- Switched to automake 1.11.6, see CVE-2012-3386.
- #14669: Fixed extraction of CC from gmp.h.
- Fixed case of intermediate zero real or imaginary part in mpc_fma,
found by hydra with GMP_CHECK_RANDOMIZE=1346362345.
This is on top of the following changes from version 1.0
- Licence change towards LGPLv3+ for the code and GFDLv1.3+ (with no
invariant sections) for the documentation.
- 100% of all lines are covered by tests
- Renamed functions
. mpc_mul_2exp to mpc_mul_2ui
. mpc_div_2exp to mpc_div_2ui
- 0^0, which returned (NaN,NaN) previously, now returns (1,+0).
- Removed compatibility with K&R compilers, which was untestable due
to lack of such compilers.
- New functions
. mpc_log10
. mpc_mul_2si, mpc_div_2si
- Speed-ups
. mpc_fma
- Bug fixes
. mpc_div and mpc_norm now return a value indicating the effective
rounding direction, as the other functions.
. mpc_mul, mpc_sqr and mpc_norm now return correct results even if
there are over- or underflows during the computation.
. mpc_asin, mpc_proj, mpc_sqr: Wrong result when input variable has
infinite part and equals output variable is corrected.
. mpc_fr_sub: Wrong return value for imaginary part is corrected.
Convert to the new LIB_DEPENDS standard and remove hard-coded
.so versions from a couple of dependent ports.
Bump PORTREVISIONS of all dependent ports.
PR: 183141
Approved by: portmgr (bdrewery)
The numexpr package evaluates multiple-operator array expressions many times
faster than NumPy can. It accepts the expression as a string, analyzes it,
rewrites it more efficiently, and compiles it to faster Python code on the fly.
It's the next best thing to writing the expression in C and compiling it with
a specialized just-in-time (JIT) compiler, i.e. it does not require a compiler
at runtime.
Also, numexpr has support for the Intel VML (Vector Math Library) -- integrated
in Intel MKL (Math Kernel Library) --, allowing nice speed-ups when computing
transcendental functions (like trigonometrical, exponentials...) on top of
Intel-compatible platforms. This support also allows to use multiple cores in
your computations.
WWW: http://code.google.com/p/numexpr/
PR: ports/148372
Submitted by: Ju Pengfei <jupengfei@gmail.com>
Feature safe: yes
