From johann.cohentanugi at gmail.com  Tue Apr  1 07:57:01 2014
From: johann.cohentanugi at gmail.com (Johann cohen-tanugi)
Date: Tue, 1 Apr 2014 13:57:01 +0200
Subject: [SciPy-Dev] Levenberg-Marquardt Implementation
In-Reply-To: <lein6k$lsp$1@ger.gmane.org>
References: <CAAq9_B5PDUxdXs_p=r4Xn6dbq_Xy3GTFCKGXN=d80pFnOs=EGA@mail.gmail.com>
	<CAOyNq8pZ=N3Evf7vhiiuh+2Tpio4rhUdj1xHZRiX+bcBSXX-LA@mail.gmail.com>
	<lein6k$lsp$1@ger.gmane.org>
Message-ID: <CAJ_f96c6-U3r5QopnHeP_SkOrn1k-Vkt=ht=ifD3bPrF=Us6sw@mail.gmail.com>

FWIW I came across a python LM implementation of the minpack LM routine in
https://code.google.com/p/astrolibpy/source/browse/mpfit/mpfit.py
The license is GPLv3 though.
Johann


On Tue, Feb 25, 2014 at 7:25 PM, Pauli Virtanen <pav at iki.fi> wrote:

> 25.02.2014 17:45, Benny Malengier kirjoitti:
> > This is present already, See
> >
> http://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.leastsq.html
> >
> > which wraps: http://www.math.utah.edu/software/minpack/minpack
> > /lmder.html
>
> The current leastsq implementation does not support sparse Jacobians
> or constraints.
>
> MINPACK does dense QR factorizations, and this approach doesn't work
> well for problems where the number of variables is too big. This was one
> of our the GSoC topic ideas [1] --- if you have suggestions on how to
> improve these, please speak up.
>
> [1] https://github.com/scipy/scipy/wiki/GSoC-project-ideas
>
> --
> Pauli Virtanen
>
>
> _______________________________________________
> SciPy-Dev mailing list
> SciPy-Dev at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-dev
>
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From newville at cars.uchicago.edu  Tue Apr  1 14:11:33 2014
From: newville at cars.uchicago.edu (Matt Newville)
Date: Tue, 1 Apr 2014 13:11:33 -0500
Subject: [SciPy-Dev] SciPy-Dev Digest, Vol 126, Issue 1
In-Reply-To: <mailman.3.1396371602.28485.scipy-dev@scipy.org>
References: <mailman.3.1396371602.28485.scipy-dev@scipy.org>
Message-ID: <CA+7ESbpw+EeFrf4JsNVjLQS_+=AFcGH3EeF-tWWHXcc5bo1Aew@mail.gmail.com>

> Date: Tue, 1 Apr 2014 13:57:01 +0200
> From: Johann cohen-tanugi <johann.cohentanugi at gmail.com>
> Subject: Re: [SciPy-Dev] Levenberg-Marquardt Implementation
> To: SciPy Developers List <scipy-dev at scipy.org>
> Message-ID:
>         <CAJ_f96c6-U3r5QopnHeP_SkOrn1k-Vkt=ht=ifD3bPrF=Us6sw at mail.gmail.com>
> Content-Type: text/plain; charset="iso-8859-1"
>
> FWIW I came across a python LM implementation of the minpack LM routine in
> https://code.google.com/p/astrolibpy/source/browse/mpfit/mpfit.py
> The license is GPLv3 though.
> Johann

The code (originally in IDL by Craig Markwardt at U Wisconsin and
available at http://www.physics.wisc.edu/~craigm/idl/) and the
translation into Python by Mark Rivers (with whom I work) shown in the
link you gave (and I could post separately) is actually not restricted
by GPLv3 and is under a more permissive BSD-like license:

 Translated from MINPACK-1 in FORTRAN, Apr-Jul 1998, CM
 Copyright (C) 1997-2002, Craig Markwardt
 This software is provided as is without any warranty whatsoever.
 Permission to use, copy, modify, and distribute modified or
 unmodified copies is granted, provided this copyright and disclaimer
 are included unchanged.

   Translated from MPFIT (Craig Markwardt's IDL package) to Python,
   August, 2002.  Mark Rivers

Astrolib.py may be distributing this under the GPLv3, but this code is
more permissively available.

Whether one wants to use this version (as opposed to the version in
scipy.optimize.leastsq) is another matter -- mpfit.py is in pure
python, and considerably slower than leastsq.   It might be worth
considering wrapping Markwardt's C version of MPFIT (which came after
the 2002 translation by Mark Rivers), but I have not looked into doing
this.

MPFIT does handle constraints to some extent (box constraints are
handled  tied values between parameters rare handled in a klunky way).
  Lmfit-py (which we're now using in place of MPFIT), uses a different
bounds implementations (that as described by MINUIT which makes
getting uncertainties for values near the boundaries more reliable)
and has a more flexible means for constraining parameter values to one
another.

I don't know of any implementation that handles "sparse Jacobians"
well.... I'm not sure I know of a problem that clearly fits that
category, though I can believe they exist.

--Matt Newville


From boyfarrell at gmail.com  Wed Apr  2 01:46:35 2014
From: boyfarrell at gmail.com (Daniel Farrell)
Date: Wed, 2 Apr 2014 14:46:35 +0900
Subject: [SciPy-Dev] Is odeint re-entrant?
Message-ID: <CAOiOdSDf7jL=2soYoi7AgHNAXqfPZW-V9DGbu1V9N3U2khy_PA@mail.gmail.com>

Dear list,

The docs warn that the lsoda, vode, zvode solvers in scipy.integrate.ode
are not re-entrant. How about the the implementation for
scipy.integrate.odeint? The docs state that is used the lsoda solver, so I
guess that it is not re-entrant.

To check my understanding, re-entrant prevents me from running multiple
instances of the above solvers on different processes?

Best wishes,

Dan
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From benny.malengier at gmail.com  Wed Apr  2 04:26:47 2014
From: benny.malengier at gmail.com (Benny Malengier)
Date: Wed, 2 Apr 2014 10:26:47 +0200
Subject: [SciPy-Dev] Is odeint re-entrant?
In-Reply-To: <CAOiOdSDf7jL=2soYoi7AgHNAXqfPZW-V9DGbu1V9N3U2khy_PA@mail.gmail.com>
References: <CAOiOdSDf7jL=2soYoi7AgHNAXqfPZW-V9DGbu1V9N3U2khy_PA@mail.gmail.com>
Message-ID: <CAOyNq8oVWpXom5ZLS9UV-w8hNa8ytOQjmkJS2ZbUBKzZL8n=Xw@mail.gmail.com>

2014-04-02 7:46 GMT+02:00 Daniel Farrell <boyfarrell at gmail.com>:

> Dear list,
>
> The docs warn that the lsoda, vode, zvode solvers in scipy.integrate.ode
> are not re-entrant. How about the the implementation for
> scipy.integrate.odeint? The docs state that is used the lsoda solver, so I
> guess that it is not re-entrant.
>
> To check my understanding, re-entrant prevents me from running multiple
> instances of the above solvers on different processes?
>

It would be strange if different processes were not possible, I think it's
more a question of those solvers storing internal state, so that you can
call step to advance one step, and still use BDF. So, it should only be in
a single process. Starting a new problem calls initiate and resets the
internal state of the solver.

The modern cvode does not have this warning, so you can certainly use that
to instantiate different problems at the same time in a thread, eg via my
code https://github.com/bmcage/odes.

Benny


>
> Best wishes,
>
> Dan
>
>
> _______________________________________________
> SciPy-Dev mailing list
> SciPy-Dev at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-dev
>
>
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From sturla.molden at gmail.com  Wed Apr  2 11:51:49 2014
From: sturla.molden at gmail.com (Sturla Molden)
Date: Wed, 02 Apr 2014 17:51:49 +0200
Subject: [SciPy-Dev] Levenberg-Marquardt Implementation
In-Reply-To: <lej4id$ucb$1@ger.gmane.org>
References: <CAAq9_B5PDUxdXs_p=r4Xn6dbq_Xy3GTFCKGXN=d80pFnOs=EGA@mail.gmail.com>	<CAOyNq8pZ=N3Evf7vhiiuh+2Tpio4rhUdj1xHZRiX+bcBSXX-LA@mail.gmail.com>	<lein6k$lsp$1@ger.gmane.org>	<CAOyNq8rc+MsQX7AXwqY4-JUJRktLOAcdQemYbCkPZiUiFsVKfg@mail.gmail.com>
	<lej4id$ucb$1@ger.gmane.org>
Message-ID: <lhhbml$gh1$1@ger.gmane.org>

On 25/02/14 23:13, Pauli Virtanen wrote:

> Porting LMDER to Python has been done (a several times), but as such, it
> doesn't have so much advantage over MINPACK.

It would allow us to use optimized LAPACK for linear least squares, 
instead of the MINPACK supplied QR routines.

Sturla




From ralf.gommers at gmail.com  Thu Apr  3 16:14:24 2014
From: ralf.gommers at gmail.com (Ralf Gommers)
Date: Thu, 3 Apr 2014 22:14:24 +0200
Subject: [SciPy-Dev] ANN: Scipy 0.14.0 release candidate 1
Message-ID: <CABL7CQgih6dwtyEHMTc-N0Rtk5yTRMJ5Mf8iqXMYfq5657myVA@mail.gmail.com>

Hi,

I'm pleased to announce the availability of the first release candidate of
Scipy 0.14.0. Please try this RC and report any issues on the scipy-dev
mailing list. A significant number of fixes for scipy.sparse went in after
the beta release, so users of that module may want to test this release
carefully.

Source tarballs, binaries and the full release notes can be found at
https://sourceforge.net/projects/scipy/files/scipy/0.14.0rc1/. The final
release will follow in one week if no new issues are found.

A big thank you to everyone who contributed to this release!

Ralf
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From matthew.brett at gmail.com  Thu Apr  3 17:20:58 2014
From: matthew.brett at gmail.com (Matthew Brett)
Date: Thu, 3 Apr 2014 14:20:58 -0700
Subject: [SciPy-Dev] [Numpy-discussion] ANN: Scipy 0.14.0 release
	candidate 1
In-Reply-To: <CABL7CQgih6dwtyEHMTc-N0Rtk5yTRMJ5Mf8iqXMYfq5657myVA@mail.gmail.com>
References: <CABL7CQgih6dwtyEHMTc-N0Rtk5yTRMJ5Mf8iqXMYfq5657myVA@mail.gmail.com>
Message-ID: <CAH6Pt5o4EbyOXxgx_uwpws6pTqYq+tw91se8EsPi_LX1CWGNKQ@mail.gmail.com>

Hi Ralf,

On Thu, Apr 3, 2014 at 1:14 PM, Ralf Gommers <ralf.gommers at gmail.com> wrote:
> Hi,
>
> I'm pleased to announce the availability of the first release candidate of
> Scipy 0.14.0. Please try this RC and report any issues on the scipy-dev
> mailing list. A significant number of fixes for scipy.sparse went in after
> the beta release, so users of that module may want to test this release
> carefully.
>
> Source tarballs, binaries and the full release notes can be found at
> https://sourceforge.net/projects/scipy/files/scipy/0.14.0rc1/. The final
> release will follow in one week if no new issues are found.
>
> A big thank you to everyone who contributed to this release!

Would you ming git-tag'ing?  I'll build some wheels...

Cheers,

Matthew


From cgohlke at uci.edu  Thu Apr  3 17:34:09 2014
From: cgohlke at uci.edu (Christoph Gohlke)
Date: Thu, 03 Apr 2014 14:34:09 -0700
Subject: [SciPy-Dev] ANN: Scipy 0.14.0 release candidate 1
In-Reply-To: <CABL7CQgih6dwtyEHMTc-N0Rtk5yTRMJ5Mf8iqXMYfq5657myVA@mail.gmail.com>
References: <CABL7CQgih6dwtyEHMTc-N0Rtk5yTRMJ5Mf8iqXMYfq5657myVA@mail.gmail.com>
Message-ID: <533DD3D1.1090102@uci.edu>

On 4/3/2014 1:14 PM, Ralf Gommers wrote:
> Hi,
>
> I'm pleased to announce the availability of the first release candidate
> of Scipy 0.14.0. Please try this RC and report any issues on the
> scipy-dev mailing list. A significant number of fixes for scipy.sparse
> went in after the beta release, so users of that module may want to test
> this release carefully.
>
> Source tarballs, binaries and the full release notes can be found at
> https://sourceforge.net/projects/scipy/files/scipy/0.14.0rc1/. The final
> release will follow in one week if no new issues are found.
>
> A big thank you to everyone who contributed to this release!
>
> Ralf
>
>

Hi Ralf,

scipy.sparse.linalg fails many tests on Windows on all Python versions. 
I attached the results for Python 2.7 with the official binaries 
(scipy-0.14.0rc1-win32-superpack-python2.7, 32 errors) and my msvc/MKL 
builds for 32 bit (scipy-0.14.0c1.win32-py2.7, 35 errors) and 64 bit 
(scipy-0.14.0c1.win-amd64-py2.7, 4 errors, 10 failures). Other Python 
versions also fail. The 64 bit failures/errors were previously reported 
for Python 3.4 only at <https://github.com/scipy/scipy/issues/3306>. 
I'll try to build and test with numpy 1.7.x and see if that helps.

Christoph
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Running unit tests for scipy
NumPy version 1.8.1
NumPy is installed in X:\Python27\lib\site-packages\numpy
SciPy version 0.14.0rc1
SciPy is installed in X:\Python27\lib\site-packages\scipy
Python version 2.7.6 (default, Nov 10 2013, 19:24:18) [MSC v.1500 32 bit (Intel)]
nose version 1.3.1
X:\Python27\lib\site-packages\numpy\lib\utils.py:134: DeprecationWarning: `scipy.lib.blas` is deprecated, use `scipy.lin
alg.blas` instead!
  warnings.warn(depdoc, DeprecationWarning)
X:\Python27\lib\site-packages\numpy\lib\utils.py:134: DeprecationWarning: `scipy.lib.lapack` is deprecated, use `scipy.l
inalg.lapack` instead!
  warnings.warn(depdoc, DeprecationWarning)
........................................................................................................................
....................................................................................................................K...
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.........K..K...........................................................................................................
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.................................................................SSSSSS......SSSSSS......SSSS...........................
.................................................................0-th dimension must be fixed to 3 but got 15
..0-th dimension must be fixed to 3 but got 5
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======================================================================
ERROR: test_twodiags (test_linsolve.TestLinsolve)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\dsolve\tests\test_linsolve.py", line 72, in test_twodiags
    x = spsolve(Asp,b)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\dsolve\linsolve.py", line 143, in spsolve
    b, flag, options=options)
ValueError: sparse matrix arrays must be 1-D C-contigous and of proper sizes and types

======================================================================
ERROR: test_linsolve.TestSplu.test_spilu_smoketest
----------------------------------------------------------------------
Traceback (most recent call last):
  File "X:\Python27\lib\site-packages\nose\case.py", line 197, in runTest
    self.test(*self.arg)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\dsolve\tests\test_linsolve.py", line 297, in test_spilu_smoket
est
    self._smoketest(spilu, check, np.complex128)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\dsolve\tests\test_linsolve.py", line 242, in _smoketest
    lu = spxlu(A)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\dsolve\linsolve.py", line 309, in spilu
    ilu=True, options=_options)
ValueError: sparse matrix arrays must be 1-D C-contigous and of proper sizes and types

======================================================================
ERROR: test_arpack.test_hermitian_modes(True, <std-hermitian>, 'D', 2, 'SM', None, 0.5, <class 'scipy.sparse.csr.csr_mat
rix'>)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "X:\Python27\lib\site-packages\nose\case.py", line 197, in runTest
    self.test(*self.arg)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\tests\test_arpack.py", line 241, in eval_evec
    eval, evec = eigs_func(ac, k, **kwargs)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 1467, in eigsh
    OPinv=OPinv)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 1260, in eigs
    symmetric=False, tol=tol)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 1030, in get_OPinv_matvec
    return SpLuInv(A.tocsc()).matvec
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 898, in __init__
    self.M_lu = splu(M)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\dsolve\linsolve.py", line 242, in splu
    ilu=False, options=_options)
ValueError: sparse matrix arrays must be 1-D C-contigous and of proper sizes and types

======================================================================
ERROR: test_arpack.test_hermitian_modes(True, <std-hermitian>, 'D', 2, 'LA', None, 0.5, <class 'scipy.sparse.csr.csr_mat
rix'>)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "X:\Python27\lib\site-packages\nose\case.py", line 197, in runTest
    self.test(*self.arg)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\tests\test_arpack.py", line 241, in eval_evec
    eval, evec = eigs_func(ac, k, **kwargs)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 1467, in eigsh
    OPinv=OPinv)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 1260, in eigs
    symmetric=False, tol=tol)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 1030, in get_OPinv_matvec
    return SpLuInv(A.tocsc()).matvec
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 898, in __init__
    self.M_lu = splu(M)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\dsolve\linsolve.py", line 242, in splu
    ilu=False, options=_options)
ValueError: sparse matrix arrays must be 1-D C-contigous and of proper sizes and types

======================================================================
ERROR: test_arpack.test_hermitian_modes(True, <gen-hermitian>, 'D', 2, 'LM', None, 0.5, <class 'scipy.sparse.csr.csr_mat
rix'>)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "X:\Python27\lib\site-packages\nose\case.py", line 197, in runTest
    self.test(*self.arg)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\tests\test_arpack.py", line 235, in eval_evec
    eval, evec = eigs_func(ac, k, bc, **kwargs)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 1467, in eigsh
    OPinv=OPinv)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 1260, in eigs
    symmetric=False, tol=tol)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 1046, in get_OPinv_matvec
    return SpLuInv(OP.tocsc()).matvec
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 898, in __init__
    self.M_lu = splu(M)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\dsolve\linsolve.py", line 242, in splu
    ilu=False, options=_options)
ValueError: sparse matrix arrays must be 1-D C-contigous and of proper sizes and types

======================================================================
ERROR: test_arpack.test_hermitian_modes(True, <gen-hermitian>, 'D', 2, 'SM', None, 0.5, <class 'scipy.sparse.csr.csr_mat
rix'>)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "X:\Python27\lib\site-packages\nose\case.py", line 197, in runTest
    self.test(*self.arg)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\tests\test_arpack.py", line 235, in eval_evec
    eval, evec = eigs_func(ac, k, bc, **kwargs)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 1467, in eigsh
    OPinv=OPinv)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 1260, in eigs
    symmetric=False, tol=tol)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 1046, in get_OPinv_matvec
    return SpLuInv(OP.tocsc()).matvec
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 898, in __init__
    self.M_lu = splu(M)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\dsolve\linsolve.py", line 242, in splu
    ilu=False, options=_options)
ValueError: sparse matrix arrays must be 1-D C-contigous and of proper sizes and types

======================================================================
ERROR: test_arpack.test_real_nonsymmetric_modes(False, <std-real-nonsym>, 'f', 2, 'LM', None, (0.1+0.1j), <class 'scipy.
sparse.csr.csr_matrix'>, 'i')
----------------------------------------------------------------------
Traceback (most recent call last):
  File "X:\Python27\lib\site-packages\nose\case.py", line 197, in runTest
    self.test(*self.arg)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\tests\test_arpack.py", line 241, in eval_evec
    eval, evec = eigs_func(ac, k, **kwargs)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 1260, in eigs
    symmetric=False, tol=tol)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 1030, in get_OPinv_matvec
    return SpLuInv(A.tocsc()).matvec
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 898, in __init__
    self.M_lu = splu(M)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\dsolve\linsolve.py", line 242, in splu
    ilu=False, options=_options)
ValueError: sparse matrix arrays must be 1-D C-contigous and of proper sizes and types

======================================================================
ERROR: test_arpack.test_real_nonsymmetric_modes(False, <std-real-nonsym>, 'f', 2, 'LR', None, (0.1+0.1j), <class 'scipy.
sparse.csr.csr_matrix'>, 'r')
----------------------------------------------------------------------
Traceback (most recent call last):
  File "X:\Python27\lib\site-packages\nose\case.py", line 197, in runTest
    self.test(*self.arg)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\tests\test_arpack.py", line 241, in eval_evec
    eval, evec = eigs_func(ac, k, **kwargs)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 1260, in eigs
    symmetric=False, tol=tol)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 1030, in get_OPinv_matvec
    return SpLuInv(A.tocsc()).matvec
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 898, in __init__
    self.M_lu = splu(M)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\dsolve\linsolve.py", line 242, in splu
    ilu=False, options=_options)
ValueError: sparse matrix arrays must be 1-D C-contigous and of proper sizes and types

======================================================================
ERROR: test_arpack.test_real_nonsymmetric_modes(False, <std-real-nonsym>, 'f', 2, 'LR', None, (0.1+0.1j), <class 'scipy.
sparse.csr.csr_matrix'>, 'i')
----------------------------------------------------------------------
Traceback (most recent call last):
  File "X:\Python27\lib\site-packages\nose\case.py", line 197, in runTest
    self.test(*self.arg)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\tests\test_arpack.py", line 241, in eval_evec
    eval, evec = eigs_func(ac, k, **kwargs)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 1260, in eigs
    symmetric=False, tol=tol)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 1030, in get_OPinv_matvec
    return SpLuInv(A.tocsc()).matvec
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 898, in __init__
    self.M_lu = splu(M)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\dsolve\linsolve.py", line 242, in splu
    ilu=False, options=_options)
ValueError: sparse matrix arrays must be 1-D C-contigous and of proper sizes and types

======================================================================
ERROR: test_arpack.test_real_nonsymmetric_modes(False, <std-real-nonsym>, 'd', 2, 'LM', None, (0.1+0.1j), <class 'scipy.
sparse.csr.csr_matrix'>, 'r')
----------------------------------------------------------------------
Traceback (most recent call last):
  File "X:\Python27\lib\site-packages\nose\case.py", line 197, in runTest
    self.test(*self.arg)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\tests\test_arpack.py", line 241, in eval_evec
    eval, evec = eigs_func(ac, k, **kwargs)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 1260, in eigs
    symmetric=False, tol=tol)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 1030, in get_OPinv_matvec
    return SpLuInv(A.tocsc()).matvec
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 898, in __init__
    self.M_lu = splu(M)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\dsolve\linsolve.py", line 242, in splu
    ilu=False, options=_options)
ValueError: sparse matrix arrays must be 1-D C-contigous and of proper sizes and types

======================================================================
ERROR: test_arpack.test_real_nonsymmetric_modes(False, <std-real-nonsym>, 'd', 2, 'LR', None, (0.1+0.1j), <class 'scipy.
sparse.csr.csr_matrix'>, 'r')
----------------------------------------------------------------------
Traceback (most recent call last):
  File "X:\Python27\lib\site-packages\nose\case.py", line 197, in runTest
    self.test(*self.arg)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\tests\test_arpack.py", line 241, in eval_evec
    eval, evec = eigs_func(ac, k, **kwargs)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 1260, in eigs
    symmetric=False, tol=tol)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 1030, in get_OPinv_matvec
    return SpLuInv(A.tocsc()).matvec
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 898, in __init__
    self.M_lu = splu(M)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\dsolve\linsolve.py", line 242, in splu
    ilu=False, options=_options)
ValueError: sparse matrix arrays must be 1-D C-contigous and of proper sizes and types

======================================================================
ERROR: test_arpack.test_real_nonsymmetric_modes(False, <std-real-nonsym>, 'd', 2, 'LR', None, (0.1+0.1j), <class 'scipy.
sparse.csr.csr_matrix'>, 'i')
----------------------------------------------------------------------
Traceback (most recent call last):
  File "X:\Python27\lib\site-packages\nose\case.py", line 197, in runTest
    self.test(*self.arg)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\tests\test_arpack.py", line 241, in eval_evec
    eval, evec = eigs_func(ac, k, **kwargs)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 1260, in eigs
    symmetric=False, tol=tol)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 1030, in get_OPinv_matvec
    return SpLuInv(A.tocsc()).matvec
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 898, in __init__
    self.M_lu = splu(M)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\dsolve\linsolve.py", line 242, in splu
    ilu=False, options=_options)
ValueError: sparse matrix arrays must be 1-D C-contigous and of proper sizes and types

======================================================================
ERROR: test_arpack.test_real_nonsymmetric_modes(False, <gen-real-nonsym>, 'd', 2, 'LM', None, (0.1+0.1j), <class 'scipy.
sparse.csr.csr_matrix'>, 'i')
----------------------------------------------------------------------
Traceback (most recent call last):
  File "X:\Python27\lib\site-packages\nose\case.py", line 197, in runTest
    self.test(*self.arg)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\tests\test_arpack.py", line 235, in eval_evec
    eval, evec = eigs_func(ac, k, bc, **kwargs)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 1260, in eigs
    symmetric=False, tol=tol)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 1046, in get_OPinv_matvec
    return SpLuInv(OP.tocsc()).matvec
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 898, in __init__
    self.M_lu = splu(M)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\dsolve\linsolve.py", line 242, in splu
    ilu=False, options=_options)
ValueError: sparse matrix arrays must be 1-D C-contigous and of proper sizes and types

======================================================================
ERROR: test_arpack.test_real_nonsymmetric_modes(False, <gen-real-nonsym>, 'd', 2, 'LR', None, (0.1+0.1j), <class 'scipy.
sparse.csr.csr_matrix'>, 'r')
----------------------------------------------------------------------
Traceback (most recent call last):
  File "X:\Python27\lib\site-packages\nose\case.py", line 197, in runTest
    self.test(*self.arg)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\tests\test_arpack.py", line 235, in eval_evec
    eval, evec = eigs_func(ac, k, bc, **kwargs)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 1260, in eigs
    symmetric=False, tol=tol)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 1046, in get_OPinv_matvec
    return SpLuInv(OP.tocsc()).matvec
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 898, in __init__
    self.M_lu = splu(M)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\dsolve\linsolve.py", line 242, in splu
    ilu=False, options=_options)
ValueError: sparse matrix arrays must be 1-D C-contigous and of proper sizes and types

======================================================================
ERROR: test_arpack.test_real_nonsymmetric_modes(False, <gen-real-nonsym>, 'd', 2, 'LI', None, (0.1+0.1j), <class 'scipy.
sparse.csr.csr_matrix'>, 'r')
----------------------------------------------------------------------
Traceback (most recent call last):
  File "X:\Python27\lib\site-packages\nose\case.py", line 197, in runTest
    self.test(*self.arg)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\tests\test_arpack.py", line 235, in eval_evec
    eval, evec = eigs_func(ac, k, bc, **kwargs)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 1260, in eigs
    symmetric=False, tol=tol)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 1046, in get_OPinv_matvec
    return SpLuInv(OP.tocsc()).matvec
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 898, in __init__
    self.M_lu = splu(M)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\dsolve\linsolve.py", line 242, in splu
    ilu=False, options=_options)
ValueError: sparse matrix arrays must be 1-D C-contigous and of proper sizes and types

======================================================================
ERROR: test_arpack.test_real_nonsymmetric_modes(False, <gen-real-nonsym>, 'd', 2, 'LI', None, (0.1+0.1j), <class 'scipy.
sparse.csr.csr_matrix'>, 'i')
----------------------------------------------------------------------
Traceback (most recent call last):
  File "X:\Python27\lib\site-packages\nose\case.py", line 197, in runTest
    self.test(*self.arg)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\tests\test_arpack.py", line 235, in eval_evec
    eval, evec = eigs_func(ac, k, bc, **kwargs)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 1260, in eigs
    symmetric=False, tol=tol)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 1046, in get_OPinv_matvec
    return SpLuInv(OP.tocsc()).matvec
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 898, in __init__
    self.M_lu = splu(M)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\dsolve\linsolve.py", line 242, in splu
    ilu=False, options=_options)
ValueError: sparse matrix arrays must be 1-D C-contigous and of proper sizes and types

======================================================================
ERROR: test_arpack.test_complex_nonsymmetric_modes(False, <std-cmplx-nonsym>, 'D', 2, 'LM', None, (0.1+0.1j), <class 'sc
ipy.sparse.csr.csr_matrix'>)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "X:\Python27\lib\site-packages\nose\case.py", line 197, in runTest
    self.test(*self.arg)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\tests\test_arpack.py", line 241, in eval_evec
    eval, evec = eigs_func(ac, k, **kwargs)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 1260, in eigs
    symmetric=False, tol=tol)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 1030, in get_OPinv_matvec
    return SpLuInv(A.tocsc()).matvec
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 898, in __init__
    self.M_lu = splu(M)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\dsolve\linsolve.py", line 242, in splu
    ilu=False, options=_options)
ValueError: sparse matrix arrays must be 1-D C-contigous and of proper sizes and types

======================================================================
ERROR: test_arpack.test_complex_nonsymmetric_modes(False, <std-cmplx-nonsym>, 'D', 2, 'LM', None, 0.1, <class 'scipy.spa
rse.csr.csr_matrix'>)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "X:\Python27\lib\site-packages\nose\case.py", line 197, in runTest
    self.test(*self.arg)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\tests\test_arpack.py", line 241, in eval_evec
    eval, evec = eigs_func(ac, k, **kwargs)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 1260, in eigs
    symmetric=False, tol=tol)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 1030, in get_OPinv_matvec
    return SpLuInv(A.tocsc()).matvec
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 898, in __init__
    self.M_lu = splu(M)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\dsolve\linsolve.py", line 242, in splu
    ilu=False, options=_options)
ValueError: sparse matrix arrays must be 1-D C-contigous and of proper sizes and types

======================================================================
ERROR: test_arpack.test_complex_nonsymmetric_modes(False, <std-cmplx-nonsym>, 'D', 2, 'LR', None, (0.1+0.1j), <class 'sc
ipy.sparse.csr.csr_matrix'>)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "X:\Python27\lib\site-packages\nose\case.py", line 197, in runTest
    self.test(*self.arg)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\tests\test_arpack.py", line 241, in eval_evec
    eval, evec = eigs_func(ac, k, **kwargs)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 1260, in eigs
    symmetric=False, tol=tol)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 1030, in get_OPinv_matvec
    return SpLuInv(A.tocsc()).matvec
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 898, in __init__
    self.M_lu = splu(M)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\dsolve\linsolve.py", line 242, in splu
    ilu=False, options=_options)
ValueError: sparse matrix arrays must be 1-D C-contigous and of proper sizes and types

======================================================================
ERROR: test_arpack.test_complex_nonsymmetric_modes(False, <std-cmplx-nonsym>, 'D', 2, 'LR', None, 0.1, <class 'scipy.spa
rse.csr.csr_matrix'>)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "X:\Python27\lib\site-packages\nose\case.py", line 197, in runTest
    self.test(*self.arg)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\tests\test_arpack.py", line 241, in eval_evec
    eval, evec = eigs_func(ac, k, **kwargs)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 1260, in eigs
    symmetric=False, tol=tol)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 1030, in get_OPinv_matvec
    return SpLuInv(A.tocsc()).matvec
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 898, in __init__
    self.M_lu = splu(M)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\dsolve\linsolve.py", line 242, in splu
    ilu=False, options=_options)
ValueError: sparse matrix arrays must be 1-D C-contigous and of proper sizes and types

======================================================================
ERROR: test_arpack.test_complex_nonsymmetric_modes(False, <std-cmplx-nonsym>, 'D', 2, 'LI', None, 0.1, <class 'scipy.spa
rse.csr.csr_matrix'>)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "X:\Python27\lib\site-packages\nose\case.py", line 197, in runTest
    self.test(*self.arg)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\tests\test_arpack.py", line 241, in eval_evec
    eval, evec = eigs_func(ac, k, **kwargs)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 1260, in eigs
    symmetric=False, tol=tol)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 1030, in get_OPinv_matvec
    return SpLuInv(A.tocsc()).matvec
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 898, in __init__
    self.M_lu = splu(M)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\dsolve\linsolve.py", line 242, in splu
    ilu=False, options=_options)
ValueError: sparse matrix arrays must be 1-D C-contigous and of proper sizes and types

======================================================================
ERROR: test_arpack.test_complex_nonsymmetric_modes(False, <std-cmplx-nonsym>, 'F', 2, 'LI', None, (0.1+0.1j), <class 'sc
ipy.sparse.csr.csr_matrix'>)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "X:\Python27\lib\site-packages\nose\case.py", line 197, in runTest
    self.test(*self.arg)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\tests\test_arpack.py", line 241, in eval_evec
    eval, evec = eigs_func(ac, k, **kwargs)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 1260, in eigs
    symmetric=False, tol=tol)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 1030, in get_OPinv_matvec
    return SpLuInv(A.tocsc()).matvec
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 898, in __init__
    self.M_lu = splu(M)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\dsolve\linsolve.py", line 242, in splu
    ilu=False, options=_options)
ValueError: sparse matrix arrays must be 1-D C-contigous and of proper sizes and types

======================================================================
ERROR: test_arpack.test_complex_nonsymmetric_modes(False, <gen-cmplx-nonsym>, 'D', 2, 'LM', None, (0.1+0.1j), <class 'sc
ipy.sparse.csr.csr_matrix'>)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "X:\Python27\lib\site-packages\nose\case.py", line 197, in runTest
    self.test(*self.arg)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\tests\test_arpack.py", line 235, in eval_evec
    eval, evec = eigs_func(ac, k, bc, **kwargs)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 1260, in eigs
    symmetric=False, tol=tol)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 1046, in get_OPinv_matvec
    return SpLuInv(OP.tocsc()).matvec
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 898, in __init__
    self.M_lu = splu(M)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\dsolve\linsolve.py", line 242, in splu
    ilu=False, options=_options)
ValueError: sparse matrix arrays must be 1-D C-contigous and of proper sizes and types

======================================================================
ERROR: test_arpack.test_complex_nonsymmetric_modes(False, <gen-cmplx-nonsym>, 'D', 2, 'LR', None, (0.1+0.1j), <class 'sc
ipy.sparse.csr.csr_matrix'>)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "X:\Python27\lib\site-packages\nose\case.py", line 197, in runTest
    self.test(*self.arg)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\tests\test_arpack.py", line 235, in eval_evec
    eval, evec = eigs_func(ac, k, bc, **kwargs)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 1260, in eigs
    symmetric=False, tol=tol)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 1046, in get_OPinv_matvec
    return SpLuInv(OP.tocsc()).matvec
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 898, in __init__
    self.M_lu = splu(M)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\dsolve\linsolve.py", line 242, in splu
    ilu=False, options=_options)
ValueError: sparse matrix arrays must be 1-D C-contigous and of proper sizes and types

======================================================================
ERROR: test_arpack.test_complex_nonsymmetric_modes(False, <gen-cmplx-nonsym>, 'D', 2, 'LR', None, 0.1, <class 'scipy.spa
rse.csr.csr_matrix'>)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "X:\Python27\lib\site-packages\nose\case.py", line 197, in runTest
    self.test(*self.arg)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\tests\test_arpack.py", line 235, in eval_evec
    eval, evec = eigs_func(ac, k, bc, **kwargs)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 1260, in eigs
    symmetric=False, tol=tol)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 1046, in get_OPinv_matvec
    return SpLuInv(OP.tocsc()).matvec
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 898, in __init__
    self.M_lu = splu(M)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\dsolve\linsolve.py", line 242, in splu
    ilu=False, options=_options)
ValueError: sparse matrix arrays must be 1-D C-contigous and of proper sizes and types

======================================================================
ERROR: test_arpack.test_complex_nonsymmetric_modes(False, <gen-cmplx-nonsym>, 'D', 2, 'LI', None, 0.1, <class 'scipy.spa
rse.csr.csr_matrix'>)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "X:\Python27\lib\site-packages\nose\case.py", line 197, in runTest
    self.test(*self.arg)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\tests\test_arpack.py", line 235, in eval_evec
    eval, evec = eigs_func(ac, k, bc, **kwargs)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 1260, in eigs
    symmetric=False, tol=tol)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 1046, in get_OPinv_matvec
    return SpLuInv(OP.tocsc()).matvec
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 898, in __init__
    self.M_lu = splu(M)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\dsolve\linsolve.py", line 242, in splu
    ilu=False, options=_options)
ValueError: sparse matrix arrays must be 1-D C-contigous and of proper sizes and types

======================================================================
ERROR: test_padecases_dtype_sparse_complex (test_matfuncs.TestExpM)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\tests\test_matfuncs.py", line 149, in test_padecases_dtype_spa
rse_complex
    assert_array_almost_equal_nulp(expm(a).toarray(), e, nulp=100)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\matfuncs.py", line 599, in expm
    return _solve_P_Q(U, V, structure=structure)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\matfuncs.py", line 657, in _solve_P_Q
    return spsolve(Q, P)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\dsolve\linsolve.py", line 151, in spsolve
    Afactsolve = factorized(A)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\dsolve\linsolve.py", line 366, in factorized
    return splu(A).solve
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\dsolve\linsolve.py", line 242, in splu
    ilu=False, options=_options)
ValueError: sparse matrix arrays must be 1-D C-contigous and of proper sizes and types

======================================================================
ERROR: test_base.TestCOO.test_solve
----------------------------------------------------------------------
Traceback (most recent call last):
  File "X:\Python27\lib\site-packages\nose\case.py", line 197, in runTest
    self.test(*self.arg)
  File "X:\Python27\lib\site-packages\scipy\sparse\tests\test_base.py", line 1920, in test_solve
    x = splu(A).solve(r)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\dsolve\linsolve.py", line 242, in splu
    ilu=False, options=_options)
ValueError: sparse matrix arrays must be 1-D C-contigous and of proper sizes and types

======================================================================
ERROR: test_base.TestCOONonCanonical.test_solve
----------------------------------------------------------------------
Traceback (most recent call last):
  File "X:\Python27\lib\site-packages\nose\case.py", line 197, in runTest
    self.test(*self.arg)
  File "X:\Python27\lib\site-packages\scipy\sparse\tests\test_base.py", line 1920, in test_solve
    x = splu(A).solve(r)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\dsolve\linsolve.py", line 242, in splu
    ilu=False, options=_options)
ValueError: sparse matrix arrays must be 1-D C-contigous and of proper sizes and types

======================================================================
ERROR: test_base.TestCSR.test_solve
----------------------------------------------------------------------
Traceback (most recent call last):
  File "X:\Python27\lib\site-packages\nose\case.py", line 197, in runTest
    self.test(*self.arg)
  File "X:\Python27\lib\site-packages\scipy\sparse\tests\test_base.py", line 1920, in test_solve
    x = splu(A).solve(r)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\dsolve\linsolve.py", line 242, in splu
    ilu=False, options=_options)
ValueError: sparse matrix arrays must be 1-D C-contigous and of proper sizes and types

======================================================================
ERROR: test_base.TestDIA.test_solve
----------------------------------------------------------------------
Traceback (most recent call last):
  File "X:\Python27\lib\site-packages\nose\case.py", line 197, in runTest
    self.test(*self.arg)
  File "X:\Python27\lib\site-packages\scipy\sparse\tests\test_base.py", line 1920, in test_solve
    x = splu(A).solve(r)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\dsolve\linsolve.py", line 242, in splu
    ilu=False, options=_options)
ValueError: sparse matrix arrays must be 1-D C-contigous and of proper sizes and types

======================================================================
ERROR: test_base.TestLIL.test_solve
----------------------------------------------------------------------
Traceback (most recent call last):
  File "X:\Python27\lib\site-packages\nose\case.py", line 197, in runTest
    self.test(*self.arg)
  File "X:\Python27\lib\site-packages\scipy\sparse\tests\test_base.py", line 1920, in test_solve
    x = splu(A).solve(r)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\dsolve\linsolve.py", line 242, in splu
    ilu=False, options=_options)
ValueError: sparse matrix arrays must be 1-D C-contigous and of proper sizes and types

----------------------------------------------------------------------
Ran 16538 tests in 164.003s

