Re: [Numpy-discussion] State-of-the-art to use a C/C++ library from Python

[Thiago Franco Moraes]

I think you can  use ffi.from_buffer and ffi.cast from cffi.


On Fri, Sep 2, 2016 at 8:53 AM Carl Kleffner  wrote:

> fork / extension of cffiwrap:
>
>
> *"cfficloak - A simple but flexible module for creating object-oriented,
> pythonic CFFI wrappers.This is an extension of
> https://bitbucket.org/memotype/cffiwrap
> "*
>
> 2016-09-02 13:46 GMT+02:00 Sebastian Haase :
>
>> How do these two relate to each other !?
>> - Sebastian
>>
>>
>> On Fri, Sep 2, 2016 at 12:33 PM, Carl Kleffner 
>> wrote:
>>
>>> maybe https://bitbucket.org/memotype/cffiwrap or
>>> https://github.com/andrewleech/cfficloak helps?
>>>
>>> C.
>>>
>>>
>>> 2016-09-02 11:16 GMT+02:00 Nathaniel Smith :
>>>
 On Fri, Sep 2, 2016 at 1:16 AM, Peter Creasey
  wrote:
 >> Date: Wed, 31 Aug 2016 13:28:21 +0200
 >> From: Michael Bieri 
 >>
 >> I'm not quite sure which approach is state-of-the-art as of 2016.
 How would
 >> you do it if you had to make a C/C++ library available in Python
 right now?
 >>
 >> In my case, I have a C library with some scientific functions on
 matrices
 >> and vectors. You will typically call a few functions to configure the
 >> computation, then hand over some pointers to existing buffers
 containing
 >> vector data, then start the computation, and finally read back the
 data.
 >> The library also can use MPI to parallelize.
 >>
 >
 > Depending on how minimal and universal you want to keep things, I use
 > the ctypes approach quite often, i.e. treat your numpy inputs an
 > outputs as arrays of doubles etc using the ndpointer(...) syntax. I
 > find it works well if you have a small number of well-defined
 > functions (not too many options) which are numerically very heavy.
 > With this approach I usually wrap each method in python to check the
 > inputs for contiguity, pass in the sizes etc. and allocate the numpy
 > array for the result.

 FWIW, the broader Python community seems to have largely deprecated
 ctypes in favor of cffi. Unfortunately I don't know if anyone has
 written helpers like numpy.ctypeslib for cffi...

 -n

 --
 Nathaniel J. Smith -- https://vorpus.org
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 NumPy-Discussion@scipy.org
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>>>
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Re: [Numpy-discussion] State-of-the-art to use a C/C++ library from Python

[Carl Kleffner]

fork / extension of cffiwrap:


*"cfficloak - A simple but flexible module for creating object-oriented,
pythonic CFFI wrappers.This is an extension of
https://bitbucket.org/memotype/cffiwrap
"*

2016-09-02 13:46 GMT+02:00 Sebastian Haase :

