From denis.akhiyarov at gmail.com  Mon May  1 03:44:57 2017
From: denis.akhiyarov at gmail.com (Denis Akhiyarov)
Date: Mon, 01 May 2017 07:44:57 +0000
Subject: [SciPy-User] Install Scipy with Anaconda's MKL libraries
In-Reply-To: <CAHCaCk+tYbh8AXuhWkTAtnjfCTqFjt4rFT9_XOP++bArRmsepQ@mail.gmail.com>
References: <CALO6Ft47hEd8RMtHF2nRAji84nztS8h-hm-qyojwv3kZn0-Zkw@mail.gmail.com>
 <CALO6Ft55X=Q9hhPUNwn-pZ1JRS+FnGLxdubLph9qtN4WkQUtnw@mail.gmail.com>
 <CAHCaCk+tYbh8AXuhWkTAtnjfCTqFjt4rFT9_XOP++bArRmsepQ@mail.gmail.com>
Message-ID: <CALxxJLTUAjOupyKROxaYPAr+7VOTFm5kj70mEZApZUpAzgE=pA@mail.gmail.com>

I still suggest Intel+MSVC compilers, since you can use trial version or
request license for open-source projects from Intel. This is what Anaconda
team is using. Also this is what Christoph Gohlke wheels are based on:

http://www.lfd.uci.edu/~gohlke/pythonlibs/#scipy

If you end up with m2w64, here is lapack for conda, you may still have to
modify paths:

https://anaconda.org/conda-forge/lapack

And blas:

https://anaconda.org/search?q=Blas

On Sun, Apr 30, 2017, 5:22 PM Matthieu Brucher <matthieu.brucher at gmail.com>
wrote:

> Why do you want to pay Intel? You can install the MKL and develop with it,
> no sweat.
>
> 2017-04-30 22:41 GMT+01:00 David Hagen <david at drhagen.com>:
>
>> > Welcome to the world of pain with building scientific packages from
>> source on Windows!
>>
>> I am beginning to feel it.
>>
>> > You need Fortran and C/C++ compilers on Windows to build scipy from
>> source
>>
>> I have MinGW-w64 installed, which seems to be the recommended method.
>>
>> > I?m pretty sure that anaconda does not come with the development files
>> for MKL, only the runtime files.
>>
>> I understand now. It looks like MKL is not the way to go unless I want to
>> pay Intel.
>>
>> > If you don't need mkl and lapack/blas is good enough, then
>> m2w64-toolchain from conda should have all necessary dependencies for
>> building scipy.
>>
>> My only goal is to install and use Scipy master somewhere where it won't
>> break my stable installation. I thought Anaconda would be a good place to
>> start because once I activate an Anaconda environment, I should be able to
>> treat like a normal Python installation and follow the normal
>> install-from-source instructions. I went ahead and installed that
>> m2w64-toolchain package, but it still doesn't find any BLAS/LAPACK. Maybe I
>> should change my question to: how do I install Scipy on Windows from
>> source? Though when I search for this specifically on the web, the answer
>> seems to be "Don't.". It seems that MinGW-w64 and ATLAS are recommended by
>> Scipy. Do you know of a conda/pip package that provides ATLAS for building
>> Scipy or another more suitable BLAS/LAPACK?
>>
>> _______________________________________________
>> SciPy-User mailing list
>> SciPy-User at python.org
>> https://mail.python.org/mailman/listinfo/scipy-user
>>
>>
>
>
> --
> Information System Engineer, Ph.D.
> Blog: http://blog.audio-tk.com/
> LinkedIn: http://www.linkedin.com/in/matthieubrucher
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at python.org
> https://mail.python.org/mailman/listinfo/scipy-user
>
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From mohamed.ibnalkadi at gmail.com  Mon May  1 03:51:40 2017
From: mohamed.ibnalkadi at gmail.com (Mohamed IBN AL KADI)
Date: Mon, 1 May 2017 09:51:40 +0200
Subject: [SciPy-User] SciPy-User Digest, Vol 165, Issue 1
In-Reply-To: <mailman.6165.1493624709.2950.scipy-user@python.org>
References: <mailman.6165.1493624709.2950.scipy-user@python.org>
Message-ID: <CAC=6zur5HjMZV+R6uyY5yrTAmNDDa7NfxFy-LkCEFxbkai+0Yg@mail.gmail.com>

I want to unscribe please.

On 1 May 2017 9:47 a.m., <scipy-user-request at python.org> wrote:

> Send SciPy-User mailing list submissions to
>         scipy-user at python.org
>
> To subscribe or unsubscribe via the World Wide Web, visit
>         https://mail.python.org/mailman/listinfo/scipy-user
> or, via email, send a message with subject or body 'help' to
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>         scipy-user-owner at python.org
>
> When replying, please edit your Subject line so it is more specific
> than "Re: Contents of SciPy-User digest..."
>
>
> Today's Topics:
>
>    1. Re: Install Scipy with Anaconda's MKL libraries (David Hagen)
>    2. Re: Install Scipy with Anaconda's MKL libraries (Matthieu Brucher)
>    3. Re: Install Scipy with Anaconda's MKL libraries (Denis Akhiyarov)
>
>
> ----------------------------------------------------------------------
>
> Message: 1
> Date: Sun, 30 Apr 2017 17:41:05 -0400
> From: David Hagen <david at drhagen.com>
> To: scipy-user at python.org
> Subject: Re: [SciPy-User] Install Scipy with Anaconda's MKL libraries
> Message-ID:
>         <CALO6Ft55X=Q9hhPUNwn-pZ1JRS+FnGLxdubLph9qtN4WkQUtnw at mail.
> gmail.com>
> Content-Type: text/plain; charset="utf-8"
>
> > Welcome to the world of pain with building scientific packages from
> source on Windows!
>
> I am beginning to feel it.
>
> > You need Fortran and C/C++ compilers on Windows to build scipy from
> source
>
> I have MinGW-w64 installed, which seems to be the recommended method.
>
> > I?m pretty sure that anaconda does not come with the development files
> for MKL, only the runtime files.
>
> I understand now. It looks like MKL is not the way to go unless I want to
> pay Intel.
>
> > If you don't need mkl and lapack/blas is good enough, then
> m2w64-toolchain from conda should have all necessary dependencies for
> building scipy.
>
> My only goal is to install and use Scipy master somewhere where it won't
> break my stable installation. I thought Anaconda would be a good place to
> start because once I activate an Anaconda environment, I should be able to
> treat like a normal Python installation and follow the normal
> install-from-source instructions. I went ahead and installed that
> m2w64-toolchain package, but it still doesn't find any BLAS/LAPACK. Maybe I
> should change my question to: how do I install Scipy on Windows from
> source? Though when I search for this specifically on the web, the answer
> seems to be "Don't.". It seems that MinGW-w64 and ATLAS are recommended by
> Scipy. Do you know of a conda/pip package that provides ATLAS for building
> Scipy or another more suitable BLAS/LAPACK?
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> attachments/20170430/3a767c3a/attachment-0001.html>
>
> ------------------------------
>
> Message: 2
> Date: Sun, 30 Apr 2017 23:22:03 +0100
> From: Matthieu Brucher <matthieu.brucher at gmail.com>
> To: SciPy Users List <scipy-user at python.org>
> Subject: Re: [SciPy-User] Install Scipy with Anaconda's MKL libraries
> Message-ID:
>         <CAHCaCk+tYbh8AXuhWkTAtnjfCTqFjt4rFT9_
> XOP++bArRmsepQ at mail.gmail.com>
> Content-Type: text/plain; charset="utf-8"
>
> Why do you want to pay Intel? You can install the MKL and develop with it,
> no sweat.
>
> 2017-04-30 22:41 GMT+01:00 David Hagen <david at drhagen.com>:
>
> > > Welcome to the world of pain with building scientific packages from
> > source on Windows!
> >
> > I am beginning to feel it.
> >
> > > You need Fortran and C/C++ compilers on Windows to build scipy from
> > source
> >
> > I have MinGW-w64 installed, which seems to be the recommended method.
> >
> > > I?m pretty sure that anaconda does not come with the development files
> > for MKL, only the runtime files.
> >
> > I understand now. It looks like MKL is not the way to go unless I want to
> > pay Intel.
> >
> > > If you don't need mkl and lapack/blas is good enough, then
> > m2w64-toolchain from conda should have all necessary dependencies for
> > building scipy.
> >
> > My only goal is to install and use Scipy master somewhere where it won't
> > break my stable installation. I thought Anaconda would be a good place to
> > start because once I activate an Anaconda environment, I should be able
> to
> > treat like a normal Python installation and follow the normal
> > install-from-source instructions. I went ahead and installed that
> > m2w64-toolchain package, but it still doesn't find any BLAS/LAPACK.
> Maybe I
> > should change my question to: how do I install Scipy on Windows from
> > source? Though when I search for this specifically on the web, the answer
> > seems to be "Don't.". It seems that MinGW-w64 and ATLAS are recommended
> by
> > Scipy. Do you know of a conda/pip package that provides ATLAS for
> building
> > Scipy or another more suitable BLAS/LAPACK?
> >
> > _______________________________________________
> > SciPy-User mailing list
> > SciPy-User at python.org
> > https://mail.python.org/mailman/listinfo/scipy-user
> >
> >
>
>
> --
> Information System Engineer, Ph.D.
> Blog: http://blog.audio-tk.com/
> LinkedIn: http://www.linkedin.com/in/matthieubrucher
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> attachments/20170430/30c78312/attachment-0001.html>
>
> ------------------------------
>
> Message: 3
> Date: Mon, 01 May 2017 07:44:57 +0000
> From: Denis Akhiyarov <denis.akhiyarov at gmail.com>
> To: SciPy Users List <scipy-user at python.org>
> Subject: Re: [SciPy-User] Install Scipy with Anaconda's MKL libraries
> Message-ID:
>         <CALxxJLTUAjOupyKROxaYPAr+7VOTFm5kj70mEZApZUpAzgE=pA@
> mail.gmail.com>
> Content-Type: text/plain; charset="utf-8"
>
> I still suggest Intel+MSVC compilers, since you can use trial version or
> request license for open-source projects from Intel. This is what Anaconda
> team is using. Also this is what Christoph Gohlke wheels are based on:
>
> http://www.lfd.uci.edu/~gohlke/pythonlibs/#scipy
>
> If you end up with m2w64, here is lapack for conda, you may still have to
> modify paths:
>
> https://anaconda.org/conda-forge/lapack
>
> And blas:
>
> https://anaconda.org/search?q=Blas
>
> On Sun, Apr 30, 2017, 5:22 PM Matthieu Brucher <matthieu.brucher at gmail.com
> >
> wrote:
>
> > Why do you want to pay Intel? You can install the MKL and develop with
> it,
> > no sweat.
> >
> > 2017-04-30 22:41 GMT+01:00 David Hagen <david at drhagen.com>:
> >
> >> > Welcome to the world of pain with building scientific packages from
> >> source on Windows!
> >>
> >> I am beginning to feel it.
> >>
> >> > You need Fortran and C/C++ compilers on Windows to build scipy from
> >> source
> >>
> >> I have MinGW-w64 installed, which seems to be the recommended method.
> >>
> >> > I?m pretty sure that anaconda does not come with the development files
> >> for MKL, only the runtime files.
> >>
> >> I understand now. It looks like MKL is not the way to go unless I want
> to
> >> pay Intel.
> >>
> >> > If you don't need mkl and lapack/blas is good enough, then
> >> m2w64-toolchain from conda should have all necessary dependencies for
> >> building scipy.
> >>
> >> My only goal is to install and use Scipy master somewhere where it won't
> >> break my stable installation. I thought Anaconda would be a good place
> to
> >> start because once I activate an Anaconda environment, I should be able
> to
> >> treat like a normal Python installation and follow the normal
> >> install-from-source instructions. I went ahead and installed that
> >> m2w64-toolchain package, but it still doesn't find any BLAS/LAPACK.
> Maybe I
> >> should change my question to: how do I install Scipy on Windows from
> >> source? Though when I search for this specifically on the web, the
> answer
> >> seems to be "Don't.". It seems that MinGW-w64 and ATLAS are recommended
> by
> >> Scipy. Do you know of a conda/pip package that provides ATLAS for
> building
> >> Scipy or another more suitable BLAS/LAPACK?
> >>
> >> _______________________________________________
> >> SciPy-User mailing list
> >> SciPy-User at python.org
> >> https://mail.python.org/mailman/listinfo/scipy-user
> >>
> >>
> >
> >
> > --
> > Information System Engineer, Ph.D.
> > Blog: http://blog.audio-tk.com/
> > LinkedIn: http://www.linkedin.com/in/matthieubrucher
> > _______________________________________________
> > SciPy-User mailing list
> > SciPy-User at python.org
> > https://mail.python.org/mailman/listinfo/scipy-user
> >
> -------------- next part --------------
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>
> ------------------------------
>
> Subject: Digest Footer
>
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at python.org
> https://mail.python.org/mailman/listinfo/scipy-user
>
>
> ------------------------------
>
> End of SciPy-User Digest, Vol 165, Issue 1
> ******************************************
>
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From david at drhagen.com  Mon May  1 19:40:19 2017
From: david at drhagen.com (David Hagen)
Date: Mon, 1 May 2017 19:40:19 -0400
Subject: [SciPy-User] Install Scipy with Anaconda's MKL libraries
In-Reply-To: <CALxxJLTUAjOupyKROxaYPAr+7VOTFm5kj70mEZApZUpAzgE=pA@mail.gmail.com>
References: <CALO6Ft47hEd8RMtHF2nRAji84nztS8h-hm-qyojwv3kZn0-Zkw@mail.gmail.com>
 <CALO6Ft55X=Q9hhPUNwn-pZ1JRS+FnGLxdubLph9qtN4WkQUtnw@mail.gmail.com>
 <CAHCaCk+tYbh8AXuhWkTAtnjfCTqFjt4rFT9_XOP++bArRmsepQ@mail.gmail.com>
 <CALxxJLTUAjOupyKROxaYPAr+7VOTFm5kj70mEZApZUpAzgE=pA@mail.gmail.com>
Message-ID: <CALO6Ft7K89B5Qd+tOJpmvKrN7YmBm0JKSDZhM_1aegN457HfWA@mail.gmail.com>

I'll try to stick with MinGW-w64 for now, but I don't even get to the
compilation phase. If I install lapack and blas from conda-forge, it still
says that lapack/blas are not found, but you indicated that I need to set
some paths. Are there instructions for this? I have no idea what
environment variables to set in order to tell Scipy to use these packages.

On Mon, May 1, 2017 at 3:44 AM, Denis Akhiyarov <denis.akhiyarov at gmail.com>
wrote:

> I still suggest Intel+MSVC compilers, since you can use trial version or
> request license for open-source projects from Intel. This is what Anaconda
> team is using. Also this is what Christoph Gohlke wheels are based on:
>
> http://www.lfd.uci.edu/~gohlke/pythonlibs/#scipy
>
> If you end up with m2w64, here is lapack for conda, you may still have to
> modify paths:
>
> https://anaconda.org/conda-forge/lapack
>
> And blas:
>
> https://anaconda.org/search?q=Blas
>
> On Sun, Apr 30, 2017, 5:22 PM Matthieu Brucher <matthieu.brucher at gmail.com>
> wrote:
>
>> Why do you want to pay Intel? You can install the MKL and develop with
>> it, no sweat.
>>
>> 2017-04-30 22:41 GMT+01:00 David Hagen <david at drhagen.com>:
>>
>>> > Welcome to the world of pain with building scientific packages from
>>> source on Windows!
>>>
>>> I am beginning to feel it.
>>>
>>> > You need Fortran and C/C++ compilers on Windows to build scipy from
>>> source
>>>
>>> I have MinGW-w64 installed, which seems to be the recommended method.
>>>
>>> > I?m pretty sure that anaconda does not come with the development files
>>> for MKL, only the runtime files.
>>>
>>> I understand now. It looks like MKL is not the way to go unless I want
>>> to pay Intel.
>>>
>>> > If you don't need mkl and lapack/blas is good enough, then
>>> m2w64-toolchain from conda should have all necessary dependencies for
>>> building scipy.
>>>
>>> My only goal is to install and use Scipy master somewhere where it won't
>>> break my stable installation. I thought Anaconda would be a good place to
>>> start because once I activate an Anaconda environment, I should be able to
>>> treat like a normal Python installation and follow the normal
>>> install-from-source instructions. I went ahead and installed that
>>> m2w64-toolchain package, but it still doesn't find any BLAS/LAPACK. Maybe I
>>> should change my question to: how do I install Scipy on Windows from
>>> source? Though when I search for this specifically on the web, the answer
>>> seems to be "Don't.". It seems that MinGW-w64 and ATLAS are recommended by
>>> Scipy. Do you know of a conda/pip package that provides ATLAS for building
>>> Scipy or another more suitable BLAS/LAPACK?
>>>
>>> _______________________________________________
>>> SciPy-User mailing list
>>> SciPy-User at python.org
>>> https://mail.python.org/mailman/listinfo/scipy-user
>>>
>>>
>>
>>
>> --
>> Information System Engineer, Ph.D.
>> Blog: http://blog.audio-tk.com/
>> LinkedIn: http://www.linkedin.com/in/matthieubrucher
>> _______________________________________________
>> SciPy-User mailing list
>> SciPy-User at python.org
>> https://mail.python.org/mailman/listinfo/scipy-user
>>
>
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at python.org
> https://mail.python.org/mailman/listinfo/scipy-user
>
>
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From immudzen at gmail.com  Tue May  2 04:00:09 2017
From: immudzen at gmail.com (William Heymann)
Date: Tue, 2 May 2017 10:00:09 +0200
Subject: [SciPy-User] Install Scipy with Anaconda's MKL libraries
In-Reply-To: <CALO6Ft7K89B5Qd+tOJpmvKrN7YmBm0JKSDZhM_1aegN457HfWA@mail.gmail.com>
References: <CALO6Ft47hEd8RMtHF2nRAji84nztS8h-hm-qyojwv3kZn0-Zkw@mail.gmail.com>
 <CALO6Ft55X=Q9hhPUNwn-pZ1JRS+FnGLxdubLph9qtN4WkQUtnw@mail.gmail.com>
 <CAHCaCk+tYbh8AXuhWkTAtnjfCTqFjt4rFT9_XOP++bArRmsepQ@mail.gmail.com>
 <CALxxJLTUAjOupyKROxaYPAr+7VOTFm5kj70mEZApZUpAzgE=pA@mail.gmail.com>
 <CALO6Ft7K89B5Qd+tOJpmvKrN7YmBm0JKSDZhM_1aegN457HfWA@mail.gmail.com>
Message-ID: <CAKWqKotdbQYdabmU2UKR2BgxfpiJBLXyQ5vdCSfA0t7iUdM_DA@mail.gmail.com>

Intel has made MKL, TBB, and a few other things completely free to use,
even in a commercial project. Visual Studio is also free unless you are a
very large company.

https://software.intel.com/en-us/articles/free-mkl

I have been using that for other projects without any problems and
compiling with Visual Studio has been very easy.

On Tue, May 2, 2017 at 1:40 AM, David Hagen <david at drhagen.com> wrote:

> I'll try to stick with MinGW-w64 for now, but I don't even get to the
> compilation phase. If I install lapack and blas from conda-forge, it still
> says that lapack/blas are not found, but you indicated that I need to set
> some paths. Are there instructions for this? I have no idea what
> environment variables to set in order to tell Scipy to use these packages.
>
> On Mon, May 1, 2017 at 3:44 AM, Denis Akhiyarov <denis.akhiyarov at gmail.com
> > wrote:
>
>> I still suggest Intel+MSVC compilers, since you can use trial version or
>> request license for open-source projects from Intel. This is what Anaconda
>> team is using. Also this is what Christoph Gohlke wheels are based on:
>>
>> http://www.lfd.uci.edu/~gohlke/pythonlibs/#scipy
>>
>> If you end up with m2w64, here is lapack for conda, you may still have to
>> modify paths:
>>
>> https://anaconda.org/conda-forge/lapack
>>
>> And blas:
>>
>> https://anaconda.org/search?q=Blas
>>
>> On Sun, Apr 30, 2017, 5:22 PM Matthieu Brucher <
>> matthieu.brucher at gmail.com> wrote:
>>
>>> Why do you want to pay Intel? You can install the MKL and develop with
>>> it, no sweat.
>>>
>>> 2017-04-30 22:41 GMT+01:00 David Hagen <david at drhagen.com>:
>>>
>>>> > Welcome to the world of pain with building scientific packages from
>>>> source on Windows!
>>>>
>>>> I am beginning to feel it.
>>>>
>>>> > You need Fortran and C/C++ compilers on Windows to build scipy from
>>>> source
>>>>
>>>> I have MinGW-w64 installed, which seems to be the recommended method.
>>>>
>>>> > I?m pretty sure that anaconda does not come with the development
>>>> files for MKL, only the runtime files.
>>>>
>>>> I understand now. It looks like MKL is not the way to go unless I want
>>>> to pay Intel.
>>>>
>>>> > If you don't need mkl and lapack/blas is good enough, then
>>>> m2w64-toolchain from conda should have all necessary dependencies for
>>>> building scipy.
>>>>
>>>> My only goal is to install and use Scipy master somewhere where it
>>>> won't break my stable installation. I thought Anaconda would be a good
>>>> place to start because once I activate an Anaconda environment, I should be
>>>> able to treat like a normal Python installation and follow the normal
>>>> install-from-source instructions. I went ahead and installed that
>>>> m2w64-toolchain package, but it still doesn't find any BLAS/LAPACK. Maybe I
>>>> should change my question to: how do I install Scipy on Windows from
>>>> source? Though when I search for this specifically on the web, the answer
>>>> seems to be "Don't.". It seems that MinGW-w64 and ATLAS are recommended by
>>>> Scipy. Do you know of a conda/pip package that provides ATLAS for building
>>>> Scipy or another more suitable BLAS/LAPACK?
>>>>
>>>> _______________________________________________
>>>> SciPy-User mailing list
>>>> SciPy-User at python.org
>>>> https://mail.python.org/mailman/listinfo/scipy-user
>>>>
>>>>
>>>
>>>
>>> --
>>> Information System Engineer, Ph.D.
>>> Blog: http://blog.audio-tk.com/
>>> LinkedIn: http://www.linkedin.com/in/matthieubrucher
>>> _______________________________________________
>>> SciPy-User mailing list
>>> SciPy-User at python.org
>>> https://mail.python.org/mailman/listinfo/scipy-user
>>>
>>
>> _______________________________________________
>> SciPy-User mailing list
>> SciPy-User at python.org
>> https://mail.python.org/mailman/listinfo/scipy-user
>>
>>
>
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at python.org
> https://mail.python.org/mailman/listinfo/scipy-user
>
>
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From gideon.simpson at gmail.com  Tue May  2 22:22:19 2017
From: gideon.simpson at gmail.com (Gideon Simpson)
Date: Tue, 2 May 2017 22:22:19 -0400
Subject: [SciPy-User] scipy.optimize and vectorization (or not)
Message-ID: <516A9270-5E48-4674-A868-F37A8515AA13@gmail.com>

I have a scalar valued function that I?d like to minimize, but, by default, it?s not readily vectorized.  In particular, it depends parametrically on the solution of an ODE, and vaguely looks like:

def f(x, ode_soln):
	...
	val = spicy.integrate.quad(lambda t: sin(x * t) * ode_soln.sol(t),0,1)

	return val

By default, if I try to do

spicy.optimize.minimize(lambda x: f(x,ode_soln))

I get an error that indicates that minimize thinks f accepts arrays of data.  I can remedy this by writing a little looped version,

def f_vec(xvec, ode_soln):
	fvals = np.zeros_like(xvec)

	for j in range(xvec.size):
		fvals[j] = f(xvec[j], ode_soln)

	return fvals

but I?m wondering if:

1.  Is there a smarter/more elegant solution to handling the vectorized input?

2.  Is there a way to just tell minimize that it?s going to have to evaluate f one point at a time, rather than writing some other function? 

-gideon


From david at drhagen.com  Tue May  2 22:12:15 2017
From: david at drhagen.com (David Hagen)
Date: Tue, 2 May 2017 22:12:15 -0400
Subject: [SciPy-User] Install Scipy with Anaconda's MKL libraries
In-Reply-To: <CAKWqKotdbQYdabmU2UKR2BgxfpiJBLXyQ5vdCSfA0t7iUdM_DA@mail.gmail.com>
References: <CALO6Ft47hEd8RMtHF2nRAji84nztS8h-hm-qyojwv3kZn0-Zkw@mail.gmail.com>
 <CALO6Ft55X=Q9hhPUNwn-pZ1JRS+FnGLxdubLph9qtN4WkQUtnw@mail.gmail.com>
 <CAHCaCk+tYbh8AXuhWkTAtnjfCTqFjt4rFT9_XOP++bArRmsepQ@mail.gmail.com>
 <CALxxJLTUAjOupyKROxaYPAr+7VOTFm5kj70mEZApZUpAzgE=pA@mail.gmail.com>
 <CALO6Ft7K89B5Qd+tOJpmvKrN7YmBm0JKSDZhM_1aegN457HfWA@mail.gmail.com>
 <CAKWqKotdbQYdabmU2UKR2BgxfpiJBLXyQ5vdCSfA0t7iUdM_DA@mail.gmail.com>
Message-ID: <CALO6Ft71-SfExyVQvPswoCN491qojfaUH9Y3k_CUE-jc1vJayQ@mail.gmail.com>

Is there a recipe for this combination? I installed MKL from that link and
the latest Visual Studio. Scipy did not find MKL on its own. I'm sure
there's some environment variables that need to be set, but I don't know
what they are.

On Tue, May 2, 2017 at 4:00 AM, William Heymann <immudzen at gmail.com> wrote:

> Intel has made MKL, TBB, and a few other things completely free to use,
> even in a commercial project. Visual Studio is also free unless you are a
> very large company.
>
> https://software.intel.com/en-us/articles/free-mkl
>
> I have been using that for other projects without any problems and
> compiling with Visual Studio has been very easy.
>
> On Tue, May 2, 2017 at 1:40 AM, David Hagen <david at drhagen.com> wrote:
>
>> I'll try to stick with MinGW-w64 for now, but I don't even get to the
>> compilation phase. If I install lapack and blas from conda-forge, it still
>> says that lapack/blas are not found, but you indicated that I need to set
>> some paths. Are there instructions for this? I have no idea what
>> environment variables to set in order to tell Scipy to use these packages.
>>
>> On Mon, May 1, 2017 at 3:44 AM, Denis Akhiyarov <
>> denis.akhiyarov at gmail.com> wrote:
>>
>>> I still suggest Intel+MSVC compilers, since you can use trial version or
>>> request license for open-source projects from Intel. This is what Anaconda
>>> team is using. Also this is what Christoph Gohlke wheels are based on:
>>>
>>> http://www.lfd.uci.edu/~gohlke/pythonlibs/#scipy
>>>
>>> If you end up with m2w64, here is lapack for conda, you may still have
>>> to modify paths:
>>>
>>> https://anaconda.org/conda-forge/lapack
>>>
>>> And blas:
>>>
>>> https://anaconda.org/search?q=Blas
>>>
>>> On Sun, Apr 30, 2017, 5:22 PM Matthieu Brucher <
>>> matthieu.brucher at gmail.com> wrote:
>>>
>>>> Why do you want to pay Intel? You can install the MKL and develop with
>>>> it, no sweat.
>>>>
>>>> 2017-04-30 22:41 GMT+01:00 David Hagen <david at drhagen.com>:
>>>>
>>>>> > Welcome to the world of pain with building scientific packages from
>>>>> source on Windows!
>>>>>
>>>>> I am beginning to feel it.
>>>>>
>>>>> > You need Fortran and C/C++ compilers on Windows to build scipy from
>>>>> source
>>>>>
>>>>> I have MinGW-w64 installed, which seems to be the recommended method.
>>>>>
>>>>> > I?m pretty sure that anaconda does not come with the development
>>>>> files for MKL, only the runtime files.
>>>>>
>>>>> I understand now. It looks like MKL is not the way to go unless I want
>>>>> to pay Intel.
>>>>>
>>>>> > If you don't need mkl and lapack/blas is good enough, then
>>>>> m2w64-toolchain from conda should have all necessary dependencies for
>>>>> building scipy.
>>>>>
>>>>> My only goal is to install and use Scipy master somewhere where it
>>>>> won't break my stable installation. I thought Anaconda would be a good
>>>>> place to start because once I activate an Anaconda environment, I should be
>>>>> able to treat like a normal Python installation and follow the normal
>>>>> install-from-source instructions. I went ahead and installed that
>>>>> m2w64-toolchain package, but it still doesn't find any BLAS/LAPACK. Maybe I
>>>>> should change my question to: how do I install Scipy on Windows from
>>>>> source? Though when I search for this specifically on the web, the answer
>>>>> seems to be "Don't.". It seems that MinGW-w64 and ATLAS are recommended by
>>>>> Scipy. Do you know of a conda/pip package that provides ATLAS for building
>>>>> Scipy or another more suitable BLAS/LAPACK?
>>>>>
>>>>> _______________________________________________
>>>>> SciPy-User mailing list
>>>>> SciPy-User at python.org
>>>>> https://mail.python.org/mailman/listinfo/scipy-user
>>>>>
>>>>>
>>>>
>>>>
>>>> --
>>>> Information System Engineer, Ph.D.
>>>> Blog: http://blog.audio-tk.com/
>>>> LinkedIn: http://www.linkedin.com/in/matthieubrucher
>>>> _______________________________________________
>>>> SciPy-User mailing list
>>>> SciPy-User at python.org
>>>> https://mail.python.org/mailman/listinfo/scipy-user
>>>>
>>>
>>> _______________________________________________
>>> SciPy-User mailing list
>>> SciPy-User at python.org
>>> https://mail.python.org/mailman/listinfo/scipy-user
>>>
>>>
>>
>> _______________________________________________
>> SciPy-User mailing list
>> SciPy-User at python.org
>> https://mail.python.org/mailman/listinfo/scipy-user
>>
>>
>
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at python.org
> https://mail.python.org/mailman/listinfo/scipy-user
>
>
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From denis.akhiyarov at gmail.com  Tue May  2 23:15:06 2017
From: denis.akhiyarov at gmail.com (Denis Akhiyarov)
Date: Wed, 03 May 2017 03:15:06 +0000
Subject: [SciPy-User] Install Scipy with Anaconda's MKL libraries
In-Reply-To: <CALO6Ft71-SfExyVQvPswoCN491qojfaUH9Y3k_CUE-jc1vJayQ@mail.gmail.com>
References: <CALO6Ft47hEd8RMtHF2nRAji84nztS8h-hm-qyojwv3kZn0-Zkw@mail.gmail.com>
 <CALO6Ft55X=Q9hhPUNwn-pZ1JRS+FnGLxdubLph9qtN4WkQUtnw@mail.gmail.com>
 <CAHCaCk+tYbh8AXuhWkTAtnjfCTqFjt4rFT9_XOP++bArRmsepQ@mail.gmail.com>
 <CALxxJLTUAjOupyKROxaYPAr+7VOTFm5kj70mEZApZUpAzgE=pA@mail.gmail.com>
 <CALO6Ft7K89B5Qd+tOJpmvKrN7YmBm0JKSDZhM_1aegN457HfWA@mail.gmail.com>
 <CAKWqKotdbQYdabmU2UKR2BgxfpiJBLXyQ5vdCSfA0t7iUdM_DA@mail.gmail.com>
 <CALO6Ft71-SfExyVQvPswoCN491qojfaUH9Y3k_CUE-jc1vJayQ@mail.gmail.com>
Message-ID: <CALxxJLQoesk0ixxFZJHGdtfw3WQV8AftXVnshEk6TiaA_+r=gw@mail.gmail.com>

Did you follow these instructions?

https://software.intel.com/en-us/articles/numpyscipy-with-intel-mkl

On Tue, May 2, 2017, 10:00 PM David Hagen <david at drhagen.com> wrote:

> Is there a recipe for this combination? I installed MKL from that link and
> the latest Visual Studio. Scipy did not find MKL on its own. I'm sure
> there's some environment variables that need to be set, but I don't know
> what they are.
>
> On Tue, May 2, 2017 at 4:00 AM, William Heymann <immudzen at gmail.com>
> wrote:
>
>> Intel has made MKL, TBB, and a few other things completely free to use,
>> even in a commercial project. Visual Studio is also free unless you are a
>> very large company.
>>
>> https://software.intel.com/en-us/articles/free-mkl
>>
>> I have been using that for other projects without any problems and
>> compiling with Visual Studio has been very easy.
>>
>> On Tue, May 2, 2017 at 1:40 AM, David Hagen <david at drhagen.com> wrote:
>>
>>> I'll try to stick with MinGW-w64 for now, but I don't even get to the
>>> compilation phase. If I install lapack and blas from conda-forge, it still
>>> says that lapack/blas are not found, but you indicated that I need to set
>>> some paths. Are there instructions for this? I have no idea what
>>> environment variables to set in order to tell Scipy to use these packages.
>>>
>>> On Mon, May 1, 2017 at 3:44 AM, Denis Akhiyarov <
>>> denis.akhiyarov at gmail.com> wrote:
>>>
>>>> I still suggest Intel+MSVC compilers, since you can use trial version
>>>> or request license for open-source projects from Intel. This is what
>>>> Anaconda team is using. Also this is what Christoph Gohlke wheels are based
>>>> on:
>>>>
>>>> http://www.lfd.uci.edu/~gohlke/pythonlibs/#scipy
>>>>
>>>> If you end up with m2w64, here is lapack for conda, you may still have
>>>> to modify paths:
>>>>
>>>> https://anaconda.org/conda-forge/lapack
>>>>
>>>> And blas:
>>>>
>>>> https://anaconda.org/search?q=Blas
>>>>
>>>> On Sun, Apr 30, 2017, 5:22 PM Matthieu Brucher <
>>>> matthieu.brucher at gmail.com> wrote:
>>>>
>>>>> Why do you want to pay Intel? You can install the MKL and develop with
>>>>> it, no sweat.
>>>>>
>>>>> 2017-04-30 22:41 GMT+01:00 David Hagen <david at drhagen.com>:
>>>>>
>>>>>> > Welcome to the world of pain with building scientific packages from
>>>>>> source on Windows!
>>>>>>
>>>>>> I am beginning to feel it.
>>>>>>
>>>>>> > You need Fortran and C/C++ compilers on Windows to build scipy from
>>>>>> source
>>>>>>
>>>>>> I have MinGW-w64 installed, which seems to be the recommended method.
>>>>>>
>>>>>> > I?m pretty sure that anaconda does not come with the development
>>>>>> files for MKL, only the runtime files.
>>>>>>
>>>>>> I understand now. It looks like MKL is not the way to go unless I
>>>>>> want to pay Intel.
>>>>>>
>>>>>> > If you don't need mkl and lapack/blas is good enough, then
>>>>>> m2w64-toolchain from conda should have all necessary dependencies for
>>>>>> building scipy.
>>>>>>
>>>>>> My only goal is to install and use Scipy master somewhere where it
>>>>>> won't break my stable installation. I thought Anaconda would be a good
>>>>>> place to start because once I activate an Anaconda environment, I should be
>>>>>> able to treat like a normal Python installation and follow the normal
>>>>>> install-from-source instructions. I went ahead and installed that
>>>>>> m2w64-toolchain package, but it still doesn't find any BLAS/LAPACK. Maybe I
>>>>>> should change my question to: how do I install Scipy on Windows from
>>>>>> source? Though when I search for this specifically on the web, the answer
>>>>>> seems to be "Don't.". It seems that MinGW-w64 and ATLAS are recommended by
>>>>>> Scipy. Do you know of a conda/pip package that provides ATLAS for building
>>>>>> Scipy or another more suitable BLAS/LAPACK?
>>>>>>
>>>>>> _______________________________________________
>>>>>> SciPy-User mailing list
>>>>>> SciPy-User at python.org
>>>>>> https://mail.python.org/mailman/listinfo/scipy-user
>>>>>>
>>>>>>
>>>>>
>>>>>
>>>>> --
>>>>> Information System Engineer, Ph.D.
>>>>> Blog: http://blog.audio-tk.com/
>>>>> LinkedIn: http://www.linkedin.com/in/matthieubrucher
>>>>> _______________________________________________
>>>>> SciPy-User mailing list
>>>>> SciPy-User at python.org
>>>>> https://mail.python.org/mailman/listinfo/scipy-user
>>>>>
>>>>
>>>> _______________________________________________
>>>> SciPy-User mailing list
>>>> SciPy-User at python.org
>>>> https://mail.python.org/mailman/listinfo/scipy-user
>>>>
>>>>
>>>
>>> _______________________________________________
>>> SciPy-User mailing list
>>> SciPy-User at python.org
>>> https://mail.python.org/mailman/listinfo/scipy-user
>>>
>>>
>>
>> _______________________________________________
>> SciPy-User mailing list
>> SciPy-User at python.org
>> https://mail.python.org/mailman/listinfo/scipy-user
>>
>>
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at python.org
> https://mail.python.org/mailman/listinfo/scipy-user
>
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From johnl at crumpington.com  Wed May  3 07:11:16 2017
From: johnl at crumpington.com (David Lee)
Date: Wed, 3 May 2017 13:11:16 +0200
Subject: [SciPy-User] Batch / HTC system testing
Message-ID: <2c55e634-f21e-bb10-0cfa-e899de3275d2@crumpington.com>

Hi,

I hope this isn't too off-topic.

