I'm still learning proper mailing list etiquette so I'm not sure if this is where I should respond. But users just getting into debugging might also benefit from knowing this:
You can turn off optimizations when compiling numpy by passing CFLAGS to setup.py like so: *CFLAGS="-O0 -g3" python setup.py build_ext -i * **Assuming you have the source code and setup.py available * This will remove optimizations while compiling and will make it easier to see more variables. That took me a long time to figure out so I wanted to share the knowledge Thanks! On Mon, Dec 28, 2020 at 10:38 PM <numpy-discussion-requ...@python.org> wrote: > Send NumPy-Discussion mailing list submissions to > numpy-discussion@python.org > > To subscribe or unsubscribe via the World Wide Web, visit > https://mail.python.org/mailman/listinfo/numpy-discussion > or, via email, send a message with subject or body 'help' to > numpy-discussion-requ...@python.org > > You can reach the person managing the list at > numpy-discussion-ow...@python.org > > When replying, please edit your Subject line so it is more specific > than "Re: Contents of NumPy-Discussion digest..." > > > Today's Topics: > > 1. Re: Addition of new distributions: Polya-gamma (Robert Kern) > 2. Help needed GDB (Amardeep Singh) > 3. ANN: NumExpr 2.7.2 (Robert McLeod) > > > ---------------------------------------------------------------------- > > Message: 1 > Date: Mon, 28 Dec 2020 13:06:33 -0500 > From: Robert Kern <robert.k...@gmail.com> > To: Discussion of Numerical Python <numpy-discussion@python.org> > Subject: Re: [Numpy-discussion] Addition of new distributions: > Polya-gamma > Message-ID: > < > caf6fjivqplsxqvyuqcaul67vhqw0viqwtry1+l1pq4gipfd...@mail.gmail.com> > Content-Type: text/plain; charset="utf-8" > > My view is that we will not add more non-uniform distribution (i.e. "named" > statistical probability distributions like Polya-Gamma) methods to > `Generator`. I think that we might add a couple more methods to handle some > more fundamental issues (like sampling from the unit interval with control > over whether each boundary is open or closed, maybe one more variation on > shuffling) that helps write randomized algorithms. Now that we have the C > and Cython APIs which allow one to implement non-uniform distributions in > other packages, we strongly encourage that. > > As I commented on the linked PR, `scipy.stats` would be a reasonable place > for a Polya-Gamma sampling function, even if it's not feasible to implement > an `rv_continuous` class for it. You have convinced me that the nature of > the Polya-Gamma distribution warrants this. The only issue is that scipy > still depends on a pre-`Generator` version of numpy. So I recommend > implementing this function in your own package with an eye towards > contributing it to scipy later. > > On Sun, Dec 27, 2020 at 6:05 AM Zolisa Bleki <blkzol...@myuct.ac.za> > wrote: > > > Hi All, > > > > I would like to know if Numpy accepts addition of new distributions since > > the implementation of the Generator interface. If so, what is the > criteria > > for a particular distribution to be accepted? The reason why i'm asking > is > > because I would like to propose adding the Polya-gamma distribution to > > numpy, for the following reasons: > > > > 1) Polya-gamma random variables are commonly used as auxiliary variables > > during data augmentation in Bayesian sampling algorithms, which have > > wide-spread usage in Statistics and recently, Machine learning. > > 2) Since this distribution is mostly useful for random sampling, it since > > appropriate to have it in numpy and not projects like scipy [1]. > > 3) The only python/C++ implementation of the sampler available is > licensed > > under GPLv3 which I believe limits copying into packages that choose to > use > > a different license [2]. > > 4) Numpy's random API makes adding the distribution painless. > > > > I have done preliminary work on this by implementing the distribution > > sampler as decribed in [3]; see: > > https://github.com/numpy/numpy/compare/master...zoj613:polyagamma . > > There is a more efficient sampling algorithm described in a later paper > > [4], but I chose not to start with that one unless I know it is worth > > investing time in. > > > > I would appreciate your thoughts on this proposal. > > > > Regards, > > Zolisa > > > > > > Refs: > > [1] https://github.com/scipy/scipy/issues/11009 > > [2] https://github.com/slinderman/pypolyagamma > > [3] https://arxiv.org/pdf/1205.0310v1.pdf > > [4] https://arxiv.org/pdf/1405.0506.pdf > > > > > > > > Disclaimer - University of Cape Town This email is subject to UCT > policies > > and email disclaimer published on our website at > > http://www.uct.ac.za/main/email-disclaimer or obtainable from +27 21 650 > > 9111. If this email is not related to the business of UCT, it is sent by > > the sender in an individual capacity. Please report security incidents or > > abuse via https://csirt.uct.ac.za/page/report-an-incident.php. > > _______________________________________________ > > NumPy-Discussion mailing list > > NumPy-Discussion@python.org > > https://mail.python.org/mailman/listinfo/numpy-discussion > > > > > -- > Robert Kern > -------------- next part -------------- > An HTML attachment was scrubbed... > URL: < > https://mail.python.org/pipermail/numpy-discussion/attachments/20201228/f4bbd564/attachment-0001.html > > > > ------------------------------ > > Message: 2 > Date: Tue, 29 Dec 2020 13:55:03 +0800 > From: Amardeep Singh <amardee...