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MAINT, DOC: discard repeated words
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‎doc/source/dev/gitwash/development_setup.rst

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- the ``main`` branch you just cloned on your own machine
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- the ``main`` branch from your fork on GitHub, which git named
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``origin`` by default
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- the ``main`` branch on the the main NumPy repo, which you named
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- the ``main`` branch on the main NumPy repo, which you named
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``upstream``.
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::

‎doc/source/reference/c-api/iterator.rst

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may not be repeated. The following example is how normal broadcasting
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applies to a 3-D array, a 2-D array, a 1-D array and a scalar.
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**Note**: Before NumPy 1.8 ``oa_ndim == 0` was used for signalling that
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**Note**: Before NumPy 1.8 ``oa_ndim == 0` was used for signalling
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that ``op_axes`` and ``itershape`` are unused. This is deprecated and
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should be replaced with -1. Better backward compatibility may be
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achieved by using :c:func:`NpyIter_MultiNew` for this case.

‎doc/source/reference/c-api/ufunc.rst

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`numpy.dtype.num` (built-in only) that the corresponding
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function in the ``func`` array accepts. For instance, for a comparison
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ufunc with three ``ntypes``, two ``nin`` and one ``nout``, where the
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first function accepts `numpy.int32` and the the second
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first function accepts `numpy.int32` and the second
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`numpy.int64`, with both returning `numpy.bool_`, ``types`` would
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be ``(char[]) {5, 5, 0, 7, 7, 0}`` since ``NPY_INT32`` is 5,
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``NPY_INT64`` is 7, and ``NPY_BOOL`` is 0.

‎doc/source/reference/random/parallel.rst

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`~SeedSequence` avoids these problems by using successions of integer hashes
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with good `avalanche properties`_ to ensure that flipping any bit in the input
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input has about a 50% chance of flipping any bit in the output. Two input seeds
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that are very close to each other will produce initial states that are very far
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has about a 50% chance of flipping any bit in the output. Two input seeds that
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are very close to each other will produce initial states that are very far
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from each other (with very high probability). It is also constructed in such
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a way that you can provide arbitrary-sized integers or lists of integers.
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`~SeedSequence` will take all of the bits that you provide and mix them

‎doc/source/reference/swig.interface-file.rst

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* ``PyArrayObject* ary``, a NumPy array.
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Require the given ``PyArrayObject`` to to be Fortran ordered. If
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Require the given ``PyArrayObject`` to be Fortran ordered. If
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the ``PyArrayObject`` is already Fortran ordered, do nothing.
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Else, set the Fortran ordering flag and recompute the strides.
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‎doc/source/user/basics.subclassing.rst

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@@ -523,7 +523,7 @@ which inputs and outputs it converted. Hence, e.g.,
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>>> a.info
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{'inputs': [0, 1], 'outputs': [0]}
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Note that another approach would be to to use ``getattr(ufunc,
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Note that another approach would be to use ``getattr(ufunc,
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methods)(*inputs, **kwargs)`` instead of the ``super`` call. For this example,
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the result would be identical, but there is a difference if another operand
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also defines ``__array_ufunc__``. E.g., lets assume that we evalulate

‎doc/source/user/building.rst

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@@ -341,7 +341,7 @@ intended host and not the build system, set::
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where ``${ARCH_TRIPLET}`` is an architecture-dependent suffix appropriate for
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the host architecture. (This should be the name of a ``_sysconfigdata`` file,
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without the ``.py`` extension, found in in the host Python library directory.)
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without the ``.py`` extension, found in the host Python library directory.)
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When using external linear algebra libraries, include and library directories
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should be provided for the desired libraries in ``site.cfg`` as described

‎doc/source/user/c-info.how-to-extend.rst

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========= F42D =========
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The second argument passed in to the Py_InitModule function is a
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structure that makes it easy to to define functions in the module. In
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structure that makes it easy to define functions in the module. In
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the example given above, the mymethods structure would have been
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defined earlier in the file (usually right before the init{name}
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subroutine) to:

‎numpy/core/_add_newdocs.py

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dtype : data-type code, optional
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The data-type used to represent the intermediate results. Defaults
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to the data-type of the output array if such is provided, or the
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the data-type of the input array if no output array is provided.
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data-type of the input array if no output array is provided.
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out : ndarray, None, or tuple of ndarray and None, optional
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A location into which the result is stored. If not provided or None,
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a freshly-allocated array is returned. For consistency with

‎numpy/core/numeric.py

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"""
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res = empty_like(a, dtype=dtype, order=order, subok=subok, shape=shape)
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# needed instead of a 0 to get same result as zeros for for string dtypes
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# needed instead of a 0 to get same result as zeros for string dtypes
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z = zeros(1, dtype=res.dtype)
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multiarray.copyto(res, z, casting='unsafe')
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return res

‎numpy/core/src/multiarray/array_coercion.c

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*
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* The code here avoid multiple conversion of array-like objects (including
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* sequences). These objects are cached after conversion, which will require
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* additional memory, but can drastically speed up coercion from from array
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* like objects.
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* additional memory, but can drastically speed up coercion from array like
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* objects.
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*/
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‎numpy/core/src/multiarray/common.c

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/*
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* This function is now identical to the new PyArray_DiscoverDTypeAndShape
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* but only returns the the dtype. It should in most cases be slowly phased
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* out. (Which may need some refactoring to PyArray_FromAny to make it simpler)
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* but only returns the dtype. It should in most cases be slowly phased out.
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* (Which may need some refactoring to PyArray_FromAny to make it simpler)
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*/
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NPY_NO_EXPORT int
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PyArray_DTypeFromObject(PyObject *obj, int maxdims, PyArray_Descr **out_dtype)

‎numpy/core/src/multiarray/convert_datatype.c

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/* Any object object is simple (could even use the default) */
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/* Any object is simple (could even use the default) */
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static NPY_CASTING
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any_to_object_resolve_descriptors(
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PyArrayMethodObject *NPY_UNUSED(self),

‎numpy/core/src/multiarray/ctors.c

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* Thus, we check if there is an array included, in that case we
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* give a FutureWarning.
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* When the warning is removed, PyArray_Pack will have to ensure
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* that that it does not append the dimensions when creating the
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* subarrays to assign `arr[0] = obj[0]`.
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* that it does not append the dimensions when creating the subarrays
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* to assign `arr[0] = obj[0]`.
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*/
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int includes_array = 0;
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if (cache != NULL) {

‎numpy/core/src/multiarray/dtype_transfer.c

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* For casts between two dtypes with the same type (within DType casts)
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* it also wraps the `copyswapn` function.
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*
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* This function is called called from `ArrayMethod.get_loop()` when a
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* specialized cast function is missing.
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* This function is called from `ArrayMethod.get_loop()` when a specialized
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* cast function is missing.
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*
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* In general, the legacy cast functions do not support unaligned access,
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* so an ArrayMethod using this must signal that. In a few places we do

‎numpy/core/src/multiarray/nditer_constr.c

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}
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/*
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* Prepares a a constructor operand. Assumes a reference to 'op'
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* Prepares a constructor operand. Assumes a reference to 'op'
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* is owned, and that 'op' may be replaced. Fills in 'op_dataptr',
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* 'op_dtype', and may modify 'op_itflags'.
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*

‎numpy/core/src/npymath/npy_math_complex.c.src

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if (ax < SQRT_3_EPSILON / 2 && ay < SQRT_3_EPSILON / 2) {
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/*
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* z = 0 was filtered out above. All other cases must raise
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* inexact, but this is the only only that needs to do it
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* inexact, but this is the only one that needs to do it
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* explicitly.
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*/
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raise_inexact();

‎numpy/core/src/umath/dispatching.c

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PyUFunc_AddLoop(PyUFuncObject *ufunc, PyObject *info, int ignore_duplicate)
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{
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/*
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* Validate the info object, this should likely move to to a different
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* Validate the info object, this should likely move to a different
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* entry-point in the future (and is mostly unnecessary currently).
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*/
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if (!PyTuple_CheckExact(info) || PyTuple_GET_SIZE(info) != 2) {

