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278 lines
9.8 KiB
ReStructuredText
Executable file
=========================
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NumPy 1.3.0 Release Notes
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=========================
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This minor includes numerous bug fixes, official python 2.6 support, and
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several new features such as generalized ufuncs.
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Highlights
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==========
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Python 2.6 support
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~~~~~~~~~~~~~~~~~~
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Python 2.6 is now supported on all previously supported platforms, including
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windows.
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http://www.python.org/dev/peps/pep-0361/
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Generalized ufuncs
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~~~~~~~~~~~~~~~~~~
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There is a general need for looping over not only functions on scalars but also
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over functions on vectors (or arrays), as explained on
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http://scipy.org/scipy/numpy/wiki/GeneralLoopingFunctions. We propose to
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realize this concept by generalizing the universal functions (ufuncs), and
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provide a C implementation that adds ~500 lines to the numpy code base. In
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current (specialized) ufuncs, the elementary function is limited to
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element-by-element operations, whereas the generalized version supports
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"sub-array" by "sub-array" operations. The Perl vector library PDL provides a
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similar functionality and its terms are re-used in the following.
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Each generalized ufunc has information associated with it that states what the
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"core" dimensionality of the inputs is, as well as the corresponding
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dimensionality of the outputs (the element-wise ufuncs have zero core
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dimensions). The list of the core dimensions for all arguments is called the
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"signature" of a ufunc. For example, the ufunc numpy.add has signature
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"(),()->()" defining two scalar inputs and one scalar output.
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Another example is (see the GeneralLoopingFunctions page) the function
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inner1d(a,b) with a signature of "(i),(i)->()". This applies the inner product
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along the last axis of each input, but keeps the remaining indices intact. For
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example, where a is of shape (3,5,N) and b is of shape (5,N), this will return
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an output of shape (3,5). The underlying elementary function is called 3*5
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times. In the signature, we specify one core dimension "(i)" for each input and
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zero core dimensions "()" for the output, since it takes two 1-d arrays and
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returns a scalar. By using the same name "i", we specify that the two
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corresponding dimensions should be of the same size (or one of them is of size
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1 and will be broadcasted).
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The dimensions beyond the core dimensions are called "loop" dimensions. In the
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above example, this corresponds to (3,5).
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The usual numpy "broadcasting" rules apply, where the signature determines how
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the dimensions of each input/output object are split into core and loop
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dimensions:
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While an input array has a smaller dimensionality than the corresponding number
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of core dimensions, 1's are pre-pended to its shape. The core dimensions are
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removed from all inputs and the remaining dimensions are broadcasted; defining
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the loop dimensions. The output is given by the loop dimensions plus the
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output core dimensions.
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Experimental Windows 64 bits support
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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Numpy can now be built on windows 64 bits (amd64 only, not IA64), with both MS
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compilers and mingw-w64 compilers:
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This is *highly experimental*: DO NOT USE FOR PRODUCTION USE. See INSTALL.txt,
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Windows 64 bits section for more information on limitations and how to build it
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by yourself.
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New features
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============
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Formatting issues
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~~~~~~~~~~~~~~~~~
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Float formatting is now handled by numpy instead of the C runtime: this enables
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locale independent formatting, more robust fromstring and related methods.
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Special values (inf and nan) are also more consistent across platforms (nan vs
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IND/NaN, etc...), and more consistent with recent python formatting work (in
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2.6 and later).
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Nan handling in max/min
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~~~~~~~~~~~~~~~~~~~~~~~
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The maximum/minimum ufuncs now reliably propagate nans. If one of the
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arguments is a nan, then nan is retured. This affects np.min/np.max, amin/amax
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and the array methods max/min. New ufuncs fmax and fmin have been added to deal
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with non-propagating nans.
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Nan handling in sign
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~~~~~~~~~~~~~~~~~~~~
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The ufunc sign now returns nan for the sign of anan.
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New ufuncs
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~~~~~~~~~~
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#. fmax - same as maximum for integer types and non-nan floats. Returns the
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non-nan argument if one argument is nan and returns nan if both arguments
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are nan.
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#. fmin - same as minimum for integer types and non-nan floats. Returns the
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non-nan argument if one argument is nan and returns nan if both arguments
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are nan.
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#. deg2rad - converts degrees to radians, same as the radians ufunc.
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#. rad2deg - converts radians to degrees, same as the degrees ufunc.
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#. log2 - base 2 logarithm.
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#. exp2 - base 2 exponential.
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#. trunc - truncate floats to nearest integer towards zero.
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#. logaddexp - add numbers stored as logarithms and return the logarithm
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of the result.
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#. logaddexp2 - add numbers stored as base 2 logarithms and return the base 2
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logarithm of the result result.
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Masked arrays
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~~~~~~~~~~~~~
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Several new features and bug fixes, including:
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* structured arrays should now be fully supported by MaskedArray
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(r6463, r6324, r6305, r6300, r6294...)
