[sympy] Bisectors / trisectors / n-sectors for Polygons

[Ethan Ward]

There was a request for this feature (12599 
). What object should a 
bisector be? There is an implementation for triangles already, which 
returns a from vertices to segments from the vertex to the incentere. Would 
it make sense to just use lines, or rays? What points should be used to 
define the object, whether it is lines, rays or segments, for a general 
polygon?

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Re: [sympy] Re: mpmath 1.0

[Isuru Fernando]

It's packaged as "libflint" (
https://github.com/conda-forge/libflint-feedstock) due to "flint" being a
python package in PyPI

Isuru

On Mon, Oct 2, 2017 at 2:42 PM, Aaron Meurer  wrote:

> I didn't see FLINT when I searched the feedstocks
> https://github.com/conda-forge?utf8=%E2%9C%93=flint==
>
> Aaron Meurer
>
> On Mon, Oct 2, 2017 at 3:37 PM, Isuru Fernando  wrote:
> >> I think getting FLINT and python-flint on conda-forge would help a
> >> lot, as it would make it much easier for most people to install it.
> >
> >
> > FLINT and Arb are both on conda-forge for Linux and OSX. (For windows,
> I'm
> > waiting for the next flint release). You just need to package
> `python-flint`
> >
> >
> > Isuru Fernando
> >
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Re: [sympy] Re: mpmath 1.0

[Aaron Meurer]

I didn't see FLINT when I searched the feedstocks
https://github.com/conda-forge?utf8=%E2%9C%93=flint==

Aaron Meurer

On Mon, Oct 2, 2017 at 3:37 PM, Isuru Fernando  wrote:
>> I think getting FLINT and python-flint on conda-forge would help a
>> lot, as it would make it much easier for most people to install it.
>
>
> FLINT and Arb are both on conda-forge for Linux and OSX. (For windows, I'm
> waiting for the next flint release). You just need to package `python-flint`
>
>
> Isuru Fernando
>
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Re: [sympy] Re: mpmath 1.0

[Isuru Fernando]

>
> I think getting FLINT and python-flint on conda-forge would help a
> lot, as it would make it much easier for most people to install it.
>

FLINT and Arb are both on conda-forge for Linux and OSX. (For windows, I'm
waiting for the next flint release). You just need to package `python-flint`


Isuru Fernando

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[sympy] Re: mpmath 1.0

[Aaron Meurer]

On Mon, Oct 2, 2017 at 3:30 PM, Fredrik Johansson
 wrote:
> On Mon, Oct 2, 2017 at 9:10 PM, Aaron Meurer  wrote:
>>
>> That's a great blog post Fredrik. Since your blog doesn't seem to have
>> a comments box, I will add my comments here.
>
>
> Thanks!
>
>>
>> One thing I would add to the mpmath history is the SymPy 1.0 release
>> (March 2016), which officially made mpmath an external dependency of
>> SymPy. Prior to that, a copy of mpmath shipped as sympy.mpmath.
>>
>
> Good point. I've shamelessly copied this into the post.
>
>>
>> I've been using mpmath (via SymPy) myself quite a bit in my own recent
>> research (computing the CRAM approximation to exp(-t) on [0, oo) to
>> arbitrary precision). I'm always amazed at how stable mpmath is. It
>> always gives what seem to be correct answers, or fails nicely if it
>> can't. While I did find some minor holes in mpmath (I had to tweak the
>> maxsteps and tol parameters of findroot (via sympy.nsolve), see
>> https://github.com/fredrik-johansson/mpmath/issues/339), it was quite
>> easy to work around it.
>>
>> Regarding Arb, I would love to see Python bindings. I would suggest
>> writing some ArbPy wrapper library, so that people can use it in
>> Python on its own, and then we can use that to improve mpmath and
>> SymPy. There's been some interest in using something like Arb for code
>> generation. The idea is this: you can use SymPy to create a model for
>> something, and then use the codegen module to generate fast machine
>> code to compute it. But the problem is that you don't necessarily know
>> how precise that machine code is. What if there are numerical issues
>> that lead to highly inaccurate results? So the idea is to swap out the
>> backend for the code generator to something like Arb, and perform the
>> same computation with guaranteed bounds. This will obviously be slower
>> than the machine code, so you wouldn't use it in practice, but instead
>> you'd use it to get some assurance on the accuracy of your results
>> with machine floats. If the accuracy is bad, you might have to look
>> into modifying the algorithm. Or in the worst case, you just have to
>> use a slower arbitrary precision library to get the precision you
>> need. But critically, since everything is code generated, the whole
>> thing would (in theory at least) be as simple as changing some flag in
>> the code generator.
>
>
> As I wrote in the post, python-flint
> (https://github.com/fredrik-johansson/python-flint) already exists:
>
 from flint import arb, good
 arb(3).sqrt()
> [1.73205080756888 +/- 3.03e-15]
 good(lambda: arb(1) + arb("1e-1000") - arb(1), maxprec=1)
> [1.00e-1000 +/- 3e-1019]
>
> It still needs work with the setup code, documentation, tests, general code
> cleanup, and interface tweaks... volunteers are welcome.

