diff options
Diffstat (limited to 'numpy/add_newdocs.py')
| -rw-r--r-- | numpy/add_newdocs.py | 42 |
1 files changed, 24 insertions, 18 deletions
diff --git a/numpy/add_newdocs.py b/numpy/add_newdocs.py index e630ae921..ba5e11f21 100644 --- a/numpy/add_newdocs.py +++ b/numpy/add_newdocs.py @@ -5819,44 +5819,50 @@ add_newdoc('numpy.core', 'ufunc', ('at', """ at(a, indices, b=None) - Performs operation in place on array for items specified by indices. - For addition ufunc, this method is equivalent to a[indices] += b, - except that results are accumulated for indices listed more than once. - This solves the problem with a[indices] += b where each time a duplicate - index is encounted, the increment is performed on the original element. - As a result, an element that appears three times in the fancy indexing - list will only be incremented once in the final result, whereas with the - new 'at' method the original element will be incremented by three in the - final result. + Performs unbuffered in place operation on operand 'a' for elements + specified by 'indices'. For addition ufunc, this method is equivalent to + `a[indices] += b`, except that results are accumulated for elements that + are indexed more than once. For example, `a[[0,0]] += 1` will only + increment the first element once because of buffering, whereas + `add.at(a, [0,0], 1)` will increment the first element twice. Parameters ---------- a : array_like - The array to act on. - indices : array_like - Paired indices, comma separated (not colon), specifying slices to - reduce. - b : array_like or scalar - Second operation for ufuncs requiring two operands. + The array to perform in place operation on. + indices : array_like or tuple + Array like index object or slice object for indexing into first + operand. If first operand has multiple dimensions, indices can be a + tuple of array like index objects or slice objects. + b : array_like + Second operand for ufuncs requiring two operands. Operand must be + broadcastable over first operand after indexing or slicing. Examples -------- Set items 0 and 1 to their negative values: - np.negative.at(a, [0, 1]) + >>> a = np.array([1, 2, 3, 4]) + >>> np.negative.at(a, [0, 1]) + array([-1, -2, 3, 4]) :: Increment items 0 and 1, and increment item 2 twice: - np.add.at(a, [0, 1, 2, 2], 1) + >>> a = np.array([1, 2, 3, 4]) + >>> np.add.at(a, [0, 1, 2, 2], 1) + array([2, 3, 5, 4]) :: Add items 0 and 1 in first array to second array, and store results in first array: - np.add.at(a, [0, 1], b) + >>> a = np.array([1, 2, 3, 4]) + >>> b = np.array([1, 2]) + >>> np.add.at(a, [0, 1], b) + array([2, 4, 3, 4]) """)) |