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diff --git a/doc/source/release/1.8.0-notes.rst b/doc/source/release/1.8.0-notes.rst new file mode 100644 index 000000000..80c39f8bc --- /dev/null +++ b/doc/source/release/1.8.0-notes.rst @@ -0,0 +1,526 @@ +========================= +NumPy 1.8.0 Release Notes +========================= + +This release supports Python 2.6 -2.7 and 3.2 - 3.3. + + +Highlights +========== + + +* New, no 2to3, Python 2 and Python 3 are supported by a common code base. +* New, gufuncs for linear algebra, enabling operations on stacked arrays. +* New, inplace fancy indexing for ufuncs with the ``.at`` method. +* New, ``partition`` function, partial sorting via selection for fast median. +* New, ``nanmean``, ``nanvar``, and ``nanstd`` functions skipping NaNs. +* New, ``full`` and ``full_like`` functions to create value initialized arrays. +* New, ``PyUFunc_RegisterLoopForDescr``, better ufunc support for user dtypes. +* Numerous performance improvements in many areas. + + +Dropped Support +=============== + + +Support for Python versions 2.4 and 2.5 has been dropped, + +Support for SCons has been removed. + + +Future Changes +============== + + +The Datetime64 type remains experimental in this release. In 1.9 there will +probably be some changes to make it more useable. + +The diagonal method currently returns a new array and raises a +FutureWarning. In 1.9 it will return a readonly view. + +Multiple field selection from an array of structured type currently +returns a new array and raises a FutureWarning. In 1.9 it will return a +readonly view. + +The numpy/oldnumeric and numpy/numarray compatibility modules will be +removed in 1.9. + + +Compatibility notes +=================== + + +The doc/sphinxext content has been moved into its own github repository, +and is included in numpy as a submodule. See the instructions in +doc/HOWTO_BUILD_DOCS.rst.txt for how to access the content. + +.. _numpydoc: https://github.com/numpy/numpydoc + +The hash function of numpy.void scalars has been changed. Previously the +pointer to the data was hashed as an integer. Now, the hash function uses +the tuple-hash algorithm to combine the hash functions of the elements of +the scalar, but only if the scalar is read-only. + +Numpy has switched its build system to using 'separate compilation' by +default. In previous releases this was supported, but not default. This +should produce the same results as the old system, but if you're trying to +do something complicated like link numpy statically or using an unusual +compiler, then it's possible you will encounter problems. If so, please +file a bug and as a temporary workaround you can re-enable the old build +system by exporting the shell variable NPY_SEPARATE_COMPILATION=0. + +For the AdvancedNew iterator the ``oa_ndim`` flag should now be -1 to indicate +that no ``op_axes`` and ``itershape`` are passed in. The ``oa_ndim == 0`` +case, now indicates a 0-D iteration and ``op_axes`` being NULL and the old +usage is deprecated. This does not effect the ``NpyIter_New`` or +``NpyIter_MultiNew`` functions. + +The functions nanargmin and nanargmax now return np.iinfo['intp'].min for +the index in all-NaN slices. Previously the functions would raise a ValueError +for array returns and NaN for scalar returns. + +NPY_RELAXED_STRIDES_CHECKING +---------------------------- +There is a new compile time environment variable +``NPY_RELAXED_STRIDES_CHECKING``. If this variable is set to 1, then +numpy will consider more arrays to be C- or F-contiguous -- for +example, it becomes possible to have a column vector which is +considered both C- and F-contiguous simultaneously. The new definition +is more accurate, allows for faster code that makes fewer unnecessary +copies, and simplifies numpy's code internally. However, it may also +break third-party libraries that make too-strong assumptions about the +stride values of C- and F-contiguous arrays. (It is also currently +known that this breaks Cython code using memoryviews, which will be +fixed in Cython.) THIS WILL BECOME THE DEFAULT IN A FUTURE RELEASE, SO +PLEASE TEST YOUR CODE NOW AGAINST NUMPY BUILT WITH:: + + NPY_RELAXED_STRIDES_CHECKING=1 python setup.py install + +You can check whether NPY_RELAXED_STRIDES_CHECKING is in effect by +running:: + + np.ones((10, 1), order="C").flags.f_contiguous + +This will be ``True`` if relaxed strides checking is enabled, and +``False`` otherwise. The typical problem we've seen so far is C code +that works with C-contiguous arrays, and assumes that the itemsize can +be accessed by looking at the last element in the ``PyArray_STRIDES(arr)`` +array. When relaxed