MAINT: Spellcheck some files.

charris · charris · commit d4a4e99b6972 · 2014-07-06T10:48:49.000-06:00
Fix spelling and grammar in

numpy/core/src/multiarray/mapping.c
numpy/core/tests/test_indexing.py
numpy/core/tests/test_regression.py
diff --git 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
@@ -147,7 +147,7 @@ def test_boolean_indexing_onedim(self):
 
     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 @@ def test_reverse_strides_and_subspace_bufferinit(self):
         # 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 @@ def test_small_regressions(self):
         # 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 @@ def setUp(self):
             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 @@ def _get_multi_index(self, arr, indices):
         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 @@ def _get_multi_index(self, arr, indices):
         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 @@ def _get_multi_index(self, arr, indices):
 
         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 @@ def _get_multi_index(self, arr, indices):
                     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 @@ def _get_multi_index(self, arr, indices):
             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 @@ def _get_multi_index(self, arr, indices):
                     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 @@ def test_multidim(self):
         # 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
@@ -181,7 +181,7 @@ def test_endian_bool_indexing(self,level=rlevel):
         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 @@ def test_unicode_string_comparison(self,level=rlevel):
 
     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 @@ def bfb(): x[:] = np.arange(3, dtype=float)
         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)
@@ -560,15 +560,15 @@ def test_reshape_zero_strides(self, level=rlevel):
         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)
 
     # Cannot test if NPY_RELAXED_STRIDES_CHECKING changes the strides.
     # 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 @@ def test_bool_indexing_invalid_nr_elements(self, level=rlevel):
         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 @@ def test_object_array_refcounting(self, level=rlevel):
         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 @@ def test_object_array_refcounting(self, level=rlevel):
 
         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 @@ def test_object_array_refcounting(self, level=rlevel):
 
         del arr, arr0
 
-        # -- 2d copying + flattening
+        # -- 2-d copying + flattening
 
         arr  = np.zeros((5, 2), dtype=np.object_)
 
@@ -1029,8 +1029,8 @@ def test_compress_small_type(self, level=rlevel):
         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
