- Python 3.10 is now the minimum supported version.
- Better support for CPUs that do not have a power of 2 number of cores. See #479 and #490. Thanks to @avalentino.
- Allow numexpr to run with the multithreading package in Python. See PR #496. Thanks to @emmaai
- Wheels for Python 3.13 are now provided.
- The default number of 'safe' threads has been upgraded to 16 (instead of previous 8). That means that if your CPU has > 16 cores, the default is to use 16. You can always override this with the "NUMEXPR_MAX_THREADS" environment variable.
- NumPy 1.23 is now the minimum supported.
- Preliminary support for Python 3.13. Thanks to Karolina Surma.
- Fix tests on nthreads detection (closes: #479). Thanks to @avalentino.
- The build process has been modernized and now uses the pyproject.toml file for more of the configuration options.
- Support for NumPy 2.0.0. This is still experimental, so please report any issues you find. Thanks to Clément Robert and Thomas Caswell for the work.
- Avoid erroring when OMP_NUM_THREADS is empty string. Thanks to Patrick Hoefler.
- Do not warn if OMP_NUM_THREAD set.
- Support for PyPy (see PRs #467 and #740). The full test suite should pass now, at least for the 3.10 version. Thanks to @27rabbitlt for most of the work and @mgorny and @mattip for providing help and additional fixes. Fixes #463.
- Fixed more sanitizer issues (see PR #469). Thanks to @27rabbitlt.
- Modernized the test suite to avoid some warnings.
- Fix re_evaluate not taking global_dict as argument. Thanks to Teng Liu (@27rabbitlt).
- Fix parsing of simple complex numbers. Now, ne.evaluate('1.5j') works. Thanks to Teng Liu (@27rabbitlt).
- Fixes for upcoming NumPy 2.0:
- Replace npy_cdouble with C++ complex. Thanks to Teng Liu (@27rabbitlt).
- Add NE_MAXARGS for future numpy change NPY_MAXARGS. Now it is set to 64 to match NumPy 2.0 value. Thanks to Teng Liu (@27rabbitlt).
- More permissive rules in sanitizing regular expression: allow to access digits after the . with scientific notation. Thanks to Thomas Vincent.
- Don't reject double underscores that are not at the start or end of a variable name (pandas uses those), or scientific-notation numbers with digits after the decimal point. Thanks to Rebecca Palmer.
- Do not use numpy.alltrue in the test suite, as it has been deprecated (replaced by numpy.all). Thanks to Rebecca Chen.
- Wheels for Python 3.12. Wheels for 3.7 and 3.8 are not generated anymore.
The sanitization can be turned off by default by setting an environment variable,
set NUMEXPR_SANITIZE=0
Improved behavior of the blacklist to avoid triggering on private variables and scientific notation numbers.
- A validate function has been added. This function checks the inputs, returning None on success or raising an exception on invalid inputs. This function was added as numerous projects seem to be using NumExpr for parsing user inputs. re_evaluate may be called directly following validate.
- As an addendum to the use of NumExpr for parsing user inputs, is that NumExpr calls eval on the inputs. A regular expression is now applied to help sanitize the input expression string, forbidding '__', ':', and ';'. Attribute access is also banned except for '.r' for real and '.i' for imag.
- Thanks to timbrist for a fix to behavior of NumExpr with integers to negative powers. NumExpr was pre-checking integer powers for negative values, which was both inefficient and caused parsing errors in some situations. Now NumExpr will simply return 0 as a result for such cases. While NumExpr generally tries to follow NumPy behavior, performance is also critical.
- Thanks to peadar for some fixes to how NumExpr launches threads for embedded applications.
- Thanks to de11n for making parsing of the site.cfg for MKL consistent among all shared platforms.
- Support for Python 3.11 has been added.
- Thanks to Tobias Hangleiter for an improved accuracy complex expm1 function. While it is 25 % slower, it is significantly more accurate for the real component over a range of values and matches NumPy outputs much more closely.
- Thanks to Kirill Kouzoubov for a range of fixes to constants parsing that was resulting in duplicated constants of the same value.
- Thanks to Mark Harfouche for noticing that we no longer need numpy version checks. packaging is no longer a requirement as a result.
- 2.8.2 was skipped due to an error in uploading to PyPi.
- Support for Python 3.6 has been dropped due to the need to substitute the flag NPY_ARRAY_WRITEBACKIFCOPY for NPY_ARRAY_UPDATEIFCOPY. This flag change was initiated in NumPy 1.14 and finalized in 1.23. The only changes were made to cases where an unaligned constant was passed in with a pre-allocated output variable:
`
x = np.empty(5, dtype=np.uint8)[1:].view(np.int32)
ne.evaluate('3', out=x)
`
We think the risk of issues is very low, but if you are using NumExpr as a expression evaluation tool you may want to write a test for this edge case.
