Jun 8, 2014 · Jul 7, 2014 · Jul 16, 2014 · Jul 8, 2014 · Jul 10, 2014 · Jul 15, 2014
diff --git a/.gitattributes b/.gitattributes
 * text=auto
 tools/win32build/nsis_scripts/*.nsi.in eol=crlf

 # Numerical data files
 numpy/lib/tests/data/*.npy binary
diff --git a/.gitignore b/.gitignore
 *.tmp
 *.vim
 tags
 cscope.out
 # gnu global
 GPATH
 GRTAGS
 GSYMS
 GTAGS

 # Compiled source #
 ###################
 numpy/core/src/private/scalarmathmodule.h
 numpy/core/src/scalarmathmodule.c
 numpy/core/src/umath/funcs.inc
 numpy/core/src/umath/loops.c
 numpy/core/src/umath/loops.[ch]
 numpy/core/src/umath/operand_flag_tests.c
 numpy/core/src/umath/simd.inc
 numpy/core/src/umath/struct_ufunc_test.c
diff --git a/.travis.yml b/.travis.yml
    - python: 3.2
      env: USE_DEBUG=1
    - python: 2.7
      env: NPY_SEPARATE_COMPILATION=0
      env: NPY_SEPARATE_COMPILATION=0 PYTHON_OO=1
    - python: 3.3
      env: NPY_SEPARATE_COMPILATION=0
    - python: 2.7
  - ulimit -a
  - mkdir builds
  - pushd builds
  # Build into own virtualenv
  # We therefore control our own environment, avoid travis' numpy
  - virtualenv --python=python venv
  - source venv/bin/activate
  - pip install nose
  # pip install coverage
  - python -V
diff --git a/doc/HOWTO_DOCUMENT.rst.txt b/doc/HOWTO_DOCUMENT.rst.txt

 Overview
 --------
 In general, we follow the standard Python style conventions as described here:
 * `Style Guide for C Code <http://www.python.org/peps/pep-0007.html>`_
 * `Style Guide for Python Code <http://www.python.org/peps/pep-0008.html>`_
 * `Docstring Conventions <http://www.python.org/peps/pep-0257.html>`_
 We mostly follow the standard Python style conventions as described here:
 * `Style Guide for C Code <http://python.org/dev/peps/pep-0007/>`_
 * `Style Guide for Python Code <http://python.org/dev/peps/pep-0008/>`_
 * `Docstring Conventions <http://python.org/dev/peps/pep-0257/>`_

 Additional PEPs of interest regarding documentation of code:
 * `Docstring Processing Framework <http://www.python.org/peps/pep-0256.html>`_
 * `Docutils Design Specification <http://www.python.org/peps/pep-0258.html>`_
 * `Docstring Processing Framework <http://python.org/dev/peps/pep-0256/>`_
 * `Docutils Design Specification <http://python.org/dev/peps/pep-0258/>`_

 Use a code checker:
 * `pylint <http://www.logilab.org/857>`_
diff --git a/doc/release/1.8.2-notes.rst b/doc/release/1.8.2-notes.rst
 NumPy 1.8.2 Release Notes
 *************************

 This is a bugfix only release in the 1.8.x series.

 Issues fixed
 ============

 * gh-4836: partition produces wrong results for multiple selections in equal ranges
 * gh-4656: Make fftpack._raw_fft threadsafe
 * gh-4628: incorrect argument order to _copyto in in np.nanmax, np.nanmin
 * gh-4642: Hold GIL for converting dtypes types with fields
 * gh-4733: fix np.linalg.svd(b, compute_uv=False)
 * gh-4853: avoid unaligned simd load on reductions on i386
 * gh-4722: Fix seg fault converting empty string to object
 * gh-4613: Fix lack of NULL check in array_richcompare
 * gh-4774: avoid unaligned access for strided byteswap
 * gh-650: Prevent division by zero when creating arrays from some buffers
 * gh-4602: ifort has issues with optimization flag O2, use O1
diff --git a/doc/release/1.9.0-notes.rst b/doc/release/1.9.0-notes.rst

 Highlights
 ==========
 * Addition of `__numpy_ufunc__` to allow overriding ufuncs in ndarray
  subclasses.
 * Numerous performance improvements in various areas, most notably indexing and
  operations on small arrays are significantly faster.
  Indexing operations now also release the GIL.
 * String version checks will break because, e.g., '1.9' > '1.10' is True. A
  NumpyVersion class has been added that can be used for such comparisons.
 * The diagonal and diag functions will return writeable views in 1.10.0
 * The `S` and/or `a` dtypes may be changed to represent Python strings
  instead of bytes, in Python 3 these two types are very different.


