This minor includes numerous bug fixes, official python 2.6 support, and several new features such as generalized ufuncs.

Python 2.6 is now supported on all previously supported platforms, including windows.

There is a general need for looping over not only functions on scalars but also over functions on vectors (or arrays), as explained on http://scipy.org/scipy/numpy/wiki/GeneralLoopingFunctions. We propose to realize this concept by generalizing the universal functions (ufuncs), and provide a C implementation that adds ~500 lines to the numpy code base. In current (specialized) ufuncs, the elementary function is limited to element-by-element operations, whereas the generalized version supports “sub-array” by “sub-array” operations. The Perl vector library PDL provides a similar functionality and its terms are re-used in the following.

Each generalized ufunc has information associated with it that states what the “core” dimensionality of the inputs is, as well as the corresponding dimensionality of the outputs (the element-wise ufuncs have zero core dimensions). The list of the core dimensions for all arguments is called the “signature” of a ufunc. For example, the ufunc numpy.add has signature “(),()->()” defining two scalar inputs and one scalar output.

Another example is (see the GeneralLoopingFunctions page) the function inner1d(a,b) with a signature of “(i),(i)->()”. This applies the inner product along the last axis of each input, but keeps the remaining indices intact. For example, where a is of shape (3,5,N) and b is of shape (5,N), this will return an output of shape (3,5). The underlying elementary function is called 3*5 times. In the signature, we specify one core dimension “(i)” for each input and zero core dimensions “()” for the output, since it takes two 1-d arrays and returns a scalar. By using the same name “i”, we specify that the two corresponding dimensions should be of the same size (or one of them is of size 1 and will be broadcasted).

The dimensions beyond the core dimensions are called “loop” dimensions. In the above example, this corresponds to (3,5).

The usual numpy “broadcasting” rules apply, where the signature determines how the dimensions of each input/output object are split into core and loop dimensions:

While an input array has a smaller dimensionality than the corresponding number of core dimensions, 1’s are pre-pended to its shape. The core dimensions are removed from all inputs and the remaining dimensions are broadcasted; defining the loop dimensions. The output is given by the loop dimensions plus the output core dimensions.

Numpy can now be built on windows 64 bits (amd64 only, not IA64), with both MS compilers and mingw-w64 compilers:

This is *highly experimental*: DO NOT USE FOR PRODUCTION USE. See INSTALL.txt,
Windows 64 bits section for more information on limitations and how to build it
by yourself.

Float formatting is now handled by numpy instead of the C runtime: this enables locale independent formatting, more robust fromstring and related methods. Special values (inf and nan) are also more consistent across platforms (nan vs IND/NaN, etc...), and more consistent with recent python formatting work (in 2.6 and later).

The maximum/minimum ufuncs now reliably propagate nans. If one of the arguments is a nan, then nan is retured. This affects np.min/np.max, amin/amax and the array methods max/min. New ufuncs fmax and fmin have been added to deal with non-propagating nans.

The ufunc sign now returns nan for the sign of anan.

- fmax - same as maximum for integer types and non-nan floats. Returns the non-nan argument if one argument is nan and returns nan if both arguments are nan.
- fmin - same as minimum for integer types and non-nan floats. Returns the non-nan argument if one argument is nan and returns nan if both arguments are nan.
- deg2rad - converts degrees to radians, same as the radians ufunc.
- rad2deg - converts radians to degrees, same as the degrees ufunc.
- log2 - base 2 logarithm.
- exp2 - base 2 exponential.
- logaddexp - add numbers stored as logarithms and return the logarithm of the result.
- logaddexp2 - add numbers stored as base 2 logarithms and return the base 2 logarithm of the result result.

- structured arrays should now be fully supported by MaskedArray
(r6463, r6324, r6305, r6300, r6294...)