- Thanks to Matt Einhorn (@matham) for improvements to the GitHub Actions build process to add support for Apple Silicon and aarch64.
- Thanks to Biswapriyo Nath (@biswa96) for a fix to allow mingw builds on Windows.
- There have been some changes made to not import platform.machine() on sparc but it is highly advised to upgrade to Python 3.9+ to avoid this issue with the Python core package platform.
- Fixed dependency list.
- Added
pyproject.tomland modernize thesetup.pyscript. Thanks to Antonio Valentino for the PR.
- Wheels for Python 3.10 are now provided.
- Support for Python 2.7 and 3.5 has been discontinued.
- All residual support for Python 2.X syntax has been removed, and therefore the setup build no longer makes calls to the 2to3 script. The setup.py has been refactored to be more modern.
- The examples on how to link into Intel VML/MKL/oneAPI now use the dynamic library.
- Pinned Numpy versions to minimum supported version in an effort to alleviate issues seen in Windows machines not having the same MSVC runtime installed as was used to build the wheels.
- ARMv8 wheels are now available, thanks to odidev for the pull request.
- Support for Python 2.7 and 3.5 is deprecated and will be discontinued when cibuildwheels and/or GitHub Actions no longer support these versions.
- Wheels are now provided for Python 3.7, 3.5, 3.6, 3.7, 3.8, and 3.9 via GitHub Actions.
- The block size is now exported into the namespace as numexpr.__BLOCK_SIZE1__ as a read-only value.
- If using MKL, the number of threads for VML is no longer forced to 1 on loading the module. Testing has shown that VML never runs in multi-threaded mode for the default BLOCKSIZE1 of 1024 elements, and forcing to 1 can have deleterious effects on NumPy functions when built with MKL. See issue #355 for details.
- Use of ndarray.tostring() in tests has been switch to ndarray.tobytes() for future-proofing deprecation of .tostring(), if the version of NumPy is greater than 1.9.
- Added a utility method get_num_threads that returns the (maximum) number of threads currently in use by the virtual machine. The functionality of set_num_threads whereby it returns the previous value has been deprecated and will be removed in 2.8.X.
- Python 3.8 support has been added.
- Python 3.4 support is discontinued.
- The tests are now compatible with NumPy 1.18.
- site.cfg.example was updated to use the libraries tag instead of mkl_libs, which is recommended for newer version of NumPy.
- The default number of 'safe' threads has been restored to the historical limit of 8, if the environment variable "NUMEXPR_MAX_THREADS" has not been set.
- Thanks to @eltoder who fixed a small memory leak.
- Support for Python 2.6 has been dropped, as it is no longer available via TravisCI.
- A typo in the test suite that had a less than rather than greater than symbol in the NumPy version check has been corrected thanks to dhomeier.
- The file site.cfg was being accidently included in the sdists on PyPi. It has now been excluded.
- Thanks to Mike Toews for more robust handling of the thread-setting environment variables.
- With Appveyor updating to Python 3.7.1, wheels for Python 3.7 are now available in addition to those for other OSes.
- Add check to make sure that f_locals is not actually f_globals when we do the f_locals clear to avoid the #310 memory leak issue.
- Compare NumPy versions using distutils.version.LooseVersion to avoid issue #312 when working with NumPy development versions.
- As part of multibuild, wheels for Python 3.7 for Linux and MacOSX are now available on PyPI.
- Thanks to Lehman Garrison for finding and fixing a bug that exhibited memory leak-like behavior. The use in numexpr.evaluate of sys._getframe combined with .f_locals from that frame object results an extra refcount on objects in the frame that calls numexpr.evaluate, and not evaluate's frame. So if the calling frame remains in scope for a long time (such as a procedural script where numexpr is called from the base frame) garbage collection would never occur.
- Imports for the numexpr.test submodule were made lazy in the numexpr module.
- Thanks to Mark Dickinson for a fix to the thread barrier that occassionally suffered from spurious wakeups on MacOSX.
- The maximum thread count can now be set at import-time by setting the environment variable 'NUMEXPR_MAX_THREADS'. The default number of max threads was lowered from 4096 (which was deemed excessive) to 64.
- A number of imports were removed (pkg_resources) or made lazy (cpuinfo) in order to speed load-times for downstream packages (such as pandas, sympy, and tables). Import time has dropped from about 330 ms to 90 ms. Thanks to Jason Sachs for pointing out the source of the slow-down.