I'm developing an on-demand, auto-scaling batch/HTC system, and I'm 
interesting in finding a tester or two. Please get in touch privately if 
this is something that you might be interested in. I can provide some 
free compute time and support.

Take care,

David

-- 
J. David Lee


From newville at cars.uchicago.edu  Wed May  3 13:41:14 2017
From: newville at cars.uchicago.edu (Matt Newville)
Date: Wed, 3 May 2017 12:41:14 -0500
Subject: [SciPy-User] scipy.optimize and vectorization (or not)
In-Reply-To: <516A9270-5E48-4674-A868-F37A8515AA13@gmail.com>
References: <516A9270-5E48-4674-A868-F37A8515AA13@gmail.com>
Message-ID: <CA+7ESbqkjLnicyq4qSkk0gPjL+fOQj8GSS9sx9znkbA_XkdwJg@mail.gmail.com>

On Tue, May 2, 2017 at 9:22 PM, Gideon Simpson <gideon.simpson at gmail.com>
wrote:

> I have a scalar valued function that I?d like to minimize, but, by
> default, it?s not readily vectorized.  In particular, it depends
> parametrically on the solution of an ODE, and vaguely looks like:
>
> def f(x, ode_soln):
>         ...
>         val = spicy.integrate.quad(lambda t: sin(x * t) *
> ode_soln.sol(t),0,1)
>
>         return val
>
> By default, if I try to do
>
> spicy.optimize.minimize(lambda x: f(x,ode_soln))
>
> I get an error that indicates that minimize thinks f accepts arrays of
> data.


minimize() takes two arguments:  a callable function that takes an array of
values to be adjusted, and an array of starting values.  Presumably, `x` is
the array of values that you would like optimized, but you have to provide
starting values.

I don't think you need the lambda, but you might want something like:

     scipy.optimize.minimize(f, x0, args=(ode_soln,))

where x0 is an array of starting values.


> I can remedy this by writing a little looped version,
>
> def f_vec(xvec, ode_soln):
>         fvals = np.zeros_like(xvec)
>
>         for j in range(xvec.size):
>                 fvals[j] = f(xvec[j], ode_soln)
>
>         return fvals
>
> but I?m wondering if:
>
> 1.  Is there a smarter/more elegant solution to handling the vectorized
> input?
>

It does handle vectorized input, or perhaps I'm not understanding your
question.


>
> 2.  Is there a way to just tell minimize that it?s going to have to
> evaluate f one point at a time, rather than writing some other function?
>

No, or well it depends what you mean by "one point at a time".  The
objective function provided should take an array of candidate values for
the parameters and return either an array to be minimized in the
least-squares sense or the scalar cost value.

Hope that helps,

--Matt
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From david at drhagen.com  Wed May  3 21:26:42 2017
From: david at drhagen.com (David Hagen)
Date: Wed, 3 May 2017 21:26:42 -0400
Subject: [SciPy-User] Install Scipy with Anaconda's MKL libraries
In-Reply-To: <CALxxJLQoesk0ixxFZJHGdtfw3WQV8AftXVnshEk6TiaA_+r=gw@mail.gmail.com>
References: <CALO6Ft47hEd8RMtHF2nRAji84nztS8h-hm-qyojwv3kZn0-Zkw@mail.gmail.com>
 <CALO6Ft55X=Q9hhPUNwn-pZ1JRS+FnGLxdubLph9qtN4WkQUtnw@mail.gmail.com>
 <CAHCaCk+tYbh8AXuhWkTAtnjfCTqFjt4rFT9_XOP++bArRmsepQ@mail.gmail.com>
 <CALxxJLTUAjOupyKROxaYPAr+7VOTFm5kj70mEZApZUpAzgE=pA@mail.gmail.com>
 <CALO6Ft7K89B5Qd+tOJpmvKrN7YmBm0JKSDZhM_1aegN457HfWA@mail.gmail.com>
 <CAKWqKotdbQYdabmU2UKR2BgxfpiJBLXyQ5vdCSfA0t7iUdM_DA@mail.gmail.com>
 <CALO6Ft71-SfExyVQvPswoCN491qojfaUH9Y3k_CUE-jc1vJayQ@mail.gmail.com>
 <CALxxJLQoesk0ixxFZJHGdtfw3WQV8AftXVnshEk6TiaA_+r=gw@mail.gmail.com>
Message-ID: <CALO6Ft6fw66b1LahGR_p=80-xxJhYiwZNEZFLN1SMhAh65NB_g@mail.gmail.com>

So those instructions get me farther than I have gotten before.

Is this actually necessary?:

Note: You will need to rebuild Python from source files. This is due to the
downloaded Python binary on Windows might be incompatible with the Visual
Studio version you used. Otherwise you will encounter runtime crash when
run numpy or script tests.

This is what I have done so far:

1) Download Scipy zip file from Github mater branch
2) Unzip file
3) Add a site.cfg file with these contents:
[mkl]
library_dirs = C:\Program Files
(x86)\IntelSWTools\compilers_and_libraries_2017.2.187\windows\mkl\lib\intel64_win;C:\Program
Files
(x86)\IntelSWTools\compilers_and_libraries_2017.2.187\windows\compiler\lib\intel64_win
include_dirs = C:\Program Files
(x86)\IntelSWTools\compilers_and_libraries_2017.2.187\windows\mkl\include
mkl_libs =
mkl_lapack95_lp64,mkl_blas95_lp64,mkl_intel_lp64,mkl_intel_thread,mkl_core,libiomp5md
lapack_libs =
mkl_lapack95_lp64,mkl_blas95_lp64,mkl_intel_lp64,mkl_intel_thread,mkl_core,libiomp5md

4) cd into that directory
5) run: python setup.py config --compiler=intelemw --fcompiler=intelvem
build_clib --compiler=intelemw --fcompiler=intelvem build_ext
--compiler=intelemw --fcompiler=intelvem install

It gets really far into the build but this is the error that results:

building extension "scipy.spatial.qhull" sources
creating build\src.win-amd64-3.6\scipy\spatial
Could not locate executable icc
Could not locate executable ecc
Traceback (most recent call last):
  File "setup.py", line 417, in <module>
    setup_package()
  File "setup.py", line 413, in setup_package
    setup(**metadata)
  File
"C:\Anaconda3\envs\scipy_master\lib\site-packages\numpy\distutils\core.py
", line 166, in setup
    return old_setup(**new_attr)
  File "C:\Anaconda3\envs\scipy_master\lib\distutils\core.py", line 148, in
setu
p
    dist.run_commands()
  File "C:\Anaconda3\envs\scipy_master\lib\distutils\dist.py", line 955, in
run_
commands
    self.run_command(cmd)
  File "C:\Anaconda3\envs\scipy_master\lib\distutils\dist.py", line 974, in
run_
command
    cmd_obj.run()
  File
"C:\Anaconda3\envs\scipy_master\lib\site-packages\numpy\distutils\command
\build_clib.py", line 74, in run
    self.run_command('build_src')
  File "C:\Anaconda3\envs\scipy_master\lib\distutils\cmd.py", line 313, in
run_c
ommand
    self.distribution.run_command(command)
  File "C:\Anaconda3\envs\scipy_master\lib\distutils\dist.py", line 974, in
run_
command
    cmd_obj.run()
  File
"C:\Anaconda3\envs\scipy_master\lib\site-packages\numpy\distutils\command
\build_src.py", line 148, in run
    self.build_sources()
  File
"C:\Anaconda3\envs\scipy_master\lib\site-packages\numpy\distutils\command
\build_src.py", line 165, in build_sources
    self.build_extension_sources(ext)
  File
"C:\Anaconda3\envs\scipy_master\lib\site-packages\numpy\distutils\command
\build_src.py", line 324, in build_extension_sources
    sources = self.generate_sources(sources, ext)
  File
"C:\Anaconda3\envs\scipy_master\lib\site-packages\numpy\distutils\command
\build_src.py", line 377, in generate_sources
    source = func(extension, build_dir)
  File "scipy\spatial\setup.py", line 35, in get_qhull_misc_config
    if config_cmd.check_func('open_memstream', decl=True, call=True):
  File
"C:\Anaconda3\envs\scipy_master\lib\site-packages\numpy\distutils\command
\config.py", line 312, in check_func
    self._check_compiler()
  File
"C:\Anaconda3\envs\scipy_master\lib\site-packages\numpy\distutils\command
\config.py", line 51, in _check_compiler
    self.compiler.initialize()
  File
"C:\Anaconda3\envs\scipy_master\lib\site-packages\numpy\distutils\intelcc
ompiler.py", line 86, in initialize
    MSVCCompiler.initialize(self, plat_name)
  File
"C:\Anaconda3\envs\scipy_master\lib\site-packages\numpy\distutils\msvc9co
mpiler.py", line 53, in initialize
    os.environ['lib'] = _merge(environ_lib, os.environ['lib'])
  File
"C:\Anaconda3\envs\scipy_master\lib\site-packages\numpy\distutils\msvc9co
mpiler.py", line 32, in _merge
    if new in old:
TypeError: argument of type 'NoneType' is not iterable

I notice that this is calling into Numpy. Do I have modify Numpy in order
to compile Scipy? I skipped the Numpy specific steps in the instructions
because I don't need the development version of Numpy.

On Tue, May 2, 2017 at 11:15 PM, Denis Akhiyarov <denis.akhiyarov at gmail.com>
wrote:

> Did you follow these instructions?
>
> https://software.intel.com/en-us/articles/numpyscipy-with-intel-mkl
>
> On Tue, May 2, 2017, 10:00 PM David Hagen <david at drhagen.com> wrote:
>
>> Is there a recipe for this combination? I installed MKL from that link
>> and the latest Visual Studio. Scipy did not find MKL on its own. I'm sure
>> there's some environment variables that need to be set, but I don't know
>> what they are.
>>
>> On Tue, May 2, 2017 at 4:00 AM, William Heymann <immudzen at gmail.com>
>> wrote:
>>
>>> Intel has made MKL, TBB, and a few other things completely free to use,
>>> even in a commercial project. Visual Studio is also free unless you are a
>>> very large company.
>>>
>>> https://software.intel.com/en-us/articles/free-mkl
>>>
>>> I have been using that for other projects without any problems and
>>> compiling with Visual Studio has been very easy.
>>>
>>> On Tue, May 2, 2017 at 1:40 AM, David Hagen <david at drhagen.com> wrote:
>>>
>>>> I'll try to stick with MinGW-w64 for now, but I don't even get to the
>>>> compilation phase. If I install lapack and blas from conda-forge, it still
>>>> says that lapack/blas are not found, but you indicated that I need to set
>>>> some paths. Are there instructions for this? I have no idea what
>>>> environment variables to set in order to tell Scipy to use these packages.
>>>>
>>>> On Mon, May 1, 2017 at 3:44 AM, Denis Akhiyarov <
>>>> denis.akhiyarov at gmail.com> wrote:
>>>>
>>>>> I still suggest Intel+MSVC compilers, since you can use trial version
>>>>> or request license for open-source projects from Intel. This is what
>>>>> Anaconda team is using. Also this is what Christoph Gohlke wheels are based
>>>>> on:
>>>>>
>>>>> http://www.lfd.uci.edu/~gohlke/pythonlibs/#scipy
>>>>>
>>>>> If you end up with m2w64, here is lapack for conda, you may still have
>>>>> to modify paths:
>>>>>
>>>>> https://anaconda.org/conda-forge/lapack
>>>>>
>>>>> And blas:
>>>>>
>>>>> https://anaconda.org/search?q=Blas
>>>>>
>>>>> On Sun, Apr 30, 2017, 5:22 PM Matthieu Brucher <
>>>>> matthieu.brucher at gmail.com> wrote:
>>>>>
>>>>>> Why do you want to pay Intel? You can install the MKL and develop
>>>>>> with it, no sweat.
>>>>>>
>>>>>> 2017-04-30 22:41 GMT+01:00 David Hagen <david at drhagen.com>:
>>>>>>
>>>>>>> > Welcome to the world of pain with building scientific packages
>>>>>>> from source on Windows!
>>>>>>>
>>>>>>> I am beginning to feel it.
>>>>>>>
>>>>>>> > You need Fortran and C/C++ compilers on Windows to build scipy
>>>>>>> from source
>>>>>>>
>>>>>>> I have MinGW-w64 installed, which seems to be the recommended method.
>>>>>>>
>>>>>>> > I?m pretty sure that anaconda does not come with the development
>>>>>>> files for MKL, only the runtime files.
>>>>>>>
>>>>>>> I understand now. It looks like MKL is not the way to go unless I
>>>>>>> want to pay Intel.
>>>>>>>
>>>>>>> > If you don't need mkl and lapack/blas is good enough, then
>>>>>>> m2w64-toolchain from conda should have all necessary dependencies for
>>>>>>> building scipy.
>>>>>>>
>>>>>>> My only goal is to install and use Scipy master somewhere where it
>>>>>>> won't break my stable installation. I thought Anaconda would be a good
>>>>>>> place to start because once I activate an Anaconda environment, I should be
>>>>>>> able to treat like a normal Python installation and follow the normal
>>>>>>> install-from-source instructions. I went ahead and installed that
>>>>>>> m2w64-toolchain package, but it still doesn't find any BLAS/LAPACK. Maybe I
>>>>>>> should change my question to: how do I install Scipy on Windows from
>>>>>>> source? Though when I search for this specifically on the web, the answer
>>>>>>> seems to be "Don't.". It seems that MinGW-w64 and ATLAS are recommended by
>>>>>>> Scipy. Do you know of a conda/pip package that provides ATLAS for building
>>>>>>> Scipy or another more suitable BLAS/LAPACK?
>>>>>>>
>>>>>>> _______________________________________________
>>>>>>> SciPy-User mailing list
>>>>>>> SciPy-User at python.org
>>>>>>> https://mail.python.org/mailman/listinfo/scipy-user
>>>>>>>
>>>>>>>
>>>>>>
>>>>>>
>>>>>> --
>>>>>> Information System Engineer, Ph.D.
>>>>>> Blog: http://blog.audio-tk.com/
>>>>>> LinkedIn: http://www.linkedin.com/in/matthieubrucher
>>>>>> _______________________________________________
>>>>>> SciPy-User mailing list
>>>>>> SciPy-User at python.org
>>>>>> https://mail.python.org/mailman/listinfo/scipy-user
>>>>>>
>>>>>
>>>>> _______________________________________________
>>>>> SciPy-User mailing list
>>>>> SciPy-User at python.org
>>>>> https://mail.python.org/mailman/listinfo/scipy-user
>>>>>
>>>>>
>>>>
>>>> _______________________________________________
>>>> SciPy-User mailing list
>>>> SciPy-User at python.org
>>>> https://mail.python.org/mailman/listinfo/scipy-user
>>>>
>>>>
>>>
>>> _______________________________________________
>>> SciPy-User mailing list
>>> SciPy-User at python.org
>>> https://mail.python.org/mailman/listinfo/scipy-user
>>>
>>>
>> _______________________________________________
>> SciPy-User mailing list
>> SciPy-User at python.org
>> https://mail.python.org/mailman/listinfo/scipy-user
>>
>
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at python.org
> https://mail.python.org/mailman/listinfo/scipy-user
>
>
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From denis.akhiyarov at gmail.com  Thu May  4 02:58:34 2017
From: denis.akhiyarov at gmail.com (Denis Akhiyarov)
Date: Thu, 4 May 2017 01:58:34 -0500
Subject: [SciPy-User] Install Scipy with Anaconda's MKL libraries
In-Reply-To: <CALO6Ft6fw66b1LahGR_p=80-xxJhYiwZNEZFLN1SMhAh65NB_g@mail.gmail.com>
References: <CALO6Ft47hEd8RMtHF2nRAji84nztS8h-hm-qyojwv3kZn0-Zkw@mail.gmail.com>
 <CALO6Ft55X=Q9hhPUNwn-pZ1JRS+FnGLxdubLph9qtN4WkQUtnw@mail.gmail.com>
 <CAHCaCk+tYbh8AXuhWkTAtnjfCTqFjt4rFT9_XOP++bArRmsepQ@mail.gmail.com>
 <CALxxJLTUAjOupyKROxaYPAr+7VOTFm5kj70mEZApZUpAzgE=pA@mail.gmail.com>
 <CALO6Ft7K89B5Qd+tOJpmvKrN7YmBm0JKSDZhM_1aegN457HfWA@mail.gmail.com>
 <CAKWqKotdbQYdabmU2UKR2BgxfpiJBLXyQ5vdCSfA0t7iUdM_DA@mail.gmail.com>
 <CALO6Ft71-SfExyVQvPswoCN491qojfaUH9Y3k_CUE-jc1vJayQ@mail.gmail.com>
 <CALxxJLQoesk0ixxFZJHGdtfw3WQV8AftXVnshEk6TiaA_+r=gw@mail.gmail.com>
 <CALO6Ft6fw66b1LahGR_p=80-xxJhYiwZNEZFLN1SMhAh65NB_g@mail.gmail.com>
Message-ID: <CALxxJLQv-yZePFASgvQS8+YNMZU4BfCH1-bj0CL+wxeBpOM=TA@mail.gmail.com>

Ok, I followed instructions on this page:

https://software.intel.com/en-us/articles/building-numpyscipy-with-intel-mkl-and-intel-fortran-on-windows

Installed parallel_studio_xe_2016_update4 with mkl, ifort, and icc.

1. I compiled numpy (python setup.py install) from source with this
site.cfg file:

[mkl]
include_dirs = C:\Program Files
(x86)\IntelSWTools\parallel_studio_xe_2016.4.062\compilers_and_libraries_2016\windows\mkl\include
library_dirs = C:\Program Files
(x86)\IntelSWTools\parallel_studio_xe_2016.4.062\compilers_and_libraries_2016\windows\mkl\lib\intel64;C:\Program
Files
(x86)\IntelSWTools\parallel_studio_xe_2016.4.062\compilers_and_libraries_2016\windows\compiler\lib\intel64
mkl_libs = mkl_core_dll, mkl_intel_lp64_dll, mkl_intel_thread_dll
lapack_libs = mkl_lapack95_lp64


2. I replaced site.cfg file in ..\Lib\site-packages\numpy\distutils with
the one above, since this is used by scipy in step 3.

3. I compiled scipy (python setup.py install) from source.

Compilation went without errors, both packages import without errors in
python.


On Wed, May 3, 2017 at 8:26 PM, David Hagen <david at drhagen.com> wrote:

> So those instructions get me farther than I have gotten before.
>
> Is this actually necessary?:
>
> Note: You will need to rebuild Python from source files. This is due to
> the downloaded Python binary on Windows might be incompatible with the
> Visual Studio version you used. Otherwise you will encounter runtime crash
> when run numpy or script tests.
>
> This is what I have done so far:
>
> 1) Download Scipy zip file from Github mater branch
> 2) Unzip file
> 3) Add a site.cfg file with these contents:
> [mkl]
> library_dirs = C:\Program Files (x86)\IntelSWTools\compilers_
> and_libraries_2017.2.187\windows\mkl\lib\intel64_win;C:\Program Files
> (x86)\IntelSWTools\compilers_and_libraries_2017.2.187\
> windows\compiler\lib\intel64_win
> include_dirs = C:\Program Files (x86)\IntelSWTools\compilers_
> and_libraries_2017.2.187\windows\mkl\include
> mkl_libs = mkl_lapack95_lp64,mkl_blas95_lp64,mkl_intel_lp64,mkl_intel_
> thread,mkl_core,libiomp5md
> lapack_libs = mkl_lapack95_lp64,mkl_blas95_lp64,mkl_intel_lp64,mkl_intel_
> thread,mkl_core,libiomp5md
>
> 4) cd into that directory
> 5) run: python setup.py config --compiler=intelemw --fcompiler=intelvem
> build_clib --compiler=intelemw --fcompiler=intelvem build_ext
> --compiler=intelemw --fcompiler=intelvem install
>
> It gets really far into the build but this is the error that results:
>
> building extension "scipy.spatial.qhull" sources
> creating build\src.win-amd64-3.6\scipy\spatial
> Could not locate executable icc
> Could not locate executable ecc
> Traceback (most recent call last):
>   File "setup.py", line 417, in <module>
>     setup_package()
>   File "setup.py", line 413, in setup_package
>     setup(**metadata)
>   File "C:\Anaconda3\envs\scipy_master\lib\site-packages\
> numpy\distutils\core.py
> ", line 166, in setup
>     return old_setup(**new_attr)
>   File "C:\Anaconda3\envs\scipy_master\lib\distutils\core.py", line 148,
> in setu
> p
>     dist.run_commands()
>   File "C:\Anaconda3\envs\scipy_master\lib\distutils\dist.py", line 955,
> in run_
> commands
>     self.run_command(cmd)
>   File "C:\Anaconda3\envs\scipy_master\lib\distutils\dist.py", line 974,
> in run_
> command
>     cmd_obj.run()
>   File "C:\Anaconda3\envs\scipy_master\lib\site-packages\
> numpy\distutils\command
> \build_clib.py", line 74, in run
>     self.run_command('build_src')
>   File "C:\Anaconda3\envs\scipy_master\lib\distutils\cmd.py", line 313,
> in run_c
> ommand
>     self.distribution.run_command(command)
>   File "C:\Anaconda3\envs\scipy_master\lib\distutils\dist.py", line 974,
> in run_
> command
>     cmd_obj.run()
>   File "C:\Anaconda3\envs\scipy_master\lib\site-packages\
> numpy\distutils\command
> \build_src.py", line 148, in run
>     self.build_sources()
>   File "C:\Anaconda3\envs\scipy_master\lib\site-packages\
> numpy\distutils\command
> \build_src.py", line 165, in build_sources
>     self.build_extension_sources(ext)
>   File "C:\Anaconda3\envs\scipy_master\lib\site-packages\
> numpy\distutils\command
> \build_src.py", line 324, in build_extension_sources
>     sources = self.generate_sources(sources, ext)
>   File "C:\Anaconda3\envs\scipy_master\lib\site-packages\
> numpy\distutils\command
> \build_src.py", line 377, in generate_sources
>     source = func(extension, build_dir)
>   File "scipy\spatial\setup.py", line 35, in get_qhull_misc_config
>     if config_cmd.check_func('open_memstream', decl=True, call=True):
>   File "C:\Anaconda3\envs\scipy_master\lib\site-packages\
> numpy\distutils\command
> \config.py", line 312, in check_func
>     self._check_compiler()
>   File "C:\Anaconda3\envs\scipy_master\lib\site-packages\
> numpy\distutils\command
> \config.py", line 51, in _check_compiler
>     self.compiler.initialize()
>   File "C:\Anaconda3\envs\scipy_master\lib\site-packages\
> numpy\distutils\intelcc
> ompiler.py", line 86, in initialize
>     MSVCCompiler.initialize(self, plat_name)
>   File "C:\Anaconda3\envs\scipy_master\lib\site-packages\
> numpy\distutils\msvc9co
> mpiler.py", line 53, in initialize
>     os.environ['lib'] = _merge(environ_lib, os.environ['lib'])
>   File "C:\Anaconda3\envs\scipy_master\lib\site-packages\
> numpy\distutils\msvc9co
> mpiler.py", line 32, in _merge
>     if new in old:
> TypeError: argument of type 'NoneType' is not iterable
>
> I notice that this is calling into Numpy. Do I have modify Numpy in order
> to compile Scipy? I skipped the Numpy specific steps in the instructions
> because I don't need the development version of Numpy.
>
> On Tue, May 2, 2017 at 11:15 PM, Denis Akhiyarov <
> denis.akhiyarov at gmail.com> wrote:
>
>> Did you follow these instructions?
>>
>> https://software.intel.com/en-us/articles/numpyscipy-with-intel-mkl
>>
>> On Tue, May 2, 2017, 10:00 PM David Hagen <david at drhagen.com> wrote:
>>
>>> Is there a recipe for this combination? I installed MKL from that link
>>> and the latest Visual Studio. Scipy did not find MKL on its own. I'm sure
>>> there's some environment variables that need to be set, but I don't know
>>> what they are.
>>>
>>> On Tue, May 2, 2017 at 4:00 AM, William Heymann <immudzen at gmail.com>
>>> wrote:
>>>
>>>> Intel has made MKL, TBB, and a few other things completely free to use,
>>>> even in a commercial project. Visual Studio is also free unless you are a
>>>> very large company.
>>>>
>>>> https://software.intel.com/en-us/articles/free-mkl
>>>>
>>>> I have been using that for other projects without any problems and
>>>> compiling with Visual Studio has been very easy.
>>>>
>>>> On Tue, May 2, 2017 at 1:40 AM, David Hagen <david at drhagen.com> wrote:
>>>>
>>>>> I'll try to stick with MinGW-w64 for now, but I don't even get to the
>>>>> compilation phase. If I install lapack and blas from conda-forge, it still
>>>>> says that lapack/blas are not found, but you indicated that I need to set
>>>>> some paths. Are there instructions for this? I have no idea what
>>>>> environment variables to set in order to tell Scipy to use these packages.
>>>>>
>>>>> On Mon, May 1, 2017 at 3:44 AM, Denis Akhiyarov <
>>>>> denis.akhiyarov at gmail.com> wrote:
>>>>>
>>>>>> I still suggest Intel+MSVC compilers, since you can use trial version
>>>>>> or request license for open-source projects from Intel. This is what
>>>>>> Anaconda team is using. Also this is what Christoph Gohlke wheels are based
>>>>>> on:
>>>>>>
>>>>>> http://www.lfd.uci.edu/~gohlke/pythonlibs/#scipy
>>>>>>
>>>>>> If you end up with m2w64, here is lapack for conda, you may still
>>>>>> have to modify paths:
>>>>>>
>>>>>> https://anaconda.org/conda-forge/lapack
>>>>>>
>>>>>> And blas:
>>>>>>
>>>>>> https://anaconda.org/search?q=Blas
>>>>>>
>>>>>> On Sun, Apr 30, 2017, 5:22 PM Matthieu Brucher <
>>>>>> matthieu.brucher at gmail.com> wrote:
>>>>>>
>>>>>>> Why do you want to pay Intel? You can install the MKL and develop
>>>>>>> with it, no sweat.
>>>>>>>
>>>>>>> 2017-04-30 22:41 GMT+01:00 David Hagen <david at drhagen.com>:
>>>>>>>
>>>>>>>> > Welcome to the world of pain with building scientific packages
>>>>>>>> from source on Windows!
>>>>>>>>
>>>>>>>> I am beginning to feel it.
>>>>>>>>
>>>>>>>> > You need Fortran and C/C++ compilers on Windows to build scipy
>>>>>>>> from source
>>>>>>>>
>>>>>>>> I have MinGW-w64 installed, which seems to be the recommended
>>>>>>>> method.
>>>>>>>>
>>>>>>>> > I?m pretty sure that anaconda does not come with the development
>>>>>>>> files for MKL, only the runtime files.
>>>>>>>>
>>>>>>>> I understand now. It looks like MKL is not the way to go unless I
>>>>>>>> want to pay Intel.
>>>>>>>>
>>>>>>>> > If you don't need mkl and lapack/blas is good enough, then
>>>>>>>> m2w64-toolchain from conda should have all necessary dependencies for
>>>>>>>> building scipy.
>>>>>>>>
>>>>>>>> My only goal is to install and use Scipy master somewhere where it
>>>>>>>> won't break my stable installation. I thought Anaconda would be a good
>>>>>>>> place to start because once I activate an Anaconda environment, I should be
>>>>>>>> able to treat like a normal Python installation and follow the normal
>>>>>>>> install-from-source instructions. I went ahead and installed that
>>>>>>>> m2w64-toolchain package, but it still doesn't find any BLAS/LAPACK. Maybe I
>>>>>>>> should change my question to: how do I install Scipy on Windows from
>>>>>>>> source? Though when I search for this specifically on the web, the answer
>>>>>>>> seems to be "Don't.". It seems that MinGW-w64 and ATLAS are recommended by
>>>>>>>> Scipy. Do you know of a conda/pip package that provides ATLAS for building
>>>>>>>> Scipy or another more suitable BLAS/LAPACK?
>>>>>>>>
>>>>>>>> _______________________________________________
>>>>>>>> SciPy-User mailing list
>>>>>>>> SciPy-User at python.org
>>>>>>>> https://mail.python.org/mailman/listinfo/scipy-user
>>>>>>>>
>>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>> --
>>>>>>> Information System Engineer, Ph.D.
>>>>>>> Blog: http://blog.audio-tk.com/
>>>>>>> LinkedIn: http://www.linkedin.com/in/matthieubrucher
>>>>>>> _______________________________________________
>>>>>>> SciPy-User mailing list
>>>>>>> SciPy-User at python.org
>>>>>>> https://mail.python.org/mailman/listinfo/scipy-user
>>>>>>>
>>>>>>
>>>>>> _______________________________________________
>>>>>> SciPy-User mailing list
>>>>>> SciPy-User at python.org
>>>>>> https://mail.python.org/mailman/listinfo/scipy-user
>>>>>>
>>>>>>
>>>>>
>>>>> _______________________________________________
>>>>> SciPy-User mailing list
>>>>> SciPy-User at python.org
>>>>> https://mail.python.org/mailman/listinfo/scipy-user
>>>>>
>>>>>
>>>>
>>>> _______________________________________________
>>>> SciPy-User mailing list
>>>> SciPy-User at python.org
>>>> https://mail.python.org/mailman/listinfo/scipy-user
>>>>
>>>>
>>> _______________________________________________
>>> SciPy-User mailing list
>>> SciPy-User at python.org
>>> https://mail.python.org/mailman/listinfo/scipy-user
>>>
>>
>> _______________________________________________
>> SciPy-User mailing list
>> SciPy-User at python.org
>> https://mail.python.org/mailman/listinfo/scipy-user
>>
>>
>
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at python.org
> https://mail.python.org/mailman/listinfo/scipy-user
>
>
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From david at drhagen.com  Thu May  4 08:16:13 2017
From: david at drhagen.com (David Hagen)
Date: Thu, 4 May 2017 08:16:13 -0400
Subject: [SciPy-User] Install Scipy with Anaconda's MKL libraries
In-Reply-To: <CALxxJLQv-yZePFASgvQS8+YNMZU4BfCH1-bj0CL+wxeBpOM=TA@mail.gmail.com>
References: <CALO6Ft47hEd8RMtHF2nRAji84nztS8h-hm-qyojwv3kZn0-Zkw@mail.gmail.com>
 <CALO6Ft55X=Q9hhPUNwn-pZ1JRS+FnGLxdubLph9qtN4WkQUtnw@mail.gmail.com>
 <CAHCaCk+tYbh8AXuhWkTAtnjfCTqFjt4rFT9_XOP++bArRmsepQ@mail.gmail.com>
 <CALxxJLTUAjOupyKROxaYPAr+7VOTFm5kj70mEZApZUpAzgE=pA@mail.gmail.com>
 <CALO6Ft7K89B5Qd+tOJpmvKrN7YmBm0JKSDZhM_1aegN457HfWA@mail.gmail.com>
 <CAKWqKotdbQYdabmU2UKR2BgxfpiJBLXyQ5vdCSfA0t7iUdM_DA@mail.gmail.com>
 <CALO6Ft71-SfExyVQvPswoCN491qojfaUH9Y3k_CUE-jc1vJayQ@mail.gmail.com>
 <CALxxJLQoesk0ixxFZJHGdtfw3WQV8AftXVnshEk6TiaA_+r=gw@mail.gmail.com>
 <CALO6Ft6fw66b1LahGR_p=80-xxJhYiwZNEZFLN1SMhAh65NB_g@mail.gmail.com>
 <CALxxJLQv-yZePFASgvQS8+YNMZU4BfCH1-bj0CL+wxeBpOM=TA@mail.gmail.com>
Message-ID: <CALO6Ft6PNWL7G37q-tsfasGoxUk7JdX6iw38-_2-Ug2CFY5vVA@mail.gmail.com>

Am I correct that there is no free version of ifort and icc? Will this
break after the 30-trial ends?

