#XXX version-specific blurb XXX#
- Comparisons with empty strings work correctly now. Fixes #121 and PyTables #184.
- Improved setup.py so that pip can query the name and version without actually doing the installation. Thanks to Joris Borgdorff.
- Added more configuration examples for compiling with MKL/VML support. Thanks to Davide Del Vento.
- Symbol MKL_VML changed into MKL_DOMAIN_VML because the former is deprecated in newer MKL. Thanks to Nick Papior Andersen.
- Better determination of methods in cpuinfo module. Thanks to Marc Jofre.
- Improved NumPy version determination (handy for 1.10.0). Thanks to Åsmund Hjulstad.
- Benchmarks run now with both Python 2 and Python 3. Thanks to Zoran Plesivčak.
- A new contains() function has been added for detecting substrings in strings. Only plain strings (bytes) are supported for now. See PR #135 and ticket #142. Thanks to Marcin Krol.
- New version of setup.py that allows better management of NumPy dependency. See PR #133. Thanks to Aleks Bunin.
- Added support for shift-left (<<) and shift-right (>>) binary operators. See PR #131. Thanks to fish2000!
- Removed the rpath flag for the GCC linker, because it is probably not necessary and it chokes to clang.
- Site has been migrated to https://github.com/pydata/numexpr. All new tickets and PR should be directed there.
- [ENH] A conj() function for computing the conjugate of complex arrays has been added. Thanks to David Menéndez. See PR #125.
- [FIX] Fixed a DeprecationWarning derived of using oa_ndim == 0 and op_axes == NULL when using NpyIter_AdvancedNew() and NumPy 1.8. Thanks to Mark Wiebe for advise on how to fix this properly.
- The copy_args argument of NumExpr function has been brought lack. This has been mainly necessary for compatibility with PyTables < 3.0, which I decided to continue to support. Fixed #115.
- The __nonzero__ method in ExpressionNode class has been commented out. This is also for compatibility with PyTables < 3.0. See #24 for details.
- Fixed the type of some parameters in the C extension so that s390 architecture compiles. Fixes #116. Thank to Antonio Valentino for reporting and the patch.
- Fixes a secondary effect of "from numpy.testing import *", where division is imported now too, so only then necessary functions from there are imported now. Thanks to Christoph Gohlke for the patch.
- [LICENSE] Fixed a problem with the license of the numexpr/win32/pthread.{c,h} files emulating pthreads on Windows platforms. After persmission from the original authors is granted, these files adopt the MIT license and can be redistributed without problems. See issue #109 for details (https://code.google.com/p/numexpr/issues/detail?id=110).
- [ENH] Improved the algorithm to decide the initial number of threads to be used. This was necessary because by default, numexpr was using a number of threads equal to the detected number of cores, and this can be just too much for moder systems where this number can be too high (and counterporductive for performance in many cases). Now, the 'NUMEXPR_NUM_THREADS' environment variable is honored, and in case this is not present, a maximum number of 8 threads are setup initially. The new algorithm is fully described in the Users Guide now in the note of 'General routines' section: https://code.google.com/p/numexpr/wiki/UsersGuide#General_routines. Closes #110.
- [ENH] numexpr.test() returns TestResult instead of None now. Closes #111.
- [FIX] Modulus with zero with integers no longer crashes the interpreter. It nows puts a zero in the result. Fixes #107.
- [API CLEAN] Removed copy_args argument of evaluate. This should only be used by old versions of PyTables (< 3.0).
- [DOC] Documented the optimization and truediv flags of evaluate in Users Guide (https://code.google.com/p/numexpr/wiki/UsersGuide).
- Dropped compatibility with Python < 2.6.
- Improve compatibiity with Python 3:
- switch from PyString to PyBytes API (requires Python >= 2.6).
- fixed incompatibilities regarding the int/long API
- use the Py_TYPE macro
- use the PyVarObject_HEAD_INIT macro instead of PyObject_HEAD_INIT
- Fixed several issues with different platforms not supporting multithreading or subprocess properly (see tickets #75 and #77).
- Now, when trying to use pure Python boolean operators, 'and', 'or' and 'not', an error is issued suggesting that '&', '|' and '~' should be used instead (fixes #24).
- Added compatibility with Python 2.5 (2.4 is definitely not supported anymore).
- numexpr.evaluate is fully documented now, in particular the new out, order and casting parameters.
- Reduction operations are fully documented now.
- Negative axis in reductions are not supported (they have never been actually), and a ValueError will be raised if they are used.
Added support for the new iterator object in NumPy 1.6 and later.
This allows for better performance with operations that implies broadcast operations, fortran-ordered or non-native byte orderings. Performance for other scenarios is preserved (except for very small arrays).
Division in numexpr is consistent now with Python/NumPy. Fixes #22 and #58.
Constants like "2." or "2.0" must be evaluated as float, not integer. Fixes #59.
evaluate() function has received a new parameter out for storing the result in already allocated arrays. This is very useful when dealing with large arrays, and a allocating new space for keeping the result is not acceptable. Closes #56.
Maximum number of threads raised from 256 to 4096. Machines with a higher number of cores will still be able to import numexpr, but limited to 4096 (which is an absurdly high number already).
- Multithreaded operation is disabled for small arrays (< 32 KB). This allows to remove the overhead of multithreading for such a small arrays. Closes #36.
- Dividing int arrays by zero gives a 0 as result now (and not a floating point exception anymore. This behaviour mimics NumPy. Thanks to Gaëtan de Menten for the fix. Closes #37.