 Compatibility notes

 * Indexing with more then one ellipsis (``...``) is deprecated.

 Non-integer reduction axis indexes are deprecated
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 Non-integer axis indexes to reduction ufuncs like `add.reduce` or `sum` are
 deprecated.

 ``promote_types`` and string dtype
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 ``promote_types`` function now returns a valid string length when given an

 This change was already applied to the 1.8.1 release.

 ``zeros_like`` for string dtypes now returns empty strings
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 To match the `zeros` function `zeros_like` now returns an array initialized
 with empty strings instead of an array filled with `'0'`.


 New Features
 ============
 list of axes for the reduction. The ``keepdims`` keyword argument was also
 added to allow convenient broadcasting to arrays of the original shape.

 Ufunc and Dot Overrides
 ~~~~~~~~~~~~~~~~~~~~~~~
 For better compatibility with external objects you can now override
 universal functions (ufuncs), ``numpy.core._dotblas.dot``, and
 ``numpy.core.multiarray.dot`` (the numpy.dot functions). By defining a
 ``__numpy_ufunc__`` method.

 Dtype parameter added to ``np.linspace`` and ``np.logspace``
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 The returned data type from the ``linspace`` and ``logspace`` functions can
diff --git a/doc/release/1.9.1-notes.rst b/doc/release/1.9.1-notes.rst
 NumPy 1.9.1 Release Notes
 *************************

 This is a bugfix only release in the 1.9.x series.

 Issues fixed
 ============

 * gh-5184: restore linear edge behaviour of gradient to as it was in < 1.9.
  The second order behaviour is available via the `edge_order` keyword
 * gh-4007: workaround Accelerate sgemv crash on OSX 10.9
 * gh-5100: restore object dtype inference from iterable objects without `len()`
 * gh-5163: avoid gcc-4.1.2 (red hat 5) miscompilation causing a crash
 * gh-5138: fix nanmedian on arrays containing inf
 * gh-5240: fix not returning out array from ufuncs with subok=False set
 * gh-5203: copy inherited masks in MaskedArray.__array_finalize__
 * gh-2317: genfromtxt did not handle filling_values=0 correctly
 * gh-5067: restore api of npy_PyFile_DupClose in python2
 * gh-5063: cannot convert invalid sequence index to tuple
 * gh-5082: Segmentation fault with argmin() on unicode arrays
 * gh-5095: don't propagate subtypes from np.where
 * gh-5104: np.inner segfaults with SciPy's sparse matrices
 * gh-5251: Issue with fromarrays not using correct format for unicode arrays
 * gh-5136: Import dummy_threading if importing threading fails
 * gh-5148: Make numpy import when run with Python flag '-OO'
 * gh-5147: Einsum double contraction in particular order causes ValueError
 * gh-479: Make f2py work with intent(in out)
 * gh-5170: Make python2 .npy files readable in python3
 * gh-5027: Use 'll' as the default length specifier for long long
 * gh-4896: fix build error with MSVC 2013 caused by C99 complex support
 * gh-4465: Make PyArray_PutTo respect writeable flag
 * gh-5225: fix crash when using arange on datetime without dtype set
 * gh-5231: fix build in c99 mode
diff --git a/doc/release/1.9.2-notes.rst b/doc/release/1.9.2-notes.rst
 NumPy 1.9.2 Release Notes
 *************************

 This is a bugfix only release in the 1.9.x series.

 Issues fixed
 ============

 * `#5316 <https://github.com/numpy/numpy/issues/5316>`__: fix too large dtype alignment of strings and complex types
 * `#5424 <https://github.com/numpy/numpy/issues/5424>`__: fix ma.median when used on ndarrays