On Thu, May 4, 2017 at 2:58 AM, Denis Akhiyarov <denis.akhiyarov at gmail.com>
wrote:

> Ok, I followed instructions on this page:
>
> https://software.intel.com/en-us/articles/building-
> numpyscipy-with-intel-mkl-and-intel-fortran-on-windows
>
> Installed parallel_studio_xe_2016_update4 with mkl, ifort, and icc.
>
> 1. I compiled numpy (python setup.py install) from source with this
> site.cfg file:
>
> [mkl]
> include_dirs = C:\Program Files (x86)\IntelSWTools\parallel_
> studio_xe_2016.4.062\compilers_and_libraries_2016\windows\mkl\include
> library_dirs = C:\Program Files (x86)\IntelSWTools\parallel_
> studio_xe_2016.4.062\compilers_and_libraries_2016\
> windows\mkl\lib\intel64;C:\Program Files (x86)\IntelSWTools\parallel_
> studio_xe_2016.4.062\compilers_and_libraries_2016\
> windows\compiler\lib\intel64
> mkl_libs = mkl_core_dll, mkl_intel_lp64_dll, mkl_intel_thread_dll
> lapack_libs = mkl_lapack95_lp64
>
>
> 2. I replaced site.cfg file in ..\Lib\site-packages\numpy\distutils with
> the one above, since this is used by scipy in step 3.
>
> 3. I compiled scipy (python setup.py install) from source.
>
> Compilation went without errors, both packages import without errors in
> python.
>
>
> On Wed, May 3, 2017 at 8:26 PM, David Hagen <david at drhagen.com> wrote:
>
>> So those instructions get me farther than I have gotten before.
>>
>> Is this actually necessary?:
>>
>> Note: You will need to rebuild Python from source files. This is due to
>> the downloaded Python binary on Windows might be incompatible with the
>> Visual Studio version you used. Otherwise you will encounter runtime crash
>> when run numpy or script tests.
>>
>> This is what I have done so far:
>>
>> 1) Download Scipy zip file from Github mater branch
>> 2) Unzip file
>> 3) Add a site.cfg file with these contents:
>> [mkl]
>> library_dirs = C:\Program Files (x86)\IntelSWTools\compilers_a
>> nd_libraries_2017.2.187\windows\mkl\lib\intel64_win;C:\Program Files
>> (x86)\IntelSWTools\compilers_and_libraries_2017.2.187\window
>> s\compiler\lib\intel64_win
>> include_dirs = C:\Program Files (x86)\IntelSWTools\compilers_a
>> nd_libraries_2017.2.187\windows\mkl\include
>> mkl_libs = mkl_lapack95_lp64,mkl_blas95_lp64,mkl_intel_lp64,mkl_intel_t
>> hread,mkl_core,libiomp5md
>> lapack_libs = mkl_lapack95_lp64,mkl_blas95_l
>> p64,mkl_intel_lp64,mkl_intel_thread,mkl_core,libiomp5md
>>
>> 4) cd into that directory
>> 5) run: python setup.py config --compiler=intelemw --fcompiler=intelvem
>> build_clib --compiler=intelemw --fcompiler=intelvem build_ext
>> --compiler=intelemw --fcompiler=intelvem install
>>
>> It gets really far into the build but this is the error that results:
>>
>> building extension "scipy.spatial.qhull" sources
>> creating build\src.win-amd64-3.6\scipy\spatial
>> Could not locate executable icc
>> Could not locate executable ecc
>> Traceback (most recent call last):
>>   File "setup.py", line 417, in <module>
>>     setup_package()
>>   File "setup.py", line 413, in setup_package
>>     setup(**metadata)
>>   File "C:\Anaconda3\envs\scipy_master\lib\site-packages\numpy\
>> distutils\core.py
>> ", line 166, in setup
>>     return old_setup(**new_attr)
>>   File "C:\Anaconda3\envs\scipy_master\lib\distutils\core.py", line 148,
>> in setu
>> p
>>     dist.run_commands()
>>   File "C:\Anaconda3\envs\scipy_master\lib\distutils\dist.py", line 955,
>> in run_
>> commands
>>     self.run_command(cmd)
>>   File "C:\Anaconda3\envs\scipy_master\lib\distutils\dist.py", line 974,
>> in run_
>> command
>>     cmd_obj.run()
>>   File "C:\Anaconda3\envs\scipy_master\lib\site-packages\numpy\
>> distutils\command
>> \build_clib.py", line 74, in run
>>     self.run_command('build_src')
>>   File "C:\Anaconda3\envs\scipy_master\lib\distutils\cmd.py", line 313,
>> in run_c
>> ommand
>>     self.distribution.run_command(command)
>>   File "C:\Anaconda3\envs\scipy_master\lib\distutils\dist.py", line 974,
>> in run_
>> command
>>     cmd_obj.run()
>>   File "C:\Anaconda3\envs\scipy_master\lib\site-packages\numpy\
>> distutils\command
>> \build_src.py", line 148, in run
>>     self.build_sources()
>>   File "C:\Anaconda3\envs\scipy_master\lib\site-packages\numpy\
>> distutils\command
>> \build_src.py", line 165, in build_sources
>>     self.build_extension_sources(ext)
>>   File "C:\Anaconda3\envs\scipy_master\lib\site-packages\numpy\
>> distutils\command
>> \build_src.py", line 324, in build_extension_sources
>>     sources = self.generate_sources(sources, ext)
>>   File "C:\Anaconda3\envs\scipy_master\lib\site-packages\numpy\
>> distutils\command
>> \build_src.py", line 377, in generate_sources
>>     source = func(extension, build_dir)
>>   File "scipy\spatial\setup.py", line 35, in get_qhull_misc_config
>>     if config_cmd.check_func('open_memstream', decl=True, call=True):
>>   File "C:\Anaconda3\envs\scipy_master\lib\site-packages\numpy\
>> distutils\command
>> \config.py", line 312, in check_func
>>     self._check_compiler()
>>   File "C:\Anaconda3\envs\scipy_master\lib\site-packages\numpy\
>> distutils\command
>> \config.py", line 51, in _check_compiler
>>     self.compiler.initialize()
>>   File "C:\Anaconda3\envs\scipy_master\lib\site-packages\numpy\
>> distutils\intelcc
>> ompiler.py", line 86, in initialize
>>     MSVCCompiler.initialize(self, plat_name)
>>   File "C:\Anaconda3\envs\scipy_master\lib\site-packages\numpy\
>> distutils\msvc9co
>> mpiler.py", line 53, in initialize
>>     os.environ['lib'] = _merge(environ_lib, os.environ['lib'])
>>   File "C:\Anaconda3\envs\scipy_master\lib\site-packages\numpy\
>> distutils\msvc9co
>> mpiler.py", line 32, in _merge
>>     if new in old:
>> TypeError: argument of type 'NoneType' is not iterable
>>
>> I notice that this is calling into Numpy. Do I have modify Numpy in order
>> to compile Scipy? I skipped the Numpy specific steps in the instructions
>> because I don't need the development version of Numpy.
>>
>> On Tue, May 2, 2017 at 11:15 PM, Denis Akhiyarov <
>> denis.akhiyarov at gmail.com> wrote:
>>
>>> Did you follow these instructions?
>>>
>>> https://software.intel.com/en-us/articles/numpyscipy-with-intel-mkl
>>>
>>> On Tue, May 2, 2017, 10:00 PM David Hagen <david at drhagen.com> wrote:
>>>
>>>> Is there a recipe for this combination? I installed MKL from that link
>>>> and the latest Visual Studio. Scipy did not find MKL on its own. I'm sure
>>>> there's some environment variables that need to be set, but I don't know
>>>> what they are.
>>>>
>>>> On Tue, May 2, 2017 at 4:00 AM, William Heymann <immudzen at gmail.com>
>>>> wrote:
>>>>
>>>>> Intel has made MKL, TBB, and a few other things completely free to
>>>>> use, even in a commercial project. Visual Studio is also free unless you
>>>>> are a very large company.
>>>>>
>>>>> https://software.intel.com/en-us/articles/free-mkl
>>>>>
>>>>> I have been using that for other projects without any problems and
>>>>> compiling with Visual Studio has been very easy.
>>>>>
>>>>> On Tue, May 2, 2017 at 1:40 AM, David Hagen <david at drhagen.com> wrote:
>>>>>
>>>>>> I'll try to stick with MinGW-w64 for now, but I don't even get to the
>>>>>> compilation phase. If I install lapack and blas from conda-forge, it still
>>>>>> says that lapack/blas are not found, but you indicated that I need to set
>>>>>> some paths. Are there instructions for this? I have no idea what
>>>>>> environment variables to set in order to tell Scipy to use these packages.
>>>>>>
>>>>>> On Mon, May 1, 2017 at 3:44 AM, Denis Akhiyarov <
>>>>>> denis.akhiyarov at gmail.com> wrote:
>>>>>>
>>>>>>> I still suggest Intel+MSVC compilers, since you can use trial
>>>>>>> version or request license for open-source projects from Intel. This is
>>>>>>> what Anaconda team is using. Also this is what Christoph Gohlke wheels are
>>>>>>> based on:
>>>>>>>
>>>>>>> http://www.lfd.uci.edu/~gohlke/pythonlibs/#scipy
>>>>>>>
>>>>>>> If you end up with m2w64, here is lapack for conda, you may still
>>>>>>> have to modify paths:
>>>>>>>
>>>>>>> https://anaconda.org/conda-forge/lapack
>>>>>>>
>>>>>>> And blas:
>>>>>>>
>>>>>>> https://anaconda.org/search?q=Blas
>>>>>>>
>>>>>>> On Sun, Apr 30, 2017, 5:22 PM Matthieu Brucher <
>>>>>>> matthieu.brucher at gmail.com> wrote:
>>>>>>>
>>>>>>>> Why do you want to pay Intel? You can install the MKL and develop
>>>>>>>> with it, no sweat.
>>>>>>>>
>>>>>>>> 2017-04-30 22:41 GMT+01:00 David Hagen <david at drhagen.com>:
>>>>>>>>
>>>>>>>>> > Welcome to the world of pain with building scientific packages
>>>>>>>>> from source on Windows!
>>>>>>>>>
>>>>>>>>> I am beginning to feel it.
>>>>>>>>>
>>>>>>>>> > You need Fortran and C/C++ compilers on Windows to build scipy
>>>>>>>>> from source
>>>>>>>>>
>>>>>>>>> I have MinGW-w64 installed, which seems to be the recommended
>>>>>>>>> method.
>>>>>>>>>
>>>>>>>>> > I?m pretty sure that anaconda does not come with the development
>>>>>>>>> files for MKL, only the runtime files.
>>>>>>>>>
>>>>>>>>> I understand now. It looks like MKL is not the way to go unless I
>>>>>>>>> want to pay Intel.
>>>>>>>>>
>>>>>>>>> > If you don't need mkl and lapack/blas is good enough, then
>>>>>>>>> m2w64-toolchain from conda should have all necessary dependencies for
>>>>>>>>> building scipy.
>>>>>>>>>
>>>>>>>>> My only goal is to install and use Scipy master somewhere where it
>>>>>>>>> won't break my stable installation. I thought Anaconda would be a good
>>>>>>>>> place to start because once I activate an Anaconda environment, I should be
>>>>>>>>> able to treat like a normal Python installation and follow the normal
>>>>>>>>> install-from-source instructions. I went ahead and installed that
>>>>>>>>> m2w64-toolchain package, but it still doesn't find any BLAS/LAPACK. Maybe I
>>>>>>>>> should change my question to: how do I install Scipy on Windows from
>>>>>>>>> source? Though when I search for this specifically on the web, the answer
>>>>>>>>> seems to be "Don't.". It seems that MinGW-w64 and ATLAS are recommended by
>>>>>>>>> Scipy. Do you know of a conda/pip package that provides ATLAS for building
>>>>>>>>> Scipy or another more suitable BLAS/LAPACK?
>>>>>>>>>
>>>>>>>>> _______________________________________________
>>>>>>>>> SciPy-User mailing list
>>>>>>>>> SciPy-User at python.org
>>>>>>>>> https://mail.python.org/mailman/listinfo/scipy-user
>>>>>>>>>
>>>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>>>> --
>>>>>>>> Information System Engineer, Ph.D.
>>>>>>>> Blog: http://blog.audio-tk.com/
>>>>>>>> LinkedIn: http://www.linkedin.com/in/matthieubrucher
>>>>>>>> _______________________________________________
>>>>>>>> SciPy-User mailing list
>>>>>>>> SciPy-User at python.org
>>>>>>>> https://mail.python.org/mailman/listinfo/scipy-user
>>>>>>>>
>>>>>>>
>>>>>>> _______________________________________________
>>>>>>> SciPy-User mailing list
>>>>>>> SciPy-User at python.org
>>>>>>> https://mail.python.org/mailman/listinfo/scipy-user
>>>>>>>
>>>>>>>
>>>>>>
>>>>>> _______________________________________________
>>>>>> SciPy-User mailing list
>>>>>> SciPy-User at python.org
>>>>>> https://mail.python.org/mailman/listinfo/scipy-user
>>>>>>
>>>>>>
>>>>>
>>>>> _______________________________________________
>>>>> SciPy-User mailing list
>>>>> SciPy-User at python.org
>>>>> https://mail.python.org/mailman/listinfo/scipy-user
>>>>>
>>>>>
>>>> _______________________________________________
>>>> SciPy-User mailing list
>>>> SciPy-User at python.org
>>>> https://mail.python.org/mailman/listinfo/scipy-user
>>>>
>>>
>>> _______________________________________________
>>> SciPy-User mailing list
>>> SciPy-User at python.org
>>> https://mail.python.org/mailman/listinfo/scipy-user
>>>
>>>
>>
>> _______________________________________________
>> SciPy-User mailing list
>> SciPy-User at python.org
>> https://mail.python.org/mailman/listinfo/scipy-user
>>
>>
>
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at python.org
> https://mail.python.org/mailman/listinfo/scipy-user
>
>
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From s.molnar at sbcglobal.net  Thu May  4 09:20:53 2017
From: s.molnar at sbcglobal.net (Stephen P. Molnar)
Date: Thu, 4 May 2017 09:20:53 -0400
Subject: [SciPy-User] Install Scipy with Anaconda's MKL libraries
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On 05/04/2017 08:16 AM, David Hagen wrote:
> Am I correct that there is no free version of ifort and icc? Will this
> break after the 30-trial ends?
>
> On Thu, May 4, 2017 at 2:58 AM, Denis Akhiyarov
> <denis.akhiyarov at gmail.com <mailto:denis.akhiyarov at gmail.com>> wrote:
>
>     Ok, I followed instructions on this page:
>
>     https://software.intel.com/en-us/articles/building-numpyscipy-with-intel-mkl-and-intel-fortran-on-windows
>     <https://software.intel.com/en-us/articles/building-numpyscipy-with-intel-mkl-and-intel-fortran-on-windows>
>
>     Installed parallel_studio_xe_2016_update4 with mkl, ifort, and icc.
>
>     1. I compiled numpy (python setup.py install) from source with this
>     site.cfg file:
>
>     [mkl]
>     include_dirs = C:\Program Files
>     (x86)\IntelSWTools\parallel_studio_xe_2016.4.062\compilers_and_libraries_2016\windows\mkl\include
>     library_dirs = C:\Program Files
>     (x86)\IntelSWTools\parallel_studio_xe_2016.4.062\compilers_and_libraries_2016\windows\mkl\lib\intel64;C:\Program
>     Files
>     (x86)\IntelSWTools\parallel_studio_xe_2016.4.062\compilers_and_libraries_2016\windows\compiler\lib\intel64
>     mkl_libs = mkl_core_dll, mkl_intel_lp64_dll, mkl_intel_thread_dll
>     lapack_libs = mkl_lapack95_lp64
>
>
>     2. I replaced site.cfg file in ..\Lib\site-packages\numpy\distutils
>     with the one above, since this is used by scipy in step 3.
>
>     3. I compiled scipy (python setup.py install) from source.
>
>     Compilation went without errors, both packages import without errors
>     in python.
>
>
>     On Wed, May 3, 2017 at 8:26 PM, David Hagen <david at drhagen.com
>     <mailto:david at drhagen.com>> wrote:
>
>         So those instructions get me farther than I have gotten before.
>
>         Is this actually necessary?:
>
>         Note: You will need to rebuild Python from source files. This is
>         due to the downloaded Python binary on Windows might
>         be incompatible with the Visual Studio version you used.
>         Otherwise you will encounter runtime crash when run numpy or
>         script tests.
>
>         This is what I have done so far:
>
>         1) Download Scipy zip file from Github mater branch
>         2) Unzip file
>         3) Add a site.cfg file with these contents:
>         [mkl]
>         library_dirs = C:\Program Files
>         (x86)\IntelSWTools\compilers_and_libraries_2017.2.187\windows\mkl\lib\intel64_win;C:\Program
>         Files
>         (x86)\IntelSWTools\compilers_and_libraries_2017.2.187\windows\compiler\lib\intel64_win
>         include_dirs = C:\Program Files
>         (x86)\IntelSWTools\compilers_and_libraries_2017.2.187\windows\mkl\include
>         mkl_libs =
>         mkl_lapack95_lp64,mkl_blas95_lp64,mkl_intel_lp64,mkl_intel_thread,mkl_core,libiomp5md
>         lapack_libs =
>         mkl_lapack95_lp64,mkl_blas95_lp64,mkl_intel_lp64,mkl_intel_thread,mkl_core,libiomp5md
>
>         4) cd into that directory
>         5) run: python setup.py config --compiler=intelemw
>         --fcompiler=intelvem build_clib --compiler=intelemw
>         --fcompiler=intelvem build_ext --compiler=intelemw
>         --fcompiler=intelvem install
>
>         It gets really far into the build but this is the error that
>         results:
>
>         building extension "scipy.spatial.qhull" sources
>         creating build\src.win-amd64-3.6\scipy\spatial
>         Could not locate executable icc
>         Could not locate executable ecc
>         Traceback (most recent call last):
>            File "setup.py", line 417, in <module>
>              setup_package()
>            File "setup.py", line 413, in setup_package
>              setup(**metadata)
>            File
>         "C:\Anaconda3\envs\scipy_master\lib\site-packages\numpy\distutils\core.py
>         ", line 166, in setup
>              return old_setup(**new_attr)
>            File "C:\Anaconda3\envs\scipy_master\lib\distutils\core.py",
>         line 148, in setu
>         p
>              dist.run_commands()
>            File "C:\Anaconda3\envs\scipy_master\lib\distutils\dist.py",
>         line 955, in run_
>         commands
>              self.run_command(cmd)
>            File "C:\Anaconda3\envs\scipy_master\lib\distutils\dist.py",
>         line 974, in run_
>         command
>              cmd_obj.run()
>            File
>         "C:\Anaconda3\envs\scipy_master\lib\site-packages\numpy\distutils\command
>         \build_clib.py", line 74, in run
>              self.run_command('build_src')
>            File "C:\Anaconda3\envs\scipy_master\lib\distutils\cmd.py",
>         line 313, in run_c
>         ommand
>              self.distribution.run_command(command)
>            File "C:\Anaconda3\envs\scipy_master\lib\distutils\dist.py",
>         line 974, in run_
>         command
>              cmd_obj.run()
>            File
>         "C:\Anaconda3\envs\scipy_master\lib\site-packages\numpy\distutils\command
>         \build_src.py", line 148, in run
>              self.build_sources()
>            File
>         "C:\Anaconda3\envs\scipy_master\lib\site-packages\numpy\distutils\command
>         \build_src.py", line 165, in build_sources
>              self.build_extension_sources(ext)
>            File
>         "C:\Anaconda3\envs\scipy_master\lib\site-packages\numpy\distutils\command
>         \build_src.py", line 324, in build_extension_sources
>              sources = self.generate_sources(sources, ext)
>            File
>         "C:\Anaconda3\envs\scipy_master\lib\site-packages\numpy\distutils\command
>         \build_src.py", line 377, in generate_sources
>              source = func(extension, build_dir)
>            File "scipy\spatial\setup.py", line 35, in get_qhull_misc_config
>              if config_cmd.check_func('open_memstream', decl=True,
>         call=True):
>            File
>         "C:\Anaconda3\envs\scipy_master\lib\site-packages\numpy\distutils\command
>         \config.py", line 312, in check_func
>              self._check_compiler()
>            File
>         "C:\Anaconda3\envs\scipy_master\lib\site-packages\numpy\distutils\command
>         \config.py", line 51, in _check_compiler
>              self.compiler.initialize()
>            File
>         "C:\Anaconda3\envs\scipy_master\lib\site-packages\numpy\distutils\intelcc
>         ompiler.py", line 86, in initialize
>              MSVCCompiler.initialize(self, plat_name)
>            File
>         "C:\Anaconda3\envs\scipy_master\lib\site-packages\numpy\distutils\msvc9co
>         mpiler.py", line 53, in initialize
>              os.environ['lib'] = _merge(environ_lib, os.environ['lib'])
>            File
>         "C:\Anaconda3\envs\scipy_master\lib\site-packages\numpy\distutils\msvc9co
>         mpiler.py", line 32, in _merge
>              if new in old:
>         TypeError: argument of type 'NoneType' is not iterable
>
>         I notice that this is calling into Numpy. Do I have modify Numpy
>         in order to compile Scipy? I skipped the Numpy specific steps in
>         the instructions because I don't need the development version of
>         Numpy.
>
>         On Tue, May 2, 2017 at 11:15 PM, Denis Akhiyarov
>         <denis.akhiyarov at gmail.com <mailto:denis.akhiyarov at gmail.com>>
>         wrote:
>
>             Did you follow these instructions?
>
>             https://software.intel.com/en-us/articles/numpyscipy-with-intel-mkl
>             <https://software.intel.com/en-us/articles/numpyscipy-with-intel-mkl>
>
>
>             On Tue, May 2, 2017, 10:00 PM David Hagen <david at drhagen.com
>             <mailto:david at drhagen.com>> wrote:
>
>                 Is there a recipe for this combination? I installed MKL
>                 from that link and the latest Visual Studio. Scipy did
>                 not find MKL on its own. I'm sure there's some
>                 environment variables that need to be set, but I don't
>                 know what they are.
>
>                 On Tue, May 2, 2017 at 4:00 AM, William Heymann
>                 <immudzen at gmail.com <mailto:immudzen at gmail.com>> wrote:
>
>                     Intel has made MKL, TBB, and a few other things
>                     completely free to use, even in a commercial
>                     project. Visual Studio is also free unless you are a
>                     very large company.
>
>                     https://software.intel.com/en-us/articles/free-mkl
>                     <https://software.intel.com/en-us/articles/free-mkl>
>
>                     I have been using that for other projects without
>                     any problems and compiling with Visual Studio has
>                     been very easy.
>
>                     On Tue, May 2, 2017 at 1:40 AM, David Hagen
>                     <david at drhagen.com <mailto:david at drhagen.com>> wrote:
>
>                         I'll try to stick with MinGW-w64 for now, but I
>                         don't even get to the compilation phase. If I
>                         install lapack and blas from conda-forge, it
>                         still says that lapack/blas are not found, but
>                         you indicated that I need to set some paths. Are
>                         there instructions for this? I have no idea what
>                         environment variables to set in order to tell
>                         Scipy to use these packages.
>
>                         On Mon, May 1, 2017 at 3:44 AM, Denis Akhiyarov
>                         <denis.akhiyarov at gmail.com
>                         <mailto:denis.akhiyarov at gmail.com>> wrote:
>
>                             I still suggest Intel+MSVC compilers, since
>                             you can use trial version or request license
>                             for open-source projects from Intel. This is
>                             what Anaconda team is using. Also this is
>                             what Christoph Gohlke wheels are based on:
>
>                             http://www.lfd.uci.edu/~gohlke/pythonlibs/#scipy
>                             <http://www.lfd.uci.edu/~gohlke/pythonlibs/#scipy>
>
>                             If you end up with m2w64, here is lapack for
>                             conda, you may still have to modify paths:
>
>                             https://anaconda.org/conda-forge/lapack
>                             <https://anaconda.org/conda-forge/lapack>
>
>                             And blas:
>
>                             https://anaconda.org/search?q=Blas
>                             <https://anaconda.org/search?q=Blas>
>
>
>                             On Sun, Apr 30, 2017, 5:22 PM Matthieu
>                             Brucher <matthieu.brucher at gmail.com
>                             <mailto:matthieu.brucher at gmail.com>> wrote:
>
>                                 Why do you want to pay Intel? You can
>                                 install the MKL and develop with it, no
>                                 sweat.
>
>                                 2017-04-30 22:41 GMT+01:00 David Hagen
>                                 <david at drhagen.com
>                                 <mailto:david at drhagen.com>>:
>
>                                     > Welcome to the world of pain with building scientific packages from source on Windows!
>
>                                     I am beginning to feel it.
>
>                                     > You need Fortran and C/C++ compilers on Windows to build scipy from source
>
>                                     I have MinGW-w64 installed, which
>                                     seems to be the recommended method.
>
>                                     > I?m pretty sure that anaconda does not come with the development files for MKL, only the runtime files.
>
>                                     I understand now. It looks like MKL
>                                     is not the way to go unless I want
>                                     to pay Intel.
>
>                                     > If you don't need mkl and lapack/blas is good enough, then m2w64-toolchain from conda should have all necessary dependencies for building scipy.
>
>                                     My only goal is to install and use
>                                     Scipy master somewhere where it
>                                     won't break my stable installation.
>                                     I thought Anaconda would be a good
>                                     place to start because once I
>                                     activate an Anaconda environment, I
>                                     should be able to treat like a
>                                     normal Python installation and
>                                     follow the normal
>                                     install-from-source instructions. I
>                                     went ahead and installed that
>                                     m2w64-toolchain package, but it
>                                     still doesn't find any BLAS/LAPACK.
>                                     Maybe I should change my question
>                                     to: how do I install Scipy on
>                                     Windows from source? Though when I
>                                     search for this specifically on the
>                                     web, the answer seems to be
>                                     "Don't.". It seems that MinGW-w64
>                                     and ATLAS are recommended by Scipy.
>                                     Do you know of a conda/pip package
>                                     that provides ATLAS for building
>                                     Scipy or another more suitable
>                                     BLAS/LAPACK?
>
>                                     _______________________________________________
>                                     SciPy-User mailing list
>                                     SciPy-User at python.org
>                                     <mailto:SciPy-User at python.org>
>                                     https://mail.python.org/mailman/listinfo/scipy-user
>                                     <https://mail.python.org/mailman/listinfo/scipy-user>
>
>
>
>
>                                 --
>                                 Information System Engineer, Ph.D.
>                                 Blog: http://blog.audio-tk.com/
>                                 LinkedIn:
>                                 http://www.linkedin.com/in/matthieubrucher
>                                 <http://www.linkedin.com/in/matthieubrucher>
>                                 _______________________________________________
>                                 SciPy-User mailing list
>                                 SciPy-User at python.org
>                                 <mailto:SciPy-User at python.org>
>                                 https://mail.python.org/mailman/listinfo/scipy-user
>                                 <https://mail.python.org/mailman/listinfo/scipy-user>
>
>
>                             _______________________________________________
>                             SciPy-User mailing list
>                             SciPy-User at python.org
>                             <mailto:SciPy-User at python.org>
>                             https://mail.python.org/mailman/listinfo/scipy-user
>                             <https://mail.python.org/mailman/listinfo/scipy-user>
>
>
>
>                         _______________________________________________
>                         SciPy-User mailing list
>                         SciPy-User at python.org <mailto:SciPy-User at python.org>
>                         https://mail.python.org/mailman/listinfo/scipy-user
>                         <https://mail.python.org/mailman/listinfo/scipy-user>
>
>
>
>                     _______________________________________________
>                     SciPy-User mailing list
>                     SciPy-User at python.org <mailto:SciPy-User at python.org>
>                     https://mail.python.org/mailman/listinfo/scipy-user
>                     <https://mail.python.org/mailman/listinfo/scipy-user>
>
>
>                 _______________________________________________
>                 SciPy-User mailing list
>                 SciPy-User at python.org <mailto:SciPy-User at python.org>
>                 https://mail.python.org/mailman/listinfo/scipy-user
>                 <https://mail.python.org/mailman/listinfo/scipy-user>
>
>
>             _______________________________________________
>             SciPy-User mailing list
>             SciPy-User at python.org <mailto:SciPy-User at python.org>
>             https://mail.python.org/mailman/listinfo/scipy-user
>             <https://mail.python.org/mailman/listinfo/scipy-user>
>
>
>
>         _______________________________________________
>         SciPy-User mailing list
>         SciPy-User at python.org <mailto:SciPy-User at python.org>
>         https://mail.python.org/mailman/listinfo/scipy-user
>         <https://mail.python.org/mailman/listinfo/scipy-user>
>
>
>
>     _______________________________________________
>     SciPy-User mailing list
>     SciPy-User at python.org <mailto:SciPy-User at python.org>
>     https://mail.python.org/mailman/listinfo/scipy-user
>     <https://mail.python.org/mailman/listinfo/scipy-user>
>
>
>
>
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at python.org
> https://mail.python.org/mailman/listinfo/scipy-us
No, you are not correct about ifort.  It's wstill available.  ayou just 
won't receive support.