strides are in effect, this is not true (and in +fact, it never was true in some corner cases). Instead, use +``PyArray_ITEMSIZE(arr)``. + +For more information check the "Internal memory layout of an ndarray" +section in the documentation. + +Binary operations with non-arrays as second argument +---------------------------------------------------- +Binary operations of the form ``<array-or-subclass> * <non-array-subclass>`` +where ``<non-array-subclass>`` declares an ``__array_priority__`` higher than +that of ``<array-or-subclass>`` will now unconditionally return +*NotImplemented*, giving ``<non-array-subclass>`` a chance to handle the +operation. Previously, `NotImplemented` would only be returned if +``<non-array-subclass>`` actually implemented the reversed operation, and after +a (potentially expensive) array conversion of ``<non-array-subclass>`` had been +attempted. (`bug <https://github.com/numpy/numpy/issues/3375>`_, `pull request +<https://github.com/numpy/numpy/pull/3501>`_) + +Function `median` used with `overwrite_input` only partially sorts array +------------------------------------------------------------------------ +If `median` is used with `overwrite_input` option the input array will now only +be partially sorted instead of fully sorted. + +Fix to financial.npv +-------------------- +The npv function had a bug. Contrary to what the documentation stated, it +summed from indexes ``1`` to ``M`` instead of from ``0`` to ``M - 1``. The +fix changes the returned value. The mirr function called the npv function, +but worked around the problem, so that was also fixed and the return value +of the mirr function remains unchanged. + +Runtime warnings when comparing NaN numbers +------------------------------------------- +Comparing ``NaN`` floating point numbers now raises the ``invalid`` runtime +warning. If a ``NaN`` is expected the warning can be ignored using np.errstate. +E.g.:: + + with np.errstate(invalid='ignore'): + operation() + + +New Features +============ + + +Support for linear algebra on stacked arrays +-------------------------------------------- +The gufunc machinery is now used for np.linalg, allowing operations on +stacked arrays and vectors. For example:: + + >>> a + array([[[ 1., 1.], + [ 0., 1.]], + + [[ 1., 1.], + [ 0., 1.]]]) + + >>> np.linalg.inv(a) + array([[[ 1., -1.], + [ 0., 1.]], + + [[ 1., -1.], + [ 0., 1.]]]) + +In place fancy indexing for ufuncs +---------------------------------- +The function ``at`` has been added to ufunc objects to allow in place +ufuncs with no buffering when fancy indexing is used. For example, the +following will increment the first and second items in the array, and will +increment the third item twice: ``numpy.add.at(arr, [0, 1, 2, 2], 1)`` + +This is what many have mistakenly thought ``arr[[0, 1, 2, 2]] += 1`` would do, +but that does not work as the incremented value of ``arr[2]`` is simply copied +into the third slot in ``arr`` twice, not incremented twice. + +New functions `partition` and `argpartition` +-------------------------------------------- +New functions to partially sort arrays via a selection algorithm. + +A ``partition`` by index ``k`` moves the ``k`` smallest element to the front of +an array. All elements before ``k`` are then smaller or equal than the value +in position ``k`` and all elements following ``k`` are then greater or equal +than the value in position ``k``. The ordering of the values within these +bounds is undefined. +A sequence of indices can be provided to sort all of them into their sorted +position at once iterative partitioning. +This can be used to efficiently obtain order statistics like median or +percentiles of samples. +``partition`` has a linear time complexity of ``O(n)`` while a full sort has +``O(n log(n))``. + +New functions `nanmean`, `nanvar` and `nanstd` +---------------------------------------------- +New nan aware statistical functions are added. In these functions the +results are what would be obtained if nan values were omitted from all +computations. + +New functions `full` and `full_like` +------------------------------------ +New convenience functions to create arrays filled with a specific value; +complementary to the existing `zeros` and `zeros_like` functions. + +IO compatibility with large files +--------------------------------- +Large NPZ files >2GB can be loaded on 64-bit systems. + +Building against OpenBLAS +------------------------- +It is now possible to build numpy against OpenBLAS by editing site.cfg. + +New constant +------------ +Euler's constant is now exposed in numpy as euler_gamma. + +New modes for qr +---------------- +New modes 'complete', 