Thanks. It wasn't clear to me that python-flint also wraps Arb.

I think getting FLINT and python-flint on conda-forge would help a
lot, as it would make it much easier for most people to install it.

Aaron Meurer

>
> Fredrik
>
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[sympy] Re: mpmath 1.0

[Fredrik Johansson]

On Mon, Oct 2, 2017 at 9:10 PM, Aaron Meurer  wrote:

> That's a great blog post Fredrik. Since your blog doesn't seem to have
> a comments box, I will add my comments here.
>

Thanks!


> One thing I would add to the mpmath history is the SymPy 1.0 release
> (March 2016), which officially made mpmath an external dependency of
> SymPy. Prior to that, a copy of mpmath shipped as sympy.mpmath.
>
>
Good point. I've shamelessly copied this into the post.


> I've been using mpmath (via SymPy) myself quite a bit in my own recent
> research (computing the CRAM approximation to exp(-t) on [0, oo) to
> arbitrary precision). I'm always amazed at how stable mpmath is. It
> always gives what seem to be correct answers, or fails nicely if it
> can't. While I did find some minor holes in mpmath (I had to tweak the
> maxsteps and tol parameters of findroot (via sympy.nsolve), see
> https://github.com/fredrik-johansson/mpmath/issues/339), it was quite
> easy to work around it.
>
> Regarding Arb, I would love to see Python bindings. I would suggest
> writing some ArbPy wrapper library, so that people can use it in
> Python on its own, and then we can use that to improve mpmath and
> SymPy. There's been some interest in using something like Arb for code
> generation. The idea is this: you can use SymPy to create a model for
> something, and then use the codegen module to generate fast machine
> code to compute it. But the problem is that you don't necessarily know
> how precise that machine code is. What if there are numerical issues
> that lead to highly inaccurate results? So the idea is to swap out the
> backend for the code generator to something like Arb, and perform the
> same computation with guaranteed bounds. This will obviously be slower
> than the machine code, so you wouldn't use it in practice, but instead
> you'd use it to get some assurance on the accuracy of your results
> with machine floats. If the accuracy is bad, you might have to look
> into modifying the algorithm. Or in the worst case, you just have to
> use a slower arbitrary precision library to get the precision you
> need. But critically, since everything is code generated, the whole
> thing would (in theory at least) be as simple as changing some flag in
> the code generator.
>

As I wrote in the post, python-flint (
https://github.com/fredrik-johansson/python-flint) already exists:

>>> from flint import arb, good
>>> arb(3).sqrt()
[1.73205080756888 +/- 3.03e-15]
>>> good(lambda: arb(1) + arb("1e-1000") - arb(1), maxprec=1)
[1.00e-1000 +/- 3e-1019]

It still needs work with the setup code, documentation, tests, general code
cleanup, and interface tweaks... volunteers are welcome.

Fredrik

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[sympy] Re: mpmath 1.0

[Ondřej Čertík]