FAILED (KNOWNFAIL=277, SKIP=1145, errors=32)
-------------- next part --------------
Running unit tests for scipy
NumPy version 1.8.1
NumPy is installed in X:\Python27\lib\site-packages\numpy
SciPy version 0.14.0c1
SciPy is installed in X:\Python27\lib\site-packages\scipy
Python version 2.7.6 (default, Nov 10 2013, 19:24:18) [MSC v.1500 32 bit (Intel)]
nose version 1.3.1
X:\Python27\lib\site-packages\numpy\lib\utils.py:134: DeprecationWarning: `scipy.lib.blas` is deprecated, use `scipy.lin
alg.blas` instead!
  warnings.warn(depdoc, DeprecationWarning)
X:\Python27\lib\site-packages\numpy\lib\utils.py:134: DeprecationWarning: `scipy.lib.lapack` is deprecated, use `scipy.l
inalg.lapack` instead!
  warnings.warn(depdoc, DeprecationWarning)
........................................................................................................................
....................................................................................................................K...
..................................................................................................................K.....
........................................................................................................................
.........K..K................................X:\Python27\lib\site-packages\scipy\interpolate\tests\test_interpolate.py:8
33: RuntimeWarning: invalid value encountered in true_divide
  res /= cres
........................................................................................................................
........................................................................................................................
........................................................................................................................
........................................................................................................................
........................................................................................................................
........................................................................................................................
........................................................................................................................
........................................................................................................................
........................................................................................................................
........................................................................................................................
....................SSSSSS......SSSSSS......SSSS........................................................................
....................0-th dimension must be fixed to 3 but got 15
..0-th dimension must be fixed to 3 but got 5
..........................S..........K..................................................................................
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........................................................................................................................
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............................X:\Python27\lib\site-packages\scipy\optimize\linesearch.py:358: RuntimeWarning: invalid valu
e encountered in greater
  if (phi_a1 > phi0 + c1 * alpha1 * derphi0) or \
X:\Python27\lib\site-packages\scipy\optimize\linesearch.py:359: RuntimeWarning: invalid value encountered in greater_equ
al
  ((phi_a1 >= phi_a0) and (i > 1)):
........................K.....................X:\Python27\lib\site-packages\scipy\optimize\slsqp.py:334: RuntimeWarning:
 invalid value encountered in greater
  bnderr = where(bnds[:, 0] > bnds[:, 1])[0]
........................................................................................................................
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...................................E..........EE..........E...........E.................E.E......E.E......E.E......E.E..
...............E......E.E...............................................................................................
........................................................................................................................
...........................................X:\Python27\lib\site-packages\numpy\linalg\linalg.py:2117: RuntimeWarning: in
valid value encountered in absolute
  return add.reduce(abs(x), axis=col_axis).max(axis=row_axis)
..........................E.............................SS.........S....S...SSSSS.SSSSSSSSSS.....SSS..S.....S...K.......
.......S........SSSSK...SSSS..S.........S........SS.........S...S...SSSSS.SSSSSSSSSS.....SSS........S..................S
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..........S........SSSSK...SSSS..S.........S........SS.........S...S...SSSSS.SSSSSSSSSS.....SSS........S................
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.........S.KKK.K..K....K...........S.................SS................K......K.K...S..........S........................
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...............................S..............SSSSK...SSSS...................................S.........S.........K......
......SSKK......KS....S..KKSSSS.SSSSSSSSSS....KSSS..K.K......S...KKK...K...K......K.S..............SSSSK...SSSS..EK.....
..........................SK.............SSSSSSSSSSSSSSSSSSSSSSSSSSSSS..........S...S..SSS.SSSS.SSSSSSSSSS....SSSKKKSSS.
...SSSKKK......S....SSSKKKSSSKKK.................................................SSS.S..............SSSSK...SSSS..E.....
............................S.........S.........K............SSKK.......S...S..SSS.SSSS.SSSSSSSSSS....SSSKKKSSS..K.SSSKK
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....................SK..................SSSSSSSSSSSSSSSSSSSSSSSSSSS.....................................................
..........KKKK.........................KKKK................KKKK....KKKK.................................................
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S.........S....................K....SSSSSSSSSSSSSSSSSSSSSSSSSSSSSS.............SSS..KKK...............K..K....K.....SSSK
KK...................S.SSSKKK...........SSSKKKSSSKKK..........................................SS......SSS...........K...
..K....................K.K..............................................................................................
........................................................................................................................
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.........SSSSSSSSSSSSSSSSSSSSSSSSSSS..............SSS...................................SSSKKK.....................SSSKK
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........................................................................................X:\Python27\lib\site-packages\sc
ipy\stats\_distn_infrastructure.py:767: RuntimeWarning: invalid value encountered in greater
  cond = logical_and(cond, (asarray(arg) > 0))
X:\Python27\lib\site-packages\scipy\stats\_distn_infrastructure.py:2858: RuntimeWarning: invalid value encountered in gr
eater_equal
  cond1 = (k >= self.a) & (k < self.b)
X:\Python27\lib\site-packages\scipy\stats\_distn_infrastructure.py:2858: RuntimeWarning: invalid value encountered in le
ss
  cond1 = (k >= self.a) & (k < self.b)
X:\Python27\lib\site-packages\scipy\stats\_distn_infrastructure.py:2859: RuntimeWarning: invalid value encountered in gr
eater_equal
  cond2 = (k >= self.b)
X:\Python27\lib\site-packages\scipy\stats\_distn_infrastructure.py:2819: RuntimeWarning: invalid value encountered in gr
eater_equal
  cond1 = (k >= self.a) & (k < self.b)
X:\Python27\lib\site-packages\scipy\stats\_distn_infrastructure.py:2819: RuntimeWarning: invalid value encountered in le
ss
  cond1 = (k >= self.a) & (k < self.b)
X:\Python27\lib\site-packages\scipy\stats\_distn_infrastructure.py:2820: RuntimeWarning: invalid value encountered in gr
eater_equal
  cond2 = (k >= self.b)
X:\Python27\lib\site-packages\scipy\stats\_distn_infrastructure.py:2939: RuntimeWarning: invalid value encountered in gr
eater_equal
  cond1 = (k >= self.a) & (k <= self.b)
X:\Python27\lib\site-packages\scipy\stats\_distn_infrastructure.py:2939: RuntimeWarning: invalid value encountered in le
ss_equal
  cond1 = (k >= self.a) & (k <= self.b)
X:\Python27\lib\site-packages\scipy\stats\_distn_infrastructure.py:2940: RuntimeWarning: invalid value encountered in le
ss
  cond2 = (k < self.a) & cond0
X:\Python27\lib\site-packages\scipy\stats\_distn_infrastructure.py:2898: RuntimeWarning: invalid value encountered in gr
eater_equal
  cond1 = (k >= self.a) & (k <= self.b)
X:\Python27\lib\site-packages\scipy\stats\_distn_infrastructure.py:2898: RuntimeWarning: invalid value encountered in le
ss_equal
  cond1 = (k >= self.a) & (k <= self.b)
X:\Python27\lib\site-packages\scipy\stats\_distn_infrastructure.py:2899: RuntimeWarning: invalid value encountered in le
ss
  cond2 = (k < self.a) & cond0
X:\Python27\lib\site-packages\scipy\stats\_distn_infrastructure.py:2746: RuntimeWarning: invalid value encountered in gr
eater_equal
  cond1 = (k >= self.a) & (k <= self.b) & self._nonzero(k, *args)
X:\Python27\lib\site-packages\scipy\stats\_distn_infrastructure.py:2746: RuntimeWarning: invalid value encountered in le
ss_equal
  cond1 = (k >= self.a) & (k <= self.b) & self._nonzero(k, *args)
X:\Python27\lib\site-packages\scipy\stats\_distn_infrastructure.py:2782: RuntimeWarning: invalid value encountered in gr
eater_equal
  cond1 = (k >= self.a) & (k <= self.b) & self._nonzero(k, *args)
X:\Python27\lib\site-packages\scipy\stats\_distn_infrastructure.py:2782: RuntimeWarning: invalid value encountered in le
ss_equal
  cond1 = (k >= self.a) & (k <= self.b) & self._nonzero(k, *args)
X:\Python27\lib\site-packages\scipy\stats\_distn_infrastructure.py:2979: RuntimeWarning: invalid value encountered in gr
eater
  cond1 = (q > 0) & (q < 1)
X:\Python27\lib\site-packages\scipy\stats\_distn_infrastructure.py:2979: RuntimeWarning: invalid value encountered in le
ss
  cond1 = (q > 0) & (q < 1)
X:\Python27\lib\site-packages\scipy\stats\_distn_infrastructure.py:2980: RuntimeWarning: invalid value encountered in eq
ual
  cond2 = (q == 1) & cond0
X:\Python27\lib\site-packages\scipy\stats\_distn_infrastructure.py:2984: RuntimeWarning: invalid value encountered in eq
ual
  place(output, (q == 0)*(cond == cond), self.a-1)
X:\Python27\lib\site-packages\scipy\stats\_distn_infrastructure.py:3019: RuntimeWarning: invalid value encountered in gr
eater
  cond1 = (q > 0) & (q < 1)
X:\Python27\lib\site-packages\scipy\stats\_distn_infrastructure.py:3019: RuntimeWarning: invalid value encountered in le
ss
  cond1 = (q > 0) & (q < 1)
X:\Python27\lib\site-packages\scipy\stats\_distn_infrastructure.py:3020: RuntimeWarning: invalid value encountered in eq
ual
  cond2 = (q == 1) & cond0
X:\Python27\lib\site-packages\scipy\stats\_distn_infrastructure.py:3026: RuntimeWarning: invalid value encountered in eq
ual
  place(output, (q == 0)*(cond == cond), self.b)
.........................................................................................................S..S...........
........................................................................................................................
........................................................................................................................
........................................................................................................................
...X:\Python27\lib\site-packages\numpy\ma\core.py:778: RuntimeWarning: invalid value encountered in absolute
  return umath.absolute(a) * self.tolerance >= umath.absolute(b)
.............................................................S.SSS....SSSSSS............................................
.....................................................
======================================================================
ERROR: test_twodiags (test_linsolve.TestLinsolve)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\dsolve\tests\test_linsolve.py", line 72, in test_twodiags
    x = spsolve(Asp,b)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\dsolve\linsolve.py", line 143, in spsolve
    b, flag, options=options)
ValueError: sparse matrix arrays must be 1-D C-contigous and of proper sizes and types

======================================================================
ERROR: test_arpack.test_hermitian_modes(True, <std-hermitian>, 'D', 2, 'LM', None, 0.5, <class 'scipy.sparse.csr.csr_mat
rix'>)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "X:\Python27\lib\site-packages\nose\case.py", line 197, in runTest
    self.test(*self.arg)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\tests\test_arpack.py", line 241, in eval_evec
    eval, evec = eigs_func(ac, k, **kwargs)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 1467, in eigsh
    OPinv=OPinv)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 1260, in eigs
    symmetric=False, tol=tol)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 1030, in get_OPinv_matvec
    return SpLuInv(A.tocsc()).matvec
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 898, in __init__
    self.M_lu = splu(M)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\dsolve\linsolve.py", line 242, in splu
    ilu=False, options=_options)
ValueError: sparse matrix arrays must be 1-D C-contigous and of proper sizes and types

======================================================================
ERROR: test_arpack.test_hermitian_modes(True, <std-hermitian>, 'D', 2, 'SM', None, 0.5, <class 'scipy.sparse.csr.csr_mat
rix'>)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "X:\Python27\lib\site-packages\nose\case.py", line 197, in runTest
    self.test(*self.arg)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\tests\test_arpack.py", line 241, in eval_evec
    eval, evec = eigs_func(ac, k, **kwargs)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 1467, in eigsh
    OPinv=OPinv)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 1260, in eigs
    symmetric=False, tol=tol)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 1030, in get_OPinv_matvec
    return SpLuInv(A.tocsc()).matvec
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 898, in __init__
    self.M_lu = splu(M)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\dsolve\linsolve.py", line 242, in splu
    ilu=False, options=_options)
ValueError: sparse matrix arrays must be 1-D C-contigous and of proper sizes and types

======================================================================
ERROR: test_arpack.test_hermitian_modes(True, <std-hermitian>, 'D', 2, 'LA', None, 0.5, <class 'scipy.sparse.csr.csr_mat
rix'>)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "X:\Python27\lib\site-packages\nose\case.py", line 197, in runTest
    self.test(*self.arg)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\tests\test_arpack.py", line 241, in eval_evec
    eval, evec = eigs_func(ac, k, **kwargs)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 1467, in eigsh
    OPinv=OPinv)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 1260, in eigs
    symmetric=False, tol=tol)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 1030, in get_OPinv_matvec
    return SpLuInv(A.tocsc()).matvec
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 898, in __init__
    self.M_lu = splu(M)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\dsolve\linsolve.py", line 242, in splu
    ilu=False, options=_options)
ValueError: sparse matrix arrays must be 1-D C-contigous and of proper sizes and types

======================================================================
ERROR: test_arpack.test_hermitian_modes(True, <std-hermitian>, 'D', 2, 'SA', None, 0.5, <class 'scipy.sparse.csr.csr_mat
rix'>)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "X:\Python27\lib\site-packages\nose\case.py", line 197, in runTest
    self.test(*self.arg)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\tests\test_arpack.py", line 241, in eval_evec
    eval, evec = eigs_func(ac, k, **kwargs)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 1467, in eigsh
    OPinv=OPinv)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 1260, in eigs
    symmetric=False, tol=tol)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 1030, in get_OPinv_matvec
    return SpLuInv(A.tocsc()).matvec
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 898, in __init__
    self.M_lu = splu(M)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\dsolve\linsolve.py", line 242, in splu
    ilu=False, options=_options)
ValueError: sparse matrix arrays must be 1-D C-contigous and of proper sizes and types

======================================================================
ERROR: test_arpack.test_hermitian_modes(True, <gen-hermitian>, 'D', 2, 'LM', None, 0.5, <class 'scipy.sparse.csr.csr_mat
rix'>)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "X:\Python27\lib\site-packages\nose\case.py", line 197, in runTest
    self.test(*self.arg)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\tests\test_arpack.py", line 235, in eval_evec
    eval, evec = eigs_func(ac, k, bc, **kwargs)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 1467, in eigsh
    OPinv=OPinv)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 1260, in eigs
    symmetric=False, tol=tol)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 1046, in get_OPinv_matvec
    return SpLuInv(OP.tocsc()).matvec
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 898, in __init__
    self.M_lu = splu(M)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\dsolve\linsolve.py", line 242, in splu
    ilu=False, options=_options)
ValueError: sparse matrix arrays must be 1-D C-contigous and of proper sizes and types

======================================================================
ERROR: test_arpack.test_real_nonsymmetric_modes(False, <std-real-nonsym>, 'f', 2, 'LM', None, (0.1+0.1j), <class 'scipy.
sparse.csr.csr_matrix'>, 'i')
----------------------------------------------------------------------
Traceback (most recent call last):
  File "X:\Python27\lib\site-packages\nose\case.py", line 197, in runTest
    self.test(*self.arg)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\tests\test_arpack.py", line 241, in eval_evec
    eval, evec = eigs_func(ac, k, **kwargs)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 1260, in eigs
    symmetric=False, tol=tol)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 1030, in get_OPinv_matvec
    return SpLuInv(A.tocsc()).matvec
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 898, in __init__
    self.M_lu = splu(M)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\dsolve\linsolve.py", line 242, in splu
    ilu=False, options=_options)
ValueError: sparse matrix arrays must be 1-D C-contigous and of proper sizes and types

======================================================================
ERROR: test_arpack.test_real_nonsymmetric_modes(False, <std-real-nonsym>, 'f', 2, 'LR', None, (0.1+0.1j), <class 'scipy.
sparse.csr.csr_matrix'>, 'r')
----------------------------------------------------------------------
Traceback (most recent call last):
  File "X:\Python27\lib\site-packages\nose\case.py", line 197, in runTest
    self.test(*self.arg)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\tests\test_arpack.py", line 241, in eval_evec
    eval, evec = eigs_func(ac, k, **kwargs)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 1260, in eigs
    symmetric=False, tol=tol)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 1030, in get_OPinv_matvec
    return SpLuInv(A.tocsc()).matvec
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 898, in __init__
    self.M_lu = splu(M)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\dsolve\linsolve.py", line 242, in splu
    ilu=False, options=_options)
ValueError: sparse matrix arrays must be 1-D C-contigous and of proper sizes and types

======================================================================
ERROR: test_arpack.test_real_nonsymmetric_modes(False, <std-real-nonsym>, 'f', 2, 'LR', None, (0.1+0.1j), <class 'scipy.
sparse.csr.csr_matrix'>, 'i')
----------------------------------------------------------------------
Traceback (most recent call last):
  File "X:\Python27\lib\site-packages\nose\case.py", line 197, in runTest
    self.test(*self.arg)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\tests\test_arpack.py", line 241, in eval_evec
    eval, evec = eigs_func(ac, k, **kwargs)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 1260, in eigs
    symmetric=False, tol=tol)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 1030, in get_OPinv_matvec
    return SpLuInv(A.tocsc()).matvec
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 898, in __init__
    self.M_lu = splu(M)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\dsolve\linsolve.py", line 242, in splu
    ilu=False, options=_options)
ValueError: sparse matrix arrays must be 1-D C-contigous and of proper sizes and types

======================================================================
ERROR: test_arpack.test_real_nonsymmetric_modes(False, <std-real-nonsym>, 'f', 2, 'LI', None, (0.1+0.1j), <class 'scipy.
sparse.csr.csr_matrix'>, 'r')
----------------------------------------------------------------------
Traceback (most recent call last):
  File "X:\Python27\lib\site-packages\nose\case.py", line 197, in runTest
    self.test(*self.arg)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\tests\test_arpack.py", line 241, in eval_evec
    eval, evec = eigs_func(ac, k, **kwargs)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 1260, in eigs
    symmetric=False, tol=tol)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 1030, in get_OPinv_matvec
    return SpLuInv(A.tocsc()).matvec
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 898, in __init__
    self.M_lu = splu(M)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\dsolve\linsolve.py", line 242, in splu
    ilu=False, options=_options)
ValueError: sparse matrix arrays must be 1-D C-contigous and of proper sizes and types

======================================================================
ERROR: test_arpack.test_real_nonsymmetric_modes(False, <std-real-nonsym>, 'f', 2, 'LI', None, (0.1+0.1j), <class 'scipy.
sparse.csr.csr_matrix'>, 'i')
----------------------------------------------------------------------
Traceback (most recent call last):
  File "X:\Python27\lib\site-packages\nose\case.py", line 197, in runTest
    self.test(*self.arg)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\tests\test_arpack.py", line 241, in eval_evec
    eval, evec = eigs_func(ac, k, **kwargs)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 1260, in eigs
    symmetric=False, tol=tol)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 1030, in get_OPinv_matvec
    return SpLuInv(A.tocsc()).matvec
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 898, in __init__
    self.M_lu = splu(M)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\dsolve\linsolve.py", line 242, in splu
    ilu=False, options=_options)
ValueError: sparse matrix arrays must be 1-D C-contigous and of proper sizes and types

======================================================================
ERROR: test_arpack.test_real_nonsymmetric_modes(False, <std-real-nonsym>, 'd', 2, 'LM', None, (0.1+0.1j), <class 'scipy.
sparse.csr.csr_matrix'>, 'r')
----------------------------------------------------------------------
Traceback (most recent call last):
  File "X:\Python27\lib\site-packages\nose\case.py", line 197, in runTest
    self.test(*self.arg)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\tests\test_arpack.py", line 241, in eval_evec
    eval, evec = eigs_func(ac, k, **kwargs)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 1260, in eigs
    symmetric=False, tol=tol)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 1030, in get_OPinv_matvec
    return SpLuInv(A.tocsc()).matvec
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 898, in __init__
    self.M_lu = splu(M)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\dsolve\linsolve.py", line 242, in splu
    ilu=False, options=_options)
ValueError: sparse matrix arrays must be 1-D C-contigous and of proper sizes and types

======================================================================
ERROR: test_arpack.test_real_nonsymmetric_modes(False, <std-real-nonsym>, 'd', 2, 'LM', None, (0.1+0.1j), <class 'scipy.
sparse.csr.csr_matrix'>, 'i')
----------------------------------------------------------------------
Traceback (most recent call last):
  File "X:\Python27\lib\site-packages\nose\case.py", line 197, in runTest
    self.test(*self.arg)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\tests\test_arpack.py", line 241, in eval_evec
    eval, evec = eigs_func(ac, k, **kwargs)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 1260, in eigs
    symmetric=False, tol=tol)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 1030, in get_OPinv_matvec
    return SpLuInv(A.tocsc()).matvec
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 898, in __init__
    self.M_lu = splu(M)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\dsolve\linsolve.py", line 242, in splu
    ilu=False, options=_options)
ValueError: sparse matrix arrays must be 1-D C-contigous and of proper sizes and types

======================================================================
ERROR: test_arpack.test_real_nonsymmetric_modes(False, <std-real-nonsym>, 'd', 2, 'LR', None, (0.1+0.1j), <class 'scipy.
sparse.csr.csr_matrix'>, 'r')
----------------------------------------------------------------------
Traceback (most recent call last):
  File "X:\Python27\lib\site-packages\nose\case.py", line 197, in runTest
    self.test(*self.arg)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\tests\test_arpack.py", line 241, in eval_evec
    eval, evec = eigs_func(ac, k, **kwargs)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 1260, in eigs
    symmetric=False, tol=tol)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 1030, in get_OPinv_matvec
    return SpLuInv(A.tocsc()).matvec
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 898, in __init__
    self.M_lu = splu(M)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\dsolve\linsolve.py", line 242, in splu
    ilu=False, options=_options)
ValueError: sparse matrix arrays must be 1-D C-contigous and of proper sizes and types

======================================================================
ERROR: test_arpack.test_real_nonsymmetric_modes(False, <std-real-nonsym>, 'd', 2, 'LR', None, (0.1+0.1j), <class 'scipy.
sparse.csr.csr_matrix'>, 'i')
----------------------------------------------------------------------
Traceback (most recent call last):
  File "X:\Python27\lib\site-packages\nose\case.py", line 197, in runTest
    self.test(*self.arg)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\tests\test_arpack.py", line 241, in eval_evec
    eval, evec = eigs_func(ac, k, **kwargs)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 1260, in eigs
    symmetric=False, tol=tol)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 1030, in get_OPinv_matvec
    return SpLuInv(A.tocsc()).matvec
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 898, in __init__
    self.M_lu = splu(M)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\dsolve\linsolve.py", line 242, in splu
    ilu=False, options=_options)
ValueError: sparse matrix arrays must be 1-D C-contigous and of proper sizes and types

======================================================================
ERROR: test_arpack.test_real_nonsymmetric_modes(False, <gen-real-nonsym>, 'd', 2, 'LM', None, (0.1+0.1j), <class 'scipy.
sparse.csr.csr_matrix'>, 'i')
----------------------------------------------------------------------
Traceback (most recent call last):
  File "X:\Python27\lib\site-packages\nose\case.py", line 197, in runTest
    self.test(*self.arg)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\tests\test_arpack.py", line 235, in eval_evec
    eval, evec = eigs_func(ac, k, bc, **kwargs)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 1260, in eigs
    symmetric=False, tol=tol)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 1046, in get_OPinv_matvec
    return SpLuInv(OP.tocsc()).matvec
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 898, in __init__
    self.M_lu = splu(M)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\dsolve\linsolve.py", line 242, in splu
    ilu=False, options=_options)
ValueError: sparse matrix arrays must be 1-D C-contigous and of proper sizes and types

======================================================================
ERROR: test_arpack.test_real_nonsymmetric_modes(False, <gen-real-nonsym>, 'd', 2, 'LR', None, (0.1+0.1j), <class 'scipy.
sparse.csr.csr_matrix'>, 'r')
----------------------------------------------------------------------
Traceback (most recent call last):
  File "X:\Python27\lib\site-packages\nose\case.py", line 197, in runTest
    self.test(*self.arg)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\tests\test_arpack.py", line 235, in eval_evec
    eval, evec = eigs_func(ac, k, bc, **kwargs)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 1260, in eigs
    symmetric=False, tol=tol)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 1046, in get_OPinv_matvec
    return SpLuInv(OP.tocsc()).matvec
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 898, in __init__
    self.M_lu = splu(M)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\dsolve\linsolve.py", line 242, in splu
    ilu=False, options=_options)
ValueError: sparse matrix arrays must be 1-D C-contigous and of proper sizes and types

======================================================================
ERROR: test_arpack.test_real_nonsymmetric_modes(False, <gen-real-nonsym>, 'd', 2, 'LR', None, (0.1+0.1j), <class 'scipy.
sparse.csr.csr_matrix'>, 'i')
----------------------------------------------------------------------
Traceback (most recent call last):
  File "X:\Python27\lib\site-packages\nose\case.py", line 197, in runTest
    self.test(*self.arg)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\tests\test_arpack.py", line 235, in eval_evec
    eval, evec = eigs_func(ac, k, bc, **kwargs)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 1260, in eigs
    symmetric=False, tol=tol)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 1046, in get_OPinv_matvec
    return SpLuInv(OP.tocsc()).matvec
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 898, in __init__
    self.M_lu = splu(M)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\dsolve\linsolve.py", line 242, in splu
    ilu=False, options=_options)
ValueError: sparse matrix arrays must be 1-D C-contigous and of proper sizes and types

======================================================================
ERROR: test_arpack.test_real_nonsymmetric_modes(False, <gen-real-nonsym>, 'd', 2, 'LI', None, (0.1+0.1j), <class 'scipy.
sparse.csr.csr_matrix'>, 'r')
----------------------------------------------------------------------
Traceback (most recent call last):
  File "X:\Python27\lib\site-packages\nose\case.py", line 197, in runTest
    self.test(*self.arg)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\tests\test_arpack.py", line 235, in eval_evec
    eval, evec = eigs_func(ac, k, bc, **kwargs)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 1260, in eigs
    symmetric=False, tol=tol)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 1046, in get_OPinv_matvec
    return SpLuInv(OP.tocsc()).matvec
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 898, in __init__
    self.M_lu = splu(M)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\dsolve\linsolve.py", line 242, in splu
    ilu=False, options=_options)
ValueError: sparse matrix arrays must be 1-D C-contigous and of proper sizes and types

======================================================================
ERROR: test_arpack.test_complex_nonsymmetric_modes(False, <std-cmplx-nonsym>, 'D', 2, 'LM', None, (0.1+0.1j), <class 'sc
ipy.sparse.csr.csr_matrix'>)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "X:\Python27\lib\site-packages\nose\case.py", line 197, in runTest
    self.test(*self.arg)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\tests\test_arpack.py", line 241, in eval_evec
    eval, evec = eigs_func(ac, k, **kwargs)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 1260, in eigs
    symmetric=False, tol=tol)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 1030, in get_OPinv_matvec
    return SpLuInv(A.tocsc()).matvec
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 898, in __init__
    self.M_lu = splu(M)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\dsolve\linsolve.py", line 242, in splu
    ilu=False, options=_options)
ValueError: sparse matrix arrays must be 1-D C-contigous and of proper sizes and types

======================================================================
ERROR: test_arpack.test_complex_nonsymmetric_modes(False, <std-cmplx-nonsym>, 'D', 2, 'LI', None, (0.1+0.1j), <class 'sc
ipy.sparse.csr.csr_matrix'>)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "X:\Python27\lib\site-packages\nose\case.py", line 197, in runTest
    self.test(*self.arg)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\tests\test_arpack.py", line 241, in eval_evec
    eval, evec = eigs_func(ac, k, **kwargs)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 1260, in eigs
    symmetric=False, tol=tol)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 1030, in get_OPinv_matvec
    return SpLuInv(A.tocsc()).matvec
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 898, in __init__
    self.M_lu = splu(M)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\dsolve\linsolve.py", line 242, in splu
    ilu=False, options=_options)
ValueError: sparse matrix arrays must be 1-D C-contigous and of proper sizes and types

======================================================================
ERROR: test_arpack.test_complex_nonsymmetric_modes(False, <std-cmplx-nonsym>, 'D', 2, 'LI', None, 0.1, <class 'scipy.spa
rse.csr.csr_matrix'>)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "X:\Python27\lib\site-packages\nose\case.py", line 197, in runTest
    self.test(*self.arg)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\tests\test_arpack.py", line 241, in eval_evec
    eval, evec = eigs_func(ac, k, **kwargs)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 1260, in eigs
    symmetric=False, tol=tol)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 1030, in get_OPinv_matvec
    return SpLuInv(A.tocsc()).matvec
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 898, in __init__
    self.M_lu = splu(M)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\dsolve\linsolve.py", line 242, in splu
    ilu=False, options=_options)
ValueError: sparse matrix arrays must be 1-D C-contigous and of proper sizes and types

======================================================================
ERROR: test_arpack.test_complex_nonsymmetric_modes(False, <std-cmplx-nonsym>, 'F', 2, 'LM', None, (0.1+0.1j), <class 'sc
ipy.sparse.csr.csr_matrix'>)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "X:\Python27\lib\site-packages\nose\case.py", line 197, in runTest
    self.test(*self.arg)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\tests\test_arpack.py", line 241, in eval_evec
    eval, evec = eigs_func(ac, k, **kwargs)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 1260, in eigs
    symmetric=False, tol=tol)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 1030, in get_OPinv_matvec
    return SpLuInv(A.tocsc()).matvec
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 898, in __init__
    self.M_lu = splu(M)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\dsolve\linsolve.py", line 242, in splu
    ilu=False, options=_options)
ValueError: sparse matrix arrays must be 1-D C-contigous and of proper sizes and types

======================================================================
ERROR: test_arpack.test_complex_nonsymmetric_modes(False, <std-cmplx-nonsym>, 'F', 2, 'LM', None, 0.1, <class 'scipy.spa
rse.csr.csr_matrix'>)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "X:\Python27\lib\site-packages\nose\case.py", line 197, in runTest
    self.test(*self.arg)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\tests\test_arpack.py", line 241, in eval_evec
    eval, evec = eigs_func(ac, k, **kwargs)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 1260, in eigs
    symmetric=False, tol=tol)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 1030, in get_OPinv_matvec
    return SpLuInv(A.tocsc()).matvec
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 898, in __init__
    self.M_lu = splu(M)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\dsolve\linsolve.py", line 242, in splu
    ilu=False, options=_options)
ValueError: sparse matrix arrays must be 1-D C-contigous and of proper sizes and types

======================================================================
ERROR: test_arpack.test_complex_nonsymmetric_modes(False, <std-cmplx-nonsym>, 'F', 2, 'LR', None, (0.1+0.1j), <class 'sc
ipy.sparse.csr.csr_matrix'>)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "X:\Python27\lib\site-packages\nose\case.py", line 197, in runTest
    self.test(*self.arg)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\tests\test_arpack.py", line 241, in eval_evec
    eval, evec = eigs_func(ac, k, **kwargs)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 1260, in eigs
    symmetric=False, tol=tol)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 1030, in get_OPinv_matvec
    return SpLuInv(A.tocsc()).matvec
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 898, in __init__
    self.M_lu = splu(M)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\dsolve\linsolve.py", line 242, in splu
    ilu=False, options=_options)
ValueError: sparse matrix arrays must be 1-D C-contigous and of proper sizes and types

======================================================================
ERROR: test_arpack.test_complex_nonsymmetric_modes(False, <std-cmplx-nonsym>, 'F', 2, 'LR', None, 0.1, <class 'scipy.spa
rse.csr.csr_matrix'>)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "X:\Python27\lib\site-packages\nose\case.py", line 197, in runTest
    self.test(*self.arg)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\tests\test_arpack.py", line 241, in eval_evec
    eval, evec = eigs_func(ac, k, **kwargs)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 1260, in eigs
    symmetric=False, tol=tol)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 1030, in get_OPinv_matvec
    return SpLuInv(A.tocsc()).matvec
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 898, in __init__
    self.M_lu = splu(M)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\dsolve\linsolve.py", line 242, in splu
    ilu=False, options=_options)
ValueError: sparse matrix arrays must be 1-D C-contigous and of proper sizes and types

======================================================================
ERROR: test_arpack.test_complex_nonsymmetric_modes(False, <std-cmplx-nonsym>, 'F', 2, 'LI', None, (0.1+0.1j), <class 'sc
ipy.sparse.csr.csr_matrix'>)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "X:\Python27\lib\site-packages\nose\case.py", line 197, in runTest
    self.test(*self.arg)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\tests\test_arpack.py", line 241, in eval_evec
    eval, evec = eigs_func(ac, k, **kwargs)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 1260, in eigs
    symmetric=False, tol=tol)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 1030, in get_OPinv_matvec
    return SpLuInv(A.tocsc()).matvec
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 898, in __init__
    self.M_lu = splu(M)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\dsolve\linsolve.py", line 242, in splu
    ilu=False, options=_options)
ValueError: sparse matrix arrays must be 1-D C-contigous and of proper sizes and types

======================================================================
ERROR: test_arpack.test_complex_nonsymmetric_modes(False, <std-cmplx-nonsym>, 'F', 2, 'LI', None, 0.1, <class 'scipy.spa
rse.csr.csr_matrix'>)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "X:\Python27\lib\site-packages\nose\case.py", line 197, in runTest
    self.test(*self.arg)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\tests\test_arpack.py", line 241, in eval_evec
    eval, evec = eigs_func(ac, k, **kwargs)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 1260, in eigs
    symmetric=False, tol=tol)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 1030, in get_OPinv_matvec
    return SpLuInv(A.tocsc()).matvec
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 898, in __init__
    self.M_lu = splu(M)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\dsolve\linsolve.py", line 242, in splu
    ilu=False, options=_options)
ValueError: sparse matrix arrays must be 1-D C-contigous and of proper sizes and types

======================================================================
ERROR: test_arpack.test_complex_nonsymmetric_modes(False, <gen-cmplx-nonsym>, 'D', 2, 'LR', None, 0.1, <class 'scipy.spa
rse.csr.csr_matrix'>)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "X:\Python27\lib\site-packages\nose\case.py", line 197, in runTest
    self.test(*self.arg)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\tests\test_arpack.py", line 235, in eval_evec
    eval, evec = eigs_func(ac, k, bc, **kwargs)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 1260, in eigs
    symmetric=False, tol=tol)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 1046, in get_OPinv_matvec
    return SpLuInv(OP.tocsc()).matvec
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 898, in __init__
    self.M_lu = splu(M)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\dsolve\linsolve.py", line 242, in splu
    ilu=False, options=_options)
ValueError: sparse matrix arrays must be 1-D C-contigous and of proper sizes and types

======================================================================
ERROR: test_arpack.test_complex_nonsymmetric_modes(False, <gen-cmplx-nonsym>, 'D', 2, 'LI', None, (0.1+0.1j), <class 'sc
ipy.sparse.csr.csr_matrix'>)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "X:\Python27\lib\site-packages\nose\case.py", line 197, in runTest
    self.test(*self.arg)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\tests\test_arpack.py", line 235, in eval_evec
    eval, evec = eigs_func(ac, k, bc, **kwargs)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 1260, in eigs
    symmetric=False, tol=tol)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 1046, in get_OPinv_matvec
    return SpLuInv(OP.tocsc()).matvec
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 898, in __init__
    self.M_lu = splu(M)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\dsolve\linsolve.py", line 242, in splu
    ilu=False, options=_options)
ValueError: sparse matrix arrays must be 1-D C-contigous and of proper sizes and types

======================================================================
ERROR: test_arpack.test_complex_nonsymmetric_modes(False, <gen-cmplx-nonsym>, 'D', 2, 'LI', None, 0.1, <class 'scipy.spa
rse.csr.csr_matrix'>)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "X:\Python27\lib\site-packages\nose\case.py", line 197, in runTest
    self.test(*self.arg)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\tests\test_arpack.py", line 235, in eval_evec
    eval, evec = eigs_func(ac, k, bc, **kwargs)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 1260, in eigs
    symmetric=False, tol=tol)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 1046, in get_OPinv_matvec
    return SpLuInv(OP.tocsc()).matvec
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 898, in __init__
    self.M_lu = splu(M)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\dsolve\linsolve.py", line 242, in splu
    ilu=False, options=_options)
ValueError: sparse matrix arrays must be 1-D C-contigous and of proper sizes and types

======================================================================
ERROR: test_padecases_dtype_sparse_complex (test_matfuncs.TestExpM)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\tests\test_matfuncs.py", line 149, in test_padecases_dtype_spa
rse_complex
    assert_array_almost_equal_nulp(expm(a).toarray(), e, nulp=100)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\matfuncs.py", line 605, in expm
    return _solve_P_Q(U, V, structure=structure)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\matfuncs.py", line 657, in _solve_P_Q
    return spsolve(Q, P)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\dsolve\linsolve.py", line 151, in spsolve
    Afactsolve = factorized(A)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\dsolve\linsolve.py", line 366, in factorized
    return splu(A).solve
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\dsolve\linsolve.py", line 242, in splu
    ilu=False, options=_options)
ValueError: sparse matrix arrays must be 1-D C-contigous and of proper sizes and types

======================================================================
ERROR: test_base.TestBSRNonCanonical.test_solve
----------------------------------------------------------------------
Traceback (most recent call last):
  File "X:\Python27\lib\site-packages\nose\case.py", line 197, in runTest
    self.test(*self.arg)
  File "X:\Python27\lib\site-packages\scipy\sparse\tests\test_base.py", line 1920, in test_solve
    x = splu(A).solve(r)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\dsolve\linsolve.py", line 242, in splu
    ilu=False, options=_options)
ValueError: sparse matrix arrays must be 1-D C-contigous and of proper sizes and types

======================================================================
ERROR: test_base.TestCOO.test_solve
----------------------------------------------------------------------
Traceback (most recent call last):
  File "X:\Python27\lib\site-packages\nose\case.py", line 197, in runTest
    self.test(*self.arg)
  File "X:\Python27\lib\site-packages\scipy\sparse\tests\test_base.py", line 1920, in test_solve
    x = splu(A).solve(r)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\dsolve\linsolve.py", line 242, in splu
    ilu=False, options=_options)
ValueError: sparse matrix arrays must be 1-D C-contigous and of proper sizes and types

======================================================================
ERROR: test_base.TestCOONonCanonical.test_solve
----------------------------------------------------------------------
Traceback (most recent call last):
  File "X:\Python27\lib\site-packages\nose\case.py", line 197, in runTest
    self.test(*self.arg)
  File "X:\Python27\lib\site-packages\scipy\sparse\tests\test_base.py", line 1920, in test_solve
    x = splu(A).solve(r)
  File "X:\Python27\lib\site-packages\scipy\sparse\linalg\dsolve\linsolve.py", line 242, in splu
    ilu=False, options=_options)
ValueError: sparse matrix arrays must be 1-D C-contigous and of proper sizes and types

----------------------------------------------------------------------
Ran 16528 tests in 157.008s

FAILED (KNOWNFAIL=277, SKIP=1147, errors=35)
-------------- next part --------------
Running unit tests for scipy
NumPy version 1.8.1
NumPy is installed in X:\Python27-x64\lib\site-packages\numpy
SciPy version 0.14.0c1
SciPy is installed in X:\Python27-x64\lib\site-packages\scipy
Python version 2.7.6 (default, Nov 10 2013, 19:24:24) [MSC v.1500 64 bit (AMD64)]
nose version 1.3.1
X:\Python27-x64\lib\site-packages\numpy\lib\utils.py:134: DeprecationWarning: `scipy.lib.blas` is deprecated, use `scipy
.linalg.blas` instead!
  warnings.warn(depdoc, DeprecationWarning)
X:\Python27-x64\lib\site-packages\numpy\lib\utils.py:134: DeprecationWarning: `scipy.lib.lapack` is deprecated, use `sci
py.linalg.lapack` instead!
  warnings.warn(depdoc, DeprecationWarning)
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.........K..K................................X:\Python27-x64\lib\site-packages\scipy\interpolate\tests\test_interpolate.
py:833: RuntimeWarning: invalid value encountered in true_divide
  res /= cres
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....................0-th dimension must be fixed to 3 but got 15
..0-th dimension must be fixed to 3 but got 5
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............................X:\Python27-x64\lib\site-packages\scipy\optimize\linesearch.py:358: RuntimeWarning: invalid
value encountered in greater
  if (phi_a1 > phi0 + c1 * alpha1 * derphi0) or \
X:\Python27-x64\lib\site-packages\scipy\optimize\linesearch.py:359: RuntimeWarning: invalid value encountered in greater
_equal
  ((phi_a1 >= phi_a0) and (i > 1)):
........................K.....................X:\Python27-x64\lib\site-packages\scipy\optimize\slsqp.py:334: RuntimeWarn
ing: invalid value encountered in greater
  bnderr = where(bnds[:, 0] > bnds[:, 1])[0]
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.K.K...S..........S........................S................................................SS..........................
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...S...S..SSS.SSSS.SSSSSSSSSS....SSSKKKSSS....SSSKKK......S....SSSKKKSSSKKK.............................................
....SSS.S..............SSSSK...SSSS....................................S.........S.........K............SSKK.......S...S
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SS....SSSKKKSSS..S.SSSKKK......S....SSSKKKSSSKKK..........................................SS.....SSS.S..............SSSS
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...........X:\Python27-x64\lib\site-packages\scipy\stats\_distn_infrastructure.py:767: RuntimeWarning: invalid value enc
ountered in greater
  cond = logical_and(cond, (asarray(arg) > 0))
X:\Python27-x64\lib\site-packages\scipy\stats\_distn_infrastructure.py:2858: RuntimeWarning: invalid value encountered i
n greater_equal
  cond1 = (k >= self.a) & (k < self.b)
X:\Python27-x64\lib\site-packages\scipy\stats\_distn_infrastructure.py:2858: RuntimeWarning: invalid value encountered i
n less
  cond1 = (k >= self.a) & (k < self.b)
X:\Python27-x64\lib\site-packages\scipy\stats\_distn_infrastructure.py:2859: RuntimeWarning: invalid value encountered i
n greater_equal
  cond2 = (k >= self.b)
X:\Python27-x64\lib\site-packages\scipy\stats\_distn_infrastructure.py:2819: RuntimeWarning: invalid value encountered i
n greater_equal
  cond1 = (k >= self.a) & (k < self.b)
X:\Python27-x64\lib\site-packages\scipy\stats\_distn_infrastructure.py:2819: RuntimeWarning: invalid value encountered i
n less
  cond1 = (k >= self.a) & (k < self.b)
X:\Python27-x64\lib\site-packages\scipy\stats\_distn_infrastructure.py:2820: RuntimeWarning: invalid value encountered i
n greater_equal
  cond2 = (k >= self.b)
X:\Python27-x64\lib\site-packages\scipy\stats\_distn_infrastructure.py:2939: RuntimeWarning: invalid value encountered i
n greater_equal
  cond1 = (k >= self.a) & (k <= self.b)
X:\Python27-x64\lib\site-packages\scipy\stats\_distn_infrastructure.py:2939: RuntimeWarning: invalid value encountered i
n less_equal
  cond1 = (k >= self.a) & (k <= self.b)
X:\Python27-x64\lib\site-packages\scipy\stats\_distn_infrastructure.py:2940: RuntimeWarning: invalid value encountered i
n less
  cond2 = (k < self.a) & cond0
X:\Python27-x64\lib\site-packages\scipy\stats\_distn_infrastructure.py:2898: RuntimeWarning: invalid value encountered i
n greater_equal
  cond1 = (k >= self.a) & (k <= self.b)
X:\Python27-x64\lib\site-packages\scipy\stats\_distn_infrastructure.py:2898: RuntimeWarning: invalid value encountered i
n less_equal
  cond1 = (k >= self.a) & (k <= self.b)
X:\Python27-x64\lib\site-packages\scipy\stats\_distn_infrastructure.py:2899: RuntimeWarning: invalid value encountered i
n less
  cond2 = (k < self.a) & cond0
X:\Python27-x64\lib\site-packages\scipy\stats\_distn_infrastructure.py:2746: RuntimeWarning: invalid value encountered i
n greater_equal
  cond1 = (k >= self.a) & (k <= self.b) & self._nonzero(k, *args)
X:\Python27-x64\lib\site-packages\scipy\stats\_distn_infrastructure.py:2746: RuntimeWarning: invalid value encountered i
n less_equal
  cond1 = (k >= self.a) & (k <= self.b) & self._nonzero(k, *args)
X:\Python27-x64\lib\site-packages\scipy\stats\_distn_infrastructure.py:2782: RuntimeWarning: invalid value encountered i
n greater_equal
  cond1 = (k >= self.a) & (k <= self.b) & self._nonzero(k, *args)
X:\Python27-x64\lib\site-packages\scipy\stats\_distn_infrastructure.py:2782: RuntimeWarning: invalid value encountered i
n less_equal
  cond1 = (k >= self.a) & (k <= self.b) & self._nonzero(k, *args)
X:\Python27-x64\lib\site-packages\scipy\stats\_distn_infrastructure.py:2979: RuntimeWarning: invalid value encountered i
n greater
  cond1 = (q > 0) & (q < 1)
X:\Python27-x64\lib\site-packages\scipy\stats\_distn_infrastructure.py:2979: RuntimeWarning: invalid value encountered i
n less
  cond1 = (q > 0) & (q < 1)
X:\Python27-x64\lib\site-packages\scipy\stats\_distn_infrastructure.py:3019: RuntimeWarning: invalid value encountered i
n greater
  cond1 = (q > 0) & (q < 1)
X:\Python27-x64\lib\site-packages\scipy\stats\_distn_infrastructure.py:3019: RuntimeWarning: invalid value encountered i
n less
  cond1 = (q > 0) & (q < 1)
.........................................................................................................S..S...........
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...X:\Python27-x64\lib\site-packages\numpy\ma\core.py:778: RuntimeWarning: invalid value encountered in absolute
  return umath.absolute(a) * self.tolerance >= umath.absolute(b)
.............................................................S.SSS....SSSSSS............................................
.....................................................
======================================================================
ERROR: test_arpack.test_symmetric_modes(True, <gen-symmetric>, 'f', 2, 'LM', None, None, <function aslinearoperator at 0
x00000000076D9B38>, None, 'normal')
----------------------------------------------------------------------
Traceback (most recent call last):
  File "X:\Python27-x64\lib\site-packages\nose\case.py", line 197, in runTest
    self.test(*self.arg)
  File "X:\Python27-x64\lib\site-packages\scipy\sparse\linalg\eigen\arpack\tests\test_arpack.py", line 238, in eval_evec

    eval, evec = eigs_func(ac, k, bc, **kwargs)
  File "X:\Python27-x64\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 1568, in eigsh
    params.iterate()
  File "X:\Python27-x64\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 562, in iterate
    self._raise_no_convergence()
  File "X:\Python27-x64\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 369, in _raise_no_convergenc
e
    raise ArpackNoConvergence(msg % (num_iter, k_ok, self.k), ev, vec)
ArpackNoConvergence: ARPACK error -1: No convergence (121 iterations, 0/2 eigenvectors converged)

