8000 Merge pull request #6035 from charris/documentation-maintenance · njsmith/numpy@7a3926c · GitHub
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Merge pull request numpy#6035 from charris/documentation-maintenance
Documentation fixes.
2 parents 61d2a44 + f5e9adb commit 7a3926c

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‎doc/source/reference/arrays.classes.rst

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@@ -147,14 +147,14 @@ Numpy provides several hooks that classes can customize:
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The value of this attribute is used to determine what type of
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object to return in situations where there is more than one
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possibility for the Python type of the returned object. Subclasses
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inherit a default value of 1.0 for this attribute.
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inherit a default value of 0.0 for this attribute.
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.. function:: class.__array__([dtype])
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If a class (ndarray subclass or not) having the :func:`__array__`
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method is used as the output object of an :ref:`ufunc
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<ufuncs.output-type>`, results will be written to the object
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returned by :func:`__array__`. Similar conversion is done on
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returned by :func:`__array__`. Similar conversion is done on
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input arrays.
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‎doc/source/reference/arrays.interface.rst

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@@ -12,7 +12,7 @@ The Array Interface
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This page describes the numpy-specific API for accessing the contents of
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a numpy array from other C extensions. :pep:`3118` --
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:cfunc:`The Revised Buffer Protocol <PyObject_GetBuffer>` introduces
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:c:func:`The Revised Buffer Protocol <PyObject_GetBuffer>` introduces
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similar, standardized API to Python 2.6 and 3.0 for any extension
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module to use. Cython__'s buffer array support
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uses the :pep:`3118` API; see the `Cython numpy
@@ -67,7 +67,7 @@ This approach to the interface consists of the object having an
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could hold (a Python int is a C long). It is up to the code
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using this attribute to handle this appropriately; either by
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raising an error when overflow is possible, or by using
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:cdata:`Py_LONG_LONG` as the C type for the shapes.
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:c:data:`Py_LONG_LONG` as the C type for the shapes.
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**typestr** (required)
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``u`` Unsigned integer
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``f`` Floating point
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``c`` Complex floating point
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``O`` Object (i.e. the memory contains a pointer to :ctype:`PyObject`)
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``O`` Object (i.e. the memory contains a pointer to :c:type:`PyObject`)
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``S`` String (fixed-length sequence of char)
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``U`` Unicode (fixed-length sequence of :ctype:`Py_UNICODE`)
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``U`` Unicode (fixed-length sequence of :c:type:`Py_UNICODE`)
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``V`` Other (void \* -- each item is a fixed-size chunk of memory)
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===== ================================================================
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@@ -134,7 +134,7 @@ This approach to the interface consists of the object having an
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means the data area is read-only).
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This attribute can also be an object exposing the
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:cfunc:`buffer interface <PyObject_AsCharBuffer>` which
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:c:func:`buffer interface <PyObject_AsCharBuffer>` which
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will be used to share the data. If this key is not present (or
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returns :class:`None`), then memory sharing will be done
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through the buffer interface of the object itself. In this
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:const:`int` or :const:`long`). As with shape, the values may
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be larger than can be represented by a C "int" or "long"; the
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calling code should handle this appropiately, either by
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raising an error, or by using :ctype:`Py_LONG_LONG` in C. The
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raising an error, or by using :c:type:`Py_LONG_LONG` in C. The
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default is :const:`None` which implies a C-style contiguous
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memory buffer. In this model, the last dimension of the array
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varies the fastest. For example, the default strides tuple
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This approach to the array interface allows for faster access to an
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array using only one attribute lookup and a well-defined C-structure.
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.. cvar:: __array_struct__
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.. c:var:: __array_struct__
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A :ctype:`PyCObject` whose :cdata:`voidptr` member contains a
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pointer to a filled :ctype:`PyArrayInterface` structure. Memory
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for the structure is dynamically created and the :ctype:`PyCObject`
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A :c:type: `PyCObject` whose :c:data:`voidptr` member contains a
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pointer to a filled :c:type:`PyArrayInterface` structure. Memory
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for the structure is dynamically created and the :c:type:`PyCObject`
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is also created with an appropriate destructor so the retriever of
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this attribute simply has to apply :cfunc:`Py_DECREF()` to the
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this attribute simply has to apply :c:func:`Py_DECREF()` to the
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object returned by this attribute when it is finished. Also,
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either the data needs to be copied out, or a reference to the
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object exposing this attribute must be held to ensure the data is
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.. admonition:: New since June 16, 2006:
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In the past most implementations used the "desc" member of the
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:ctype:`PyCObject` itself (do not confuse this with the "descr" member of
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the :ctype:`PyArrayInterface` structure above --- they are two separate
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:c:type:`PyCObject` itself (do not confuse this with the "descr" member of
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the :c:type:`PyArrayInterface` structure above --- they are two separate
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things) to hold the pointer to the object exposing the interface.
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This is now an explicit part of the interface. Be sure to own a
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reference to the object when the :ctype:`PyCObject` is created using
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:ctype:`PyCObject_FromVoidPtrAndDesc`.
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reference to the object when the :c:type:`PyCObject` is created using
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:c:type:`PyCObject_FromVoidPtrAndDesc`.
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Type description examples