 * `#5481 <https://github.com/numpy/numpy/issues/5481>`__: Fix astype for structured array fields of different byte order
 * `#5354 <https://github.com/numpy/numpy/issues/5354>`__: fix segfault when clipping complex arrays
 * `#5524 <https://github.com/numpy/numpy/issues/5524>`__: allow np.argpartition on non ndarrays
 * `#5612 <https://github.com/numpy/numpy/issues/5612>`__: Fixes ndarray.fill to accept full range of uint64
 * `#5155 <https://github.com/numpy/numpy/issues/5155>`__: Fix loadtxt with comments=None and a string None data
 * `#4476 <https://github.com/numpy/numpy/issues/4476>`__: Masked array view fails if structured dtype has datetime component
 * `#5388 <https://github.com/numpy/numpy/issues/5388>`__: Make RandomState.set_state and RandomState.get_state threadsafe
 * `#5390 <https://github.com/numpy/numpy/issues/5390>`__: make seed, randint and shuffle threadsafe
 * `#5374 <https://github.com/numpy/numpy/issues/5374>`__: Fixed incorrect assert_array_almost_equal_nulp documentation
 * `#5393 <https://github.com/numpy/numpy/issues/5393>`__: Add support for ATLAS > 3.9.33.
 * `#5313 <https://github.com/numpy/numpy/issues/5313>`__: PyArray_AsCArray caused segfault for 3d arrays
 * `#5492 <https://github.com/numpy/numpy/issues/5492>`__: handle out of memory in rfftf
 * `#4181 <https://github.com/numpy/numpy/issues/4181>`__: fix a few bugs in the random.pareto docstring
 * `#5359 <https://github.com/numpy/numpy/issues/5359>`__: minor changes to linspace docstring
 * `#4723 <https://github.com/numpy/numpy/issues/4723>`__: fix a compile issues on AIX
diff --git a/doc/release/1.9.3-notes.rst b/doc/release/1.9.3-notes.rst
 NumPy 1.9.3 Release Notes
 *************************

 This is a bugfix only release in the 1.9.x series.

 The only changes from 1.9.2 are a fix for reading gzipped text files on Python
 3.5 and some build fixes.

 Issues fixed
 ============

 * `#5866 <https://github.com/numpy/numpy/pull/5866>`__: fix error finding
  Python headers when ``build_ext`` ``--include-dirs`` is set;
 * `#6016 <https://github.com/numpy/numpy/pull/6016>`__: fix ``np.loadtxt``
  error on Python 3.5 when reading from gzip files;
 * `#5555 <https://github.com/numpy/numpy/pull/5555>`__: Replace deprecated
  options for ifort;
 * `#6096 <https://github.com/numpy/numpy/pull/6096>`__: remove /GL for VS2015
  in check_long_double_representation;
 * `#6141 <https://github.com/numpy/numpy/pull/6141>`__: enable Visual Studio
  2015 C99 features;
 * `#6171 <https://github.com/numpy/numpy/pull/6171>`__: revert C99 complex for
  MSVC14.
diff --git a/doc/source/reference/arrays.classes.rst b/doc/source/reference/arrays.classes.rst

 Numpy provides several hooks that classes can customize:

 .. function:: class.__numpy_ufunc__(self, ufunc, method, i, inputs, **kwargs)

   .. versionadded:: 1.9

   Any class (ndarray subclass or not) can define this method to
   override behavior of Numpy's ufuncs. This works quite similarly to
   Python's ``__mul__`` and other binary operation routines.

   - *ufunc* is the ufunc object that was called.
   - *method* is a string indicating which Ufunc method was called
     (one of ``"__call__"``, ``"reduce"``, ``"reduceat"``,
     ``"accumulate"``, ``"outer"``, ``"inner"``).
   - *i* is the index of *self* in *inputs*.
   - *inputs* is a tuple of the input arguments to the ``ufunc``
   - *kwargs* is a dictionary containing the optional input arguments
     of the ufunc. The ``out`` argument is always contained in
     *kwargs*, if given. See the discussion in :ref:`ufuncs` for
     details.

   The method should return either the result of the operation, or
   :obj:`NotImplemented` if the operation requested is not
   implemented.

