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Package is "python-numexpr" Mon Mar 15 10:54:27 2021 rev:14 rq:878626 version:2.7.3 Changes: -------- --- /work/SRC/openSUSE:Factory/python-numexpr/python-numexpr.changes 2021-01-11 17:15:59.788585604 +0100 +++ /work/SRC/openSUSE:Factory/.python-numexpr.new.2401/python-numexpr.changes 2021-03-15 10:54:27.457176992 +0100 @@ -1,0 +2,15 @@ +Fri Mar 12 20:28:19 UTC 2021 - Dirk M??ller <[email protected]> + +- skip python3.6 build (no numpy) + +------------------------------------------------------------------- +Wed Mar 3 19:08:44 UTC 2021 - Arun Persaud <[email protected]> + +- update to version 2.7.3: + * 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. + +------------------------------------------------------------------- Old: ---- numexpr-2.7.2.tar.gz New: ---- numexpr-2.7.3.tar.gz ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ Other differences: ------------------ ++++++ python-numexpr.spec ++++++ --- /var/tmp/diff_new_pack.P3aVvF/_old 2021-03-15 10:54:27.921177705 +0100 +++ /var/tmp/diff_new_pack.P3aVvF/_new 2021-03-15 10:54:27.925177711 +0100 @@ -17,8 +17,9 @@ %{?!python_module:%define python_module() python-%{**} python3-%{**}} +%global skip_python36 1 Name: python-numexpr -Version: 2.7.2 +Version: 2.7.3 Release: 0 Summary: Numerical expression evaluator for NumPy License: MIT ++++++ numexpr-2.7.2.tar.gz -> numexpr-2.7.3.tar.gz ++++++ diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn' '--exclude=.svnignore' old/numexpr-2.7.2/ANNOUNCE.rst new/numexpr-2.7.3/ANNOUNCE.rst --- old/numexpr-2.7.2/ANNOUNCE.rst 2020-12-29 06:49:46.000000000 +0100 +++ new/numexpr-2.7.3/ANNOUNCE.rst 2021-03-01 05:41:15.000000000 +0100 @@ -1,41 +1,25 @@ ======================== -Announcing NumExpr 2.7.2 +Announcing NumExpr 2.7.3 ======================== Hi everyone, -It's been awhile since the last update to NumExpr, mostly as the existing scientific -Python tool chain for building wheels on PyPi became defunct and we have had to -redevelop a new one based on `cibuildwheel` and GitHub Actions. This release also -brings us support (and wheels for) Python 3.9. - -There have been a number of changes to enhance how NumExpr works when NumPy -uses MKL as a backend. +This is a maintenance release to make use of the oldest supported NumPy version +when building wheels, in an effort to alleviate issues seen on Windows machines +that do not have the latest Windows MSVC runtime installed. It also adds +wheels built via GitHub Actions for ARMv8 platforms. Project documentation is available at: http://numexpr.readthedocs.io/ -Changes from 2.7.1 to 2.7.2 +Changes from 2.7.2 to 2.7.3 --------------------------- -- 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. +- Pinned Numpy versions to minimum supported version in an effort to alleviate + issues seen in Windows machines not having the same Windows SDK installed as + was used to build the wheels. +- ARMv8 wheels are now available, thanks to `odidev` for the pull request. What's Numexpr? --------------- @@ -77,7 +61,6 @@ Let us know of any bugs, suggestions, gripes, kudos, etc. you may have. - Enjoy data! diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn' '--exclude=.svnignore' old/numexpr-2.7.2/AUTHORS.txt new/numexpr-2.7.3/AUTHORS.txt --- old/numexpr-2.7.2/AUTHORS.txt 2018-07-12 19:33:23.000000000 +0200 +++ new/numexpr-2.7.3/AUTHORS.txt 2021-03-01 05:15:44.000000000 +0100 @@ -24,4 +24,4 @@ David Cox improved readability of the Readme. Robert A. McLeod contributed bug fixes and ported the documentation to -numexpr.readthedocs.io. \ No newline at end of file +numexpr.readthedocs.io. He is the maintainer of the package since 2016. \ No newline at end of file diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn' '--exclude=.svnignore' old/numexpr-2.7.2/CMakeLists.txt