-- 
Stephen P. Molnar, Ph.D.		Life is a fuzzy set
www.molecular-modeling.net		Stochastic and multivariate
(614)312-7528 (c)
Skype: smolnar1

From david at drhagen.com  Thu May  4 10:54:41 2017
From: david at drhagen.com (David Hagen)
Date: Thu, 4 May 2017 10:54:41 -0400
Subject: [SciPy-User] Install Scipy with Anaconda's MKL libraries
In-Reply-To: <590B2AB5.9080505@sbcglobal.net>
References: <CALO6Ft47hEd8RMtHF2nRAji84nztS8h-hm-qyojwv3kZn0-Zkw@mail.gmail.com>
 <CALO6Ft55X=Q9hhPUNwn-pZ1JRS+FnGLxdubLph9qtN4WkQUtnw@mail.gmail.com>
 <CAHCaCk+tYbh8AXuhWkTAtnjfCTqFjt4rFT9_XOP++bArRmsepQ@mail.gmail.com>
 <CALxxJLTUAjOupyKROxaYPAr+7VOTFm5kj70mEZApZUpAzgE=pA@mail.gmail.com>
 <CALO6Ft7K89B5Qd+tOJpmvKrN7YmBm0JKSDZhM_1aegN457HfWA@mail.gmail.com>
 <CAKWqKotdbQYdabmU2UKR2BgxfpiJBLXyQ5vdCSfA0t7iUdM_DA@mail.gmail.com>
 <CALO6Ft71-SfExyVQvPswoCN491qojfaUH9Y3k_CUE-jc1vJayQ@mail.gmail.com>
 <CALxxJLQoesk0ixxFZJHGdtfw3WQV8AftXVnshEk6TiaA_+r=gw@mail.gmail.com>
 <CALO6Ft6fw66b1LahGR_p=80-xxJhYiwZNEZFLN1SMhAh65NB_g@mail.gmail.com>
 <CALxxJLQv-yZePFASgvQS8+YNMZU4BfCH1-bj0CL+wxeBpOM=TA@mail.gmail.com>
 <CALO6Ft6PNWL7G37q-tsfasGoxUk7JdX6iw38-_2-Ug2CFY5vVA@mail.gmail.com>
 <590B2AB5.9080505@sbcglobal.net>
Message-ID: <CALO6Ft5zFxPjoDnY13OM+=4kQvjmj3bfk5ogEPSv=1Sx7YeCzA@mail.gmail.com>

> No, you are not correct about ifort.  It's wstill available.  ayou just
won't receive support.

I must be unable to Google correctly. I can find free MKL for everyone. I
can find free ifort and icc for students. I can find free ifort and icc for
Linux for open source developers. But I can't find free ifort and icc for
Windows for open source developers. This is what I am looking at:
https://software.intel.com/en-us/articles/free-ipsxe-tools-and-libraries
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From denis.akhiyarov at gmail.com  Thu May  4 13:04:44 2017
From: denis.akhiyarov at gmail.com (Denis Akhiyarov)
Date: Thu, 4 May 2017 12:04:44 -0500
Subject: [SciPy-User] Install Scipy with Anaconda's MKL libraries
In-Reply-To: <CALO6Ft5zFxPjoDnY13OM+=4kQvjmj3bfk5ogEPSv=1Sx7YeCzA@mail.gmail.com>
References: <CALO6Ft47hEd8RMtHF2nRAji84nztS8h-hm-qyojwv3kZn0-Zkw@mail.gmail.com>
 <CALO6Ft55X=Q9hhPUNwn-pZ1JRS+FnGLxdubLph9qtN4WkQUtnw@mail.gmail.com>
 <CAHCaCk+tYbh8AXuhWkTAtnjfCTqFjt4rFT9_XOP++bArRmsepQ@mail.gmail.com>
 <CALxxJLTUAjOupyKROxaYPAr+7VOTFm5kj70mEZApZUpAzgE=pA@mail.gmail.com>
 <CALO6Ft7K89B5Qd+tOJpmvKrN7YmBm0JKSDZhM_1aegN457HfWA@mail.gmail.com>
 <CAKWqKotdbQYdabmU2UKR2BgxfpiJBLXyQ5vdCSfA0t7iUdM_DA@mail.gmail.com>
 <CALO6Ft71-SfExyVQvPswoCN491qojfaUH9Y3k_CUE-jc1vJayQ@mail.gmail.com>
 <CALxxJLQoesk0ixxFZJHGdtfw3WQV8AftXVnshEk6TiaA_+r=gw@mail.gmail.com>
 <CALO6Ft6fw66b1LahGR_p=80-xxJhYiwZNEZFLN1SMhAh65NB_g@mail.gmail.com>
 <CALxxJLQv-yZePFASgvQS8+YNMZU4BfCH1-bj0CL+wxeBpOM=TA@mail.gmail.com>
 <CALO6Ft6PNWL7G37q-tsfasGoxUk7JdX6iw38-_2-Ug2CFY5vVA@mail.gmail.com>
 <590B2AB5.9080505@sbcglobal.net>
 <CALO6Ft5zFxPjoDnY13OM+=4kQvjmj3bfk5ogEPSv=1Sx7YeCzA@mail.gmail.com>
Message-ID: <CALxxJLQqt+XaEWZmy2xKh862SPoyKuOQOk2=JH97cWPPuoAi-w@mail.gmail.com>

You can sign-up for beta program which includes Windows ifort compiler.

https://software.intel.com/en-us/articles/intel-parallel-studio-xe-2018-beta

"The beta program officially ends *June 22nd, 2017*. The beta license
provided will expire October 12, 2017."

On Thu, May 4, 2017 at 9:54 AM, David Hagen <david at drhagen.com> wrote:

> > No, you are not correct about ifort.  It's wstill available.  ayou just
> won't receive support.
>
> I must be unable to Google correctly. I can find free MKL for everyone. I
> can find free ifort and icc for students. I can find free ifort and icc for
> Linux for open source developers. But I can't find free ifort and icc for
> Windows for open source developers. This is what I am looking at:
> https://software.intel.com/en-us/articles/free-ipsxe-tools-and-libraries
>
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at python.org
> https://mail.python.org/mailman/listinfo/scipy-user
>
>
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From s.molnar at sbcglobal.net  Thu May  4 13:17:58 2017
From: s.molnar at sbcglobal.net (Stephen P. Molnar)
Date: Thu, 4 May 2017 13:17:58 -0400
Subject: [SciPy-User] Install Scipy with Anaconda's MKL libraries
In-Reply-To: <CALO6Ft5zFxPjoDnY13OM+=4kQvjmj3bfk5ogEPSv=1Sx7YeCzA@mail.gmail.com>
References: <CALO6Ft47hEd8RMtHF2nRAji84nztS8h-hm-qyojwv3kZn0-Zkw@mail.gmail.com>
 <CALO6Ft55X=Q9hhPUNwn-pZ1JRS+FnGLxdubLph9qtN4WkQUtnw@mail.gmail.com>
 <CAHCaCk+tYbh8AXuhWkTAtnjfCTqFjt4rFT9_XOP++bArRmsepQ@mail.gmail.com>
 <CALxxJLTUAjOupyKROxaYPAr+7VOTFm5kj70mEZApZUpAzgE=pA@mail.gmail.com>
 <CALO6Ft7K89B5Qd+tOJpmvKrN7YmBm0JKSDZhM_1aegN457HfWA@mail.gmail.com>
 <CAKWqKotdbQYdabmU2UKR2BgxfpiJBLXyQ5vdCSfA0t7iUdM_DA@mail.gmail.com>
 <CALO6Ft71-SfExyVQvPswoCN491qojfaUH9Y3k_CUE-jc1vJayQ@mail.gmail.com>
 <CALxxJLQoesk0ixxFZJHGdtfw3WQV8AftXVnshEk6TiaA_+r=gw@mail.gmail.com>
 <CALO6Ft6fw66b1LahGR_p=80-xxJhYiwZNEZFLN1SMhAh65NB_g@mail.gmail.com>
 <CALxxJLQv-yZePFASgvQS8+YNMZU4BfCH1-bj0CL+wxeBpOM=TA@mail.gmail.com>
 <CALO6Ft6PNWL7G37q-tsfasGoxUk7JdX6iw38-_2-Ug2CFY5vVA@mail.gmail.com>
 <590B2AB5.9080505@sbcglobal.net>
 <CALO6Ft5zFxPjoDnY13OM+=4kQvjmj3bfk5ogEPSv=1Sx7YeCzA@mail.gmail.com>
Message-ID: <590B6246.6090607@sbcglobal.net>

On 05/04/2017 10:54 AM, David Hagen wrote:
>  > No, you are not correct about ifort.  It's wstill available.  ayou
> just won't receive support.
>
> I must be unable to Google correctly. I can find free MKL for everyone.
> I can find free ifort and icc for students. I can find free ifort and
> icc for Linux for open source developers. But I can't find free ifort
> and icc for Windows for open source developers. This is what I am
> looking at:
> https://software.intel.com/en-us/articles/free-ipsxe-tools-and-libraries
>
>
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at python.org
> https://mail.python.org/mailman/listinfo/scipy-user
>

Check out:

https://software.intel.com/en-us/free-software

-- 
Stephen P. Molnar, Ph.D.		Life is a fuzzy set
www.molecular-modeling.net		Stochastic and multivariate
(614)312-7528 (c)
Skype: smolnar1

From noflaco at gmail.com  Fri May  5 19:18:02 2017
From: noflaco at gmail.com (Carlton Banks)
Date: Sat, 6 May 2017 01:18:02 +0200
Subject: [SciPy-User] multiple cores
Message-ID: <8E823DFF-2412-4E9D-A8D5-B3D4B910D755@gmail.com>

Anyone are of whether SciPy <https://docs.scipy.org/doc/scipy-0.15.1/reference/generated/scipy.optimize.differential_evolution.html> use multiple cores?

if not is there a possibility of this?
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From eulergaussriemann at gmail.com  Fri May  5 20:27:58 2017
From: eulergaussriemann at gmail.com (David Goldsmith)
Date: Fri, 5 May 2017 17:27:58 -0700
Subject: [SciPy-User] multiple cores
In-Reply-To: <8E823DFF-2412-4E9D-A8D5-B3D4B910D755@gmail.com>
References: <8E823DFF-2412-4E9D-A8D5-B3D4B910D755@gmail.com>
Message-ID: <CA+_2Pn02WjHUPz7nKVrN0MP5CH=AmXY+iMv0WEADvykXRkcy0g@mail.gmail.com>

Hi, Carlton.  This is not an area of expertise of mine, but I took the
liberty of Googling the topic and here's the top hit:
http://scipy.github.io/old-wiki/pages/ParallelProgramming.  The top line
is: "This is an archival dump of old wiki content --- see scipy.org for
current material," so I clicked on the link to go to the scipy home page
and repeated my search there...and the top hit was the same page. :-(
 Scrolling down in these search results, I see some more-or-less relevant
links, but nothing general, so I started reading the "old" page.  These are
the highlights, IMO:

"numpy/scipy are not perfect in this area, but there are some things you
can do."

"The best way to make use of a parallel processing system depend on the
task you're doing and on the parallel system you're using."  (I regard this
as particularly pertinent, absent knowledge of your system, as one thing I
do know is that Windows is, or at least was--I stopped using Windoze with
the introduction of 8--particularly "protective" of the allocation of
processors; I'm less familiar with the "friendliness" of Posix flavors,
including Linux and Mac.)

And finally:

""premature optimization is the root of all evil
<http://scipy.github.io/old-wiki/external.html?link=http://www.acm.org/ubiquity/views/v7i24_fallacy.html>"...Get
your code working first, before even thinking about parallelization. Then
ask yourself whether your code actually needs to be any faster. Don't
embark on the bug-strewn path of parallelization unless you have to."

Wiser words are rarely spoken.  Nevertheless, though billed as old, that
document _appears_ to be the most current, general document on parallel
programming w/ scipy, so it appears to be the place to start (and it _does_
have specific suggestions on how and when to parallelize scipy code).

I'd hope that more expert voices will chime in, but _sometimes_ that takes
a little while, so I wanted to give you something to mull in the meantime.

FWIW,

DLG

On Fri, May 5, 2017 at 4:18 PM, Carlton Banks <noflaco at gmail.com> wrote:

> Anyone are of whether SciPy
> <https://docs.scipy.org/doc/scipy-0.15.1/reference/generated/scipy.optimize.differential_evolution.html> use
> multiple cores?
>
> if not is there a possibility of this?
>
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at python.org
> https://mail.python.org/mailman/listinfo/scipy-user
>
>
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From david at drhagen.com  Sat May  6 10:24:26 2017
From: david at drhagen.com (David Hagen)
Date: Sat, 6 May 2017 10:24:26 -0400
Subject: [SciPy-User] Install Scipy with Anaconda's MKL libraries
In-Reply-To: <CALO6Ft53irwAsYuAo=v5fBZPX6ZeP=kkAqMuVrLdq4org7tt=Q@mail.gmail.com>
References: <CALO6Ft47hEd8RMtHF2nRAji84nztS8h-hm-qyojwv3kZn0-Zkw@mail.gmail.com>
 <CALO6Ft55X=Q9hhPUNwn-pZ1JRS+FnGLxdubLph9qtN4WkQUtnw@mail.gmail.com>
 <CAHCaCk+tYbh8AXuhWkTAtnjfCTqFjt4rFT9_XOP++bArRmsepQ@mail.gmail.com>
 <CALxxJLTUAjOupyKROxaYPAr+7VOTFm5kj70mEZApZUpAzgE=pA@mail.gmail.com>
 <CALO6Ft7K89B5Qd+tOJpmvKrN7YmBm0JKSDZhM_1aegN457HfWA@mail.gmail.com>
 <CAKWqKotdbQYdabmU2UKR2BgxfpiJBLXyQ5vdCSfA0t7iUdM_DA@mail.gmail.com>
 <CALO6Ft71-SfExyVQvPswoCN491qojfaUH9Y3k_CUE-jc1vJayQ@mail.gmail.com>
 <CALxxJLQoesk0ixxFZJHGdtfw3WQV8AftXVnshEk6TiaA_+r=gw@mail.gmail.com>
 <CALO6Ft6fw66b1LahGR_p=80-xxJhYiwZNEZFLN1SMhAh65NB_g@mail.gmail.com>
 <CALxxJLQv-yZePFASgvQS8+YNMZU4BfCH1-bj0CL+wxeBpOM=TA@mail.gmail.com>
 <CALO6Ft6PNWL7G37q-tsfasGoxUk7JdX6iw38-_2-Ug2CFY5vVA@mail.gmail.com>
 <590B2AB5.9080505@sbcglobal.net>
 <CALO6Ft5zFxPjoDnY13OM+=4kQvjmj3bfk5ogEPSv=1Sx7YeCzA@mail.gmail.com>
 <590B6246.6090607@sbcglobal.net>
 <CALO6Ft53irwAsYuAo=v5fBZPX6ZeP=kkAqMuVrLdq4org7tt=Q@mail.gmail.com>
Message-ID: <CALO6Ft4+B4MULYefCdxrifuEPm0sF5Jq-Y0+XKuPgFcNPLxeXA@mail.gmail.com>

I noticed that I had two versions of Visual Studio installed: Microsoft
Visual C++ 14.0 and Microsoft Visual Studio 2017. I thought that this might
be confusing the installer, so I uninstalled the older Microsoft Visual C++
14.0. However, the Numpy installation now gives a new error:

error: Microsoft Visual C++ 14.0 is required. Get it with "Microsoft Visual
C++
Build Tools": http://landinghub.visualstudio.com/visual-cpp-build-tools

This seems to make it clear that uninstalling was a mistake, but the Numpy
build instructions <https://www.scipy.org/scipylib/building/windows.html>
say to specifically get the latest version of Visual Studio.
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From david at drhagen.com  Sat May  6 10:09:02 2017
From: david at drhagen.com (David Hagen)
Date: Sat, 6 May 2017 10:09:02 -0400
Subject: [SciPy-User] Install Scipy with Anaconda's MKL libraries
In-Reply-To: <590B6246.6090607@sbcglobal.net>
References: <CALO6Ft47hEd8RMtHF2nRAji84nztS8h-hm-qyojwv3kZn0-Zkw@mail.gmail.com>
 <CALO6Ft55X=Q9hhPUNwn-pZ1JRS+FnGLxdubLph9qtN4WkQUtnw@mail.gmail.com>
 <CAHCaCk+tYbh8AXuhWkTAtnjfCTqFjt4rFT9_XOP++bArRmsepQ@mail.gmail.com>
 <CALxxJLTUAjOupyKROxaYPAr+7VOTFm5kj70mEZApZUpAzgE=pA@mail.gmail.com>
 <CALO6Ft7K89B5Qd+tOJpmvKrN7YmBm0JKSDZhM_1aegN457HfWA@mail.gmail.com>
 <CAKWqKotdbQYdabmU2UKR2BgxfpiJBLXyQ5vdCSfA0t7iUdM_DA@mail.gmail.com>
 <CALO6Ft71-SfExyVQvPswoCN491qojfaUH9Y3k_CUE-jc1vJayQ@mail.gmail.com>
 <CALxxJLQoesk0ixxFZJHGdtfw3WQV8AftXVnshEk6TiaA_+r=gw@mail.gmail.com>
 <CALO6Ft6fw66b1LahGR_p=80-xxJhYiwZNEZFLN1SMhAh65NB_g@mail.gmail.com>
 <CALxxJLQv-yZePFASgvQS8+YNMZU4BfCH1-bj0CL+wxeBpOM=TA@mail.gmail.com>
 <CALO6Ft6PNWL7G37q-tsfasGoxUk7JdX6iw38-_2-Ug2CFY5vVA@mail.gmail.com>
 <590B2AB5.9080505@sbcglobal.net>
 <CALO6Ft5zFxPjoDnY13OM+=4kQvjmj3bfk5ogEPSv=1Sx7YeCzA@mail.gmail.com>
 <590B6246.6090607@sbcglobal.net>
Message-ID: <CALO6Ft53irwAsYuAo=v5fBZPX6ZeP=kkAqMuVrLdq4org7tt=Q@mail.gmail.com>

I am following Stephen's instructions with the Beta distribution of Intel
Parallel Studio. The compilation of Numpy fails with this error:

LINK : fatal error LNK1158: cannot run 'rc.exe'

It also has this message during setup:

customize IntelVisualFCompiler
Could not locate executable ifort
Could not locate executable ifl

and this:

Found executable C:\Program Files (x86)\Microsoft Visual Studio
14.0\VC\BIN\x86_amd64\cl.exe

Do I need to configure Numpy to use the Parallel Studio compilers rather
than whatever else I may have installed?
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From david at drhagen.com  Sat May  6 11:23:20 2017
From: david at drhagen.com (David Hagen)
Date: Sat, 6 May 2017 11:23:20 -0400
Subject: [SciPy-User] Install Scipy with Anaconda's MKL libraries
In-Reply-To: <CALO6Ft4+B4MULYefCdxrifuEPm0sF5Jq-Y0+XKuPgFcNPLxeXA@mail.gmail.com>
References: <CALO6Ft47hEd8RMtHF2nRAji84nztS8h-hm-qyojwv3kZn0-Zkw@mail.gmail.com>
 <CALO6Ft55X=Q9hhPUNwn-pZ1JRS+FnGLxdubLph9qtN4WkQUtnw@mail.gmail.com>
 <CAHCaCk+tYbh8AXuhWkTAtnjfCTqFjt4rFT9_XOP++bArRmsepQ@mail.gmail.com>
 <CALxxJLTUAjOupyKROxaYPAr+7VOTFm5kj70mEZApZUpAzgE=pA@mail.gmail.com>
 <CALO6Ft7K89B5Qd+tOJpmvKrN7YmBm0JKSDZhM_1aegN457HfWA@mail.gmail.com>
 <CAKWqKotdbQYdabmU2UKR2BgxfpiJBLXyQ5vdCSfA0t7iUdM_DA@mail.gmail.com>
 <CALO6Ft71-SfExyVQvPswoCN491qojfaUH9Y3k_CUE-jc1vJayQ@mail.gmail.com>
 <CALxxJLQoesk0ixxFZJHGdtfw3WQV8AftXVnshEk6TiaA_+r=gw@mail.gmail.com>
 <CALO6Ft6fw66b1LahGR_p=80-xxJhYiwZNEZFLN1SMhAh65NB_g@mail.gmail.com>
 <CALxxJLQv-yZePFASgvQS8+YNMZU4BfCH1-bj0CL+wxeBpOM=TA@mail.gmail.com>
 <CALO6Ft6PNWL7G37q-tsfasGoxUk7JdX6iw38-_2-Ug2CFY5vVA@mail.gmail.com>
 <590B2AB5.9080505@sbcglobal.net>
 <CALO6Ft5zFxPjoDnY13OM+=4kQvjmj3bfk5ogEPSv=1Sx7YeCzA@mail.gmail.com>
 <590B6246.6090607@sbcglobal.net>
 <CALO6Ft53irwAsYuAo=v5fBZPX6ZeP=kkAqMuVrLdq4org7tt=Q@mail.gmail.com>
 <CALO6Ft4+B4MULYefCdxrifuEPm0sF5Jq-Y0+XKuPgFcNPLxeXA@mail.gmail.com>
Message-ID: <CALO6Ft7kpPWFQ6saEt2kuTADOg473DbqCb-i3P8HpqXWjzmF=Q@mail.gmail.com>

I reinstalled Microsoft Visual C++ 14.0. Then following various advice on
the web, I copied rc.exe and rcdll.exe from

C:\Program Files (x86)\Windows Kits\8.1\bin\x64

into

C:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\bin\x86_amd64

It seems that MSVC++ 14.0 and that weird file copying is necessary for
compiling Numpy. I can now install Numpy from source using python setup.py
install. I then copied the site.cfg into every numpy/distutils directory I
could find.

However, compiling Scipy gives a LNK2001 linking error with some _gfortran
symbols:

mach.lib(d1mach.o) : error LNK2001: unresolved external symbol
_gfortran_st_write
mach.lib(d1mach.o) : error LNK2001: unresolved external symbol
_gfortran_st_write_done
mach.lib(d1mach.o) : error LNK2001: unresolved external symbol
_gfortran_stop_numeric_f08
mach.lib(d1mach.o) : error LNK2001: unresolved external symbol
_gfortran_transfer_character_write
mach.lib(d1mach.o) : error LNK2001: unresolved external symbol
_gfortran_transfer_integer_write
mach.lib(d1mach.o) : error LNK2001: unresolved external symbol
_gfortran_stop_string
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From eulergaussriemann at gmail.com  Sat May  6 11:25:46 2017
From: eulergaussriemann at gmail.com (David Goldsmith)
Date: Sat, 06 May 2017 15:25:46 +0000
Subject: [SciPy-User] Install Scipy with Anaconda's MKL libraries
In-Reply-To: <CALO6Ft4+B4MULYefCdxrifuEPm0sF5Jq-Y0+XKuPgFcNPLxeXA@mail.gmail.com>
References: <CALO6Ft47hEd8RMtHF2nRAji84nztS8h-hm-qyojwv3kZn0-Zkw@mail.gmail.com>
 <CALO6Ft55X=Q9hhPUNwn-pZ1JRS+FnGLxdubLph9qtN4WkQUtnw@mail.gmail.com>
 <CAHCaCk+tYbh8AXuhWkTAtnjfCTqFjt4rFT9_XOP++bArRmsepQ@mail.gmail.com>
 <CALxxJLTUAjOupyKROxaYPAr+7VOTFm5kj70mEZApZUpAzgE=pA@mail.gmail.com>
 <CALO6Ft7K89B5Qd+tOJpmvKrN7YmBm0JKSDZhM_1aegN457HfWA@mail.gmail.com>
 <CAKWqKotdbQYdabmU2UKR2BgxfpiJBLXyQ5vdCSfA0t7iUdM_DA@mail.gmail.com>
 <CALO6Ft71-SfExyVQvPswoCN491qojfaUH9Y3k_CUE-jc1vJayQ@mail.gmail.com>
 <CALxxJLQoesk0ixxFZJHGdtfw3WQV8AftXVnshEk6TiaA_+r=gw@mail.gmail.com>
 <CALO6Ft6fw66b1LahGR_p=80-xxJhYiwZNEZFLN1SMhAh65NB_g@mail.gmail.com>
 <CALxxJLQv-yZePFASgvQS8+YNMZU4BfCH1-bj0CL+wxeBpOM=TA@mail.gmail.com>
 <CALO6Ft6PNWL7G37q-tsfasGoxUk7JdX6iw38-_2-Ug2CFY5vVA@mail.gmail.com>
 <590B2AB5.9080505@sbcglobal.net>
 <CALO6Ft5zFxPjoDnY13OM+=4kQvjmj3bfk5ogEPSv=1Sx7YeCzA@mail.gmail.com>
 <590B6246.6090607@sbcglobal.net>
 <CALO6Ft53irwAsYuAo=v5fBZPX6ZeP=kkAqMuVrLdq4org7tt=Q@mail.gmail.com>
 <CALO6Ft4+B4MULYefCdxrifuEPm0sF5Jq-Y0+XKuPgFcNPLxeXA@mail.gmail.com>
Message-ID: <CA+_2Pn2zur6q-zRpxDiH9B3YtgHt4hWL0v5OueC9nPufidM8vg@mail.gmail.com>

Hi, David.  You (re-?)learned a frustrating lesson: when testing the
hypothesis that an installation is "in the way," before/instead of
uninstalling, rename/move.

Now that i've moralized, with evidence of a "bug in the doc", my belief is
that you will be asked to file a ticket (doc bugs are considered just as
legitimate bugs as code bugs).  The only exception to this i can see is if
someone else chimes in with a reasonable explanation as to why that's a
"feature," not a bug, but in this instance, given what you say, that
doesn't seem likely.

DLG

On Sat, May 6, 2017 at 7:50 AM David Hagen <david at drhagen.com> wrote:

> I noticed that I had two versions of Visual Studio installed: Microsoft
> Visual C++ 14.0 and Microsoft Visual Studio 2017. I thought that this might
> be confusing the installer, so I uninstalled the older Microsoft Visual C++
> 14.0. However, the Numpy installation now gives a new error:
>
> error: Microsoft Visual C++ 14.0 is required. Get it with "Microsoft
> Visual C++
> Build Tools": http://landinghub.visualstudio.com/visual-cpp-build-tools
>
> This seems to make it clear that uninstalling was a mistake, but the Numpy
> build instructions <https://www.scipy.org/scipylib/building/windows.html>
> say to specifically get the latest version of Visual Studio.
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at python.org
> https://mail.python.org/mailman/listinfo/scipy-user
>
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From jaredvacanti at gmail.com  Sun May  7 14:45:17 2017
From: jaredvacanti at gmail.com (Jared Vacanti)
Date: Sun, 7 May 2017 13:45:17 -0500
Subject: [SciPy-User] Fitting Polynomial With Shape Restrictions
Message-ID: <CAJ49pGZ7M7AUnc+iyBhoWxJ2htjYUmebyqAuzTR2AeDDXxqxhA@mail.gmail.com>

I am trying to fit a polynomial to observational data with shape
restrictions - in this particular case monotonicity (decreasing) of the
function and an always positive second derivative.

Some of the interpolation classes have a mathematical "built-in"
restriction - like scipy.interpolate.Rbf's thin-plate roughness penalty
imposes some restrictions, but it's not explicit or adjustable.

What are my options for imposing boundary conditions or shape restrictions
on the spline?

I have sample data here:

import pandas as pd
df = pd.read_csv("https://bpaste.net/raw/3e20878b5237")

or available independently at https://bpaste.net/raw/3e20878b5237

I have tried using a interior point convex optimization solver, but the
results seem to be numerically finicky. Are there other alternatives?
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From josef.pktd at gmail.com  Sun May  7 15:02:22 2017
From: josef.pktd at gmail.com (josef.pktd at gmail.com)
Date: Sun, 7 May 2017 15:02:22 -0400
Subject: [SciPy-User] Fitting Polynomial With Shape Restrictions
In-Reply-To: <CAJ49pGZ7M7AUnc+iyBhoWxJ2htjYUmebyqAuzTR2AeDDXxqxhA@mail.gmail.com>
References: <CAJ49pGZ7M7AUnc+iyBhoWxJ2htjYUmebyqAuzTR2AeDDXxqxhA@mail.gmail.com>
Message-ID: <CAMMTP+CJMTRk8a3Zqrixn7iEju-QO2bmbZnGADs-MG_3Ro=C2Q@mail.gmail.com>

On Sun, May 7, 2017 at 2:45 PM, Jared Vacanti <jaredvacanti at gmail.com>
wrote:

> I am trying to fit a polynomial to observational data with shape
> restrictions - in this particular case monotonicity (decreasing) of the
> function and an always positive second derivative.
>
> Some of the interpolation classes have a mathematical "built-in"
> restriction - like scipy.interpolate.Rbf's thin-plate roughness penalty
> imposes some restrictions, but it's not explicit or adjustable.
>
> What are my options for imposing boundary conditions or shape restrictions
> on the spline?
>
> I have sample data here:
>
> import pandas as pd
> df = pd.read_csv("https://bpaste.net/raw/3e20878b5237")
>
> or available independently at https://bpaste.net/raw/3e20878b5237
>
> I have tried using a interior point convex optimization solver, but the
> results seem to be numerically finicky. Are there other alternatives?
>

As far as I know, pchip is the only one with monotonicity constraints
https://docs.scipy.org/doc/scipy/reference/generated/scipy.interpolate.pchip_interpolate.html

Josef



>
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at python.org
> https://mail.python.org/mailman/listinfo/scipy-user
>
>
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From jaredvacanti at gmail.com  Sun May  7 15:12:32 2017
From: jaredvacanti at gmail.com (Jared Vacanti)
Date: Sun, 7 May 2017 14:12:32 -0500
Subject: [SciPy-User] Fitting Polynomial With Shape Restrictions
In-Reply-To: <CAMMTP+CJMTRk8a3Zqrixn7iEju-QO2bmbZnGADs-MG_3Ro=C2Q@mail.gmail.com>
References: <CAJ49pGZ7M7AUnc+iyBhoWxJ2htjYUmebyqAuzTR2AeDDXxqxhA@mail.gmail.com>
 <CAMMTP+CJMTRk8a3Zqrixn7iEju-QO2bmbZnGADs-MG_3Ro=C2Q@mail.gmail.com>
Message-ID: <CAJ49pGYZWHq-3DdCT59K1c2MPD70HE8PUKT9pn7brsNXD9mT=Q@mail.gmail.com>

Are there other techniques of fitting splines (perhaps using scipy.optimize
?) where I can impose boundary conditions myself, even outside of the
options provided in scipy.interpolate?

On Sun, May 7, 2017 at 2:02 PM, <josef.pktd at gmail.com> wrote:

>
>
> On Sun, May 7, 2017 at 2:45 PM, Jared Vacanti <jaredvacanti at gmail.com>
> wrote:
>
>> I am trying to fit a polynomial to observational data with shape
>> restrictions - in this particular case monotonicity (decreasing) of the
>> function and an always positive second derivative.
>>
>> Some of the interpolation classes have a mathematical "built-in"
>> restriction - like scipy.interpolate.Rbf's thin-plate roughness penalty
>> imposes some restrictions, but it's not explicit or adjustable.
>>
>> What are my options for imposing boundary conditions or shape
>> restrictions on the spline?
>>
>> I have sample data here:
>>
>> import pandas as pd
>> df = pd.read_csv("https://bpaste.net/raw/3e20878b5237")
>>
>> or available independently at https://bpaste.net/raw/3e20878b5237
>>
>> I have tried using a interior point convex optimization solver, but the
>> results seem to be numerically finicky. Are there other alternatives?
>>
>
> As far as I know, pchip is the only one with monotonicity constraints
> https://docs.scipy.org/doc/scipy/reference/generated/
> scipy.interpolate.pchip_interpolate.html
>
> Josef
>
>
>
>>
>> _______________________________________________
>> SciPy-User mailing list
>> SciPy-User at python.org
>> https://mail.python.org/mailman/listinfo/scipy-user
>>
>>
>
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at python.org
> https://mail.python.org/mailman/listinfo/scipy-user
>
>
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From evgeny.burovskiy at gmail.com  Sun May  7 15:34:12 2017
From: evgeny.burovskiy at gmail.com (Evgeni Burovski)
Date: Sun, 7 May 2017 22:34:12 +0300
Subject: [SciPy-User] Fitting Polynomial With Shape Restrictions
In-Reply-To: <CAJ49pGYZWHq-3DdCT59K1c2MPD70HE8PUKT9pn7brsNXD9mT=Q@mail.gmail.com>
References: <CAJ49pGZ7M7AUnc+iyBhoWxJ2htjYUmebyqAuzTR2AeDDXxqxhA@mail.gmail.com>
 <CAMMTP+CJMTRk8a3Zqrixn7iEju-QO2bmbZnGADs-MG_3Ro=C2Q@mail.gmail.com>
 <CAJ49pGYZWHq-3DdCT59K1c2MPD70HE8PUKT9pn7brsNXD9mT=Q@mail.gmail.com>
Message-ID: <CAMRo0isPXQjdwGYc3G25Nad-kFsQ7tJKYETiS8SpiyJrNs0NWQ@mail.gmail.com>

On Sun, May 7, 2017 at 10:12 PM, Jared Vacanti <jaredvacanti at gmail.com> wrote:
> Are there other techniques of fitting splines (perhaps using scipy.optimize
> ?) where I can impose boundary conditions myself, even outside of the
> options provided in scipy.interpolate?