> How do these two relate to each other !?
> - Sebastian
>
>
> On Fri, Sep 2, 2016 at 12:33 PM, Carl Kleffner 
> wrote:
>
>> maybe https://bitbucket.org/memotype/cffiwrap or
>> https://github.com/andrewleech/cfficloak helps?
>>
>> C.
>>
>>
>> 2016-09-02 11:16 GMT+02:00 Nathaniel Smith :
>>
>>> On Fri, Sep 2, 2016 at 1:16 AM, Peter Creasey
>>>  wrote:
>>> >> Date: Wed, 31 Aug 2016 13:28:21 +0200
>>> >> From: Michael Bieri 
>>> >>
>>> >> I'm not quite sure which approach is state-of-the-art as of 2016. How
>>> would
>>> >> you do it if you had to make a C/C++ library available in Python
>>> right now?
>>> >>
>>> >> In my case, I have a C library with some scientific functions on
>>> matrices
>>> >> and vectors. You will typically call a few functions to configure the
>>> >> computation, then hand over some pointers to existing buffers
>>> containing
>>> >> vector data, then start the computation, and finally read back the
>>> data.
>>> >> The library also can use MPI to parallelize.
>>> >>
>>> >
>>> > Depending on how minimal and universal you want to keep things, I use
>>> > the ctypes approach quite often, i.e. treat your numpy inputs an
>>> > outputs as arrays of doubles etc using the ndpointer(...) syntax. I
>>> > find it works well if you have a small number of well-defined
>>> > functions (not too many options) which are numerically very heavy.
>>> > With this approach I usually wrap each method in python to check the
>>> > inputs for contiguity, pass in the sizes etc. and allocate the numpy
>>> > array for the result.
>>>
>>> FWIW, the broader Python community seems to have largely deprecated
>>> ctypes in favor of cffi. Unfortunately I don't know if anyone has
>>> written helpers like numpy.ctypeslib for cffi...
>>>
>>> -n
>>>
>>> --
>>> Nathaniel J. Smith -- https://vorpus.org
>>> ___
>>> NumPy-Discussion mailing list
>>> NumPy-Discussion@scipy.org
>>> https://mail.scipy.org/mailman/listinfo/numpy-discussion
>>>
>>
>>
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>>
>
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Re: [Numpy-discussion] State-of-the-art to use a C/C++ library from Python

[Sebastian Haase]

How do these two relate to each other !?
- Sebastian


On Fri, Sep 2, 2016 at 12:33 PM, Carl Kleffner  wrote:

> maybe https://bitbucket.org/memotype/cffiwrap or https://github.com/
> andrewleech/cfficloak helps?
>
> C.
>
>
> 2016-09-02 11:16 GMT+02:00 Nathaniel Smith :
>
>> On Fri, Sep 2, 2016 at 1:16 AM, Peter Creasey
>>  wrote:
>> >> Date: Wed, 31 Aug 2016 13:28:21 +0200
>> >> From: Michael Bieri 
>> >>
>> >> I'm not quite sure which approach is state-of-the-art as of 2016. How
>> would
>> >> you do it if you had to make a C/C++ library available in Python right
>> now?
>> >>
>> >> In my case, I have a C library with some scientific functions on
>> matrices
>> >> and vectors. You will typically call a few functions to configure the
>> >> computation, then hand over some pointers to existing buffers
>> containing
>> >> vector data, then start the computation, and finally read back the
>> data.
>> >> The library also can use MPI to parallelize.
>> >>
>> >
>> > Depending on how minimal and universal you want to keep things, I use
>> > the ctypes approach quite often, i.e. treat your numpy inputs an
>> > outputs as arrays of doubles etc using the ndpointer(...) syntax. I
>> > find it works well if you have a small number of well-defined
>> > functions (not too many options) which are numerically very heavy.
>> > With this approach I usually wrap each method in python to check the
>> > inputs for contiguity, pass in the sizes etc. and allocate the numpy
>> > array for the result.
>>
>> FWIW, the broader Python community seems to have largely deprecated
>> ctypes in favor of cffi. Unfortunately I don't know if anyone has
>> written helpers like numpy.ctypeslib for cffi...
>>
>> -n
>>
>> --
>> Nathaniel J. Smith -- https://vorpus.org
>> ___
>> NumPy-Discussion mailing list
>> NumPy-Discussion@scipy.org
>> https://mail.scipy.org/mailman/listinfo/numpy-discussion
>>
>
>
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Re: [Numpy-discussion] State-of-the-art to use a C/C++ library from Python

[Carl Kleffner]

maybe https://bitbucket.org/memotype/cffiwrap or
https://github.com/andrewleech/cfficloak helps?

C.