‎doc/source/reference/arrays.ndarray.rst

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@@ -133,13 +133,14 @@ memory block can be accessed by some combination of the indices.
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While a C-style and Fortran-style contiguous array, which has the corresponding
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flags set, can be addressed with the above strides, the actual strides may be
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different. This can happen in two cases:
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1. If ``self.shape[k] == 1`` then for any legal index ``index[k] == 0``.
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This means that in the formula for the offset
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:math:`n_k = 0` and thus :math:`s_k n_k = 0` and the value of
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:math:`s_k` `= self.strides[k]` is arbitrary.
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2. If an array has no elements (``self.size == 0``) there is no legal index
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and the strides are never used. Any array with no elements may be
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considered C-style and Fortran-style contiguous.
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1. If ``self.shape[k] == 1`` then for any legal index ``index[k] == 0``.
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This means that in the formula for the offset :math:`n_k = 0` and thus
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:math:`s_k n_k = 0` and the value of :math:`s_k` `= self.strides[k]` is
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arbitrary.
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2. If an array has no elements (``self.size == 0``) there is no legal
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index and the strides are never used. Any array with no elements may be
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considered C-style and Fortran-style contiguous.
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Point 1. means that ``self``and ``self.squeeze()`` always have the same
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contiguity and :term:`aligned` flags value. This also means that even a high
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ndarray.imag
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ndarray.flat
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ndarray.ctypes
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__array_priority__
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.. _arrays.ndarray.array-interface:
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ndarray.item
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ndarray.tolist
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ndarray.itemset
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ndarray.setasflat
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ndarray.tostring
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ndarray.tobytes
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ndarray.tofile

‎doc/source/reference/arrays.scalars.rst

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@@ -65,15 +65,15 @@ Some of the scalar types are essentially equivalent to fundamental
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Python types and therefore inherit from them as well as from the
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generic array scalar type:
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==================== ====================
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==================== ================================
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Array scalar type Related Python type
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==================== ====================
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==================== ================================
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:class:`int_` :class:`IntType` (Python 2 only)
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:class:`float_` :class:`FloatType`
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:class:`complex_` :class:`ComplexType`
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:class:`str_` :class:`StringType`
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:class:`unicode_` :class:`UnicodeType`
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==================== ====================
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==================== ================================
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The :class:`bool_` data type is very similar to the Python
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:class:`BooleanType` but does not inherit from it because Python's
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==========
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The array scalar objects have an :obj:`array priority
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<__array_priority__>` of :cdata:`NPY_SCALAR_PRIORITY`
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<__array_priority__>` of :c:data:`NPY_SCALAR_PRIORITY`
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(-1,000,000.0). They also do not (yet) have a :attr:`ctypes <ndarray.ctypes>`
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attribute. Otherwise, they share the same attributes as arrays:
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