- When compiled with VML support, the number of threads is set to 1 for VML core, and to the number of cores for the native pthreads implementation. This leads to much better performance. Closes #39.
- Fixed different issues with reduction operations (sum, prod). The problem is that the threaded code does not work well for broadcasting or reduction operations. Now, the serial code is used in those cases. Closes #41.
- Optimization of "compilation phase" through a better hash. This can lead up to a 25% of improvement when operating with variable expressions over small arrays. Thanks to Gaëtan de Menten for the patch. Closes #43.
- The
set_num_threadsnow returns the number of previous thread setting, as stated in the docstrings.
- Mingw32 can also work with pthreads compatibility code for win32. Fixes #31.
- Fixed a problem that used to happen when running Numexpr with threads in subprocesses. It seems that threads needs to be initialized whenever a subprocess is created. Fixes #33.
- The GIL (Global Interpreter Lock) is released during computations. This should allow for better resource usage for multithreaded apps. Fixes #35.
Added support for multi-threading in pure C. This is to avoid the GIL and allows to squeeze the best performance in both multi-core machines.
David Cooke contributed a thorough refactorization of the opcode machinery for the virtual machine. With this, it is really easy to add more opcodes. See:
http://code.google.com/p/numexpr/issues/detail?id=28
as an example.
Added a couple of opcodes to VM: where_bbbb and cast_ib. The first allow to get boolean arrays out of the where function. The second allows to cast a boolean array into an integer one. Thanks to gdementen for his contribution.
Fix negation of int64 numbers. Closes #25.
Using a npy_intp datatype (instead of plain int) so as to be able to manage arrays larger than 2 GB.
- Due to an oversight,
uint32types were not properly supported. That has been solved. Fixes #19. - Function abs for computing the absolute value added. However, it
does not strictly follow NumPy conventions. See
README.txtor website docs for more info on this. Thanks to Pauli Virtanen for the patch. Fixes #20.
A new type called internally float has been implemented so as to be able to work natively with single-precision floating points. This prevents the silent upcast to double types that was taking place in previous versions, so allowing both an improved performance and an optimal usage of memory for the single-precision computations. However, the casting rules for floating point types slightly differs from those of NumPy. See:
or the README.txt file for more info on this issue.
Support for Python 2.6 added.
When linking with the MKL, added a '-rpath' option to the link step so that the paths to MKL libraries are automatically included into the runtime library search path of the final package (i.e. the user won't need to update its LD_LIBRARY_PATH or LD_RUN_PATH environment variables anymore). Fixes #16.
Support for Intel's VML (Vector Math Library) added, normally included in Intel's MKL (Math Kernel Library). In addition, when the VML support is on, several processors can be used in parallel (see the new set_vml_num_threads() function). With that, the computations of transcendental functions can be accelerated quite a few. For example, typical speed-ups when using one single core for contiguous arrays are 3x with peaks of 7.5x (for the pow() function). When using 2 cores the speed-ups are around 4x and 14x respectively. Closes #9.
Some new VML-related functions have been added:
- set_vml_accuracy_mode(mode): Set the accuracy for VML operations.
- set_vml_num_threads(nthreads): Suggests a maximum number of threads to be used in VML operations.
- get_vml_version(): Get the VML/MKL library version.
See the README.txt for more info about them.
In order to easily allow the detection of the MKL, the setup.py has been updated to use the numpy.distutils. So, if you are already used to link NumPy/SciPy with MKL, then you will find that giving VML support to numexpr works almost the same.
A new print_versions() function has been made available. This allows to quickly print the versions on which numexpr is based on. Very handy for issue reporting purposes.
The numexpr.numexpr compiler function has been renamed to numexpr.NumExpr in order to avoid name collisions with the name of the package (!). This function is mainly for internal use, so you should not need to upgrade your existing numexpr scripts.
- The case for multidimensional array operands is properly accelerated now. Added a new benchmark (based on a script provided by Andrew Collette, thanks!) for easily testing this case in the future. Closes #12.
- Added a fix to avoid the caches in numexpr to grow too much. The dictionary caches are kept now always with less than 256 entries. Closes #11.
- The VERSION file is correctly copied now (it was not present for the 1.1 tar file, I don't know exactly why). Closes #8.
Numexpr can work now in threaded environments. Fixes #2.
The test suite can be run programmatically by using
numexpr.test().Support a more complete set of functions for expressions (including those that are not supported by MSVC 7.1 compiler, like the inverse hyperbolic or log1p and expm1 functions. The complete list now is:
- where(bool, number1, number2): number
- Number1 if the bool condition is true, number2 otherwise.
- {sin,cos,tan}(float|complex): float|complex
- Trigonometric sinus, cosinus or tangent.
- {arcsin,arccos,arctan}(float|complex): float|complex
- Trigonometric inverse sinus, cosinus or tangent.
- arctan2(float1, float2): float
- Trigonometric inverse tangent of float1/float2.
- {sinh,cosh,tanh}(float|complex): float|complex
- Hyperbolic sinus, cosinus or tangent.
- {arcsinh,arccosh,arctanh}(float|complex): float|complex
- Hyperbolic inverse sinus, cosinus or tangent.
- {log,log10,log1p}(float|complex): float|complex
- Natural, base-10 and log(1+x) logarithms.
- {exp,expm1}(float|complex): float|complex
- Exponential and exponential minus one.
- sqrt(float|complex): float|complex
- Square root.
- {real,imag}(complex): float
- Real or imaginary part of complex.
- complex(float, float): complex
- Complex from real and imaginary parts.