   If one of the arguments has a :func:`__numpy_ufunc__` method, it is
   executed *instead* of the ufunc.  If more than one of the input
   arguments implements :func:`__numpy_ufunc__`, they are tried in the
   order: subclasses before superclasses, otherwise left to right. The
   first routine returning something else than :obj:`NotImplemented`
   determines the result. If all of the :func:`__numpy_ufunc__`
   operations return :obj:`NotImplemented`, a :exc:`TypeError` is
   raised.

   If an :class:`ndarray` subclass defines the :func:`__numpy_ufunc__`
   method, this disables the :func:`__array_wrap__`,
   :func:`__array_prepare__`, :data:`__array_priority__` mechanism
   described below.

   .. note:: In addition to ufuncs, :func:`__numpy_ufunc__` also
      overrides the behavior of :func:`numpy.dot` even though it is
      not an Ufunc.

   .. note:: If you also define right-hand binary operator override
      methods (such as ``__rmul__``) or comparison operations (such as
      ``__gt__``) in your class, they take precedence over the
      :func:`__numpy_ufunc__` mechanism when resolving results of
      binary operations (such as ``ndarray_obj * your_obj``).

      The technical special case is: ``ndarray.__mul__`` returns
      ``NotImplemented`` if the other object is *not* a subclass of
      :class:`ndarray`, and defines both ``__numpy_ufunc__`` and
      ``__rmul__``. Similar exception applies for the other operations
      than multiplication.

      In such a case, when computing a binary operation such as
      ``ndarray_obj * your_obj``, your ``__numpy_ufunc__`` method
      *will not* be called.  Instead, the execution passes on to your
      right-hand ``__rmul__`` operation, as per standard Python
      operator override rules.

      Similar special case applies to *in-place operations*: If you
      define ``__rmul__``, then ``ndarray_obj *= your_obj`` *will not*
      call your ``__numpy_ufunc__`` implementation. Instead, the
      default Python behavior ``ndarray_obj = ndarray_obj * your_obj``
      occurs.

      Note that the above discussion applies only to Python's builtin
      binary operation mechanism. ``np.multiply(ndarray_obj,
      your_obj)`` always calls only your ``__numpy_ufunc__``, as
      expected.

 .. function:: class.__array_finalize__(self)

   This method is called whenever the system internally allocates a
diff --git a/doc/source/reference/c-api.types-and-structures.rst b/doc/source/reference/c-api.types-and-structures.rst
        Indicates that items of this data-type must be reference
        counted (using :cfunc:`Py_INCREF` and :cfunc:`Py_DECREF` ).

    .. cvar::NPY_ITEM_LISTPICKLE
    .. cvar::NPY_LIST_PICKLE

        Indicates arrays of this data-type must be converted to a list
        before pickling.
diff --git a/doc/source/reference/routines.array-manipulation.rst b/doc/source/reference/routines.array-manipulation.rst
   asmatrix
   asfarray
   asfortranarray
   ascontiguousarray
   asarray_chkfinite
   asscalar
   require

diff --git a/doc/source/reference/routines.ma.rst b/doc/source/reference/routines.ma.rst
   ma.nonzero
   ma.shape
   ma.size
   ma.is_masked
   ma.is_mask

   ma.MaskedArray.data
   ma.MaskedArray.mask

   ma.column_stack
   ma.concatenate
   ma.append
   ma.dstack
   ma.hstack
   ma.vstack
diff --git a/doc/source/reference/routines.maskna.rst b/doc/source/reference/routines.maskna.rst
diff --git a/doc/source/reference/routines.polynomials.classes.rst b/doc/source/reference/routines.polynomials.classes.rst
 In the first case the lower bound of the integration is set to -1 and the
 integration constant is 0. In the second the constant of integration is set
