Hi, I am pleased to announce the release of NumPy 1.6.0. This release is the result of 9 months of work, and includes many new features, performance improvements and bug fixes. Some highlights are: - Re-introduction of datetime dtype support to deal with dates in arrays. - A new 16-bit floating point type. - A new iterator, which improves performance of many functions. Sources and binaries can be found at http://sourceforge.net/projects/numpy/files/NumPy/1.6.0/<http://sourceforge.net/projects/numpy/files/NumPy/1.6.0rc2/> For release notes see below. Thank you to everyone who contributed to this release. Enjoy, The NumPy developers ========================= NumPy 1.6.0 Release Notes ========================= This release includes several new features as well as numerous bug fixes and improved documentation. It is backward compatible with the 1.5.0 release, and supports Python 2.4 - 2.7 and 3.1 - 3.2. Highlights ========== * Re-introduction of datetime dtype support to deal with dates in arrays. * A new 16-bit floating point type. * A new iterator, which improves performance of many functions. New features ============ New 16-bit floating point type ------------------------------ This release adds support for the IEEE 754-2008 binary16 format, available as the data type ``numpy.half``. Within Python, the type behaves similarly to `float` or `double`, and C extensions can add support for it with the exposed half-float API. New iterator ------------ A new iterator has been added, replacing the functionality of the existing iterator and multi-iterator with a single object and API. This iterator works well with general memory layouts different from C or Fortran contiguous, and handles both standard NumPy and customized broadcasting. The buffering, automatic data type conversion, and optional output parameters, offered by ufuncs but difficult to replicate elsewhere, are now exposed by this iterator. Legendre, Laguerre, Hermite, HermiteE polynomials in ``numpy.polynomial`` ------------------------------------------------------------------------- Extend the number of polynomials available in the polynomial package. In addition, a new ``window`` attribute has been added to the classes in order to specify the range the ``domain`` maps to. This is mostly useful for the Laguerre, Hermite, and HermiteE polynomials whose natural domains are infinite and provides a more intuitive way to get the correct mapping of values without playing unnatural tricks with the domain. Fortran assumed shape array and size function support in ``numpy.f2py`` ----------------------------------------------------------------------- F2py now supports wrapping Fortran 90 routines that use assumed shape arrays. Before such routines could be called from Python but the corresponding Fortran routines received assumed shape arrays as zero length arrays which caused unpredicted results. Thanks to Lorenz Hüdepohl for pointing out the correct way to interface routines with assumed shape arrays. In addition, f2py supports now automatic wrapping of Fortran routines that use two argument ``size`` function in dimension specifications. Other new functions ------------------- ``numpy.ravel_multi_index`` : Converts a multi-index tuple into an array of flat indices, applying boundary modes to the indices. ``numpy.einsum`` : Evaluate the Einstein summation convention. Using the Einstein summation convention, many common multi-dimensional array operations can be represented in a simple fashion. This function provides a way compute such summations. ``numpy.count_nonzero`` : Counts the number of non-zero elements in an array. ``numpy.result_type`` and ``numpy.min_scalar_type`` : These functions expose the underlying type promotion used by the ufuncs and other operations to determine the types of outputs. These improve upon the ``numpy.common_type`` and ``numpy.mintypecode`` which provide similar functionality but do not match the ufunc implementation. Changes ======= ``default error handling`` -------------------------- The default error handling has been change from ``print`` to ``warn`` for all except for ``underflow``, which remains as ``ignore``. ``numpy.distutils`` ------------------- Several new compilers are supported for building Numpy: the Portland Group Fortran compiler on OS X, the PathScale compiler suite and the 64-bit Intel C compiler on Linux. ``numpy.testing`` ----------------- The testing framework gained ``numpy.testing.assert_allclose``, which provides a more convenient way to compare floating point arrays than `assert_almost_equal`, `assert_approx_equal` and `assert_array_almost_equal`. ``C API`` --------- In addition to the APIs for the new iterator and half data type, a number of other additions have been made to the C API. The type promotion mechanism used by ufuncs is exposed via ``PyArray_PromoteTypes``, ``PyArray_ResultType``, and ``PyArray_MinScalarType``. A new enumeration ``NPY_CASTING`` has been added which controls what types of casts are permitted. This is used by the new functions ``PyArray_CanCastArrayTo`` and ``PyArray_CanCastTypeTo``. A more flexible way to handle conversion of arbitrary python objects into arrays is exposed by ``PyArray_GetArrayParamsFromObject``. Deprecated features =================== The "normed" keyword in ``numpy.histogram`` is deprecated. Its functionality will be replaced by the new "density" keyword. Removed features ================ ``numpy.fft`` ------------- The functions `refft`, `refft2`, `refftn`, `irefft`, `irefft2`, `irefftn`, which were aliases for the same functions without the 'e' in the name, were removed. ``numpy.memmap`` ---------------- The `sync()` and `close()` methods of memmap were removed. Use `flush()` and "del memmap" instead. ``numpy.lib`` ------------- The deprecated functions ``numpy.unique1d``, ``numpy.setmember1d``, ``numpy.intersect1d_nu`` and ``numpy.lib.ufunclike.log2`` were removed. ``numpy.ma`` ------------ Several deprecated items were removed from the ``numpy.ma`` module:: * ``numpy.ma.MaskedArray`` "raw_data" method * ``numpy.ma.MaskedArray`` constructor "flag" keyword * ``numpy.ma.make_mask`` "flag" keyword * ``numpy.ma.allclose`` "fill_value" keyword ``numpy.distutils`` ------------------- The ``numpy.get_numpy_include`` function was removed, use ``numpy.get_include`` instead.