FITPACK has some spline fitting with convexity constraints, but I
don't think it's exposed to python:

https://github.com/scipy/scipy/blob/master/scipy/interpolate/fitpack/concon.f
https://github.com/scipy/scipy/blob/master/scipy/interpolate/fitpack/cocosp.f

Hyman et al have some discussion of constructing local interpolants
with convexity constraints:
http://math.lanl.gov/~mac/papers/numerics/DEH89.pdf
and references therein.



> On Sun, May 7, 2017 at 2:02 PM, <josef.pktd at gmail.com> wrote:
>>
>>
>>
>> On Sun, May 7, 2017 at 2:45 PM, Jared Vacanti <jaredvacanti at gmail.com>
>> wrote:
>>>
>>> I am trying to fit a polynomial to observational data with shape
>>> restrictions - in this particular case monotonicity (decreasing) of the
>>> function and an always positive second derivative.
>>>
>>> Some of the interpolation classes have a mathematical "built-in"
>>> restriction - like scipy.interpolate.Rbf's thin-plate roughness penalty
>>> imposes some restrictions, but it's not explicit or adjustable.
>>>
>>> What are my options for imposing boundary conditions or shape
>>> restrictions on the spline?
>>>
>>> I have sample data here:
>>>
>>> import pandas as pd
>>> df = pd.read_csv("https://bpaste.net/raw/3e20878b5237")
>>>
>>> or available independently at https://bpaste.net/raw/3e20878b5237
>>>
>>> I have tried using a interior point convex optimization solver, but the
>>> results seem to be numerically finicky. Are there other alternatives?
>>
>>
>> As far as I know, pchip is the only one with monotonicity constraints
>> https://docs.scipy.org/doc/scipy/reference/generated/scipy.interpolate.pchip_interpolate.html
>>
>> Josef
>>
>>
>>>
>>>
>>> _______________________________________________
>>> SciPy-User mailing list
>>> SciPy-User at python.org
>>> https://mail.python.org/mailman/listinfo/scipy-user
>>>
>>
>>
>> _______________________________________________
>> SciPy-User mailing list
>> SciPy-User at python.org
>> https://mail.python.org/mailman/listinfo/scipy-user
>>
>
>
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at python.org
> https://mail.python.org/mailman/listinfo/scipy-user
>

From eulergaussriemann at gmail.com  Sun May  7 16:01:51 2017
From: eulergaussriemann at gmail.com (David Goldsmith)
Date: Sun, 07 May 2017 20:01:51 +0000
Subject: [SciPy-User] Fitting Polynomial With Shape Restrictions
In-Reply-To: <CAJ49pGYZWHq-3DdCT59K1c2MPD70HE8PUKT9pn7brsNXD9mT=Q@mail.gmail.com>
References: <CAJ49pGZ7M7AUnc+iyBhoWxJ2htjYUmebyqAuzTR2AeDDXxqxhA@mail.gmail.com>
 <CAMMTP+CJMTRk8a3Zqrixn7iEju-QO2bmbZnGADs-MG_3Ro=C2Q@mail.gmail.com>
 <CAJ49pGYZWHq-3DdCT59K1c2MPD70HE8PUKT9pn7brsNXD9mT=Q@mail.gmail.com>
Message-ID: <CA+_2Pn1OVcBMRFsMtYdHTjCBWR69Xh+xAYo5JD-vL6vhy+3-dg@mail.gmail.com>

I'd suggest doing some advance pencil-and-paper work to determine what your
constraints imply about the relationships among the coefficients.  For
example, you mention that the second derivative should always be positive:
you can readily formalize this by writing down the second derative of your
model, adding a slack variable, which you constrain to be strictly
negative, and then set that second-derivative-plus-slack expression equal
to zero: oui la, an additional equation--which can be added to the matrix
representation of your minimization problem--to rigorously represent your
second derivative constraint!  The monotonicity requirement can be treated
similarly, as a limitation on the first derivative, though it may require
introduction of additional parameters, perhaps in the form of bounds on the
interval over which you plan to use the model.  But in any event, with a
little pre-programming effort, you should be able to readily solve your
problem without having to resort to any programming "tricks" or
"workarounds."  (I was once charged with writing a program to explore the
nine-dimensional parameter space of eighth-order polynomials to look for
sub-spaces with particular properties; too late was i informed that there
were additional "pre-constraints," e.g., monotonicity such as you describe,
and strict positiveness of the polynomial output--that i could have used to
significantly reduce both the dimensionality of and the ranges in the space
I needed to search.  This was in the area of plasma confinement simulation:
may i ask, in what area you are working?)

HTH,

DLG

On Sun, May 7, 2017 at 12:13 PM Jared Vacanti <jaredvacanti at gmail.com>
wrote:

> Are there other techniques of fitting splines (perhaps using
> scipy.optimize ?) where I can impose boundary conditions myself, even
> outside of the options provided in scipy.interpolate?
>
> On Sun, May 7, 2017 at 2:02 PM, <josef.pktd at gmail.com> wrote:
>
>>
>>
>> On Sun, May 7, 2017 at 2:45 PM, Jared Vacanti <jaredvacanti at gmail.com>
>> wrote:
>>
>>> I am trying to fit a polynomial to observational data with shape
>>> restrictions - in this particular case monotonicity (decreasing) of the
>>> function and an always positive second derivative.
>>>
>>> Some of the interpolation classes have a mathematical "built-in"
>>> restriction - like scipy.interpolate.Rbf's thin-plate roughness penalty
>>> imposes some restrictions, but it's not explicit or adjustable.
>>>
>>> What are my options for imposing boundary conditions or shape
>>> restrictions on the spline?
>>>
>>> I have sample data here:
>>>
>>> import pandas as pd
>>> df = pd.read_csv("https://bpaste.net/raw/3e20878b5237")
>>>
>>> or available independently at https://bpaste.net/raw/3e20878b5237
>>>
>>> I have tried using a interior point convex optimization solver, but the
>>> results seem to be numerically finicky. Are there other alternatives?
>>>
>>
>> As far as I know, pchip is the only one with monotonicity constraints
>> https://docs.scipy.org/doc/scipy/reference/generated/scipy.interpolate.pchip_interpolate.html
>>
>> Josef
>>
>>
>>
>>>
>>> _______________________________________________
>>> SciPy-User mailing list
>>> SciPy-User at python.org
>>> https://mail.python.org/mailman/listinfo/scipy-user
>>>
>>>
>>
>> _______________________________________________
>> SciPy-User mailing list
>> SciPy-User at python.org
>> https://mail.python.org/mailman/listinfo/scipy-user
>>
>>
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at python.org
> https://mail.python.org/mailman/listinfo/scipy-user
>
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From josef.pktd at gmail.com  Sun May  7 16:36:46 2017
From: josef.pktd at gmail.com (josef.pktd at gmail.com)
Date: Sun, 7 May 2017 16:36:46 -0400
Subject: [SciPy-User] Fitting Polynomial With Shape Restrictions
In-Reply-To: <CAMRo0isPXQjdwGYc3G25Nad-kFsQ7tJKYETiS8SpiyJrNs0NWQ@mail.gmail.com>
References: <CAJ49pGZ7M7AUnc+iyBhoWxJ2htjYUmebyqAuzTR2AeDDXxqxhA@mail.gmail.com>
 <CAMMTP+CJMTRk8a3Zqrixn7iEju-QO2bmbZnGADs-MG_3Ro=C2Q@mail.gmail.com>
 <CAJ49pGYZWHq-3DdCT59K1c2MPD70HE8PUKT9pn7brsNXD9mT=Q@mail.gmail.com>
 <CAMRo0isPXQjdwGYc3G25Nad-kFsQ7tJKYETiS8SpiyJrNs0NWQ@mail.gmail.com>
Message-ID: <CAMMTP+AZhvzwy0+dikOT_+7Xj72VPEBsyd4KXyx3smGF3+T4GQ@mail.gmail.com>

On Sun, May 7, 2017 at 3:34 PM, Evgeni Burovski <evgeny.burovskiy at gmail.com>
wrote:

> On Sun, May 7, 2017 at 10:12 PM, Jared Vacanti <jaredvacanti at gmail.com>
> wrote:
> > Are there other techniques of fitting splines (perhaps using
> scipy.optimize
> > ?) where I can impose boundary conditions myself, even outside of the
> > options provided in scipy.interpolate?
>
> FITPACK has some spline fitting with convexity constraints, but I
> don't think it's exposed to python:
>
> https://github.com/scipy/scipy/blob/master/scipy/
> interpolate/fitpack/concon.f
> https://github.com/scipy/scipy/blob/master/scipy/
> interpolate/fitpack/cocosp.f
>
> Hyman et al have some discussion of constructing local interpolants
> with convexity constraints:
> http://math.lanl.gov/~mac/papers/numerics/DEH89.pdf
> and references therein.
>
>
I looked into monotonicity and similar constraints for splines several
years ago.
I gave up because adding all the constraints that are implied on the
coefficients (values and derivatives at the knots) looked difficult, and it
is difficult to get the relevant parameters directly from the scipy splines.

AFAIR
and I was hoping for somebody to come along with more background and more
incentive than I had.

Josef



>
>
> > On Sun, May 7, 2017 at 2:02 PM, <josef.pktd at gmail.com> wrote:
> >>
> >>
> >>
> >> On Sun, May 7, 2017 at 2:45 PM, Jared Vacanti <jaredvacanti at gmail.com>
> >> wrote:
> >>>
> >>> I am trying to fit a polynomial to observational data with shape
> >>> restrictions - in this particular case monotonicity (decreasing) of the
> >>> function and an always positive second derivative.
> >>>
> >>> Some of the interpolation classes have a mathematical "built-in"
> >>> restriction - like scipy.interpolate.Rbf's thin-plate roughness penalty
> >>> imposes some restrictions, but it's not explicit or adjustable.
> >>>
> >>> What are my options for imposing boundary conditions or shape
> >>> restrictions on the spline?
> >>>
> >>> I have sample data here:
> >>>
> >>> import pandas as pd
> >>> df = pd.read_csv("https://bpaste.net/raw/3e20878b5237")
> >>>
> >>> or available independently at https://bpaste.net/raw/3e20878b5237
> >>>
> >>> I have tried using a interior point convex optimization solver, but the
> >>> results seem to be numerically finicky. Are there other alternatives?
> >>
> >>
> >> As far as I know, pchip is the only one with monotonicity constraints
> >> https://docs.scipy.org/doc/scipy/reference/generated/
> scipy.interpolate.pchip_interpolate.html
> >>
> >> Josef
> >>
> >>
> >>>
> >>>
> >>> _______________________________________________
> >>> SciPy-User mailing list
> >>> SciPy-User at python.org
> >>> https://mail.python.org/mailman/listinfo/scipy-user
> >>>
> >>
> >>
> >> _______________________________________________
> >> SciPy-User mailing list
> >> SciPy-User at python.org
> >> https://mail.python.org/mailman/listinfo/scipy-user
> >>
> >
> >
> > _______________________________________________
> > SciPy-User mailing list
> > SciPy-User at python.org
> > https://mail.python.org/mailman/listinfo/scipy-user
> >
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at python.org
> https://mail.python.org/mailman/listinfo/scipy-user
>
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From eulergaussriemann at gmail.com  Sun May  7 16:52:51 2017
From: eulergaussriemann at gmail.com (David Goldsmith)
Date: Sun, 07 May 2017 20:52:51 +0000
Subject: [SciPy-User] Fitting Polynomial With Shape Restrictions
In-Reply-To: <CAMMTP+AZhvzwy0+dikOT_+7Xj72VPEBsyd4KXyx3smGF3+T4GQ@mail.gmail.com>
References: <CAJ49pGZ7M7AUnc+iyBhoWxJ2htjYUmebyqAuzTR2AeDDXxqxhA@mail.gmail.com>
 <CAMMTP+CJMTRk8a3Zqrixn7iEju-QO2bmbZnGADs-MG_3Ro=C2Q@mail.gmail.com>
 <CAJ49pGYZWHq-3DdCT59K1c2MPD70HE8PUKT9pn7brsNXD9mT=Q@mail.gmail.com>
 <CAMRo0isPXQjdwGYc3G25Nad-kFsQ7tJKYETiS8SpiyJrNs0NWQ@mail.gmail.com>
 <CAMMTP+AZhvzwy0+dikOT_+7Xj72VPEBsyd4KXyx3smGF3+T4GQ@mail.gmail.com>
Message-ID: <CA+_2Pn2rO+fObhnwv3GRVcKjBa0T92oJD6BKM5KsBxw6bXe7KA@mail.gmail.com>

Hi, josef.  If you mean having generic code that automates the process,
then you are probably correct: there's just too many degrees of freedom to
account for all possibilities in a generic program (at least that's the way
it appears to me, too).  But using known math to work out, "by hand," these
implications in specific cases prior to using the generic code tools we do
have, largely thanks to you, is eminently "do-able."  Thanks for all you've
done for scipy, lo these many years!

DLG

On Sun, May 7, 2017 at 1:37 PM <josef.pktd at gmail.com> wrote:

> On Sun, May 7, 2017 at 3:34 PM, Evgeni Burovski <
> evgeny.burovskiy at gmail.com> wrote:
>
>> On Sun, May 7, 2017 at 10:12 PM, Jared Vacanti <jaredvacanti at gmail.com>
>> wrote:
>> > Are there other techniques of fitting splines (perhaps using
>> scipy.optimize
>> > ?) where I can impose boundary conditions myself, even outside of the
>> > options provided in scipy.interpolate?
>>
>> FITPACK has some spline fitting with convexity constraints, but I
>> don't think it's exposed to python:
>>
>>
>> https://github.com/scipy/scipy/blob/master/scipy/interpolate/fitpack/concon.f
>>
>> https://github.com/scipy/scipy/blob/master/scipy/interpolate/fitpack/cocosp.f
>>
>> Hyman et al have some discussion of constructing local interpolants
>> with convexity constraints:
>> http://math.lanl.gov/~mac/papers/numerics/DEH89.pdf
>> and references therein.
>>
>>
> I looked into monotonicity and similar constraints for splines several
> years ago.
> I gave up because adding all the constraints that are implied on the
> coefficients (values and derivatives at the knots) looked difficult, and it
> is difficult to get the relevant parameters directly from the scipy splines.
>
> AFAIR
> and I was hoping for somebody to come along with more background and more
> incentive than I had.
>
> Josef
>
>
>
>>
>>
>> > On Sun, May 7, 2017 at 2:02 PM, <josef.pktd at gmail.com> wrote:
>> >>
>> >>
>> >>
>> >> On Sun, May 7, 2017 at 2:45 PM, Jared Vacanti <jaredvacanti at gmail.com>
>> >> wrote:
>> >>>
>> >>> I am trying to fit a polynomial to observational data with shape
>> >>> restrictions - in this particular case monotonicity (decreasing) of
>> the
>> >>> function and an always positive second derivative.
>> >>>
>> >>> Some of the interpolation classes have a mathematical "built-in"
>> >>> restriction - like scipy.interpolate.Rbf's thin-plate roughness
>> penalty
>> >>> imposes some restrictions, but it's not explicit or adjustable.
>> >>>
>> >>> What are my options for imposing boundary conditions or shape
>> >>> restrictions on the spline?
>> >>>
>> >>> I have sample data here:
>> >>>
>> >>> import pandas as pd
>> >>> df = pd.read_csv("https://bpaste.net/raw/3e20878b5237")
>> >>>
>> >>> or available independently at https://bpaste.net/raw/3e20878b5237
>> >>>
>> >>> I have tried using a interior point convex optimization solver, but
>> the
>> >>> results seem to be numerically finicky. Are there other alternatives?
>> >>
>> >>
>> >> As far as I know, pchip is the only one with monotonicity constraints
>> >>
>> https://docs.scipy.org/doc/scipy/reference/generated/scipy.interpolate.pchip_interpolate.html
>> >>
>> >> Josef
>> >>
>> >>
>> >>>
>> >>>
>> >>> _______________________________________________
>> >>> SciPy-User mailing list
>> >>> SciPy-User at python.org
>> >>> https://mail.python.org/mailman/listinfo/scipy-user
>> >>>
>> >>
>> >>
>> >> _______________________________________________
>> >> SciPy-User mailing list
>> >> SciPy-User at python.org
>> >> https://mail.python.org/mailman/listinfo/scipy-user
>> >>
>> >
>> >
>> > _______________________________________________
>> > SciPy-User mailing list
>> > SciPy-User at python.org
>> > https://mail.python.org/mailman/listinfo/scipy-user
>> >
>> _______________________________________________
>> SciPy-User mailing list
>> SciPy-User at python.org
>> https://mail.python.org/mailman/listinfo/scipy-user
>>
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at python.org
> https://mail.python.org/mailman/listinfo/scipy-user
>
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From denis.akhiyarov at gmail.com  Tue May  9 00:25:58 2017
From: denis.akhiyarov at gmail.com (Denis Akhiyarov)
Date: Mon, 8 May 2017 23:25:58 -0500
Subject: [SciPy-User] Install Scipy with Anaconda's MKL libraries
In-Reply-To: <CA+_2Pn2zur6q-zRpxDiH9B3YtgHt4hWL0v5OueC9nPufidM8vg@mail.gmail.com>
References: <CALO6Ft47hEd8RMtHF2nRAji84nztS8h-hm-qyojwv3kZn0-Zkw@mail.gmail.com>
 <CALO6Ft55X=Q9hhPUNwn-pZ1JRS+FnGLxdubLph9qtN4WkQUtnw@mail.gmail.com>
 <CAHCaCk+tYbh8AXuhWkTAtnjfCTqFjt4rFT9_XOP++bArRmsepQ@mail.gmail.com>
 <CALxxJLTUAjOupyKROxaYPAr+7VOTFm5kj70mEZApZUpAzgE=pA@mail.gmail.com>
 <CALO6Ft7K89B5Qd+tOJpmvKrN7YmBm0JKSDZhM_1aegN457HfWA@mail.gmail.com>
 <CAKWqKotdbQYdabmU2UKR2BgxfpiJBLXyQ5vdCSfA0t7iUdM_DA@mail.gmail.com>
 <CALO6Ft71-SfExyVQvPswoCN491qojfaUH9Y3k_CUE-jc1vJayQ@mail.gmail.com>
 <CALxxJLQoesk0ixxFZJHGdtfw3WQV8AftXVnshEk6TiaA_+r=gw@mail.gmail.com>
 <CALO6Ft6fw66b1LahGR_p=80-xxJhYiwZNEZFLN1SMhAh65NB_g@mail.gmail.com>
 <CALxxJLQv-yZePFASgvQS8+YNMZU4BfCH1-bj0CL+wxeBpOM=TA@mail.gmail.com>
 <CALO6Ft6PNWL7G37q-tsfasGoxUk7JdX6iw38-_2-Ug2CFY5vVA@mail.gmail.com>
 <590B2AB5.9080505@sbcglobal.net>
 <CALO6Ft5zFxPjoDnY13OM+=4kQvjmj3bfk5ogEPSv=1Sx7YeCzA@mail.gmail.com>
 <590B6246.6090607@sbcglobal.net>
 <CALO6Ft53irwAsYuAo=v5fBZPX6ZeP=kkAqMuVrLdq4org7tt=Q@mail.gmail.com>
 <CALO6Ft4+B4MULYefCdxrifuEPm0sF5Jq-Y0+XKuPgFcNPLxeXA@mail.gmail.com>
 <CA+_2Pn2zur6q-zRpxDiH9B3YtgHt4hWL0v5OueC9nPufidM8vg@mail.gmail.com>
Message-ID: <CALxxJLS=WN7mSVFbLYBJxqw0ijtcB=5vwOFnQtJOBAE-gLFgiA@mail.gmail.com>

1. The documentation definitely needs improvements.

The difference here is that CPython requirements for MSVC compilers from
Visual Studio are not the same as ifort requirements for Visual Studio.
For example MSVC compiler for Python 2.7 (aka.ms/vcpython27) is likely not
enough to install ifort.
So 2.7 - MSVC 2008, 3.4 - MSVC 2010, 3.5-3.6 MSVC 2015/2017:
https://wiki.python.org/moin/WindowsCompilers

Here are ifort requirements for VS: https://software.intel.com
/en-us/articles/troubleshooting-fortran-integration-issues-w
ith-visual-studio

2. VS 2017 is supported by setuptools 34.4.0+

3. Anyway I followed Intel article about scipy and ifort using VS 2015 with
C++ compiler installed. In python 3.5, 64-bit both scipy and numpy compile
without any issues even with beta 2018 ifort compiler. Don't forget to use
the command prompt provided by Intel, Step 3 - Configuration:

https://software.intel.com/en-us/articles/building-numpyscipy-with-intel-mkl-and-intel-fortran-on-windows









On Sat, May 6, 2017 at 10:25 AM, David Goldsmith <
eulergaussriemann at gmail.com> wrote:

> Hi, David.  You (re-?)learned a frustrating lesson: when testing the
> hypothesis that an installation is "in the way," before/instead of
> uninstalling, rename/move.
>
> Now that i've moralized, with evidence of a "bug in the doc", my belief is
> that you will be asked to file a ticket (doc bugs are considered just as
> legitimate bugs as code bugs).  The only exception to this i can see is if
> someone else chimes in with a reasonable explanation as to why that's a
> "feature," not a bug, but in this instance, given what you say, that
> doesn't seem likely.
>
> DLG
>
> On Sat, May 6, 2017 at 7:50 AM David Hagen <david at drhagen.com> wrote:
>
>> I noticed that I had two versions of Visual Studio installed: Microsoft
>> Visual C++ 14.0 and Microsoft Visual Studio 2017. I thought that this might
>> be confusing the installer, so I uninstalled the older Microsoft Visual C++
>> 14.0. However, the Numpy installation now gives a new error:
>>
>> error: Microsoft Visual C++ 14.0 is required. Get it with "Microsoft
>> Visual C++
>> Build Tools": http://landinghub.visualstudio.com/visual-cpp-build-tools
>>
>> This seems to make it clear that uninstalling was a mistake, but the Numpy
>> build instructions <https://www.scipy.org/scipylib/building/windows.html>
>> say to specifically get the latest version of Visual Studio.
>> _______________________________________________
>> SciPy-User mailing list
>> SciPy-User at python.org
>> https://mail.python.org/mailman/listinfo/scipy-user
>>
>
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at python.org
> https://mail.python.org/mailman/listinfo/scipy-user
>
>
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From david at drhagen.com  Tue May  9 06:44:57 2017
From: david at drhagen.com (David Hagen)
Date: Tue, 9 May 2017 06:44:57 -0400
Subject: [SciPy-User] Install Scipy with Anaconda's MKL libraries
In-Reply-To: <CALxxJLS=WN7mSVFbLYBJxqw0ijtcB=5vwOFnQtJOBAE-gLFgiA@mail.gmail.com>
References: <CALO6Ft47hEd8RMtHF2nRAji84nztS8h-hm-qyojwv3kZn0-Zkw@mail.gmail.com>
 <CALO6Ft55X=Q9hhPUNwn-pZ1JRS+FnGLxdubLph9qtN4WkQUtnw@mail.gmail.com>
 <CAHCaCk+tYbh8AXuhWkTAtnjfCTqFjt4rFT9_XOP++bArRmsepQ@mail.gmail.com>
 <CALxxJLTUAjOupyKROxaYPAr+7VOTFm5kj70mEZApZUpAzgE=pA@mail.gmail.com>
 <CALO6Ft7K89B5Qd+tOJpmvKrN7YmBm0JKSDZhM_1aegN457HfWA@mail.gmail.com>
 <CAKWqKotdbQYdabmU2UKR2BgxfpiJBLXyQ5vdCSfA0t7iUdM_DA@mail.gmail.com>
 <CALO6Ft71-SfExyVQvPswoCN491qojfaUH9Y3k_CUE-jc1vJayQ@mail.gmail.com>
 <CALxxJLQoesk0ixxFZJHGdtfw3WQV8AftXVnshEk6TiaA_+r=gw@mail.gmail.com>
 <CALO6Ft6fw66b1LahGR_p=80-xxJhYiwZNEZFLN1SMhAh65NB_g@mail.gmail.com>
 <CALxxJLQv-yZePFASgvQS8+YNMZU4BfCH1-bj0CL+wxeBpOM=TA@mail.gmail.com>
 <CALO6Ft6PNWL7G37q-tsfasGoxUk7JdX6iw38-_2-Ug2CFY5vVA@mail.gmail.com>
 <590B2AB5.9080505@sbcglobal.net>
 <CALO6Ft5zFxPjoDnY13OM+=4kQvjmj3bfk5ogEPSv=1Sx7YeCzA@mail.gmail.com>
 <590B6246.6090607@sbcglobal.net>
 <CALO6Ft53irwAsYuAo=v5fBZPX6ZeP=kkAqMuVrLdq4org7tt=Q@mail.gmail.com>
 <CALO6Ft4+B4MULYefCdxrifuEPm0sF5Jq-Y0+XKuPgFcNPLxeXA@mail.gmail.com>
 <CA+_2Pn2zur6q-zRpxDiH9B3YtgHt4hWL0v5OueC9nPufidM8vg@mail.gmail.com>
 <CALxxJLS=WN7mSVFbLYBJxqw0ijtcB=5vwOFnQtJOBAE-gLFgiA@mail.gmail.com>
Message-ID: <CALO6Ft7+bkDwQc7Js0Sk4OtiSR11zmHhJE2XOigXcuy0j2VjEA@mail.gmail.com>

> The difference here is that CPython requirements for MSVC compilers from
Visual Studio are not the same as ifort requirements for Visual Studio.

I currently have Python 3.6.1. I have both Visual Studio C++ 14.0 (I think
this is the same thing as VS2015) and Visual Studio 2017 installed. I have
Parallel Studio XE 2018 Beta installed. I am not sure if that is all
compatible because 2018 Beta isn't on there. Should I uninstall VS 2017?

> VS 2017 is supported by setuptools 34.4.0+

Should I upgrade from 27.2.0, which for whatever reason is the version that
conda installs with Python 3.6.1?

> Anyway I followed Intel article about scipy and ifort using VS 2015 with
C++ compiler installed.

I am able to run plain python setup.py install on the latest version of
Numpy and it installs fine. (Doing the same with Scipy errors out with
missing symbols described previously.) However, running the install command
given for Intel64 in the link it fails. I followed everything except for
recompile Python. It gets to here:

running config
running build_clib
running build_src
build_src
building py_modules sources
building library "npymath" sources
Could not locate executable icc
Could not locate executable ecc

And then throws a NoneType is not iterable exception:

...
File "c:\numpy-1.12.1\numpy\distutils\msvc9compiler.py", line 53, in
initialize
  os.environ['lib'] = _merge(environ_lib, os.environ['lib'])
File "c:\numpy-1.12.1\numpy\distutils\msvc9compiler.py", line 32, in _merge
  if new in old:
TypeError: argument of type 'NoneType' is not iterable

This happen both with and without modifying the compile options. It looks
like it can't find icc, but without a real error, it is difficult to tell
if that was a notify, warning, or error.

It installs with just python setup.py install. But if I do that is that the
reason I get symbol errors trying to install Scipy? Is it just managing to
muddle through with one of my other compilers but not be linkable by Scipy?
I have Intel C++ Compiler installed as part of the Parallel Studio install.
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From robert.pollak at jku.at  Wed May 10 03:14:42 2017
From: robert.pollak at jku.at (Robert Pollak)
Date: Wed, 10 May 2017 09:14:42 +0200
Subject: [SciPy-User] EuroSciPy 2017
In-Reply-To: <20170208180203.GJ14363@pi-x230>
References: <0a08235d-eb73-56ee-b937-df840d4b4176@jku.at>
 <20170208180203.GJ14363@pi-x230>
Message-ID: <8a4db233-6ada-77c7-985c-9104e24f51c7@jku.at>

Dear EuroSciPy organizers,

as it's May already and I don't see anything announced, I would like to
ask again about (the date of) this year's conference.

Best regards,
Robert


On 2017-02-08 19:02, Pierre de Buyl wrote to scipy-user:
> Dear Robert,
> 
> The official contact information for EuroSciPy is euroscipy-org at python.org or
> https://twitter.com/EuroSciPy
> 
> The dates are not set yet as we are trying to arrange for the venue (the
> previous venue could not be taken at our typical dates).
> 
> The official announcement will be posted on our Twitter account and also on the
> NumPy and SciPy lists.
> 
> Best regards,
> 
> Pierre
> 
> 
> On Tue, Feb 07, 2017 at 04:28:01PM +0100, Robert Pollak wrote to scipy-user:
>> Dear EuroSciPy team,
>>
>> I have noticed that EuroSciPy 2017 in Erlangen is announced as
>> "upcoming" on https://conference.scipy.org/ . Is there already a date fixed?
>>
>> Best regards,
>> Robert


From charlesr.harris at gmail.com  Wed May 10 21:48:34 2017
From: charlesr.harris at gmail.com (Charles R Harris)
Date: Wed, 10 May 2017 19:48:34 -0600
Subject: [SciPy-User] NumPy v1.13.0rc1 released.
Message-ID: <CAB6mnx+78KwqkfUN65RQMefXNBG4myLWKCmGjTQ8GYTg4Qjd5Q@mail.gmail.com>

Hi All,

I'm please to announce the NumPy 1.13.0rc1 release. This release supports
Python 2.7 and 3.4-3.6 and contains many new features. It is one of the
most ambitious releases in the last several years. Some of the highlights
and new functions are

*Highlights*

   - Operations like ``a + b + c`` will reuse temporaries on some
   platforms, resulting in less memory use and faster execution.
   - Inplace operations check if inputs overlap outputs and create
   temporaries to avoid problems.
   - New __array_ufunc__ attribute provides improved ability for classes to
   override default ufunc behavior.
   -  New np.block function for creating blocked arrays.


*New functions*

   - New ``np.positive`` ufunc.
   - New ``np.divmod`` ufunc provides more efficient divmod.
   - New ``np.isnat`` ufunc tests for NaT special values.
   - New ``np.heaviside`` ufunc computes the Heaviside function.
   - New ``np.isin`` function, improves on ``in1d``.
   - New ``np.block`` function for creating blocked arrays.
   - New ``PyArray_MapIterArrayCopyIfOverlap`` added to NumPy C-API.

Wheels for the pre-release are available on PyPI. Source tarballs,
zipfiles, release notes, and the Changelog are available on github
<https://github.com/numpy/numpy/releases/tag/v1.13.0rc1>.

A total of 100 people contributed to this release.  People with a "+" by
their
names contributed a patch for the first time.