new/numexpr-2.7.3/CMakeLists.txt --- old/numexpr-2.7.2/CMakeLists.txt 2018-07-12 19:33:23.000000000 +0200 +++ new/numexpr-2.7.3/CMakeLists.txt 1970-01-01 01:00:00.000000000 +0100 @@ -1,104 +0,0 @@ -# WARNING! EXPERIMENTAL! Use setup.py if you're not familiar with cmake. -# -# We recommend that you create a separate directory for the build, -# so that the build files aren't mixed in with the source files. -# e.g. -# $ mkdir build-cmake -# $ cd build-cmake -# $ cmake .. -# $ make -# -# MacOSX Notes: -# On MacOSX, it may default to 64-bit, even if your Python is 32-bit. -# The linker will give NO WARNING, and the resulting .so file will -# fail to load in Python. To fix this, run cmake as follows, -# assuming you're in <source dir>/build-cmake/: -# cmake -DCMAKE_OSX_ARCHITECTURES=i386 .. -# To debug this issue, you can compare "lipo -info `which python`" -# with "lipo -info numexpr.so". They should have the same platform info. -# -# Further problems on OS X appear to be related to EPD Python. CMake's -# default detection may be detecting the wrong python to link against. -# -# Windows Notes: -# Python 2.7 is built with Visual C++ 9 (aka 2008). This is the one -# you should pick when running cmake-gui. Be sure to switch the build -# configuration from Debug to Release (or RelWithDebInfo) in Visual Studio. - -project(numexpr) - -cmake_minimum_required(VERSION 2.8) - -# Force the default build type to be Release, because a Debug -# build doesn't work properly with the default Python build -if(NOT CMAKE_BUILD_TYPE) - set(CMAKE_BUILD_TYPE "Release" CACHE STRING - "Choose the type of build, options are: Debug Release -RelWithDebInfo MinSizeRel." - FORCE) -endif() - -set(CMAKE_MODULE_PATH ${PROJECT_SOURCE_DIR}) - -find_package(PythonInterp REQUIRED) -find_package(PythonLibsNew REQUIRED) -find_package(NumPy REQUIRED) - -# Default install location for Python packages -if(CMAKE_INSTALL_PREFIX_INITIALIZED_TO_DEFAULT) - set(CMAKE_INSTALL_PREFIX "${PYTHON_SITE_PACKAGES}" CACHE STRING - "Choose the Python module directory (default site-packages)" FORCE) -endif() - -# Require version >= 1.6 -if(NUMPY_VERSION_DECIMAL LESS 10600) - message(FATAL_ERROR, - "NumExpr requires NumPy >= 1.6") -endif() - -include_directories( - ${PYTHON_INCLUDE_DIRS} - ${NUMPY_INCLUDE_DIRS} - ) - -set(numexpr_SRC - numexpr/interpreter.cpp - numexpr/module.cpp - numexpr/numexpr_object.cpp - numexpr/complex_functions.hpp - numexpr/functions.hpp - numexpr/interpreter.hpp - numexpr/module.hpp - numexpr/missing_posix_functions.hpp - numexpr/msvc_function_stubs.hpp - numexpr/numexpr_config.hpp - numexpr/numexpr_object.hpp - numexpr/opcodes.hpp - ) -if(CMAKE_HOST_WIN32) - set(numexpr_SRC - ${numexpr_SRC} - numexpr/win32/pthread.c - ) -endif() - -python_add_module(interpreter ${numexpr_SRC}) - -# Generate __config__.py. This is a dummy placeholder, as I -# don't know why it's here. -file(WRITE "${PROJECT_BINARY_DIR}/__config__.py" - "# This file is generated by a CMakeFiles.txt configuration\n" - "__all__ = ['get_info','show']\n" - "def get_info(name):\n" - " return None\n" - "def show():\n" - " print('someone called show()')\n") - -# Install all the Python scripts -install(DIRECTORY numexpr DESTINATION "${CMAKE_INSTALL_PREFIX}" - FILES_MATCHING PATTERN "*.py") -# Install __config__.py -install(FILES "${PROJECT_BINARY_DIR}/__config__.py" - DESTINATION "${CMAKE_INSTALL_PREFIX}/numexpr") -# Install the module -install(TARGETS interpreter DESTINATION "${CMAKE_INSTALL_PREFIX}/numexpr") diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn' '--exclude=.svnignore' old/numexpr-2.7.2/INSTALL.rst new/numexpr-2.7.3/INSTALL.rst --- old/numexpr-2.7.2/INSTALL.rst 2018-07-12 19:33:23.000000000 +0200 +++ new/numexpr-2.7.3/INSTALL.rst 1970-01-01 