On Sat, May 14, 2011 at 3:54 AM, Ralf Gommers <ralf.gommers@googlemail.com>wrote:
Hi,
I am pleased to announce the release of NumPy 1.6.0. This release is the result of 9 months of work, and includes many new features, performance improvements and bug fixes. Some highlights are:
- Re-introduction of datetime dtype support to deal with dates in arrays. - A new 16-bit floating point type. - A new iterator, which improves performance of many functions.
Sources and binaries can be found at http://sourceforge.net/projects/numpy/files/NumPy/1.6.0/<http://sourceforge.net/projects/numpy/files/NumPy/1.6.0rc2/> For release notes see below.
Thank you to everyone who contributed to this release.
Enjoy, The NumPy developers
========================= NumPy 1.6.0 Release Notes =========================
This release includes several new features as well as numerous bug fixes and improved documentation. It is backward compatible with the 1.5.0 release, and supports Python 2.4 - 2.7 and 3.1 - 3.2.
Highlights ==========
* Re-introduction of datetime dtype support to deal with dates in arrays.
* A new 16-bit floating point type.
* A new iterator, which improves performance of many functions.
New features ============
New 16-bit floating point type ------------------------------
This release adds support for the IEEE 754-2008 binary16 format, available as the data type ``numpy.half``. Within Python, the type behaves similarly to `float` or `double`, and C extensions can add support for it with the exposed half-float API.
New iterator ------------
A new iterator has been added, replacing the functionality of the existing iterator and multi-iterator with a single object and API. This iterator works well with general memory layouts different from C or Fortran contiguous, and handles both standard NumPy and customized broadcasting. The buffering, automatic data type conversion, and optional output parameters, offered by ufuncs but difficult to replicate elsewhere, are now exposed by this iterator.
Legendre, Laguerre, Hermite, HermiteE polynomials in ``numpy.polynomial`` -------------------------------------------------------------------------
Extend the number of polynomials available in the polynomial package. In addition, a new ``window`` attribute has been added to the classes in order to specify the range the ``domain`` maps to. This is mostly useful for the Laguerre, Hermite, and HermiteE polynomials whose natural domains are infinite and provides a more intuitive way to get the correct mapping of values without playing unnatural tricks with the domain.
Fortran assumed shape array and size function support in ``numpy.f2py`` -----------------------------------------------------------------------
F2py now supports wrapping Fortran 90 routines that use assumed shape arrays. Before such routines could be called from Python but the corresponding Fortran routines received assumed shape arrays as zero length arrays which caused unpredicted results. Thanks to Lorenz Hüdepohl for pointing out the correct way to interface routines with assumed shape arrays.
In addition, f2py supports now automatic wrapping of Fortran routines that use two argument ``size`` function in dimension specifications.
Other new functions -------------------
``numpy.ravel_multi_index`` : Converts a multi-index tuple into an array of flat indices, applying boundary modes to the indices.
``numpy.einsum`` : Evaluate the Einstein summation convention. Using the Einstein summation convention, many common multi-dimensional array operations can be represented in a simple fashion. This function provides a way compute such summations.
