[Numpy-discussion] NumPy-Discussion Digest, Vol 171, Issue 39

Tue Dec 29 17:34:25 EST 2020

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-request at python.org>
wrote:

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> 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.kern at gmail.com>
> To: Discussion of Numerical Python <numpy-discussion at python.org>
> Subject: Re: [Numpy-discussion] Addition of new distributions:
>         Polya-gamma
> Message-ID:
>         <
> CAF6FJivqpLsXqvyUQcAUL67VHQw0viQWTry1+L1pq4GipfDdhw at 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 <BLKZOL001 at 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
> >
> >
> >
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>
> --
> Robert Kern
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> ------------------------------
>
> Message: 2
> Date: Tue, 29 Dec 2020 13:55:03 +0800
> From: Amardeep Singh <amardeepjk at gmail.com>
> To: numpy-discussion at python.org
> Subject: [Numpy-discussion] Help needed GDB
> Message-ID:
>         <
> CAJmcdX6dgoh_FKdHKxQB1_GPLdYw3+0PMG_ne_T5BBhEzMmGmg at 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.
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> ------------------------------
>
> Message: 3
> Date: Mon, 28 Dec 2020 22:38:07 -0800
> From: Robert McLeod <robbmcleod at gmail.com>
> To: python-announce-list at python.org,  Discussion of Numerical Python
>         <numpy-discussion at python.org>, pydata at googlegroups.com
> Subject: [Numpy-discussion] ANN: NumExpr 2.7.2
> Message-ID:
>         <
> CAEFUWWV0T8ACcZbS7N56zXtPZEBgMFVQQ7whjhgxVFttgO6DTQ at 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
> robbmcleod at gmail.com
> robert.mcleod at hitachi-hhtc.ca
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