On Mon, Oct 2, 2017, at 01:10 PM, Aaron Meurer wrote:
> That's a great blog post Fredrik. Since your blog doesn't seem to have
> a comments box, I will add my comments here.
> 
> One thing I would add to the mpmath history is the SymPy 1.0 release
> (March 2016), which officially made mpmath an external dependency of
> SymPy. Prior to that, a copy of mpmath shipped as sympy.mpmath.
> 
> I've been using mpmath (via SymPy) myself quite a bit in my own recent
> research (computing the CRAM approximation to exp(-t) on [0, oo) to
> arbitrary precision). I'm always amazed at how stable mpmath is. It
> always gives what seem to be correct answers, or fails nicely if it
> can't. While I did find some minor holes in mpmath (I had to tweak the
> maxsteps and tol parameters of findroot (via sympy.nsolve), see
> https://github.com/fredrik-johansson/mpmath/issues/339), it was quite
> easy to work around it.
> 
> Regarding Arb, I would love to see Python bindings. I would suggest
> writing some ArbPy wrapper library, so that people can use it in
> Python on its own, and then we can use that to improve mpmath and
> SymPy. There's been some interest in using something like Arb for code
> generation. The idea is this: you can use SymPy to create a model for
> something, and then use the codegen module to generate fast machine
> code to compute it. But the problem is that you don't necessarily know
> how precise that machine code is. What if there are numerical issues
> that lead to highly inaccurate results? So the idea is to swap out the
> backend for the code generator to something like Arb, and perform the
> same computation with guaranteed bounds. This will obviously be slower
> than the machine code, so you wouldn't use it in practice, but instead
> you'd use it to get some assurance on the accuracy of your results
> with machine floats. If the accuracy is bad, you might have to look
> into modifying the algorithm. Or in the worst case, you just have to
> use a slower arbitrary precision library to get the precision you
> need. But critically, since everything is code generated, the whole
> thing would (in theory at least) be as simple as changing some flag in
> the code generator.

I would mention that besides Sage and Nemo, also SymEngine uses Arb as a
possible backend for evaluating symbolic expressions:

https://github.com/symengine/symengine/blob/ce810e73d628a89406f9e66a8aa5b52655b96404/symengine/eval_arb.cpp

Ondrej

> 
> Aaron Meurer
> 
> 
> On Mon, Oct 2, 2017 at 8:26 AM, Fredrik Johansson
>  wrote:
> > As promised, here is the extended blog post:
> >
> > http://fredrikj.net/blog/2017/10/mpmath-1-0-and-a-ten-year-retrospective/
> >
> > Fredrik
> >
> > --
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[sympy] Re: mpmath 1.0

[Aaron Meurer]

That's a great blog post Fredrik. Since your blog doesn't seem to have
a comments box, I will add my comments here.

One thing I would add to the mpmath history is the SymPy 1.0 release
(March 2016), which officially made mpmath an external dependency of
SymPy. Prior to that, a copy of mpmath shipped as sympy.mpmath.

I've been using mpmath (via SymPy) myself quite a bit in my own recent
research (computing the CRAM approximation to exp(-t) on [0, oo) to
arbitrary precision). I'm always amazed at how stable mpmath is. It
always gives what seem to be correct answers, or fails nicely if it
can't. While I did find some minor holes in mpmath (I had to tweak the
maxsteps and tol parameters of findroot (via sympy.nsolve), see
https://github.com/fredrik-johansson/mpmath/issues/339), it was quite
easy to work around it.

Regarding Arb, I would love to see Python bindings. I would suggest
writing some ArbPy wrapper library, so that people can use it in
Python on its own, and then we can use that to improve mpmath and
SymPy. There's been some interest in using something like Arb for code
generation. The idea is this: you can use SymPy to create a model for
something, and then use the codegen module to generate fast machine
code to compute it. But the problem is that you don't necessarily know
how precise that machine code is. What if there are numerical issues
that lead to highly inaccurate results? So the idea is to swap out the
backend for the code generator to something like Arb, and perform the
same computation with guaranteed bounds. This will obviously be slower
than the machine code, so you wouldn't use it in practice, but instead
you'd use it to get some assurance on the accuracy of your results
with machine floats. If the accuracy is bad, you might have to look
into modifying the algorithm. Or in the worst case, you just have to
use a slower arbitrary precision library to get the precision you
need. But critically, since everything is code generated, the whole
thing would (in theory at least) be as simple as changing some flag in
the code generator.

Aaron Meurer


On Mon, Oct 2, 2017 at 8:26 AM, Fredrik Johansson
 wrote:
> As promised, here is the extended blog post:
>
> http://fredrikj.net/blog/2017/10/mpmath-1-0-and-a-ten-year-retrospective/
>
> Fredrik
>
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[sympy] Re: mpmath 1.0

[Fredrik Johansson]

As promised, here is the extended blog post:

http://fredrikj.net/blog/2017/10/mpmath-1-0-and-a-ten-year-retrospective/

Fredrik

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Re: [sympy] S.EmptySet.as_relational(x) -> ?

[Chris Smith]

As long as x is generic, it seems like something like this would preserve 
the fidelity of original
```
>>> eq = x**2 < 0
>>> ans = Piecewise((False, Eq(im(x), 0)), (True, Eq(re(x), 0)), (x > oo, 
True))
```
The both are True or False at the same values of x and they both raise a 
TypeError if `x -> 2 + I`. Once an assumption is placed on x, like being 
real, this collapses to False as expected.