``numpy.count_nonzero`` : Counts the number of non-zero elements in an array.
``numpy.result_type`` and ``numpy.min_scalar_type`` : These functions expose the underlying type promotion used by the ufuncs and other operations to determine the types of outputs. These improve upon the ``numpy.common_type`` and ``numpy.mintypecode`` which provide similar functionality but do not match the ufunc implementation.
Changes =======
``default error handling`` --------------------------
The default error handling has been change from ``print`` to ``warn`` for all except for ``underflow``, which remains as ``ignore``.
``numpy.distutils`` -------------------
Several new compilers are supported for building Numpy: the Portland Group Fortran compiler on OS X, the PathScale compiler suite and the 64-bit Intel C compiler on Linux.
``numpy.testing`` -----------------
The testing framework gained ``numpy.testing.assert_allclose``, which provides a more convenient way to compare floating point arrays than `assert_almost_equal`, `assert_approx_equal` and `assert_array_almost_equal`.
``C API`` ---------
In addition to the APIs for the new iterator and half data type, a number of other additions have been made to the C API. The type promotion mechanism used by ufuncs is exposed via ``PyArray_PromoteTypes``, ``PyArray_ResultType``, and ``PyArray_MinScalarType``. A new enumeration ``NPY_CASTING`` has been added which controls what types of casts are permitted. This is used by the new functions ``PyArray_CanCastArrayTo`` and ``PyArray_CanCastTypeTo``. A more flexible way to handle conversion of arbitrary python objects into arrays is exposed by ``PyArray_GetArrayParamsFromObject``.
Deprecated features ===================
The "normed" keyword in ``numpy.histogram`` is deprecated. Its functionality will be replaced by the new "density" keyword.
Removed features ================
``numpy.fft`` -------------
The functions `refft`, `refft2`, `refftn`, `irefft`, `irefft2`, `irefftn`, which were aliases for the same functions without the 'e' in the name, were removed.
``numpy.memmap`` ----------------
The `sync()` and `close()` methods of memmap were removed. Use `flush()` and "del memmap" instead.
``numpy.lib`` -------------
The deprecated functions ``numpy.unique1d``, ``numpy.setmember1d``, ``numpy.intersect1d_nu`` and ``numpy.lib.ufunclike.log2`` were removed.
``numpy.ma`` ------------
Several deprecated items were removed from the ``numpy.ma`` module::
* ``numpy.ma.MaskedArray`` "raw_data" method * ``numpy.ma.MaskedArray`` constructor "flag" keyword * ``numpy.ma.make_mask`` "flag" keyword * ``numpy.ma.allclose`` "fill_value" keyword
``numpy.distutils`` -------------------
The ``numpy.get_numpy_include`` function was removed, use ``numpy.get_include`` instead.
The link is http://sourceforge.net/projects/numpy/files/NumPy/1.6.0/ Chuck
On Sat, May 14, 2011 at 3:09 PM, Charles R Harris <charlesr.harris@gmail.com
wrote:
On Sat, May 14, 2011 at 3:54 AM, Ralf Gommers <ralf.gommers@googlemail.com
wrote:
Hi,
I am pleased to announce the release of NumPy 1.6.0. This release is the result of 9 months of work, and includes many new features, performance improvements and bug fixes. Some highlights are:
- Re-introduction of datetime dtype support to deal with dates in arrays. - A new 16-bit floating point type. - A new iterator, which improves performance of many functions.
The link is http://sourceforge.net/projects/numpy/files/NumPy/1.6.0/
The OS X binaries are also up now.
Ralf
Dear all, I have OSX 10.6.7, XCode 4, and Python.org python 2.6.6 and 2.7.1, where 2.7 is 64-bit. With 2.7, easy_install successfully compiles and installs the package, both over the web and with an explicit download. With 2.6, there seems to be a problem with attempting to compile the PPC architecture, seen in this error. /usr/libexec/gcc/powerpc-apple-darwin10/4.0.1/as: assembler (/usr/bin/../libexec/gcc/darwin/ppc/as or /usr/bin/../local/libexec/gcc/darwin/ppc/as) for architecture ppc not installed If I explicitly set CFLAGS and LDFLAGS to have "-arch i386 -arch x86_64" in them as appropriate, it seems to work... Is this a correctable bug in the packaging, or just a quirk of my setup? Andrew On 15/05/2011 17:39, Ralf Gommers wrote:
On Sat, May 14, 2011 at 3:09 PM, Charles R Harris <charlesr.harris@gmail.com <mailto:charlesr.harris@gmail.com>> wrote:
On Sat, May 14, 2011 at 3:54 AM, Ralf Gommers <ralf.gommers@googlemail.com <mailto:ralf.gommers@googlemail.com>> wrote:
Hi,
I am pleased to announce the release of NumPy 1.6.0. This release is the result of 9 months of work, and includes many new features, performance improvements and bug fixes. Some highlights are:
- Re-introduction of datetime dtype support to deal with dates in arrays. - A new 16-bit floating point type. - A new iterator, which improves performance of many functions.