 to 1 as well. Differentiation is simpler since the only option is the
 number times the polynomial is differentiated::
 numberoftimes the polynomial is differentiated::

    >>> p = P([1, 2, 3])
    >>> p.deriv(1)
    >>> import matplotlib.pyplot as plt
    >>> from numpy.polynomial import Chebyshev as T
    >>> x = np.linspace(-1, 1, 100)
    >>> for i in range(6): ax = plt.plot(x, T.basis(i)(x), lw=2, label="T_%d"%i)
    >>> for i in range(6): ax = plt.plot(x, T.basis(i)(x), lw=2, label="$T_%d$"%i)
    ...
    >>> plt.legend(loc="upper left")
    <matplotlib.legend.Legend object at 0x3b3ee10>
    >>> import matplotlib.pyplot as plt
    >>> from numpy.polynomial import Chebyshev as T
    >>> x = np.linspace(-2, 2, 100)
    >>> for i in range(6): ax = plt.plot(x, T.basis(i)(x), lw=2, label="T_%d"%i)
    >>> for i in range(6): ax = plt.plot(x, T.basis(i)(x), lw=2, label="$T_%d$"%i)
    ...
    >>> plt.legend(loc="lower right")
    <matplotlib.legend.Legend object at 0x3b3ee10>
diff --git a/doc/source/release.rst b/doc/source/release.rst
 Release Notes
 *************

 .. include:: ../release/1.9.2-notes.rst
 .. include:: ../release/1.9.1-notes.rst
 .. include:: ../release/1.9.0-notes.rst
 .. include:: ../release/1.8.2-notes.rst
 .. include:: ../release/1.8.1-notes.rst
 .. include:: ../release/1.8.0-notes.rst
 .. include:: ../release/1.7.2-notes.rst
 .. include:: ../release/1.7.1-notes.rst
Original file line number	Diff line number	Diff line change
		@@ -1,2 +1,5 @@
		* text=auto
		tools/win32build/nsis_scripts/*.nsi.in eol=crlf

		# Numerical data files
		numpy/lib/tests/data/*.npy binary
Original file line number	Diff line number	Diff line change
Expand Up		@@ -15,6 +15,12 @@
		*.tmp
		*.vim
		tags
		cscope.out
		# gnu global
		GPATH
		GRTAGS
		GSYMS
		GTAGS

		# Compiled source #
		###################
Expand DownExpand Up		@@ -123,7 +129,7 @@ numpy/core/src/private/npy_partition.h
		numpy/core/src/private/scalarmathmodule.h
		numpy/core/src/scalarmathmodule.c
		numpy/core/src/umath/funcs.inc
		numpy/core/src/umath/loops.c
		numpy/core/src/umath/loops.[ch]
		numpy/core/src/umath/operand_flag_tests.c
		numpy/core/src/umath/simd.inc
		numpy/core/src/umath/struct_ufunc_test.c
Expand Down
Original file line number	Diff line number	Diff line change
Expand Up		@@ -13,7 +13,7 @@ matrix:
		- python: 3.2
		env: USE_DEBUG=1
		- python: 2.7
		env: NPY_SEPARATE_COMPILATION=0
		env: NPY_SEPARATE_COMPILATION=0 PYTHON_OO=1
		- python: 3.3
		env: NPY_SEPARATE_COMPILATION=0
		- python: 2.7
Expand All		@@ -31,6 +31,10 @@ before_install:
		- ulimit -a
		- mkdir builds
		- pushd builds
		# Build into own virtualenv
		# We therefore control our own environment, avoid travis' numpy
		- virtualenv --python=python venv
		- source venv/bin/activate
		- pip install nose
		# pip install coverage
		- python -V
Expand Down
Original file line number	Diff line number	Diff line change
Expand Up		@@ -30,14 +30,14 @@ A Guide to NumPy/SciPy Documentation

		Overview
		--------
		In general, we follow the standard Python style conventions as described here:
		* `Style Guide for C Code <http://www.python.org/peps/pep-0007.html>`_
		* `Style Guide for Python Code <http://www.python.org/peps/pep-0008.html>`_
		* `Docstring Conventions <http://www.python.org/peps/pep-0257.html>`_
		We mostly follow the standard Python style conventions as described here:
		* `Style Guide for C Code <http://python.org/dev/peps/pep-0007/>`_
		* `Style Guide for Python Code <http://python.org/dev/peps/pep-0008/>`_
		* `Docstring Conventions <http://python.org/dev/peps/pep-0257/>`_

		Additional PEPs of interest regarding documentation of code:
		* `Docstring Processing Framework <http://www.python.org/peps/pep-0256.html>`_
		* `Docutils Design Specification <http://www.python.org/peps/pep-0258.html>`_
		* `Docstring Processing Framework <http://python.org/dev/peps/pep-0256/>`_
		* `Docutils Design Specification <http://python.org/dev/peps/pep-0258/>`_

		Use a code checker:
		* `pylint <http://www.logilab.org/857>`_
Expand Down
Original file line number	Diff line number	Diff line change
		@@ -0,0 +1,19 @@
		NumPy 1.8.2 Release Notes
		*************************

		This is a bugfix only release in the 1.8.x series.

		Issues fixed
		============

		* gh-4836: partition produces wrong results for multiple selections in equal ranges
		* gh-4656: Make fftpack._raw_fft threadsafe
		* gh-4628: incorrect argument order to _copyto in in np.nanmax, np.nanmin
		* gh-4642: Hold GIL for converting dtypes types with fields
		* gh-4733: fix np.linalg.svd(b, compute_uv=False)
		* gh-4853: avoid unaligned simd load on reductions on i386
		* gh-4722: Fix seg fault converting empty string to object
		* gh-4613: Fix lack of NULL check in array_richcompare
		* gh-4774: avoid unaligned access for strided byteswap
		* gh-650: Prevent division by zero when creating arrays from some buffers
		* gh-4602: ifort has issues with optimization flag O2, use O1
Original file line number	Diff line number	Diff line change
Expand Up		@@ -6,8 +6,6 @@ This release supports Python 2.6 - 2.7 and 3.2 - 3.4.

		Highlights
		==========
		* Addition of `__numpy_ufunc__` to allow overriding ufuncs in ndarray
		subclasses.
		* Numerous performance improvements in various areas, most notably indexing and
		operations on small arrays are significantly faster.
		Indexing operations now also release the GIL.
Expand DownExpand Up		@@ -35,6 +33,8 @@ Future Changes
		* String version checks will break because, e.g., '1.9' > '1.10' is True. A
		NumpyVersion class has been added that can be used for such comparisons.
		* The diagonal and diag functions will return writeable views in 1.10.0
		* The `S` and/or `a` dtypes may be changed to represent Python strings
		instead of bytes, in Python 3 these two types are very different.


		Compatibility notes
Expand DownExpand Up		@@ -176,6 +176,11 @@ introduced in advanced indexing operations:

		* Indexing with more then one ellipsis (``...``) is deprecated.

		Non-integer reduction axis indexes are deprecated
		~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
		Non-integer axis indexes to reduction ufuncs like `add.reduce` or `sum` are
		deprecated.

		``promote_types`` and string dtype
		~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
		``promote_types`` function now returns a valid string length when given an
Expand DownExpand Up		@@ -240,6 +245,11 @@ a problem cannot occur.

		This change was already applied to the 1.8.1 release.

		``zeros_like`` for string dtypes now returns empty strings
		~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
		To match the `zeros` function `zeros_like` now returns an array initialized
		with empty strings instead of an array filled with `'0'`.


		New Features
		============
Expand All		@@ -257,13 +267,6 @@ ufunc reductions do since 1.7. One can now say axis=(index, index) to pick a
		list of axes for the reduction. The ``keepdims`` keyword argument was also
		added to allow convenient broadcasting to arrays of the original shape.

		Ufunc and Dot Overrides
		~~~~~~~~~~~~~~~~~~~~~~~
		For better compatibility with external objects you can now override
		universal functions (ufuncs), ``numpy.core._dotblas.dot``, and
		``numpy.core.multiarray.dot`` (the numpy.dot functions). By defining a
		``__numpy_ufunc__`` method.