'reduced', and 'raw' have been added to the qr +factorization and the old 'full' and 'economic' modes are deprecated. +The 'reduced' mode replaces the old 'full' mode and is the default as was +the 'full' mode, so backward compatibility can be maintained by not +specifying the mode. + +The 'complete' mode returns a full dimensional factorization, which can be +useful for obtaining a basis for the orthogonal complement of the range +space. The 'raw' mode returns arrays that contain the Householder +reflectors and scaling factors that can be used in the future to apply q +without needing to convert to a matrix. The 'economic' mode is simply +deprecated, there isn't much use for it and it isn't any more efficient +than the 'raw' mode. + +New `invert` argument to `in1d` +------------------------------- +The function `in1d` now accepts a `invert` argument which, when `True`, +causes the returned array to be inverted. + +Advanced indexing using `np.newaxis` +------------------------------------ +It is now possible to use `np.newaxis`/`None` together with index +arrays instead of only in simple indices. This means that +``array[np.newaxis, [0, 1]]`` will now work as expected and select the first +two rows while prepending a new axis to the array. + + +C-API +----- +New ufuncs can now be registered with builtin input types and a custom +output type. Before this change, NumPy wouldn't be able to find the right +ufunc loop function when the ufunc was called from Python, because the ufunc +loop signature matching logic wasn't looking at the output operand type. +Now the correct ufunc loop is found, as long as the user provides an output +argument with the correct output type. + +runtests.py +----------- +A simple test runner script ``runtests.py`` was added. It also builds Numpy via +``setup.py build`` and can be used to run tests easily during development. + + +Improvements +============ + +IO performance improvements +--------------------------- +Performance in reading large files was improved by chunking (see also IO compatibility). + +Performance improvements to `pad` +--------------------------------- +The `pad` function has a new implementation, greatly improving performance for +all inputs except `mode=<function>` (retained for backwards compatibility). +Scaling with dimensionality is dramatically improved for rank >= 4. + +Performance improvements to `isnan`, `isinf`, `isfinite` and `byteswap` +----------------------------------------------------------------------- +`isnan`, `isinf`, `isfinite` and `byteswap` have been improved to take +advantage of compiler builtins to avoid expensive calls to libc. +This improves performance of these operations by about a factor of two on gnu +libc systems. + +Performance improvements via SSE2 vectorization +----------------------------------------------- +Several functions have been optimized to make use of SSE2 CPU SIMD instructions. + +* Float32 and float64: + * base math (`add`, `subtract`, `divide`, `multiply`) + * `sqrt` + * `minimum/maximum` + * `absolute` +* Bool: + * `logical_or` + * `logical_and` + * `logical_not` + +This improves performance of these operations up to 4x/2x for float32/float64 +and up to 10x for bool depending on the location of the data in the CPU caches. +The performance gain is greatest for in-place operations. + +In order to use the improved functions the SSE2 instruction set must be enabled +at compile time. It is enabled by default on x86_64 systems. On x86_32 with a +capable CPU it must be enabled by passing the appropriate flag to the CFLAGS +build variable (-msse2 with gcc). + +Performance improvements to `median` +------------------------------------ +`median` is now implemented in terms of `partition` instead of `sort` which +reduces its time complexity from O(n log(n)) to O(n). +If used with the `overwrite_input` option the array will now only be partially +sorted instead of fully sorted. + + +Overrideable operand flags in ufunc C-API +----------------------------------------- +When creating a ufunc, the default ufunc operand flags can be overridden +via the new op_flags attribute of the ufunc object. For example, to set +the operand flag for the first input to read/write: + +PyObject \*ufunc = PyUFunc_FromFuncAndData(...); +ufunc->op_flags[0] = NPY_ITER_READWRITE; + +This allows a ufunc to perform an operation in place. Also, global nditer flags +can be overridden via the new iter_flags attribute of the ufunc object. +For example, to set the reduce flag for a ufunc: + +ufunc->iter_flags = NPY_ITER_REDUCE_OK; + + +Changes +======= + + +General +------- +The function np.take now allows 0-d arrays as indices. + +The