======================================================================
ERROR: test_arpack.test_symmetric_modes(True, <gen-symmetric>, 'f', 2, 'SM', None, None, <function aslinearoperator at 0
x00000000076D9B38>, None, 'normal')
----------------------------------------------------------------------
Traceback (most recent call last):
  File "X:\Python27-x64\lib\site-packages\nose\case.py", line 197, in runTest
    self.test(*self.arg)
  File "X:\Python27-x64\lib\site-packages\scipy\sparse\linalg\eigen\arpack\tests\test_arpack.py", line 238, in eval_evec

    eval, evec = eigs_func(ac, k, bc, **kwargs)
  File "X:\Python27-x64\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 1568, in eigsh
    params.iterate()
  File "X:\Python27-x64\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 562, in iterate
    self._raise_no_convergence()
  File "X:\Python27-x64\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 369, in _raise_no_convergenc
e
    raise ArpackNoConvergence(msg % (num_iter, k_ok, self.k), ev, vec)
ArpackNoConvergence: ARPACK error -1: No convergence (121 iterations, 1/2 eigenvectors converged)

======================================================================
ERROR: test_arpack.test_symmetric_modes(True, <gen-symmetric>, 'f', 2, 'SA', None, None, <function aslinearoperator at 0
x00000000076D9B38>, None, 'normal')
----------------------------------------------------------------------
Traceback (most recent call last):
  File "X:\Python27-x64\lib\site-packages\nose\case.py", line 197, in runTest
    self.test(*self.arg)
  File "X:\Python27-x64\lib\site-packages\scipy\sparse\linalg\eigen\arpack\tests\test_arpack.py", line 238, in eval_evec

    eval, evec = eigs_func(ac, k, bc, **kwargs)
  File "X:\Python27-x64\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 1568, in eigsh
    params.iterate()
  File "X:\Python27-x64\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 562, in iterate
    self._raise_no_convergence()
  File "X:\Python27-x64\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 369, in _raise_no_convergenc
e
    raise ArpackNoConvergence(msg % (num_iter, k_ok, self.k), ev, vec)
ArpackNoConvergence: ARPACK error -1: No convergence (121 iterations, 1/2 eigenvectors converged)

======================================================================
ERROR: test_arpack.test_symmetric_modes(True, <gen-symmetric>, 'f', 2, 'BE', None, None, <function aslinearoperator at 0
x00000000076D9B38>, None, 'normal')
----------------------------------------------------------------------
Traceback (most recent call last):
  File "X:\Python27-x64\lib\site-packages\nose\case.py", line 197, in runTest
    self.test(*self.arg)
  File "X:\Python27-x64\lib\site-packages\scipy\sparse\linalg\eigen\arpack\tests\test_arpack.py", line 238, in eval_evec

    eval, evec = eigs_func(ac, k, bc, **kwargs)
  File "X:\Python27-x64\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 1568, in eigsh
    params.iterate()
  File "X:\Python27-x64\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 562, in iterate
    self._raise_no_convergence()
  File "X:\Python27-x64\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 369, in _raise_no_convergenc
e
    raise ArpackNoConvergence(msg % (num_iter, k_ok, self.k), ev, vec)
ArpackNoConvergence: ARPACK error -1: No convergence (121 iterations, 0/2 eigenvectors converged)

======================================================================
FAIL: test_arpack.test_symmetric_modes(True, <gen-symmetric>, 'f', 2, 'LA', None, None, <function aslinearoperator at 0x
00000000076D9B38>, None, 'normal')
----------------------------------------------------------------------
Traceback (most recent call last):
  File "X:\Python27-x64\lib\site-packages\nose\case.py", line 197, in runTest
    self.test(*self.arg)
  File "X:\Python27-x64\lib\site-packages\scipy\sparse\linalg\eigen\arpack\tests\test_arpack.py", line 270, in eval_evec

    assert_allclose_cc(eval, exact_eval, rtol=rtol, atol=atol, err_msg=err)
  File "X:\Python27-x64\lib\site-packages\scipy\sparse\linalg\eigen\arpack\tests\test_arpack.py", line 130, in assert_al
lclose_cc
    assert_allclose(actual, conj(desired), **kw)
  File "X:\Python27-x64\lib\site-packages\numpy\testing\utils.py", line 1183, in assert_allclose
    verbose=verbose, header=header)
  File "X:\Python27-x64\lib\site-packages\numpy\testing\utils.py", line 644, in assert_array_compare
    raise AssertionError(msg)
AssertionError:
Not equal to tolerance rtol=0.00178814, atol=0.000357628
error for eigsh:general, typ=f, which=LA, sigma=None, mattype=aslinearoperator, OPpart=None, mode=normal
(mismatch 100.0%)
 x: array([ 179.96405029,  249.00842285], dtype=float32)
 y: array([  5.23087454-0.j,  34.52712250-0.j], dtype=complex64)

======================================================================
FAIL: test_arpack.test_real_nonsymmetric_modes(False, <gen-real-nonsym>, 'f', 2, 'LM', None, None, <function aslinearope
rator at 0x00000000076D9B38>, None)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "X:\Python27-x64\lib\site-packages\nose\case.py", line 197, in runTest
    self.test(*self.arg)
  File "X:\Python27-x64\lib\site-packages\scipy\sparse\linalg\eigen\arpack\tests\test_arpack.py", line 270, in eval_evec

    assert_allclose_cc(eval, exact_eval, rtol=rtol, atol=atol, err_msg=err)
  File "X:\Python27-x64\lib\site-packages\scipy\sparse\linalg\eigen\arpack\tests\test_arpack.py", line 130, in assert_al
lclose_cc
    assert_allclose(actual, conj(desired), **kw)
  File "X:\Python27-x64\lib\site-packages\numpy\testing\utils.py", line 1183, in assert_allclose
    verbose=verbose, header=header)
  File "X:\Python27-x64\lib\site-packages\numpy\testing\utils.py", line 644, in assert_array_compare
    raise AssertionError(msg)
AssertionError:
Not equal to tolerance rtol=0.00178814, atol=0.000357628
error for eigs:general, typ=f, which=LM, sigma=None, mattype=aslinearoperator, OPpart=None, mode=normal
(mismatch 100.0%)
 x: array([ 0.23179591+0.83416843j,  0.23179591-0.83416843j], dtype=complex64)
 y: array([  2.00296926-0.j, -13.60746479-0.j], dtype=complex64)

======================================================================
FAIL: test_arpack.test_real_nonsymmetric_modes(False, <gen-real-nonsym>, 'f', 2, 'LR', None, None, <function aslinearope
rator at 0x00000000076D9B38>, None)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "X:\Python27-x64\lib\site-packages\nose\case.py", line 197, in runTest
    self.test(*self.arg)
  File "X:\Python27-x64\lib\site-packages\scipy\sparse\linalg\eigen\arpack\tests\test_arpack.py", line 270, in eval_evec

    assert_allclose_cc(eval, exact_eval, rtol=rtol, atol=atol, err_msg=err)
  File "X:\Python27-x64\lib\site-packages\scipy\sparse\linalg\eigen\arpack\tests\test_arpack.py", line 130, in assert_al
lclose_cc
    assert_allclose(actual, conj(desired), **kw)
  File "X:\Python27-x64\lib\site-packages\numpy\testing\utils.py", line 1183, in assert_allclose
    verbose=verbose, header=header)
  File "X:\Python27-x64\lib\site-packages\numpy\testing\utils.py", line 644, in assert_array_compare
    raise AssertionError(msg)
AssertionError:
Not equal to tolerance rtol=0.00178814, atol=0.000357628
error for eigs:general, typ=f, which=LR, sigma=None, mattype=aslinearoperator, OPpart=None, mode=normal
(mismatch 100.0%)
 x: array([ 0.60024601+0.j,  0.76261067+0.j], dtype=complex64)
 y: array([ 0.77774024+1.81930017j,  2.00296926-0.j        ], dtype=complex64)

======================================================================
FAIL: test_arpack.test_real_nonsymmetric_modes(False, <gen-real-nonsym>, 'f', 2, 'LI', None, None, <function aslinearope
rator at 0x00000000076D9B38>, None)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "X:\Python27-x64\lib\site-packages\nose\case.py", line 197, in runTest
    self.test(*self.arg)
  File "X:\Python27-x64\lib\site-packages\scipy\sparse\linalg\eigen\arpack\tests\test_arpack.py", line 270, in eval_evec

    assert_allclose_cc(eval, exact_eval, rtol=rtol, atol=atol, err_msg=err)
  File "X:\Python27-x64\lib\site-packages\scipy\sparse\linalg\eigen\arpack\tests\test_arpack.py", line 130, in assert_al
lclose_cc
    assert_allclose(actual, conj(desired), **kw)
  File "X:\Python27-x64\lib\site-packages\numpy\testing\utils.py", line 1183, in assert_allclose
    verbose=verbose, header=header)
  File "X:\Python27-x64\lib\site-packages\numpy\testing\utils.py", line 644, in assert_array_compare
    raise AssertionError(msg)
AssertionError:
Not equal to tolerance rtol=0.00178814, atol=0.000357628
error for eigs:general, typ=f, which=LI, sigma=None, mattype=aslinearoperator, OPpart=None, mode=normal
(mismatch 100.0%)
 x: array([ 0.2973851+0.36813122j,  0.2973851-0.36813122j], dtype=complex64)
 y: array([ 0.77774024-1.81930017j,  0.77774024+1.81930017j], dtype=complex64)

======================================================================
FAIL: test_iterative.test_convergence(<function gmres at 0x00000000076FAF98>, <poisson1d>)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "X:\Python27-x64\lib\site-packages\nose\case.py", line 197, in runTest
    self.test(*self.arg)
  File "X:\Python27-x64\lib\site-packages\scipy\sparse\linalg\isolve\tests\test_iterative.py", line 196, in check_conver
gence
    assert_normclose(A.dot(x), b, tol=tol)
  File "X:\Python27-x64\lib\site-packages\scipy\sparse\linalg\isolve\tests\test_iterative.py", line 178, in assert_normc
lose
    assert_(residual < tolerance, msg=msg)
  File "X:\Python27-x64\lib\site-packages\numpy\testing\utils.py", line 44, in assert_
    raise AssertionError(msg)
AssertionError: residual (129.548) not smaller than tolerance 1.43318

======================================================================
FAIL: test_iterative.test_convergence(<function gmres at 0x00000000076FAF98>, <neg-poisson1d>)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "X:\Python27-x64\lib\site-packages\nose\case.py", line 197, in runTest
    self.test(*self.arg)
  File "X:\Python27-x64\lib\site-packages\scipy\sparse\linalg\isolve\tests\test_iterative.py", line 196, in check_conver
gence
    assert_normclose(A.dot(x), b, tol=tol)
  File "X:\Python27-x64\lib\site-packages\scipy\sparse\linalg\isolve\tests\test_iterative.py", line 178, in assert_normc
lose
    assert_(residual < tolerance, msg=msg)
  File "X:\Python27-x64\lib\site-packages\numpy\testing\utils.py", line 44, in assert_
    raise AssertionError(msg)
AssertionError: residual (151.212) not smaller than tolerance 1.43318

======================================================================
FAIL: test_iterative.test_convergence(<function gmres at 0x00000000076FAF98>, <rand-diag>)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "X:\Python27-x64\lib\site-packages\nose\case.py", line 197, in runTest
    self.test(*self.arg)
  File "X:\Python27-x64\lib\site-packages\scipy\sparse\linalg\isolve\tests\test_iterative.py", line 196, in check_conver
gence
    assert_normclose(A.dot(x), b, tol=tol)
  File "X:\Python27-x64\lib\site-packages\scipy\sparse\linalg\isolve\tests\test_iterative.py", line 178, in assert_normc
lose
    assert_(residual < tolerance, msg=msg)
  File "X:\Python27-x64\lib\site-packages\numpy\testing\utils.py", line 44, in assert_
    raise AssertionError(msg)
AssertionError: residual (5.93818) not smaller than tolerance 0.168819

======================================================================
FAIL: test_iterative.test_convergence(<function gmres at 0x00000000076FAF98>, <rand>)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "X:\Python27-x64\lib\site-packages\nose\case.py", line 197, in runTest
    self.test(*self.arg)
  File "X:\Python27-x64\lib\site-packages\scipy\sparse\linalg\isolve\tests\test_iterative.py", line 196, in check_conver
gence
    assert_normclose(A.dot(x), b, tol=tol)
  File "X:\Python27-x64\lib\site-packages\scipy\sparse\linalg\isolve\tests\test_iterative.py", line 178, in assert_normc
lose
    assert_(residual < tolerance, msg=msg)
  File "X:\Python27-x64\lib\site-packages\numpy\testing\utils.py", line 44, in assert_
    raise AssertionError(msg)
AssertionError: residual (2.25456) not smaller than tolerance 0.0374166

======================================================================
FAIL: test_iterative.test_convergence(<function gmres at 0x00000000076FAF98>, <rand-sym>)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "X:\Python27-x64\lib\site-packages\nose\case.py", line 197, in runTest
    self.test(*self.arg)
  File "X:\Python27-x64\lib\site-packages\scipy\sparse\linalg\isolve\tests\test_iterative.py", line 196, in check_conver
gence
    assert_normclose(A.dot(x), b, tol=tol)
  File "X:\Python27-x64\lib\site-packages\scipy\sparse\linalg\isolve\tests\test_iterative.py", line 178, in assert_normc
lose
    assert_(residual < tolerance, msg=msg)
  File "X:\Python27-x64\lib\site-packages\numpy\testing\utils.py", line 44, in assert_
    raise AssertionError(msg)
AssertionError: residual (0.64031) not smaller than tolerance 0.0374166

======================================================================
FAIL: test_iterative.test_convergence(<function gmres at 0x00000000076FAF98>, <rand-sym-pd>)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "X:\Python27-x64\lib\site-packages\nose\case.py", line 197, in runTest
    self.test(*self.arg)
  File "X:\Python27-x64\lib\site-packages\scipy\sparse\linalg\isolve\tests\test_iterative.py", line 196, in check_conver
gence
    assert_normclose(A.dot(x), b, tol=tol)
  File "X:\Python27-x64\lib\site-packages\scipy\sparse\linalg\isolve\tests\test_iterative.py", line 178, in assert_normc
lose
    assert_(residual < tolerance, msg=msg)
  File "X:\Python27-x64\lib\site-packages\numpy\testing\utils.py", line 44, in assert_
    raise AssertionError(msg)
AssertionError: residual (8.30071) not smaller than tolerance 0.142829

----------------------------------------------------------------------
Ran 16528 tests in 172.190s

FAILED (KNOWNFAIL=277, SKIP=1147, errors=4, failures=10)

From jaime.frio at gmail.com  Thu Apr  3 17:35:03 2014
From: jaime.frio at gmail.com (=?ISO-8859-1?Q?Jaime_Fern=E1ndez_del_R=EDo?=)
Date: Thu, 3 Apr 2014 14:35:03 -0700
Subject: [SciPy-Dev] Algorithm used in minimum_filter1d
Message-ID: <CAPOWHWmg9_ff1gFvHSYasR4gB24tn6zUGL6sVHH3Gf_A_Kk3Aw@mail.gmail.com>

Hi,

A PR of mine was merged yesterday that uses an improved algorithm for the
computation of 1D minimum filters in ndimage, see here:

https://github.com/scipy/scipy/pull/3517

Looking a little more into this, I have found out that pandas' rolling_min
function uses a different algorithm. They attribute it to bottleneck, which
attributes it to Richard Harter:

http://www.richardhartersworld.com/cri/2001/slidingmin.html

Diving a little deeper in google scholar, the origin of the algorithm seems
to be this 1996 paper by Scott C. Douglas:

http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.42.2777&rep=rep1&type=pdf

In this last paper I found out that I had reinvented the MINLINE algorithm,
and that the one used in pandas and bottleneck is known as MINLIST.

If the sequence has n items and the filter is of size m, MINLIST has
guaranteed O(n) performance, but needs O(m) extra storage, while MINLINE
only has expected O(n) performance, and O(n.m) worst case, but uses O(1)
extra storage. Comparing pandas rolling_min with the new scipy's
minimum_filter1d, scipy runs about 3-4x faster for random data of any
length, although it is also several times slower for large windows on
strictly increasing sequences.

Would implementing MINLIST be a better option than MINLINE? It will likely
make the function slower than the old scipy implementation for small
windows, and the extra storage is annoying. But it does have a more
consistent performance over all possible inputs. In a way it is like
choosing between quicksort and mergesort. Perhaps implementing both and
having a user selectable switch is the way to go.

But before putting it all together, I'd like to hear some opinions on
whether this is worth it at all, and what the preferred option is.

Jaime

-- 
(\__/)
( O.o)
( > <) Este es Conejo. Copia a Conejo en tu firma y ay?dale en sus planes
de dominaci?n mundial.
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From cgohlke at uci.edu  Thu Apr  3 18:11:17 2014
From: cgohlke at uci.edu (Christoph Gohlke)
Date: Thu, 03 Apr 2014 15:11:17 -0700
Subject: [SciPy-Dev] ANN: Scipy 0.14.0 release candidate 1
In-Reply-To: <533DD3D1.1090102@uci.edu>
References: <CABL7CQgih6dwtyEHMTc-N0Rtk5yTRMJ5Mf8iqXMYfq5657myVA@mail.gmail.com>
	<533DD3D1.1090102@uci.edu>
Message-ID: <533DDC85.9000700@uci.edu>

On 4/3/2014 2:34 PM, Christoph Gohlke wrote:
> On 4/3/2014 1:14 PM, Ralf Gommers wrote:
>> Hi,
>>
>> I'm pleased to announce the availability of the first release candidate
>> of Scipy 0.14.0. Please try this RC and report any issues on the
>> scipy-dev mailing list. A significant number of fixes for scipy.sparse
>> went in after the beta release, so users of that module may want to test
>> this release carefully.
>>
>> Source tarballs, binaries and the full release notes can be found at
>> https://sourceforge.net/projects/scipy/files/scipy/0.14.0rc1/. The final
>> release will follow in one week if no new issues are found.
>>
>> A big thank you to everyone who contributed to this release!
>>
>> Ralf
>>
>>
>
> Hi Ralf,
>
> scipy.sparse.linalg fails many tests on Windows on all Python versions.
> I attached the results for Python 2.7 with the official binaries
> (scipy-0.14.0rc1-win32-superpack-python2.7, 32 errors) and my msvc/MKL
> builds for 32 bit (scipy-0.14.0c1.win32-py2.7, 35 errors) and 64 bit
> (scipy-0.14.0c1.win-amd64-py2.7, 4 errors, 10 failures). Other Python
> versions also fail. The 64 bit failures/errors were previously reported
> for Python 3.4 only at <https://github.com/scipy/scipy/issues/3306>.
> I'll try to build and test with numpy 1.7.x and see if that helps.
>
> Christoph
>

All tests pass when building and testing with numpy 1.7.2 instead of 
numpy 1.8.1 (tested on 32 and 64 bit Python 2.7).

Christoph


From benny.malengier at gmail.com  Fri Apr  4 03:19:15 2014
From: benny.malengier at gmail.com (Benny Malengier)
Date: Fri, 4 Apr 2014 09:19:15 +0200
Subject: [SciPy-Dev] Algorithm used in minimum_filter1d
In-Reply-To: <CAPOWHWmg9_ff1gFvHSYasR4gB24tn6zUGL6sVHH3Gf_A_Kk3Aw@mail.gmail.com>
References: <CAPOWHWmg9_ff1gFvHSYasR4gB24tn6zUGL6sVHH3Gf_A_Kk3Aw@mail.gmail.com>
Message-ID: <CAOyNq8pFnw2Ly3RJu2+yajcTRqoCdwH=6hT_p24TJf95fLUskQ@mail.gmail.com>

2014-04-03 23:35 GMT+02:00 Jaime Fern?ndez del R?o <jaime.frio at gmail.com>:

> Hi,
>
> A PR of mine was merged yesterday that uses an improved algorithm for the
> computation of 1D minimum filters in ndimage, see here:
>
> https://github.com/scipy/scip <https://github.com/scipy/scipy/pull/3517>
> Klik hier als je wilt Beantwoo
> y/pull/3517 <https://github.com/scipy/scipy/pull/3517>
>
> Looking a little more into this, I have found out that pandas' rolling_min
> function uses a different algorithm. They attribute it to bottleneck, which
> attributes it to Richard Harter:
>
> http://www.richardhartersworld.com/cri/2001/slidingmin.html
>
> Diving a little deeper in google scholar, the origin of the algorithm
> seems to be this 1996 paper by Scott C. Douglas:
>
>
> http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.42.2777&rep=rep1&type=pdf
>
> In this last paper I found out that I had reinvented the MINLINE
> algorithm, and that the one used in pandas and bottleneck is known as
> MINLIST.
>
> If the sequence has n items and the filter is of size m, MINLIST has
> guaranteed O(n) performance, but needs O(m) extra storage, while MINLINE
> only has expected O(n) performance, and O(n.m) worst case, but uses O(1)
> extra storage. Comparing pandas rolling_min with the new scipy's
> minimum_filter1d, scipy runs about 3-4x faster for random data of any
> length, although it is also several times slower for large windows on
> strictly increasing sequences.
>
> Would implementing MINLIST be a better option than MINLINE? It will likely
> make the function slower than the old scipy implementation for small
> windows, and the extra storage is annoying. But it does have a more
> consistent performance over all possible inputs. In a way it is like
> choosing between quicksort and mergesort. Perhaps implementing both and
> having a user selectable switch is the way to go.
>

Adding the reference to the docs is first step.
As both seem to have merit, a switch seems reasonable.

Benny


>
> But before putting it all together, I'd like to hear some opinions on
> whether this is worth it at all, and what the preferred option is.
>
> Jaime
>
> --
> (\__/)
> ( O.o)
> ( > <) Este es Conejo. Copia a Conejo en tu firma y ay?dale en sus planes
> de dominaci?n mundial.
>
> _______________________________________________
> SciPy-Dev mailing list
> SciPy-Dev at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-dev
>
>
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From ralf.gommers at gmail.com  Fri Apr  4 16:44:16 2014
From: ralf.gommers at gmail.com (Ralf Gommers)
Date: Fri, 4 Apr 2014 22:44:16 +0200
Subject: [SciPy-Dev] [Numpy-discussion] ANN: Scipy 0.14.0 release
 candidate 1
In-Reply-To: <CAH6Pt5o4EbyOXxgx_uwpws6pTqYq+tw91se8EsPi_LX1CWGNKQ@mail.gmail.com>
References: <CABL7CQgih6dwtyEHMTc-N0Rtk5yTRMJ5Mf8iqXMYfq5657myVA@mail.gmail.com>
	<CAH6Pt5o4EbyOXxgx_uwpws6pTqYq+tw91se8EsPi_LX1CWGNKQ@mail.gmail.com>
Message-ID: <CABL7CQhcYkCKt2Np2HPoYr3MtRdRHZeV10tUJP4MQQOtXcZMdA@mail.gmail.com>

On Thu, Apr 3, 2014 at 11:20 PM, Matthew Brett <matthew.brett at gmail.com>wrote:

> Hi Ralf,
>
> On Thu, Apr 3, 2014 at 1:14 PM, Ralf Gommers <ralf.gommers at gmail.com>
> wrote:
> > Hi,
> >
> > I'm pleased to announce the availability of the first release candidate
> of
> > Scipy 0.14.0. Please try this RC and report any issues on the scipy-dev
> > mailing list. A significant number of fixes for scipy.sparse went in
> after
> > the beta release, so users of that module may want to test this release
> > carefully.
> >
> > Source tarballs, binaries and the full release notes can be found at
> > https://sourceforge.net/projects/scipy/files/scipy/0.14.0rc1/. The final
> > release will follow in one week if no new issues are found.
> >
> > A big thank you to everyone who contributed to this release!
>
> Would you ming git-tag'ing?


Done.


> I'll build some wheels...
>

Thanks. If you want to do that for testing, then go for it. Probably they
won't get downloaded a lot from SF though, so if you'd want to do only the
final release (which can be put on PyPi) that's also fine of course.

Cheers,
Ralf


>
> Cheers,
>
> Matthew
> _______________________________________________
> SciPy-Dev mailing list
> SciPy-Dev at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-dev
>
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From matthew.brett at gmail.com  Sun Apr  6 02:11:42 2014
From: matthew.brett at gmail.com (Matthew Brett)
Date: Sat, 5 Apr 2014 23:11:42 -0700
Subject: [SciPy-Dev] [Numpy-discussion] ANN: Scipy 0.14.0 release
 candidate 1
In-Reply-To: <CABL7CQhcYkCKt2Np2HPoYr3MtRdRHZeV10tUJP4MQQOtXcZMdA@mail.gmail.com>
References: <CABL7CQgih6dwtyEHMTc-N0Rtk5yTRMJ5Mf8iqXMYfq5657myVA@mail.gmail.com>
	<CAH6Pt5o4EbyOXxgx_uwpws6pTqYq+tw91se8EsPi_LX1CWGNKQ@mail.gmail.com>
	<CABL7CQhcYkCKt2Np2HPoYr3MtRdRHZeV10tUJP4MQQOtXcZMdA@mail.gmail.com>
Message-ID: <CAH6Pt5pLO_mpXZq1bzUrAHgu9a+Br_Svc00rBOPMM3=1Bt_zLg@mail.gmail.com>

Hi,

On Fri, Apr 4, 2014 at 1:44 PM, Ralf Gommers <ralf.gommers at gmail.com> wrote:
>
>
>
> On Thu, Apr 3, 2014 at 11:20 PM, Matthew Brett <matthew.brett at gmail.com>
> wrote:
>>
>> Hi Ralf,
>>
>> On Thu, Apr 3, 2014 at 1:14 PM, Ralf Gommers <ralf.gommers at gmail.com>
>> wrote:
>> > Hi,
>> >
>> > I'm pleased to announce the availability of the first release candidate
>> > of
>> > Scipy 0.14.0. Please try this RC and report any issues on the scipy-dev
>> > mailing list. A significant number of fixes for scipy.sparse went in
>> > after
>> > the beta release, so users of that module may want to test this release
>> > carefully.
>> >
>> > Source tarballs, binaries and the full release notes can be found at
>> > https://sourceforge.net/projects/scipy/files/scipy/0.14.0rc1/. The final
>> > release will follow in one week if no new issues are found.
>> >
>> > A big thank you to everyone who contributed to this release!
>>
>> Would you ming git-tag'ing?
>
>
> Done.

Just FYI - this version describes itself as "0.14.0c1" because of a
missing 'd' in:

 +VERSION             = '%d.%d.%rc1' % (MAJOR, MINOR, MICRO)

I've built wheels for testing, here:

https://nipy.bic.berkeley.edu/scipy_installers/scipy-0.14.0c1-cp27-none-macosx_10_6_intel.whl
https://nipy.bic.berkeley.edu/scipy_installers/scipy-0.14.0c1-cp33-cp33m-macosx_10_6_intel.whl
https://nipy.bic.berkeley.edu/scipy_installers/scipy-0.14.0c1-cp34-cp34m-macosx_10_6_intel.whl

Usual procedure:

pip install -U pip
pip install --pre -f https://nipy.bic.berkeley.edu/scipy_installers scipy

should work, for a python.org  Python.  Please do let me know of any problems.

Cheers,

Matthew


From sholzergr at gmail.com  Sun Apr  6 07:18:21 2014
From: sholzergr at gmail.com (Severin Holzer-Graf)
Date: Sun, 6 Apr 2014 13:18:21 +0200
Subject: [SciPy-Dev] sparse.block_diag improvement
Message-ID: <CAFk72czyideNDnZd6CkyHVGF-31P04Myxva29_BdM8-gZEX3-g@mail.gmail.com>

Hi,

i had to generate very large sparse block diagonal matrices.
I found out that the existing sparse.block_diag is way to slow for
this and took over 30 secs to generate the matrix i needed.
The reason why the current sparse.block_diag implementation does that
very slow is because it generates everything in a row-wise manner,
which involves looping and a lot of memory.

I wrote my own method that creates the indptr and indices directly.
This works for equally shaped matrices that should be arranged in a
sparse block diagonal matrix.
A benchmark can be found here: http://de.tinypic.com/r/30ue59h/8
It's about a x600 speedup.

My question is, is there interest in integrating this in Scipy, and if
yes, how can this be done best?
I tried extending the current block_diag in a fork, but the indexing
trick only works for equally sized matrices. So either add a check in
the beginning of the existing block_diag and than executing the fast
way, or writing another function, especially for equally sized
matrices.

Best
Severin


From pav at iki.fi  Mon Apr  7 04:28:10 2014
From: pav at iki.fi (Pauli Virtanen)
Date: Mon, 7 Apr 2014 08:28:10 +0000 (UTC)
Subject: [SciPy-Dev] =?utf-8?q?sparse=2Eblock=5Fdiag_improvement?=
References: <CAFk72czyideNDnZd6CkyHVGF-31P04Myxva29_BdM8-gZEX3-g@mail.gmail.com>
Message-ID: <loom.20140407T102600-489@post.gmane.org>

Severin Holzer-Graf <sholzergr <at> gmail.com> writes:
[clip]
> My question is, is there interest in integrating this in Scipy, and if
> yes, how can this be done best?
> I tried extending the current block_diag in a fork, but the indexing
> trick only works for equally sized matrices. So either add a check in
> the beginning of the existing block_diag and than executing the fast
> way, or writing another function, especially for equally sized
> matrices.

The best way to do it is probably to write the "fast path"
as a separate function with a name starting with "_", and
add an if statement to block_diag calling that in cases
where it's possible to do it in a better way.

-- 
Pauli Virtanen



From sholzergr at gmail.com  Mon Apr  7 05:43:38 2014
From: sholzergr at gmail.com (Severin Holzer-Graf)
Date: Mon, 7 Apr 2014 11:43:38 +0200
Subject: [SciPy-Dev] sparse.block_diag improvement
In-Reply-To: <loom.20140407T102600-489@post.gmane.org>
References: <CAFk72czyideNDnZd6CkyHVGF-31P04Myxva29_BdM8-gZEX3-g@mail.gmail.com>
	<loom.20140407T102600-489@post.gmane.org>
Message-ID: <CAFk72cwCRGYm7defVigz7zx_0GyvzG_Go6q9YE=TDGMMeETdcw@mail.gmail.com>

2014-04-07 10:28 GMT+02:00 Pauli Virtanen <pav at iki.fi>:
> Severin Holzer-Graf <sholzergr <at> gmail.com> writes:
> [clip]
>> My question is, is there interest in integrating this in Scipy, and if
>> yes, how can this be done best?
>> I tried extending the current block_diag in a fork, but the indexing
>> trick only works for equally sized matrices. So either add a check in
>> the beginning of the existing block_diag and than executing the fast
>> way, or writing another function, especially for equally sized
>> matrices.
>
> The best way to do it is probably to write the "fast path"
> as a separate function with a name starting with "_", and
> add an if statement to block_diag calling that in cases
> where it's possible to do it in a better way.

Will do so and come back to you then.

There's also a case that happend to me quite often.
If i want to convert a dense matrix row's to a sparse block_diag
matrix. Then the data concatenation can be skipped and replaced by a
simple ".ravel()".
Should we add a optional parameter to block_diag or rather to this
automatically, if the supplied list consists only of one matrix?

-- 
Severin


From nouiz at nouiz.org  Mon Apr  7 14:37:09 2014
From: nouiz at nouiz.org (=?ISO-8859-1?Q?Fr=E9d=E9ric_Bastien?=)
Date: Mon, 7 Apr 2014 14:37:09 -0400
Subject: [SciPy-Dev] [Numpy-discussion] ANN: Scipy 0.14.0 release
 candidate 1
In-Reply-To: <CAH6Pt5pLO_mpXZq1bzUrAHgu9a+Br_Svc00rBOPMM3=1Bt_zLg@mail.gmail.com>
References: <CABL7CQgih6dwtyEHMTc-N0Rtk5yTRMJ5Mf8iqXMYfq5657myVA@mail.gmail.com>
	<CAH6Pt5o4EbyOXxgx_uwpws6pTqYq+tw91se8EsPi_LX1CWGNKQ@mail.gmail.com>
	<CABL7CQhcYkCKt2Np2HPoYr3MtRdRHZeV10tUJP4MQQOtXcZMdA@mail.gmail.com>
	<CAH6Pt5pLO_mpXZq1bzUrAHgu9a+Br_Svc00rBOPMM3=1Bt_zLg@mail.gmail.com>
Message-ID: <CADKKbtgxH58VhUb+0Pkzd8RDjL6j5fprRvPVv76B_JTCSv3hLw@mail.gmail.com>

Theano tests (we use practicly just the sparse packages) passed.

thanks

Fr?d?ric

On Sun, Apr 6, 2014 at 2:11 AM, Matthew Brett <matthew.brett at gmail.com> wrote:
> Hi,
>
> On Fri, Apr 4, 2014 at 1:44 PM, Ralf Gommers <ralf.gommers at gmail.com> wrote:
>>
>>
>>
>> On Thu, Apr 3, 2014 at 11:20 PM, Matthew Brett <matthew.brett at gmail.com>
>> wrote:
>>>
>>> Hi Ralf,
>>>
>>> On Thu, Apr 3, 2014 at 1:14 PM, Ralf Gommers <ralf.gommers at gmail.com>
>>> wrote:
>>> > Hi,
>>> >
>>> > I'm pleased to announce the availability of the first release candidate
>>> > of
>>> > Scipy 0.14.0. Please try this RC and report any issues on the scipy-dev
>>> > mailing list. A significant number of fixes for scipy.sparse went in
>>> > after
>>> > the beta release, so users of that module may want to test this release
>>> > carefully.
>>> >
>>> > Source tarballs, binaries and the full release notes can be found at
>>> > https://sourceforge.net/projects/scipy/files/scipy/0.14.0rc1/. The final
>>> > release will follow in one week if no new issues are found.
>>> >
>>> > A big thank you to everyone who contributed to this release!
>>>
>>> Would you ming git-tag'ing?
>>
>>
>> Done.
>
> Just FYI - this version describes itself as "0.14.0c1" because of a
> missing 'd' in:
>
>  +VERSION             = '%d.%d.%rc1' % (MAJOR, MINOR, MICRO)
>
> I've built wheels for testing, here:
>
> https://nipy.bic.berkeley.edu/scipy_installers/scipy-0.14.0c1-cp27-none-macosx_10_6_intel.whl
> https://nipy.bic.berkeley.edu/scipy_installers/scipy-0.14.0c1-cp33-cp33m-macosx_10_6_intel.whl
> https://nipy.bic.berkeley.edu/scipy_installers/scipy-0.14.0c1-cp34-cp34m-macosx_10_6_intel.whl
>
> Usual procedure:
>
> pip install -U pip
> pip install --pre -f https://nipy.bic.berkeley.edu/scipy_installers scipy
>
> should work, for a python.org  Python.  Please do let me know of any problems.
>
> Cheers,
>
> Matthew
> _______________________________________________
> SciPy-Dev mailing list
> SciPy-Dev at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-dev


From warren.weckesser at gmail.com  Tue Apr  8 08:00:31 2014
From: warren.weckesser at gmail.com (Warren Weckesser)
Date: Tue, 8 Apr 2014 08:00:31 -0400
Subject: [SciPy-Dev] wiki.scipy.org is down.
Message-ID: <CAGzF1udfyP5DUiC6MbbzAFDOHN+4qhk85yJdm2KcmpChLa1iWA@mail.gmail.com>

The wiki site, wiki.scipy.org, appears to be down.

Warren


From robert.kern at gmail.com  Tue Apr  8 08:24:27 2014
From: robert.kern at gmail.com (Robert Kern)
Date: Tue, 8 Apr 2014 13:24:27 +0100
Subject: [SciPy-Dev] wiki.scipy.org is down.
In-Reply-To: <CAGzF1udfyP5DUiC6MbbzAFDOHN+4qhk85yJdm2KcmpChLa1iWA@mail.gmail.com>
References: <CAGzF1udfyP5DUiC6MbbzAFDOHN+4qhk85yJdm2KcmpChLa1iWA@mail.gmail.com>
Message-ID: <CAF6FJitRsHdRGiY-yA05xOtnEe+1_+_9Q3oDGbdYLo+qj0XZTw@mail.gmail.com>

On Tue, Apr 8, 2014 at 1:00 PM, Warren Weckesser
<warren.weckesser at gmail.com> wrote:
> The wiki site, wiki.scipy.org, appears to be down.

Thanks. Ran out of available semaphores. I cleared out the stale ones
from previous Apache restarts and kicked Apache to life again.

-- 
Robert Kern


From njs at pobox.com  Thu Apr 10 15:45:49 2014
From: njs at pobox.com (Nathaniel Smith)
Date: Thu, 10 Apr 2014 20:45:49 +0100
Subject: [SciPy-Dev] Fwd: [Python-Dev] PEP 465: A dedicated infix operator
	for matrix multiplication
Message-ID: <CAPJVwBmi58mAdz+=_tyasA=sAY07yMhcTntC=MrGOGeia5U_Jg@mail.gmail.com>

Hey all,

Given the sometimes rocky history of collaboration between numerical
Python and core Python, I thought it might be helpful to flag this
posting for broader distribution -- it gives one perspective on how
the core devs see things. (And is certainly consistent with my
experience around PEP 465.)

(Nick is, among other things, core Python's packaging czar, and
previously on record [1] as wishing for more feedback on how the
current energy around python packaging could take our needs into
account.)