2016-09-02 11:16 GMT+02:00 Nathaniel Smith :

> On Fri, Sep 2, 2016 at 1:16 AM, Peter Creasey
>  wrote:
> >> Date: Wed, 31 Aug 2016 13:28:21 +0200
> >> From: Michael Bieri 
> >>
> >> I'm not quite sure which approach is state-of-the-art as of 2016. How
> would
> >> you do it if you had to make a C/C++ library available in Python right
> now?
> >>
> >> In my case, I have a C library with some scientific functions on
> matrices
> >> and vectors. You will typically call a few functions to configure the
> >> computation, then hand over some pointers to existing buffers containing
> >> vector data, then start the computation, and finally read back the data.
> >> The library also can use MPI to parallelize.
> >>
> >
> > Depending on how minimal and universal you want to keep things, I use
> > the ctypes approach quite often, i.e. treat your numpy inputs an
> > outputs as arrays of doubles etc using the ndpointer(...) syntax. I
> > find it works well if you have a small number of well-defined
> > functions (not too many options) which are numerically very heavy.
> > With this approach I usually wrap each method in python to check the
> > inputs for contiguity, pass in the sizes etc. and allocate the numpy
> > array for the result.
>
> FWIW, the broader Python community seems to have largely deprecated
> ctypes in favor of cffi. Unfortunately I don't know if anyone has
> written helpers like numpy.ctypeslib for cffi...
>
> -n
>
> --
> Nathaniel J. Smith -- https://vorpus.org
> ___
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> NumPy-Discussion@scipy.org
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Re: [Numpy-discussion] State-of-the-art to use a C/C++ library from Python

[Nathaniel Smith]

On Fri, Sep 2, 2016 at 1:16 AM, Peter Creasey
 wrote:
>> Date: Wed, 31 Aug 2016 13:28:21 +0200
>> From: Michael Bieri 
>>
>> I'm not quite sure which approach is state-of-the-art as of 2016. How would
>> you do it if you had to make a C/C++ library available in Python right now?
>>
>> In my case, I have a C library with some scientific functions on matrices
>> and vectors. You will typically call a few functions to configure the
>> computation, then hand over some pointers to existing buffers containing
>> vector data, then start the computation, and finally read back the data.
>> The library also can use MPI to parallelize.
>>
>
> Depending on how minimal and universal you want to keep things, I use
> the ctypes approach quite often, i.e. treat your numpy inputs an
> outputs as arrays of doubles etc using the ndpointer(...) syntax. I
> find it works well if you have a small number of well-defined
> functions (not too many options) which are numerically very heavy.
> With this approach I usually wrap each method in python to check the
> inputs for contiguity, pass in the sizes etc. and allocate the numpy
> array for the result.

FWIW, the broader Python community seems to have largely deprecated
ctypes in favor of cffi. Unfortunately I don't know if anyone has
written helpers like numpy.ctypeslib for cffi...

-n

-- 
Nathaniel J. Smith -- https://vorpus.org
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Re: [Numpy-discussion] State-of-the-art to use a C/C++ library from Python

[Peter Creasey]

> Date: Wed, 31 Aug 2016 13:28:21 +0200
> From: Michael Bieri 
>
> I'm not quite sure which approach is state-of-the-art as of 2016. How would
> you do it if you had to make a C/C++ library available in Python right now?
>
> In my case, I have a C library with some scientific functions on matrices
> and vectors. You will typically call a few functions to configure the
> computation, then hand over some pointers to existing buffers containing
> vector data, then start the computation, and finally read back the data.
> The library also can use MPI to parallelize.
>

Depending on how minimal and universal you want to keep things, I use
the ctypes approach quite often, i.e. treat your numpy inputs an
outputs as arrays of doubles etc using the ndpointer(...) syntax. I
find it works well if you have a small number of well-defined
functions (not too many options) which are numerically very heavy.
With this approach I usually wrap each method in python to check the
inputs for contiguity, pass in the sizes etc. and allocate the numpy
array for the result.

Peter
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