		Dtype parameter added to ``np.linspace`` and ``np.logspace``
		~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
		The returned data type from the ``linspace`` and ``logspace`` functions can
Expand Down
Original file line number	Diff line number	Diff line change
		@@ -0,0 +1,33 @@
		NumPy 1.9.1 Release Notes
		*************************

		This is a bugfix only release in the 1.9.x series.

		Issues fixed
		============

		* gh-5184: restore linear edge behaviour of gradient to as it was in < 1.9.
		The second order behaviour is available via the `edge_order` keyword
		* gh-4007: workaround Accelerate sgemv crash on OSX 10.9
		* gh-5100: restore object dtype inference from iterable objects without `len()`
		* gh-5163: avoid gcc-4.1.2 (red hat 5) miscompilation causing a crash
		* gh-5138: fix nanmedian on arrays containing inf
		* gh-5240: fix not returning out array from ufuncs with subok=False set
		* gh-5203: copy inherited masks in MaskedArray.__array_finalize__
		* gh-2317: genfromtxt did not handle filling_values=0 correctly
		* gh-5067: restore api of npy_PyFile_DupClose in python2
		* gh-5063: cannot convert invalid sequence index to tuple
		* gh-5082: Segmentation fault with argmin() on unicode arrays
		* gh-5095: don't propagate subtypes from np.where
		* gh-5104: np.inner segfaults with SciPy's sparse matrices
		* gh-5251: Issue with fromarrays not using correct format for unicode arrays
		* gh-5136: Import dummy_threading if importing threading fails
		* gh-5148: Make numpy import when run with Python flag '-OO'
		* gh-5147: Einsum double contraction in particular order causes ValueError
		* gh-479: Make f2py work with intent(in out)
		* gh-5170: Make python2 .npy files readable in python3
		* gh-5027: Use 'll' as the default length specifier for long long
		* gh-4896: fix build error with MSVC 2013 caused by C99 complex support
		* gh-4465: Make PyArray_PutTo respect writeable flag
		* gh-5225: fix crash when using arange on datetime without dtype set
		* gh-5231: fix build in c99 mode
Original file line number	Diff line number	Diff line change
		@@ -0,0 +1,25 @@
		NumPy 1.9.2 Release Notes
		*************************

		This is a bugfix only release in the 1.9.x series.

		Issues fixed
		============

		* `#5316 <https://github.com/numpy/numpy/issues/5316>`__: fix too large dtype alignment of strings and complex types
		* `#5424 <https://github.com/numpy/numpy/issues/5424>`__: fix ma.median when used on ndarrays
		* `#5481 <https://github.com/numpy/numpy/issues/5481>`__: Fix astype for structured array fields of different byte order
		* `#5354 <https://github.com/numpy/numpy/issues/5354>`__: fix segfault when clipping complex arrays
		* `#5524 <https://github.com/numpy/numpy/issues/5524>`__: allow np.argpartition on non ndarrays
		* `#5612 <https://github.com/numpy/numpy/issues/5612>`__: Fixes ndarray.fill to accept full range of uint64
		* `#5155 <https://github.com/numpy/numpy/issues/5155>`__: Fix loadtxt with comments=None and a string None data
		* `#4476 <https://github.com/numpy/numpy/issues/4476>`__: Masked array view fails if structured dtype has datetime component
		* `#5388 <https://github.com/numpy/numpy/issues/5388>`__: Make RandomState.set_state and RandomState.get_state threadsafe
		* `#5390 <https://github.com/numpy/numpy/issues/5390>`__: make seed, randint and shuffle threadsafe
		* `#5374 <https://github.com/numpy/numpy/issues/5374>`__: Fixed incorrect assert_array_almost_equal_nulp documentation
		* `#5393 <https://github.com/numpy/numpy/issues/5393>`__: Add support for ATLAS > 3.9.33.
		* `#5313 <https://github.com/numpy/numpy/issues/5313>`__: PyArray_AsCArray caused segfault for 3d arrays
		* `#5492 <https://github.com/numpy/numpy/issues/5492>`__: handle out of memory in rfftf
		* `#4181 <https://github.com/numpy/numpy/issues/4181>`__: fix a few bugs in the random.pareto docstring
		* `#5359 <https://github.com/numpy/numpy/issues/5359>`__: minor changes to linspace docstring
		* `#4723 <https://github.com/numpy/numpy/issues/4723>`__: fix a compile issues on AIX
Original file line number	Diff line number	Diff line change
		@@ -0,0 +1,23 @@
		NumPy 1.9.3 Release Notes
		*************************

		This is a bugfix only release in the 1.9.x series.

		The only changes from 1.9.2 are a fix for reading gzipped text files on Python
		3.5 and some build fixes.