-n

[1] https://ncoghlan_devs-python-notes.readthedocs.org/en/latest/pep_ideas/core_packaging_api.html#a-long-caveat-on-this-essay

---------- Forwarded message ----------
From: Nick Coghlan <ncoghlan at gmail.com>
Date: Tue, Apr 8, 2014 at 1:32 PM
Subject: Re: [Python-Dev] PEP 465: A dedicated infix operator for
matrix multiplication
To: Bj?rn Lindqvist <bjourne at gmail.com>
Cc: Sturla Molden <sturla.molden at gmail.com>, "python-dev at python.org"
<python-dev at python.org>


On 8 April 2014 21:24, Bj?rn Lindqvist <bjourne at gmail.com> wrote:
> 2014-04-08 12:23 GMT+02:00 Sturla Molden <sturla.molden at gmail.com>:
>> Bj?rn Lindqvist <bjourne at gmail.com> wrote:
>>
>>> import numpy as np
>>> from numpy.linalg import inv, solve
>>>
>>> # Using dot function:
>>> S = np.dot((np.dot(H, beta) - r).T,
>>>            np.dot(inv(np.dot(np.dot(H, V), H.T)), np.dot(H, beta) - r))
>>>
>>> # Using dot method:
>>> S = (H.dot(beta) - r).T.dot(inv(H.dot(V).dot(H.T))).dot(H.dot(beta) - r)
>>>
>>> Don't keep your reader hanging! Tell us what the magical variables H,
>>> beta, r and V are. And why import solve when you aren't using it?
>>> Curious readers that aren't very good at matrix math, like me, should
>>> still be able to follow your logic. Even if it is just random data,
>>> it's better than nothing!
>>
>> Perhaps. But you don't need to know matrix multiplication to see that those
>> expressions are not readable. And by extension, you can still imagine that
>> bugs can easily hide in unreadable code.
>>
>> Matrix multiplications are used extensively in anything from engineering to
>> statistics to computer graphics (2D and 3D). This operator will be a good
>> thing for a lot of us.
>
> All I ask for is to be able to see that with my own eyes. Maybe there
> is some drastic improvement I can invent to make the algorithm much
> more readable? Then the PEP:s motivation falls down. I don't want to
> have to believe that the code the pep author came up with is the most
> optimal. I want to prove that for myself.

Note that the relationship between the CPython core development team
and the Numeric Python community is based heavily on trust - we don't
expect them to teach us to become numeric experts, we just expect them
to explain themselves well enough to persuade us that a core language
or interpreter change is the only realistic way to achieve a
particular goal. This does occasionally result in quirky patterns of
feature adoption, as things like extended slicing, full rich
comparison support, ellipsis support, rich buffer API support, and now
matrix multiplication support, were added for the numeric community's
benefit without necessarily offering any immediately obvious benefit
for those not using the numeric Python stack - it was only later that
they became pervasively adopted throughout the standard library (with
matmul, for example, a follow on proposal to allow tuples, lists and
arrays to handle vector dot products may make sense).

This particular problem has been kicking around long enough, and is
sufficiently familiar to several of us, that what's in the PEP already
presents a compelling rationale for the *folks that need to be
convinced* (which is primarily Guido, but if enough of the other core
devs think something is a questionable idea, we can often talk him out
of it - that's not the case here though).

Attempting to condense that preceding 10+ years of history *into the
PEP itself* wouldn't be a good return on investment - the links to the
earlier PEPs are there, as are the links to these discussion threads.

Cheers,
Nick.

P.S. We've been relatively successful in getting a similar trust based
dynamic off the ground for the packaging and distribution community
over the past year or so. The next big challenge in trust based
delegation for the core development team will likely be around a
Python 3.5+ only WSGI 2.0 (that can assume the Python 3 text model,
the restoration of binary interpolation, the availability of asyncio,
etc), but most of the likely principals there are still burned out
from the WSGI 1.1 debate and the Python 3 transition in general :(

>
>
> --
> mvh/best regards Bj?rn Lindqvist
> _______________________________________________
> Python-Dev mailing list
> Python-Dev at python.org
> https://mail.python.org/mailman/listinfo/python-dev
> Unsubscribe: https://mail.python.org/mailman/options/python-dev/ncoghlan%40gmail.com



--
Nick Coghlan   |   ncoghlan at gmail.com   |   Brisbane, Australia
_______________________________________________
Python-Dev mailing list
Python-Dev at python.org
https://mail.python.org/mailman/listinfo/python-dev
Unsubscribe: https://mail.python.org/mailman/options/python-dev/njs%40pobox.com


-- 
Nathaniel J. Smith
Postdoctoral researcher - Informatics - University of Edinburgh
http://vorpus.org


From charlesr.harris at gmail.com  Sun Apr 13 01:42:08 2014
From: charlesr.harris at gmail.com (Charles R Harris)
Date: Sat, 12 Apr 2014 23:42:08 -0600
Subject: [SciPy-Dev] Errors with numpy-devel
Message-ID: <CAB6mnx+o8E3cV-N7HVhxH-dtxih+zKQNKotF9rJkaKfYuAjr1Q@mail.gmail.com>

Hi All,

I get 75 errors and 3 failures when testing against current numpy on my
machine. Most of the errors are due to either the deprecation of the binary
'-' operator for booleans or to the deprecation of double ellipsis for
indexing, i.e., '(..., ...)' . The remainder look like two numerical
precision problems and one I can't immediately identify.

The main question I have is what is the best way to deal with the
deprecations?


FAIL: test_lsmr.TestLSMR.testBidiagonalA
----------------------------------------------------------------------
Traceback (most recent call last):
  File "/usr/lib/python2.7/site-packages/nose/case.py", line 197, in runTest
    self.test(*self.arg)
  File
"/home/charris/.local/lib/python2.7/site-packages/scipy/sparse/linalg/isolve/tests/test_lsmr.py",
line 60, in testBidiagonalA
    self.assertCompatibleSystem(A,xtrue)
  File
"/home/charris/.local/lib/python2.7/site-packages/scipy/sparse/linalg/isolve/tests/test_lsmr.py",
line 40, in assertCompatibleSystem
    assert_almost_equal(norm(x - xtrue), 0, 6)
  File
"/home/charris/.local/lib/python2.7/site-packages/numpy/testing/utils.py",
line 486, in assert_almost_equal
    raise AssertionError(_build_err_msg())
AssertionError:
Arrays are not almost equal to 6 decimals
 ACTUAL: 6.048630163037888e-07
 DESIRED: 0

======================================================================
FAIL: test_qhull.TestUtilities.test_degenerate_barycentric_transforms
----------------------------------------------------------------------
Traceback (most recent call last):
  File "/usr/lib/python2.7/site-packages/nose/case.py", line 197, in runTest
    self.test(*self.arg)
  File
"/home/charris/.local/lib/python2.7/site-packages/numpy/testing/decorators.py",
line 146, in skipper_func
    return f(*args, **kwargs)
  File
"/home/charris/.local/lib/python2.7/site-packages/scipy/spatial/tests/test_qhull.py",
line 296, in test_degenerate_barycentric_transforms
    assert_(bad_count < 20, bad_count)
  File
"/home/charris/.local/lib/python2.7/site-packages/numpy/testing/utils.py",
line 50, in assert_
    raise AssertionError(smsg)
AssertionError: 20

======================================================================
FAIL: test_trim (test_mstats_basic.TestTrimming)
----------------------------------------------------------------------
Traceback (most recent call last):
  File
"/home/charris/.local/lib/python2.7/site-packages/scipy/stats/tests/test_mstats_basic.py",
line 270, in test_trim
    assert_equal(trimx._mask.ravel(),[1]*20+[0]*70+[1]*20)
  File
"/home/charris/.local/lib/python2.7/site-packages/numpy/ma/testutils.py",
line 123, in assert_equal
    return assert_array_equal(actual, desired, err_msg)
  File
"/home/charris/.local/lib/python2.7/site-packages/numpy/ma/testutils.py",
line 196, in assert_array_equal
    header='Arrays are not equal')
  File
"/home/charris/.local/lib/python2.7/site-packages/numpy/ma/testutils.py",
line 189, in assert_array_compare
    verbose=verbose, header=header)
  File
"/home/charris/.local/lib/python2.7/site-packages/numpy/testing/utils.py",
line 660, in assert_array_compare
    raise AssertionError(msg)
AssertionError:
Arrays are not equal

(mismatch 9.09090909091%)
 x: array([ True,  True,  True,  True,  True,  True,  True,  True,  True,
        True,  True,  True,  True,  True,  True,  True,  True,  True,
        True,  True, False, False, False, False, False, False, False,...
 y: array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0,
0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0,...

Chuck
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From ralf.gommers at gmail.com  Sun Apr 13 16:58:52 2014
From: ralf.gommers at gmail.com (Ralf Gommers)
Date: Sun, 13 Apr 2014 22:58:52 +0200
Subject: [SciPy-Dev] Errors with numpy-devel
In-Reply-To: <CAB6mnx+o8E3cV-N7HVhxH-dtxih+zKQNKotF9rJkaKfYuAjr1Q@mail.gmail.com>
References: <CAB6mnx+o8E3cV-N7HVhxH-dtxih+zKQNKotF9rJkaKfYuAjr1Q@mail.gmail.com>
Message-ID: <CABL7CQiig9r6RdDyvUrc_Z=+D6noHkZUdM18NG9BwCVNE2_iQA@mail.gmail.com>

On Sun, Apr 13, 2014 at 7:42 AM, Charles R Harris <charlesr.harris at gmail.com
> wrote:

> Hi All,
>
> I get 75 errors and 3 failures when testing against current numpy on my
> machine. Most of the errors are due to either the deprecation of the binary
> '-' operator for booleans or to the deprecation of double ellipsis for
> indexing, i.e., '(..., ...)' . The remainder look like two numerical
> precision problems and one I can't immediately identify.
>
> The main question I have is what is the best way to deal with the
> deprecations?
>

Fix them? It's not that much work; I'd like to still get that into 0.14.0
if possible. Will have a look.

The QHull test I've seen fail randomly on various platforms, it has a real
issue. The other two look like the tests need adapting.

Ralf


>
> FAIL: test_lsmr.TestLSMR.testBidiagonalA
> ----------------------------------------------------------------------
> Traceback (most recent call last):
>   File "/usr/lib/python2.7/site-packages/nose/case.py", line 197, in
> runTest
>     self.test(*self.arg)
>   File
> "/home/charris/.local/lib/python2.7/site-packages/scipy/sparse/linalg/isolve/tests/test_lsmr.py",
> line 60, in testBidiagonalA
>     self.assertCompatibleSystem(A,xtrue)
>   File
> "/home/charris/.local/lib/python2.7/site-packages/scipy/sparse/linalg/isolve/tests/test_lsmr.py",
> line 40, in assertCompatibleSystem
>     assert_almost_equal(norm(x - xtrue), 0, 6)
>   File
> "/home/charris/.local/lib/python2.7/site-packages/numpy/testing/utils.py",
> line 486, in assert_almost_equal
>     raise AssertionError(_build_err_msg())
> AssertionError:
> Arrays are not almost equal to 6 decimals
>  ACTUAL: 6.048630163037888e-07
>  DESIRED: 0
>
> ======================================================================
> FAIL: test_qhull.TestUtilities.test_degenerate_barycentric_transforms
> ----------------------------------------------------------------------
> Traceback (most recent call last):
>   File "/usr/lib/python2.7/site-packages/nose/case.py", line 197, in
> runTest
>     self.test(*self.arg)
>   File
> "/home/charris/.local/lib/python2.7/site-packages/numpy/testing/decorators.py",
> line 146, in skipper_func
>     return f(*args, **kwargs)
>   File
> "/home/charris/.local/lib/python2.7/site-packages/scipy/spatial/tests/test_qhull.py",
> line 296, in test_degenerate_barycentric_transforms
>     assert_(bad_count < 20, bad_count)
>   File
> "/home/charris/.local/lib/python2.7/site-packages/numpy/testing/utils.py",
> line 50, in assert_
>     raise AssertionError(smsg)
> AssertionError: 20
>
> ======================================================================
> FAIL: test_trim (test_mstats_basic.TestTrimming)
> ----------------------------------------------------------------------
> Traceback (most recent call last):
>   File
> "/home/charris/.local/lib/python2.7/site-packages/scipy/stats/tests/test_mstats_basic.py",
> line 270, in test_trim
>     assert_equal(trimx._mask.ravel(),[1]*20+[0]*70+[1]*20)
>   File
> "/home/charris/.local/lib/python2.7/site-packages/numpy/ma/testutils.py",
> line 123, in assert_equal
>     return assert_array_equal(actual, desired, err_msg)
>   File
> "/home/charris/.local/lib/python2.7/site-packages/numpy/ma/testutils.py",
> line 196, in assert_array_equal
>     header='Arrays are not equal')
>   File
> "/home/charris/.local/lib/python2.7/site-packages/numpy/ma/testutils.py",
> line 189, in assert_array_compare
>     verbose=verbose, header=header)
>   File
> "/home/charris/.local/lib/python2.7/site-packages/numpy/testing/utils.py",
> line 660, in assert_array_compare
>     raise AssertionError(msg)
> AssertionError:
> Arrays are not equal
>
> (mismatch 9.09090909091%)
>  x: array([ True,  True,  True,  True,  True,  True,  True,  True,  True,
>         True,  True,  True,  True,  True,  True,  True,  True,  True,
>         True,  True, False, False, False, False, False, False, False,...
>  y: array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0,
> 0, 0,
>        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
>        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
> 0,...
>
> Chuck
>
>
>
> _______________________________________________
> SciPy-Dev mailing list
> SciPy-Dev at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-dev
>
>
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From benny.malengier at gmail.com  Tue Apr 15 02:32:09 2014
From: benny.malengier at gmail.com (Benny Malengier)
Date: Tue, 15 Apr 2014 08:32:09 +0200
Subject: [SciPy-Dev] libflame integrated LAPACK
Message-ID: <CAOyNq8oQk26WHmmmubFoeTrTxNoQj96yqbA1HfGdKBcuGiCD2Q@mail.gmail.com>

I think the announcement of libflame is of interest for scipy,
http://www.cs.utexas.edu/~flame/web/.
It seems all is under a license scipy can  use:

From: Field G. Van Zee field at cs.utexas.edu
Date: April 07, 2014
Subject: The union of libflame and LAPACK

Sponsored by an NSF Software Infrastructure for Sustained
Innovation grant, we have been developing a new, vertically
integrated dense linear algebra software stack. At the bottom of
this software stack is the BLAS-like Library Instantiation Software
(BLIS). Above this, targeting sequential and multithreaded
architectures is libflame. At the top of the stack is Elemental
for distributed memory architectures.

libflame targets roughly the same layer as does LAPACK, and now we
have incorporated the LAPACK code base into libflame. For those
operations where libflame has the native functionality, the LAPACK
code becomes an interface. For all other operations, the netlib
implementation provides that functionality. We affectionately call
this new union "flapack", which offers the following benefits:

1) The libflame implementation of LAPACK is entirely coded in C.
No Fortran libraries or compilers are required.
2) The libflame library builds upon the BLIS interface. This
interface, unlike the BLAS, allows for arbitrary row and column
stride. While some applications may benefit from this (e.g., those
that perform computation with slices of tensors), from a
development and maintainability point of view it allows more
functionality to be supported with less code.
3) The union of the two libraries allows users to benefit from both
the LAPACK and libflame code base, within one package.
4) "flapack" passes the LAPACK test suite on platforms where we
have tested this. (There is one exception of a test case that
involves packed matrices that we believe is not in general use.)

The library is available under a 3-clause BSD license at:
https://github.com/flame/libflame
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From manojkumarsivaraj334 at gmail.com  Tue Apr 15 09:24:06 2014
From: manojkumarsivaraj334 at gmail.com (Manoj Kumar)
Date: Tue, 15 Apr 2014 18:54:06 +0530
Subject: [SciPy-Dev] Mp3 support for scipy.io and some help
Message-ID: <CAFQAd-=AxAKSOZJ-t06_aoUw3+Td+k5hQME3tB1RjPnYZNTXBA@mail.gmail.com>

Hello Scipy-Devs,

I was working on an application that involves analyzing sound files.
Basically, I would like to extract information like frequencies, amplitudes
so that I can plot a frequency time transform. Since mp3 is the most widely
used format, I would have to write my own function to transform the mp3
format into a numpy array (similar to what .wav
does). Would scipy be interested in such a thing?

However, Lars tells me there a lot of patent issues. Is there any
workaround to this?
Related Issue: https://github.com/scipy/scipy/issues/3536

Sorry if this was off topic and any help would be greatly appreciated.

-- 
Regards,
Manoj Kumar,
Mech Undergrad
http://manojbits.wordpress.com
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From matthieu.brucher at gmail.com  Tue Apr 15 09:27:57 2014
From: matthieu.brucher at gmail.com (Matthieu Brucher)
Date: Tue, 15 Apr 2014 14:27:57 +0100
Subject: [SciPy-Dev] Mp3 support for scipy.io and some help
In-Reply-To: <CAFQAd-=AxAKSOZJ-t06_aoUw3+Td+k5hQME3tB1RjPnYZNTXBA@mail.gmail.com>
References: <CAFQAd-=AxAKSOZJ-t06_aoUw3+Td+k5hQME3tB1RjPnYZNTXBA@mail.gmail.com>
Message-ID: <CAHCaCkLv6A=NF1dLvitP_eGThQ=6cEz3MaFmTjmnn+DYB9dXkg@mail.gmail.com>

Hi,

Indeed, if you distribute the software, you need to pay for the
patents license. So not an currently option:
http://en.wikipedia.org/wiki/LAME
You may be able to extract the mp3 decoder implemented here:
http://audiotools.sourceforge.net/

Cheers,

Matthieu

2014-04-15 14:24 GMT+01:00 Manoj Kumar <manojkumarsivaraj334 at gmail.com>:
> Hello Scipy-Devs,
>
> I was working on an application that involves analyzing sound files.
> Basically, I would like to extract information like frequencies, amplitudes
> so that I can plot a frequency time transform. Since mp3 is the most widely
> used format, I would have to write my own function to transform the mp3
> format into a numpy array (similar to what .wav
> does). Would scipy be interested in such a thing?
>
> However, Lars tells me there a lot of patent issues. Is there any workaround
> to this?
> Related Issue: https://github.com/scipy/scipy/issues/3536
>
> Sorry if this was off topic and any help would be greatly appreciated.
>
> --
> Regards,
> Manoj Kumar,
> Mech Undergrad
> http://manojbits.wordpress.com
>
> _______________________________________________
> SciPy-Dev mailing list
> SciPy-Dev at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-dev
>



-- 
Information System Engineer, Ph.D.
Blog: http://matt.eifelle.com
LinkedIn: http://www.linkedin.com/in/matthieubrucher
Music band: http://liliejay.com/


From njs at pobox.com  Tue Apr 15 09:41:07 2014
From: njs at pobox.com (Nathaniel Smith)
Date: Tue, 15 Apr 2014 15:41:07 +0200
Subject: [SciPy-Dev] Mp3 support for scipy.io and some help
In-Reply-To: <CAHCaCkLv6A=NF1dLvitP_eGThQ=6cEz3MaFmTjmnn+DYB9dXkg@mail.gmail.com>
References: <CAFQAd-=AxAKSOZJ-t06_aoUw3+Td+k5hQME3tB1RjPnYZNTXBA@mail.gmail.com>
	<CAHCaCkLv6A=NF1dLvitP_eGThQ=6cEz3MaFmTjmnn+DYB9dXkg@mail.gmail.com>
Message-ID: <CAPJVwBm+k236d_aqzMMov+_60DG=aj0Yb=JYzTFa85BLv8-PGA@mail.gmail.com>

Not sure what the patent situation is; Debian and Fedora both seem to
be shipping mp3 decoders at this point though, unless I'm misreading
something (at least they seem to have libmad packages).

I'd probably start by trying pymad (GPL in case that matters to you):
   https://github.com/jaqx0r/pymad
(which is also shipped by Debian and Fedora).

-n

On Tue, Apr 15, 2014 at 3:27 PM, Matthieu Brucher
<matthieu.brucher at gmail.com> wrote:
> Hi,
>
> Indeed, if you distribute the software, you need to pay for the
> patents license. So not an currently option:
> http://en.wikipedia.org/wiki/LAME
> You may be able to extract the mp3 decoder implemented here:
> http://audiotools.sourceforge.net/
>
> Cheers,
>
> Matthieu
>
> 2014-04-15 14:24 GMT+01:00 Manoj Kumar <manojkumarsivaraj334 at gmail.com>:
>> Hello Scipy-Devs,
>>
>> I was working on an application that involves analyzing sound files.
>> Basically, I would like to extract information like frequencies, amplitudes
>> so that I can plot a frequency time transform. Since mp3 is the most widely
>> used format, I would have to write my own function to transform the mp3
>> format into a numpy array (similar to what .wav
>> does). Would scipy be interested in such a thing?
>>
>> However, Lars tells me there a lot of patent issues. Is there any workaround
>> to this?
>> Related Issue: https://github.com/scipy/scipy/issues/3536
>>
>> Sorry if this was off topic and any help would be greatly appreciated.
>>
>> --
>> Regards,
>> Manoj Kumar,
>> Mech Undergrad
>> http://manojbits.wordpress.com
>>
>> _______________________________________________
>> SciPy-Dev mailing list
>> SciPy-Dev at scipy.org
>> http://mail.scipy.org/mailman/listinfo/scipy-dev
>>
>
>
>
> --
> Information System Engineer, Ph.D.
> Blog: http://matt.eifelle.com
> LinkedIn: http://www.linkedin.com/in/matthieubrucher
> Music band: http://liliejay.com/
> _______________________________________________
> SciPy-Dev mailing list
> SciPy-Dev at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-dev



-- 
Nathaniel J. Smith
Postdoctoral researcher - Informatics - University of Edinburgh
http://vorpus.org


From deshpande.jaidev at gmail.com  Tue Apr 15 09:36:43 2014
From: deshpande.jaidev at gmail.com (Jaidev Deshpande)
Date: Tue, 15 Apr 2014 19:06:43 +0530
Subject: [SciPy-Dev] Mp3 support for scipy.io and some help
In-Reply-To: <CAHCaCkLv6A=NF1dLvitP_eGThQ=6cEz3MaFmTjmnn+DYB9dXkg@mail.gmail.com>
References: <CAFQAd-=AxAKSOZJ-t06_aoUw3+Td+k5hQME3tB1RjPnYZNTXBA@mail.gmail.com>
	<CAHCaCkLv6A=NF1dLvitP_eGThQ=6cEz3MaFmTjmnn+DYB9dXkg@mail.gmail.com>
Message-ID: <CAB=suEmQ+9dNAbvA4GqBHGmFF67zDawdkBx5bZJWWhkwv36V5w@mail.gmail.com>

On Tue, Apr 15, 2014 at 6:57 PM, Matthieu Brucher <
matthieu.brucher at gmail.com> wrote:

> Hi,
>
> Indeed, if you distribute the software, you need to pay for the
> patents license. So not an currently option:
> http://en.wikipedia.org/wiki/LAME
> You may be able to extract the mp3 decoder implemented here:
> http://audiotools.sourceforge.net/
>
> Cheers,
>
> Matthieu
>
> 2014-04-15 14:24 GMT+01:00 Manoj Kumar <manojkumarsivaraj334 at gmail.com>:
> > Hello Scipy-Devs,
> >
> > I was working on an application that involves analyzing sound files.
> > Basically, I would like to extract information like frequencies,
> amplitudes
> > so that I can plot a frequency time transform. Since mp3 is the most
> widely
> > used format, I would have to write my own function to transform the mp3
> > format into a numpy array (similar to what .wav
> > does). Would scipy be interested in such a thing?
> >
> > However, Lars tells me there a lot of patent issues. Is there any
> workaround
> > to this?
>


Yes, there are patent issues. Check this out -
http://www.mega-nerd.com/libsndfile/FAQ.html#Q020



> > Related Issue: https://github.com/scipy/scipy/issues/3536
> >
> > Sorry if this was off topic and any help would be greatly appreciated.
> >
> > --
> > Regards,
> > Manoj Kumar,
> > Mech Undergrad
> > http://manojbits.wordpress.com
> >
> > _______________________________________________
> > SciPy-Dev mailing list
> > SciPy-Dev at scipy.org
> > http://mail.scipy.org/mailman/listinfo/scipy-dev
> >
>
>
>
> --
> Information System Engineer, Ph.D.
> Blog: http://matt.eifelle.com
> LinkedIn: http://www.linkedin.com/in/matthieubrucher
> Music band: http://liliejay.com/
> _______________________________________________
> SciPy-Dev mailing list
> SciPy-Dev at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-dev
>



-- 
JD
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From richard9404 at gmail.com  Tue Apr 15 10:45:32 2014
From: richard9404 at gmail.com (Richard Tsai)
Date: Tue, 15 Apr 2014 22:45:32 +0800
Subject: [SciPy-Dev] Mp3 support for scipy.io and some help
In-Reply-To: <CAPJVwBm+k236d_aqzMMov+_60DG=aj0Yb=JYzTFa85BLv8-PGA@mail.gmail.com>
References: <CAFQAd-=AxAKSOZJ-t06_aoUw3+Td+k5hQME3tB1RjPnYZNTXBA@mail.gmail.com>
	<CAHCaCkLv6A=NF1dLvitP_eGThQ=6cEz3MaFmTjmnn+DYB9dXkg@mail.gmail.com>
	<CAPJVwBm+k236d_aqzMMov+_60DG=aj0Yb=JYzTFa85BLv8-PGA@mail.gmail.com>
Message-ID: <CALkpnheH0Ay7BNRZq1EXK2pyCpGQ+Hn2oZw0WWZ8rzFpFZNzJg@mail.gmail.com>

2014-04-15 21:41 GMT+08:00 Nathaniel Smith <njs at pobox.com>:

> Not sure what the patent situation is; Debian and Fedora both seem to
> be shipping mp3 decoders at this point though, unless I'm misreading
> something (at least they seem to have libmad packages).
>
> I'd probably start by trying pymad (GPL in case that matters to you):
>    https://github.com/jaqx0r/pymad
> (which is also shipped by Debian and Fedora).
>
>
Fedora puts libmad in RPMFusion, which is designed for avoiding patent
issues. I don't know how Debian does it. Maybe they just ignore the patent?
Patent is different from copyright so many hackers just ignore it.

I think it is not a good idea get involve with these issues. At least the
users
would not be able to use scipy with it in commercial products. But I know
little
about patent laws and the mp3 patent doesn't work in my country. Maybe some
one can clarify it for us?

Richard
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From ralf.gommers at gmail.com  Tue Apr 15 15:03:17 2014
From: ralf.gommers at gmail.com (Ralf Gommers)
Date: Tue, 15 Apr 2014 21:03:17 +0200
Subject: [SciPy-Dev] EuroSciPy 2014 abstract submission deadline extended
Message-ID: <CABL7CQjncRVX-8NrKjpg753DOL1YLrim27zJo4GvjZPanE1P2g@mail.gmail.com>

Hi,

In response to a number of requests, the organizing committee of the
EuroSciPy 2014 conference has extended the deadline for abstract submission
by 12 days, to Sunday April 27th 2014, 23:59:59 UTC. Up to then, new
abstracts may be submitted on https://www.euroscipy.org/2014/ . We are very
much looking forward to your submissions to the conference.

EuroSciPy 2014 is the annual European conference for scientists using
Python. It will be held August 27-30 2014 in Cambridge, UK. This year
promises to be an exciting event again. The number of abstract submissions
is already above the level of the 2013 edition, and the first keynote has
been announced:

    Steven G. Johnson (MIT), Crossing Language Barriers with Julia, Scipy,
and IPython

Questions regarding abstract submission may be addressed to euroscipy
-org at python.org

Best regards,
Ralf Gommers (Program Chair)
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From nils106 at googlemail.com  Tue Apr 15 15:19:32 2014
From: nils106 at googlemail.com (Nils Wagner)
Date: Tue, 15 Apr 2014 21:19:32 +0200
Subject: [SciPy-Dev] Errors with numpy-devel
In-Reply-To: <CABL7CQiig9r6RdDyvUrc_Z=+D6noHkZUdM18NG9BwCVNE2_iQA@mail.gmail.com>
References: <CAB6mnx+o8E3cV-N7HVhxH-dtxih+zKQNKotF9rJkaKfYuAjr1Q@mail.gmail.com>
	<CABL7CQiig9r6RdDyvUrc_Z=+D6noHkZUdM18NG9BwCVNE2_iQA@mail.gmail.com>
Message-ID: <CAE7vhgOqjL_6wFjKQfSXoFXVHyES9X-CnCxu5oUkh5=HhkgEfA@mail.gmail.com>

>>> from scipy import stats
>>> stats.test()
Running unit tests for scipy.stats
NumPy version 1.9.0.dev-61c699e
NumPy is installed in /home/nils/local/lib64/python2.7/site-packages/numpy
SciPy version 0.15.0.dev-87df1db
SciPy is installed in /home/nils/local/lib64/python2.7/site-packages/scipy
Python version 2.7.3 (default, Apr 14 2012, 08:58:41) [GCC]
nose version 1.3.1

...

======================================================================
ERROR: test_gmean (test_mstats_basic.TestCompareWithStats)
----------------------------------------------------------------------
Traceback (most recent call last):
  File
"/home/nils/local/lib64/python2.7/site-packages/scipy/stats/tests/test_mstats_basic.py",
line 763, in test_gmean
    rm = stats.mstats.gmean(abs(xm))
  File
"/home/nils/local/lib64/python2.7/site-packages/scipy/stats/stats.py", line
516, in gmean
    log_a = np.log(a)
  File "/home/nils/local/lib64/python2.7/site-packages/numpy/ma/core.py",
line 2837, in __array_wrap__
    d = filled(domain(*args), True)
  File "/home/nils/local/lib64/python2.7/site-packages/numpy/ma/core.py",
line 795, in __call__
    return umath.less_equal(x, self.critical_value)
RuntimeWarning: invalid value encountered in less_equal

======================================================================
ERROR: test_hmean (test_mstats_basic.TestCompareWithStats)
----------------------------------------------------------------------
Traceback (most recent call last):
  File
"/home/nils/local/lib64/python2.7/site-packages/scipy/stats/tests/test_mstats_basic.py",
line 775, in test_hmean
    rm = stats.mstats.hmean(abs(xm))
  File
"/home/nils/local/lib64/python2.7/site-packages/scipy/stats/stats.py", line
562, in hmean
    if np.all(a > 0):  # Harmonic mean only defined if greater than zero
RuntimeWarning: invalid value encountered in greater

======================================================================
ERROR: test_tmax (test_mstats_basic.TestCompareWithStats)
----------------------------------------------------------------------
Traceback (most recent call last):
  File
"/home/nils/local/lib64/python2.7/site-packages/scipy/stats/tests/test_mstats_basic.py",
line 908, in test_tmax
    stats.mstats.tmax(xm,2.), 10)
  File
"/home/nils/local/lib64/python2.7/site-packages/scipy/stats/mstats_basic.py",
line 1383, in tmax
    am = trima(a, (None, upperlimit), (False, inclusive))
  File
"/home/nils/local/lib64/python2.7/site-packages/scipy/stats/mstats_basic.py",
line 1018, in trima
    condition |= (a > upper_lim)
RuntimeWarning: invalid value encountered in greater

======================================================================
ERROR: test_tmin (test_mstats_basic.TestCompareWithStats)
----------------------------------------------------------------------
Traceback (most recent call last):
  File
"/home/nils/local/lib64/python2.7/site-packages/scipy/stats/tests/test_mstats_basic.py",
line 919, in test_tmin
    stats.mstats.tmin(xm,lowerlimit=-1.), 10)
  File
"/home/nils/local/lib64/python2.7/site-packages/scipy/stats/mstats_basic.py",
line 1376, in tmin
    am = trima(a, (lowerlimit, None), (inclusive, False))
  File
"/home/nils/local/lib64/python2.7/site-packages/scipy/stats/mstats_basic.py",
line 1013, in trima
    condition |= (a < lower_lim)
RuntimeWarning: invalid value encountered in less

======================================================================
ERROR: test_tsem (test_mstats_basic.TestCompareWithStats)
----------------------------------------------------------------------
Traceback (most recent call last):
  File
"/home/nils/local/lib64/python2.7/site-packages/scipy/stats/tests/test_mstats_basic.py",
line 958, in test_tsem
    stats.mstats.tsem(xm,limits=(-2.,2.)),
  File
"/home/nils/local/lib64/python2.7/site-packages/scipy/stats/mstats_basic.py",
line 1393, in tsem
    am = trima(a.ravel(), limits, inclusive)
  File
"/home/nils/local/lib64/python2.7/site-packages/scipy/stats/mstats_basic.py",
line 1013, in trima
    condition |= (a < lower_lim)
RuntimeWarning: invalid value encountered in less

======================================================================
ERROR: test_zmap (test_mstats_basic.TestCompareWithStats)
----------------------------------------------------------------------
Traceback (most recent call last):
  File
"/home/nils/local/lib64/python2.7/site-packages/scipy/stats/tests/test_mstats_basic.py",
line 927, in test_zmap
    zm = stats.mstats.zmap(xm,ym)
  File
"/home/nils/local/lib64/python2.7/site-packages/scipy/stats/stats.py", line
2063, in zmap
    return (scores - mns) / sstd
  File "/home/nils/local/lib64/python2.7/site-packages/numpy/ma/core.py",
line 3735, in __truediv__
    return true_divide(self, other)
  File "/home/nils/local/lib64/python2.7/site-packages/numpy/ma/core.py",
line 1089, in __call__
    m |= filled(domain(da, db), True)
  File "/home/nils/local/lib64/python2.7/site-packages/numpy/ma/core.py",
line 783, in __call__
    return umath.absolute(a) * self.tolerance >= umath.absolute(b)
RuntimeWarning: invalid value encountered in greater_equal

======================================================================
FAIL: test_example1a (test_morestats.TestAndersonKSamp)
----------------------------------------------------------------------
Traceback (most recent call last):
  File
"/home/nils/local/lib64/python2.7/site-packages/scipy/stats/tests/test_morestats.py",
line 97, in test_example1a
    midrank=False)
  File
"/home/nils/local/lib64/python2.7/site-packages/numpy/testing/utils.py",
line 1593, in assert_warns
    % func.__name__)
AssertionError: No warning raised when calling anderson_ksamp

======================================================================
FAIL: test_tmean (test_mstats_basic.TestCompareWithStats)
----------------------------------------------------------------------
Traceback (most recent call last):
  File
"/home/nils/local/lib64/python2.7/site-packages/scipy/stats/tests/test_mstats_basic.py",
line 901, in test_tmean
    assert_equal(stats.tmean(x),stats.mstats.tmean(xm))
  File
"/home/nils/local/lib64/python2.7/site-packages/numpy/ma/testutils.py",
line 100, in assert_equal
    raise AssertionError(msg)
AssertionError:
Items are not equal:
 ACTUAL: 0.040175456835823721
 DESIRED: 0.040175456835823714

======================================================================
FAIL: test_trim (test_mstats_basic.TestTrimming)
----------------------------------------------------------------------
Traceback (most recent call last):
  File
"/home/nils/local/lib64/python2.7/site-packages/scipy/stats/tests/test_mstats_basic.py",
line 274, in test_trim
    assert_equal(trimx._mask.ravel(),[1]*20+[0]*70+[1]*20)
  File
"/home/nils/local/lib64/python2.7/site-packages/numpy/ma/testutils.py",
line 123, in assert_equal
    return assert_array_equal(actual, desired, err_msg)
  File
"/home/nils/local/lib64/python2.7/site-packages/numpy/ma/testutils.py",
line 196, in assert_array_equal
    header='Arrays are not equal')
  File
"/home/nils/local/lib64/python2.7/site-packages/numpy/ma/testutils.py",
line 189, in assert_array_compare
    verbose=verbose, header=header)
  File
"/home/nils/local/lib64/python2.7/site-packages/numpy/testing/utils.py",
line 660, in assert_array_compare
    raise AssertionError(msg)
AssertionError:
Arrays are not equal

(mismatch 9.09090909091%)
 x: array([ True,  True,  True,  True,  True,  True,  True,  True,  True,
        True,  True,  True,  True,  True,  True,  True,  True,  True,
        True,  True, False, False, False, False, False, False, False,...
 y: array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0,
0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0,...

----------------------------------------------------------------------
Ran 1957 tests in 50.047s

FAILED (KNOWNFAIL=4, SKIP=3, errors=6, failures=3)
<nose.result.TextTestResult run=1957 errors=6 failures=3>



On Sun, Apr 13, 2014 at 10:58 PM, Ralf Gommers <ralf.gommers at gmail.com>wrote:

>
>
>
> On Sun, Apr 13, 2014 at 7:42 AM, Charles R Harris <
> charlesr.harris at gmail.com> wrote:
>
>> Hi All,
>>
>> I get 75 errors and 3 failures when testing against current numpy on my
>> machine. Most of the errors are due to either the deprecation of the binary
>> '-' operator for booleans or to the deprecation of double ellipsis for
>> indexing, i.e., '(..., ...)' . The remainder look like two numerical
>> precision problems and one I can't immediately identify.
>>
>> The main question I have is what is the best way to deal with the
>> deprecations?
>>
>
> Fix them? It's not that much work; I'd like to still get that into 0.14.0
> if possible. Will have a look.
>
> The QHull test I've seen fail randomly on various platforms, it has a real
> issue. The other two look like the tests need adapting.
>
> Ralf
>
>
>>
>> FAIL: test_lsmr.TestLSMR.testBidiagonalA
>> ----------------------------------------------------------------------
>> Traceback (most recent call last):
>>   File "/usr/lib/python2.7/site-packages/nose/case.py", line 197, in
>> runTest
>>     self.test(*self.arg)
>>   File
>> "/home/charris/.local/lib/python2.7/site-packages/scipy/sparse/linalg/isolve/tests/test_lsmr.py",
>> line 60, in testBidiagonalA
>>     self.assertCompatibleSystem(A,xtrue)
>>   File
>> "/home/charris/.local/lib/python2.7/site-packages/scipy/sparse/linalg/isolve/tests/test_lsmr.py",
>> line 40, in assertCompatibleSystem
>>     assert_almost_equal(norm(x - xtrue), 0, 6)
>>   File
>> "/home/charris/.local/lib/python2.7/site-packages/numpy/testing/utils.py",
>> line 486, in assert_almost_equal
>>     raise AssertionError(_build_err_msg())
>> AssertionError:
>> Arrays are not almost equal to 6 decimals
>>  ACTUAL: 6.048630163037888e-07
>>  DESIRED: 0
>>
>> ======================================================================
>> FAIL: test_qhull.TestUtilities.test_degenerate_barycentric_transforms
>> ----------------------------------------------------------------------
>> Traceback (most recent call last):
>>   File "/usr/lib/python2.7/site-packages/nose/case.py", line 197, in
>> runTest
>>     self.test(*self.arg)
>>   File
>> "/home/charris/.local/lib/python2.7/site-packages/numpy/testing/decorators.py",
>> line 146, in skipper_func
>>     return f(*args, **kwargs)
>>   File
>> "/home/charris/.local/lib/python2.7/site-packages/scipy/spatial/tests/test_qhull.py",
>> line 296, in test_degenerate_barycentric_transforms
>>     assert_(bad_count < 20, bad_count)
>>   File
>> "/home/charris/.local/lib/python2.7/site-packages/numpy/testing/utils.py",
>> line 50, in assert_
>>     raise AssertionError(smsg)
>> AssertionError: 20
>>
>> ======================================================================
>> FAIL: test_trim (test_mstats_basic.TestTrimming)
>> ----------------------------------------------------------------------
>> Traceback (most recent call last):
>>   File
>> "/home/charris/.local/lib/python2.7/site-packages/scipy/stats/tests/test_mstats_basic.py",
>> line 270, in test_trim
>>     assert_equal(trimx._mask.ravel(),[1]*20+[0]*70+[1]*20)
>>   File
>> "/home/charris/.local/lib/python2.7/site-packages/numpy/ma/testutils.py",
>> line 123, in assert_equal
>>     return assert_array_equal(actual, desired, err_msg)
>>   File
>> "/home/charris/.local/lib/python2.7/site-packages/numpy/ma/testutils.py",
>> line 196, in assert_array_equal
>>     header='Arrays are not equal')
>>   File
>> "/home/charris/.local/lib/python2.7/site-packages/numpy/ma/testutils.py",
>> line 189, in assert_array_compare
>>     verbose=verbose, header=header)
>>   File
>> "/home/charris/.local/lib/python2.7/site-packages/numpy/testing/utils.py",
>> line 660, in assert_array_compare
>>     raise AssertionError(msg)
>> AssertionError:
>> Arrays are not equal
>>
>> (mismatch 9.09090909091%)
>>  x: array([ True,  True,  True,  True,  True,  True,  True,  True,  True,
>>         True,  True,  True,  True,  True,  True,  True,  True,  True,
>>         True,  True, False, False, False, False, False, False, False,...
>>  y: array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0,
>> 0, 0,
>>        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
>> 0,
>>        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
>> 0,...
>>
>> Chuck
>>
>>
>>
>> _______________________________________________
>> SciPy-Dev mailing list
>> SciPy-Dev at scipy.org
>> http://mail.scipy.org/mailman/listinfo/scipy-dev
>>
>>
>
> _______________________________________________
> SciPy-Dev mailing list
> SciPy-Dev at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-dev
>
>
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From charlesnwoods at gmail.com  Tue Apr 15 17:38:21 2014
From: charlesnwoods at gmail.com (Nathan Woods)
Date: Tue, 15 Apr 2014 15:38:21 -0600
Subject: [SciPy-Dev] Multivariate ctypes integration PR
In-Reply-To: <CAAYHtCQTCdtLe_ezw++-E+6F5MfyXLoiK8rOg0ydTOuX2ums-A@mail.gmail.com>
References: <CAEpsjT40tznXWYWN-eGUhUSg_ijKYrYREv0RS9XYezrD6eh6Nw@mail.gmail.com>
	<CAAYHtCQTCdtLe_ezw++-E+6F5MfyXLoiK8rOg0ydTOuX2ums-A@mail.gmail.com>
Message-ID: <CAAYHtCR8307yXnzpZYuhLSdpepNuC3ZSMkzV6-anBjVpa+ok5A@mail.gmail.com>

Hey everyone,

Sorry to be the squeaky wheel again, but this needs to see some movement
pretty soon. We only have another few weeks where we can work on this
enhancement before the school year ends.