01:00:00.000000000 +0100 @@ -1,54 +0,0 @@ -================== -Installing Numexpr -================== - -These are instructions for installing Numexpr on Unix systems. For -Windows, it is best to install it from binaries. However, you should -note that, for the time being, we cannot provide Windows binaries with -MKL support. - - -Building -======== - -This version of `Numexpr` requires Python 2.6 or greater, -and NumPy 1.6 or greater. - -It's built in the standard Python way:: - - $ python setup.py build - $ python setup.py install - -You can test `numexpr` with: - - $ python -c "import numexpr; numexpr.test()" - - -Enabling Intel's MKL support -============================ - -numexpr includes support for Intel's MKL library. This allows for -better performance on Intel architectures, mainly when evaluating -transcendental functions (trigonometrical, exponential...). It also -enables numexpr using several CPU cores. - -If you have Intel's MKL, just copy the `site.cfg.example` that comes -in the distribution to `site.cfg` and edit the latter giving proper -directions on how to find your MKL libraries in your system. After -doing this, you can proceed with the usual building instructions -listed above. - -Pay attention to the messages during the building process in order to -know whether MKL has been detected or not. Finally, you can check the -speed-ups on your machine by running the `bench/vml_timing.py` script -(you can play with different parameters to the -`set_vml_accuracy_mode()` and `set_vml_num_threads()` functions in the -script so as to see how it would affect performance). - - - -.. Local Variables: -.. mode: text -.. coding: utf-8 -.. fill-column: 70 -.. End: diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn' '--exclude=.svnignore' old/numexpr-2.7.2/PKG-INFO new/numexpr-2.7.3/PKG-INFO --- old/numexpr-2.7.2/PKG-INFO 2020-12-29 07:34:37.000000000 +0100 +++ new/numexpr-2.7.3/PKG-INFO 2021-03-03 19:11:42.000000000 +0100 @@ -1,6 +1,6 @@ Metadata-Version: 1.0 Name: numexpr -Version: 2.7.2 +Version: 2.7.3 Summary: Fast numerical expression evaluator for NumPy Home-page: https://github.com/pydata/numexpr Author: David M. Cooke, Francesc Alted and others @@ -76,6 +76,69 @@ In order to get a better idea on the different speed-ups that can be achieved on your platform, run the provided benchmarks. + Installation + ------------ + + From wheels + ^^^^^^^^^^^ + + NumExpr is available for install via `pip` for a wide range of platforms and + Python versions (which may be browsed at: https://pypi.org/project/numexpr/#files). + Installation can be performed as:: + + pip install numexpr + + If you are using the Anaconda or Miniconda distribution of Python you may prefer + to use the `conda` package manager in this case:: + + conda install numexpr + + From Source + ^^^^^^^^^^^ + + On most `Nix systems your compilers will already be present. However if you + are using a virtual environment with a substantially newer version of Python than + your system Python you may be prompted to install a new version of `gcc` or `clang`. + + For Windows, you will need to install the Microsoft Visual C++ Build Tools + (which are free) first.The version depends on which version of Python you have + installed: + + https://wiki.python.org/moin/WindowsCompilers + + For Python 3.6+ simply installating the latest version of MSVC build tools should + be sufficient. Note that wheels found via pip do not include MKL support. Wheels + available via `conda` will have MKL, if the MKL backend is used for NumPy. + + See `requirements.txt` for the required version of NumPy. + + NumExpr is built in the standard Python way:: + + python setup.py build install + + You can test `numexpr` with:: + + python -c "import numexpr; numexpr.test()" + + Do not test