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* Minor bug fixes (r6356, r6352, r6335, r6299, r6298)
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* Improved support for __iter__ (r6326)
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* made baseclass, sharedmask and hardmask accesible to the user (but
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read-only)
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* doc update
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gfortran support on windows
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~~~~~~~~~~~~~~~~~~~~~~~~~~~
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Gfortran can now be used as a fortran compiler for numpy on windows, even when
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the C compiler is Visual Studio (VS 2005 and above; VS 2003 will NOT work).
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Gfortran + Visual studio does not work on windows 64 bits (but gcc + gfortran
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does). It is unclear whether it will be possible to use gfortran and visual
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studio at all on x64.
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Arch option for windows binary
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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Automatic arch detection can now be bypassed from the command line for the superpack installed:
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numpy-1.3.0-superpack-win32.exe /arch=nosse
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will install a numpy which works on any x86, even if the running computer
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supports SSE set.
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Deprecated features
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===================
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Histogram
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~~~~~~~~~
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The semantics of histogram has been modified to fix long-standing issues
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with outliers handling. The main changes concern
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#. the definition of the bin edges, now including the rightmost edge, and
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#. the handling of upper outliers, now ignored rather than tallied in the
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rightmost bin.
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The previous behavior is still accessible using `new=False`, but this is
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deprecated, and will be removed entirely in 1.4.0.
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Documentation changes
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=====================
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A lot of documentation has been added. Both user guide and references can be
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built from sphinx.
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New C API
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=========
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Multiarray API
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~~~~~~~~~~~~~~
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The following functions have been added to the multiarray C API:
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* PyArray_GetEndianness: to get runtime endianness
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Ufunc API
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~~~~~~~~~~~~~~
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The following functions have been added to the ufunc API:
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* PyUFunc_FromFuncAndDataAndSignature: to declare a more general ufunc
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(generalized ufunc).
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New defines
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~~~~~~~~~~~
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New public C defines are available for ARCH specific code through numpy/npy_cpu.h:
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* NPY_CPU_X86: x86 arch (32 bits)
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* NPY_CPU_AMD64: amd64 arch (x86_64, NOT Itanium)
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* NPY_CPU_PPC: 32 bits ppc
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* NPY_CPU_PPC64: 64 bits ppc
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* NPY_CPU_SPARC: 32 bits sparc
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* NPY_CPU_SPARC64: 64 bits sparc
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* NPY_CPU_S390: S390
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* NPY_CPU_IA64: ia64
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* NPY_CPU_PARISC: PARISC
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New macros for CPU endianness has been added as well (see internal changes
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below for details):
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* NPY_BYTE_ORDER: integer
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* NPY_LITTLE_ENDIAN/NPY_BIG_ENDIAN defines
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Those provide portable alternatives to glibc endian.h macros for platforms
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without it.
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Portable NAN, INFINITY, etc...
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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npy_math.h now makes available several portable macro to get NAN, INFINITY:
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* NPY_NAN: equivalent to NAN, which is a GNU extension
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* NPY_INFINITY: equivalent to C99 INFINITY
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* NPY_PZERO, NPY_NZERO: positive and negative zero respectively
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Corresponding single and extended precision macros are available as well. All
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references to NAN, or home-grown computation of NAN on the fly have been
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removed for consistency.
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Internal changes
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================
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numpy.core math configuration revamp
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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This should make the porting to new platforms easier, and more robust. In
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particular, the configuration stage does not need to execute any code on the
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target platform, which is a first step toward cross-compilation.
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http://projects.scipy.org/numpy/browser/trunk/doc/neps/math_config_clean.txt
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umath refactor
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~~~~~~~~~~~~~~
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A lot of code cleanup for umath/ufunc code (charris).
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Improvements to build warnings
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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Numpy can now build with -W -Wall without warnings
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http://projects.scipy.org/numpy/browser/trunk/doc/neps/warnfix.txt
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Separate core math library
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~~~~~~~~~~~~~~~~~~~~~~~~~~
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The core math functions (sin, cos, etc... for basic C types) have been put into
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a separate library; it acts as a compatibility layer, to support most C99 maths
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functions (real only for now). The library includes platform-specific fixes for
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various maths functions, such as using those versions should be more robust
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than using your platform functions directly. The API for existing functions is
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exactly the same as the C99 math functions API; the only difference is the npy
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prefix (npy_cos vs cos).
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The core library will be made available to any extension in 1.4.0.
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CPU arch detection
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~~~~~~~~~~~~~~~~~~
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npy_cpu.h defines numpy specific CPU defines, such as NPY_CPU_X86, etc...
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Those are portable across OS and toolchains, and set up when the header is
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parsed, so that they can be safely used even in the case of cross-compilation
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(the values is not set when numpy is built), or for multi-arch binaries (e.g.
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fat binaries on Max OS X).
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npy_endian.h defines numpy specific endianness defines, modeled on the glibc
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endian.h. NPY_BYTE_ORDER is equivalent to BYTE_ORDER, and one of
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NPY_LITTLE_ENDIAN or NPY_BIG_ENDIAN is defined. As for CPU archs, those are set
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when the header is parsed by the compiler, and as such can be used for
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cross-compilation and multi-arch binaries.
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