		Issues fixed
		============

		* `#5866 <https://github.com/numpy/numpy/pull/5866>`__: fix error finding
		Python headers when ``build_ext`` ``--include-dirs`` is set;
		* `#6016 <https://github.com/numpy/numpy/pull/6016>`__: fix ``np.loadtxt``
		error on Python 3.5 when reading from gzip files;
		* `#5555 <https://github.com/numpy/numpy/pull/5555>`__: Replace deprecated
		options for ifort;
		* `#6096 <https://github.com/numpy/numpy/pull/6096>`__: remove /GL for VS2015
		in check_long_double_representation;
		* `#6141 <https://github.com/numpy/numpy/pull/6141>`__: enable Visual Studio
		2015 C99 features;
		* `#6171 <https://github.com/numpy/numpy/pull/6171>`__: revert C99 complex for
		MSVC14.
Original file line number	Diff line number	Diff line change
Expand Up		@@ -39,76 +39,6 @@ Special attributes and methods

		Numpy provides several hooks that classes can customize:

		.. function:: class.__numpy_ufunc__(self, ufunc, method, i, inputs, **kwargs)

		.. versionadded:: 1.9

		Any class (ndarray subclass or not) can define this method to
		override behavior of Numpy's ufuncs. This works quite similarly to
		Python's ``__mul__`` and other binary operation routines.

		- ufunc is the ufunc object that was called.
		- method is a string indicating which Ufunc method was called
		(one of ``"__call__"``, ``"reduce"``, ``"reduceat"``,
		``"accumulate"``, ``"outer"``, ``"inner"``).
		- i is the index of self in inputs.
		- inputs is a tuple of the input arguments to the ``ufunc``
		- kwargs is a dictionary containing the optional input arguments
		of the ufunc. The ``out`` argument is always contained in
		kwargs, if given. See the discussion in :ref:`ufuncs` for
		details.

		The method should return either the result of the operation, or
		:obj:`NotImplemented` if the operation requested is not
		implemented.

		If one of the arguments has a :func:`__numpy_ufunc__` method, it is
		executed instead of the ufunc. If more than one of the input
		arguments implements :func:`__numpy_ufunc__`, they are tried in the
		order: subclasses before superclasses, otherwise left to right. The
		first routine returning something else than :obj:`NotImplemented`
		determines the result. If all of the :func:`__numpy_ufunc__`
		operations return :obj:`NotImplemented`, a :exc:`TypeError` is
		raised.

		If an :class:`ndarray` subclass defines the :func:`__numpy_ufunc__`
		method, this disables the :func:`__array_wrap__`,
		:func:`__array_prepare__`, :data:`__array_priority__` mechanism
		described below.

		.. note:: In addition to ufuncs, :func:`__numpy_ufunc__` also
		overrides the behavior of :func:`numpy.dot` even though it is
		not an Ufunc.

		.. note:: If you also define right-hand binary operator override
		methods (such as ``__rmul__``) or comparison operations (such as
		``__gt__``) in your class, they take precedence over the
		:func:`__numpy_ufunc__` mechanism when resolving results of
		binary operations (such as ``ndarray_obj * your_obj``).

		The technical special case is: ``ndarray.__mul__`` returns
		``NotImplemented`` if the other object is not a subclass of
		:class:`ndarray`, and defines both ``__numpy_ufunc__`` and
		``__rmul__``. Similar exception applies for the other operations
		than multiplication.

		In such a case, when computing a binary operation such as
		``ndarray_obj * your_obj``, your ``__numpy_ufunc__`` method
		will not be called. Instead, the execution passes on to your
		right-hand ``__rmul__`` operation, as per standard Python
		operator override rules.

		Similar special case applies to in-place operations: If you
		define ``__rmul__``, then ``ndarray_obj = your_obj`` will not*
		call your ``__numpy_ufunc__`` implementation. Instead, the
		default Python behavior ``ndarray_obj = ndarray_obj * your_obj``
		occurs.

		Note that the above discussion applies only to Python's builtin
		binary operation mechanism. ``np.multiply(ndarray_obj,
		your_obj)`` always calls only your ``__numpy_ufunc__``, as
		expected.

		.. function:: class.__array_finalize__(self)

		This method is called whenever the system internally allocates a
Expand Down