NumExpr in the source directory or you will generate import errors. + + Enable Intel?? MKL support + ^^^^^^^^^^^^^^^^^^^^^^^^^ + + NumExpr includes support for Intel's MKL library. This may provide better + performance on Intel architectures, mainly when evaluating transcendental + functions (trigonometrical, exponential...). + + If you have Intel's MKL, copy the `site.cfg.example` that comes with the + distribution to `site.cfg` and edit the latter file to provide correct paths to + the MKL libraries in your system. After doing this, you can proceed with the + usual building instructions listed above. + + Pay attention to the messages during the building process in order to know + whether MKL has been detected or not. Finally, you can check the speed-ups on + your machine by running the `bench/vml_timing.py` script (you can play with + different parameters to the `set_vml_accuracy_mode()` and `set_vml_num_threads()` + functions in the script so as to see how it would affect performance). Usage ----- diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn' '--exclude=.svnignore' old/numexpr-2.7.2/README.rst new/numexpr-2.7.3/README.rst --- old/numexpr-2.7.2/README.rst 2020-12-29 06:50:30.000000000 +0100 +++ new/numexpr-2.7.3/README.rst 2021-03-01 05:15:44.000000000 +0100 @@ -68,6 +68,69 @@ In order to get a better idea on the different speed-ups that can be achieved on your platform, run the provided benchmarks. +Installation +------------ + +From wheels +^^^^^^^^^^^ + +NumExpr is available for install via `pip` for a wide range of platforms and +Python versions (which may be browsed at: https://pypi.org/project/numexpr/#files). +Installation can be performed as:: + + pip install numexpr + +If you are using the Anaconda or Miniconda distribution of Python you may prefer +to use the `conda` package manager in this case:: + + conda install numexpr + +From Source +^^^^^^^^^^^ + +On most `Nix systems your compilers will already be present. However if you +are using a virtual environment with a substantially newer version of Python than +your system Python you may be prompted to install a new version of `gcc` or `clang`. + +For Windows, you will need to install the Microsoft Visual C++ Build Tools +(which are free) first.The version depends on which version of Python you have +installed: + +https://wiki.python.org/moin/WindowsCompilers + +For Python 3.6+ simply installating the latest version of MSVC build tools should +be sufficient. Note that wheels found via pip do not include MKL support. Wheels +available via `conda` will have MKL, if the MKL backend is used for NumPy. + +See `requirements.txt` for the required version of NumPy. + +NumExpr is built in the standard Python way:: + + python setup.py build install + +You can test `numexpr` with:: + + python -c "import numexpr; numexpr.test()" + +Do not test NumExpr in the source directory or you will generate import errors. + +Enable Intel?? MKL support +^^^^^^^^^^^^^^^^^^^^^^^^^ + +NumExpr includes support for Intel's MKL library. This may provide better +performance on Intel architectures, mainly when evaluating transcendental +functions (trigonometrical, exponential...). + +If you have Intel's MKL, copy the `site.cfg.example` that comes with the +distribution to `site.cfg` and edit the latter file to provide correct paths to +the MKL libraries in your system. After doing this, you can proceed with the +usual building instructions listed above. + +Pay attention to the messages during the building process in order to know +whether MKL has been detected or not. Finally, you can check the speed-ups on +your machine by running the `bench/vml_timing.py` script (you can play with +different parameters to the `set_vml_accuracy_mode()` and `set_vml_num_threads()` +functions in the script