   - A. Jesse Jiryu Davis +
   - Alessandro Pietro Bardelli +
   - Alex Rothberg +
   - Alexander Shadchin
   - Allan Haldane
   - Andres Guzman-Ballen +
   - Antoine Pitrou
   - Antony Lee
   - B R S Recht +
   - Baurzhan Muftakhidinov +
   - Ben Rowland
   - Benda Xu +
   - Blake Griffith
   - Bradley Wogsland +
   - Brandon Carter +
   - CJ Carey
   - Charles Harris
   - Danny Hermes +
   - Duke Vijitbenjaronk +
   - Egor Klenin +
   - Elliott Forney +
   - Elliott M Forney +
   - Endolith
   - Eric Wieser
   - Erik M. Bray
   - Eugene +
   - Evan Limanto +
   - Felix Berkenkamp +
   - Fran?ois Bissey +
   - Frederic Bastien
   - Greg Young
   - Gregory R. Lee
   - Importance of Being Ernest +
   - Jaime Fernandez
   - Jakub Wilk +
   - James Cowgill +
   - James Sanders
   - Jean Utke +
   - Jesse Thoren +
   - Jim Crist +
   - Joerg Behrmann +
   - John Kirkham
   - Jonathan Helmus
   - Jonathan L Long
   - Jonathan Tammo Siebert +
   - Joseph Fox-Rabinovitz
   - Joshua Loyal +
   - Juan Nunez-Iglesias +
   - Julian Taylor
   - Kirill Balunov +
   - Likhith Chitneni +
   - Lo?c Est?ve
   - Mads Ohm Larsen
   - Marein K?nings +
   - Marten van Kerkwijk
   - Martin Thoma
   - Martino Sorbaro +
   - Marvin Schmidt +
   - Matthew Brett
   - Matthias Bussonnier +
   - Matthias C. M. Troffaes +
   - Matti Picus
   - Michael Seifert
   - Mikhail Pak +
   - Mortada Mehyar
   - Nathaniel J. Smith
   - Nick Papior
   - Oscar Villellas +
   - Pauli Virtanen
   - Pavel Potocek
   - Pete Peeradej Tanruangporn +
   - Philipp A +
   - Ralf Gommers
   - Robert Kern
   - Roland Kaufmann +
   - Ronan Lamy
   - Sami Salonen +
   - Sanchez Gonzalez Alvaro
   - Sebastian Berg
   - Shota Kawabuchi
   - Simon Gibbons
   - Stefan Otte
   - Stefan Peterson +
   - Stephan Hoyer
   - S?ren Fuglede J?rgensen +
   - Takuya Akiba
   - Tom Boyd +
   - Ville Skytt? +
   - Warren Weckesser
   - Wendell Smith
   - Yu Feng
   - Zixu Zhao +
   - Z? Vin?cius +
   - aha66 +
   - davidjn +
   - drabach +
   - drlvk +
   - jsh9 +
   - solarjoe +
   - zengi +

Cheers,

Chuck
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From njs at pobox.com  Wed May 10 22:15:56 2017
From: njs at pobox.com (Nathaniel Smith)
Date: Wed, 10 May 2017 19:15:56 -0700
Subject: [SciPy-User] [Numpy-discussion] NumPy v1.13.0rc1 released.
In-Reply-To: <CAJXewOnEZ4O+=0uHfn1N4LtWFDmc_3LDkDMj9YmJmf-UwMBrTQ@mail.gmail.com>
References: <CAB6mnx+78KwqkfUN65RQMefXNBG4myLWKCmGjTQ8GYTg4Qjd5Q@mail.gmail.com>
 <CAJXewOnEZ4O+=0uHfn1N4LtWFDmc_3LDkDMj9YmJmf-UwMBrTQ@mail.gmail.com>
Message-ID: <CAPJVwB=ZRNT4OYw9_U+1cXebCw=XUj_3+2h0kO4M5E6bwqC4Qg@mail.gmail.com>

On Wed, May 10, 2017 at 7:06 PM, Nathan Goldbaum <nathan12343 at gmail.com> wrote:
> Hi Chuck,
>
> Is there a docs build for this release somewhere? I'd like to find an
> authoritative reference about __array_ufunc__, which I'd hesistated on
> looking into until now for fear about the API changing.

A sort-of-rendered version of the end-user docs can be seen here:
  https://github.com/numpy/numpy/blob/master/doc/source/reference/arrays.classes.rst

And the NEP has been updated to hopefully provide a more spec-like
description of the final version:
  https://github.com/numpy/numpy/blob/master/doc/neps/ufunc-overrides.rst

Note that the API is "provisional" in 1.13, i.e. it *might* change in
backwards-incompatible ways:
  https://docs.python.org/3/glossary.html#term-provisional-api

-n

-- 
Nathaniel J. Smith -- https://vorpus.org

From kenny.vermeers at gmail.com  Thu May 11 06:12:05 2017
From: kenny.vermeers at gmail.com (Kenny Vermeers)
Date: Thu, 11 May 2017 12:12:05 +0200
Subject: [SciPy-User] Arc length of parametric spline
Message-ID: <CAOdQN1HrDOFgr+8qD9GiZgQ0T0-AiC8Un7pjL0Z1Brf0RUVu+w@mail.gmail.com>

Hi,

I have a curve given by x,y points and interpolated it using splines with
parameter t. t is in [0-1] by default but I would like to remap this to
[0-arclength]. My plan was to calculate the arc length analytically or
numerically using the light blue highlighted formula in
http://tutorial.math.lamar.edu/Classes/CalcII/ParaArcLength.aspx i.e.
int_0to1(sqrt((dx/dt)^2 + (dy/dt)^2))dt

In order to do this I need the splines dx/dt and dy/dt,. I was going to do
this as follows
tck, u = interpolate.splprep([x, y])
dspl = interpolate.splder(tck)
hoping to get dx/dt and dy/dt and then solving the integral. Unfortunately
that doesn't work since splder doesn't seem to accept parametric splines.
Do you have any suggestions on how to get the spline parameter from 0 to
arclen?

Thanks!

Kenny
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From nadavh at visionsense.com  Thu May 11 09:09:51 2017
From: nadavh at visionsense.com (Nadav Horesh)
Date: Thu, 11 May 2017 13:09:51 +0000
Subject: [SciPy-User] [Numpy-discussion] NumPy v1.13.0rc1 released.
In-Reply-To: <CAB6mnx+78KwqkfUN65RQMefXNBG4myLWKCmGjTQ8GYTg4Qjd5Q@mail.gmail.com>
References: <CAB6mnx+78KwqkfUN65RQMefXNBG4myLWKCmGjTQ8GYTg4Qjd5Q@mail.gmail.com>
Message-ID: <BY2PR08MB095EEBCC6A9A7FE15381931CBED0@BY2PR08MB095.namprd08.prod.outlook.com>

There is a change to "expand_dims" function, that it is now does not allow axis = a.ndims.

This influences matplotlib function get_bending_matrices in triinterpolate.py


  Nadav

________________________________
From: NumPy-Discussion <numpy-discussion-bounces+nadavh=visionsense.com at python.org> on behalf of Charles R Harris <charlesr.harris at gmail.com>
Sent: 11 May 2017 04:48:34
To: numpy-discussion; SciPy-User; SciPy Developers List; python-announce-list at python.org
Subject: [Numpy-discussion] NumPy v1.13.0rc1 released.

Hi All,

I'm please to announce the NumPy 1.13.0rc1 release. This release supports Python 2.7 and 3.4-3.6 and contains many new features. It is one of the most ambitious releases in the last several years. Some of the highlights and new functions are

Highlights

  *   Operations like ``a + b + c`` will reuse temporaries on some platforms, resulting in less memory use and faster execution.
  *   Inplace operations check if inputs overlap outputs and create temporaries to avoid problems.
  *   New __array_ufunc__ attribute provides improved ability for classes to override default ufunc behavior.
  *    New np.block function for creating blocked arrays.

New functions

  *   New ``np.positive`` ufunc.
  *   New ``np.divmod`` ufunc provides more efficient divmod.
  *   New ``np.isnat`` ufunc tests for NaT special values.
  *   New ``np.heaviside`` ufunc computes the Heaviside function.
  *   New ``np.isin`` function, improves on ``in1d``.
  *   New ``np.block`` function for creating blocked arrays.
  *   New ``PyArray_MapIterArrayCopyIfOverlap`` added to NumPy C-API.

Wheels for the pre-release are available on PyPI. Source tarballs, zipfiles, release notes, and the Changelog are available on github<https://github.com/numpy/numpy/releases/tag/v1.13.0rc1>.

A total of 100 people contributed to this release.  People with a "+" by their
names contributed a patch for the first time.

  *   A. Jesse Jiryu Davis +
  *   Alessandro Pietro Bardelli +
  *   Alex Rothberg +
  *   Alexander Shadchin
  *   Allan Haldane
  *   Andres Guzman-Ballen +
  *   Antoine Pitrou
  *   Antony Lee
  *   B R S Recht +
  *   Baurzhan Muftakhidinov +
  *   Ben Rowland
  *   Benda Xu +
  *   Blake Griffith
  *   Bradley Wogsland +
  *   Brandon Carter +
  *   CJ Carey
  *   Charles Harris
  *   Danny Hermes +
  *   Duke Vijitbenjaronk +
  *   Egor Klenin +
  *   Elliott Forney +
  *   Elliott M Forney +
  *   Endolith
  *   Eric Wieser
  *   Erik M. Bray
  *   Eugene +
  *   Evan Limanto +
  *   Felix Berkenkamp +
  *   Fran?ois Bissey +
  *   Frederic Bastien
  *   Greg Young
  *   Gregory R. Lee
  *   Importance of Being Ernest +
  *   Jaime Fernandez
  *   Jakub Wilk +
  *   James Cowgill +
  *   James Sanders
  *   Jean Utke +
  *   Jesse Thoren +
  *   Jim Crist +
  *   Joerg Behrmann +
  *   John Kirkham
  *   Jonathan Helmus
  *   Jonathan L Long
  *   Jonathan Tammo Siebert +
  *   Joseph Fox-Rabinovitz
  *   Joshua Loyal +
  *   Juan Nunez-Iglesias +
  *   Julian Taylor
  *   Kirill Balunov +
  *   Likhith Chitneni +
  *   Lo?c Est?ve
  *   Mads Ohm Larsen
  *   Marein K?nings +
  *   Marten van Kerkwijk
  *   Martin Thoma
  *   Martino Sorbaro +
  *   Marvin Schmidt +
  *   Matthew Brett
  *   Matthias Bussonnier +
  *   Matthias C. M. Troffaes +
  *   Matti Picus
  *   Michael Seifert
  *   Mikhail Pak +
  *   Mortada Mehyar
  *   Nathaniel J. Smith
  *   Nick Papior
  *   Oscar Villellas +
  *   Pauli Virtanen
  *   Pavel Potocek
  *   Pete Peeradej Tanruangporn +
  *   Philipp A +
  *   Ralf Gommers
  *   Robert Kern
  *   Roland Kaufmann +
  *   Ronan Lamy
  *   Sami Salonen +
  *   Sanchez Gonzalez Alvaro
  *   Sebastian Berg
  *   Shota Kawabuchi
  *   Simon Gibbons
  *   Stefan Otte
  *   Stefan Peterson +
  *   Stephan Hoyer
  *   S?ren Fuglede J?rgensen +
  *   Takuya Akiba
  *   Tom Boyd +
  *   Ville Skytt? +
  *   Warren Weckesser
  *   Wendell Smith
  *   Yu Feng
  *   Zixu Zhao +
  *   Z? Vin?cius +
  *   aha66 +
  *   davidjn +
  *   drabach +
  *   drlvk +
  *   jsh9 +
  *   solarjoe +
  *   zengi +

Cheers,

Chuck
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From shoyer at gmail.com  Thu May 11 12:23:20 2017
From: shoyer at gmail.com (Stephan Hoyer)
Date: Thu, 11 May 2017 09:23:20 -0700
Subject: [SciPy-User] [Numpy-discussion] NumPy v1.13.0rc1 released.
In-Reply-To: <CAL1kJvANUVy0WuSs7cJX6hmsCkpEH=hYt-hQ_bVhCUnWNWh69w@mail.gmail.com>
References: <CAB6mnx+78KwqkfUN65RQMefXNBG4myLWKCmGjTQ8GYTg4Qjd5Q@mail.gmail.com>
 <BY2PR08MB095EEBCC6A9A7FE15381931CBED0@BY2PR08MB095.namprd08.prod.outlook.com>
 <CAL1kJvANUVy0WuSs7cJX6hmsCkpEH=hYt-hQ_bVhCUnWNWh69w@mail.gmail.com>
Message-ID: <CAEQ_Tvf7UXYZ+o7J6ybPySgLBpkL_cnce5MrWyGhPdkdRSJu4w@mail.gmail.com>

Also, as friendly reminder, GitHub is a better place for bug reports than
mailing lists with hundreds of subscribers :).

On Thu, May 11, 2017 at 6:56 AM, Eric Wieser <wieser.eric+numpy at gmail.com>
wrote:

> Nadav: Can you provide a testcase that fails?
>
> I don't think you're correct - it works just fine when `axis = a.ndims` -
> the issue arises when `axis > a.ndims`, but I'd argue that in that case an
> error is correct behaviour. But still a change, so perhaps needs a release
> note entry
>
> On Thu, 11 May 2017 at 14:25 Nadav Horesh <nadavh at visionsense.com> wrote:
>
>> There is a change to "expand_dims" function, that it is now does not
>> allow axis = a.ndims.
>>
>> This influences matplotlib function get_bending_matrices in
>> triinterpolate.py
>>
>>
>>   Nadav
>> ------------------------------
>> *From:* NumPy-Discussion <numpy-discussion-bounces+nadavh=
>> visionsense.com at python.org> on behalf of Charles R Harris <
>> charlesr.harris at gmail.com>
>> *Sent:* 11 May 2017 04:48:34
>> *To:* numpy-discussion; SciPy-User; SciPy Developers List;
>> python-announce-list at python.org
>> *Subject:* [Numpy-discussion] NumPy v1.13.0rc1 released.
>>
>> Hi All,
>>
>> I'm please to announce the NumPy 1.13.0rc1 release. This release supports
>> Python 2.7 and 3.4-3.6 and contains many new features. It is one of the
>> most ambitious releases in the last several years. Some of the highlights
>> and new functions are
>>
>> *Highlights*
>>
>>    - Operations like ``a + b + c`` will reuse temporaries on some
>>    platforms, resulting in less memory use and faster execution.
>>    - Inplace operations check if inputs overlap outputs and create
>>    temporaries to avoid problems.
>>    - New __array_ufunc__ attribute provides improved ability for classes
>>    to override default ufunc behavior.
>>    -  New np.block function for creating blocked arrays.
>>
>>
>> *New functions*
>>
>>    - New ``np.positive`` ufunc.
>>    - New ``np.divmod`` ufunc provides more efficient divmod.
>>    - New ``np.isnat`` ufunc tests for NaT special values.
>>    - New ``np.heaviside`` ufunc computes the Heaviside function.
>>    - New ``np.isin`` function, improves on ``in1d``.
>>    - New ``np.block`` function for creating blocked arrays.
>>    - New ``PyArray_MapIterArrayCopyIfOverlap`` added to NumPy C-API.
>>
>> Wheels for the pre-release are available on PyPI. Source tarballs,
>> zipfiles, release notes, and the Changelog are available on github
>> <https://github.com/numpy/numpy/releases/tag/v1.13.0rc1>.
>>
>> A total of 100 people contributed to this release.  People with a "+" by
>> their
>> names contributed a patch for the first time.
>>
>>    - A. Jesse Jiryu Davis +
>>    - Alessandro Pietro Bardelli +
>>    - Alex Rothberg +
>>    - Alexander Shadchin
>>    - Allan Haldane
>>    - Andres Guzman-Ballen +
>>    - Antoine Pitrou
>>    - Antony Lee
>>    - B R S Recht +
>>    - Baurzhan Muftakhidinov +
>>    - Ben Rowland
>>    - Benda Xu +
>>    - Blake Griffith
>>    - Bradley Wogsland +
>>    - Brandon Carter +
>>    - CJ Carey
>>    - Charles Harris
>>    - Danny Hermes +
>>    - Duke Vijitbenjaronk +
>>    - Egor Klenin +
>>    - Elliott Forney +
>>    - Elliott M Forney +
>>    - Endolith
>>    - Eric Wieser
>>    - Erik M. Bray
>>    - Eugene +
>>    - Evan Limanto +
>>    - Felix Berkenkamp +
>>    - Fran?ois Bissey +
>>    - Frederic Bastien
>>    - Greg Young
>>    - Gregory R. Lee
>>    - Importance of Being Ernest +
>>    - Jaime Fernandez
>>    - Jakub Wilk +
>>    - James Cowgill +
>>    - James Sanders
>>    - Jean Utke +
>>    - Jesse Thoren +
>>    - Jim Crist +
>>    - Joerg Behrmann +
>>    - John Kirkham
>>    - Jonathan Helmus
>>    - Jonathan L Long
>>    - Jonathan Tammo Siebert +
>>    - Joseph Fox-Rabinovitz
>>    - Joshua Loyal +
>>    - Juan Nunez-Iglesias +
>>    - Julian Taylor
>>    - Kirill Balunov +
>>    - Likhith Chitneni +
>>    - Lo?c Est?ve
>>    - Mads Ohm Larsen
>>    - Marein K?nings +
>>    - Marten van Kerkwijk
>>    - Martin Thoma
>>    - Martino Sorbaro +
>>    - Marvin Schmidt +
>>    - Matthew Brett
>>    - Matthias Bussonnier +
>>    - Matthias C. M. Troffaes +
>>    - Matti Picus
>>    - Michael Seifert
>>    - Mikhail Pak +
>>    - Mortada Mehyar
>>    - Nathaniel J. Smith
>>    - Nick Papior
>>    - Oscar Villellas +
>>    - Pauli Virtanen
>>    - Pavel Potocek
>>    - Pete Peeradej Tanruangporn +
>>    - Philipp A +
>>    - Ralf Gommers
>>    - Robert Kern
>>    - Roland Kaufmann +
>>    - Ronan Lamy
>>    - Sami Salonen +
>>    - Sanchez Gonzalez Alvaro
>>    - Sebastian Berg
>>    - Shota Kawabuchi
>>    - Simon Gibbons
>>    - Stefan Otte
>>    - Stefan Peterson +
>>    - Stephan Hoyer
>>    - S?ren Fuglede J?rgensen +
>>    - Takuya Akiba
>>    - Tom Boyd +
>>    - Ville Skytt? +
>>    - Warren Weckesser
>>    - Wendell Smith
>>    - Yu Feng
>>    - Zixu Zhao +
>>    - Z? Vin?cius +
>>    - aha66 +
>>    - davidjn +
>>    - drabach +
>>    - drlvk +
>>    - jsh9 +
>>    - solarjoe +
>>    - zengi +
>>
>> Cheers,
>>
>> Chuck
>> _______________________________________________
>> NumPy-Discussion mailing list
>> NumPy-Discussion at python.org
>> https://mail.python.org/mailman/listinfo/numpy-discussion
>>
>
> _______________________________________________
> NumPy-Discussion mailing list
> NumPy-Discussion at python.org
> https://mail.python.org/mailman/listinfo/numpy-discussion
>
>
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From pav at iki.fi  Thu May 11 15:05:08 2017
From: pav at iki.fi (Pauli Virtanen)
Date: Thu, 11 May 2017 21:05:08 +0200
Subject: [SciPy-User] Arc length of parametric spline
In-Reply-To: <CAOdQN1HrDOFgr+8qD9GiZgQ0T0-AiC8Un7pjL0Z1Brf0RUVu+w@mail.gmail.com>
References: <CAOdQN1HrDOFgr+8qD9GiZgQ0T0-AiC8Un7pjL0Z1Brf0RUVu+w@mail.gmail.com>
Message-ID: <3100705a-1b26-ad1a-f2a9-6365cf403b34@iki.fi>

Kenny Vermeers kirjoitti 11.05.2017 klo 12:12:
> In order to do this I need the splines dx/dt and dy/dt,. I was going to
> do this as follows
> tck, u = interpolate.splprep([x, y])
> dspl = interpolate.splder(tck)
> hoping to get dx/dt and dy/dt and then solving the integral.
> Unfortunately that doesn't work since splder doesn't seem to accept
> parametric splines. Do you have any suggestions on how to get the spline
> parameter from 0 to arclen?

You can grab the x(u) and y(u) splines from the output of splprep. It is
a bit ugly because splprep has a different padding convention than splrep.

t, c, k = tck
cx = np.concatenate([c[0], np.zeros(len(t) - len(c[0]))])
cy = np.concatenate([c[1], np.zeros(len(t) - len(c[1]))])
tck_x = (t, cx, k)
tck_y = (t, cy, k)

From david at drhagen.com  Sat May 13 07:50:51 2017
From: david at drhagen.com (David Hagen)
Date: Sat, 13 May 2017 07:50:51 -0400
Subject: [SciPy-User] Install Scipy with Anaconda's MKL libraries
In-Reply-To: <CALO6Ft7+bkDwQc7Js0Sk4OtiSR11zmHhJE2XOigXcuy0j2VjEA@mail.gmail.com>
References: <CALO6Ft47hEd8RMtHF2nRAji84nztS8h-hm-qyojwv3kZn0-Zkw@mail.gmail.com>
 <CALO6Ft55X=Q9hhPUNwn-pZ1JRS+FnGLxdubLph9qtN4WkQUtnw@mail.gmail.com>
 <CAHCaCk+tYbh8AXuhWkTAtnjfCTqFjt4rFT9_XOP++bArRmsepQ@mail.gmail.com>
 <CALxxJLTUAjOupyKROxaYPAr+7VOTFm5kj70mEZApZUpAzgE=pA@mail.gmail.com>
 <CALO6Ft7K89B5Qd+tOJpmvKrN7YmBm0JKSDZhM_1aegN457HfWA@mail.gmail.com>
 <CAKWqKotdbQYdabmU2UKR2BgxfpiJBLXyQ5vdCSfA0t7iUdM_DA@mail.gmail.com>
 <CALO6Ft71-SfExyVQvPswoCN491qojfaUH9Y3k_CUE-jc1vJayQ@mail.gmail.com>
 <CALxxJLQoesk0ixxFZJHGdtfw3WQV8AftXVnshEk6TiaA_+r=gw@mail.gmail.com>
 <CALO6Ft6fw66b1LahGR_p=80-xxJhYiwZNEZFLN1SMhAh65NB_g@mail.gmail.com>
 <CALxxJLQv-yZePFASgvQS8+YNMZU4BfCH1-bj0CL+wxeBpOM=TA@mail.gmail.com>
 <CALO6Ft6PNWL7G37q-tsfasGoxUk7JdX6iw38-_2-Ug2CFY5vVA@mail.gmail.com>
 <590B2AB5.9080505@sbcglobal.net>
 <CALO6Ft5zFxPjoDnY13OM+=4kQvjmj3bfk5ogEPSv=1Sx7YeCzA@mail.gmail.com>
 <590B6246.6090607@sbcglobal.net>
 <CALO6Ft53irwAsYuAo=v5fBZPX6ZeP=kkAqMuVrLdq4org7tt=Q@mail.gmail.com>
 <CALO6Ft4+B4MULYefCdxrifuEPm0sF5Jq-Y0+XKuPgFcNPLxeXA@mail.gmail.com>
 <CA+_2Pn2zur6q-zRpxDiH9B3YtgHt4hWL0v5OueC9nPufidM8vg@mail.gmail.com>
 <CALxxJLS=WN7mSVFbLYBJxqw0ijtcB=5vwOFnQtJOBAE-gLFgiA@mail.gmail.com>
 <CALO6Ft7+bkDwQc7Js0Sk4OtiSR11zmHhJE2XOigXcuy0j2VjEA@mail.gmail.com>
Message-ID: <CALO6Ft4_Da7xm7THhfq0cDkszd8d8zEXqM79OBJS+2w5zUES+A@mail.gmail.com>

I uninstalled all versions of MSVS and installed Visual Studio 2015 update
3. This did not fix anything, but at least I ruled out conflicts there.

To get past the NoneType error from my previous message I had to fix a bug
in Numpy; namely, in the file

numpy\distutils\msvc9compiler.py

the _merge function fails if old is None. I merely swap the two `if`
statements and it got past. I will submit a pull request.

Then the installer couldn't find icl.exe. I also had to add this to my PATH
environment variable:

C:\Program Files
(x86)\IntelSWTools\compilers_and_libraries_2018.0.065\windows\bin\intel64

Then I got this error

LINK : fatal error LNK1104: cannot open file 'libiomp5md.lib'

which I tracked down to MPI. The instructions here
<https://software.intel.com/en-us/articles/building-numpyscipy-with-intel-mkl-and-intel-fortran-on-windows>
had
told me to add several compiler options in numpy/distutils/intelccompiler.py,
one of which was openmp. Once I reverted the compiler options to their
original state, this error went away.

Then I got this error

LINK : fatal error LNK1104: cannot open file 'libmmd.lib'

I tracked this down to the compiler libraries not be on the library path so
I added this to the LIB environment variable:

C:\Program Files
(x86)\IntelSWTools\compilers_and_libraries_2018.0.065\windows\compiler\lib\intel64

Finally Numpy installed and imports.

But while it was nice to learn how to install Numpy from source, the main
goal was to install Scipy from source. I thought that installing Numpy from
source would fix my earlier problems with unresolved external _gfortran
symbols. Alas, I still get this error while installing Scipy:

building 'scipy.integrate._quadpack' extension
compiling C sources
C:\Program Files
(x86)\IntelSWTools\compilers_and_libraries_2018.0.065\windows\bin\intel64\icl.exe
/c /nologo /O3 /MD /W3 /Qstd=c99 -DSCIPY_MKL_H -DHAVE_CBLAS -
Iscipy\_lib\src -I"C:\Program Files
(x86)\IntelSWTools\parallel_studio_xe_2018.0.019\compilers_and_libraries_2018\windows\mkl\include"
-IC:\Anaconda3\envs\numpy
_source\lib\site-packages\numpy-1.12.1-py3.6-win-amd64.egg\numpy\core\include
-IC:\Anaconda3\envs\numpy_source\include
-IC:\Anaconda3\envs\numpy_source\include
/Tcscipy\integrate\_quadpackmodule.c
/Fobuild\temp.win-amd64-3.6\Release\scipy\integrate\_quadpackmodule.obj
could not find library 'quadpack' in directories ['C:\\Program Files
(x86)\\Inte
lSWTools\\parallel_studio_xe_2018.0.019\\compilers_and_libraries_2018\\windows\\
mkl\\lib\\intel64']
...
   Creating library
build\temp.win-amd64-3.6\Release\scipy\integrate\_quadpack.c
p36-win_amd64.lib and object
build\temp.win-amd64-3.6\Release\scipy\integrate\_quadpack.cp36-win_amd64.exp
mach.lib(d1mach.o) : error LNK2019: unresolved external symbol
_gfortran_st_write referenced in function d1mach_
mach.lib(d1mach.o) : error LNK2019: unresolved external symbol
_gfortran_st_write_done referenced in function d1mach_
mach.lib(d1mach.o) : error LNK2019: unresolved external symbol
_gfortran_stop_numeric_f08 referenced in function d1mach_
mach.lib(d1mach.o) : error LNK2019: unresolved external symbol
_gfortran_transfer_character_write referenced in function d1mach_
mach.lib(d1mach.o) : error LNK2019: unresolved external symbol
_gfortran_transfer_integer_write referenced in function d1mach_
mach.lib(d1mach.o) : error LNK2019: unresolved external symbol
_gfortran_stop_string referenced in function d1mach_
build\lib.win-amd64-3.6\scipy\integrate\_quadpack.cp36-win_amd64.pyd :
fatal error LNK1120: 6 unresolved externals

*What does this even mean?*
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From david at drhagen.com  Sat May 13 20:37:16 2017
From: david at drhagen.com (David Hagen)
Date: Sat, 13 May 2017 20:37:16 -0400
Subject: [SciPy-User] Install Scipy with Anaconda's MKL libraries
In-Reply-To: <CALO6Ft4_Da7xm7THhfq0cDkszd8d8zEXqM79OBJS+2w5zUES+A@mail.gmail.com>
References: <CALO6Ft47hEd8RMtHF2nRAji84nztS8h-hm-qyojwv3kZn0-Zkw@mail.gmail.com>
 <CALO6Ft55X=Q9hhPUNwn-pZ1JRS+FnGLxdubLph9qtN4WkQUtnw@mail.gmail.com>
 <CAHCaCk+tYbh8AXuhWkTAtnjfCTqFjt4rFT9_XOP++bArRmsepQ@mail.gmail.com>
 <CALxxJLTUAjOupyKROxaYPAr+7VOTFm5kj70mEZApZUpAzgE=pA@mail.gmail.com>
 <CALO6Ft7K89B5Qd+tOJpmvKrN7YmBm0JKSDZhM_1aegN457HfWA@mail.gmail.com>
 <CAKWqKotdbQYdabmU2UKR2BgxfpiJBLXyQ5vdCSfA0t7iUdM_DA@mail.gmail.com>
 <CALO6Ft71-SfExyVQvPswoCN491qojfaUH9Y3k_CUE-jc1vJayQ@mail.gmail.com>
 <CALxxJLQoesk0ixxFZJHGdtfw3WQV8AftXVnshEk6TiaA_+r=gw@mail.gmail.com>
 <CALO6Ft6fw66b1LahGR_p=80-xxJhYiwZNEZFLN1SMhAh65NB_g@mail.gmail.com>
 <CALxxJLQv-yZePFASgvQS8+YNMZU4BfCH1-bj0CL+wxeBpOM=TA@mail.gmail.com>
 <CALO6Ft6PNWL7G37q-tsfasGoxUk7JdX6iw38-_2-Ug2CFY5vVA@mail.gmail.com>
 <590B2AB5.9080505@sbcglobal.net>
 <CALO6Ft5zFxPjoDnY13OM+=4kQvjmj3bfk5ogEPSv=1Sx7YeCzA@mail.gmail.com>
 <590B6246.6090607@sbcglobal.net>
 <CALO6Ft53irwAsYuAo=v5fBZPX6ZeP=kkAqMuVrLdq4org7tt=Q@mail.gmail.com>
 <CALO6Ft4+B4MULYefCdxrifuEPm0sF5Jq-Y0+XKuPgFcNPLxeXA@mail.gmail.com>
 <CA+_2Pn2zur6q-zRpxDiH9B3YtgHt4hWL0v5OueC9nPufidM8vg@mail.gmail.com>
 <CALxxJLS=WN7mSVFbLYBJxqw0ijtcB=5vwOFnQtJOBAE-gLFgiA@mail.gmail.com>
 <CALO6Ft7+bkDwQc7Js0Sk4OtiSR11zmHhJE2XOigXcuy0j2VjEA@mail.gmail.com>
 <CALO6Ft4_Da7xm7THhfq0cDkszd8d8zEXqM79OBJS+2w5zUES+A@mail.gmail.com>
Message-ID: <CALO6Ft56XqM7JnHfsh8yEJs2Q1ebJwsnsJrA8qc4Lw3Uub3X6g@mail.gmail.com>

Ok, I solved everything. Here's the solution to the unresolved external
symbols error: *DELETE THE BUILD FOLDER*. If Scipy starts compiling with
one compiler, it will used the cached binaries when linking later even if
it is now using a different compiler. Deleting the build folder forces it
to start over.
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From eulergaussriemann at gmail.com  Sat May 13 21:35:46 2017
From: eulergaussriemann at gmail.com (David Goldsmith)
Date: Sun, 14 May 2017 01:35:46 +0000
Subject: [SciPy-User] Install Scipy with Anaconda's MKL libraries
In-Reply-To: <CALO6Ft56XqM7JnHfsh8yEJs2Q1ebJwsnsJrA8qc4Lw3Uub3X6g@mail.gmail.com>
References: <CALO6Ft47hEd8RMtHF2nRAji84nztS8h-hm-qyojwv3kZn0-Zkw@mail.gmail.com>
 <CALO6Ft55X=Q9hhPUNwn-pZ1JRS+FnGLxdubLph9qtN4WkQUtnw@mail.gmail.com>
 <CAHCaCk+tYbh8AXuhWkTAtnjfCTqFjt4rFT9_XOP++bArRmsepQ@mail.gmail.com>
 <CALxxJLTUAjOupyKROxaYPAr+7VOTFm5kj70mEZApZUpAzgE=pA@mail.gmail.com>
 <CALO6Ft7K89B5Qd+tOJpmvKrN7YmBm0JKSDZhM_1aegN457HfWA@mail.gmail.com>
 <CAKWqKotdbQYdabmU2UKR2BgxfpiJBLXyQ5vdCSfA0t7iUdM_DA@mail.gmail.com>
 <CALO6Ft71-SfExyVQvPswoCN491qojfaUH9Y3k_CUE-jc1vJayQ@mail.gmail.com>
 <CALxxJLQoesk0ixxFZJHGdtfw3WQV8AftXVnshEk6TiaA_+r=gw@mail.gmail.com>
 <CALO6Ft6fw66b1LahGR_p=80-xxJhYiwZNEZFLN1SMhAh65NB_g@mail.gmail.com>
 <CALxxJLQv-yZePFASgvQS8+YNMZU4BfCH1-bj0CL+wxeBpOM=TA@mail.gmail.com>
 <CALO6Ft6PNWL7G37q-tsfasGoxUk7JdX6iw38-_2-Ug2CFY5vVA@mail.gmail.com>
 <590B2AB5.9080505@sbcglobal.net>
 <CALO6Ft5zFxPjoDnY13OM+=4kQvjmj3bfk5ogEPSv=1Sx7YeCzA@mail.gmail.com>
 <590B6246.6090607@sbcglobal.net>
 <CALO6Ft53irwAsYuAo=v5fBZPX6ZeP=kkAqMuVrLdq4org7tt=Q@mail.gmail.com>
 <CALO6Ft4+B4MULYefCdxrifuEPm0sF5Jq-Y0+XKuPgFcNPLxeXA@mail.gmail.com>
 <CA+_2Pn2zur6q-zRpxDiH9B3YtgHt4hWL0v5OueC9nPufidM8vg@mail.gmail.com>
 <CALxxJLS=WN7mSVFbLYBJxqw0ijtcB=5vwOFnQtJOBAE-gLFgiA@mail.gmail.com>
 <CALO6Ft7+bkDwQc7Js0Sk4OtiSR11zmHhJE2XOigXcuy0j2VjEA@mail.gmail.com>
 <CALO6Ft4_Da7xm7THhfq0cDkszd8d8zEXqM79OBJS+2w5zUES+A@mail.gmail.com>
 <CALO6Ft56XqM7JnHfsh8yEJs2Q1ebJwsnsJrA8qc4Lw3Uub3X6g@mail.gmail.com>
Message-ID: <CA+_2Pn2GmBdiJR71mfEErfsAjHqod2tjQXh6gB5KSO0VX=Aa+Q@mail.gmail.com>

Ah yes, the old "start the build fresh each time" problem--been there, done
that.  Even if something says i shouldn't have to start over, i no longer
trust that: always start with a fresh build.