Minor bug fixes (r6356, r6352, r6335, r6299, r6298)

Improved support for __iter__ (r6326)

made baseclass, sharedmask and hardmask accesible to the user (but

read-only) * doc update

Gfortran can now be used as a fortran compiler for numpy on windows, even when the C compiler is Visual Studio (VS 2005 and above; VS 2003 will NOT work). Gfortran + Visual studio does not work on windows 64 bits (but gcc + gfortran does). It is unclear whether it will be possible to use gfortran and visual studio at all on x64.

Automatic arch detection can now be bypassed from the command line for the superpack installed:

numpy-1.3.0-superpack-win32.exe /arch=nosse

will install a numpy which works on any x86, even if the running computer supports SSE set.

The semantics of histogram has been modified to fix long-standing issues with outliers handling. The main changes concern

- the definition of the bin edges, now including the rightmost edge, and
- the handling of upper outliers, now ignored rather than tallied in the rightmost bin.

The previous behavior is still accessible using *new=False*, but this is
deprecated, and will be removed entirely in 1.4.0.

A lot of documentation has been added. Both user guide and references can be built from sphinx.

The following functions have been added to the multiarray C API:

- PyArray_GetEndianness: to get runtime endianness

The following functions have been added to the ufunc API:

- PyUFunc_FromFuncAndDataAndSignature: to declare a more general ufunc
(generalized ufunc).

New public C defines are available for ARCH specific code through numpy/npy_cpu.h:

- NPY_CPU_X86: x86 arch (32 bits)
- NPY_CPU_AMD64: amd64 arch (x86_64, NOT Itanium)
- NPY_CPU_PPC: 32 bits ppc
- NPY_CPU_PPC64: 64 bits ppc
- NPY_CPU_SPARC: 32 bits sparc
- NPY_CPU_SPARC64: 64 bits sparc
- NPY_CPU_S390: S390
- NPY_CPU_PARISC: PARISC

New macros for CPU endianness has been added as well (see internal changes below for details):

- NPY_BYTE_ORDER: integer
- NPY_LITTLE_ENDIAN/NPY_BIG_ENDIAN defines

Those provide portable alternatives to glibc endian.h macros for platforms without it.

npy_math.h now makes available several portable macro to get NAN, INFINITY:

- NPY_NAN: equivalent to NAN, which is a GNU extension
- NPY_INFINITY: equivalent to C99 INFINITY
- NPY_PZERO, NPY_NZERO: positive and negative zero respectively

Corresponding single and extended precision macros are available as well. All references to NAN, or home-grown computation of NAN on the fly have been removed for consistency.

This should make the porting to new platforms easier, and more robust. In particular, the configuration stage does not need to execute any code on the target platform, which is a first step toward cross-compilation.

http://projects.scipy.org/numpy/browser/trunk/doc/neps/math_config_clean.txt

A lot of code cleanup for umath/ufunc code (charris).

Numpy can now build with -W -Wall without warnings

http://projects.scipy.org/numpy/browser/trunk/doc/neps/warnfix.txt

The core math functions (sin, cos, etc... for basic C types) have been put into a separate library; it acts as a compatibility layer, to support most C99 maths functions (real only for now). The library includes platform-specific fixes for various maths functions, such as using those versions should be more robust than using your platform functions directly. The API for existing functions is exactly the same as the C99 math functions API; the only difference is the npy prefix (npy_cos vs cos).

The core library will be made available to any extension in 1.4.0.

npy_cpu.h defines numpy specific CPU defines, such as NPY_CPU_X86, etc... Those are portable across OS and toolchains, and set up when the header is parsed, so that they can be safely used even in the case of cross-compilation (the values is not set when numpy is built), or for multi-arch binaries (e.g. fat binaries on Max OS X).

npy_endian.h defines numpy specific endianness defines, modeled on the glibc endian.h. NPY_BYTE_ORDER is equivalent to BYTE_ORDER, and one of NPY_LITTLE_ENDIAN or NPY_BIG_ENDIAN is defined. As for CPU archs, those are set when the header is parsed by the compiler, and as such can be used for cross-compilation and multi-arch binaries.