expand tests ffftn

heat/fft/fft.py

@@ -117,6 +117,7 @@ def __fftn_op(x: DNDarray, fftn_op: callable, **kwargs) -> DNDarray:
         local_x = x.larray
     except AttributeError:
         raise TypeError("x must be a DNDarray, is {}".format(type(x)))
+
     original_split = x.split
 
     # sanitize kwargs
@@ -126,6 +127,7 @@ def __fftn_op(x: DNDarray, fftn_op: callable, **kwargs) -> DNDarray:
     if repeated_axes:
         raise NotImplementedError("Multiple transforms over the same axis not implemented yet.")
     s = kwargs.get("s", None)
+    s = sanitize_axis(x.gshape, s)
     norm = kwargs.get("norm", None)
 
     # non-distributed DNDarray
@@ -142,6 +144,7 @@ def __fftn_op(x: DNDarray, fftn_op: callable, **kwargs) -> DNDarray:
         for i, axis in enumerate(axes):
             output_shape[axis] = s[i]
     else:
+        axes = tuple(range(x.ndim))
         s = tuple(output_shape[axis] for axis in axes)
     output_shape = tuple(output_shape)
 
@@ -170,6 +173,14 @@ def __fftn_op(x: DNDarray, fftn_op: callable, **kwargs) -> DNDarray:
         )
         x = x.transpose(transpose_axes)
 
+    # original split is 0 and fft is along axis 0
+    if x.ndim == 1:
+        _ = x.resplit(axis=None)
+        result = __fftn_op(_, fftn_op, **kwargs)
+        del _
+        result.resplit_(axis=0)
+        return result
+
     # redistribute x from axis 0 to 1
     _ = x.resplit(axis=1)
     # FFT along axis 0 (now non-split)

heat/fft/tests/test_fft.py

@@ -62,7 +62,7 @@ def test_fft(self):
 
     def test_ifft(self):
         # 1D non-distributed
-        x = ht.random.randn(6)
+        x = ht.random.randn(6, dtype=ht.float64)
         x_fft = ht.fft.fft(x)
         y = ht.fft.ifft(x_fft)
         self.assertIsInstance(y, ht.DNDarray)
@@ -88,7 +88,22 @@ def test_irfft2(self):
         pass
 
     def test_fftn(self):
-        pass
+        # 1D non-distributed
+        x = ht.random.randn(6)
+        y = ht.fft.fftn(x)
+        np_y = np.fft.fftn(x.numpy())
+        self.assertIsInstance(y, ht.DNDarray)
+        self.assertEqual(y.shape, x.shape)
+        self.assert_array_equal(y, np_y)
+
+        # 1D distributed
+        x = ht.random.randn(6, split=0)
+        y = ht.fft.fftn(x)
+        np_y = np.fft.fftn(x.numpy())
+        self.assertIsInstance(y, ht.DNDarray)
+        self.assertEqual(y.shape, x.shape)
+        self.assertTrue(y.split == 0)
+        self.assert_array_equal(y, np_y)
 
     def test_ifftn(self):
         pass