DLG

On Sat, May 13, 2017 at 6:01 PM David Hagen <david at drhagen.com> wrote:

> Ok, I solved everything. Here's the solution to the unresolved external
> symbols error: *DELETE THE BUILD FOLDER*. If Scipy starts compiling with
> one compiler, it will used the cached binaries when linking later even if
> it is now using a different compiler. Deleting the build folder forces it
> to start over.
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at python.org
> https://mail.python.org/mailman/listinfo/scipy-user
>
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From david at drhagen.com  Sun May 14 17:37:13 2017
From: david at drhagen.com (David Hagen)
Date: Sun, 14 May 2017 17:37:13 -0400
Subject: [SciPy-User] Install Scipy with Anaconda's MKL libraries
In-Reply-To: <CA+_2Pn2GmBdiJR71mfEErfsAjHqod2tjQXh6gB5KSO0VX=Aa+Q@mail.gmail.com>
References: <CALO6Ft47hEd8RMtHF2nRAji84nztS8h-hm-qyojwv3kZn0-Zkw@mail.gmail.com>
 <CALO6Ft55X=Q9hhPUNwn-pZ1JRS+FnGLxdubLph9qtN4WkQUtnw@mail.gmail.com>
 <CAHCaCk+tYbh8AXuhWkTAtnjfCTqFjt4rFT9_XOP++bArRmsepQ@mail.gmail.com>
 <CALxxJLTUAjOupyKROxaYPAr+7VOTFm5kj70mEZApZUpAzgE=pA@mail.gmail.com>
 <CALO6Ft7K89B5Qd+tOJpmvKrN7YmBm0JKSDZhM_1aegN457HfWA@mail.gmail.com>
 <CAKWqKotdbQYdabmU2UKR2BgxfpiJBLXyQ5vdCSfA0t7iUdM_DA@mail.gmail.com>
 <CALO6Ft71-SfExyVQvPswoCN491qojfaUH9Y3k_CUE-jc1vJayQ@mail.gmail.com>
 <CALxxJLQoesk0ixxFZJHGdtfw3WQV8AftXVnshEk6TiaA_+r=gw@mail.gmail.com>
 <CALO6Ft6fw66b1LahGR_p=80-xxJhYiwZNEZFLN1SMhAh65NB_g@mail.gmail.com>
 <CALxxJLQv-yZePFASgvQS8+YNMZU4BfCH1-bj0CL+wxeBpOM=TA@mail.gmail.com>
 <CALO6Ft6PNWL7G37q-tsfasGoxUk7JdX6iw38-_2-Ug2CFY5vVA@mail.gmail.com>
 <590B2AB5.9080505@sbcglobal.net>
 <CALO6Ft5zFxPjoDnY13OM+=4kQvjmj3bfk5ogEPSv=1Sx7YeCzA@mail.gmail.com>
 <590B6246.6090607@sbcglobal.net>
 <CALO6Ft53irwAsYuAo=v5fBZPX6ZeP=kkAqMuVrLdq4org7tt=Q@mail.gmail.com>
 <CALO6Ft4+B4MULYefCdxrifuEPm0sF5Jq-Y0+XKuPgFcNPLxeXA@mail.gmail.com>
 <CA+_2Pn2zur6q-zRpxDiH9B3YtgHt4hWL0v5OueC9nPufidM8vg@mail.gmail.com>
 <CALxxJLS=WN7mSVFbLYBJxqw0ijtcB=5vwOFnQtJOBAE-gLFgiA@mail.gmail.com>
 <CALO6Ft7+bkDwQc7Js0Sk4OtiSR11zmHhJE2XOigXcuy0j2VjEA@mail.gmail.com>
 <CALO6Ft4_Da7xm7THhfq0cDkszd8d8zEXqM79OBJS+2w5zUES+A@mail.gmail.com>
 <CALO6Ft56XqM7JnHfsh8yEJs2Q1ebJwsnsJrA8qc4Lw3Uub3X6g@mail.gmail.com>
 <CA+_2Pn2GmBdiJR71mfEErfsAjHqod2tjQXh6gB5KSO0VX=Aa+Q@mail.gmail.com>
Message-ID: <CALO6Ft6jzK4tMWyhuEjY2geVUiu48-QDNdfLmopJvZbRm3Sf1w@mail.gmail.com>

I spoke too soon. I "successfully" compiled Numpy and Scipy from master and
both can be imported. However, some Scipy items don't import. It doesn't
really matter what exactly I import. If I run scipy.test() everything dies.
Can someone interpret this error message?

>>> from scipy.special import roots_legendre
Traceback (most recent call last):
  File "__init__.pxd", line 987, in numpy.import_array
(scipy\special\_ufuncs_cx
x.cxx:3729)
RuntimeError: module compiled against API version 0xb but this version of
numpy
is 0xa

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "C:\Anaconda3\envs\numpy_scipy\lib\site-packages\scipy-
1.0.0.dev0+unknown
-py3.6-win-amd64.egg\scipy\special\__init__.py", line 640, in <module>
    from ._ufuncs import *
  File "scipy\special\_ufuncs.pyx", line 1, in init scipy.special._ufuncs
(scipy
\special\_ufuncs.c:39403)
  File "scipy\special\_ufuncs_extra_code_common.pxi", line 34, in init
scipy.spe
cial._ufuncs_cxx (scipy\special\_ufuncs_cxx.cxx:4376)
  File "__init__.pxd", line 989, in numpy.import_array
(scipy\special\_ufuncs_cx
x.cxx:3772)
ImportError: numpy.core.multiarray failed to import
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From denis.akhiyarov at gmail.com  Mon May 15 17:42:37 2017
From: denis.akhiyarov at gmail.com (Denis Akhiyarov)
Date: Mon, 15 May 2017 16:42:37 -0500
Subject: [SciPy-User] Install Scipy with Anaconda's MKL libraries
In-Reply-To: <CALO6Ft6jzK4tMWyhuEjY2geVUiu48-QDNdfLmopJvZbRm3Sf1w@mail.gmail.com>
References: <CALO6Ft47hEd8RMtHF2nRAji84nztS8h-hm-qyojwv3kZn0-Zkw@mail.gmail.com>
 <CALO6Ft55X=Q9hhPUNwn-pZ1JRS+FnGLxdubLph9qtN4WkQUtnw@mail.gmail.com>
 <CAHCaCk+tYbh8AXuhWkTAtnjfCTqFjt4rFT9_XOP++bArRmsepQ@mail.gmail.com>
 <CALxxJLTUAjOupyKROxaYPAr+7VOTFm5kj70mEZApZUpAzgE=pA@mail.gmail.com>
 <CALO6Ft7K89B5Qd+tOJpmvKrN7YmBm0JKSDZhM_1aegN457HfWA@mail.gmail.com>
 <CAKWqKotdbQYdabmU2UKR2BgxfpiJBLXyQ5vdCSfA0t7iUdM_DA@mail.gmail.com>
 <CALO6Ft71-SfExyVQvPswoCN491qojfaUH9Y3k_CUE-jc1vJayQ@mail.gmail.com>
 <CALxxJLQoesk0ixxFZJHGdtfw3WQV8AftXVnshEk6TiaA_+r=gw@mail.gmail.com>
 <CALO6Ft6fw66b1LahGR_p=80-xxJhYiwZNEZFLN1SMhAh65NB_g@mail.gmail.com>
 <CALxxJLQv-yZePFASgvQS8+YNMZU4BfCH1-bj0CL+wxeBpOM=TA@mail.gmail.com>
 <CALO6Ft6PNWL7G37q-tsfasGoxUk7JdX6iw38-_2-Ug2CFY5vVA@mail.gmail.com>
 <590B2AB5.9080505@sbcglobal.net>
 <CALO6Ft5zFxPjoDnY13OM+=4kQvjmj3bfk5ogEPSv=1Sx7YeCzA@mail.gmail.com>
 <590B6246.6090607@sbcglobal.net>
 <CALO6Ft53irwAsYuAo=v5fBZPX6ZeP=kkAqMuVrLdq4org7tt=Q@mail.gmail.com>
 <CALO6Ft4+B4MULYefCdxrifuEPm0sF5Jq-Y0+XKuPgFcNPLxeXA@mail.gmail.com>
 <CA+_2Pn2zur6q-zRpxDiH9B3YtgHt4hWL0v5OueC9nPufidM8vg@mail.gmail.com>
 <CALxxJLS=WN7mSVFbLYBJxqw0ijtcB=5vwOFnQtJOBAE-gLFgiA@mail.gmail.com>
 <CALO6Ft7+bkDwQc7Js0Sk4OtiSR11zmHhJE2XOigXcuy0j2VjEA@mail.gmail.com>
 <CALO6Ft4_Da7xm7THhfq0cDkszd8d8zEXqM79OBJS+2w5zUES+A@mail.gmail.com>
 <CALO6Ft56XqM7JnHfsh8yEJs2Q1ebJwsnsJrA8qc4Lw3Uub3X6g@mail.gmail.com>
 <CA+_2Pn2GmBdiJR71mfEErfsAjHqod2tjQXh6gB5KSO0VX=Aa+Q@mail.gmail.com>
 <CALO6Ft6jzK4tMWyhuEjY2geVUiu48-QDNdfLmopJvZbRm3Sf1w@mail.gmail.com>
Message-ID: <CALxxJLSO+=y8ukLZakEv6gLqV0GzcYKtOLBn=bTjFiDWSghOgQ@mail.gmail.com>

It looks like you have multiple numpy versions installed. Did you uninstall
numpy, scipy with both pip and conda before building and installing numpy
and scipy from source? I presume that you also built numpy from clean
source without cache?

Since you are using conda environments, make sure that you activate it
before the build process and before launching python interpreter.

On Sun, May 14, 2017 at 4:37 PM, David Hagen <david at drhagen.com> wrote:

> I spoke too soon. I "successfully" compiled Numpy and Scipy from master
> and both can be imported. However, some Scipy items don't import. It
> doesn't really matter what exactly I import. If I run scipy.test()
> everything dies. Can someone interpret this error message?
>
> >>> from scipy.special import roots_legendre
> Traceback (most recent call last):
>   File "__init__.pxd", line 987, in numpy.import_array
> (scipy\special\_ufuncs_cx
> x.cxx:3729)
> RuntimeError: module compiled against API version 0xb but this version of
> numpy
> is 0xa
>
> During handling of the above exception, another exception occurred:
>
> Traceback (most recent call last):
>   File "<stdin>", line 1, in <module>
>   File "C:\Anaconda3\envs\numpy_scipy\lib\site-packages\scipy-1.0.
> 0.dev0+unknown
> -py3.6-win-amd64.egg\scipy\special\__init__.py", line 640, in <module>
>     from ._ufuncs import *
>   File "scipy\special\_ufuncs.pyx", line 1, in init scipy.special._ufuncs
> (scipy
> \special\_ufuncs.c:39403)
>   File "scipy\special\_ufuncs_extra_code_common.pxi", line 34, in init
> scipy.spe
> cial._ufuncs_cxx (scipy\special\_ufuncs_cxx.cxx:4376)
>   File "__init__.pxd", line 989, in numpy.import_array
> (scipy\special\_ufuncs_cx
> x.cxx:3772)
> ImportError: numpy.core.multiarray failed to import
>
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at python.org
> https://mail.python.org/mailman/listinfo/scipy-user
>
>
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From david at drhagen.com  Mon May 15 20:48:03 2017
From: david at drhagen.com (David Hagen)
Date: Mon, 15 May 2017 20:48:03 -0400
Subject: [SciPy-User] Install Scipy with Anaconda's MKL libraries
In-Reply-To: <CALxxJLSO+=y8ukLZakEv6gLqV0GzcYKtOLBn=bTjFiDWSghOgQ@mail.gmail.com>
References: <CALO6Ft47hEd8RMtHF2nRAji84nztS8h-hm-qyojwv3kZn0-Zkw@mail.gmail.com>
 <CALO6Ft55X=Q9hhPUNwn-pZ1JRS+FnGLxdubLph9qtN4WkQUtnw@mail.gmail.com>
 <CAHCaCk+tYbh8AXuhWkTAtnjfCTqFjt4rFT9_XOP++bArRmsepQ@mail.gmail.com>
 <CALxxJLTUAjOupyKROxaYPAr+7VOTFm5kj70mEZApZUpAzgE=pA@mail.gmail.com>
 <CALO6Ft7K89B5Qd+tOJpmvKrN7YmBm0JKSDZhM_1aegN457HfWA@mail.gmail.com>
 <CAKWqKotdbQYdabmU2UKR2BgxfpiJBLXyQ5vdCSfA0t7iUdM_DA@mail.gmail.com>
 <CALO6Ft71-SfExyVQvPswoCN491qojfaUH9Y3k_CUE-jc1vJayQ@mail.gmail.com>
 <CALxxJLQoesk0ixxFZJHGdtfw3WQV8AftXVnshEk6TiaA_+r=gw@mail.gmail.com>
 <CALO6Ft6fw66b1LahGR_p=80-xxJhYiwZNEZFLN1SMhAh65NB_g@mail.gmail.com>
 <CALxxJLQv-yZePFASgvQS8+YNMZU4BfCH1-bj0CL+wxeBpOM=TA@mail.gmail.com>
 <CALO6Ft6PNWL7G37q-tsfasGoxUk7JdX6iw38-_2-Ug2CFY5vVA@mail.gmail.com>
 <590B2AB5.9080505@sbcglobal.net>
 <CALO6Ft5zFxPjoDnY13OM+=4kQvjmj3bfk5ogEPSv=1Sx7YeCzA@mail.gmail.com>
 <590B6246.6090607@sbcglobal.net>
 <CALO6Ft53irwAsYuAo=v5fBZPX6ZeP=kkAqMuVrLdq4org7tt=Q@mail.gmail.com>
 <CALO6Ft4+B4MULYefCdxrifuEPm0sF5Jq-Y0+XKuPgFcNPLxeXA@mail.gmail.com>
 <CA+_2Pn2zur6q-zRpxDiH9B3YtgHt4hWL0v5OueC9nPufidM8vg@mail.gmail.com>
 <CALxxJLS=WN7mSVFbLYBJxqw0ijtcB=5vwOFnQtJOBAE-gLFgiA@mail.gmail.com>
 <CALO6Ft7+bkDwQc7Js0Sk4OtiSR11zmHhJE2XOigXcuy0j2VjEA@mail.gmail.com>
 <CALO6Ft4_Da7xm7THhfq0cDkszd8d8zEXqM79OBJS+2w5zUES+A@mail.gmail.com>
 <CALO6Ft56XqM7JnHfsh8yEJs2Q1ebJwsnsJrA8qc4Lw3Uub3X6g@mail.gmail.com>
 <CA+_2Pn2GmBdiJR71mfEErfsAjHqod2tjQXh6gB5KSO0VX=Aa+Q@mail.gmail.com>
 <CALO6Ft6jzK4tMWyhuEjY2geVUiu48-QDNdfLmopJvZbRm3Sf1w@mail.gmail.com>
 <CALxxJLSO+=y8ukLZakEv6gLqV0GzcYKtOLBn=bTjFiDWSghOgQ@mail.gmail.com>
Message-ID: <CALO6Ft4dLmAZ0CQQYvZ-F0nKpjMVifVsnuZtToNjNsy+YujU6A@mail.gmail.com>

> It looks like you have multiple numpy versions installed. Did you
uninstall numpy, scipy with both pip and conda before building and
installing numpy and scipy from source? I presume that you also built numpy
from clean source without cache?

I have numpy installed in a separate conda environment because I don't want
to lose my production environment while playing with Numpy and Scipy
master. I got this error after creating a fresh conda environment,
activating it, conda installing python and cython in it, and then running
setup.py on freshly downloaded numpy and scipy master folders. How can I
check if my other Numpy copies are visible in the search path for modules?
Is there a command I can run to diagnose the version compatibility of the
numpy and scipy modules that are imported and where they are located?
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From david at drhagen.com  Wed May 17 20:40:58 2017
From: david at drhagen.com (David Hagen)
Date: Wed, 17 May 2017 20:40:58 -0400
Subject: [SciPy-User] Install Scipy with Anaconda's MKL libraries
In-Reply-To: <CALO6Ft4dLmAZ0CQQYvZ-F0nKpjMVifVsnuZtToNjNsy+YujU6A@mail.gmail.com>
References: <CALO6Ft47hEd8RMtHF2nRAji84nztS8h-hm-qyojwv3kZn0-Zkw@mail.gmail.com>
 <CALO6Ft55X=Q9hhPUNwn-pZ1JRS+FnGLxdubLph9qtN4WkQUtnw@mail.gmail.com>
 <CAHCaCk+tYbh8AXuhWkTAtnjfCTqFjt4rFT9_XOP++bArRmsepQ@mail.gmail.com>
 <CALxxJLTUAjOupyKROxaYPAr+7VOTFm5kj70mEZApZUpAzgE=pA@mail.gmail.com>
 <CALO6Ft7K89B5Qd+tOJpmvKrN7YmBm0JKSDZhM_1aegN457HfWA@mail.gmail.com>
 <CAKWqKotdbQYdabmU2UKR2BgxfpiJBLXyQ5vdCSfA0t7iUdM_DA@mail.gmail.com>
 <CALO6Ft71-SfExyVQvPswoCN491qojfaUH9Y3k_CUE-jc1vJayQ@mail.gmail.com>
 <CALxxJLQoesk0ixxFZJHGdtfw3WQV8AftXVnshEk6TiaA_+r=gw@mail.gmail.com>
 <CALO6Ft6fw66b1LahGR_p=80-xxJhYiwZNEZFLN1SMhAh65NB_g@mail.gmail.com>
 <CALxxJLQv-yZePFASgvQS8+YNMZU4BfCH1-bj0CL+wxeBpOM=TA@mail.gmail.com>
 <CALO6Ft6PNWL7G37q-tsfasGoxUk7JdX6iw38-_2-Ug2CFY5vVA@mail.gmail.com>
 <590B2AB5.9080505@sbcglobal.net>
 <CALO6Ft5zFxPjoDnY13OM+=4kQvjmj3bfk5ogEPSv=1Sx7YeCzA@mail.gmail.com>
 <590B6246.6090607@sbcglobal.net>
 <CALO6Ft53irwAsYuAo=v5fBZPX6ZeP=kkAqMuVrLdq4org7tt=Q@mail.gmail.com>
 <CALO6Ft4+B4MULYefCdxrifuEPm0sF5Jq-Y0+XKuPgFcNPLxeXA@mail.gmail.com>
 <CA+_2Pn2zur6q-zRpxDiH9B3YtgHt4hWL0v5OueC9nPufidM8vg@mail.gmail.com>
 <CALxxJLS=WN7mSVFbLYBJxqw0ijtcB=5vwOFnQtJOBAE-gLFgiA@mail.gmail.com>
 <CALO6Ft7+bkDwQc7Js0Sk4OtiSR11zmHhJE2XOigXcuy0j2VjEA@mail.gmail.com>
 <CALO6Ft4_Da7xm7THhfq0cDkszd8d8zEXqM79OBJS+2w5zUES+A@mail.gmail.com>
 <CALO6Ft56XqM7JnHfsh8yEJs2Q1ebJwsnsJrA8qc4Lw3Uub3X6g@mail.gmail.com>
 <CA+_2Pn2GmBdiJR71mfEErfsAjHqod2tjQXh6gB5KSO0VX=Aa+Q@mail.gmail.com>
 <CALO6Ft6jzK4tMWyhuEjY2geVUiu48-QDNdfLmopJvZbRm3Sf1w@mail.gmail.com>
 <CALxxJLSO+=y8ukLZakEv6gLqV0GzcYKtOLBn=bTjFiDWSghOgQ@mail.gmail.com>
 <CALO6Ft4dLmAZ0CQQYvZ-F0nKpjMVifVsnuZtToNjNsy+YujU6A@mail.gmail.com>
Message-ID: <CALO6Ft5aOcz-VHseg8XAr_dJZKvOwSv2LXmZ=4Hn2RmkkExLBQ@mail.gmail.com>

> It looks like you have multiple numpy versions installed. Did you
uninstall numpy, scipy with both pip and conda before building and
installing numpy and scipy from source? I presume that you also built numpy
from clean source without cache?

I can confirm it was another version of Numpy installed alongside. Anaconda
had pulled it in when I installed matplotlib. Once I removed it and
installed matplotlib from pip, then everything worked fine.
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From charlesr.harris at gmail.com  Thu May 18 17:42:23 2017
From: charlesr.harris at gmail.com (Charles R Harris)
Date: Thu, 18 May 2017 15:42:23 -0600
Subject: [SciPy-User] NumPy 1.13.0rc2 released
Message-ID: <CAB6mnxLY6oLKQBAC3H0rib6vWR29oZsVQdPKqsqtzDwQAy_=9w@mail.gmail.com>

Hi All,

I'm pleased to announce the NumPy 1.13.0rc2 release. This release supports
Python 2.7 and 3.4-3.6 and contains many new features. It is one of the
most ambitious releases in the last several years. Some of the highlights
and new functions are

*Highlights*

   - Operations like ``a + b + c`` will reuse temporaries on some
   platforms, resulting in less memory use and faster execution.
   - Inplace operations check if inputs overlap outputs and create
   temporaries to avoid problems.
   - New __array_ufunc__ attribute provides improved ability for classes to
   override default ufunc behavior.
   - New np.block function for creating blocked arrays.


*New functions*

   - New ``np.positive`` ufunc.
   - New ``np.divmod`` ufunc provides more efficient divmod.
   - New ``np.isnat`` ufunc tests for NaT special values.
   - New ``np.heaviside`` ufunc computes the Heaviside function.
   - New ``np.isin`` function, improves on ``in1d``.
   - New ``np.block`` function for creating blocked arrays.
   - New ``PyArray_MapIterArrayCopyIfOverlap`` added to NumPy C-API.

Wheels for the pre-release are available on PyPI. Source tarballs,
zipfiles, release notes, and the changelog are available on github
<https://github.com/numpy/numpy/releases/tag/v1.13.0rc2>.

A total of 102 people contributed to this release.  People with a "+" by
their
names contributed a patch for the first time.

   - A. Jesse Jiryu Davis +
   - Alessandro Pietro Bardelli +
   - Alex Rothberg +
   - Alexander Shadchin
   - Allan Haldane
   - Andres Guzman-Ballen +
   - Antoine Pitrou
   - Antony Lee
   - B R S Recht +
   - Baurzhan Muftakhidinov +
   - Ben Rowland
   - Benda Xu +
   - Blake Griffith
   - Bradley Wogsland +
   - Brandon Carter +
   - CJ Carey
   - Charles Harris
   - Christoph Gohlke
   - Danny Hermes +
   - David Hagen +
   - David Nicholson +
   - Duke Vijitbenjaronk +
   - Egor Klenin +
   - Elliott Forney +
   - Elliott M Forney +
   - Endolith
   - Eric Wieser
   - Erik M. Bray
   - Eugene +
   - Evan Limanto +
   - Felix Berkenkamp +
   - Fran?ois Bissey +
   - Frederic Bastien
   - Greg Young
   - Gregory R. Lee
   - Importance of Being Ernest +
   - Jaime Fernandez
   - Jakub Wilk +
   - James Cowgill +
   - James Sanders
   - Jean Utke +
   - Jesse Thoren +
   - Jim Crist +
   - Joerg Behrmann +
   - John Kirkham
   - Jonathan Helmus
   - Jonathan L Long
   - Jonathan Tammo Siebert +
   - Joseph Fox-Rabinovitz
   - Joshua Loyal +
   - Juan Nunez-Iglesias +
   - Julian Taylor
   - Kirill Balunov +
   - Likhith Chitneni +
   - Lo?c Est?ve
   - Mads Ohm Larsen
   - Marein K?nings +
   - Marten van Kerkwijk
   - Martin Thoma
   - Martino Sorbaro +
   - Marvin Schmidt +
   - Matthew Brett
   - Matthias Bussonnier +
   - Matthias C. M. Troffaes +
   - Matti Picus
   - Michael Seifert
   - Mikhail Pak +
   - Mortada Mehyar
   - Nathaniel J. Smith
   - Nick Papior
   - Oscar Villellas +
   - Pauli Virtanen
   - Pavel Potocek
   - Pete Peeradej Tanruangporn +
   - Philipp A +
   - Ralf Gommers
   - Robert Kern
   - Roland Kaufmann +
   - Ronan Lamy
   - Sami Salonen +
   - Sanchez Gonzalez Alvaro
   - Sebastian Berg
   - Shota Kawabuchi
   - Simon Gibbons
   - Stefan Otte
   - Stefan Peterson +
   - Stephan Hoyer
   - S?ren Fuglede J?rgensen +
   - Takuya Akiba
   - Tom Boyd +
   - Ville Skytt? +
   - Warren Weckesser
   - Wendell Smith
   - Yu Feng
   - Zixu Zhao +
   - Z? Vin?cius +
   - aha66 +
   - drabach +
   - drlvk +
   - jsh9 +
   - solarjoe +
   - zengi +

Cheers,

Chuck
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From cimrman3 at ntc.zcu.cz  Fri May 19 04:52:37 2017
From: cimrman3 at ntc.zcu.cz (Robert Cimrman)
Date: Fri, 19 May 2017 10:52:37 +0200
Subject: [SciPy-User] ANN: SfePy 2017.2
Message-ID: <dd27e819-98db-d1bc-c1fd-cb86bcc29133@ntc.zcu.cz>

I am pleased to announce release 2017.2 of SfePy.

Description
-----------

SfePy (simple finite elements in Python) is a software for solving systems of
coupled partial differential equations by the finite element method or by the
isogeometric analysis (limited support). It is distributed under the new BSD
license.

Home page: http://sfepy.org
Mailing list: https://mail.python.org/mm3/mailman3/lists/sfepy.python.org/
Git (source) repository, issue tracker: https://github.com/sfepy/sfepy

Highlights of this release
--------------------------

- simplified and unified implementation of some homogenized coefficients
- support for saving custom structured data to HDF5 files
- new tutorial on preparing meshes using FreeCAD/OpenSCAD and Gmsh

For full release notes see http://docs.sfepy.org/doc/release_notes.html#id1
(rather long and technical).

Cheers,
Robert Cimrman

---

Contributors to this release in alphabetical order:

Robert Cimrman
Jan Heczko
Lubos Kejzlar
Vladimir Lukes
Matyas Novak

From schilling.klaus at web.de  Fri May 19 12:59:20 2017
From: schilling.klaus at web.de (klaus schilling)
Date: Fri, 19 May 2017 18:59:20 +0200
Subject: [SciPy-User] Alergia algorithm
Message-ID: <87tw4gbx5j.fsf@web.de>

is there already a module implementing, on top of sci-py, the
Alergia-algorithm for the construction of stochastic finite automata
from prefix tree models?

Klaus Schilling

From kirillbalunov at gmail.com  Thu May 25 16:48:30 2017
From: kirillbalunov at gmail.com (Kirill Balunov)
Date: Thu, 25 May 2017 23:48:30 +0300
Subject: [SciPy-User] scipy optimization: inequality constraint type
Message-ID: <CABwCVQCSRGtgGmicUOyfzRxqQxE--7uSHZYt28UupDcKGzEM3w@mail.gmail.com>

Hi,
I've tried some scipy optimization routines, they work great!!! But I
wondered, why historically for inequality constraints the type was chosen
to be "greater than or equal" type? This is inconsistent with the classical
formulation of non-linear programming problems.

Thanks!

-gdg
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From eulergaussriemann at gmail.com  Thu May 25 17:07:54 2017
From: eulergaussriemann at gmail.com (David Goldsmith)
Date: Thu, 25 May 2017 21:07:54 +0000
Subject: [SciPy-User] scipy optimization: inequality constraint type
In-Reply-To: <CABwCVQCSRGtgGmicUOyfzRxqQxE--7uSHZYt28UupDcKGzEM3w@mail.gmail.com>
References: <CABwCVQCSRGtgGmicUOyfzRxqQxE--7uSHZYt28UupDcKGzEM3w@mail.gmail.com>
Message-ID: <CA+_2Pn2yyk3tZfeQNQUg0-Pxwg6DFArKNxrhVcn=1x_2z2R+2g@mail.gmail.com>

I would assume that it's because the "or equal to" option allows greater
flexibility: if that criterion is allowed by the problem, and the algorithm
can find such a solution (e.g., by checking all such corner points), then
that's better than not even providing the option, yes?  And if you try to
"rig" the strict inequality approach by allowing for a little extra room
around the corner, then the exact solution might not be found, yes?
Indeed, i'm no expert, but i did have a course in this, and IIRC, if your
problem allows for equality, then you _must_ separately check all the
corners, yes?  (In other words, what you state about the "classical
formulation" is not what i was taught: I was taught that the specifics of
the problem dictate whether any given inequality should be strict or
"weak.")

DLG

On Thu, May 25, 2017 at 1:49 PM Kirill Balunov <kirillbalunov at gmail.com>
wrote:

> Hi,
> I've tried some scipy optimization routines, they work great!!! But I
> wondered, why historically for inequality constraints the type was chosen
> to be "greater than or equal" type? This is inconsistent with the classical
> formulation of non-linear programming problems.
>
> Thanks!
>
> -gdg
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at python.org
> https://mail.python.org/mailman/listinfo/scipy-user
>
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From kirillbalunov at gmail.com  Thu May 25 17:28:45 2017
From: kirillbalunov at gmail.com (Kirill Balunov)
Date: Fri, 26 May 2017 00:28:45 +0300
Subject: [SciPy-User] scipy optimization: inequality constraint type
In-Reply-To: <CA+_2Pn2yyk3tZfeQNQUg0-Pxwg6DFArKNxrhVcn=1x_2z2R+2g@mail.gmail.com>
References: <CABwCVQCSRGtgGmicUOyfzRxqQxE--7uSHZYt28UupDcKGzEM3w@mail.gmail.com>
 <CA+_2Pn2yyk3tZfeQNQUg0-Pxwg6DFArKNxrhVcn=1x_2z2R+2g@mail.gmail.com>
Message-ID: <CABwCVQBe-h=swxPaSC3sJVVQJJEGP2X4XDHW+XqCemAZM+cSrQ@mail.gmail.com>

I'm sorry, perhaps I should more clearly formulate the question. David you
are totally right. What I mean by classical: is "less than or equal"
type. Of course it's a question of a sign, but still...