The big challenge that we believe needs to be fixed is with the tests. We
need a way to compile (or distribute) a C function from source to  a
library with a given name, generally and at install time, in order to
include the C route tests in the SciPy test suite. Is there a way to use
distutils for this?

If there's anything else that we can do on our end to speed things up, we'd
be more than happy to help however we can.

N


On Mon, Mar 31, 2014 at 1:27 PM, Nathan Woods <charlesnwoods at gmail.com>wrote:

> We had a question about how to implement one of the suggested improvements:
>
> "- The code should more carefully check that the whole ctypes signature
>   matches"
>
> From what we can tell (see e.g.
> http://stackoverflow.com/questions/18775389/is-there-a-way-to-load-the-constant-values-stored-in-a-header-file-via-ctypes),
> there is no way to automatically generate the ctypes function signature
> directly from a .h header file or c function prototype. This means that any
> ctypes library function must be assumed to have the function signature that
> the user claims it does (by specifying argtypes & restype). Unfortunately,
> the ctypes library load & specification commands are executed by the user,
> not by the library, so there's no way to enforce the function signature.
>
> Maybe there's a way in Cython or f2py, but we think that ctypes should
> probably be supported anyway, even if it is with an "use at own risk"
> disclaimer.
>
> Nathan
>
>
>
> On Fri, Mar 14, 2014 at 12:08 PM, Brian Lee Newsom <
> Brian.Newsom at colorado.edu> wrote:
>
>> Thank you to everyone involved in assisting to move this forward. It is
>> definitely a confusing project for me but something I am committed to
>> finishing and getting in the library as soon as possible.  I appreciate the
>> help and I will do my best to address the issues Pauli presented.
>>
>> Thanks,
>> Brian
>>
>> _______________________________________________
>> SciPy-Dev mailing list
>> SciPy-Dev at scipy.org
>> http://mail.scipy.org/mailman/listinfo/scipy-dev
>>
>>
>
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From christoph.sawade at googlemail.com  Wed Apr 16 08:16:41 2014
From: christoph.sawade at googlemail.com (Christoph Sawade)
Date: Wed, 16 Apr 2014 14:16:41 +0200
Subject: [SciPy-Dev] Fwd: Binomial proportion confidence interval
In-Reply-To: <CAKxiPZM6+8cunbJe+LdgHaquoLXTcTennogMVtoVAJHcVkANoA@mail.gmail.com>
References: <CAKxiPZPmYokbYfy3k2fJhDaHmMXxyYXeYutJNCdCk6azupg7yA@mail.gmail.com>
	<CAKxiPZM6+8cunbJe+LdgHaquoLXTcTennogMVtoVAJHcVkANoA@mail.gmail.com>
Message-ID: <CAKxiPZO7XyHXSMJ3-g8jdyP5NTTfqVNrMMR_Kws=Ui_Q-WSQQQ@mail.gmail.com>

Hi,

I would like to add approximate intervals [1] to the scipy project.
Approximate intervals often have higher statistical power than exact
intervals for binomial random variables.
Example usage can be found in the "binom R" package [2]. In analogy to [3],
I see two possible ways to integrate these methods into the scipy API:

 (a,b) = scipy.stats.binom.approx_interval(alpha, loc, scale,
method='wilson')
(a,b) = scipy.stats.binom.wilson_interval(alpha, loc, scale),

where (a,b) are the end-points of range that contain 100*alpha % of the
rv's possible values.

What do you think?

Best, Christoph

[1] http://www-stat.wharton.upenn.edu/~tcai/paper/Binomial-StatSci.pdf
[2] http://cran.r-project.org/web/packages/binom/index.html
[3]
http://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.rv_discrete.interval.html#scipy.stats.rv_discrete.interval
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From josef.pktd at gmail.com  Wed Apr 16 08:22:39 2014
From: josef.pktd at gmail.com (josef.pktd at gmail.com)
Date: Wed, 16 Apr 2014 08:22:39 -0400
Subject: [SciPy-Dev] Fwd: Binomial proportion confidence interval
In-Reply-To: <CAKxiPZO7XyHXSMJ3-g8jdyP5NTTfqVNrMMR_Kws=Ui_Q-WSQQQ@mail.gmail.com>
References: <CAKxiPZPmYokbYfy3k2fJhDaHmMXxyYXeYutJNCdCk6azupg7yA@mail.gmail.com>
	<CAKxiPZM6+8cunbJe+LdgHaquoLXTcTennogMVtoVAJHcVkANoA@mail.gmail.com>
	<CAKxiPZO7XyHXSMJ3-g8jdyP5NTTfqVNrMMR_Kws=Ui_Q-WSQQQ@mail.gmail.com>
Message-ID: <CAMMTP+C_6qX52h3tVyxzpCbE1N6JGSJmpRptTgHg95xdZ9A5NQ@mail.gmail.com>

On Wed, Apr 16, 2014 at 8:16 AM, Christoph Sawade
<christoph.sawade at googlemail.com> wrote:
> Hi,
>
> I would like to add approximate intervals [1] to the scipy project.
> Approximate intervals often have higher statistical power than exact
> intervals for binomial random variables.
> Example usage can be found in the "binom R" package [2]. In analogy to [3],
> I see two possible ways to integrate these methods into the scipy API:
>
> (a,b) = scipy.stats.binom.approx_interval(alpha, loc, scale,
> method='wilson')
> (a,b) = scipy.stats.binom.wilson_interval(alpha, loc, scale),
>
> where (a,b) are the end-points of range that contain 100*alpha % of the rv's
> possible values.
>
> What do you think?

I added them to statsmodels
http://statsmodels.sourceforge.net/devel/generated/statsmodels.stats.proportion.proportion_confint.html
together with other functions supporting proportions

http://statsmodels.sourceforge.net/devel/stats.html#proportion

Josef


>
> Best, Christoph
>
> [1] http://www-stat.wharton.upenn.edu/~tcai/paper/Binomial-StatSci.pdf
> [2] http://cran.r-project.org/web/packages/binom/index.html
> [3]
> http://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.rv_discrete.interval.html#scipy.stats.rv_discrete.interval
>
>
>
>
>
> _______________________________________________
> SciPy-Dev mailing list
> SciPy-Dev at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-dev
>


From deil.christoph at googlemail.com  Wed Apr 16 08:25:14 2014
From: deil.christoph at googlemail.com (Christoph Deil)
Date: Wed, 16 Apr 2014 14:25:14 +0200
Subject: [SciPy-Dev] Binomial proportion confidence interval
In-Reply-To: <CAMMTP+C_6qX52h3tVyxzpCbE1N6JGSJmpRptTgHg95xdZ9A5NQ@mail.gmail.com>
References: <CAKxiPZPmYokbYfy3k2fJhDaHmMXxyYXeYutJNCdCk6azupg7yA@mail.gmail.com>
	<CAKxiPZM6+8cunbJe+LdgHaquoLXTcTennogMVtoVAJHcVkANoA@mail.gmail.com>
	<CAKxiPZO7XyHXSMJ3-g8jdyP5NTTfqVNrMMR_Kws=Ui_Q-WSQQQ@mail.gmail.com>
	<CAMMTP+C_6qX52h3tVyxzpCbE1N6JGSJmpRptTgHg95xdZ9A5NQ@mail.gmail.com>
Message-ID: <7424CEE5-5BCE-45DD-A1D9-FED5D5DD0895@gmail.com>


On 16 Apr 2014, at 14:22, josef.pktd at gmail.com wrote:

> On Wed, Apr 16, 2014 at 8:16 AM, Christoph Sawade
> <christoph.sawade at googlemail.com> wrote:
>> Hi,
>> 
>> I would like to add approximate intervals [1] to the scipy project.
>> Approximate intervals often have higher statistical power than exact
>> intervals for binomial random variables.
>> Example usage can be found in the "binom R" package [2]. In analogy to [3],
>> I see two possible ways to integrate these methods into the scipy API:
>> 
>> (a,b) = scipy.stats.binom.approx_interval(alpha, loc, scale,
>> method='wilson')
>> (a,b) = scipy.stats.binom.wilson_interval(alpha, loc, scale),
>> 
>> where (a,b) are the end-points of range that contain 100*alpha % of the rv's
>> possible values.
>> 
>> What do you think?
> 
> I added them to statsmodels
> http://statsmodels.sourceforge.net/devel/generated/statsmodels.stats.proportion.proportion_confint.html
> together with other functions supporting proportions
> 
> http://statsmodels.sourceforge.net/devel/stats.html#proportion
> 
> Josef

I didn?t look in detail, but FYI there?s also these binomial stats methods in Astropy:
http://docs.astropy.org/en/latest/api/astropy.stats.binned_binom_proportion.html
http://docs.astropy.org/en/latest/api/astropy.stats.binom_conf_interval.html

Seems some people use those a lot, i.e. `scipy.stats` would be an appropriate place?

Christoph

> 
> 
>> 
>> Best, Christoph
>> 
>> [1] http://www-stat.wharton.upenn.edu/~tcai/paper/Binomial-StatSci.pdf
>> [2] http://cran.r-project.org/web/packages/binom/index.html
>> [3]
>> http://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.rv_discrete.interval.html#scipy.stats.rv_discrete.interval
>> 
>> 
>> 
>> 
>> 
>> _______________________________________________
>> SciPy-Dev mailing list
>> SciPy-Dev at scipy.org
>> http://mail.scipy.org/mailman/listinfo/scipy-dev
>> 
> _______________________________________________
> SciPy-Dev mailing list
> SciPy-Dev at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-dev

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From ralf.gommers at gmail.com  Wed Apr 16 17:00:48 2014
From: ralf.gommers at gmail.com (Ralf Gommers)
Date: Wed, 16 Apr 2014 23:00:48 +0200
Subject: [SciPy-Dev] Binomial proportion confidence interval
In-Reply-To: <7424CEE5-5BCE-45DD-A1D9-FED5D5DD0895@gmail.com>
References: <CAKxiPZPmYokbYfy3k2fJhDaHmMXxyYXeYutJNCdCk6azupg7yA@mail.gmail.com>
	<CAKxiPZM6+8cunbJe+LdgHaquoLXTcTennogMVtoVAJHcVkANoA@mail.gmail.com>
	<CAKxiPZO7XyHXSMJ3-g8jdyP5NTTfqVNrMMR_Kws=Ui_Q-WSQQQ@mail.gmail.com>
	<CAMMTP+C_6qX52h3tVyxzpCbE1N6JGSJmpRptTgHg95xdZ9A5NQ@mail.gmail.com>
	<7424CEE5-5BCE-45DD-A1D9-FED5D5DD0895@gmail.com>
Message-ID: <CABL7CQitw3rEa6fu=6W1G4Lcrv6Xvs43vA4OQDumZ2RjK=p2jQ@mail.gmail.com>

On Wed, Apr 16, 2014 at 2:25 PM, Christoph Deil <
deil.christoph at googlemail.com> wrote:

>
> On 16 Apr 2014, at 14:22, josef.pktd at gmail.com wrote:
>
> On Wed, Apr 16, 2014 at 8:16 AM, Christoph Sawade
> <christoph.sawade at googlemail.com> wrote:
>
> Hi,
>
> I would like to add approximate intervals [1] to the scipy project.
> Approximate intervals often have higher statistical power than exact
> intervals for binomial random variables.
> Example usage can be found in the "binom R" package [2]. In analogy to [3],
> I see two possible ways to integrate these methods into the scipy API:
>
> (a,b) = scipy.stats.binom.approx_interval(alpha, loc, scale,
> method='wilson')
> (a,b) = scipy.stats.binom.wilson_interval(alpha, loc, scale),
>
> where (a,b) are the end-points of range that contain 100*alpha % of the
> rv's
> possible values.
>
> What do you think?
>
>
> I added them to statsmodels
>
> http://statsmodels.sourceforge.net/devel/generated/statsmodels.stats.proportion.proportion_confint.html
> together with other functions supporting proportions
>
> http://statsmodels.sourceforge.net/devel/stats.html#proportion
>
> Josef
>
>
> I didn?t look in detail, but FYI there?s also these binomial stats methods
> in Astropy:
>
> http://docs.astropy.org/en/latest/api/astropy.stats.binned_binom_proportion.html
>
> http://docs.astropy.org/en/latest/api/astropy.stats.binom_conf_interval.html
>
> Seems some people use those a lot, i.e. `scipy.stats` would be an
> appropriate place?
>

It doesn't seem to me like this is used often enough that it *has to be* in
scipy, but it could be. If the statsmodels and astropy devs agree that this
makes sense, maybe their functions can be merged into something that works
for both projects and then contributed to scipy.stats?

Ralf



>
> Christoph
>
>
>
>
> Best, Christoph
>
> [1] http://www-stat.wharton.upenn.edu/~tcai/paper/Binomial-StatSci.pdf
> [2] http://cran.r-project.org/web/packages/binom/index.html
> [3]
>
> http://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.rv_discrete.interval.html#scipy.stats.rv_discrete.interval
>
>
>
>
>
> _______________________________________________
> SciPy-Dev mailing list
> SciPy-Dev at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-dev
>
> _______________________________________________
> SciPy-Dev mailing list
> SciPy-Dev at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-dev
>
>
>
> _______________________________________________
> SciPy-Dev mailing list
> SciPy-Dev at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-dev
>
>
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From ralf.gommers at gmail.com  Mon Apr 21 15:11:57 2014
From: ralf.gommers at gmail.com (Ralf Gommers)
Date: Mon, 21 Apr 2014 21:11:57 +0200
Subject: [SciPy-Dev] Errors with numpy-devel
In-Reply-To: <CAE7vhgOqjL_6wFjKQfSXoFXVHyES9X-CnCxu5oUkh5=HhkgEfA@mail.gmail.com>
References: <CAB6mnx+o8E3cV-N7HVhxH-dtxih+zKQNKotF9rJkaKfYuAjr1Q@mail.gmail.com>
	<CABL7CQiig9r6RdDyvUrc_Z=+D6noHkZUdM18NG9BwCVNE2_iQA@mail.gmail.com>
	<CAE7vhgOqjL_6wFjKQfSXoFXVHyES9X-CnCxu5oUkh5=HhkgEfA@mail.gmail.com>
Message-ID: <CABL7CQiZZ6p4_L_cGQakoaKFb4xquAFNcRvVFbLmPrO0uf+iTw@mail.gmail.com>

On Tue, Apr 15, 2014 at 9:19 PM, Nils Wagner <nils106 at googlemail.com> wrote:

> >>> from scipy import stats
> >>> stats.test()
> Running unit tests for scipy.stats
> NumPy version 1.9.0.dev-61c699e
> NumPy is installed in /home/nils/local/lib64/python2.7/site-packages/numpy
> SciPy version 0.15.0.dev-87df1db
> SciPy is installed in /home/nils/local/lib64/python2.7/site-packages/scipy
> Python version 2.7.3 (default, Apr 14 2012, 08:58:41) [GCC]
> nose version 1.3.1
>
> ...
>
> ======================================================================
> ERROR: test_gmean (test_mstats_basic.TestCompareWithStats)
>
> ----------------------------------------------------------------------
> Traceback (most recent call last):
>   File
> "/home/nils/local/lib64/python2.7/site-packages/scipy/stats/tests/test_mstats_basic.py",
> line 763, in test_gmean
>     rm = stats.mstats.gmean(abs(xm))
>   File
> "/home/nils/local/lib64/python2.7/site-packages/scipy/stats/stats.py", line
> 516, in gmean
>     log_a = np.log(a)
>   File "/home/nils/local/lib64/python2.7/site-packages/numpy/ma/core.py",
> line 2837, in __array_wrap__
>     d = filled(domain(*args), True)
>   File "/home/nils/local/lib64/python2.7/site-packages/numpy/ma/core.py",
> line 795, in __call__
>     return umath.less_equal(x, self.critical_value)
> RuntimeWarning: invalid value encountered in less_equal
>
> ======================================================================
> ERROR: test_hmean (test_mstats_basic.TestCompareWithStats)
>
> ----------------------------------------------------------------------
> Traceback (most recent call last):
>   File
> "/home/nils/local/lib64/python2.7/site-packages/scipy/stats/tests/test_mstats_basic.py",
> line 775, in test_hmean
>     rm = stats.mstats.hmean(abs(xm))
>   File
> "/home/nils/local/lib64/python2.7/site-packages/scipy/stats/stats.py", line
> 562, in hmean
>     if np.all(a > 0):  # Harmonic mean only defined if greater than zero
> RuntimeWarning: invalid value encountered in greater
>
> ======================================================================
> ERROR: test_tmax (test_mstats_basic.TestCompareWithStats)
>
> ----------------------------------------------------------------------
> Traceback (most recent call last):
>   File
> "/home/nils/local/lib64/python2.7/site-packages/scipy/stats/tests/test_mstats_basic.py",
> line 908, in test_tmax
>     stats.mstats.tmax(xm,2.), 10)
>   File
> "/home/nils/local/lib64/python2.7/site-packages/scipy/stats/mstats_basic.py",
> line 1383, in tmax
>     am = trima(a, (None, upperlimit), (False, inclusive))
>   File
> "/home/nils/local/lib64/python2.7/site-packages/scipy/stats/mstats_basic.py",
> line 1018, in trima
>     condition |= (a > upper_lim)
> RuntimeWarning: invalid value encountered in greater
>
> ======================================================================
> ERROR: test_tmin (test_mstats_basic.TestCompareWithStats)
>
> ----------------------------------------------------------------------
> Traceback (most recent call last):
>   File
> "/home/nils/local/lib64/python2.7/site-packages/scipy/stats/tests/test_mstats_basic.py",
> line 919, in test_tmin
>     stats.mstats.tmin(xm,lowerlimit=-1.), 10)
>   File
> "/home/nils/local/lib64/python2.7/site-packages/scipy/stats/mstats_basic.py",
> line 1376, in tmin
>     am = trima(a, (lowerlimit, None), (inclusive, False))
>   File
> "/home/nils/local/lib64/python2.7/site-packages/scipy/stats/mstats_basic.py",
> line 1013, in trima
>     condition |= (a < lower_lim)
> RuntimeWarning: invalid value encountered in less
>
> ======================================================================
> ERROR: test_tsem (test_mstats_basic.TestCompareWithStats)
>
> ----------------------------------------------------------------------
> Traceback (most recent call last):
>   File
> "/home/nils/local/lib64/python2.7/site-packages/scipy/stats/tests/test_mstats_basic.py",
> line 958, in test_tsem
>     stats.mstats.tsem(xm,limits=(-2.,2.)),
>   File
> "/home/nils/local/lib64/python2.7/site-packages/scipy/stats/mstats_basic.py",
> line 1393, in tsem
>     am = trima(a.ravel(), limits, inclusive)
>   File
> "/home/nils/local/lib64/python2.7/site-packages/scipy/stats/mstats_basic.py",
> line 1013, in trima
>     condition |= (a < lower_lim)
> RuntimeWarning: invalid value encountered in less
>
> ======================================================================
> ERROR: test_zmap (test_mstats_basic.TestCompareWithStats)
>
> ----------------------------------------------------------------------
> Traceback (most recent call last):
>   File
> "/home/nils/local/lib64/python2.7/site-packages/scipy/stats/tests/test_mstats_basic.py",
> line 927, in test_zmap
>     zm = stats.mstats.zmap(xm,ym)
>   File
> "/home/nils/local/lib64/python2.7/site-packages/scipy/stats/stats.py", line
> 2063, in zmap
>     return (scores - mns) / sstd
>   File "/home/nils/local/lib64/python2.7/site-packages/numpy/ma/core.py",
> line 3735, in __truediv__
>     return true_divide(self, other)
>   File "/home/nils/local/lib64/python2.7/site-packages/numpy/ma/core.py",
> line 1089, in __call__
>     m |= filled(domain(da, db), True)
>   File "/home/nils/local/lib64/python2.7/site-packages/numpy/ma/core.py",
> line 783, in __call__
>     return umath.absolute(a) * self.tolerance >= umath.absolute(b)
> RuntimeWarning: invalid value encountered in greater_equal
>
> ======================================================================
> FAIL: test_example1a (test_morestats.TestAndersonKSamp)
>
> ----------------------------------------------------------------------
> Traceback (most recent call last):
>   File
> "/home/nils/local/lib64/python2.7/site-packages/scipy/stats/tests/test_morestats.py",
> line 97, in test_example1a
>     midrank=False)
>   File
> "/home/nils/local/lib64/python2.7/site-packages/numpy/testing/utils.py",
> line 1593, in assert_warns
>     % func.__name__)
> AssertionError: No warning raised when calling anderson_ksamp
>
> ======================================================================
> FAIL: test_tmean (test_mstats_basic.TestCompareWithStats)
>
> ----------------------------------------------------------------------
> Traceback (most recent call last):
>   File
> "/home/nils/local/lib64/python2.7/site-packages/scipy/stats/tests/test_mstats_basic.py",
> line 901, in test_tmean
>     assert_equal(stats.tmean(x),stats.mstats.tmean(xm))
>   File
> "/home/nils/local/lib64/python2.7/site-packages/numpy/ma/testutils.py",
> line 100, in assert_equal
>     raise AssertionError(msg)
> AssertionError:
> Items are not equal:
>  ACTUAL: 0.040175456835823721
>  DESIRED: 0.040175456835823714
>
>
> ======================================================================
> FAIL: test_trim (test_mstats_basic.TestTrimming)
> ----------------------------------------------------------------------
> Traceback (most recent call last):
>   File
> "/home/nils/local/lib64/python2.7/site-packages/scipy/stats/tests/test_mstats_basic.py",
> line 274, in test_trim
>
>     assert_equal(trimx._mask.ravel(),[1]*20+[0]*70+[1]*20)
>   File
> "/home/nils/local/lib64/python2.7/site-packages/numpy/ma/testutils.py",
> line 123, in assert_equal
>     return assert_array_equal(actual, desired, err_msg)
>   File
> "/home/nils/local/lib64/python2.7/site-packages/numpy/ma/testutils.py",
> line 196, in assert_array_equal
>
>     header='Arrays are not equal')
>   File
> "/home/nils/local/lib64/python2.7/site-packages/numpy/ma/testutils.py",
> line 189, in assert_array_compare
>     verbose=verbose, header=header)
>   File
> "/home/nils/local/lib64/python2.7/site-packages/numpy/testing/utils.py",
> line 660, in assert_array_compare
>
>     raise AssertionError(msg)
> AssertionError:
> Arrays are not equal
>
> (mismatch 9.09090909091%)
>  x: array([ True,  True,  True,  True,  True,  True,  True,  True,  True,
>         True,  True,  True,  True,  True,  True,  True,  True,  True,
>         True,  True, False, False, False, False, False, False, False,...
>  y: array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0,
> 0, 0,
>        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
>        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
> 0,...
>
> ----------------------------------------------------------------------
> Ran 1957 tests in 50.047s
>
> FAILED (KNOWNFAIL=4, SKIP=3, errors=6, failures=3)
> <nose.result.TextTestResult run=1957 errors=6 failures=3>
>
>
>
> On Sun, Apr 13, 2014 at 10:58 PM, Ralf Gommers <ralf.gommers at gmail.com>wrote:
>
>>
>>
>>
>> On Sun, Apr 13, 2014 at 7:42 AM, Charles R Harris <
>> charlesr.harris at gmail.com> wrote:
>>
>>> Hi All,
>>>
>>> I get 75 errors and 3 failures when testing against current numpy on my
>>> machine. Most of the errors are due to either the deprecation of the binary
>>> '-' operator for booleans or to the deprecation of double ellipsis for
>>> indexing, i.e., '(..., ...)' . The remainder look like two numerical
>>> precision problems and one I can't immediately identify.
>>>
>>> The main question I have is what is the best way to deal with the
>>> deprecations?
>>>
>>
>> Fix them? It's not that much work; I'd like to still get that into 0.14.0
>> if possible. Will have a look.
>>
>> The QHull test I've seen fail randomly on various platforms, it has a
>> real issue. The other two look like the tests need adapting.
>>
>> Ralf
>>
>>
>>>
>>> FAIL: test_lsmr.TestLSMR.testBidiagonalA
>>> ----------------------------------------------------------------------
>>> Traceback (most recent call last):
>>>   File "/usr/lib/python2.7/site-packages/nose/case.py", line 197, in
>>> runTest
>>>     self.test(*self.arg)
>>>   File
>>> "/home/charris/.local/lib/python2.7/site-packages/scipy/sparse/linalg/isolve/tests/test_lsmr.py",
>>> line 60, in testBidiagonalA
>>>     self.assertCompatibleSystem(A,xtrue)
>>>   File
>>> "/home/charris/.local/lib/python2.7/site-packages/scipy/sparse/linalg/isolve/tests/test_lsmr.py",
>>> line 40, in assertCompatibleSystem
>>>     assert_almost_equal(norm(x - xtrue), 0, 6)
>>>   File
>>> "/home/charris/.local/lib/python2.7/site-packages/numpy/testing/utils.py",
>>> line 486, in assert_almost_equal
>>>     raise AssertionError(_build_err_msg())
>>> AssertionError:
>>> Arrays are not almost equal to 6 decimals
>>>  ACTUAL: 6.048630163037888e-07
>>>  DESIRED: 0
>>>
>>> ======================================================================
>>> FAIL: test_qhull.TestUtilities.test_degenerate_barycentric_transforms
>>> ----------------------------------------------------------------------
>>> Traceback (most recent call last):
>>>   File "/usr/lib/python2.7/site-packages/nose/case.py", line 197, in
>>> runTest
>>>     self.test(*self.arg)
>>>   File
>>> "/home/charris/.local/lib/python2.7/site-packages/numpy/testing/decorators.py",
>>> line 146, in skipper_func
>>>     return f(*args, **kwargs)
>>>   File
>>> "/home/charris/.local/lib/python2.7/site-packages/scipy/spatial/tests/test_qhull.py",
>>> line 296, in test_degenerate_barycentric_transforms
>>>     assert_(bad_count < 20, bad_count)
>>>   File
>>> "/home/charris/.local/lib/python2.7/site-packages/numpy/testing/utils.py",
>>> line 50, in assert_
>>>     raise AssertionError(smsg)
>>> AssertionError: 20
>>>
>>> ======================================================================
>>> FAIL: test_trim (test_mstats_basic.TestTrimming)
>>> ----------------------------------------------------------------------
>>> Traceback (most recent call last):
>>>   File
>>> "/home/charris/.local/lib/python2.7/site-packages/scipy/stats/tests/test_mstats_basic.py",
>>> line 270, in test_trim
>>>     assert_equal(trimx._mask.ravel(),[1]*20+[0]*70+[1]*20)
>>>   File
>>> "/home/charris/.local/lib/python2.7/site-packages/numpy/ma/testutils.py",
>>> line 123, in assert_equal
>>>     return assert_array_equal(actual, desired, err_msg)
>>>   File
>>> "/home/charris/.local/lib/python2.7/site-packages/numpy/ma/testutils.py",
>>> line 196, in assert_array_equal
>>>     header='Arrays are not equal')
>>>   File
>>> "/home/charris/.local/lib/python2.7/site-packages/numpy/ma/testutils.py",
>>> line 189, in assert_array_compare
>>>     verbose=verbose, header=header)
>>>   File
>>> "/home/charris/.local/lib/python2.7/site-packages/numpy/testing/utils.py",
>>> line 660, in assert_array_compare
>>>     raise AssertionError(msg)
>>> AssertionError:
>>> Arrays are not equal
>>>
>>> (mismatch 9.09090909091%)
>>>  x: array([ True,  True,  True,  True,  True,  True,  True,  True,  True,
>>>         True,  True,  True,  True,  True,  True,  True,  True,  True,
>>>         True,  True, False, False, False, False, False, False, False,...
>>>  y: array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
>>> 0, 0, 0,
>>>        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
>>> 0,
>>>        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
>>> 0,...
>>>
>>>
>>>

Except for the Qhull one, all of these should be fixed by
https://github.com/scipy/scipy/pull/3564.

Ralf
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From wcardoen at gmail.com  Mon Apr 21 19:07:39 2014
From: wcardoen at gmail.com (Wim R. Cardoen)
Date: Mon, 21 Apr 2014 17:07:39 -0600
Subject: [SciPy-Dev] Errors in Scipy 0.13.3 & Python 3.3.5
Message-ID: <CABNPx=ynM1mmuk8GSwuHOTTFjOQOMU4J6B8iX5LMOZS8K=nPzA@mail.gmail.com>

Hello

I compiled scipy 0.13.3 successfully
on a Centos 6 machine.
When I ran the test suite I obtained the following 5 errors
(I used OpenBlas (single threaded) for the BLAS/LAPACK library and
SuiteSparse for the AMD,UMFPACK libraries)


======================================================================
FAIL: test_arpack.test_real_nonsymmetric_modes(False, <std-real-nonsym>,
'f', 2, 'LM', None, 0.1, <function asarray at 0x7f989ca49b00>, 'r')
----------------------------------------------------------------------
Traceback (most recent call last):
  File
"/software/pkg/python/3.3.5/lib/python3.3/site-packages/nose-1.3.1-py3.3.egg/nose/case.py",
line 198, in runTest
    self.test(*self.arg)
  File
"/software/pkg/python/3.3.5/lib/python3.3/site-packages/scipy/sparse/linalg/eigen/arpack/tests/test_arpack.py",
line 259, in eval_evec
    assert_allclose(LHS, RHS, rtol=rtol, atol=atol, err_msg=err)
  File
"/software/pkg/python/3.3.5/lib/python3.3/site-packages/numpy/testing/utils.py",
line 1183, in assert_allclose
    verbose=verbose, header=header)
  File
"/software/pkg/python/3.3.5/lib/python3.3/site-packages/numpy/testing/utils.py",
line 644, in assert_array_compare
    raise AssertionError(msg)
AssertionError:
Not equal to tolerance rtol=0.000357628, atol=0.000357628
error for eigs:standard, typ=f, which=LM, sigma=0.1, mattype=asarray,
OPpart=r, mode=normal
(mismatch 100.0%)
 x: array([[-0.11649324+0.j, -0.05435310+0.j],
       [ 0.13801208+0.j, -0.05894032+0.j],
       [-0.21229853+0.j, -0.02815625+0.j],...
 y: array([[-0.05698580+0.08018474j, -0.05435318+0.j        ],
       [ 0.06439485-0.09061003j, -0.05894023+0.j        ],
       [-0.12503998+0.17594382j, -0.02815617+0.j        ],...

======================================================================
FAIL: test_arpack.test_real_nonsymmetric_modes(False, <std-real-nonsym>,
'f', 2, 'LR', None, None, <function aslinearoperator at 0x7f988cd9fb90>,
None)
----------------------------------------------------------------------
Traceback (most recent call last):
  File
"/software/pkg/python/3.3.5/lib/python3.3/site-packages/nose-1.3.1-py3.3.egg/nose/case.py",
line 198, in runTest
    self.test(*self.arg)
  File
"/software/pkg/python/3.3.5/lib/python3.3/site-packages/scipy/sparse/linalg/eigen/arpack/tests/test_arpack.py",
line 259, in eval_evec
    assert_allclose(LHS, RHS, rtol=rtol, atol=atol, err_msg=err)
  File
"/software/pkg/python/3.3.5/lib/python3.3/site-packages/numpy/testing/utils.py",
line 1183, in assert_allclose
    verbose=verbose, header=header)
  File
"/software/pkg/python/3.3.5/lib/python3.3/site-packages/numpy/testing/utils.py",
line 644, in assert_array_compare
    raise AssertionError(msg)
AssertionError:
Not equal to tolerance rtol=0.00178814, atol=0.000357628
error for eigs:standard, typ=f, which=LR, sigma=None,
mattype=aslinearoperator, OPpart=None, mode=normal
(mismatch 100.0%)
 x: array([[ 0.13884316+0.j, -1.07112074+0.j],
       [-0.08962911+0.j, -1.39801252+0.j],
       [ 0.21701422+0.j, -0.93968379+0.j],...
 y: array([[ 0.06020537+0.08471544j, -1.07112110+0.j        ],
       [-0.07497437-0.10549703j, -1.39801240+0.j        ],
       [ 0.13326693+0.18752094j, -0.93968397+0.j        ],...

======================================================================
FAIL: test_basic.test_xlogy
----------------------------------------------------------------------
Traceback (most recent call last):
  File
"/software/pkg/python/3.3.5/lib/python3.3/site-packages/nose-1.3.1-py3.3.egg/nose/case.py",
line 198, in runTest
    self.test(*self.arg)
  File
"/software/pkg/python/3.3.5/lib/python3.3/site-packages/scipy/special/tests/test_basic.py",
line 2736, in test_xlogy
    assert_func_equal(special.xlogy, w2, z2, rtol=1e-13, atol=1e-13)
  File
"/software/pkg/python/3.3.5/lib/python3.3/site-packages/scipy/special/_testutils.py",
line 87, in assert_func_equal
    fdata.check()
  File
"/software/pkg/python/3.3.5/lib/python3.3/site-packages/scipy/special/_testutils.py",
line 292, in check
    assert_(False, "\n".join(msg))
  File
"/software/pkg/python/3.3.5/lib/python3.3/site-packages/numpy/testing/utils.py",
line 44, in assert_
    raise AssertionError(msg)
AssertionError:
Max |adiff|: 712.209
Max |rdiff|: 1027.5
Bad results (3 out of 6) for the following points (in output 0):
                            0j                        (nan+0j)
=>                        (-0+0j) !=                     (nan+nanj)
(rdiff                            0.0)
                        (1+0j)                          (2+0j) =>
(-711.5155851371305+0.7853981633976757j) !=        (0.6931471805599453+0j)
(rdiff             1027.5006309578175)
                        (1+0j)                              1j =>
(-711.5155851371305+0.7853981633976757j) !=            1.5707963267948966j
(rdiff              452.9651658054808)

======================================================================
FAIL: test_lambertw.test_values
----------------------------------------------------------------------
Traceback (most recent call last):
  File
"/software/pkg/python/3.3.5/lib/python3.3/site-packages/nose-1.3.1-py3.3.egg/nose/case.py",
line 198, in runTest
    self.test(*self.arg)
  File
"/software/pkg/python/3.3.5/lib/python3.3/site-packages/scipy/special/tests/test_lambertw.py",
line 21, in test_values
    assert_equal(lambertw(inf,1).real, inf)
  File
"/software/pkg/python/3.3.5/lib/python3.3/site-packages/numpy/testing/utils.py",
line 304, in assert_equal
    raise AssertionError(msg)
AssertionError:
Items are not equal:
 ACTUAL: nan
 DESIRED: inf

======================================================================
FAIL: test_lambertw.test_ufunc
----------------------------------------------------------------------
Traceback (most recent call last):
  File
"/software/pkg/python/3.3.5/lib/python3.3/site-packages/numpy/testing/utils.py",
line 581, in chk_same_position
    assert_array_equal(x_id, y_id)
  File
"/software/pkg/python/3.3.5/lib/python3.3/site-packages/numpy/testing/utils.py",
line 718, in assert_array_equal
    verbose=verbose, header='Arrays are not equal')
  File
"/software/pkg/python/3.3.5/lib/python3.3/site-packages/numpy/testing/utils.py",
line 644, in assert_array_compare
    raise AssertionError(msg)
AssertionError:
Arrays are not equal

(mismatch 66.66666666666666%)
 x: array([False,  True,  True], dtype=bool)
 y: array([False, False, False], dtype=bool)

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File
"/software/pkg/python/3.3.5/lib/python3.3/site-packages/nose-1.3.1-py3.3.egg/nose/case.py",
line 198, in runTest
    self.test(*self.arg)
  File
"/software/pkg/python/3.3.5/lib/python3.3/site-packages/scipy/special/tests/test_lambertw.py",
line 93, in test_ufunc
    lambertw(r_[0., e, 1.]), r_[0., 1., 0.567143290409783873])
  File
"/software/pkg/python/3.3.5/lib/python3.3/site-packages/numpy/testing/utils.py",
line 811, in assert_array_almost_equal
    header=('Arrays are not almost equal to %d decimals' % decimal))
  File
"/software/pkg/python/3.3.5/lib/python3.3/site-packages/numpy/testing/utils.py",
line 607, in assert_array_compare
    chk_same_position(x_isnan, y_isnan, hasval='nan')
  File
"/software/pkg/python/3.3.5/lib/python3.3/site-packages/numpy/testing/utils.py",
line 587, in chk_same_position
    raise AssertionError(msg)
AssertionError:
Arrays are not almost equal to 6 decimals

x and y nan location mismatch:
 x: array([  0.+0.j,  nan+0.j,  nan+0.j])
 y: array([ 0.   ,  1.   ,  0.567])

----------------------------------------------------------------------
Ran 8775 tests in 239.751s

<scipy-dev at scipy.org>FAILED (KNOWNFAIL=114, SKIP=220, failures=5)
Running unit tests for scipy
NumPy version 1.8.1
NumPy is installed in
/software/pkg/python/3.3.5/lib/python3.3/site-packages/numpy
SciPy version 0.13.3
SciPy is installed in
/software/pkg/python/3.3.5/lib/python3.3/site-packages/scipy
Python version 3.3.5 (default, Apr 16 2014, 19:42:58) [GCC 4.4.7 20120313
(Red Hat 4.4.7-4)]
nose version 1.3.1

Do you have any fix for these errors?

Thanks in advance.

Wim


-- 
---------------------------------------------------------------
Wim R. Cardoen, PhD
Staff Scientist,
Center for High Performance Computing
University of Utah
---------------------------------------------------------------
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From matthew.brett at gmail.com  Mon Apr 21 21:05:53 2014
From: matthew.brett at gmail.com (Matthew Brett)
Date: Mon, 21 Apr 2014 18:05:53 -0700
Subject: [SciPy-Dev] Powell failure on MingW windows build - any insights?
Message-ID: <CAH6Pt5q6Z+w4pFzKjTsoPDY2e-ZUAr1qXSz2K92s2jgNYUOAng@mail.gmail.com>

Hi,

I'm experimenting with Carl Kleffner's MingW-w64 builds of numpy and scipy.

Numpy now passes all tests for me (building with Carl's toolchain and
ATLAS 64-bit).

Scipy fails 2 tests only, both using the Powell routine.

Errors look like this:

======================================================================
FAIL: Powell (direction set) optimization routine
----------------------------------------------------------------------
Traceback (most recent call last):
  File "D:\devel\py27\lib\site-packages\nose\case.py", line 197, in runTest
    self.test(*self.arg)
  File "D:\devel\py27\lib\site-packages\scipy\optimize\tests\test_optimize.py",
line 209, in test_powell
    atol=1e-14, rtol=1e-7)
  File "D:\devel\py27\lib\site-packages\numpy\testing\utils.py", line
1181, in assert_allclose
    verbose=verbose, header=header)
  File "D:\devel\py27\lib\site-packages\numpy\testing\utils.py", line
644, in assert_array_compare
    raise AssertionError(msg)
AssertionError:
Not equal to tolerance rtol=1e-07, atol=1e-14

(mismatch 100.0%)
 x: array([[ 0.75077639, -0.44156936,  0.47100962],
       [ 0.75077639, -0.44156936,  0.48052496],
       [ 1.50155279, -0.88313872,  0.95153458],...
 y: array([[ 0.72949016, -0.44156936,  0.47100962],
       [ 0.72949016, -0.44156936,  0.48052496],
       [ 1.45898031, -0.88313872,  0.95153458],...

Does anyone have any insight as to what might be going on here?  Same
failure on Windows 32 and 64 bit, with ATLAS or OpenBLAS...

