<html><head><meta http-equiv="content-type" content="text/html; charset=utf-8"></head><body dir="auto"><div>when this is final will want to pick this up in the 3.2 full dep Travis build (if its not already)<br><br></div><div><br>Begin forwarded message:<br><br></div><blockquote type="cite"><div><b>From:</b> Francesc Alted <<a href="mailto:faltet@gmail.com">faltet@gmail.com</a>><br><b>Date:</b> April 14, 2013, 4:19:31 PM EDT<br><b>To:</b> <a href="mailto:numexpr@googlegroups.com">numexpr@googlegroups.com</a><br><b>Cc:</b> Discussion list for PyTables <<a href="mailto:pytables-users@lists.sourceforge.net">pytables-users@lists.sourceforge.net</a>><br><b>Subject:</b> <b>[Pytables-users] ANN: numexpr 2.1 RC1 available!</b><br><b>Reply-To:</b> Discussion list for PyTables <<a href="mailto:pytables-users@lists.sourceforge.net">pytables-users@lists.sourceforge.net</a>><br><br></div></blockquote><blockquote type="cite"><div><span>============================</span><br><span> Announcing Numexpr 2.1RC1</span><br><span>============================</span><br><span></span><br><span>Numexpr is a fast numerical expression evaluator for NumPy. With it,</span><br><span>expressions that operate on arrays (like "3*a+4*b") are accelerated</span><br><span>and use less memory than doing the same calculation in Python.</span><br><span></span><br><span>It wears multi-threaded capabilities, as well as support for Intel's</span><br><span>VML library, which allows for squeezing the last drop of performance</span><br><span>out of your multi-core processors.</span><br><span></span><br><span>What's new</span><br><span>==========</span><br><span></span><br><span>This version adds compatibility for Python 3. A bunch of thanks to </span><br><span>Antonio Valentino for his excelent work on this.I apologize for taking </span><br><span>so long in releasing his contributions.</span><br><span></span><br><span>In case you want to know more in detail what has changed in this</span><br><span>version, see:</span><br><span></span><br><span><a href="http://code.google.com/p/numexpr/wiki/ReleaseNotes">http://code.google.com/p/numexpr/wiki/ReleaseNotes</a></span><br><span></span><br><span>or have a look at RELEASE_NOTES.txt in the tarball.</span><br><span></span><br><span>Where I can find Numexpr?</span><br><span>=========================</span><br><span></span><br><span>The project is hosted at Google code in:</span><br><span></span><br><span><a href="http://code.google.com/p/numexpr/">http://code.google.com/p/numexpr/</a></span><br><span></span><br><span>This is a release candidate 1, so it will not be available on the PyPi </span><br><span>repository. I'll post it there when the final version will released.</span><br><span></span><br><span>Share your experience</span><br><span>=====================</span><br><span></span><br><span>Let us know of any bugs, suggestions, gripes, kudos, etc. you may</span><br><span>have.</span><br><span></span><br><span></span><br><span>Enjoy!</span><br><span></span><br><span>--</span><br><span>Francesc Alted</span><br><span></span><br><span></span><br><span>------------------------------------------------------------------------------</span><br><span>Precog is a next-generation analytics platform capable of advanced</span><br><span>analytics on semi-structured data. The platform includes APIs for building</span><br><span>apps and a phenomenal toolset for data science. Developers can use</span><br><span>our toolset for easy data analysis & visualization. Get a free account!</span><br><span><a href="http://www2.precog.com/precogplatform/slashdotnewsletter">http://www2.precog.com/precogplatform/slashdotnewsletter</a></span><br><span>_______________________________________________</span><br><span>Pytables-users mailing list</span><br><span><a href="mailto:Pytables-users@lists.sourceforge.net">Pytables-users@lists.sourceforge.net</a></span><br><span><a href="https://lists.sourceforge.net/lists/listinfo/pytables-users">https://lists.sourceforge.net/lists/listinfo/pytables-users</a></span><br></div></blockquote></body></html>