‎numpy/core/src/umath/ufunc_type_resolution.c

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/* Usually a failure, but let the the default version handle it */
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/* Usually a failure, but let the default version handle it */
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operands, type_tup, out_dtypes);
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}

‎numpy/core/tests/test_indexing.py

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class TestArrayToIndexDeprecation:
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"""Creating an index from array not 0-D is an error.
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"""
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def test_array_to_index_error(self):

‎numpy/doc/ufuncs.py

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>>> np.add.reduce(np.arange(10).reshape(2,5),axis=1)
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**.accumulate(arr)** applies the binary operator and generates an an
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**.accumulate(arr)** applies the binary operator and generates an
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equivalently shaped array that includes the accumulated amount for each
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‎numpy/lib/function_base.py

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Parameters
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----------
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x : ndarray
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Array (possibly multi-dimensional) of values for which to to
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calculate ``sinc(x)``.
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Array (possibly multi-dimensional) of values for which to calculate
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``sinc(x)``.
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‎numpy/lib/histograms.py

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with non-normal datasets.
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'scott'
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Less robust estimator that that takes into account data
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variability and data size.
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Less robust estimator that takes into account data variability
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and data size.
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‎numpy/lib/nanfunctions.py

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Compute a/b ignoring invalid results. If `a` is an array the division
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is done in place. If `a` is a scalar, then its type is preserved in the
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output. If out is None, then then a is used instead so that the
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division is in place. Note that this is only called with `a` an inexact
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type.
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output. If out is None, then a is used instead so that the division
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is in place. Note that this is only called with `a` an inexact type.
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‎numpy/polynomial/chebyshev.py

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If `x` is a list or tuple, it is converted to an ndarray, otherwise
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it is left unchanged and treated as a scalar. In either case, `x`
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or its elements must support addition and multiplication with
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with themselves and with the elements of `c`.
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themselves and with the elements of `c`.
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c : array_like
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Array of coefficients ordered so that the coefficients for terms of
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degree n are contained in c[n]. If `c` is multidimensional the

‎numpy/polynomial/hermite.py

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it is left unchanged and treated as a scalar. In either case, `x`
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or its elements must support addition and multiplication with
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with themselves and with the elements of `c`.
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themselves and with the elements of `c`.
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c : array_like
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Array of coefficients ordered so that the coefficients for terms of
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‎numpy/polynomial/laguerre.py

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it is left unchanged and treated as a scalar. In either case, `x`
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or its elements must support addition and multiplication with
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with themselves and with the elements of `c`.
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themselves and with the elements of `c`.
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‎numpy/polynomial/legendre.py

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it is left unchanged and treated as a scalar. In either case, `x`
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with themselves and with the elements of `c`.
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themselves and with the elements of `c`.
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‎numpy/random/_common.pyx

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‎numpy/random/_examples/cython/extending.pyx

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# is only necessary if the GIL is also released, as in this example.
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‎numpy/random/bit_generator.pyx

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This method directly exposes the the raw underlying pseudo-random
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‎numpy/random/tests/test_generator_mt19937.py

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# the sha256 hashes of the initial and the final states' keys and
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# the position of the initial and the final state.
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‎numpy/testing/_private/parameterized.py

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"""
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tl;dr: all code code is licensed under simplified BSD, unless stated otherwise.
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tl;dr: all code is licensed under simplified BSD, unless stated otherwise.
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Unless stated otherwise in the source files, all code is copyright 2010 David
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Wolever <david@wolever.net>. All rights reserved.

‎tools/swig/README

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Testing
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-------
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The tests are a good example of what we are trying to do with numpy.i.
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The files related to testing are are in the test subdirectory::
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The files related to testing are in the test subdirectory::
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Vector.h
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Vector.cxx

‎tools/swig/numpy.i

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return success;
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/* Require the given PyArrayObject to to be Fortran ordered. If the
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/* Require the given PyArrayObject to be Fortran ordered. If the
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* the PyArrayObject is already Fortran ordered, do nothing. Else,
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* set the Fortran ordering flag and recompute the strides.
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*/

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