Many thanks for any pointers,

Matthew


From richard9404 at gmail.com  Tue Apr 22 02:59:04 2014
From: richard9404 at gmail.com (Richard Tsai)
Date: Tue, 22 Apr 2014 14:59:04 +0800
Subject: [SciPy-Dev] GSoC Draft Proposal: Rewrite and improve cluster
 package in Cython
In-Reply-To: <CALkpnhfnWZ+LBcJNgminubKN90PHPbr27SEVTWufgjpCegs23A@mail.gmail.com>
References: <CALkpnheu9SFbQme7TCqOFfrf9KOx6Q=aAUUes0NY=ffDHZFC-w@mail.gmail.com>
	<CALc6Xo4cy7rT=HrPF1eEvRW18AkzNdy3h7Tf_PnJAMV6vQ4VsA@mail.gmail.com>
	<CALkpnhdn1-XxGaWyUuBJVCWK2reVmph279kBuJt5nxHh8O3dbw@mail.gmail.com>
	<CALc6Xo4w0TSkAMktBR7D4H15vA45AV8ECS8WQDfRqUcEE=2Mww@mail.gmail.com>
	<CALkpnhewPi27nsfRmrrDTb1t+9LJchTP5NfFY+KRNRc67aHv-g@mail.gmail.com>
	<CABL7CQgkakQ2qSYCVCrkVBAh3Q+L9e57-pKmW7aE=p5LE=80Pw@mail.gmail.com>
	<CALkpnhcFsCEmUxDtrbAmnE8GpLgOsQGBPKf1VeFkMEWx_7pL0w@mail.gmail.com>
	<CABL7CQhhZL=b23oCV7dUB=-tYJDgUnPXAYSFuCyx7UqEAC3=DQ@mail.gmail.com>
	<CALkpnhfnWZ+LBcJNgminubKN90PHPbr27SEVTWufgjpCegs23A@mail.gmail.com>
Message-ID: <CALkpnhf3LwP__T-KEtWaTMAmHz29b8U2q+CLDLo+rtyLML5cQg@mail.gmail.com>

2014-03-21 22:18 GMT+08:00 Richard Tsai <richard9404 at gmail.com>:

> Hi all,
>
> I've posted my proposal to melange but there's still some potential
> features to the package (cluster) I want to discuss here.
>
> The first one is about the stopping criterion of kmeans/kmeans. These two
> functions are using the average distance from observations to their
> corresponding centroids currently. But a more accurate exiting condition
> will be the average *squared* distance. Besides, the average centroids
> moving distance, and the changes of the results of vq are both better than
> the original one.
> Second, finding convex hulls of hierarchical clustering seems interesting
> but I'm not sure if there's a demand for it.
> The third one is gap statistics for automatic determination of k in
> kmeans. David supposed that it should be scikit-learn territory and I plan
> to put it to the end.
>
> I'm not sure if these features are proper to be integrated into cluster
> and Ralf doubts that there's some overlap with scikit-learn so I post them
> here to discuss at his suggestion. I've also made my proposal public:
> http://www.google-melange.com/gsoc/proposal/public/google/gsoc2014/richardtsai/5629499534213120
> Comments/suggestions are welcome.
>
> Regards,
> Richard
>

Hi all,

I've received emails from GSoC saying that my proposal has been
accepted. Thanks
to those who have help me with my application!

I'll submit the required materials soon then make a more detailed plan and
prepare for coding. If you have any thoughts about my project, please
discuss with me!

Richard
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From warren.weckesser at gmail.com  Tue Apr 22 08:22:31 2014
From: warren.weckesser at gmail.com (Warren Weckesser)
Date: Tue, 22 Apr 2014 08:22:31 -0400
Subject: [SciPy-Dev] GSoC Draft Proposal: Rewrite and improve cluster
 package in Cython
In-Reply-To: <CALkpnhf3LwP__T-KEtWaTMAmHz29b8U2q+CLDLo+rtyLML5cQg@mail.gmail.com>
References: <CALkpnheu9SFbQme7TCqOFfrf9KOx6Q=aAUUes0NY=ffDHZFC-w@mail.gmail.com>
	<CALc6Xo4cy7rT=HrPF1eEvRW18AkzNdy3h7Tf_PnJAMV6vQ4VsA@mail.gmail.com>
	<CALkpnhdn1-XxGaWyUuBJVCWK2reVmph279kBuJt5nxHh8O3dbw@mail.gmail.com>
	<CALc6Xo4w0TSkAMktBR7D4H15vA45AV8ECS8WQDfRqUcEE=2Mww@mail.gmail.com>
	<CALkpnhewPi27nsfRmrrDTb1t+9LJchTP5NfFY+KRNRc67aHv-g@mail.gmail.com>
	<CABL7CQgkakQ2qSYCVCrkVBAh3Q+L9e57-pKmW7aE=p5LE=80Pw@mail.gmail.com>
	<CALkpnhcFsCEmUxDtrbAmnE8GpLgOsQGBPKf1VeFkMEWx_7pL0w@mail.gmail.com>
	<CABL7CQhhZL=b23oCV7dUB=-tYJDgUnPXAYSFuCyx7UqEAC3=DQ@mail.gmail.com>
	<CALkpnhfnWZ+LBcJNgminubKN90PHPbr27SEVTWufgjpCegs23A@mail.gmail.com>
	<CALkpnhf3LwP__T-KEtWaTMAmHz29b8U2q+CLDLo+rtyLML5cQg@mail.gmail.com>
Message-ID: <CAGzF1ufLuTQEwMoZTTMU1Dm+i2f36OyEBDn-H5y1yciA+==8Lg@mail.gmail.com>

On 4/22/14, Richard Tsai <richard9404 at gmail.com> wrote:
> 2014-03-21 22:18 GMT+08:00 Richard Tsai <richard9404 at gmail.com>:
>
>> Hi all,
>>
>> I've posted my proposal to melange but there's still some potential
>> features to the package (cluster) I want to discuss here.
>>
>> The first one is about the stopping criterion of kmeans/kmeans. These two
>> functions are using the average distance from observations to their
>> corresponding centroids currently. But a more accurate exiting condition
>> will be the average *squared* distance. Besides, the average centroids
>> moving distance, and the changes of the results of vq are both better
>> than
>> the original one.
>> Second, finding convex hulls of hierarchical clustering seems interesting
>> but I'm not sure if there's a demand for it.
>> The third one is gap statistics for automatic determination of k in
>> kmeans. David supposed that it should be scikit-learn territory and I
>> plan
>> to put it to the end.
>>
>> I'm not sure if these features are proper to be integrated into cluster
>> and Ralf doubts that there's some overlap with scikit-learn so I post
>> them
>> here to discuss at his suggestion. I've also made my proposal public:
>> http://www.google-melange.com/gsoc/proposal/public/google/gsoc2014/richardtsai/5629499534213120
>> Comments/suggestions are welcome.
>>
>> Regards,
>> Richard
>>
>
> Hi all,
>
> I've received emails from GSoC saying that my proposal has been
> accepted. Thanks
> to those who have help me with my application!
>
> I'll submit the required materials soon then make a more detailed plan and
> prepare for coding. If you have any thoughts about my project, please
> discuss with me!
>
> Richard
>


Congratulations, Richard!  That's great news.

Warren


From matthew.brett at gmail.com  Tue Apr 22 15:51:37 2014
From: matthew.brett at gmail.com (Matthew Brett)
Date: Tue, 22 Apr 2014 12:51:37 -0700
Subject: [SciPy-Dev] Powell failure on MingW windows build - any
	insights?
In-Reply-To: <CAH6Pt5q6Z+w4pFzKjTsoPDY2e-ZUAr1qXSz2K92s2jgNYUOAng@mail.gmail.com>
References: <CAH6Pt5q6Z+w4pFzKjTsoPDY2e-ZUAr1qXSz2K92s2jgNYUOAng@mail.gmail.com>
Message-ID: <CAH6Pt5rKkiwAoue3bMBzdt8NbiXS3=AFQxt726p5X6-th-SZNg@mail.gmail.com>

Hi,

On Mon, Apr 21, 2014 at 6:05 PM, Matthew Brett <matthew.brett at gmail.com> wrote:
> Hi,
>
> I'm experimenting with Carl Kleffner's MingW-w64 builds of numpy and scipy.
>
> Numpy now passes all tests for me (building with Carl's toolchain and
> ATLAS 64-bit).
>
> Scipy fails 2 tests only, both using the Powell routine.
>
> Errors look like this:
>
> ======================================================================
> FAIL: Powell (direction set) optimization routine
> ----------------------------------------------------------------------
> Traceback (most recent call last):
>   File "D:\devel\py27\lib\site-packages\nose\case.py", line 197, in runTest
>     self.test(*self.arg)
>   File "D:\devel\py27\lib\site-packages\scipy\optimize\tests\test_optimize.py",
> line 209, in test_powell
>     atol=1e-14, rtol=1e-7)
>   File "D:\devel\py27\lib\site-packages\numpy\testing\utils.py", line
> 1181, in assert_allclose
>     verbose=verbose, header=header)
>   File "D:\devel\py27\lib\site-packages\numpy\testing\utils.py", line
> 644, in assert_array_compare
>     raise AssertionError(msg)
> AssertionError:
> Not equal to tolerance rtol=1e-07, atol=1e-14
>
> (mismatch 100.0%)
>  x: array([[ 0.75077639, -0.44156936,  0.47100962],
>        [ 0.75077639, -0.44156936,  0.48052496],
>        [ 1.50155279, -0.88313872,  0.95153458],...
>  y: array([[ 0.72949016, -0.44156936,  0.47100962],
>        [ 0.72949016, -0.44156936,  0.48052496],
>        [ 1.45898031, -0.88313872,  0.95153458],...
>
> Does anyone have any insight as to what might be going on here?  Same
> failure on Windows 32 and 64 bit, with ATLAS or OpenBLAS...

To follow up:

The test_powell test tests that the estimated parameters for an
example function are nearly the same as a previous example run, for
iterations 34 through 38.  It therefore asserts that the optimization
path is exactly the same, up until iteration 38.

There is a separate test above that the solution is nearly the same
and the number of iterations are similar.

In the test code, there is:

        # However, some leeway must be added: the exact evaluation
        # count is sensitive to numerical error, and floating-point
        # computations are not bit-for-bit reproducible across
        # machines, and when using e.g. MKL, data alignment
        # etc. affect the rounding error.

In fact, what seems to have happened in this test in my case, is that
tiny O(eps) differences in the result of the exp function have led to
a divergent path earlier than step 34. The end solution is the same,
and the number of iterations is similar (those tests do pass).

It seems to me the iteration path test is not a good test.  It's
likely that these kind of tiny differences will lead to divergent
paths, and testing that steps 34 though 38 are the same seems
arbitrary and difficult to justify.

I think that test should be removed, leaving the check for the same
solution and roughly the same number of iterations.  Is that
reasonable?

Cheers,

Matthew


From pav at iki.fi  Tue Apr 22 16:08:10 2014
From: pav at iki.fi (Pauli Virtanen)
Date: Tue, 22 Apr 2014 23:08:10 +0300
Subject: [SciPy-Dev] Powell failure on MingW windows build - any
	insights?
In-Reply-To: <CAH6Pt5rKkiwAoue3bMBzdt8NbiXS3=AFQxt726p5X6-th-SZNg@mail.gmail.com>
References: <CAH6Pt5q6Z+w4pFzKjTsoPDY2e-ZUAr1qXSz2K92s2jgNYUOAng@mail.gmail.com>
	<CAH6Pt5rKkiwAoue3bMBzdt8NbiXS3=AFQxt726p5X6-th-SZNg@mail.gmail.com>
Message-ID: <lj6i7b$d9s$1@ger.gmane.org>

22.04.2014 22:51, Matthew Brett kirjoitti:
[clip]
> It seems to me the iteration path test is not a good test.  It's 
> likely that these kind of tiny differences will lead to divergent 
> paths, and testing that steps 34 though 38 are the same seems 
> arbitrary and difficult to justify.
> 
> I think that test should be removed, leaving the check for the same
> solution and roughly the same number of iterations.  Is that 
> reasonable?

The point of those tests is to safeguard against unintended changes
when refactoring the codes. An error added e.g. in line search code
can still make the code converge, and just checking iteration counts
in an easy test does not necessarily reveal regressions.

Having the check that far (34 evals) in the tail may however be
excessive. The other routines check earlier only after a few
iterations, so if you can see how much of the trace is reproducible,
that should be OK.

-- 
Pauli Virtanen



From ralf.gommers at gmail.com  Tue Apr 22 16:40:23 2014
From: ralf.gommers at gmail.com (Ralf Gommers)
Date: Tue, 22 Apr 2014 22:40:23 +0200
Subject: [SciPy-Dev] welcome to Scipy GSoC'14 students!
Message-ID: <CABL7CQjTVA1_9PWfPBg9Lp6y=-e2Wd2Vfcd37_NuDgfr6DnuDw@mail.gmail.com>

Hi all,

The accepted projects for Google Summer of Code have been announced:
congratulations and a warm welcome to Janani Padmanbhan and Richard Tsai!

Janani will be working on spherical harmonic and hypergemetric functions in
scipy.special [1], with Pauli and Stefan as main mentors, and Evgeni and me
also pitching in.

Richard will be working on rewriting scipy.cluster in Cython and improving
it further [2], with Charles as main mentor and David W-F as domain expert
providing help.

>From now until May 19th is the community bonding period [3] - the period of
time when students further get to know the project, figure out people &
processes within the project in more detail, and prepare for the actual
coding period. Please help them find their way! And Richard, Janani, feel
free to ask questions, explore or work on issues / documentation / website
/ whatever you think is interesting.

Cheers,
Ralf


[1]
http://www.google-melange.com/gsoc/proposal/public/google/gsoc2014/jennystone/5629499534213120

[2]
http://www.google-melange.com/gsoc/proposal/public/google/gsoc2014/richardtsai/5629499534213120

[3]
http://googlesummerofcode.blogspot.nl/2007/04/so-what-is-this-community-bonding-all.html
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From warren.weckesser at gmail.com  Tue Apr 22 16:47:07 2014
From: warren.weckesser at gmail.com (Warren Weckesser)
Date: Tue, 22 Apr 2014 16:47:07 -0400
Subject: [SciPy-Dev] welcome to Scipy GSoC'14 students!
In-Reply-To: <CABL7CQjTVA1_9PWfPBg9Lp6y=-e2Wd2Vfcd37_NuDgfr6DnuDw@mail.gmail.com>
References: <CABL7CQjTVA1_9PWfPBg9Lp6y=-e2Wd2Vfcd37_NuDgfr6DnuDw@mail.gmail.com>
Message-ID: <CAGzF1uf_zJULgXkvhrM=0C5B6Li64AQime0aF=Cs+Pqcm9bsXw@mail.gmail.com>

On Tue, Apr 22, 2014 at 4:40 PM, Ralf Gommers <ralf.gommers at gmail.com>wrote:

> Hi all,
>
> The accepted projects for Google Summer of Code have been announced:
> congratulations and a warm welcome to Janani Padmanbhan and Richard Tsai!
>
> Janani will be working on spherical harmonic and hypergemetric functions
> in scipy.special [1], with Pauli and Stefan as main mentors, and Evgeni and
> me also pitching in.
>
> Richard will be working on rewriting scipy.cluster in Cython and improving
> it further [2], with Charles as main mentor and David W-F as domain expert
> providing help.
>
>

Congratulations Richard and Janani!  Looking forward to your contributions
this summer.

Best regards,

Warren

P.S.  Thanks to the mentors, too!



> From now until May 19th is the community bonding period [3] - the period
> of time when students further get to know the project, figure out people &
> processes within the project in more detail, and prepare for the actual
> coding period. Please help them find their way! And Richard, Janani, feel
> free to ask questions, explore or work on issues / documentation / website
> / whatever you think is interesting.
>
> Cheers,
> Ralf
>
>
> [1]
> http://www.google-melange.com/gsoc/proposal/public/google/gsoc2014/jennystone/5629499534213120
>
> [2]
> http://www.google-melange.com/gsoc/proposal/public/google/gsoc2014/richardtsai/5629499534213120
>
> [3]
> http://googlesummerofcode.blogspot.nl/2007/04/so-what-is-this-community-bonding-all.html
>
>
>
> _______________________________________________
> SciPy-Dev mailing list
> SciPy-Dev at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-dev
>
>
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From ralf.gommers at gmail.com  Tue Apr 22 17:39:34 2014
From: ralf.gommers at gmail.com (Ralf Gommers)
Date: Tue, 22 Apr 2014 23:39:34 +0200
Subject: [SciPy-Dev] Errors in Scipy 0.13.3 & Python 3.3.5
In-Reply-To: <CABNPx=ynM1mmuk8GSwuHOTTFjOQOMU4J6B8iX5LMOZS8K=nPzA@mail.gmail.com>
References: <CABNPx=ynM1mmuk8GSwuHOTTFjOQOMU4J6B8iX5LMOZS8K=nPzA@mail.gmail.com>
Message-ID: <CABL7CQi2va5kCvDzi4ZssJfm1GwO6pPcDLVWiR8DfqQNo9NFFw@mail.gmail.com>

On Tue, Apr 22, 2014 at 1:07 AM, Wim R. Cardoen <wcardoen at gmail.com> wrote:

> Hello
>
> I compiled scipy 0.13.3 successfully
> on a Centos 6 machine.
> When I ran the test suite I obtained the following 5 errors
> (I used OpenBlas (single threaded) for the BLAS/LAPACK library and
> SuiteSparse for the AMD,UMFPACK libraries)
>
>
> ======================================================================
> FAIL: test_arpack.test_real_nonsymmetric_modes(False, <std-real-nonsym>,
> 'f', 2, 'LM', None, 0.1, <function asarray at 0x7f989ca49b00>, 'r')
> ----------------------------------------------------------------------
> Traceback (most recent call last):
>   File
> "/software/pkg/python/3.3.5/lib/python3.3/site-packages/nose-1.3.1-py3.3.egg/nose/case.py",
> line 198, in runTest
>     self.test(*self.arg)
>   File
> "/software/pkg/python/3.3.5/lib/python3.3/site-packages/scipy/sparse/linalg/eigen/arpack/tests/test_arpack.py",
> line 259, in eval_evec
>     assert_allclose(LHS, RHS, rtol=rtol, atol=atol, err_msg=err)
>   File
> "/software/pkg/python/3.3.5/lib/python3.3/site-packages/numpy/testing/utils.py",
> line 1183, in assert_allclose
>     verbose=verbose, header=header)
>   File
> "/software/pkg/python/3.3.5/lib/python3.3/site-packages/numpy/testing/utils.py",
> line 644, in assert_array_compare
>     raise AssertionError(msg)
> AssertionError:
> Not equal to tolerance rtol=0.000357628, atol=0.000357628
> error for eigs:standard, typ=f, which=LM, sigma=0.1, mattype=asarray,
> OPpart=r, mode=normal
> (mismatch 100.0%)
>  x: array([[-0.11649324+0.j, -0.05435310+0.j],
>        [ 0.13801208+0.j, -0.05894032+0.j],
>        [-0.21229853+0.j, -0.02815625+0.j],...
>  y: array([[-0.05698580+0.08018474j, -0.05435318+0.j        ],
>        [ 0.06439485-0.09061003j, -0.05894023+0.j        ],
>        [-0.12503998+0.17594382j, -0.02815617+0.j        ],...
>
> ======================================================================
> FAIL: test_arpack.test_real_nonsymmetric_modes(False, <std-real-nonsym>,
> 'f', 2, 'LR', None, None, <function aslinearoperator at 0x7f988cd9fb90>,
> None)
> ----------------------------------------------------------------------
> Traceback (most recent call last):
>   File
> "/software/pkg/python/3.3.5/lib/python3.3/site-packages/nose-1.3.1-py3.3.egg/nose/case.py",
> line 198, in runTest
>     self.test(*self.arg)
>   File
> "/software/pkg/python/3.3.5/lib/python3.3/site-packages/scipy/sparse/linalg/eigen/arpack/tests/test_arpack.py",
> line 259, in eval_evec
>     assert_allclose(LHS, RHS, rtol=rtol, atol=atol, err_msg=err)
>   File
> "/software/pkg/python/3.3.5/lib/python3.3/site-packages/numpy/testing/utils.py",
> line 1183, in assert_allclose
>     verbose=verbose, header=header)
>   File
> "/software/pkg/python/3.3.5/lib/python3.3/site-packages/numpy/testing/utils.py",
> line 644, in assert_array_compare
>     raise AssertionError(msg)
> AssertionError:
> Not equal to tolerance rtol=0.00178814, atol=0.000357628
> error for eigs:standard, typ=f, which=LR, sigma=None,
> mattype=aslinearoperator, OPpart=None, mode=normal
> (mismatch 100.0%)
>  x: array([[ 0.13884316+0.j, -1.07112074+0.j],
>        [-0.08962911+0.j, -1.39801252+0.j],
>        [ 0.21701422+0.j, -0.93968379+0.j],...
>  y: array([[ 0.06020537+0.08471544j, -1.07112110+0.j        ],
>        [-0.07497437-0.10549703j, -1.39801240+0.j        ],
>        [ 0.13326693+0.18752094j, -0.93968397+0.j        ],...
>
> ======================================================================
> FAIL: test_basic.test_xlogy
> ----------------------------------------------------------------------
> Traceback (most recent call last):
>   File
> "/software/pkg/python/3.3.5/lib/python3.3/site-packages/nose-1.3.1-py3.3.egg/nose/case.py",
> line 198, in runTest
>     self.test(*self.arg)
>   File
> "/software/pkg/python/3.3.5/lib/python3.3/site-packages/scipy/special/tests/test_basic.py",
> line 2736, in test_xlogy
>     assert_func_equal(special.xlogy, w2, z2, rtol=1e-13, atol=1e-13)
>   File
> "/software/pkg/python/3.3.5/lib/python3.3/site-packages/scipy/special/_testutils.py",
> line 87, in assert_func_equal
>     fdata.check()
>   File
> "/software/pkg/python/3.3.5/lib/python3.3/site-packages/scipy/special/_testutils.py",
> line 292, in check
>     assert_(False, "\n".join(msg))
>   File
> "/software/pkg/python/3.3.5/lib/python3.3/site-packages/numpy/testing/utils.py",
> line 44, in assert_
>     raise AssertionError(msg)
> AssertionError:
> Max |adiff|: 712.209
> Max |rdiff|: 1027.5
> Bad results (3 out of 6) for the following points (in output 0):
>                             0j                        (nan+0j)
> =>                        (-0+0j) !=                     (nan+nanj)
> (rdiff                            0.0)
>                         (1+0j)                          (2+0j) =>
> (-711.5155851371305+0.7853981633976757j) !=        (0.6931471805599453+0j)
> (rdiff             1027.5006309578175)
>                         (1+0j)                              1j =>
> (-711.5155851371305+0.7853981633976757j) !=            1.5707963267948966j
> (rdiff              452.9651658054808)
>
> ======================================================================
> FAIL: test_lambertw.test_values
> ----------------------------------------------------------------------
> Traceback (most recent call last):
>   File
> "/software/pkg/python/3.3.5/lib/python3.3/site-packages/nose-1.3.1-py3.3.egg/nose/case.py",
> line 198, in runTest
>     self.test(*self.arg)
>   File
> "/software/pkg/python/3.3.5/lib/python3.3/site-packages/scipy/special/tests/test_lambertw.py",
> line 21, in test_values
>     assert_equal(lambertw(inf,1).real, inf)
>   File
> "/software/pkg/python/3.3.5/lib/python3.3/site-packages/numpy/testing/utils.py",
> line 304, in assert_equal
>     raise AssertionError(msg)
> AssertionError:
> Items are not equal:
>  ACTUAL: nan
>  DESIRED: inf
>
> ======================================================================
> FAIL: test_lambertw.test_ufunc
> ----------------------------------------------------------------------
> Traceback (most recent call last):
>   File
> "/software/pkg/python/3.3.5/lib/python3.3/site-packages/numpy/testing/utils.py",
> line 581, in chk_same_position
>     assert_array_equal(x_id, y_id)
>   File
> "/software/pkg/python/3.3.5/lib/python3.3/site-packages/numpy/testing/utils.py",
> line 718, in assert_array_equal
>     verbose=verbose, header='Arrays are not equal')
>   File
> "/software/pkg/python/3.3.5/lib/python3.3/site-packages/numpy/testing/utils.py",
> line 644, in assert_array_compare
>     raise AssertionError(msg)
> AssertionError:
> Arrays are not equal
>
> (mismatch 66.66666666666666%)
>  x: array([False,  True,  True], dtype=bool)
>  y: array([False, False, False], dtype=bool)
>
> During handling of the above exception, another exception occurred:
>
> Traceback (most recent call last):
>   File
> "/software/pkg/python/3.3.5/lib/python3.3/site-packages/nose-1.3.1-py3.3.egg/nose/case.py",
> line 198, in runTest
>     self.test(*self.arg)
>   File
> "/software/pkg/python/3.3.5/lib/python3.3/site-packages/scipy/special/tests/test_lambertw.py",
> line 93, in test_ufunc
>     lambertw(r_[0., e, 1.]), r_[0., 1., 0.567143290409783873])
>   File
> "/software/pkg/python/3.3.5/lib/python3.3/site-packages/numpy/testing/utils.py",
> line 811, in assert_array_almost_equal
>     header=('Arrays are not almost equal to %d decimals' % decimal))
>   File
> "/software/pkg/python/3.3.5/lib/python3.3/site-packages/numpy/testing/utils.py",
> line 607, in assert_array_compare
>     chk_same_position(x_isnan, y_isnan, hasval='nan')
>   File
> "/software/pkg/python/3.3.5/lib/python3.3/site-packages/numpy/testing/utils.py",
> line 587, in chk_same_position
>     raise AssertionError(msg)
> AssertionError:
> Arrays are not almost equal to 6 decimals
>
> x and y nan location mismatch:
>  x: array([  0.+0.j,  nan+0.j,  nan+0.j])
>  y: array([ 0.   ,  1.   ,  0.567])
>
> ----------------------------------------------------------------------
> Ran 8775 tests in 239.751s
>
> <scipy-dev at scipy.org>FAILED (KNOWNFAIL=114, SKIP=220, failures=5)
> Running unit tests for scipy
> NumPy version 1.8.1
> NumPy is installed in
> /software/pkg/python/3.3.5/lib/python3.3/site-packages/numpy
> SciPy version 0.13.3
> SciPy is installed in
> /software/pkg/python/3.3.5/lib/python3.3/site-packages/scipy
> Python version 3.3.5 (default, Apr 16 2014, 19:42:58) [GCC 4.4.7 20120313
> (Red Hat 4.4.7-4)]
> nose version 1.3.1
>
> Do you have any fix for these errors?
>

Hi Wim, thanks for the report. These all look new to me; I don't have an
easy fix. The Arpack ones should have been fixed in 0.13.3 (they were a
major issue on several platforms for 0.11.x/0.12.x), but apparently there's
still something not 100% right.

Could you open an issue on Github? Adding details about which compilers you
used and about which processor may also be helpful.

Ralf



>
> Thanks in advance.
>
> Wim
>
>
> --
> ---------------------------------------------------------------
> Wim R. Cardoen, PhD
> Staff Scientist,
> Center for High Performance Computing
> University of Utah
> ---------------------------------------------------------------
>
> _______________________________________________
> SciPy-Dev mailing list
> SciPy-Dev at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-dev
>
>
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From jenny.stone125 at gmail.com  Tue Apr 22 18:06:20 2014
From: jenny.stone125 at gmail.com (Jennifer Janani)
Date: Wed, 23 Apr 2014 03:36:20 +0530
Subject: [SciPy-Dev] welcome to Scipy GSoC'14 students!
In-Reply-To: <CAGzF1uf_zJULgXkvhrM=0C5B6Li64AQime0aF=Cs+Pqcm9bsXw@mail.gmail.com>
References: <CABL7CQjTVA1_9PWfPBg9Lp6y=-e2Wd2Vfcd37_NuDgfr6DnuDw@mail.gmail.com>
	<CAGzF1uf_zJULgXkvhrM=0C5B6Li64AQime0aF=Cs+Pqcm9bsXw@mail.gmail.com>
Message-ID: <CAO_-KLh1-QqKJ+Nx0XN195yd60e2qq6AujnncsSkjOW1kVPP=A@mail.gmail.com>

Thanks a lot everyone, for having been highly supportive and helping out
with this.
Looking forward to working with this great community this summer.
Janani


On Wed, Apr 23, 2014 at 2:17 AM, Warren Weckesser <
warren.weckesser at gmail.com> wrote:

>
> On Tue, Apr 22, 2014 at 4:40 PM, Ralf Gommers <ralf.gommers at gmail.com>wrote:
>
>> Hi all,
>>
>> The accepted projects for Google Summer of Code have been announced:
>> congratulations and a warm welcome to Janani Padmanbhan and Richard Tsai!
>>
>> Janani will be working on spherical harmonic and hypergemetric functions
>> in scipy.special [1], with Pauli and Stefan as main mentors, and Evgeni and
>> me also pitching in.
>>
>> Richard will be working on rewriting scipy.cluster in Cython and
>> improving it further [2], with Charles as main mentor and David W-F as
>> domain expert providing help.
>>
>>
>
> Congratulations Richard and Janani!  Looking forward to your contributions
> this summer.
>
> Best regards,
>
> Warren
>
> P.S.  Thanks to the mentors, too!
>
>
>
>> From now until May 19th is the community bonding period [3] - the period
>> of time when students further get to know the project, figure out people &
>> processes within the project in more detail, and prepare for the actual
>> coding period. Please help them find their way! And Richard, Janani, feel
>> free to ask questions, explore or work on issues / documentation / website
>> / whatever you think is interesting.
>>
>> Cheers,
>> Ralf
>>
>>
>> [1]
>> http://www.google-melange.com/gsoc/proposal/public/google/gsoc2014/jennystone/5629499534213120
>>
>> [2]
>> http://www.google-melange.com/gsoc/proposal/public/google/gsoc2014/richardtsai/5629499534213120
>>
>> [3]
>> http://googlesummerofcode.blogspot.nl/2007/04/so-what-is-this-community-bonding-all.html
>>
>>
>>
>> _______________________________________________
>> SciPy-Dev mailing list
>> SciPy-Dev at scipy.org
>> http://mail.scipy.org/mailman/listinfo/scipy-dev
>>
>>
>
> _______________________________________________
> SciPy-Dev mailing list
> SciPy-Dev at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-dev
>
>
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From matthew.brett at gmail.com  Tue Apr 22 19:17:47 2014
From: matthew.brett at gmail.com (Matthew Brett)
Date: Tue, 22 Apr 2014 16:17:47 -0700
Subject: [SciPy-Dev] Powell failure on MingW windows build - any
	insights?
In-Reply-To: <lj6i7b$d9s$1@ger.gmane.org>
References: <CAH6Pt5q6Z+w4pFzKjTsoPDY2e-ZUAr1qXSz2K92s2jgNYUOAng@mail.gmail.com>
	<CAH6Pt5rKkiwAoue3bMBzdt8NbiXS3=AFQxt726p5X6-th-SZNg@mail.gmail.com>
	<lj6i7b$d9s$1@ger.gmane.org>
Message-ID: <CAH6Pt5r_XrsdtJFodwMZ73ufZR+usLhiAx_8YiDJ6rcC9chg-Q@mail.gmail.com>

Hi,

On Tue, Apr 22, 2014 at 1:08 PM, Pauli Virtanen <pav at iki.fi> wrote:
> 22.04.2014 22:51, Matthew Brett kirjoitti:
> [clip]
>> It seems to me the iteration path test is not a good test.  It's
>> likely that these kind of tiny differences will lead to divergent
>> paths, and testing that steps 34 though 38 are the same seems
>> arbitrary and difficult to justify.
>>
>> I think that test should be removed, leaving the check for the same
>> solution and roughly the same number of iterations.  Is that
>> reasonable?
>
> The point of those tests is to safeguard against unintended changes
> when refactoring the codes. An error added e.g. in line search code
> can still make the code converge, and just checking iteration counts
> in an easy test does not necessarily reveal regressions.
>
> Having the check that far (34 evals) in the tail may however be
> excessive. The other routines check earlier only after a few
> iterations, so if you can see how much of the trace is reproducible,
> that should be OK.

The course is the same through iteration 14 - is that acceptable as a length?

Cheers,

Matthew


From wcardoen at gmail.com  Tue Apr 22 21:41:02 2014
From: wcardoen at gmail.com (Wim R. Cardoen)
Date: Tue, 22 Apr 2014 19:41:02 -0600
Subject: [SciPy-Dev] Errors in Scipy 0.13.3 & Python 3.3.5
In-Reply-To: <CABL7CQi2va5kCvDzi4ZssJfm1GwO6pPcDLVWiR8DfqQNo9NFFw@mail.gmail.com>
References: <CABNPx=ynM1mmuk8GSwuHOTTFjOQOMU4J6B8iX5LMOZS8K=nPzA@mail.gmail.com>
	<CABL7CQi2va5kCvDzi4ZssJfm1GwO6pPcDLVWiR8DfqQNo9NFFw@mail.gmail.com>
Message-ID: <CABNPx=wA4eVdtLUUjXyaKnNfohG6Q0xzOjkLP9NBQf7afBKiPw@mail.gmail.com>

Hi Ralf,

Thanks for your reply.
I just submitted an issue on Github.
Please let me know if you want the output of the compilation and linking
process for scipy.

Best regards,

Wim


On Tue, Apr 22, 2014 at 3:39 PM, Ralf Gommers <ralf.gommers at gmail.com>wrote:

>
>
>
> On Tue, Apr 22, 2014 at 1:07 AM, Wim R. Cardoen <wcardoen at gmail.com>wrote:
>
>> Hello
>>
>> I compiled scipy 0.13.3 successfully
>> on a Centos 6 machine.
>> When I ran the test suite I obtained the following 5 errors
>> (I used OpenBlas (single threaded) for the BLAS/LAPACK library and
>> SuiteSparse for the AMD,UMFPACK libraries)
>>
>>
>> ======================================================================
>> FAIL: test_arpack.test_real_nonsymmetric_modes(False, <std-real-nonsym>,
>> 'f', 2, 'LM', None, 0.1, <function asarray at 0x7f989ca49b00>, 'r')
>> ----------------------------------------------------------------------
>> Traceback (most recent call last):
>>   File
>> "/software/pkg/python/3.3.5/lib/python3.3/site-packages/nose-1.3.1-py3.3.egg/nose/case.py",
>> line 198, in runTest
>>     self.test(*self.arg)
>>   File
>> "/software/pkg/python/3.3.5/lib/python3.3/site-packages/scipy/sparse/linalg/eigen/arpack/tests/test_arpack.py",
>> line 259, in eval_evec
>>     assert_allclose(LHS, RHS, rtol=rtol, atol=atol, err_msg=err)
>>   File
>> "/software/pkg/python/3.3.5/lib/python3.3/site-packages/numpy/testing/utils.py",
>> line 1183, in assert_allclose
>>     verbose=verbose, header=header)
>>   File
>> "/software/pkg/python/3.3.5/lib/python3.3/site-packages/numpy/testing/utils.py",
>> line 644, in assert_array_compare
>>     raise AssertionError(msg)
>> AssertionError:
>> Not equal to tolerance rtol=0.000357628, atol=0.000357628
>> error for eigs:standard, typ=f, which=LM, sigma=0.1, mattype=asarray,
>> OPpart=r, mode=normal
>> (mismatch 100.0%)
>>  x: array([[-0.11649324+0.j, -0.05435310+0.j],
>>        [ 0.13801208+0.j, -0.05894032+0.j],
>>        [-0.21229853+0.j, -0.02815625+0.j],...
>>  y: array([[-0.05698580+0.08018474j, -0.05435318+0.j        ],
>>        [ 0.06439485-0.09061003j, -0.05894023+0.j        ],
>>        [-0.12503998+0.17594382j, -0.02815617+0.j        ],...
>>
>> ======================================================================
>> FAIL: test_arpack.test_real_nonsymmetric_modes(False, <std-real-nonsym>,
>> 'f', 2, 'LR', None, None, <function aslinearoperator at 0x7f988cd9fb90>,
>> None)
>> ----------------------------------------------------------------------
>> Traceback (most recent call last):
>>   File
>> "/software/pkg/python/3.3.5/lib/python3.3/site-packages/nose-1.3.1-py3.3.egg/nose/case.py",
>> line 198, in runTest
>>     self.test(*self.arg)
>>   File
>> "/software/pkg/python/3.3.5/lib/python3.3/site-packages/scipy/sparse/linalg/eigen/arpack/tests/test_arpack.py",
>> line 259, in eval_evec
>>     assert_allclose(LHS, RHS, rtol=rtol, atol=atol, err_msg=err)
>>   File
>> "/software/pkg/python/3.3.5/lib/python3.3/site-packages/numpy/testing/utils.py",
>> line 1183, in assert_allclose
>>     verbose=verbose, header=header)
>>   File
>> "/software/pkg/python/3.3.5/lib/python3.3/site-packages/numpy/testing/utils.py",
>> line 644, in assert_array_compare
>>     raise AssertionError(msg)
>> AssertionError:
>> Not equal to tolerance rtol=0.00178814, atol=0.000357628
>> error for eigs:standard, typ=f, which=LR, sigma=None,
>> mattype=aslinearoperator, OPpart=None, mode=normal
>> (mismatch 100.0%)
>>  x: array([[ 0.13884316+0.j, -1.07112074+0.j],
>>        [-0.08962911+0.j, -1.39801252+0.j],
>>        [ 0.21701422+0.j, -0.93968379+0.j],...
>>  y: array([[ 0.06020537+0.08471544j, -1.07112110+0.j        ],
>>        [-0.07497437-0.10549703j, -1.39801240+0.j        ],
>>        [ 0.13326693+0.18752094j, -0.93968397+0.j        ],...
>>
>> ======================================================================
>> FAIL: test_basic.test_xlogy
>> ----------------------------------------------------------------------
>> Traceback (most recent call last):
>>   File
>> "/software/pkg/python/3.3.5/lib/python3.3/site-packages/nose-1.3.1-py3.3.egg/nose/case.py",
>> line 198, in runTest
>>     self.test(*self.arg)
>>   File
>> "/software/pkg/python/3.3.5/lib/python3.3/site-packages/scipy/special/tests/test_basic.py",
>> line 2736, in test_xlogy
>>     assert_func_equal(special.xlogy, w2, z2, rtol=1e-13, atol=1e-13)
>>   File
>> "/software/pkg/python/3.3.5/lib/python3.3/site-packages/scipy/special/_testutils.py",
>> line 87, in assert_func_equal
>>     fdata.check()
>>   File
>> "/software/pkg/python/3.3.5/lib/python3.3/site-packages/scipy/special/_testutils.py",
>> line 292, in check
>>     assert_(False, "\n".join(msg))
>>   File
>> "/software/pkg/python/3.3.5/lib/python3.3/site-packages/numpy/testing/utils.py",
>> line 44, in assert_
>>     raise AssertionError(msg)
>> AssertionError:
>> Max |adiff|: 712.209
>> Max |rdiff|: 1027.5
>> Bad results (3 out of 6) for the following points (in output 0):
>>                             0j                        (nan+0j)
>> =>                        (-0+0j) !=                     (nan+nanj)
>> (rdiff                            0.0)
>>                         (1+0j)                          (2+0j) =>
>> (-711.5155851371305+0.7853981633976757j) !=        (0.6931471805599453+0j)
>> (rdiff             1027.5006309578175)
>>                         (1+0j)                              1j =>
>> (-711.5155851371305+0.7853981633976757j) !=            1.5707963267948966j
>> (rdiff              452.9651658054808)
>>
>> ======================================================================
>> FAIL: test_lambertw.test_values
>> ----------------------------------------------------------------------
>> Traceback (most recent call last):
>>   File
>> "/software/pkg/python/3.3.5/lib/python3.3/site-packages/nose-1.3.1-py3.3.egg/nose/case.py",
>> line 198, in runTest
>>     self.test(*self.arg)
>>   File
>> "/software/pkg/python/3.3.5/lib/python3.3/site-packages/scipy/special/tests/test_lambertw.py",
>> line 21, in test_values
>>     assert_equal(lambertw(inf,1).real, inf)
>>   File
>> "/software/pkg/python/3.3.5/lib/python3.3/site-packages/numpy/testing/utils.py",
>> line 304, in assert_equal
>>     raise AssertionError(msg)
>> AssertionError:
>> Items are not equal:
>>  ACTUAL: nan
>>  DESIRED: inf
>>
>> ======================================================================
>> FAIL: test_lambertw.test_ufunc
>> ----------------------------------------------------------------------
>> Traceback (most recent call last):
>>   File
>> "/software/pkg/python/3.3.5/lib/python3.3/site-packages/numpy/testing/utils.py",
>> line 581, in chk_same_position
>>     assert_array_equal(x_id, y_id)
>>   File
>> "/software/pkg/python/3.3.5/lib/python3.3/site-packages/numpy/testing/utils.py",
>> line 718, in assert_array_equal
>>     verbose=verbose, header='Arrays are not equal')
>>   File
>> "/software/pkg/python/3.3.5/lib/python3.3/site-packages/numpy/testing/utils.py",
>> line 644, in assert_array_compare
>>     raise AssertionError(msg)
>> AssertionError:
>> Arrays are not equal
>>
>> (mismatch 66.66666666666666%)
>>  x: array([False,  True,  True], dtype=bool)
>>  y: array([False, False, False], dtype=bool)
>>
>> During handling of the above exception, another exception occurred:
>>
>> Traceback (most recent call last):
>>   File
>> "/software/pkg/python/3.3.5/lib/python3.3/site-packages/nose-1.3.1-py3.3.egg/nose/case.py",
>> line 198, in runTest
>>     self.test(*self.arg)
>>   File
>> "/software/pkg/python/3.3.5/lib/python3.3/site-packages/scipy/special/tests/test_lambertw.py",
>> line 93, in test_ufunc
>>     lambertw(r_[0., e, 1.]), r_[0., 1., 0.567143290409783873])
>>   File
>> "/software/pkg/python/3.3.5/lib/python3.3/site-packages/numpy/testing/utils.py",
>> line 811, in assert_array_almost_equal
>>     header=('Arrays are not almost equal to %d decimals' % decimal))
>>   File
>> "/software/pkg/python/3.3.5/lib/python3.3/site-packages/numpy/testing/utils.py",
>> line 607, in assert_array_compare
>>     chk_same_position(x_isnan, y_isnan, hasval='nan')
>>   File
>> "/software/pkg/python/3.3.5/lib/python3.3/site-packages/numpy/testing/utils.py",
>> line 587, in chk_same_position
>>     raise AssertionError(msg)
>> AssertionError:
>> Arrays are not almost equal to 6 decimals
>>
>> x and y nan location mismatch:
>>  x: array([  0.+0.j,  nan+0.j,  nan+0.j])
>>  y: array([ 0.   ,  1.   ,  0.567])
>>
>> ----------------------------------------------------------------------
>> Ran 8775 tests in 239.751s
>>
>> <scipy-dev at scipy.org>FAILED (KNOWNFAIL=114, SKIP=220, failures=5)
>> Running unit tests for scipy
>> NumPy version 1.8.1
>> NumPy is installed in
>> /software/pkg/python/3.3.5/lib/python3.3/site-packages/numpy
>> SciPy version 0.13.3
>> SciPy is installed in
>> /software/pkg/python/3.3.5/lib/python3.3/site-packages/scipy
>> Python version 3.3.5 (default, Apr 16 2014, 19:42:58) [GCC 4.4.7 20120313
>> (Red Hat 4.4.7-4)]
>> nose version 1.3.1
>>
>> Do you have any fix for these errors?
>>
>
> Hi Wim, thanks for the report. These all look new to me; I don't have an
> easy fix. The Arpack ones should have been fixed in 0.13.3 (they were a
> major issue on several platforms for 0.11.x/0.12.x), but apparently there's
> still something not 100% right.
>
> Could you open an issue on Github? Adding details about which compilers
> you used and about which processor may also be helpful.
>
> Ralf
>
>
>
>>
>> Thanks in advance.
>>
>> Wim
>>
>>
>> --
>> ---------------------------------------------------------------
>> Wim R. Cardoen, PhD
>> Staff Scientist,
>> Center for High Performance Computing
>> University of Utah
>> ---------------------------------------------------------------
>>
>> _______________________________________________
>> SciPy-Dev mailing list
>> SciPy-Dev at scipy.org
>> http://mail.scipy.org/mailman/listinfo/scipy-dev
>>
>>
>
> _______________________________________________
> SciPy-Dev mailing list
> SciPy-Dev at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-dev
>
>
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From Jiri.Krtek at rsj.com  Wed Apr 23 07:38:03 2014
From: Jiri.Krtek at rsj.com (Jiri Krtek)
Date: Wed, 23 Apr 2014 11:38:03 +0000
Subject: [SciPy-Dev] kmeans with weights to scipy.cluster
Message-ID: <79B4F5CBAD42654FAC33C4E46F8C592903A3BDB0@RSJPHA02.int.rsj.cz>

Hi all,

I need to take into consideration weights in k-means. Specifically, the first (Assignment) step of the k-means algorithm remains the same, but in the second (Update) step there is not a simple average, but weighted average. I searched for some implementation of such kind of algorithm in Python, but I wasn't successful. So I hacked a little bit the kmeans2 function from scipy.cluster. I want to ask if it sounds interesting. Would you add it to scipy.cluster module?

