Fixed issue #48

mkl_fft/_numpy_fft.py

@@ -1,4 +1,4 @@
-g#!/usr/bin/env python
+#!/usr/bin/env python
 # Copyright (c) 2017-2019, Intel Corporation
 #
 # Redistribution and use in source and binary forms, with or without

mkl_fft/_pydfti.pyx

@@ -912,6 +912,48 @@ def _iter_fftnd(a, s=None, axes=None, function=fft, overwrite_arg=False, scale_f
     return a
 
 
+def flat_to_multi(ind, shape):
+    nd = len(shape)
+    m_ind = [-1] * nd
+    j = ind
+    for i in range(nd):
+        si = shape[nd-1-i]
+        q = j // si
+        r = j - si * q
+        m_ind[nd-1-i] = r
+        j = q
+    return m_ind
+
+
+def iter_complementary(x, axes, func, kwargs, result):
+    if axes is None:
+        return func(x, **kwargs)
+    x_shape = x.shape
+    nd = x.ndim
+    r = list(range(nd))
+    sl = [slice(None, None, None)] * nd
+    if not isinstance(axes, tuple):
+        axes = (axes,)
+    for ai in axes:
+        r[ai] = None
+    size = 1
+    sub_shape = []
+    dual_ind = []
+    for ri in r:
+        if ri is not None:
+            size *= x_shape[ri]
+            sub_shape.append(x_shape[ri])
+            dual_ind.append(ri)
+
+    for ind in range(size):
+        m_ind = flat_to_multi(ind, sub_shape)
+        for k1, k2 in zip(dual_ind, m_ind):
+            sl[k1] = k2
+        np.copyto(result[tuple(sl)], func(x[tuple(sl)], **kwargs))
+
+    return result
+
+
 def _direct_fftnd(x, overwrite_arg=False, direction=+1, double fsc=1.0):
     """Perform n-dimensional FFT over all axes"""
     cdef int err
@@ -988,6 +1030,7 @@ def _direct_fftnd(x, overwrite_arg=False, direction=+1, double fsc=1.0):
 
         return f_arr
 
+
 def _check_shapes_for_direct(xs, shape, axes):
     if len(axes) > 7: # Intel MKL supports up to 7D
        return False
@@ -1006,6 +1049,14 @@ def _check_shapes_for_direct(xs, shape, axes):
     return True
 
 
+def _output_dtype(dt):
+    if dt == np.double:
+        return np.cdouble
+    if dt == np.single:
+        return np.csingle
+    return dt
+
+
 def _fftnd_impl(x, shape=None, axes=None, overwrite_x=False, direction=+1, double fsc=1.0):
     if direction not in [-1, +1]:
         raise ValueError("Direction of FFT should +1 or -1")
@@ -1026,10 +1077,20 @@ def _fftnd_impl(x, shape=None, axes=None, overwrite_x=False, direction=+1, doubl
     if _direct:
         return _direct_fftnd(x, overwrite_arg=overwrite_x, direction=direction, fsc=fsc)
     else:
-        sc = (<object> fsc)**(1/x.ndim)
-        return _iter_fftnd(x, s=shape, axes=axes,
-                           overwrite_arg=overwrite_x, scale_function=lambda n: sc,
-                           function=fft if direction == 1 else ifft)
+        if (shape is None):
+            x = np.asarray(x)
+            res = np.empty(x.shape, dtype=_output_dtype(x.dtype))
+            return iter_complementary(
+                x, axes,
+                _direct_fftnd,
+                {'overwrite_arg': overwrite_x, 'direction': direction, 'fsc': fsc},
+                res
+                )
+        else:
+            sc = (<object> fsc)**(1/x.ndim)
+            return _iter_fftnd(x, s=shape, axes=axes,
+                               overwrite_arg=overwrite_x, scale_function=lambda n: sc,
+                               function=fft if direction == 1 else ifft)
 
 
 def fft2(x, shape=None, axes=(-2,-1), overwrite_x=False, forward_scale=1.0):

mkl_fft/tests/test_fftnd.py

@@ -99,6 +99,24 @@ def test_matrix4(self):
             assert_allclose(t_strided, t_contig, rtol=r_tol, atol=a_tol)
 
 
+    def test_matrix5(self):
+        """fftn of strided array is same as fftn of a contiguous copy"""
+        rs = rnd.RandomState(1234)
+        x = rs.randn(6, 11, 12, 13)
+        y = x[::-2, :, :, ::3]
+        r_tol, a_tol = _get_rtol_atol(y)
+        f = mkl_fft.fftn(y, axes=(1,2))
+        for i0 in range(y.shape[0]):
+            for i3 in range(y.shape[3]):
+                assert_allclose(
+                    f[i0, :, :, i3],
+                    mkl_fft.fftn(y[i0, :, : , i3]),
+                    rtol=r_tol, atol=a_tol
+                )
+
+
+
+
 class Test_Regressions(TestCase):
 
     def setUp(self):
@@ -129,6 +147,7 @@ def test_rfftn_numpy(self):
                 tr_rfft = np.transpose(mkl_fft.rfftn_numpy(x, axes=a), a)
                 assert_allclose(rfft_tr, tr_rfft, rtol=r_tol, atol=a_tol)
 
+
 class Test_Scales(TestCase):
     def setUp(self):
         pass