so as to see how it would affect performance). Usage ----- diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn' '--exclude=.svnignore' old/numexpr-2.7.2/RELEASE_NOTES.rst new/numexpr-2.7.3/RELEASE_NOTES.rst --- old/numexpr-2.7.2/RELEASE_NOTES.rst 2020-12-29 02:27:40.000000000 +0100 +++ new/numexpr-2.7.3/RELEASE_NOTES.rst 2021-03-01 05:40:50.000000000 +0100 @@ -2,6 +2,15 @@ Release notes for Numexpr 2.7 series ==================================== +Changes from 2.7.2 to 2.7.3 +--------------------------- + +- 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. + + Changes from 2.7.1 to 2.7.2 --------------------------- diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn' '--exclude=.svnignore' old/numexpr-2.7.2/numexpr/tests/test_numexpr.py new/numexpr-2.7.3/numexpr/tests/test_numexpr.py --- old/numexpr-2.7.2/numexpr/tests/test_numexpr.py 2020-08-26 01:26:50.000000000 +0200 +++ new/numexpr-2.7.3/numexpr/tests/test_numexpr.py 2021-03-01 05:15:44.000000000 +0100 @@ -1,3 +1,4 @@ + ################################################################### # Numexpr - Fast numerical array expression evaluator for NumPy. # @@ -991,11 +992,17 @@ with _environment('OMP_NUM_THREADS', '5'): # NUMEXPR_NUM_THREADS has priority with _environment('NUMEXPR_NUM_THREADS', '3'): - self.assertEquals(3, numexpr._init_num_threads()) + if 'sparc' in platform.machine(): + self.assertEqual(1, numexpr._init_num_threads()) + else: + self.assertEqual(3, numexpr._init_num_threads()) def test_omp_num_threads(self): with _environment('OMP_NUM_THREADS', '5'): - self.assertEquals(5, numexpr._init_num_threads()) + if 'sparc' in platform.machine(): + self.assertEqual(1, numexpr._init_num_threads()) + else: + self.assertEqual(5, numexpr._init_num_threads()) def test_vml_threads_round_trip(self): n_threads = 3 diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn' '--exclude=.svnignore' old/numexpr-2.7.2/numexpr/version.py new/numexpr-2.7.3/numexpr/version.py --- old/numexpr-2.7.2/numexpr/version.py 2020-08-26 01:03:08.000000000 +0200 +++ new/numexpr-2.7.3/numexpr/version.py 2021-03-03 19:11:41.000000000 +0100 @@ -1,11 +1,3 @@ -################################################################### -# Numexpr - Fast numerical array expression evaluator for NumPy. -# -# License: MIT -# Author: See AUTHORS.txt -# -# See LICENSE.txt and LICENSES/*.txt for details about copyright and -# rights to use. -#################################################################### - -version = '2.7.2' +# THIS FILE IS GENERATED BY `SETUP.PY` +version = '2.7.3' +numpy_build_version = '1.19.2' diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn' '--exclude=.svnignore' old/numexpr-2.7.2/numexpr.egg-info/PKG-INFO new/numexpr-2.7.3/numexpr.egg-info/PKG-INFO --- old/numexpr-2.7.2/numexpr.egg-info/PKG-INFO 2020-12-29 07:34:37.000000000 +0100 +++ new/numexpr-2.7.3/numexpr.egg-info/PKG-INFO 2021-03-03 19:11:42.000000000 +0100 @@ -1,6 +1,6 @@ Metadata-Version: 1.0 Name: numexpr -Version: 2.7.2 +Version: 2.7.3 Summary: Fast numerical expression evaluator for NumPy Home-page: https://github.com/pydata/numexpr Author: David M. Cooke, Francesc Alted and others @@ -76,6 +76,69 @@ In order to get a better idea on the different speed-ups that can be achieved on your platform, run the provided benchmarks. + Installation + ------------ + + From wheels + ^^^^^^^^^^^ + + NumExpr is available for install via `pip` for a wide range of platforms and + Python versions (which may be browsed at: https://pypi.org/project/numexpr/#files). + Installation can be performed as:: + + pip install numexpr + + If you are using the Anaconda or Miniconda distribution of Python you may prefer + to use the `conda` package manager in this case:: + + conda install numexpr + + From Source + ^^^^^^^^^^^ + + On