,
                    *(dtype_tuple[1], test_shape, dtype, s, kwargs))


    def test_overwrite_input(self):
        '''Test the overwrite_input flag
        '''
        dtype_tuple = self.io_dtypes[functions[self.func]]

        for dtype in dtype_tuple[0]:
            for test_shape, s, _kwargs in self.test_data:
                s = None

                kwargs = _kwargs.copy()
                self.validate(dtype_tuple[1], test_shape, dtype, s, kwargs)

                self.check_arg(self.overwrite_input_flag, (True, False),
                        dtype_tuple[1], test_shape, dtype, s, kwargs)

    def test_input_maintained(self):
        '''Test to make sure the input is maintained by default.
        '''
        dtype_tuple = self.io_dtypes[functions[self.func]]
        for dtype in dtype_tuple[0]:
            for test_shape, s, kwargs in self.test_data:

                input_array = dtype_tuple[1](test_shape, dtype)

                orig_input_array = input_array.copy()

                getattr(self.test_interface, self.func)(
                        input_array, s, **kwargs)

                self.assertTrue(
                        numpy.alltrue(input_array == orig_input_array))

    def test_on_non_writeable_array_issue_92(self):
        '''Test to make sure that locked arrays work.

        Regression test for issue 92.
        '''
        def copy_with_writeable(array_to_copy):
            array_copy = array_to_copy.copy()
            array_copy.flags.writeable = array_to_copy.flags.writeable
            return array_copy

        dtype_tuple = self.io_dtypes[functions[self.func]]

        def array_type(test_shape, dtype):
            a = dtype_tuple[1](test_shape, dtype)
            a.flags.writeable = False
            return a

        for dtype in dtype_tuple[0]:
            for test_shape, s, kwargs in self.test_data:
                s = None

                self.validate(array_type,
                              test_shape, dtype, s, kwargs,
                              copy_func=copy_with_writeable)

    def test_overwrite_input_for_issue_92(self):
        '''Tests that trying to overwrite a locked array fails.
        '''
        a = numpy.zeros((4,))
        a.flags.writeable = False
        self.assertRaisesRegex(
            ValueError,
            'overwrite_input cannot be True when the ' +
            'input array flags.writeable is False',
            interfaces.numpy_fft.fft,
            a, overwrite_input=True)


class InterfacesNumpyFFTTestIFFT(InterfacesNumpyFFTTestFFT):
    func = 'ifft'

class InterfacesNumpyFFTTestRFFT(InterfacesNumpyFFTTestFFT):
    func = 'rfft'

class InterfacesNumpyFFTTestIRFFT(InterfacesNumpyFFTTestFFT):
    func = 'irfft'
    realinv = True

class InterfacesNumpyFFTTestHFFT(InterfacesNumpyFFTTestFFT):
    func = 'hfft'
    realinv = True

class InterfacesNumpyFFTTestIHFFT(InterfacesNumpyFFTTestFFT):
    func = 'ihfft'

class InterfacesNumpyFFTTestFFT2(InterfacesNumpyFFTTestFFT):
    axes_kw = 'axes'
    func = 'ifft2'
    test_shapes = (
            ((128, 64), {'axes': None}),
            ((128, 32), {'axes': None}),
            ((128, 32, 4), {'axes': (0, 2)}),
            ((59, 100), {'axes': (-2, -1)}),
            ((32, 32), {'axes': (-2, -1), 'norm': 'ortho'}),
            ((64, 128, 16), {'axes': (0, 2)}),
            ((4, 6, 8, 4), {'axes': (0, 3)}),
            )

    invalid_args = (
            ((100,), ((100, 200),), ValueError, 'Shape error'),
            ((100, 200), ((100, 200, 100),), ValueError, 'Shape error'),
            ((100,), ((100, 200), (-3, -2, -1)), ValueError, 'Shape error'),
            ((100, 200), (100, -1), TypeError, ''),
            ((100, 200), ((100, 200), (-3, -2)), IndexError, 'Invalid axes'),
            ((100, 200), ((100,), (-3,)), IndexError, 'Invalid axes'),
            # pass invalid normalisation string
            ((100, 200), ((100,), (-3,), 'invalid_norm'), ValueError, ''))

    def test_shape_and_s_different_lengths(self):
        dtype_tuple = self.io_dtypes[functions[self.func]]
        for dtype in dtype_tuple[0]:
            for test_shape, s, _kwargs in self.test_data:
                kwargs = copy.copy(_kwargs)
                try:
                    s = s[1:]
                except TypeError:
                    self.skipTest('Not meaningful test on 1d arrays.')

                del kwargs['axes']
                self.validate(dtype_tuple[1],
                        test_shape, dtype, s, kwargs)


class InterfacesNumpyFFTTestIFFT2(InterfacesNumpyFFTTestFFT2):
    func = 'ifft2'

class InterfacesNumpyFFTTestRFFT2(InterfacesNumpyFFTTestFFT2):
    func = 'rfft2'

class InterfacesNumpyFFTTestIRFFT2(InterfacesNumpyFFTTestFFT2):
    func = 'irfft2'
    realinv = True

class InterfacesNumpyFFTTestFFTN(InterfacesNumpyFFTTestFFT2):
    func = 'ifftn'
    test_shapes = (
            ((128, 32, 4), {'axes': None}),
            ((64, 128, 16), {'axes': (0, 1, 2)}),
            ((4, 6, 8, 4), {'axes': (0, 3, 1)}),
            ((4, 6, 4, 4), {'axes': (0, 3, 1), 'norm': 'ortho'}),
            ((4, 6, 8, 4), {'axes': (0, 3, 1, 2)}),
            )

class InterfacesNumpyFFTTestIFFTN(InterfacesNumpyFFTTestFFTN):
    func = 'ifftn'

class InterfacesNumpyFFTTestRFFTN(InterfacesNumpyFFTTestFFTN):
    func = 'rfftn'

class InterfacesNumpyFFTTestIRFFTN(InterfacesNumpyFFTTestFFTN):
    func = 'irfftn'
    realinv = True

test_cases = (
        InterfacesNumpyFFTTestModule,
        InterfacesNumpyFFTTestFFT,
        InterfacesNumpyFFTTestIFFT,
        InterfacesNumpyFFTTestRFFT,
        InterfacesNumpyFFTTestIRFFT,
        InterfacesNumpyFFTTestHFFT,
        InterfacesNumpyFFTTestIHFFT,
        InterfacesNumpyFFTTestFFT2,
        InterfacesNumpyFFTTestIFFT2,
        InterfacesNumpyFFTTestRFFT2,
        InterfacesNumpyFFTTestIRFFT2,
        InterfacesNumpyFFTTestFFTN,
        InterfacesNumpyFFTTestIFFTN,
        InterfacesNumpyFFTTestRFFTN,
        InterfacesNumpyFFTTestIRFFTN,)

#test_set = {'InterfacesNumpyFFTTestHFFT': ('test_valid',)}
test_set = None

if __name__ == '__main__':

    run_test_suites(test_cases, test_set)