Regards,
Jiri
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From sturla.molden at gmail.com  Wed Apr 23 14:25:58 2014
From: sturla.molden at gmail.com (Sturla Molden)
Date: Wed, 23 Apr 2014 18:25:58 +0000 (UTC)
Subject: [SciPy-Dev] Powell failure on MingW windows build - any
	insights?
References: <CAH6Pt5q6Z+w4pFzKjTsoPDY2e-ZUAr1qXSz2K92s2jgNYUOAng@mail.gmail.com>
	<CAH6Pt5rKkiwAoue3bMBzdt8NbiXS3=AFQxt726p5X6-th-SZNg@mail.gmail.com>
	<lj6i7b$d9s$1@ger.gmane.org>
	<CAH6Pt5r_XrsdtJFodwMZ73ufZR+usLhiAx_8YiDJ6rcC9chg-Q@mail.gmail.com>
Message-ID: <2004312289419969465.345476sturla.molden-gmail.com@news.gmane.org>

Matthew Brett <matthew.brett at gmail.com> wrote:

> The course is the same through iteration 14 - is that acceptable as a length?

Why not try this?

#pragma summon cthulhu

http://www.tedunangst.com/flak/post/worst-common-denominator-programming



From pav at iki.fi  Wed Apr 23 14:29:58 2014
From: pav at iki.fi (Pauli Virtanen)
Date: Wed, 23 Apr 2014 21:29:58 +0300
Subject: [SciPy-Dev] Powell failure on MingW windows build - any
	insights?
In-Reply-To: <CAH6Pt5r_XrsdtJFodwMZ73ufZR+usLhiAx_8YiDJ6rcC9chg-Q@mail.gmail.com>
References: <CAH6Pt5q6Z+w4pFzKjTsoPDY2e-ZUAr1qXSz2K92s2jgNYUOAng@mail.gmail.com>	<CAH6Pt5rKkiwAoue3bMBzdt8NbiXS3=AFQxt726p5X6-th-SZNg@mail.gmail.com>	<lj6i7b$d9s$1@ger.gmane.org>
	<CAH6Pt5r_XrsdtJFodwMZ73ufZR+usLhiAx_8YiDJ6rcC9chg-Q@mail.gmail.com>
Message-ID: <lj90r6$762$1@ger.gmane.org>

23.04.2014 02:17, Matthew Brett kirjoitti:
[clip]
>> Having the check that far (34 evals) in the tail may however be 
>> excessive. The other routines check earlier only after a few 
>> iterations, so if you can see how much of the trace is
>> reproducible, that should be OK.
> 
> The course is the same through iteration 14 - is that acceptable as
> a length?

Should be OK.

-- 
Pauli Virtanen



From matthew.brett at gmail.com  Wed Apr 23 18:52:38 2014
From: matthew.brett at gmail.com (Matthew Brett)
Date: Wed, 23 Apr 2014 15:52:38 -0700
Subject: [SciPy-Dev] Powell failure on MingW windows build - any
	insights?
In-Reply-To: <lj90r6$762$1@ger.gmane.org>
References: <CAH6Pt5q6Z+w4pFzKjTsoPDY2e-ZUAr1qXSz2K92s2jgNYUOAng@mail.gmail.com>
	<CAH6Pt5rKkiwAoue3bMBzdt8NbiXS3=AFQxt726p5X6-th-SZNg@mail.gmail.com>
	<lj6i7b$d9s$1@ger.gmane.org>
	<CAH6Pt5r_XrsdtJFodwMZ73ufZR+usLhiAx_8YiDJ6rcC9chg-Q@mail.gmail.com>
	<lj90r6$762$1@ger.gmane.org>
Message-ID: <CAH6Pt5rtjXeguZ2Bb1oDD-5kRR-OQNQu8wM+L2tTaqEqi31WCg@mail.gmail.com>

Hi,

On Wed, Apr 23, 2014 at 11:29 AM, Pauli Virtanen <pav at iki.fi> wrote:
> 23.04.2014 02:17, Matthew Brett kirjoitti:
> [clip]
>>> Having the check that far (34 evals) in the tail may however be
>>> excessive. The other routines check earlier only after a few
>>> iterations, so if you can see how much of the trace is
>>> reproducible, that should be OK.
>>
>> The course is the same through iteration 14 - is that acceptable as
>> a length?
>
> Should be OK.

Here are values 0:15

array([[ 0.        ,  0.        ,  0.        ],
       [ 0.        ,  0.        ,  0.        ],
       [ 1.        ,  0.        ,  0.        ],
       [-1.618034  ,  0.        ,  0.        ],
       [ 0.        ,  0.        ,  0.        ],
       [-0.61803397,  0.        ,  0.        ],
       [ 0.381966  ,  0.        ,  0.        ],
       [ 0.190983  ,  0.        ,  0.        ],
       [ 0.61803397,  0.        ,  0.        ],
       [ 0.76393201,  0.        ,  0.        ],
       [ 0.85410196,  0.        ,  0.        ],
       [ 0.70820392,  0.        ,  0.        ],
       [ 0.79837386,  0.        ,  0.        ],
       [ 0.74264577,  0.        ,  0.        ],
       [ 0.72949016,  0.        ,  0.        ]])

I wonder if it matters that this only covers the search for the first
value in the vector?

Cheers,

Matthew


From matthew.brett at gmail.com  Wed Apr 23 19:17:00 2014
From: matthew.brett at gmail.com (Matthew Brett)
Date: Wed, 23 Apr 2014 16:17:00 -0700
Subject: [SciPy-Dev] Powell failure on MingW windows build - any
	insights?
In-Reply-To: <CAH6Pt5rtjXeguZ2Bb1oDD-5kRR-OQNQu8wM+L2tTaqEqi31WCg@mail.gmail.com>
References: <CAH6Pt5q6Z+w4pFzKjTsoPDY2e-ZUAr1qXSz2K92s2jgNYUOAng@mail.gmail.com>
	<CAH6Pt5rKkiwAoue3bMBzdt8NbiXS3=AFQxt726p5X6-th-SZNg@mail.gmail.com>
	<lj6i7b$d9s$1@ger.gmane.org>
	<CAH6Pt5r_XrsdtJFodwMZ73ufZR+usLhiAx_8YiDJ6rcC9chg-Q@mail.gmail.com>
	<lj90r6$762$1@ger.gmane.org>
	<CAH6Pt5rtjXeguZ2Bb1oDD-5kRR-OQNQu8wM+L2tTaqEqi31WCg@mail.gmail.com>
Message-ID: <CAH6Pt5rzsszk+z+A-R6_M0LWeZxfySWd1fgyPC2NGgxiJxfukA@mail.gmail.com>

On Wed, Apr 23, 2014 at 3:52 PM, Matthew Brett <matthew.brett at gmail.com> wrote:
> Hi,
>
> On Wed, Apr 23, 2014 at 11:29 AM, Pauli Virtanen <pav at iki.fi> wrote:
>> 23.04.2014 02:17, Matthew Brett kirjoitti:
>> [clip]
>>>> Having the check that far (34 evals) in the tail may however be
>>>> excessive. The other routines check earlier only after a few
>>>> iterations, so if you can see how much of the trace is
>>>> reproducible, that should be OK.
>>>
>>> The course is the same through iteration 14 - is that acceptable as
>>> a length?
>>
>> Should be OK.
>
> Here are values 0:15
>
> array([[ 0.        ,  0.        ,  0.        ],
>        [ 0.        ,  0.        ,  0.        ],
>        [ 1.        ,  0.        ,  0.        ],
>        [-1.618034  ,  0.        ,  0.        ],
>        [ 0.        ,  0.        ,  0.        ],
>        [-0.61803397,  0.        ,  0.        ],
>        [ 0.381966  ,  0.        ,  0.        ],
>        [ 0.190983  ,  0.        ,  0.        ],
>        [ 0.61803397,  0.        ,  0.        ],
>        [ 0.76393201,  0.        ,  0.        ],
>        [ 0.85410196,  0.        ,  0.        ],
>        [ 0.70820392,  0.        ,  0.        ],
>        [ 0.79837386,  0.        ,  0.        ],
>        [ 0.74264577,  0.        ,  0.        ],
>        [ 0.72949016,  0.        ,  0.        ]])
>
> I wonder if it matters that this only covers the search for the first
> value in the vector?

I guess an alternative would be to have several possible paths, say up
until 30, where we have agreed that differences between these paths
are due to acceptable differences in rounding error.   Was there a
reason to choose 34:39 previously?  For example, was this a constant
part of the path across machines, compared to other parts?

Cheers,

Matthew


From richard9404 at gmail.com  Thu Apr 24 01:50:33 2014
From: richard9404 at gmail.com (Richard Tsai)
Date: Thu, 24 Apr 2014 13:50:33 +0800
Subject: [SciPy-Dev] kmeans with weights to scipy.cluster
In-Reply-To: <79B4F5CBAD42654FAC33C4E46F8C592903A3BDB0@RSJPHA02.int.rsj.cz>
References: <79B4F5CBAD42654FAC33C4E46F8C592903A3BDB0@RSJPHA02.int.rsj.cz>
Message-ID: <CALkpnhfNnnhETdySX14jqzD==0wcbd+7hyXNZ+p3vipZUgS+xg@mail.gmail.com>

2014-04-23 19:38 GMT+08:00 Jiri Krtek <Jiri.Krtek at rsj.com>:

>  Hi all,
>
>
>
> I need to take into consideration weights in k-means. Specifically, the
> first (Assignment) step of the k-means algorithm remains the same, but in
> the second (Update) step there is not a simple average, but weighted
> average. I searched for some implementation of such kind of algorithm in
> Python, but I wasn?t successful. So I hacked a little bit the kmeans2
> function from scipy.cluster. I want to ask if it sounds interesting. Would
> you add it to scipy.cluster module?
>
>
>
> Regards,
>
> Jiri
>

Hi Jiri,

That sounds interesting. Could you provide some more details about your
implementation? You can also just post your code to Github. Besides, it
might be better to provide this feature in `kmeans` function as well :)

Richard
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From ralf.gommers at gmail.com  Thu Apr 24 02:21:47 2014
From: ralf.gommers at gmail.com (Ralf Gommers)
Date: Thu, 24 Apr 2014 08:21:47 +0200
Subject: [SciPy-Dev] ANN: Scipy 0.14.0 release candidate 2
Message-ID: <CABL7CQiRqrrSVBXj=jJn_AKiWwwwtb-w9OFDfpkcT4iX2DAQ_g@mail.gmail.com>

Hi,

I'm pleased to announce the availability of the second release candidate of
Scipy 0.14.0. It includes fixes for sparse.linalg with MKL and for changes
in numpy master. Please try this RC and report any issues on the scipy-dev
mailing list.

Source tarballs, binaries and the full release notes can be found at
http://sourceforge.net/projects/scipy/files/scipy/0.14.0rc2/. The final
release will follow in one week if no new issues are found.

Thanks to everyone who contributed to this release, and in particular to
Christoph Gohlke and Pauli Virtanen for fixing the major issue in 0.14.0rc1.

Ralf
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From warren.weckesser at gmail.com  Thu Apr 24 05:04:18 2014
From: warren.weckesser at gmail.com (Warren Weckesser)
Date: Thu, 24 Apr 2014 05:04:18 -0400
Subject: [SciPy-Dev] ANN: Scipy 0.14.0 release candidate 2
In-Reply-To: <CABL7CQiRqrrSVBXj=jJn_AKiWwwwtb-w9OFDfpkcT4iX2DAQ_g@mail.gmail.com>
References: <CABL7CQiRqrrSVBXj=jJn_AKiWwwwtb-w9OFDfpkcT4iX2DAQ_g@mail.gmail.com>
Message-ID: <CAGzF1ue9bOYTJuiuNY6JVYsM-hcN71mfRUr8oBRPm4_OBP+MNA@mail.gmail.com>

On 4/24/14, Ralf Gommers <ralf.gommers at gmail.com> wrote:
> Hi,
>
> I'm pleased to announce the availability of the second release candidate of
> Scipy 0.14.0. It includes fixes for sparse.linalg with MKL and for changes
> in numpy master. Please try this RC and report any issues on the scipy-dev
> mailing list.
>
> Source tarballs, binaries and the full release notes can be found at
> http://sourceforge.net/projects/scipy/files/scipy/0.14.0rc2/. The final
> release will follow in one week if no new issues are found.
>
> Thanks to everyone who contributed to this release, and in particular to
> Christoph Gohlke and Pauli Virtanen for fixing the major issue in
> 0.14.0rc1.
>
> Ralf
>


Hey Ralf,

I just noticed that the list of pull requests in the release notes
includes those that were closed without being merged (e.g. #3218).
Issue here: https://github.com/scipy/scipy/issues/3571

I'll look into fixing the script (scipy/tools/gh_lists.py), but if
someone fixes it before me, I won't complain. :)

Warren


Warren


From Jiri.Krtek at rsj.com  Thu Apr 24 05:07:03 2014
From: Jiri.Krtek at rsj.com (Jiri Krtek)
Date: Thu, 24 Apr 2014 09:07:03 +0000
Subject: [SciPy-Dev] kmeans with weights to scipy.cluster
In-Reply-To: <CALkpnhfNnnhETdySX14jqzD==0wcbd+7hyXNZ+p3vipZUgS+xg@mail.gmail.com>
References: <79B4F5CBAD42654FAC33C4E46F8C592903A3BDB0@RSJPHA02.int.rsj.cz>
	<CALkpnhfNnnhETdySX14jqzD==0wcbd+7hyXNZ+p3vipZUgS+xg@mail.gmail.com>
Message-ID: <79B4F5CBAD42654FAC33C4E46F8C592903A3D2A9@RSJPHA02.int.rsj.cz>

I only added a few rows to the code of kmeans2 and _kmeans2 functions. Actually, I added to kmeans2 function only new optional parameter weights. The same I did in the function _kmeans2, but there I also added the testing if weights is None and the subsequent 2 rows. I also imported average from numpy.

Here it is:

def kmeans2(data, k, iter=10, thresh=1e-5, minit='random', missing='warn', weights=None):

    if missing not in _valid_miss_meth:
        raise ValueError("Unkown missing method: %s" % str(missing))
    # If data is rank 1, then we have 1 dimension problem.
    nd = ndim(data)
    if nd == 1:
        d = 1
        # raise ValueError("Input of rank 1 not supported yet")
    elif nd == 2:
        d = data.shape[1]
    else:
        raise ValueError("Input of rank > 2 not supported")

    if size(data) < 1:
        raise ValueError("Input has 0 items.")

    # If k is not a single value, then it should be compatible with data's
    # shape
    if size(k) > 1 or minit == 'matrix':
        if not nd == ndim(k):
            raise ValueError("k is not an int and has not same rank than data")
        if d == 1:
            nc = len(k)
        else:
            (nc, dc) = k.shape
            if not dc == d:
                raise ValueError("k is not an int and has not same rank than\
                        data")
        clusters = k.copy()
    else:
        try:
            nc = int(k)
        except TypeError:
            raise ValueError("k (%s) could not be converted to an integer " % str(k))

        if nc < 1:
            raise ValueError("kmeans2 for 0 clusters ? (k was %s)" % str(k))

        if not nc == k:
            warn("k was not an integer, was converted.")
        try:
            init = _valid_init_meth[minit]
        except KeyError:
            raise ValueError("unknown init method %s" % str(minit))
        clusters = init(data, k)

    if int(iter) < 1:
        raise ValueError("iter = %s is not valid.  iter must be a positive integer." % iter)

    return _kmeans2(data, clusters, iter, nc, _valid_miss_meth[missing], weights)


def _kmeans2(data, code, niter, nc, missing, weights=None):
    """ "raw" version of kmeans2. Do not use directly.

    Run k-means with a given initial codebook.

    """
    for i in range(niter):
        # Compute the nearest neighbour for each obs
        # using the current code book
        label = vq(data, code)[0]
        # Update the code by computing centroids using the new code book
        for j in range(nc):
            mbs = where(label == j)
            if mbs[0].size > 0:
                if weights is not None:
                    code[j] = average(data[mbs], axis=0, weights=weights[mbs])
                else:
                    code[j] = mean(data[mbs], axis=0)
            else:
                missing()

    return code, label


I haven?t hacked the initial centroids creation yet, but it should be hacked. For example when minit=?points?, then the points should have the probability of selection given by their weights (because when some point has weight much higher than other points, it should be near the center, or even it should be the center and should be alone in its cluster). If minit=?random?, then the mean and cov in the _krandinit function should be affected by weights.

Jiri

From: scipy-dev-bounces at scipy.org [mailto:scipy-dev-bounces at scipy.org] On Behalf Of Richard Tsai
Sent: Thursday, April 24, 2014 7:51 AM
To: SciPy Developers List
Subject: Re: [SciPy-Dev] kmeans with weights to scipy.cluster


2014-04-23 19:38 GMT+08:00 Jiri Krtek <Jiri.Krtek at rsj.com<mailto:Jiri.Krtek at rsj.com>>:
Hi all,

I need to take into consideration weights in k-means. Specifically, the first (Assignment) step of the k-means algorithm remains the same, but in the second (Update) step there is not a simple average, but weighted average. I searched for some implementation of such kind of algorithm in Python, but I wasn?t successful. So I hacked a little bit the kmeans2 function from scipy.cluster. I want to ask if it sounds interesting. Would you add it to scipy.cluster module?

Regards,
Jiri

Hi Jiri,

That sounds interesting. Could you provide some more details about your implementation? You can also just post your code to Github. Besides, it might be better to provide this feature in `kmeans` function as well :)

Richard
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From matthew.brett at gmail.com  Thu Apr 24 17:56:47 2014
From: matthew.brett at gmail.com (Matthew Brett)
Date: Thu, 24 Apr 2014 14:56:47 -0700
Subject: [SciPy-Dev] 64-bit windows numpy / scipy wheels for testing
Message-ID: <CAH6Pt5r47rOKXYfdiqaGQihV+yzrq2dc9rmYbboNb=HUbrtEpA@mail.gmail.com>

Hi,

Thanks to Cark Kleffner's toolchain and some help from Clint Whaley
(main author of ATLAS), I've built 64-bit windows numpy and scipy
wheels for testing.

The build uses Carl's custom mingw-w64 build with static linking.

There are two harmless test failures on scipy (being discussed on the
list at the moment) - tests otherwise clean.

Wheels are here:

https://nipy.bic.berkeley.edu/scipy_installers/numpy-1.8.1-cp27-none-win_amd64.whl
https://nipy.bic.berkeley.edu/scipy_installers/scipy-0.13.3-cp27-none-win_amd64.whl

You can test with:

pip install -U pip # to upgrade pip to latest
pip install -f https://nipy.bic.berkeley.edu/scipy_installers numpy scipy

Please do send feedback.

ATLAS binary here:

https://nipy.bic.berkeley.edu/scipy_installers/atlas_builds/atlas-64-full-sse2.tar.bz2

Many thanks for Carl in particular for doing all the hard work,

Cheers,

Matthew


From jtaylor.debian at googlemail.com  Thu Apr 24 18:35:39 2014
From: jtaylor.debian at googlemail.com (Julian Taylor)
Date: Fri, 25 Apr 2014 00:35:39 +0200
Subject: [SciPy-Dev] [Numpy-discussion] 64-bit windows numpy / scipy
	wheels for testing
In-Reply-To: <CAH6Pt5r47rOKXYfdiqaGQihV+yzrq2dc9rmYbboNb=HUbrtEpA@mail.gmail.com>
References: <CAH6Pt5r47rOKXYfdiqaGQihV+yzrq2dc9rmYbboNb=HUbrtEpA@mail.gmail.com>
Message-ID: <535991BB.7000102@googlemail.com>

On 24.04.2014 23:56, Matthew Brett wrote:
> Hi,
> 
> Thanks to Cark Kleffner's toolchain and some help from Clint Whaley
> (main author of ATLAS), I've built 64-bit windows numpy and scipy
> wheels for testing.
> 
> The build uses Carl's custom mingw-w64 build with static linking.
> 
> There are two harmless test failures on scipy (being discussed on the
> list at the moment) - tests otherwise clean.
> 

This is great news, thanks for working on this.

Have you already documented the procedure used to create the wheels?
I would like to be able to reproduce the builds.

Is it possible to add this toolchain and build procedure to the
vagrant/fabric based numpy release virtual machine setup?
The current version doing linux + win32 builds is available here:
https://github.com/juliantaylor/numpy-vendor

The windows builds are done in a linux guest using wine. Wine also seems
to support win64.

The mac build procedure would also needs updating.

Cheers,
Julian


From richard9404 at gmail.com  Fri Apr 25 04:21:02 2014
From: richard9404 at gmail.com (Richard Tsai)
Date: Fri, 25 Apr 2014 16:21:02 +0800
Subject: [SciPy-Dev] kmeans with weights to scipy.cluster
In-Reply-To: <79B4F5CBAD42654FAC33C4E46F8C592903A3D2A9@RSJPHA02.int.rsj.cz>
References: <79B4F5CBAD42654FAC33C4E46F8C592903A3BDB0@RSJPHA02.int.rsj.cz>
	<CALkpnhfNnnhETdySX14jqzD==0wcbd+7hyXNZ+p3vipZUgS+xg@mail.gmail.com>
	<79B4F5CBAD42654FAC33C4E46F8C592903A3D2A9@RSJPHA02.int.rsj.cz>
Message-ID: <CALkpnhfbWusyd3ihpjcWbwg2kNiNmKNdsw+zhojVMSU8XY6CrQ@mail.gmail.com>

2014-04-24 17:07 GMT+08:00 Jiri Krtek <Jiri.Krtek at rsj.com>:

>  I only added a few rows to the code of kmeans2 and _kmeans2 functions.
> Actually, I added to kmeans2 function only new optional parameter weights.
> The same I did in the function _kmeans2, but there I also added the testing
> if weights is None and the subsequent 2 rows. I also imported average from
> numpy.
>

kmeans2 should set the weights to all 1 when None is given then the branch
in the loop in _kmeans2 could be avoided. Besides, kmeans2 should also
check the size of weights.


>
>
> Here it is:
>
>
>
> def kmeans2(data, k, iter=10, thresh=1e-5, minit='random', missing='warn',
> weights=None):
>
>
>
>     if missing not in _valid_miss_meth:
>
>         raise ValueError("Unkown missing method: %s" % str(missing))
>
>     # If data is rank 1, then we have 1 dimension problem.
>
>     nd = ndim(data)
>
>     if nd == 1:
>
>         d = 1
>
>         # raise ValueError("Input of rank 1 not supported yet")
>
>     elif nd == 2:
>
>         d = data.shape[1]
>
>     else:
>
>         raise ValueError("Input of rank > 2 not supported")
>
>
>
>     if size(data) < 1:
>
>         raise ValueError("Input has 0 items.")
>
>
>
>     # If k is not a single value, then it should be compatible with data's
>
>     # shape
>
>     if size(k) > 1 or minit == 'matrix':
>
>         if not nd == ndim(k):
>
>             raise ValueError("k is not an int and has not same rank than
> data")
>
>         if d == 1:
>
>             nc = len(k)
>
>         else:
>
>             (nc, dc) = k.shape
>
>             if not dc == d:
>
>                 raise ValueError("k is not an int and has not same rank
> than\
>
>                         data")
>
>         clusters = k.copy()
>
>     else:
>
>         try:
>
>             nc = int(k)
>
>         except TypeError:
>
>             raise ValueError("k (%s) could not be converted to an integer
> " % str(k))
>
>
>
>         if nc < 1:
>
>             raise ValueError("kmeans2 for 0 clusters ? (k was %s)" %
> str(k))
>
>
>
>         if not nc == k:
>
>             warn("k was not an integer, was converted.")
>
>         try:
>
>             init = _valid_init_meth[minit]
>
>         except KeyError:
>
>             raise ValueError("unknown init method %s" % str(minit))
>
>         clusters = init(data, k)
>
>
>
>     if int(iter) < 1:
>
>         raise ValueError("iter = %s is not valid.  iter must be a positive
> integer." % iter)
>
>
>
>     return _kmeans2(data, clusters, iter, nc, _valid_miss_meth[missing],
> weights)
>
>
>
>
>
> def _kmeans2(data, code, niter, nc, missing, weights=None):
>
>     """ "raw" version of kmeans2. Do not use directly.
>
>
>
>     Run k-means with a given initial codebook.
>
>
>
>     """
>
>     for i in range(niter):
>
>         # Compute the nearest neighbour for each obs
>
>         # using the current code book
>
>         label = vq(data, code)[0]
>
>         # Update the code by computing centroids using the new code book
>
>         for j in range(nc):
>
>             mbs = where(label == j)
>
>             if mbs[0].size > 0:
>
>                 if weights is not None:
>
>                     code[j] = average(data[mbs], axis=0,
> weights=weights[mbs])
>
>                 else:
>
>                     code[j] = mean(data[mbs], axis=0)
>
>             else:
>
>                 missing()
>
>
>
>     return code, label
>
>
>
>
>
> I haven?t hacked the initial centroids creation yet, but it should be
> hacked. For example when minit=?points?, then the points should have the
> probability of selection given by their weights (because when some point
> has weight much higher than other points, it should be near the center, or
> even it should be the center and should be alone in its cluster). If
> minit=?random?, then the mean and cov in the _krandinit function should be
> affected by weights.
>

I think the minit='points' case can be implemented with np.random.choice.
As for the minit='random' case, I am not so sure. Maybe scale the
observation matrix according to weights before passing to _krandinit?


>
>
 Jiri
>

Consider making a pull request on Github?

Richard
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From kdavies4 at gmail.com  Sun Apr 27 03:07:35 2014
From: kdavies4 at gmail.com (Kevin Davies)
Date: Sat, 26 Apr 2014 21:07:35 -1000
Subject: [SciPy-Dev] requesting code review: physical constants
Message-ID: <535CACB7.4080006@gmail.com>

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From kdavies4 at gmail.com  Sun Apr 27 03:38:56 2014
From: kdavies4 at gmail.com (Kevin Davies)
Date: Sat, 26 Apr 2014 21:38:56 -1000
Subject: [SciPy-Dev] requesting code review: added close matches to
	constants.codata.value()
Message-ID: <535CB410.1010803@gmail.com>

I changed scipy.constants.codata.values() to suggest alternative 
spellings when an entry isn't found.  Instead of a key error, 
values('magnetic constant') prints what is shown in this screenshot.

Here's the code comparison:
https://github.com/kdavies4/scipy/compare/suggest-codata

This introduces a dependency on difflib, which I haven't added to the 
installation.
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From njs at pobox.com  Sun Apr 27 03:50:40 2014
From: njs at pobox.com (Nathaniel Smith)
Date: Sun, 27 Apr 2014 08:50:40 +0100
Subject: [SciPy-Dev] requesting code review: added close matches to
	constants.codata.value()
In-Reply-To: <535CB410.1010803@gmail.com>
References: <535CB410.1010803@gmail.com>
Message-ID: <CAPJVwBkae3Z83EW376XCT4h1DCjEFivYkyWGD9f9tW4BiMCNGA@mail.gmail.com>

Never call print from a library. Raise a KeyError with a custom message if
you want to do this.

difflib is built in to python, it's not a "dependency".
On 27 Apr 2014 08:39, "Kevin Davies" <kdavies4 at gmail.com> wrote:

> I changed scipy.constants.codata.values() to suggest alternative
> spellings when an entry isn't found.  Instead of a key error,
> values('magnetic constant') prints what is shown in this screenshot.
>
> Here's the code comparison:
> https://github.com/kdavies4/scipy/compare/suggest-codata
>
> This introduces a dependency on difflib, which I haven't added to the
> installation.
>
> _______________________________________________
> SciPy-Dev mailing list
> SciPy-Dev at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-dev
>
>
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From pav at iki.fi  Sun Apr 27 08:02:11 2014
From: pav at iki.fi (Pauli Virtanen)
Date: Sun, 27 Apr 2014 15:02:11 +0300
Subject: [SciPy-Dev] requesting code review: physical constants
In-Reply-To: <535CACB7.4080006@gmail.com>
References: <535CACB7.4080006@gmail.com>
Message-ID: <ljirk3$qpn$1@ger.gmane.org>

27.04.2014 10:07, Kevin Davies kirjoitti:
> I changed constants.py to calculate more of the physical constants
> directly instead of lookup in the codata tables.  I think it's best
> to encode the underlying algebraic relations where they exist.

This is actually currently done at the end of `codata.py`, via the
exact_values stuff. It would make sense to move all of that processing
there, so that constants returned by `value()` are consistent with the
named ones.

[clip]
> This change introduces four additional constants to
> scipy.constants:
> 
> 1. magnetic quantum (Phi_0) 2. conductance quantum (G_0) 3. Faraday
> constant (F) 4. characteristic impedance of vacuum (Z_0)

Adding these should be OK.

-- 
Pauli Virtanen




From kdavies4 at gmail.com  Sun Apr 27 12:23:30 2014
From: kdavies4 at gmail.com (Kevin Davies)
Date: Sun, 27 Apr 2014 06:23:30 -1000
Subject: [SciPy-Dev] requesting code review: physical constants
In-Reply-To: <ljirk3$qpn$1@ger.gmane.org>
References: <535CACB7.4080006@gmail.com> <ljirk3$qpn$1@ger.gmane.org>
Message-ID: <535D2F02.5010006@gmail.com>

Thanks, good point.  I can move it to exact_values, but it'll be a long 
dictionary if we make exact_values comprehensive.

On 04/27/2014 02:02 AM, Pauli Virtanen wrote:
> 27.04.2014 10:07, Kevin Davies kirjoitti:
>> I changed constants.py to calculate more of the physical constants
>> directly instead of lookup in the codata tables.  I think it's best
>> to encode the underlying algebraic relations where they exist.
> This is actually currently done at the end of `codata.py`, via the
> exact_values stuff. It would make sense to move all of that processing
> there, so that constants returned by `value()` are consistent with the
> named ones.
>
> [clip]
>> This change introduces four additional constants to
>> scipy.constants:
>>
>> 1. magnetic quantum (Phi_0) 2. conductance quantum (G_0) 3. Faraday
>> constant (F) 4. characteristic impedance of vacuum (Z_0)
> Adding these should be OK.
>



From nils106 at googlemail.com  Sun Apr 27 12:47:23 2014
From: nils106 at googlemail.com (Nils Wagner)
Date: Sun, 27 Apr 2014 18:47:23 +0200
Subject: [SciPy-Dev] Test failures
Message-ID: <CAE7vhgMq3WAm7z681kMZ+bEq+0YP3Gn+_xbUPZDg01=vKRmS0g@mail.gmail.com>

======================================================================
ERROR: test_scalar (test_size_check.TestDummyArray)
----------------------------------------------------------------------
Traceback (most recent call last):
  File
"/home/nils/local/lib64/python2.7/site-packages/scipy/weave/tests/test_size_check.py",
line 60, in test_scalar
    self.generic_check(x,y,desired)
  File
"/home/nils/local/lib64/python2.7/site-packages/scipy/weave/tests/test_size_check.py",
line 128, in generic_check
    assert_array_equal(actual,desired)
  File
"/home/nils/local/lib64/python2.7/site-packages/numpy/testing/utils.py",
line 734, in assert_array_equal
    verbose=verbose, header='Arrays are not equal')
  File
"/home/nils/local/lib64/python2.7/site-packages/numpy/testing/utils.py",
line 587, in assert_array_compare
    x = array(x, copy=False, subok=True)
ValueError: setting an array element with a sequence.

======================================================================
ERROR: test_x1 (test_size_check.TestDummyArray)
----------------------------------------------------------------------
Traceback (most recent call last):
  File
"/home/nils/local/lib64/python2.7/site-packages/scipy/weave/tests/test_size_check.py",
line 65, in test_x1
    self.generic_check(x,y,desired)
  File
"/home/nils/local/lib64/python2.7/site-packages/scipy/weave/tests/test_size_check.py",
line 128, in generic_check
    assert_array_equal(actual,desired)
  File
"/home/nils/local/lib64/python2.7/site-packages/numpy/testing/utils.py",
line 734, in assert_array_equal
    verbose=verbose, header='Arrays are not equal')
  File
"/home/nils/local/lib64/python2.7/site-packages/numpy/testing/utils.py",
line 587, in assert_array_compare
    x = array(x, copy=False, subok=True)
ValueError: setting an array element with a sequence.

======================================================================
ERROR: test_x_y (test_size_check.TestDummyArray)
----------------------------------------------------------------------
Traceback (most recent call last):
  File
"/home/nils/local/lib64/python2.7/site-packages/scipy/weave/tests/test_size_check.py",
line 75, in test_x_y
    self.generic_check(x,y,desired)
  File
"/home/nils/local/lib64/python2.7/site-packages/scipy/weave/tests/test_size_check.py",
line 128, in generic_check
    assert_array_equal(actual,desired)
  File
"/home/nils/local/lib64/python2.7/site-packages/numpy/testing/utils.py",
line 734, in assert_array_equal
    verbose=verbose, header='Arrays are not equal')
  File
"/home/nils/local/lib64/python2.7/site-packages/numpy/testing/utils.py",
line 587, in assert_array_compare
    x = array(x, copy=False, subok=True)
ValueError: setting an array element with a sequence.

======================================================================
ERROR: test_x_y2 (test_size_check.TestDummyArray)
----------------------------------------------------------------------
Traceback (most recent call last):
  File
"/home/nils/local/lib64/python2.7/site-packages/scipy/weave/tests/test_size_check.py",
line 80, in test_x_y2
    self.generic_check(x,y,desired)
  File
"/home/nils/local/lib64/python2.7/site-packages/scipy/weave/tests/test_size_check.py",
line 128, in generic_check
    assert_array_equal(actual,desired)
  File
"/home/nils/local/lib64/python2.7/site-packages/numpy/testing/utils.py",
line 734, in assert_array_equal
    verbose=verbose, header='Arrays are not equal')
  File
"/home/nils/local/lib64/python2.7/site-packages/numpy/testing/utils.py",
line 587, in assert_array_compare
    x = array(x, copy=False, subok=True)
ValueError: setting an array element with a sequence.

======================================================================
ERROR: test_x_y3 (test_size_check.TestDummyArray)
----------------------------------------------------------------------
Traceback (most recent call last):
  File
"/home/nils/local/lib64/python2.7/site-packages/scipy/weave/tests/test_size_check.py",
line 85, in test_x_y3
    self.generic_check(x,y,desired)
  File
"/home/nils/local/lib64/python2.7/site-packages/scipy/weave/tests/test_size_check.py",
line 128, in generic_check
    assert_array_equal(actual,desired)
  File
"/home/nils/local/lib64/python2.7/site-packages/numpy/testing/utils.py",
line 734, in assert_array_equal
    verbose=verbose, header='Arrays are not equal')
  File
"/home/nils/local/lib64/python2.7/site-packages/numpy/testing/utils.py",
line 587, in assert_array_compare
    x = array(x, copy=False, subok=True)
ValueError: setting an array element with a sequence.

======================================================================
ERROR: test_x_y4 (test_size_check.TestDummyArray)
----------------------------------------------------------------------
Traceback (most recent call last):
  File
"/home/nils/local/lib64/python2.7/site-packages/scipy/weave/tests/test_size_check.py",
line 90, in test_x_y4
    self.generic_check(x,y,desired)
  File
"/home/nils/local/lib64/python2.7/site-packages/scipy/weave/tests/test_size_check.py",
line 128, in generic_check
    assert_array_equal(actual,desired)
  File
"/home/nils/local/lib64/python2.7/site-packages/numpy/testing/utils.py",
line 734, in assert_array_equal
    verbose=verbose, header='Arrays are not equal')
  File
"/home/nils/local/lib64/python2.7/site-packages/numpy/testing/utils.py",
line 587, in assert_array_compare
    x = array(x, copy=False, subok=True)
ValueError: setting an array element with a sequence.

======================================================================
ERROR: test_x_y5 (test_size_check.TestDummyArray)
----------------------------------------------------------------------
Traceback (most recent call last):
  File
"/home/nils/local/lib64/python2.7/site-packages/scipy/weave/tests/test_size_check.py",
line 95, in test_x_y5
    self.generic_check(x,y,desired)
  File
"/home/nils/local/lib64/python2.7/site-packages/scipy/weave/tests/test_size_check.py",
line 128, in generic_check
    assert_array_equal(actual,desired)
  File
"/home/nils/local/lib64/python2.7/site-packages/numpy/testing/utils.py",
line 734, in assert_array_equal
    verbose=verbose, header='Arrays are not equal')
  File
"/home/nils/local/lib64/python2.7/site-packages/numpy/testing/utils.py",
line 587, in assert_array_compare
    x = array(x, copy=False, subok=True)
ValueError: setting an array element with a sequence.

======================================================================
ERROR: test_x_y6 (test_size_check.TestDummyArray)
----------------------------------------------------------------------
Traceback (most recent call last):
  File
"/home/nils/local/lib64/python2.7/site-packages/scipy/weave/tests/test_size_check.py",
line 100, in test_x_y6
    self.generic_check(x,y,desired)
  File
"/home/nils/local/lib64/python2.7/site-packages/scipy/weave/tests/test_size_check.py",
line 128, in generic_check
    assert_array_equal(actual,desired)
  File
"/home/nils/local/lib64/python2.7/site-packages/numpy/testing/utils.py",
line 734, in assert_array_equal
    verbose=verbose, header='Arrays are not equal')
  File
"/home/nils/local/lib64/python2.7/site-packages/numpy/testing/utils.py",
line 587, in assert_array_compare
    x = array(x, copy=False, subok=True)
ValueError: setting an array element with a sequence.

======================================================================
ERROR: test_x_y7 (test_size_check.TestDummyArray)
----------------------------------------------------------------------
Traceback (most recent call last):
  File
"/home/nils/local/lib64/python2.7/site-packages/scipy/weave/tests/test_size_check.py",
line 105, in test_x_y7
    self.generic_check(x,y,desired)
  File
"/home/nils/local/lib64/python2.7/site-packages/scipy/weave/tests/test_size_check.py",
line 128, in generic_check
    assert_array_equal(actual,desired)
  File
"/home/nils/local/lib64/python2.7/site-packages/numpy/testing/utils.py",
line 734, in assert_array_equal
    verbose=verbose, header='Arrays are not equal')
  File
"/home/nils/local/lib64/python2.7/site-packages/numpy/testing/utils.py",
line 587, in assert_array_compare
    x = array(x, copy=False, subok=True)
ValueError: setting an array element with a sequence.

