diff options
Diffstat (limited to 'numpy/lib/arraysetops.py')
| -rw-r--r-- | numpy/lib/arraysetops.py | 20 |
1 files changed, 16 insertions, 4 deletions
diff --git a/numpy/lib/arraysetops.py b/numpy/lib/arraysetops.py index 0f2d082c5..22687b941 100644 --- a/numpy/lib/arraysetops.py +++ b/numpy/lib/arraysetops.py @@ -270,20 +270,33 @@ def unique(ar, return_index=False, return_inverse=False, # Must reshape to a contiguous 2D array for this to work... orig_shape, orig_dtype = ar.shape, ar.dtype - ar = ar.reshape(orig_shape[0], -1) + ar = ar.reshape(orig_shape[0], np.prod(orig_shape[1:], dtype=np.intp)) ar = np.ascontiguousarray(ar) dtype = [('f{i}'.format(i=i), ar.dtype) for i in range(ar.shape[1])] + # At this point, `ar` has shape `(n, m)`, and `dtype` is a structured + # data type with `m` fields where each field has the data type of `ar`. + # In the following, we create the array `consolidated`, which has + # shape `(n,)` with data type `dtype`. try: - consolidated = ar.view(dtype) + if ar.shape[1] > 0: + consolidated = ar.view(dtype) + else: + # If ar.shape[1] == 0, then dtype will be `np.dtype([])`, which is + # a data type with itemsize 0, and the call `ar.view(dtype)` will + # fail. Instead, we'll use `np.empty` to explicitly create the + # array with shape `(len(ar),)`. Because `dtype` in this case has + # itemsize 0, the total size of the result is still 0 bytes. + consolidated = np.empty(len(ar), dtype=dtype) except TypeError: # There's no good way to do this for object arrays, etc... msg = 'The axis argument to unique is not supported for dtype {dt}' raise TypeError(msg.format(dt=ar.dtype)) def reshape_uniq(uniq): + n = len(uniq) uniq = uniq.view(orig_dtype) - uniq = uniq.reshape(-1, *orig_shape[1:]) + uniq = uniq.reshape(n, *orig_shape[1:]) uniq = np.moveaxis(uniq, 0, axis) return uniq @@ -783,4 +796,3 @@ def setdiff1d(ar1, ar2, assume_unique=False): ar1 = unique(ar1) ar2 = unique(ar2) return ar1[in1d(ar1, ar2, assume_unique=True, invert=True)] - |