separate compilation mode is now enabled by default. + +Several changes to np.insert and np.delete: + +* Previously, negative indices and indices that pointed past the end of + the array were simply ignored. Now, this will raise a Future or Deprecation + Warning. In the future they will be treated like normal indexing treats + them -- negative indices will wrap around, and out-of-bound indices will + generate an error. +* Previously, boolean indices were treated as if they were integers (always + referring to either the 0th or 1st item in the array). In the future, they + will be treated as masks. In this release, they raise a FutureWarning + warning of this coming change. +* In Numpy 1.7. np.insert already allowed the syntax + `np.insert(arr, 3, [1,2,3])` to insert multiple items at a single position. + In Numpy 1.8. this is also possible for `np.insert(arr, [3], [1, 2, 3])`. + +Padded regions from np.pad are now correctly rounded, not truncated. + +C-API Array Additions +--------------------- +Four new functions have been added to the array C-API. + +* PyArray_Partition +* PyArray_ArgPartition +* PyArray_SelectkindConverter +* PyDataMem_NEW_ZEROED + +C-API Ufunc Additions +--------------------- +One new function has been added to the ufunc C-API that allows to register +an inner loop for user types using the descr. + +* PyUFunc_RegisterLoopForDescr + +C-API Developer Improvements +---------------------------- +The ``PyArray_Type`` instance creation function ``tp_new`` now +uses ``tp_basicsize`` to determine how much memory to allocate. +In previous releases only ``sizeof(PyArrayObject)`` bytes of +memory were allocated, often requiring C-API subtypes to +reimplement ``tp_new``. + +Deprecations +============ + +The 'full' and 'economic' modes of qr factorization are deprecated. + +General +------- +The use of non-integer for indices and most integer arguments has been +deprecated. Previously float indices and function arguments such as axes or +shapes were truncated to integers without warning. For example +`arr.reshape(3., -1)` or `arr[0.]` will trigger a deprecation warning in +NumPy 1.8., and in some future version of NumPy they will raise an error. + + +Authors +======= + +This release contains work by the following people who contributed at least +one patch to this release. The names are in alphabetical order by first name: + +* 87 +* Adam Ginsburg + +* Adam Griffiths + +* Alexander Belopolsky + +* Alex Barth + +* Alex Ford + +* Andreas Hilboll + +* Andreas Kloeckner + +* Andreas Schwab + +* Andrew Horton + +* argriffing + +* Arink Verma + +* Bago Amirbekian + +* Bartosz Telenczuk + +* bebert218 + +* Benjamin Root + +* Bill Spotz + +* Bradley M. Froehle +* Carwyn Pelley + +* Charles Harris +* Chris +* Christian Brueffer + +* Christoph Dann + +* Christoph Gohlke +* Dan Hipschman + +* Daniel + +* Dan Miller + +* daveydave400 + +* David Cournapeau +* David Warde-Farley +* Denis Laxalde +* dmuellner + +* Edward Catmur + +* Egor Zindy + +* endolith +* Eric Firing +* Eric Fode +* Eric Moore + +* Eric Price + +* Fazlul Shahriar + +* Félix Hartmann + +* Fernando Perez +* Frank B + +* Frank Breitling + +* Frederic +* Gabriel +* GaelVaroquaux +* Guillaume Gay + +* Han Genuit +* HaroldMills + +* hklemm + +* jamestwebber + +* Jason Madden + +* Jay Bourque +* jeromekelleher + +* Jesús Gómez + +* jmozmoz + +* jnothman + +* Johannes Schönberger + +* John Benediktsson + +* John Salvatier + +* John Stechschulte + +* Jonathan Waltman + +* Joon Ro + +* Jos de Kloe + +* Joseph Martinot-Lagarde + +* Josh Warner (Mac) + +* Jostein Bø Fløystad + +* Juan Luis Cano Rodríguez + +* Julian Taylor + +* Julien Phalip + +* K.-Michael Aye + +* Kumar Appaiah + +* Lars Buitinck +* Leon Weber + +* Luis Pedro Coelho +* Marcin Juszkiewicz +* Mark Wiebe +* Marten van Kerkwijk + +* Martin Baeuml + +* Martin Spacek +* Martin Teichmann + +* Matt Davis + +* Matthew Brett +* Maximilian Albert + +* m-d-w + +* Michael Droettboom +* mwtoews + +* Nathaniel J. Smith +* Nicolas Scheffer + +* Nils Werner + +* ochoadavid + +* Ondřej Čertík +* ovillellas + +* Paul Ivanov +* Pauli Virtanen +* peterjc +* Ralf Gommers +* Raul Cota + +* Richard Hattersley + +* Robert Costa + +* Robert Kern +* Rob Ruana + +* Ronan Lamy +* Sandro Tosi +* Sascha Peilicke + +* Sebastian Berg +* Skipper Seabold +* Stefan van der Walt +* Steve + +* Takafumi Arakaki + +* Thomas Robitaille + +* Tomas Tomecek + +* Travis E. Oliphant +* Valentin Haenel +* Vladimir Rutsky + +* Warren Weckesser +* Yaroslav Halchenko +* Yury V. Zaytsev + + +A total of 119 people contributed to this release. +People with a "+" by their names contributed a patch for the first time. |