- Thanks to Alvaro Lopez Ortega for updates to benchmarks to be compatible with Python 3.
- Travis and AppVeyor now fail if the test module fails or errors.
- Thanks to Mahdi Ben Jelloul for a patch that removed a bug where constants in where calls would raise a ValueError.
- Fixed a bug whereby all-constant power operations would lead to infinite recursion.
- Christoph Gohlke noticed a lack of coverage for the 2.6.3 floor and ceil functions for MKL that caused seg-faults in test, so thanks to him for that.
- Documentation now available at readthedocs.io.
- Support for floor() and ceil() functions added by Caleb P. Burns.
- NumPy requirement increased from 1.6 to 1.7 due to changes in iterator flags (#245).
- Sphinx autodocs support added for documentation on readthedocs.org.
- Fixed a bug where complex constants would return an error, fixing problems with sympy when using NumExpr as a backend.
- Fix for #277 whereby arrays of shape (1,...) would be reduced as if they were full reduction. Behavoir now matches that of NumPy.
- String literals are automatically encoded into 'ascii' bytes for convience (see #281).
- Updates to keep with API changes in newer NumPy versions (#228). Thanks to Oleksandr Pavlyk.
- Removed several warnings (#226 and #227). Thanks to Oleksander Pavlyk.
- Fix bugs in function stringcontains() (#230). Thanks to Alexander Shadchin.
- Detection of the POWER processor (#232). Thanks to Breno Leitao.
- Fix pow result casting (#235). Thanks to Fernando Seiti Furusato.
- Fix integers to negative integer powers (#240). Thanks to Antonio Valentino.
- Detect numpy exceptions in expression evaluation (#240). Thanks to Antonio Valentino.
- Better handling of RC versions (#243). Thanks to Antonio Valentino.
- Fixed a performance regression in some situations as consequence of increasing too much the BLOCK_SIZE1 constant. After more careful benchmarks (both in VML and non-VML modes), the value has been set again to 1024 (down from 8192). The benchmarks have been made with a relatively new processor (Intel Xeon E3-1245 v5 @ 3.50GHz), so they should work well for a good range of processors again.
- Added NetBSD support to CPU detection. Thanks to Thomas Klausner.
- Introduced a new re_evaluate() function for re-evaluating the previous executed array expression without any check. This is meant for accelerating loops that are re-evaluating the same expression repeatedly without changing anything else than the operands. If unsure, use evaluate() which is safer.
- The BLOCK_SIZE1 and BLOCK_SIZE2 constants have been re-checked in order to find a value maximizing most of the benchmarks in bench/ directory. The new values (8192 and 16 respectively) give somewhat better results (~5%) overall. The CPU used for fine tuning is a relatively new Haswell processor (E3-1240 v3).
- The '--name' flag for setup.py returning the name of the package is honored now (issue #215).
- conj() and abs() actually added as VML-powered functions, preventing the same problems than log10() before (PR #212). Thanks to Tom Kooij for the fix!
- Fix for log10() and conj() functions. These produced wrong results when numexpr was compiled with Intel's MKL (which is a popular build since Anaconda ships it by default) and non-contiguous data (issue #210). Thanks to Arne de Laat and Tom Kooij for reporting and providing a nice test unit.
- Fix that allows numexpr-powered apps to be profiled with pympler. Thanks to @nbecker.
- Added locking for allowing the use of numexpr in multi-threaded callers (this does not prevent numexpr to use multiple cores simultaneously). (PR #199, Antoine Pitrou, PR #200, Jenn Olsen).
- Added new min() and max() functions (PR #195, CJ Carey).
- Fixed some UserWarnings in Solaris (PR #189, Graham Jones).
- Better handling of MSVC defines. (#168, Francesc Alted).
- Undone a 'fix' for a harmless data race. (#185 Benedikt Reinartz, Francesc Alted).
- Ignore NumPy warnings (overflow/underflow, divide by zero and others) that only show up in Python3. Masking these warnings in tests is fine because all the results are checked to be valid. (#183, Francesc Alted).
- Fix bad #ifdef for including stdint on Windows (PR #186, Mike Sarahan).
- Honor OMP_NUM_THREADS as a fallback in case NUMEXPR_NUM_THREADS is not set. Fixes #161. (PR #175, Stefan Erb).
- Added support for AppVeyor (PR #178 Andrea Bedini)
- Fix to allow numexpr to be imported after eventlet.monkey_patch(), as suggested in #118 (PR #180 Ben Moran).
- Fix harmless data race that triggers false positives in ThreadSanitizer. (PR #179, Clement Courbet).
- Fixed some string tests on Python 3 (PR #182, Antonio Valentino).
- 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.