The link is http://sourceforge.net/projects/numpy/files/NumPy/1.6.0/
The OS X binaries are also up now.
Ralf
_______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
On Mon, May 16, 2011 at 12:41 PM, Andrew Jaffe <a.h.jaffe@gmail.com> wrote:
Dear all,
I have OSX 10.6.7, XCode 4, and Python.org python 2.6.6 and 2.7.1, where 2.7 is 64-bit.
With 2.7, easy_install successfully compiles and installs the package, both over the web and with an explicit download.
With 2.6, there seems to be a problem with attempting to compile the PPC architecture, seen in this error.
Please just use "python setup.py install", easy_install is very unreliable.
/usr/libexec/gcc/powerpc-apple-darwin10/4.0.1/as: assembler (/usr/bin/../libexec/gcc/darwin/ppc/as or /usr/bin/../local/libexec/gcc/darwin/ppc/as) for architecture ppc not installed
XCode 4 does not support PPC, use version 3.2. Then it should work as
advertized. Cheers, Ralf
On 16/05/2011 18:45, Ralf Gommers wrote:
On Mon, May 16, 2011 at 12:41 PM, Andrew Jaffe <a.h.jaffe@gmail.com <mailto:a.h.jaffe@gmail.com>> wrote:
Dear all,
I have OSX 10.6.7, XCode 4, and Python.org python 2.6.6 and 2.7.1, where 2.7 is 64-bit.
With 2.7, easy_install successfully compiles and installs the package, both over the web and with an explicit download.
With 2.6, there seems to be a problem with attempting to compile the PPC architecture, seen in this error.
Please just use "python setup.py install", easy_install is very unreliable.
/usr/libexec/gcc/powerpc-apple-darwin10/4.0.1/as: assembler (/usr/bin/../libexec/gcc/darwin/ppc/as or /usr/bin/../local/libexec/gcc/darwin/ppc/as) for architecture ppc not installed
XCode 4 does not support PPC, use version 3.2. Then it should work as advertized.
Aha, thanks! But they're both installed, but how do force this? (Does 64-bit 2.7 not care about PPC?) Yours, Andrew
On Mon, May 16, 2011 at 8:30 PM, Andrew Jaffe <a.h.jaffe@gmail.com> wrote:
On 16/05/2011 18:45, Ralf Gommers wrote:
On Mon, May 16, 2011 at 12:41 PM, Andrew Jaffe <a.h.jaffe@gmail.com <mailto:a.h.jaffe@gmail.com>> wrote:
Dear all,
I have OSX 10.6.7, XCode 4, and Python.org python 2.6.6 and 2.7.1,
where
2.7 is 64-bit.
With 2.7, easy_install successfully compiles and installs the
package,
both over the web and with an explicit download.
With 2.6, there seems to be a problem with attempting to compile the
PPC
architecture, seen in this error.
Please just use "python setup.py install", easy_install is very
unreliable.
/usr/libexec/gcc/powerpc-apple-darwin10/4.0.1/as: assembler (/usr/bin/../libexec/gcc/darwin/ppc/as or /usr/bin/../local/libexec/gcc/darwin/ppc/as) for architecture ppc not installed
XCode 4 does not support PPC, use version 3.2. Then it should work as advertized.
Aha, thanks!
But they're both installed, but how do force this?
You have to figure out where the right compiler is located. Then try "export CC=/path/to/compiler". Or if you have MacPorts installed you can use its gcc_select. Or google for another solution.
(Does 64-bit 2.7 not care about PPC?)
No, it's an Intel 32 + 64-bit bundle. Cheers, Ralf
participants (3)
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Andrew Jaffe -
Charles R Harris -
Ralf Gommers