diff options
| -rw-r--r-- | numpy/core/src/multiarray/mapping.c | 18 | ||||
| -rw-r--r-- | numpy/core/tests/test_indexing.py | 28 | ||||
| -rw-r--r-- | numpy/core/tests/test_regression.py | 24 |
3 files changed, 35 insertions, 35 deletions
diff --git a/numpy/core/src/multiarray/mapping.c b/numpy/core/src/multiarray/mapping.c index 20488fb85..1255b15ea 100644 --- a/numpy/core/src/multiarray/mapping.c +++ b/numpy/core/src/multiarray/mapping.c @@ -1224,7 +1224,7 @@ array_assign_boolean_subscript(PyArrayObject *self, if (needs_api) { /* - * FIXME?: most assignment operations stop after the first occurance + * FIXME?: most assignment operations stop after the first occurrence * of an error. Boolean does not currently, but should at least * report the error. (This is only relevant for things like str->int * casts which call into python) @@ -1439,7 +1439,7 @@ array_subscript(PyArrayObject *self, PyObject *op) /* * TODO: Should this be a view or not? The only reason not would be * optimization (i.e. of array[...] += 1) I think. - * Before, it was just self for a single Ellipis. + * Before, it was just self for a single ellipsis. */ result = PyArray_View(self, NULL, NULL); /* A single ellipsis, so no need to decref */ @@ -1664,7 +1664,7 @@ array_assign_item(PyArrayObject *self, Py_ssize_t i, PyObject *op) /* * This fallback takes the old route of `arr.flat[index] = values` * for one dimensional `arr`. The route can sometimes fail slightly - * different (ValueError instead of IndexError), in which case we + * differently (ValueError instead of IndexError), in which case we * warn users about the change. But since it does not actually care *at all* * about shapes, it should only fail for out of bound indexes or * casting errors. @@ -2448,7 +2448,7 @@ PyArray_MapIterCheckIndices(PyArrayMapIterObject *mit) NPY_BEGIN_THREADS_DEF; if (mit->size == 0) { - /* All indices got broadcasted away, do *not* check as it always was */ + /* All indices got broadcast away, do *not* check as it always was */ return 0; } @@ -2671,7 +2671,7 @@ PyArray_MapIterNew(npy_index_info *indices , int index_num, int index_type, * 1. No subspace iteration is necessary, so the extra_op can * be included into the index iterator (it will be buffered) * 2. Subspace iteration is necessary, so the extra op is iterated - * independendly, and the iteration order is fixed at C (could + * independently, and the iteration order is fixed at C (could * also use Fortran order if the array is Fortran order). * In this case the subspace iterator is not buffered. * @@ -2864,7 +2864,7 @@ PyArray_MapIterNew(npy_index_info *indices , int index_num, int index_type, NPY_ITER_GROWINNER; /* - * For a single 1-d operand, guarantee itertion order + * For a single 1-d operand, guarantee iteration order * (scipy used this). Note that subspace may be used. */ if ((mit->numiter == 1) && (PyArray_NDIM(index_arrays[0]) == 1)) { @@ -3076,7 +3076,7 @@ PyArray_MapIterNew(npy_index_info *indices , int index_num, int index_type, fail: /* - * Check whether the operand was not broadcastable and replace the error + * Check whether the operand could not be broadcast and replace the error * in that case. This should however normally be found early with a * direct goto to broadcast_error */ @@ -3091,7 +3091,7 @@ PyArray_MapIterNew(npy_index_info *indices , int index_num, int index_type, /* (j < 0 is currently impossible, extra_op is reshaped) */ j >= 0 && PyArray_DIM(extra_op, i) != mit->dimensions[j]) { - /* extra_op cannot be broadcasted to the indexing result */ + /* extra_op cannot be broadcast to the indexing result */ goto broadcast_error; } } @@ -3151,7 +3151,7 @@ PyArray_MapIterNew(npy_index_info *indices , int index_num, int index_type, * that most of this public API is currently not guaranteed * to stay the same between versions. If you plan on using * it, please consider adding more utility functions here - * to accomodate new features. + * to accommodate new features. */ NPY_NO_EXPORT PyObject * PyArray_MapIterArray(PyArrayObject * a, PyObject * index) diff --git a/numpy/core/tests/test_indexing.py b/numpy/core/tests/test_indexing.py index 2dfaf6f3c..bb1341455 100644 --- a/numpy/core/tests/test_indexing.py +++ b/numpy/core/tests/test_indexing.py @@ -147,7 +147,7 @@ class TestIndexing(TestCase): def test_boolean_assignment_value_mismatch(self): # A boolean assignment should fail when the shape of the values - # cannot be broadcasted to the subscription. (see also gh-3458) + # cannot be broadcast to the subscription. (see also gh-3458) a = np.arange(4) def f(a, v): a[a > -1] = v @@ -188,12 +188,12 @@ class TestIndexing(TestCase): # If the strides are not reversed, the 0 in the arange comes last. assert_equal(a[0], 0) - # This also tests that the subspace buffer is initiliazed: + # This also tests that the subspace buffer is initialized: a = np.ones((5, 2)) c = np.arange(10).reshape(5, 2)[::-1] a[b, :] = c assert_equal(a[0], [0, 1]) - + def test_reversed_strides_result_allocation(self): # Test a bug when calculating the output strides for a result array # when the subspace size was 1 (and test other cases as well) @@ -346,7 +346,7 @@ class TestIndexing(TestCase): # Reference count of intp for index checks a = np.array([0]) refcount = sys.getrefcount(np.dtype(np.intp)) - # item setting always checks indices in seperate function: + # item setting always checks indices in separate function: a[np.array([0], dtype=np.intp)] = 1 a[np.array([0], dtype=np.uint8)] = 1 assert_raises(IndexError, a.__setitem__, @@ -537,11 +537,11 @@ class TestMultiIndexingAutomated(TestCase): These test use code to mimic the C-Code indexing for selection. NOTE: * This still lacks tests for complex item setting. - * If you change behavoir of indexing, you might want to modify + * If you change behavior of indexing, you might want to modify these tests to try more combinations. * Behavior was written to match numpy version 1.8. (though a first version matched 1.7.) - * Only tuple indicies are supported by the mimicing code. + * Only tuple indices are supported by the mimicking code. (and tested as of writing this) * Error types should match most of the time as long as there is only one error. For multiple errors, what gets raised @@ -564,7 +564,7 @@ class TestMultiIndexingAutomated(TestCase): slice(4, -1, -2), slice(None, None, -3), # Some Fancy indexes: - np.empty((0, 1, 1), dtype=np.intp), # empty broadcastable + np.empty((0, 1, 1), dtype=np.intp), # empty and can be broadcast np.array([0, 1, -2]), np.array([[2], [0], [1]]), np.array([[0, -1], [0, 1]], dtype=np.dtype('intp').newbyteorder()), @@ -611,7 +611,7 @@ class TestMultiIndexingAutomated(TestCase): fancy_dim = 0 # NOTE: This is a funny twist (and probably OK to change). # The boolean array has illegal indexes, but this is - # allowed if the broadcasted fancy-indices are 0-sized. + # allowed if the broadcast fancy-indices are 0-sized. # This variable is to catch that case. error_unless_broadcast_to_empty = False @@ -656,7 +656,7 @@ class TestMultiIndexingAutomated(TestCase): if arr.ndim - ndim < 0: # we can't take more dimensions then we have, not even for 0-d arrays. # since a[()] makes sense, but not a[(),]. We will raise an error - # lateron, unless a broadcasting error occurs first. + # later on, unless a broadcasting error occurs first. raise IndexError if ndim == 0 and not None in in_indices: @@ -668,7 +668,7 @@ class TestMultiIndexingAutomated(TestCase): for ax, indx in enumerate(in_indices): if isinstance(indx, slice): - # convert to an index array anways: + # convert to an index array indx = np.arange(*indx.indices(arr.shape[ax])) indices.append(['s', indx]) continue @@ -701,7 +701,7 @@ class TestMultiIndexingAutomated(TestCase): indx = flat_indx else: # This could be changed, a 0-d boolean index can - # make sense (even outide the 0-d indexed array case) + # make sense (even outside the 0-d indexed array case) # Note that originally this is could be interpreted as # integer in the full integer special case. raise IndexError @@ -753,7 +753,7 @@ class TestMultiIndexingAutomated(TestCase): arr = arr.transpose(*(fancy_axes + axes)) # We only have one 'f' index now and arr is transposed accordingly. - # Now handle newaxes by reshaping... + # Now handle newaxis by reshaping... ax = 0 for indx in indices: if indx[0] == 'f': @@ -771,7 +771,7 @@ class TestMultiIndexingAutomated(TestCase): res = np.broadcast(*indx[1:]) # raises ValueError... else: res = indx[1] - # unfortunatly the indices might be out of bounds. So check + # unfortunately the indices might be out of bounds. So check # that first, and use mode='wrap' then. However only if # there are any indices... if res.size != 0: @@ -909,7 +909,7 @@ class TestMultiIndexingAutomated(TestCase): # spot and the simple ones in one other spot. with warnings.catch_warnings(): # This is so that np.array(True) is not accepted in a full integer - # index, when running the file seperatly. + # index, when running the file separately. warnings.filterwarnings('error', '', DeprecationWarning) for simple_pos in [0, 2, 3]: tocheck = [self.fill_indices, self.complex_indices, diff --git a/numpy/core/tests/test_regression.py b/numpy/core/tests/test_regression.py index 316ee33d3..726f0efb9 100644 --- a/numpy/core/tests/test_regression.py +++ b/numpy/core/tests/test_regression.py @@ -181,7 +181,7 @@ class TestRegression(TestCase): assert_(np.all(b[yb] > 0.5)) def test_endian_where(self,level=rlevel): - """GitHuB issue #369""" + """GitHub issue #369""" net = np.zeros(3, dtype='>f4') net[1] = 0.00458849 net[2] = 0.605202 @@ -290,7 +290,7 @@ class TestRegression(TestCase): def test_tobytes_FORTRANORDER_discontiguous(self,level=rlevel): """Fix in r2836""" - # Create discontiguous Fortran-ordered array + # Create non-contiguous Fortran ordered array x = np.array(np.random.rand(3, 3), order='F')[:, :2] assert_array_almost_equal(x.ravel(), np.fromstring(x.tobytes())) @@ -311,7 +311,7 @@ class TestRegression(TestCase): self.assertRaises(ValueError, bfb) def test_nonarray_assignment(self): - # See also Issue gh-2870, test for nonarray assignment + # See also Issue gh-2870, test for non-array assignment # and equivalent unsafe casted array assignment a = np.arange(10) b = np.ones(10, dtype=bool) @@ -560,7 +560,7 @@ class TestRegression(TestCase): assert_(a.reshape(5, 1).strides[0] == 0) def test_reshape_zero_size(self, level=rlevel): - """Github Issue #2700, setting shape failed for 0-sized arrays""" + """GitHub Issue #2700, setting shape failed for 0-sized arrays""" a = np.ones((0, 2)) a.shape = (-1, 2) @@ -568,7 +568,7 @@ class TestRegression(TestCase): # With NPY_RELAXED_STRIDES_CHECKING the test becomes superfluous. @dec.skipif(np.ones(1).strides[0] == np.iinfo(np.intp).max) def test_reshape_trailing_ones_strides(self): - # Github issue gh-2949, bad strides for trailing ones of new shape + # GitHub issue gh-2949, bad strides for trailing ones of new shape a = np.zeros(12, dtype=np.int32)[::2] # not contiguous strides_c = (16, 8, 8, 8) strides_f = (8, 24, 48, 48) @@ -756,9 +756,9 @@ class TestRegression(TestCase): s = np.ones(10, dtype=float) x = np.array((15,), dtype=float) def ia(x, s, v): x[(s>0)]=v - # After removing deprecation, the following is are ValueErrors. + # After removing deprecation, the following are ValueErrors. # This might seem odd as compared to the value error below. This - # is due to the fact that the new code always use "nonzero" logic + # is due to the fact that the new code always uses "nonzero" logic # and the boolean special case is not taken. self.assertRaises(IndexError, ia, x, s, np.zeros(9, dtype=float)) self.assertRaises(IndexError, ia, x, s, np.zeros(11, dtype=float)) @@ -848,7 +848,7 @@ class TestRegression(TestCase): cnt0_b = cnt(b) cnt0_c = cnt(c) - # -- 0d -> 1d broadcasted slice assignment + # -- 0d -> 1-d broadcast slice assignment arr = np.zeros(5, dtype=np.object_) @@ -865,7 +865,7 @@ class TestRegression(TestCase): del arr - # -- 1d -> 2d broadcasted slice assignment + # -- 1-d -> 2-d broadcast slice assignment arr = np.zeros((5, 2), dtype=np.object_) arr0 = np.zeros(2, dtype=np.object_) @@ -884,7 +884,7 @@ class TestRegression(TestCase): del arr, arr0 - # -- 2d copying + flattening + # -- 2-d copying + flattening arr = np.zeros((5, 2), dtype=np.object_) @@ -1029,8 +1029,8 @@ class TestRegression(TestCase): b = np.zeros((2, 1), dtype = np.single) try: a.compress([True, False], axis = 1, out = b) - raise AssertionError("compress with an out which cannot be " \ - "safely casted should not return "\ + raise AssertionError("compress with an out which cannot be " + "safely casted should not return " "successfully") except TypeError: pass |