@gmail.com> > To: numpy-discussion@python.org > Subject: [Numpy-discussion] Help needed GDB > Message-ID: > < > cajmcdx6dgoh_fkdhkxqb1_gpldyw3+0pmg_ne_t5bbhezmm...@mail.gmail.com> > Content-Type: text/plain; charset="utf-8" > > Hi All, > > I am trying to debug c code of numpy via gdb.Can someone help me with this? > i am getting " Python scripting is not supported in this copy of GDB". How > to install python supported gdb on win10? > > > https://numpy.org/doc/stable/dev/development_environment.html > > I am following the steps in the docs. machine is windows 10. > > Debugging > <https://numpy.org/doc/stable/dev/development_environment.html#debugging> > > Another frequently asked question is ?How do I debug C code inside NumPy??. > First, ensure that you have gdb installed on your system with the Python > extensions (often the default on Linux). You can see which version of > Python is running inside gdb to verify your setup: > > (gdb) python>import > sys>print(sys.version_info)>endsys.version_info(major=3, minor=7, > micro=0, releaselevel='final', serial=0) > > > > > $ gdb -v > GNU gdb (GDB) 7.6.1 > This GDB was configured as "mingw32". > > $ gdb > (gdb) python > >import sys > >print(sys.version_info) > >end > (gdb) Python scripting is not supported in this copy of GDB. > -------------- next part -------------- > An HTML attachment was scrubbed... > URL: < > https://mail.python.org/pipermail/numpy-discussion/attachments/20201229/268f82ab/attachment-0001.html > > > > ------------------------------ > > Message: 3 > Date: Mon, 28 Dec 2020 22:38:07 -0800 > From: Robert McLeod <robbmcl...@gmail.com> > To: python-announce-l...@python.org, Discussion of Numerical Python > <numpy-discussion@python.org>, pyd...@googlegroups.com > Subject: [Numpy-discussion] ANN: NumExpr 2.7.2 > Message-ID: > < > caefuwwv0t8acczbs7n56zxtpzebgmfvqq7whjhgxvfttgo6...@mail.gmail.com> > Content-Type: text/plain; charset="utf-8" > > ======================== > Announcing NumExpr 2.7.2 > ======================== > > 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. > > Project documentation is available at: > > http://numexpr.readthedocs.io/ > > Changes from 2.7.1 to 2.7.2 > --------------------------- > > - 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. > > What's Numexpr? > --------------- > > Numexpr is a fast numerical expression evaluator for NumPy. With it, > expressions that operate on arrays (like "3*a+4*b") are accelerated > and use less memory than doing the same calculation in Python. > > It has multi-threaded capabilities, as well as support for Intel's > MKL (Math Kernel Library), which allows an extremely fast evaluation > of transcendental functions (sin, cos, tan, exp, log...) while > squeezing the last drop of performance out of your multi-core > processors. Look here for a some benchmarks of numexpr using MKL: > > https://github.com/pydata/numexpr/wiki/NumexprMKL > > Its only dependency is NumPy (MKL is optional), so it works well as an > easy-to-deploy, easy-to-use, computational engine for projects that > don't want to adopt other solutions requiring more heavy dependencies. > > Where I can find Numexpr? > ------------------------- > > The project is hosted at GitHub in: > > https://github.com/pydata/numexpr > > You can get the packages from PyPI as well (but not for RC releases): > > http://pypi.python.org/pypi/numexpr > > Documentation is hosted at: > > http://numexpr.readthedocs.io/en/latest/ > > Share your experience > --------------------- > > Let us know of any bugs, suggestions, gripes, kudos, etc. you may > have. > > Enjoy data! > > > -- > Robert McLeod > robbmcl...@gmail.com > robert.mcl...@hitachi-hhtc.ca > -------------- next part -------------- > An HTML attachment was scrubbed... > URL: < > https://mail.python.org/pipermail/numpy-discussion/attachments/20201228/f4240dab/attachment.html > > > > ------------------------------ > > Subject: Digest Footer > > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@python.org > https://mail.python.org/mailman/listinfo/numpy-discussion > > > ------------------------------ > > End of NumPy-Discussion Digest, Vol 171, Issue 39 > ************************************************* >
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