-gdg

2017-05-26 0:07 GMT+03:00 David Goldsmith <eulergaussriemann at gmail.com>:

> I would assume that it's because the "or equal to" option allows greater
> flexibility: if that criterion is allowed by the problem, and the algorithm
> can find such a solution (e.g., by checking all such corner points), then
> that's better than not even providing the option, yes?  And if you try to
> "rig" the strict inequality approach by allowing for a little extra room
> around the corner, then the exact solution might not be found, yes?
> Indeed, i'm no expert, but i did have a course in this, and IIRC, if your
> problem allows for equality, then you _must_ separately check all the
> corners, yes?  (In other words, what you state about the "classical
> formulation" is not what i was taught: I was taught that the specifics of
> the problem dictate whether any given inequality should be strict or
> "weak.")
>
> DLG
>
> On Thu, May 25, 2017 at 1:49 PM Kirill Balunov <kirillbalunov at gmail.com>
> wrote:
>
>> Hi,
>> I've tried some scipy optimization routines, they work great!!! But I
>> wondered, why historically for inequality constraints the type was chosen
>> to be "greater than or equal" type? This is inconsistent with the classical
>> formulation of non-linear programming problems.
>>
>> Thanks!
>>
>> -gdg
>> _______________________________________________
>> SciPy-User mailing list
>> SciPy-User at python.org
>> https://mail.python.org/mailman/listinfo/scipy-user
>>
>
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at python.org
> https://mail.python.org/mailman/listinfo/scipy-user
>
>
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From eulergaussriemann at gmail.com  Thu May 25 17:36:01 2017
From: eulergaussriemann at gmail.com (David Goldsmith)
Date: Thu, 25 May 2017 21:36:01 +0000
Subject: [SciPy-User] scipy optimization: inequality constraint type
In-Reply-To: <CABwCVQBe-h=swxPaSC3sJVVQJJEGP2X4XDHW+XqCemAZM+cSrQ@mail.gmail.com>
References: <CABwCVQCSRGtgGmicUOyfzRxqQxE--7uSHZYt28UupDcKGzEM3w@mail.gmail.com>
 <CA+_2Pn2yyk3tZfeQNQUg0-Pxwg6DFArKNxrhVcn=1x_2z2R+2g@mail.gmail.com>
 <CABwCVQBe-h=swxPaSC3sJVVQJJEGP2X4XDHW+XqCemAZM+cSrQ@mail.gmail.com>
Message-ID: <CA+_2Pn3zwM5bfvnzmtq33cquDN9YTWO10QSPL7Lmzp_F=fCgWg@mail.gmail.com>

Ah, yes, that convention i am familiar with; maybe to accommodate the
"inflexibility" of less numerate potential users (who may be fixated, e.g.,
on wanting to maximize profit or yield)?  Of course, at some point such
people may want to minimize something, so hopefully they have someone
around to tell them to simply multiply by negative one. ;-)

DLG

On Thu, May 25, 2017 at 2:29 PM Kirill Balunov <kirillbalunov at gmail.com>
wrote:

> I'm sorry, perhaps I should more clearly formulate the question. David you
> are totally right. What I mean by classical: is "less than or equal"
> type. Of course it's a question of a sign, but still...
>
> -gdg
>
> 2017-05-26 0:07 GMT+03:00 David Goldsmith <eulergaussriemann at gmail.com>:
>
>> I would assume that it's because the "or equal to" option allows greater
>> flexibility: if that criterion is allowed by the problem, and the algorithm
>> can find such a solution (e.g., by checking all such corner points), then
>> that's better than not even providing the option, yes?  And if you try to
>> "rig" the strict inequality approach by allowing for a little extra room
>> around the corner, then the exact solution might not be found, yes?
>> Indeed, i'm no expert, but i did have a course in this, and IIRC, if your
>> problem allows for equality, then you _must_ separately check all the
>> corners, yes?  (In other words, what you state about the "classical
>> formulation" is not what i was taught: I was taught that the specifics of
>> the problem dictate whether any given inequality should be strict or
>> "weak.")
>>
>> DLG
>>
>> On Thu, May 25, 2017 at 1:49 PM Kirill Balunov <kirillbalunov at gmail.com>
>> wrote:
>>
>>> Hi,
>>> I've tried some scipy optimization routines, they work great!!! But I
>>> wondered, why historically for inequality constraints the type was chosen
>>> to be "greater than or equal" type? This is inconsistent with the classical
>>> formulation of non-linear programming problems.
>>>
>>> Thanks!
>>>
>>> -gdg
>>> _______________________________________________
>>> SciPy-User mailing list
>>> SciPy-User at python.org
>>> https://mail.python.org/mailman/listinfo/scipy-user
>>>
>>
>> _______________________________________________
>> SciPy-User mailing list
>> SciPy-User at python.org
>> https://mail.python.org/mailman/listinfo/scipy-user
>>
>>
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at python.org
> https://mail.python.org/mailman/listinfo/scipy-user
>
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From kirillbalunov at gmail.com  Thu May 25 17:53:03 2017
From: kirillbalunov at gmail.com (Kirill Balunov)
Date: Fri, 26 May 2017 00:53:03 +0300
Subject: [SciPy-User] scipy optimization: inequality constraint type
In-Reply-To: <CA+_2Pn3zwM5bfvnzmtq33cquDN9YTWO10QSPL7Lmzp_F=fCgWg@mail.gmail.com>
References: <CABwCVQCSRGtgGmicUOyfzRxqQxE--7uSHZYt28UupDcKGzEM3w@mail.gmail.com>
 <CA+_2Pn2yyk3tZfeQNQUg0-Pxwg6DFArKNxrhVcn=1x_2z2R+2g@mail.gmail.com>
 <CABwCVQBe-h=swxPaSC3sJVVQJJEGP2X4XDHW+XqCemAZM+cSrQ@mail.gmail.com>
 <CA+_2Pn3zwM5bfvnzmtq33cquDN9YTWO10QSPL7Lmzp_F=fCgWg@mail.gmail.com>
Message-ID: <CABwCVQAC0wFoTTT13czxzyGRXKuexOETybLVf3tW0wiJPdW0zQ@mail.gmail.com>

No, no, the constraints only affect the feseable set of the problem. Min or
Max depends on the sign of objective function. From mathemtical point of
view, the problem is that the KKT conditions are derived for standard
formulation (with "less than ..") of NLP.

Cheers,

-gdg


2017-05-26 0:36 GMT+03:00 David Goldsmith <eulergaussriemann at gmail.com>:

> Ah, yes, that convention i am familiar with; maybe to accommodate the
> "inflexibility" of less numerate potential users (who may be fixated, e.g.,
> on wanting to maximize profit or yield)?  Of course, at some point such
> people may want to minimize something, so hopefully they have someone
> around to tell them to simply multiply by negative one. ;-)
>
> DLG
>
> On Thu, May 25, 2017 at 2:29 PM Kirill Balunov <kirillbalunov at gmail.com>
> wrote:
>
>> I'm sorry, perhaps I should more clearly formulate the question. David
>> you are totally right. What I mean by classical: is "less than or equal"
>> type. Of course it's a question of a sign, but still...
>>
>> -gdg
>>
>> 2017-05-26 0:07 GMT+03:00 David Goldsmith <eulergaussriemann at gmail.com>:
>>
>>> I would assume that it's because the "or equal to" option allows greater
>>> flexibility: if that criterion is allowed by the problem, and the algorithm
>>> can find such a solution (e.g., by checking all such corner points), then
>>> that's better than not even providing the option, yes?  And if you try to
>>> "rig" the strict inequality approach by allowing for a little extra room
>>> around the corner, then the exact solution might not be found, yes?
>>> Indeed, i'm no expert, but i did have a course in this, and IIRC, if your
>>> problem allows for equality, then you _must_ separately check all the
>>> corners, yes?  (In other words, what you state about the "classical
>>> formulation" is not what i was taught: I was taught that the specifics of
>>> the problem dictate whether any given inequality should be strict or
>>> "weak.")
>>>
>>> DLG
>>>
>>> On Thu, May 25, 2017 at 1:49 PM Kirill Balunov <kirillbalunov at gmail.com>
>>> wrote:
>>>
>>>> Hi,
>>>> I've tried some scipy optimization routines, they work great!!! But I
>>>> wondered, why historically for inequality constraints the type was chosen
>>>> to be "greater than or equal" type? This is inconsistent with the classical
>>>> formulation of non-linear programming problems.
>>>>
>>>> Thanks!
>>>>
>>>> -gdg
>>>> _______________________________________________
>>>> SciPy-User mailing list
>>>> SciPy-User at python.org
>>>> https://mail.python.org/mailman/listinfo/scipy-user
>>>>
>>>
>>> _______________________________________________
>>> SciPy-User mailing list
>>> SciPy-User at python.org
>>> https://mail.python.org/mailman/listinfo/scipy-user
>>>
>>>
>> _______________________________________________
>> SciPy-User mailing list
>> SciPy-User at python.org
>> https://mail.python.org/mailman/listinfo/scipy-user
>>
>
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at python.org
> https://mail.python.org/mailman/listinfo/scipy-user
>
>
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From eulergaussriemann at gmail.com  Thu May 25 18:03:46 2017
From: eulergaussriemann at gmail.com (David Goldsmith)
Date: Thu, 25 May 2017 22:03:46 +0000
Subject: [SciPy-User] scipy optimization: inequality constraint type
In-Reply-To: <CABwCVQAC0wFoTTT13czxzyGRXKuexOETybLVf3tW0wiJPdW0zQ@mail.gmail.com>
References: <CABwCVQCSRGtgGmicUOyfzRxqQxE--7uSHZYt28UupDcKGzEM3w@mail.gmail.com>
 <CA+_2Pn2yyk3tZfeQNQUg0-Pxwg6DFArKNxrhVcn=1x_2z2R+2g@mail.gmail.com>
 <CABwCVQBe-h=swxPaSC3sJVVQJJEGP2X4XDHW+XqCemAZM+cSrQ@mail.gmail.com>
 <CA+_2Pn3zwM5bfvnzmtq33cquDN9YTWO10QSPL7Lmzp_F=fCgWg@mail.gmail.com>
 <CABwCVQAC0wFoTTT13czxzyGRXKuexOETybLVf3tW0wiJPdW0zQ@mail.gmail.com>
Message-ID: <CA+_2Pn0B=dsyuCz_N9zpTa=10W6dyB=tLEAnWpgKR8u8ks=T1Q@mail.gmail.com>

The KKT reference exceeds my numeracy...

Anyway, i doubt this is the case, but if it's really a problem, you can
always write wrappers to automate the desired transformations, yes?

DLG

On The, May 25, 2017 at 2:53 PM Kirill Balunov <kirillbalunov at gmail.com>
wrote:

> No, no, the constraints only affect the feseable set of the problem. Min
> or Max depends on the sign of objective function. From mathemtical point of
> view, the problem is that the KKT conditions are derived for standard
> formulation (with "less than ..") of NLP.
>
> Cheers,
>
> -gdg
>
>
> 2017-05-26 0:36 GMT+03:00 David Goldsmith <eulergaussriemann at gmail.com>:
>
>> Ah, yes, that convention i am familiar with; maybe to accommodate the
>> "inflexibility" of less numerate potential users (who may be fixated, e.g.,
>> on wanting to maximize profit or yield)?  Of course, at some point such
>> people may want to minimize something, so hopefully they have someone
>> around to tell them to simply multiply by negative one. ;-)
>>
>> DLG
>>
>> On Thu, May 25, 2017 at 2:29 PM Kirill Balunov <kirillbalunov at gmail.com>
>> wrote:
>>
>>> I'm sorry, perhaps I should more clearly formulate the question. David
>>> you are totally right. What I mean by classical: is "less than or equal"
>>> type. Of course it's a question of a sign, but still...
>>>
>>> -gdg
>>>
>>> 2017-05-26 0:07 GMT+03:00 David Goldsmith <eulergaussriemann at gmail.com>:
>>>
>>>> I would assume that it's because the "or equal to" option allows
>>>> greater flexibility: if that criterion is allowed by the problem, and the
>>>> algorithm can find such a solution (e.g., by checking all such corner
>>>> points), then that's better than not even providing the option, yes?  And
>>>> if you try to "rig" the strict inequality approach by allowing for a little
>>>> extra room around the corner, then the exact solution might not be found,
>>>> yes?  Indeed, i'm no expert, but i did have a course in this, and IIRC, if
>>>> your problem allows for equality, then you _must_ separately check all the
>>>> corners, yes?  (In other words, what you state about the "classical
>>>> formulation" is not what i was taught: I was taught that the specifics of
>>>> the problem dictate whether any given inequality should be strict or
>>>> "weak.")
>>>>
>>>> DLG
>>>>
>>>> On Thu, May 25, 2017 at 1:49 PM Kirill Balunov <kirillbalunov at gmail.com>
>>>> wrote:
>>>>
>>>>> Hi,
>>>>> I've tried some scipy optimization routines, they work great!!! But I
>>>>> wondered, why historically for inequality constraints the type was chosen
>>>>> to be "greater than or equal" type? This is inconsistent with the classical
>>>>> formulation of non-linear programming problems.
>>>>>
>>>>> Thanks!
>>>>>
>>>>> -gdg
>>>>> _______________________________________________
>>>>> SciPy-User mailing list
>>>>> SciPy-User at python.org
>>>>> https://mail.python.org/mailman/listinfo/scipy-user
>>>>>
>>>>
>>>> _______________________________________________
>>>> SciPy-User mailing list
>>>> SciPy-User at python.org
>>>> https://mail.python.org/mailman/listinfo/scipy-user
>>>>
>>>>
>>> _______________________________________________
>>> SciPy-User mailing list
>>> SciPy-User at python.org
>>> https://mail.python.org/mailman/listinfo/scipy-user
>>>
>>
>> _______________________________________________
>> SciPy-User mailing list
>> SciPy-User at python.org
>> https://mail.python.org/mailman/listinfo/scipy-user
>>
>>
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at python.org
> https://mail.python.org/mailman/listinfo/scipy-user
>
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From kirillbalunov at gmail.com  Fri May 26 07:18:26 2017
From: kirillbalunov at gmail.com (Kirill Balunov)
Date: Fri, 26 May 2017 14:18:26 +0300
Subject: [SciPy-User] scipy optimization: inequality constraint type
In-Reply-To: <CA+_2Pn0B=dsyuCz_N9zpTa=10W6dyB=tLEAnWpgKR8u8ks=T1Q@mail.gmail.com>
References: <CABwCVQCSRGtgGmicUOyfzRxqQxE--7uSHZYt28UupDcKGzEM3w@mail.gmail.com>
 <CA+_2Pn2yyk3tZfeQNQUg0-Pxwg6DFArKNxrhVcn=1x_2z2R+2g@mail.gmail.com>
 <CABwCVQBe-h=swxPaSC3sJVVQJJEGP2X4XDHW+XqCemAZM+cSrQ@mail.gmail.com>
 <CA+_2Pn3zwM5bfvnzmtq33cquDN9YTWO10QSPL7Lmzp_F=fCgWg@mail.gmail.com>
 <CABwCVQAC0wFoTTT13czxzyGRXKuexOETybLVf3tW0wiJPdW0zQ@mail.gmail.com>
 <CA+_2Pn0B=dsyuCz_N9zpTa=10W6dyB=tLEAnWpgKR8u8ks=T1Q@mail.gmail.com>
Message-ID: <CABwCVQBtb5MVxmu1znafQ5kZ1WNL41GSmiAEtBk4xrzo+WkPqw@mail.gmail.com>

David, KKT conditions are first-order necessary conditions which are
applicable if some assumptions are satisfied (regularity, continuity,..).
The points which satisfy KKT are said to be stationary (or candidate)
points (min, max or inflation). Internally this conditions use gradient of
Lagrange function -> c^T * x + lambda^T * h(x) + mu^T * g(x) (this is
classical notation), where h(x) are equality and g(x) are inequality (g(x)
<= 0) constraints respectively. Also there is a restriction among others
for `mu` to be non-negative. This is classical formulation of NLP (which
looks for minimum). Of course mathematics is simply a human game with some
rules. So you can choose from four cases (for "inequality term"):
1) +  mu^T * g(x),  mu >= 0,  g(x) <=0   (classical)
2) -  mu^T * g(x), mu >= 0, g(x) >= 0
3) + mu^T * g(x),  mu <= 0,  g(x) >= 0    ( awkward)
4) - mu^T * g(x),  mu => 0,  g(x) <= 0    (awkward)

T?e last two are awkward they roughly crash the dual problem principle (the
related maximization problem but in Lagrange multipliers). The first two
look good but the  the second one is very unusual. That is why I ask about
historical reasons, why this form was chosen?

-gdg



2017-05-26 1:03 GMT+03:00 David Goldsmith <eulergaussriemann at gmail.com>:

> The KKT reference exceeds my numeracy...
>
> Anyway, i doubt this is the case, but if it's really a problem, you can
> always write wrappers to automate the desired transformations, yes?
>
> DLG
>
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From kirillbalunov at gmail.com  Fri May 26 07:39:04 2017
From: kirillbalunov at gmail.com (Kirill Balunov)
Date: Fri, 26 May 2017 14:39:04 +0300
Subject: [SciPy-User] scipy optimization: inequality constraint type
In-Reply-To: <CABwCVQBtb5MVxmu1znafQ5kZ1WNL41GSmiAEtBk4xrzo+WkPqw@mail.gmail.com>
References: <CABwCVQCSRGtgGmicUOyfzRxqQxE--7uSHZYt28UupDcKGzEM3w@mail.gmail.com>
 <CA+_2Pn2yyk3tZfeQNQUg0-Pxwg6DFArKNxrhVcn=1x_2z2R+2g@mail.gmail.com>
 <CABwCVQBe-h=swxPaSC3sJVVQJJEGP2X4XDHW+XqCemAZM+cSrQ@mail.gmail.com>
 <CA+_2Pn3zwM5bfvnzmtq33cquDN9YTWO10QSPL7Lmzp_F=fCgWg@mail.gmail.com>
 <CABwCVQAC0wFoTTT13czxzyGRXKuexOETybLVf3tW0wiJPdW0zQ@mail.gmail.com>
 <CA+_2Pn0B=dsyuCz_N9zpTa=10W6dyB=tLEAnWpgKR8u8ks=T1Q@mail.gmail.com>
 <CABwCVQBtb5MVxmu1znafQ5kZ1WNL41GSmiAEtBk4xrzo+WkPqw@mail.gmail.com>
Message-ID: <CABwCVQCLJVOx-aeWud1OZf8sc8ppZgY-Lpw=+AEoFn8MQiagOw@mail.gmail.com>

I'm sorry, I understand that's because the only solver for constrained
optimization in scipy is *SLSQP*?

But, It is not good idea to write about a particular case as if it is a
general ->

In documentation -> "In general, the optimization problems are of the form:
..."
https://docs.scipy.org/doc/scipy-0.18.1/reference/generated/scipy.optimize.minimize.html

Which is not true in general :)


-gdg

2017-05-26 14:18 GMT+03:00 Kirill Balunov <kirillbalunov at gmail.com>:

> David, KKT conditions are first-order necessary conditions which are
> applicable if some assumptions are satisfied (regularity, continuity,..).
> The points which satisfy KKT are said to be stationary (or candidate)
> points (min, max or inflation). Internally this conditions use gradient of
> Lagrange function -> c^T * x + lambda^T * h(x) + mu^T * g(x) (this is
> classical notation), where h(x) are equality and g(x) are inequality (g(x)
> <= 0) constraints respectively. Also there is a restriction among others
> for `mu` to be non-negative. This is classical formulation of NLP (which
> looks for minimum). Of course mathematics is simply a human game with some
> rules. So you can choose from four cases (for "inequality term"):
> 1) +  mu^T * g(x),  mu >= 0,  g(x) <=0   (classical)
> 2) -  mu^T * g(x), mu >= 0, g(x) >= 0
> 3) + mu^T * g(x),  mu <= 0,  g(x) >= 0    ( awkward)
> 4) - mu^T * g(x),  mu => 0,  g(x) <= 0    (awkward)
>
> T?e last two are awkward they roughly crash the dual problem principle
> (the related maximization problem but in Lagrange multipliers). The first
> two look good but the  the second one is very unusual. That is why I ask
> about historical reasons, why this form was chosen?
>
> -gdg
>
>
>
> 2017-05-26 1:03 GMT+03:00 David Goldsmith <eulergaussriemann at gmail.com>:
>
>> The KKT reference exceeds my numeracy...
>>
>> Anyway, i doubt this is the case, but if it's really a problem, you can
>> always write wrappers to automate the desired transformations, yes?
>>
>> DLG
>>
>
>
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From eulergaussriemann at gmail.com  Fri May 26 11:15:11 2017
From: eulergaussriemann at gmail.com (David Goldsmith)
Date: Fri, 26 May 2017 15:15:11 +0000
Subject: [SciPy-User] scipy optimization: inequality constraint type
In-Reply-To: <CABwCVQCLJVOx-aeWud1OZf8sc8ppZgY-Lpw=+AEoFn8MQiagOw@mail.gmail.com>
References: <CABwCVQCSRGtgGmicUOyfzRxqQxE--7uSHZYt28UupDcKGzEM3w@mail.gmail.com>
 <CA+_2Pn2yyk3tZfeQNQUg0-Pxwg6DFArKNxrhVcn=1x_2z2R+2g@mail.gmail.com>
 <CABwCVQBe-h=swxPaSC3sJVVQJJEGP2X4XDHW+XqCemAZM+cSrQ@mail.gmail.com>
 <CA+_2Pn3zwM5bfvnzmtq33cquDN9YTWO10QSPL7Lmzp_F=fCgWg@mail.gmail.com>
 <CABwCVQAC0wFoTTT13czxzyGRXKuexOETybLVf3tW0wiJPdW0zQ@mail.gmail.com>
 <CA+_2Pn0B=dsyuCz_N9zpTa=10W6dyB=tLEAnWpgKR8u8ks=T1Q@mail.gmail.com>
 <CABwCVQBtb5MVxmu1znafQ5kZ1WNL41GSmiAEtBk4xrzo+WkPqw@mail.gmail.com>
 <CABwCVQCLJVOx-aeWud1OZf8sc8ppZgY-Lpw=+AEoFn8MQiagOw@mail.gmail.com>
Message-ID: <CA+_2Pn0rEji_Qp_pUnLe=HidkKfMk=kWS9VDHWsQoAPFbr=fcw@mail.gmail.com>

It looks like it could simply be a typo: could you be troubled to file a
ticket?  Worst case: someone will explain, in that record, why it isn't a
typo; best case: it will get fixed.  Thanks for your conscientiousness.

DLG

On Fri, May 26, 2017 at 4:40 AM Kirill Balunov <kirillbalunov at gmail.com>
wrote:

> I'm sorry, I understand that's because the only solver for constrained
> optimization in scipy is *SLSQP*?
>
> But, It is not good idea to write about a particular case as if it is a
> general ->
>
> In documentation -> "In general, the optimization problems are of the
> form: ..."
>
> https://docs.scipy.org/doc/scipy-0.18.1/reference/generated/scipy.optimize.minimize.html
>
> Which is not true in general :)
>
>
> -gdg
>
> 2017-05-26 14:18 GMT+03:00 Kirill Balunov <kirillbalunov at gmail.com>:
>
>> David, KKT conditions are first-order necessary conditions which are
>> applicable if some assumptions are satisfied (regularity, continuity,..).
>> The points which satisfy KKT are said to be stationary (or candidate)
>> points (min, max or inflation). Internally this conditions use gradient of
>> Lagrange function -> c^T * x + lambda^T * h(x) + mu^T * g(x) (this is
>> classical notation), where h(x) are equality and g(x) are inequality (g(x)
>> <= 0) constraints respectively. Also there is a restriction among others
>> for `mu` to be non-negative. This is classical formulation of NLP (which
>> looks for minimum). Of course mathematics is simply a human game with some
>> rules. So you can choose from four cases (for "inequality term"):
>> 1) +  mu^T * g(x),  mu >= 0,  g(x) <=0   (classical)
>> 2) -  mu^T * g(x), mu >= 0, g(x) >= 0
>> 3) + mu^T * g(x),  mu <= 0,  g(x) >= 0    ( awkward)
>> 4) - mu^T * g(x),  mu => 0,  g(x) <= 0    (awkward)
>>
>> T?e last two are awkward they roughly crash the dual problem principle
>> (the related maximization problem but in Lagrange multipliers). The first
>> two look good but the  the second one is very unusual. That is why I ask
>> about historical reasons, why this form was chosen?
>>
>> -gdg
>>
>>
>>
>> 2017-05-26 1:03 GMT+03:00 David Goldsmith <eulergaussriemann at gmail.com>:
>>
>>> The KKT reference exceeds my numeracy...
>>>
>>> Anyway, i doubt this is the case, but if it's really a problem, you can
>>> always write wrappers to automate the desired transformations, yes?
>>>
>>> DLG
>>>
>>
>>
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at python.org
> https://mail.python.org/mailman/listinfo/scipy-user
>
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From edwardlrichards at gmail.com  Sat May 27 13:22:45 2017
From: edwardlrichards at gmail.com (Edward Richards)
Date: Sat, 27 May 2017 10:22:45 -0700
Subject: [SciPy-User] low memory labeled component clustering
Message-ID: <5929B5E5.8000408@gmail.com>

I would like a LCC algorithm that produces sparse results. What I have 
in mind would be similar to MATLAB's bwconncomp and return a sparse data 
structure instead of a dense label array. I am new to image processing, 
and have only a surface understanding of the LCC algorithm. I am hoping 
that the experts out there can let me know if my goal is tenable (or 
hopefully already implemented), before I dive too deeply into the 
ndimage.label source code.

I am working on a detection algorithm, and the data is 3D and can be 
very large in shape but sparse. I would like to process a bunch of data 
at once to avoid edge effects. Currently I am maxing out my computers 
RAM (32 GB) processing a (50, 200, 523264) element array. This array has 
only ~3 million non-zero elements, or 0.00056 sparsity. For each cluster 
I only need; the max value, it's location, and the number of elements.

Currently, the data is stored as a list of sparse matrices. Is it 
possible to have the only 3D representation of the data be a Boolean 
array that I pass to a clustering algorithm? For my problem, it is 
better to take reasonable performance hits than hold temporary arrays in 
memory.

Thank you,
Ned

From mainakjas at gmail.com  Sun May 28 19:58:46 2017
From: mainakjas at gmail.com (Mainak Jas)
Date: Mon, 29 May 2017 01:58:46 +0200
Subject: [SciPy-User] scale parameter at 0
Message-ID: <CANzrpG28k7oFaekLwSGSb5KL8xkoR8KNnsoJ9_bTkRYgZe08BQ@mail.gmail.com>

Hi everyone,

I was wondering if anyone had an insight into what happens in scipy when
the scale parameter becomes 0. In particular, I was looking into l?vy
stable distribution. If we do in scipy:

>>> from scipy.stats import levy_stable
>>> levy_stable.rvs(alpha=0.99, beta=1, scale=0, loc=1, size=5)

I get:

>>> array([ 1.,  1.,  1.,  1.,  1.])

But when I look in wikipedia
<https://en.wikipedia.org/wiki/L%C3%A9vy_distribution>, it tells me that
the scale parameter should be greater than 0. Maybe it's a more general
question for all distributions, but I'm interested in this one
particularly. Why doesn't scipy throw an error when scale parameter is 0?

Thanks.

Best regards,
Mainak
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From josef.pktd at gmail.com  Sun May 28 20:34:26 2017
From: josef.pktd at gmail.com (josef.pktd at gmail.com)
Date: Sun, 28 May 2017 20:34:26 -0400
Subject: [SciPy-User] scale parameter at 0
In-Reply-To: <CANzrpG28k7oFaekLwSGSb5KL8xkoR8KNnsoJ9_bTkRYgZe08BQ@mail.gmail.com>
References: <CANzrpG28k7oFaekLwSGSb5KL8xkoR8KNnsoJ9_bTkRYgZe08BQ@mail.gmail.com>
Message-ID: <CAMMTP+BDgCQuZegL1av2fwCrMabE7KT6n4GESmZe-cz19MeRxA@mail.gmail.com>

On Sun, May 28, 2017 at 7:58 PM, Mainak Jas <mainakjas at gmail.com> wrote:
> Hi everyone,
>
> I was wondering if anyone had an insight into what happens in scipy when the
> scale parameter becomes 0. In particular, I was looking into l?vy stable
> distribution. If we do in scipy:
>
>>>> from scipy.stats import levy_stable
>>>> levy_stable.rvs(alpha=0.99, beta=1, scale=0, loc=1, size=5)
>
> I get:
>
>>>> array([ 1.,  1.,  1.,  1.,  1.])
>
> But when I look in wikipedia, it tells me that the scale parameter should be
> greater than 0. Maybe it's a more general question for all distributions,
> but I'm interested in this one particularly. Why doesn't scipy throw an
> error when scale parameter is 0?

scale defines the spread of the distribution, and is equal to the
standard deviation in the normal distribution.

As the scale goes to zero, the distribution collapses to a single
point, i.e. a Dirac measure. Whether that makes numerical sense for a
distribution depends on the floating point computation details.

In general it can still be useful to allow for the degenerate case
with scale=0, but I don't remember much discussion about it. From what
I remember scale=0 produces for many methods the correct limit for
scale -> 0, then there is no real reason to exclude it.
(A long time ago there was a discussion about adding a degenerate one
point distribution, but it doesn't fit well in any of the current base
classes.)

Josef


>
> Thanks.
>
> Best regards,
> Mainak
>
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at python.org
> https://mail.python.org/mailman/listinfo/scipy-user
>

From mainakjas at gmail.com  Mon May 29 11:03:16 2017
From: mainakjas at gmail.com (Mainak Jas)
Date: Mon, 29 May 2017 17:03:16 +0200
Subject: [SciPy-User] scale parameter at 0
In-Reply-To: <CAMMTP+BDgCQuZegL1av2fwCrMabE7KT6n4GESmZe-cz19MeRxA@mail.gmail.com>
References: <CANzrpG28k7oFaekLwSGSb5KL8xkoR8KNnsoJ9_bTkRYgZe08BQ@mail.gmail.com>
 <CAMMTP+BDgCQuZegL1av2fwCrMabE7KT6n4GESmZe-cz19MeRxA@mail.gmail.com>
Message-ID: <CANzrpG2TCNn7vqbS8-VSsX2wef-Ouf7T8qxUaGTfxKd_ffqMcQ@mail.gmail.com>

Hi Josef,

scale defines the spread of the distribution, and is equal to the
> standard deviation in the normal distribution.
>
> As the scale goes to zero, the distribution collapses to a single
> point, i.e. a Dirac measure. Whether that makes numerical sense for a
> distribution depends on the floating point computation details.
>
> In general it can still be useful to allow for the degenerate case
> with scale=0, but I don't remember much discussion about it. From what
> I remember scale=0 produces for many methods the correct limit for
> scale -> 0, then there is no real reason to exclude it.
> (A long time ago there was a discussion about adding a degenerate one
> point distribution, but it doesn't fit well in any of the current base
> classes.)
>

Thanks a lot for the explanation. However, in this particular case for the
l?vy stable distribution, I am not sure if it produces the correct limit for
scale -> 0. I worked out the Math with a colleague which I provide here:
https://www.overleaf.com/read/qgwtvtrkcrqc. Please let me know if we
missed something.

Mainak


> Josef
>
>
> >
> > Thanks.
> >
> > Best regards,
> > Mainak
> >
> > _______________________________________________
> > SciPy-User mailing list
> > SciPy-User at python.org
> > https://mail.python.org/mailman/listinfo/scipy-user
> >
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at python.org
> https://mail.python.org/mailman/listinfo/scipy-user
>
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From davidmenhur at gmail.com  Mon May 29 11:33:44 2017
From: davidmenhur at gmail.com (=?UTF-8?B?RGHPgGlk?=)
Date: Mon, 29 May 2017 17:33:44 +0200
Subject: [SciPy-User] scale parameter at 0
In-Reply-To: <CANzrpG2TCNn7vqbS8-VSsX2wef-Ouf7T8qxUaGTfxKd_ffqMcQ@mail.gmail.com>
References: <CANzrpG28k7oFaekLwSGSb5KL8xkoR8KNnsoJ9_bTkRYgZe08BQ@mail.gmail.com>
 <CAMMTP+BDgCQuZegL1av2fwCrMabE7KT6n4GESmZe-cz19MeRxA@mail.gmail.com>
 <CANzrpG2TCNn7vqbS8-VSsX2wef-Ouf7T8qxUaGTfxKd_ffqMcQ@mail.gmail.com>
Message-ID: <CAJhcF=26uK5xyGf1n79-0guqf8_n3ceLEsf+Vv=ZeDMoDgzDJA@mail.gmail.com>

On 29 May 2017 at 17:03, Mainak Jas <mainakjas at gmail.com> wrote:

> I worked out the Math with a colleague which I provide here:
> https://www.overleaf.com/read/qgwtvtrkcrqc. Please let me know if we
> missed something.
>

You are using a property of the symetric stable distribution, but you
consider beta = 1, which is asymmetric.

/David.
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From mainakjas at gmail.com  Mon May 29 11:42:29 2017
From: mainakjas at gmail.com (Mainak Jas)
Date: Mon, 29 May 2017 17:42:29 +0200
Subject: [SciPy-User] scale parameter at 0
In-Reply-To: <CAJhcF=26uK5xyGf1n79-0guqf8_n3ceLEsf+Vv=ZeDMoDgzDJA@mail.gmail.com>
References: <CANzrpG28k7oFaekLwSGSb5KL8xkoR8KNnsoJ9_bTkRYgZe08BQ@mail.gmail.com>
 <CAMMTP+BDgCQuZegL1av2fwCrMabE7KT6n4GESmZe-cz19MeRxA@mail.gmail.com>
 <CANzrpG2TCNn7vqbS8-VSsX2wef-Ouf7T8qxUaGTfxKd_ffqMcQ@mail.gmail.com>
 <CAJhcF=26uK5xyGf1n79-0guqf8_n3ceLEsf+Vv=ZeDMoDgzDJA@mail.gmail.com>
Message-ID: <CANzrpG1PZpyKp-sRsDDouXnJKJJ__PoVeE=v6hb+GaehB=mC5w@mail.gmail.com>

On Mon, May 29, 2017 at 5:33 PM, Da?id <davidmenhur at gmail.com> wrote:

>
> On 29 May 2017 at 17:03, Mainak Jas <mainakjas at gmail.com> wrote:
>
>> I worked out the Math with a colleague which I provide here:
>> https://www.overleaf.com/read/qgwtvtrkcrqc. Please let me know if we
>> missed something.
>>
>
> You are using a property of the symetric stable distribution, but you
> consider beta = 1, which is asymmetric.
>

Yes, but the symmetric stable distribution can be expressed in terms of the
positive stable distribution (which is asymmetric) according to Equation (2)

Mainak


> /David.
>
>
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at python.org
> https://mail.python.org/mailman/listinfo/scipy-user
>
>
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