On Sunday, October 1, 2017 at 11:04:48 PM UTC-5, Aaron Meurer wrote:
>
> True and False seem the best. It could return something like 1 = 0, but 
> that would just evaluate to False anyway. 
>
> Aaron Meurer 
>
> On Sun, Oct 1, 2017 at 11:42 PM Chris Smith  > wrote:
>
>> When solving an inequality that is true for all reals, e.g. `x**2 >= 0`, 
>> the relational (-oo < x) & (x < oo) is returned by 
>> `solve_univariate_inequality`. This is good because it enforces the 
>> condition that x be real: substitution of x with I, for example, will fail. 
>> But if the relationship is never true for any real numbers, S.EmptySet is 
>> returned and as a relational, this comes back as False. Should a relational 
>> like x > oo be returned instead?
>>
>> ```
>> >>> from sympy.abc import x
>> >>> S.Reals.as_relational(x)
>> (-oo < x) & (x < oo)
>> >>> S.EmptySet.as_relational(x)
>> False
>> >>> S.UniversalSet.as_relational(x)
>> True
>> ```
>>
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>>  
>> 
>> .
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>>
>

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[sympy] Re: mpmath 1.0

[Fredrik Johansson]

On Mon, Oct 2, 2017 at 7:30 AM, Aaron Meurer  wrote:

> That sounds probable. I know a lot of the point releases to python fix SSL
> issues. Maybe try using a miniconda python install.
>
>
>
I installed 3.6 and that did the trick. https://pypi.python.org/pypi/mpmath
now has the latest version.

Fredrik

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Re: [sympy] Re: PyCon India - 2017

[Shekhar Prasad Rajak]

Hello all,

The workshop proposal has been accepted.  We need to improve slides and
tutorials in the repo :
https://github.com/Shekharrajak/PyCon-SymPy-SymEngine  and plan it
accordingly..

Please confirm once the availability.

The speakers are :


   - Amit Kumar , Core Developer & GSoC-cer at
   SymPy
   - Shekhar Prasad Rajak  : NIT Warangal
   | Core Developer at SymPy GSoC 2016 | Solvers, Sets
   - Shikhar Jaiswal  : IIT Patna | Student
   Developer at SymPy GSoC 2017 | SymPy - SymEngine Integration and Python
   Wrappers



Cheers,
Shekhar

On Sun, Aug 6, 2017 at 11:08 PM, Shekhar Prasad Rajak <
shekharrajak.1...@gmail.com> wrote:

> The same proposal is added in the wiki : https://github.com/sympy/
> sympy/wiki/PyCon-India-2017-Proposal . Feel free to edit and let me know
> the mistakes.
>
> --
> Shekhar
>
> On Saturday, 8 July 2017 10:11:41 UTC+5:30, Shekhar Prasad Rajak wrote:
>>
>> Hello all,
>>
>> This post is regarding upcoming PyCon India in Delhi. I am interested to
>> attend this event and for the talk or workshop on SymPy.  Let me know if
>> someone is available and willing to join.
>>
>> Related links:
>>
>> - PyCon India 2017 : https://in.pycon.org/2017/ ,
>> https://in.pycon.org/cfp/2017/proposals/
>>
>> - PyCon India 2015  : https://in.pycon.org/cfp/py
>> con-india-2015/proposals/symbolic-computation-with-python-sympy~bqYrb/
>>
>> - PyDelhi Conference 2016 :  https://cfp.pydelhi.org/pydel
>> hi-conference-2016/proposals/symbolic-computation-with-
>> python-using-sympy/
>>
>> Last year I put proposal and got some reviews. I am pasting some lines
>> here :
>>
>> 1. note that we except tutorials and workshops to be highly interactive,
>> and ideally it should not contain material that can simply be found on
>> SymPy's online documentation. So please make detailed material and upload
>> it here. (IPython notebooks would be great.)
>>
>> 2. please consider having a few exercises on symengine. It would be
>> especially good if you can make the tutorial more about sympy as a wrapper
>> around symengine.
>>
>>
>> Cheers,
>> Shekhar 
>>
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-- 
Sincerely,
Shekhar Prasad Rajak
Contact : +918142478937,+919713899217
Address : NIT Warangal,Telangana,506004
More info  

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