diff options
Diffstat (limited to 'numpy/lib')
| -rw-r--r-- | numpy/lib/arraysetops.py | 37 | ||||
| -rw-r--r-- | numpy/lib/tests/test_arraysetops.py | 11 |
2 files changed, 31 insertions, 17 deletions
diff --git a/numpy/lib/arraysetops.py b/numpy/lib/arraysetops.py index 7b103ef3e..e8eda297f 100644 --- a/numpy/lib/arraysetops.py +++ b/numpy/lib/arraysetops.py @@ -148,16 +148,15 @@ def unique(ar, return_index=False, return_inverse=False, .. versionadded:: 1.9.0 axis : int or None, optional - The axis to operate on. If None, `ar` will be flattened beforehand. - Otherwise, duplicate items will be removed along the provided axis, - with all the other axes belonging to the each of the unique elements. - Object arrays or structured arrays that contain objects are not - supported if the `axis` kwarg is used. + The axis to operate on. If None, `ar` will be flattened. If an integer, + the subarrays indexed by the given axis will be flattened and treated + as the elements of a 1-D array with the dimension of the given axis, + see the notes for more details. Object arrays or structured arrays + that contain objects are not supported if the `axis` kwarg is used. The + default is None. .. versionadded:: 1.13.0 - - Returns ------- unique : ndarray @@ -179,6 +178,17 @@ def unique(ar, return_index=False, return_inverse=False, numpy.lib.arraysetops : Module with a number of other functions for performing set operations on arrays. + Notes + ----- + When an axis is specified the subarrays indexed by the axis are sorted. + This is done by making the specified axis the first dimension of the array + and then flattening the subarrays in C order. The flattened subarrays are + then viewed as a structured type with each element given a label, with the + effect that we end up with a 1-D array of structured types that can be + treated in the same way as any other 1-D array. The result is that the + flattened subarrays are sorted in lexicographic order starting with the + first element. + Examples -------- >>> np.unique([1, 1, 2, 2, 3, 3]) @@ -223,25 +233,18 @@ def unique(ar, return_index=False, return_inverse=False, ret = _unique1d(ar, return_index, return_inverse, return_counts) return _unpack_tuple(ret) + # axis was specified and not None try: ar = np.swapaxes(ar, axis, 0) except np.AxisError: # this removes the "axis1" or "axis2" prefix from the error message raise np.AxisError(axis, ar.ndim) - orig_shape, orig_dtype = ar.shape, ar.dtype # 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 = np.ascontiguousarray(ar) - - if ar.dtype.char in (np.typecodes['AllInteger'] + - np.typecodes['Datetime'] + 'S'): - # Optimization: Creating a view of your data with a np.void data type of - # size the number of bytes in a full row. Handles any type where items - # have a unique binary representation, i.e. 0 is only 0, not +0 and -0. - dtype = np.dtype((np.void, ar.dtype.itemsize * ar.shape[1])) - else: - dtype = [('f{i}'.format(i=i), ar.dtype) for i in range(ar.shape[1])] + dtype = [('f{i}'.format(i=i), ar.dtype) for i in range(ar.shape[1])] try: consolidated = ar.view(dtype) diff --git a/numpy/lib/tests/test_arraysetops.py b/numpy/lib/tests/test_arraysetops.py index 17415d8fe..8286834a4 100644 --- a/numpy/lib/tests/test_arraysetops.py +++ b/numpy/lib/tests/test_arraysetops.py @@ -4,6 +4,8 @@ from __future__ import division, absolute_import, print_function import numpy as np +import sys + from numpy.testing import ( run_module_suite, assert_array_equal, assert_equal, assert_raises, ) @@ -453,6 +455,15 @@ class TestUnique(object): assert_array_equal(v.data, v2.data, msg) assert_array_equal(v.mask, v2.mask, msg) + def test_unique_sort_order_with_axis(self): + # These tests fail if sorting along axis is done by treating subarrays + # as unsigned byte strings. See gh-10495. + fmt = "sort order incorrect for integer type '%s'" + for dt in 'bhilq': + a = np.array([[-1],[0]], dt) + b = np.unique(a, axis=0) + assert_array_equal(a, b, fmt % dt) + def _run_axis_tests(self, dtype): data = np.array([[0, 1, 0, 0], [1, 0, 0, 0], |