most `Nix systems your compilers will already be present. However if you + are using a virtual environment with a substantially newer version of Python than + your system Python you may be prompted to install a new version of `gcc` or `clang`. + + For Windows, you will need to install the Microsoft Visual C++ Build Tools + (which are free) first.The version depends on which version of Python you have + installed: + + https://wiki.python.org/moin/WindowsCompilers + + For Python 3.6+ simply installating the latest version of MSVC build tools should + be sufficient. Note that wheels found via pip do not include MKL support. Wheels + available via `conda` will have MKL, if the MKL backend is used for NumPy. + + See `requirements.txt` for the required version of NumPy. + + NumExpr is built in the standard Python way:: + + python setup.py build install + + You can test `numexpr` with:: + + python -c "import numexpr; numexpr.test()" + + Do not test NumExpr in the source directory or you will generate import errors. + + Enable Intel?? MKL support + ^^^^^^^^^^^^^^^^^^^^^^^^^ + + NumExpr includes support for Intel's MKL library. This may provide better + performance on Intel architectures, mainly when evaluating transcendental + functions (trigonometrical, exponential...). + + If you have Intel's MKL, copy the `site.cfg.example` that comes with the + distribution to `site.cfg` and edit the latter file to provide correct paths to + the MKL libraries in your system. After doing this, you can proceed with the + usual building instructions listed above. + + Pay attention to the messages during the building process in order to know + whether MKL has been detected or not. Finally, you can check the speed-ups on + your machine by running the `bench/vml_timing.py` script (you can play with + different parameters to the `set_vml_accuracy_mode()` and `set_vml_num_threads()` + functions in the script so as to see how it would affect performance). Usage ----- diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn' '--exclude=.svnignore' old/numexpr-2.7.2/numexpr.egg-info/SOURCES.txt new/numexpr-2.7.3/numexpr.egg-info/SOURCES.txt --- old/numexpr-2.7.2/numexpr.egg-info/SOURCES.txt 2020-12-29 07:34:37.000000000 +0100 +++ new/numexpr-2.7.3/numexpr.egg-info/SOURCES.txt 2021-03-03 19:11:42.000000000 +0100 @@ -1,7 +1,5 @@ ANNOUNCE.rst AUTHORS.txt -CMakeLists.txt -INSTALL.rst LICENSE.txt MANIFEST.in README.rst diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn' '--exclude=.svnignore' old/numexpr-2.7.2/setup.py new/numexpr-2.7.3/setup.py --- old/numexpr-2.7.2/setup.py 2020-01-26 23:53:38.000000000 +0100 +++ new/numexpr-2.7.3/setup.py 2021-03-01 05:41:30.000000000 +0100 @@ -13,8 +13,8 @@ import sys, os, os.path as op, io from distutils.command.clean import clean -if sys.version_info < (2, 6): - raise RuntimeError("must use python 2.6 or greater") +if sys.version_info < (2, 7): + raise RuntimeError("must use python 2.7 or greater") try: import setuptools @@ -27,9 +27,24 @@ with io.open('README.rst', encoding='utf-8') as f: LONG_DESCRIPTION = f.read() -# Fetch the version for numexpr (will be put in variable `version`) -with open(os.path.join('numexpr', 'version.py')) as f: - exec(f.read()) +# # Fetch the version for numexpr (will be put in variable `version`) +# with open(os.path.join('numexpr', 'version.py')) as f: +# exec(f.read()) +major_ver = 2 +minor_ver = 7 +nano_ver = 3 +branch = '' + +version = '%d.%d.%d%s' % (major_ver, minor_ver, nano_ver, branch) +with open('numexpr/version.py', 'w') as fh: + fh.write('# THIS FILE IS GENERATED BY `SETUP.PY`\n') + fh.write("version = '%s'\n" % version) + try: + import numpy + fh.write("numpy_build_version = '%s'\n" % numpy.__version__) + except ImportError: + pass + def setup_package(): metadata = dict(