======================================================================
ERROR: test_y1 (test_size_check.TestDummyArray)
----------------------------------------------------------------------
Traceback (most recent call last):
  File
"/home/nils/local/lib64/python2.7/site-packages/scipy/weave/tests/test_size_check.py",
line 70, in test_y1
    self.generic_check(x,y,desired)
  File
"/home/nils/local/lib64/python2.7/site-packages/scipy/weave/tests/test_size_check.py",
line 128, in generic_check
    assert_array_equal(actual,desired)
  File
"/home/nils/local/lib64/python2.7/site-packages/numpy/testing/utils.py",
line 734, in assert_array_equal
    verbose=verbose, header='Arrays are not equal')
  File
"/home/nils/local/lib64/python2.7/site-packages/numpy/testing/utils.py",
line 587, in assert_array_compare
    x = array(x, copy=False, subok=True)
ValueError: setting an array element with a sequence.

======================================================================
FAIL: test_trim (test_mstats_basic.TestTrimming)
----------------------------------------------------------------------
Traceback (most recent call last):
  File
"/home/nils/local/lib64/python2.7/site-packages/scipy/stats/tests/test_mstats_basic.py",
line 277, in test_trim
    assert_equal(trimx._mask.ravel(), expected)
  File
"/home/nils/local/lib64/python2.7/site-packages/numpy/ma/testutils.py",
line 123, in assert_equal
    return assert_array_equal(actual, desired, err_msg)
  File
"/home/nils/local/lib64/python2.7/site-packages/numpy/ma/testutils.py",
line 196, in assert_array_equal
    header='Arrays are not equal')
  File
"/home/nils/local/lib64/python2.7/site-packages/numpy/ma/testutils.py",
line 189, in assert_array_compare
    verbose=verbose, header=header)
  File
"/home/nils/local/lib64/python2.7/site-packages/numpy/testing/utils.py",
line 660, in assert_array_compare
    raise AssertionError(msg)
AssertionError:
Arrays are not equal

(mismatch 9.09090909091%)
 x: array([ True,  True,  True,  True,  True,  True,  True,  True,  True,
        True,  True,  True,  True,  True,  True,  True,  True,  True,
        True,  True, False, False, False, False, False, False, False,...
 y: array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0,
0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0,...

----------------------------------------------------------------------
Ran 16585 tests in 660.049s

FAILED (KNOWNFAIL=277, SKIP=904, errors=10, failures=1)



>>> numpy.__version__
'1.9.0.dev-7bab957'
>>> scipy.__version__
'0.15.0.dev-58d8117'
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From kdavies4 at gmail.com  Sun Apr 27 13:03:58 2014
From: kdavies4 at gmail.com (Kevin Davies)
Date: Sun, 27 Apr 2014 07:03:58 -1000
Subject: [SciPy-Dev] requesting code review: physical constants
In-Reply-To: <ljirk3$qpn$1@ger.gmane.org>
References: <535CACB7.4080006@gmail.com> <ljirk3$qpn$1@ger.gmane.org>
Message-ID: <535D387E.5080400@gmail.com>

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From kdavies4 at gmail.com  Sun Apr 27 14:51:27 2014
From: kdavies4 at gmail.com (Kevin Davies)
Date: Sun, 27 Apr 2014 08:51:27 -1000
Subject: [SciPy-Dev] requesting code review: added close matches to
	constants.codata.value()
In-Reply-To: <CAPJVwBkae3Z83EW376XCT4h1DCjEFivYkyWGD9f9tW4BiMCNGA@mail.gmail.com>
References: <535CB410.1010803@gmail.com>
	<CAPJVwBkae3Z83EW376XCT4h1DCjEFivYkyWGD9f9tW4BiMCNGA@mail.gmail.com>
Message-ID: <535D51AF.1090305@gmail.com>

Thanks.  Unfortunately it looks like I can't raise a KeyError with a 
multi-line message.  Newlines are printed literally as "\n".  Do you 
have any suggestions?


From pierre.haessig at crans.org  Mon Apr 28 08:48:19 2014
From: pierre.haessig at crans.org (Pierre Haessig)
Date: Mon, 28 Apr 2014 14:48:19 +0200
Subject: [SciPy-Dev] requesting code review: added close matches to
	constants.codata.value()
In-Reply-To: <535D51AF.1090305@gmail.com>
References: <535CB410.1010803@gmail.com>	<CAPJVwBkae3Z83EW376XCT4h1DCjEFivYkyWGD9f9tW4BiMCNGA@mail.gmail.com>
	<535D51AF.1090305@gmail.com>
Message-ID: <535E4E13.6080006@crans.org>

Hi Kevin,

Just a random thought:  what about putting your recommendation system in
a separate "search" function ?
The KeyError raised by value(...) could be one-line message pointing to
this search function.

That function, meant to be used interactively unlike value(), could use
prints I guess.

best,
Pierre


From robert.kern at gmail.com  Mon Apr 28 08:53:52 2014
From: robert.kern at gmail.com (Robert Kern)
Date: Mon, 28 Apr 2014 13:53:52 +0100
Subject: [SciPy-Dev] requesting code review: added close matches to
	constants.codata.value()
In-Reply-To: <535E4E13.6080006@crans.org>
References: <535CB410.1010803@gmail.com>
	<CAPJVwBkae3Z83EW376XCT4h1DCjEFivYkyWGD9f9tW4BiMCNGA@mail.gmail.com>
	<535D51AF.1090305@gmail.com> <535E4E13.6080006@crans.org>
Message-ID: <CAF6FJivEnLcOwUVSWr3F6dci-fzXE2SeVDWEGgB+Y8pj0x36sg@mail.gmail.com>

On Mon, Apr 28, 2014 at 1:48 PM, Pierre Haessig
<pierre.haessig at crans.org> wrote:
> Hi Kevin,
>
> Just a random thought:  what about putting your recommendation system in
> a separate "search" function ?
> The KeyError raised by value(...) could be one-line message pointing to
> this search function.

The argument to KeyError should be the key that is causing the
problem, not a natural language message. That's why his attempt to use
a multi-line string got printed as its repr().

> That function, meant to be used interactively unlike value(), could use
> prints I guess.

But yes, I agree that adding a function specifically documented to be
used interactively would be a better place for this functionality.

-- 
Robert Kern


From njs at pobox.com  Mon Apr 28 09:01:32 2014
From: njs at pobox.com (Nathaniel Smith)
Date: Mon, 28 Apr 2014 14:01:32 +0100
Subject: [SciPy-Dev] requesting code review: added close matches to
	constants.codata.value()
In-Reply-To: <535D51AF.1090305@gmail.com>
References: <535CB410.1010803@gmail.com>
	<CAPJVwBkae3Z83EW376XCT4h1DCjEFivYkyWGD9f9tW4BiMCNGA@mail.gmail.com>
	<535D51AF.1090305@gmail.com>
Message-ID: <CAPJVwBk5vqjQrMvUYhhQS+PPOGKT3PPW+GqyttUkfhkE7yzFwQ@mail.gmail.com>

Another option (don't know if it's a good one):

class ConstantKeyError(KeyError):
    def __str__(self):
        key = self.args[0]
        similar = ...
        return "Constant %r not found -- perhaps you wanted one of:
%s" % (key, ", ".join(similar))

-n

On Sun, Apr 27, 2014 at 7:51 PM, Kevin Davies <kdavies4 at gmail.com> wrote:
> Thanks.  Unfortunately it looks like I can't raise a KeyError with a
> multi-line message.  Newlines are printed literally as "\n".  Do you
> have any suggestions?
> _______________________________________________
> SciPy-Dev mailing list
> SciPy-Dev at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-dev



-- 
Nathaniel J. Smith
Postdoctoral researcher - Informatics - University of Edinburgh
http://vorpus.org


From charlesnwoods at gmail.com  Mon Apr 28 11:46:20 2014
From: charlesnwoods at gmail.com (Nathan Woods)
Date: Mon, 28 Apr 2014 09:46:20 -0600
Subject: [SciPy-Dev] Multivariate ctypes integration PR
In-Reply-To: <CAAYHtCR8307yXnzpZYuhLSdpepNuC3ZSMkzV6-anBjVpa+ok5A@mail.gmail.com>
References: <CAEpsjT40tznXWYWN-eGUhUSg_ijKYrYREv0RS9XYezrD6eh6Nw@mail.gmail.com>
	<CAAYHtCQTCdtLe_ezw++-E+6F5MfyXLoiK8rOg0ydTOuX2ums-A@mail.gmail.com>
	<CAAYHtCR8307yXnzpZYuhLSdpepNuC3ZSMkzV6-anBjVpa+ok5A@mail.gmail.com>
Message-ID: <CAAYHtCQaxx2H_JaXCQGXAX=8+_h-T=8rGa=6WNRxg2F+DmreWw@mail.gmail.com>

Anyone? Anyone?

>From the lack of response to my email two weeks ago, I guess that there
isn't really a good way to distribute a library function for testing. When
ctypes functionality was initially included, the test function used was
part of the standard library, to avoid this same problem. I propose that we
therefore do one of two things.

One, we incorporate the changes as a user-beware feature, and strongly
suggest that users who are willing to do the work necessary to use the
functionality (a minority, presumably) do their own testing. In order to
use the speed improvements here, a user has to code their function in C,
compile it to a library, and load that library using ctypes. Then, that
ctypes function can be passed to scipy. I think we can agree that anyone
who is willing to put in that work for performance can be expected to run a
test or two to make sure everything is working.

Two, we shelve the project until a better solution is available. This will
probably require some alternative to ctypes, which is just too clunky to
distribute well. Unfortunately, I think that shelving this till later will
essentially kill it at this point.

Nathan


On Tue, Apr 15, 2014 at 3:38 PM, Nathan Woods <charlesnwoods at gmail.com>wrote:

> Hey everyone,
>
> Sorry to be the squeaky wheel again, but this needs to see some movement
> pretty soon. We only have another few weeks where we can work on this
> enhancement before the school year ends.
>
> The big challenge that we believe needs to be fixed is with the tests. We
> need a way to compile (or distribute) a C function from source to  a
> library with a given name, generally and at install time, in order to
> include the C route tests in the SciPy test suite. Is there a way to use
> distutils for this?
>
> If there's anything else that we can do on our end to speed things up,
> we'd be more than happy to help however we can.
>
> N
>
>
> On Mon, Mar 31, 2014 at 1:27 PM, Nathan Woods <charlesnwoods at gmail.com>wrote:
>
>> We had a question about how to implement one of the suggested
>> improvements:
>>
>> "- The code should more carefully check that the whole ctypes signature
>>   matches"
>>
>>  From what we can tell (see e.g.
>> http://stackoverflow.com/questions/18775389/is-there-a-way-to-load-the-constant-values-stored-in-a-header-file-via-ctypes),
>> there is no way to automatically generate the ctypes function signature
>> directly from a .h header file or c function prototype. This means that any
>> ctypes library function must be assumed to have the function signature that
>> the user claims it does (by specifying argtypes & restype). Unfortunately,
>> the ctypes library load & specification commands are executed by the user,
>> not by the library, so there's no way to enforce the function signature.
>>
>> Maybe there's a way in Cython or f2py, but we think that ctypes should
>> probably be supported anyway, even if it is with an "use at own risk"
>> disclaimer.
>>
>> Nathan
>>
>>
>>
>> On Fri, Mar 14, 2014 at 12:08 PM, Brian Lee Newsom <
>> Brian.Newsom at colorado.edu> wrote:
>>
>>> Thank you to everyone involved in assisting to move this forward. It is
>>> definitely a confusing project for me but something I am committed to
>>> finishing and getting in the library as soon as possible.  I appreciate the
>>> help and I will do my best to address the issues Pauli presented.
>>>
>>> Thanks,
>>> Brian
>>>
>>> _______________________________________________
>>> SciPy-Dev mailing list
>>> SciPy-Dev at scipy.org
>>> http://mail.scipy.org/mailman/listinfo/scipy-dev
>>>
>>>
>>
>
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From robert.kern at gmail.com  Mon Apr 28 11:53:26 2014
From: robert.kern at gmail.com (Robert Kern)
Date: Mon, 28 Apr 2014 16:53:26 +0100
Subject: [SciPy-Dev] Multivariate ctypes integration PR
In-Reply-To: <CAAYHtCQaxx2H_JaXCQGXAX=8+_h-T=8rGa=6WNRxg2F+DmreWw@mail.gmail.com>
References: <CAEpsjT40tznXWYWN-eGUhUSg_ijKYrYREv0RS9XYezrD6eh6Nw@mail.gmail.com>
	<CAAYHtCQTCdtLe_ezw++-E+6F5MfyXLoiK8rOg0ydTOuX2ums-A@mail.gmail.com>
	<CAAYHtCR8307yXnzpZYuhLSdpepNuC3ZSMkzV6-anBjVpa+ok5A@mail.gmail.com>
	<CAAYHtCQaxx2H_JaXCQGXAX=8+_h-T=8rGa=6WNRxg2F+DmreWw@mail.gmail.com>
Message-ID: <CAF6FJiu_=RWH71ys64rYn-Nmex+MTA+tLY1XPnf+vE7_3pLFzg@mail.gmail.com>

On Mon, Apr 28, 2014 at 4:46 PM, Nathan Woods <charlesnwoods at gmail.com> wrote:
> Anyone? Anyone?
>
> From the lack of response to my email two weeks ago, I guess that there
> isn't really a good way to distribute a library function for testing. When
> ctypes functionality was initially included, the test function used was part
> of the standard library, to avoid this same problem. I propose that we
> therefore do one of two things.

The stdlib tests ctypes by building a Python extension module
`_ctypes_test` along with all of the other stdlib extension modules.
It is careful to `EXPORT` all of the symbols that will be needed. We
could do the same here. We will just have an extra _private extension
module built along with all of the others while scipy is building. It
will not be compiled during the unit test.

http://hg.python.org/cpython/file/a14012352f65/Modules/_ctypes/_ctypes_test.c
http://hg.python.org/cpython/file/a14012352f65/Lib/ctypes/test/test_cfuncs.py#l7

-- 
Robert Kern


From db at edx.org  Mon Apr 28 11:55:45 2014
From: db at edx.org (David Baumgold)
Date: Mon, 28 Apr 2014 11:55:45 -0400
Subject: [SciPy-Dev] wheels on PyPI?
In-Reply-To: <6B9AFD6A2B9E477CBB6D979CF46A5DC9@edx.org>
References: <6B9AFD6A2B9E477CBB6D979CF46A5DC9@edx.org>
Message-ID: <9B1D27822EB44B9CA1F947948EB3E85E@edx.org>

Hey all, I work for edX (http://code.edx.org), and we include NumPy and SciPy in our software platform. We have a lot of tests for our code, and running them requires making a virtual environment and installing all of our dependencies each time ? but NumPy and SciPy take a long time to compile. We?ve actually built Python wheel (http://pythonwheels.com) files for all of our dependencies, which we host on an AWS server: http://edx-wheels.s3-website-us-east-1.amazonaws.com It would be super convenient if we could get wheel files for NumPy and SciPy on PyPI (https://pypi.python.org/pypi), so that its easier for us to install them in our test environment ? and I imagine it would help a whole lot of other people, as well! Who has the authorization to upload new packages to the NumPy and SciPy projects on PyPI, and how can I contribute our wheel files back to the community? Alternatively, how can I help the SciPy project build and distribute wheel files automatically, as part of the standard release process? Thanks for the info!
-David Baumgold

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From robert.kern at gmail.com  Mon Apr 28 12:08:15 2014
From: robert.kern at gmail.com (Robert Kern)
Date: Mon, 28 Apr 2014 17:08:15 +0100
Subject: [SciPy-Dev] wheels on PyPI?
In-Reply-To: <9B1D27822EB44B9CA1F947948EB3E85E@edx.org>
References: <6B9AFD6A2B9E477CBB6D979CF46A5DC9@edx.org>
	<9B1D27822EB44B9CA1F947948EB3E85E@edx.org>
Message-ID: <CAF6FJits9i_6Eq49-SaUYBH_enMR-hwDq1kB6zNQrPBdoaenEQ@mail.gmail.com>

On Mon, Apr 28, 2014 at 4:55 PM, David Baumgold <db at edx.org> wrote:
> Hey all, I work for edX, and we include NumPy and SciPy in our software
> platform. We have a lot of tests for our code, and running them requires
> making a virtual environment and installing all of our dependencies each
> time ? but NumPy and SciPy take a long time to compile. We?ve actually built
> Python wheel files for all of our dependencies, which we host on an AWS
> server: http://edx-wheels.s3-website-us-east-1.amazonaws.com It would be
> super convenient if we could get wheel files for NumPy and SciPy on PyPI, so
> that its easier for us to install them in our test environment ? and I
> imagine it would help a whole lot of other people, as well! Who has the
> authorization to upload new packages to the NumPy and SciPy projects on
> PyPI, and how can I contribute our wheel files back to the community?
> Alternatively, how can I help the SciPy project build and distribute wheel
> files automatically, as part of the standard release process? Thanks for the
> info!

It looks like you only have Linux and OS X wheels for numpy and scipy.
The general consensus, Python-community-wide, is that only noarch and
Windows wheels should be put up on PyPI. The PyPI server even enforces
this, at this present time. Linux and even OS X environments are a
little too variable.

The stumbling block for getting official Windows wheels up has been
finding working, not-embarassingly-out-of-date accelerated BLAS and
LAPACK libraries that work. If you look on the recent numpy-discussion
threads on this subject, it appears that this will be resolved
soonish. Or at least, there are people actively working on it, and you
can expect regular Windows wheels to be uploaded to PyPI as soon as it
is possible to do so.

-- 
Robert Kern


From kdavies4 at gmail.com  Mon Apr 28 12:47:42 2014
From: kdavies4 at gmail.com (Kevin Davies)
Date: Mon, 28 Apr 2014 06:47:42 -1000
Subject: [SciPy-Dev] requesting code review: added close matches to
	constants.codata.value()
In-Reply-To: <CAF6FJivEnLcOwUVSWr3F6dci-fzXE2SeVDWEGgB+Y8pj0x36sg@mail.gmail.com>
References: <535CB410.1010803@gmail.com>	<CAPJVwBkae3Z83EW376XCT4h1DCjEFivYkyWGD9f9tW4BiMCNGA@mail.gmail.com>	<535D51AF.1090305@gmail.com>
	<535E4E13.6080006@crans.org>
	<CAF6FJivEnLcOwUVSWr3F6dci-fzXE2SeVDWEGgB+Y8pj0x36sg@mail.gmail.com>
Message-ID: <535E862E.8070209@gmail.com>

Thanks for everyone's help.  It sounds like the best option is to add 
this  recommendation system to constants.codata.find().  Please take a look:
https://github.com/kdavies4/scipy/compare/codata-find


On 04/28/2014 02:53 AM, Robert Kern wrote:
> On Mon, Apr 28, 2014 at 1:48 PM, Pierre Haessig
> <pierre.haessig at crans.org> wrote:
>> Hi Kevin,
>>
>> Just a random thought:  what about putting your recommendation system in
>> a separate "search" function ?
>> The KeyError raised by value(...) could be one-line message pointing to
>> this search function.
> The argument to KeyError should be the key that is causing the
> problem, not a natural language message. That's why his attempt to use
> a multi-line string got printed as its repr().
>
>> That function, meant to be used interactively unlike value(), could use
>> prints I guess.
> But yes, I agree that adding a function specifically documented to be
> used interactively would be a better place for this functionality.
>



From sidharth.n.kashyap at gmail.com  Mon Apr 28 12:48:10 2014
From: sidharth.n.kashyap at gmail.com (sidharth kashyap)
Date: Mon, 28 Apr 2014 17:48:10 +0100
Subject: [SciPy-Dev] Scipy build errors - undefined reference to `Py* -
	CentOS6
Message-ID: <CAAL5kX9he8GNP1653=J_k1vD3Wx_6AV7aztwD4-BBCxK+K3_AQ@mail.gmail.com>

Hi,

I am getting the below errors when I try to build Scipy.

build/temp.linux-x86_64-2.7/build/src.linux-x86_64-2.7/_fftpackmodule.o:
In function `f2py_rout__fftpack_destroy_dst1_cache':
scipy-0.13.3/scipy/fftpack/build/src.linux-x86_64-2.7/_fftpackmodule.c:3458:
undefined reference to `PyArg_ParseTupleAndKeywords'
scipy-0.13.3/scipy/fftpack/build/src.linux-x86_64-2.7/_fftpackmodule.c:3469:
undefined reference to `PyErr_Occurred'
scipy-0.13.3/scipy/fftpack/build/src.linux-x86_64-2.7/_fftpackmodule.c:3479:
undefined reference to `Py_BuildValue'
build/temp.linux-x86_64-2.7/build/src.linux-x86_64-2.7/_fftpackmodule.o:
In function `f2py_rout__fftpack_destroy_dst2_cache':
scipy-0.13.3/scipy/fftpack/build/src.linux-x86_64-2.7/_fftpackmodule.c:3397:
undefined reference to `PyArg_ParseTupleAndKeywords'
scipy-0.13.3/scipy/fftpack/build/src.linux-x86_64-2.7/_fftpackmodule.c:3408:
undefined reference to `PyErr_Occurred'
scipy-0.13.3/scipy/fftpack/build/src.linux-x86_64-2.7/_fftpackmodule.c:3418:
undefined reference to `Py_BuildValue'
build/temp.linux-x86_64-2.7/build/src.linux-x86_64-2.7/_fftpackmodule.o:
In function `f2py_rout__fftpack_destroy_ddst1_cache':
scipy-0.13.3/scipy/fftpack/build/src.linux-x86_64-2.7/_fftpackmodule.c:3336:
undefined reference to `PyArg_ParseTupleAndKeywords'
scipy-0.13.3/scipy/fftpack/build/src.linux-x86_64-2.7/_fftpackmodule.c:3347:
undefined reference to `PyErr_Occurred'
scipy-0.13.3/scipy/fftpack/build/src.linux-x86_64-2.7/_fftpackmodule.c:3357:
undefined reference to `Py_BuildValue'
build/temp.linux-x86_64-2.7/build/src.linux-x86_64-2.7/_fftpackmodule.o:
In function `f2py_rout__fftpack_destroy_ddst2_cache':
scipy-0.13.3/scipy/fftpack/build/src.linux-x86_64-2.7/_fftpackmodule.c:3275:
undefined reference to `PyArg_ParseTupleAndKeywords'
scipy-0.13.3/scipy/fftpack/build/src.linux-x86_64-2.7/_fftpackmodule.c:3286:
undefined reference to `PyErr_Occurred'
scipy-0.13.3/scipy/fftpack/build/src.linux-x86_64-2.7/_fftpackmodule.c:3296:
undefined reference to `Py_BuildValue'
build/temp.linux-x86_64-2.7/build/src.linux-x86_64-2.7/_fftpackmodule.o:
In function `f2py_rout__fftpack_destroy_dct1_cache':
scipy-0.13.3/scipy/fftpack/build/src.linux-x86_64-2.7/_fftpackmodule.c:2542:
undefined reference to `PyArg_ParseTupleAndKeywords'
scipy-0.13.3/scipy/fftpack/build/src.linux-x86_64-2.7/_fftpackmodule.c:2553:
undefined reference to `PyErr_Occurred'
scipy-0.13.3/scipy/fftpack/build/src.linux-x86_64-2.7/_fftpackmodule.c:2563:
undefined reference to `Py_BuildValue'
build/temp.linux-x86_64-2.7/build/src.linux-x86_64-2.7/_fftpackmodule.o:
In function `f2py_rout__fftpack_destroy_dct2_cache':
scipy-0.13.3/scipy/fftpack/build/src.linux-x86_64-2.7/_fftpackmodule.c:2481:
undefined reference to `PyArg_ParseTupleAndKeywords'
scipy-0.13.3/scipy/fftpack/build/src.linux-x86_64-2.7/_fftpackmodule.c:2492:
undefined reference to `PyErr_Occurred'
scipy-0.13.3/scipy/fftpack/build/src.linux-x86_64-2.7/_fftpackmodule.c:2502:
undefined reference to `Py_BuildValue'


The build progresses well till fftpack

Here is the command used to build

 python setup.py build --fcompiler=gnu95 --force --verbose --debug

Python Version:
python --version
Python 2.7.3


Numpy version:
>>> import numpy
>>> numpy.__version__
'1.6.2'


All the sources: python, numpy and atlas have been compiled using the
same version of gcc (4.6.2)

LD_LIBRARY_PATH has the path for the objects  located at
./python/2.7.3/gnu-4.6.2/lib

I have also put in INCLUDE, INCLUDE_PATH, C_INCLUDE_PATH have the path
to the header files (Python.h)

The command at which it fails:

gfortran -Wall -L/app/libraries/expat/2.0.1/gnu-4.6.2/lib -lexpat -g
build/temp.linux-x86_64-2.7/build/src.linux-x86_64-2.7/_fftpackmodule.o
build/temp.linux-x86_64-2.7/src/zfft.o
build/temp.linux-x86_64-2.7/src/drfft.o
build/temp.linux-x86_64-2.7/src/zrfft.o
build/temp.linux-x86_64-2.7/src/zfftnd.o
build/temp.linux-x86_64-2.7/build/src.linux-x86_64-2.7/src/dct.o
build/temp.linux-x86_64-2.7/build/src.linux-x86_64-2.7/src/dst.o
build/temp.linux-x86_64-2.7/build/src.linux-x86_64-2.7/fortranobject.o
-Lbuild/temp.linux-x86_64-2.7 -ldfftpack -lfftpack -lgfortran -o
build/lib.linux-x86_64-2.7/fftpack/_fftpack.so

The error:

scipy-0.13.3/scipy/fftpack/build/src.linux-x86_64-2.7/fortranobject.c:959:
undefined reference to `PyCObject_AsVoidPtr'
collect2: ld returned 1 exit status


I have tried to put in -l and -L options to include the library paths.

Any help on this is highly appreciated

Should I include any other build parameters?

Thanks,
Sid


From ralf.gommers at gmail.com  Mon Apr 28 12:56:10 2014
From: ralf.gommers at gmail.com (Ralf Gommers)
Date: Mon, 28 Apr 2014 18:56:10 +0200
Subject: [SciPy-Dev] 1.0 roadmap: weave
In-Reply-To: <loom.20131128T214641-166@post.gmane.org>
References: <alpine.DEB.2.02.1309251815560.8021@ptpcp8>
	<l1vd74$ol7$1@ger.gmane.org>
	<alpine.DEB.2.02.1309260950420.11235@ptpcp8>
	<loom.20130926T140607-460@post.gmane.org>
	<loom.20131128T214641-166@post.gmane.org>
Message-ID: <CABL7CQhhayqtp=x_Jo5Xi0=NtuF5XbB-o=-Hw2OBGDAXEjyCVg@mail.gmail.com>

On Thu, Nov 28, 2013 at 10:03 PM, Maxim Imakaev <imakaev at mit.edu> wrote:

> Hi,
>
> Pauli Virtanen <pav <at> iki.fi> writes:
>
> >
> > Arnd Baecker <arnd.baecker <at> web.de> writes:
> > [clip]
> > > Finally, to answer Roberts question whether I could volunteer
> > > to maintain weave as a separate package:
> > > not sure - maybe with some helping hands (currently I have no clue what
> > > this would require) it might be possible.
> > > The important aspect is the long term perspective:
> > > How many people would be interested in this and maybe even in a python
> 3
> > > port and actively use weave also for new code, or
> > > is the general impression, that the more modern tools (cython, ...)
> > > should be used?
> >
> > Also, let's say that if someone with personal interest in keeping
> > it working addresses the issues within the next few years, then
> > the pressure of splitting it out decreases quite a lot.
> >
> > In the big picture, weave is relatively "mature" code base, and
> > keeping it working is probably not too big apart from the Py3 port.
> >
>
> I found this two-month-old thread while checking (again) for any solutions
> regarding weave.inline and python 3.x compatibility.
>
> Our lab is another example of what Arnd was talking about: I'm a graduate
> student close to the end of my Ph.D, and I built many parts of our code
> using weave.inline. Now I'm looking for ways to make my code compatible
> with
> future generation. And weave.inline is the only package which holds us at
> python 2.x.
>
> Did someone start porting weave.inline since this thread ended on Sep 26?
> What are the chances someone will do this in the nearest future?
>

They were small, it looks like.

I spent some time this weekend on packaging weave as a separate package:
https://github.com/rgommers/weave. My proposal is to:
- release this under the name "Weave".
- put it on PyPi and at https://github.com/scipy/weave
- deprecate scipy.weave for 0.15.0
- add an extra envvar to trigger running the scipy.weave tests marked as
slow. don't run those by default when executing ``scipy.test('full')``.
this will fix the timeout issues we currently have on TravisCi.
- remove scipy.weave for 0.17.0 or 1.0.0, whichever comes first.

Thoughts?

Ralf
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From ralf.gommers at gmail.com  Mon Apr 28 13:04:19 2014
From: ralf.gommers at gmail.com (Ralf Gommers)
Date: Mon, 28 Apr 2014 19:04:19 +0200
Subject: [SciPy-Dev] wheels on PyPI?
In-Reply-To: <CAF6FJits9i_6Eq49-SaUYBH_enMR-hwDq1kB6zNQrPBdoaenEQ@mail.gmail.com>
References: <6B9AFD6A2B9E477CBB6D979CF46A5DC9@edx.org>
	<9B1D27822EB44B9CA1F947948EB3E85E@edx.org>
	<CAF6FJits9i_6Eq49-SaUYBH_enMR-hwDq1kB6zNQrPBdoaenEQ@mail.gmail.com>
Message-ID: <CABL7CQhObA0ZQjWQGTXyopwztUu=zfSY1v2ggU+F15xPnq4prw@mail.gmail.com>

On Mon, Apr 28, 2014 at 6:08 PM, Robert Kern <robert.kern at gmail.com> wrote:

> On Mon, Apr 28, 2014 at 4:55 PM, David Baumgold <db at edx.org> wrote:
> > Hey all, I work for edX, and we include NumPy and SciPy in our software
> > platform. We have a lot of tests for our code, and running them requires
> > making a virtual environment and installing all of our dependencies each
> > time ? but NumPy and SciPy take a long time to compile. We?ve actually
> built
> > Python wheel files for all of our dependencies, which we host on an AWS
> > server: http://edx-wheels.s3-website-us-east-1.amazonaws.com It would be
> > super convenient if we could get wheel files for NumPy and SciPy on
> PyPI, so
> > that its easier for us to install them in our test environment ? and I
> > imagine it would help a whole lot of other people, as well! Who has the
> > authorization to upload new packages to the NumPy and SciPy projects on
> > PyPI, and how can I contribute our wheel files back to the community?
> > Alternatively, how can I help the SciPy project build and distribute
> wheel
> > files automatically, as part of the standard release process? Thanks for
> the
> > info!
>
> It looks like you only have Linux and OS X wheels for numpy and scipy.
> The general consensus, Python-community-wide, is that only noarch and
> Windows wheels should be put up on PyPI. The PyPI server even enforces
> this, at this present time. Linux and even OS X environments are a
> little too variable.
>

For Linux that's correct, but for OS X we are going to put those on PyPi.
Actually, for Numpy those are already there thanks to Matthew's efforts:
https://pypi.python.org/pypi/numpy. For Scipy we plan to do the same for
0.14.0.

Specifically for testing on TravisCI, AstroPy hosts a wheelhouse with Linux
wheels that may be of use:
https://www.mpia-hd.mpg.de/~robitaille/wheelhouse/
https://github.com/scipy/scipy/blob/master/.travis.yml

Ralf


>
> The stumbling block for getting official Windows wheels up has been
> finding working, not-embarassingly-out-of-date accelerated BLAS and
> LAPACK libraries that work. If you look on the recent numpy-discussion
> threads on this subject, it appears that this will be resolved
> soonish. Or at least, there are people actively working on it, and you
> can expect regular Windows wheels to be uploaded to PyPI as soon as it
> is possible to do so.
>
> --
> Robert Kern
> _______________________________________________
> SciPy-Dev mailing list
> SciPy-Dev at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-dev
>
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From nils106 at googlemail.com  Mon Apr 28 14:16:04 2014
From: nils106 at googlemail.com (Nils Wagner)
Date: Mon, 28 Apr 2014 20:16:04 +0200
Subject: [SciPy-Dev] Errors with numpy-devel
In-Reply-To: <CABL7CQiZZ6p4_L_cGQakoaKFb4xquAFNcRvVFbLmPrO0uf+iTw@mail.gmail.com>
References: <CAB6mnx+o8E3cV-N7HVhxH-dtxih+zKQNKotF9rJkaKfYuAjr1Q@mail.gmail.com>
	<CABL7CQiig9r6RdDyvUrc_Z=+D6noHkZUdM18NG9BwCVNE2_iQA@mail.gmail.com>
	<CAE7vhgOqjL_6wFjKQfSXoFXVHyES9X-CnCxu5oUkh5=HhkgEfA@mail.gmail.com>
	<CABL7CQiZZ6p4_L_cGQakoaKFb4xquAFNcRvVFbLmPrO0uf+iTw@mail.gmail.com>
Message-ID: <CAE7vhgMh4Vy-_U+FcL_17ytYOSVhwpWNEAbPk5fvdZunLMsrTw@mail.gmail.com>

======================================================================
ERROR: test_scalar (test_size_check.TestDummyArray)
----------------------------------------------------------------------
Traceback (most recent call last):
  File
"/home/nils/local/lib64/python2.7/site-packages/scipy/weave/tests/test_size_check.py",
line 60, in test_scalar
    self.generic_check(x,y,desired)
  File
"/home/nils/local/lib64/python2.7/site-packages/scipy/weave/tests/test_size_check.py",
line 128, in generic_check
    assert_array_equal(actual,desired)
  File
"/home/nils/local/lib64/python2.7/site-packages/numpy/testing/utils.py",
line 734, in assert_array_equal
    verbose=verbose, header='Arrays are not equal')
  File
"/home/nils/local/lib64/python2.7/site-packages/numpy/testing/utils.py",
line 587, in assert_array_compare
    x = array(x, copy=False, subok=True)
ValueError: setting an array element with a sequence.

======================================================================
ERROR: test_x1 (test_size_check.TestDummyArray)
----------------------------------------------------------------------
Traceback (most recent call last):
  File
"/home/nils/local/lib64/python2.7/site-packages/scipy/weave/tests/test_size_check.py",
line 65, in test_x1
    self.generic_check(x,y,desired)
  File
"/home/nils/local/lib64/python2.7/site-packages/scipy/weave/tests/test_size_check.py",
line 128, in generic_check
    assert_array_equal(actual,desired)
  File
"/home/nils/local/lib64/python2.7/site-packages/numpy/testing/utils.py",
line 734, in assert_array_equal
    verbose=verbose, header='Arrays are not equal')
  File
"/home/nils/local/lib64/python2.7/site-packages/numpy/testing/utils.py",
line 587, in assert_array_compare
    x = array(x, copy=False, subok=True)
ValueError: setting an array element with a sequence.

======================================================================
ERROR: test_x_y (test_size_check.TestDummyArray)
----------------------------------------------------------------------
Traceback (most recent call last):
  File
"/home/nils/local/lib64/python2.7/site-packages/scipy/weave/tests/test_size_check.py",
line 75, in test_x_y
    self.generic_check(x,y,desired)
  File
"/home/nils/local/lib64/python2.7/site-packages/scipy/weave/tests/test_size_check.py",
line 128, in generic_check
    assert_array_equal(actual,desired)
  File
"/home/nils/local/lib64/python2.7/site-packages/numpy/testing/utils.py",
line 734, in assert_array_equal
    verbose=verbose, header='Arrays are not equal')
  File
"/home/nils/local/lib64/python2.7/site-packages/numpy/testing/utils.py",
line 587, in assert_array_compare
    x = array(x, copy=False, subok=True)
ValueError: setting an array element with a sequence.

======================================================================
ERROR: test_x_y2 (test_size_check.TestDummyArray)
----------------------------------------------------------------------
Traceback (most recent call last):
  File
"/home/nils/local/lib64/python2.7/site-packages/scipy/weave/tests/test_size_check.py",
line 80, in test_x_y2
    self.generic_check(x,y,desired)
  File
"/home/nils/local/lib64/python2.7/site-packages/scipy/weave/tests/test_size_check.py",
line 128, in generic_check
    assert_array_equal(actual,desired)
  File
"/home/nils/local/lib64/python2.7/site-packages/numpy/testing/utils.py",
line 734, in assert_array_equal
    verbose=verbose, header='Arrays are not equal')
  File
"/home/nils/local/lib64/python2.7/site-packages/numpy/testing/utils.py",
line 587, in assert_array_compare
    x = array(x, copy=False, subok=True)
ValueError: setting an array element with a sequence.

======================================================================
ERROR: test_x_y3 (test_size_check.TestDummyArray)
----------------------------------------------------------------------
Traceback (most recent call last):
  File
"/home/nils/local/lib64/python2.7/site-packages/scipy/weave/tests/test_size_check.py",
line 85, in test_x_y3
    self.generic_check(x,y,desired)
  File
"/home/nils/local/lib64/python2.7/site-packages/scipy/weave/tests/test_size_check.py",
line 128, in generic_check
    assert_array_equal(actual,desired)
  File
"/home/nils/local/lib64/python2.7/site-packages/numpy/testing/utils.py",
line 734, in assert_array_equal
    verbose=verbose, header='Arrays are not equal')
  File
"/home/nils/local/lib64/python2.7/site-packages/numpy/testing/utils.py",
line 587, in assert_array_compare
    x = array(x, copy=False, subok=True)
ValueError: setting an array element with a sequence.

======================================================================
ERROR: test_x_y4 (test_size_check.TestDummyArray)
----------------------------------------------------------------------
Traceback (most recent call last):
  File
"/home/nils/local/lib64/python2.7/site-packages/scipy/weave/tests/test_size_check.py",
line 90, in test_x_y4
    self.generic_check(x,y,desired)
  File
"/home/nils/local/lib64/python2.7/site-packages/scipy/weave/tests/test_size_check.py",
line 128, in generic_check
    assert_array_equal(actual,desired)
  File
"/home/nils/local/lib64/python2.7/site-packages/numpy/testing/utils.py",
line 734, in assert_array_equal
    verbose=verbose, header='Arrays are not equal')
  File
"/home/nils/local/lib64/python2.7/site-packages/numpy/testing/utils.py",
line 587, in assert_array_compare
    x = array(x, copy=False, subok=True)
ValueError: setting an array element with a sequence.

======================================================================
ERROR: test_x_y5 (test_size_check.TestDummyArray)
----------------------------------------------------------------------
Traceback (most recent call last):
  File
"/home/nils/local/lib64/python2.7/site-packages/scipy/weave/tests/test_size_check.py",
line 95, in test_x_y5
    self.generic_check(x,y,desired)
  File
"/home/nils/local/lib64/python2.7/site-packages/scipy/weave/tests/test_size_check.py",
line 128, in generic_check
    assert_array_equal(actual,desired)
  File
"/home/nils/local/lib64/python2.7/site-packages/numpy/testing/utils.py",
line 734, in assert_array_equal
    verbose=verbose, header='Arrays are not equal')
  File
"/home/nils/local/lib64/python2.7/site-packages/numpy/testing/utils.py",
line 587, in assert_array_compare
    x = array(x, copy=False, subok=True)
ValueError: setting an array element with a sequence.

======================================================================
ERROR: test_x_y6 (test_size_check.TestDummyArray)
----------------------------------------------------------------------
Traceback (most recent call last):
  File
"/home/nils/local/lib64/python2.7/site-packages/scipy/weave/tests/test_size_check.py",
line 100, in test_x_y6
    self.generic_check(x,y,desired)
  File
"/home/nils/local/lib64/python2.7/site-packages/scipy/weave/tests/test_size_check.py",
line 128, in generic_check
    assert_array_equal(actual,desired)
  File
"/home/nils/local/lib64/python2.7/site-packages/numpy/testing/utils.py",
line 734, in assert_array_equal
    verbose=verbose, header='Arrays are not equal')
  File
"/home/nils/local/lib64/python2.7/site-packages/numpy/testing/utils.py",
line 587, in assert_array_compare
    x = array(x, copy=False, subok=True)
ValueError: setting an array element with a sequence.

======================================================================
ERROR: test_x_y7 (test_size_check.TestDummyArray)
----------------------------------------------------------------------
Traceback (most recent call last):
  File
"/home/nils/local/lib64/python2.7/site-packages/scipy/weave/tests/test_size_check.py",
line 105, in test_x_y7
    self.generic_check(x,y,desired)
  File
"/home/nils/local/lib64/python2.7/site-packages/scipy/weave/tests/test_size_check.py",
line 128, in generic_check
    assert_array_equal(actual,desired)
  File
"/home/nils/local/lib64/python2.7/site-packages/numpy/testing/utils.py",
line 734, in assert_array_equal
    verbose=verbose, header='Arrays are not equal')
  File
"/home/nils/local/lib64/python2.7/site-packages/numpy/testing/utils.py",
line 587, in assert_array_compare
    x = array(x, copy=False, subok=True)
ValueError: setting an array element with a sequence.

======================================================================
ERROR: test_y1 (test_size_check.TestDummyArray)
----------------------------------------------------------------------
Traceback (most recent call last):
  File
"/home/nils/local/lib64/python2.7/site-packages/scipy/weave/tests/test_size_check.py",
line 70, in test_y1
    self.generic_check(x,y,desired)
  File
"/home/nils/local/lib64/python2.7/site-packages/scipy/weave/tests/test_size_check.py",
line 128, in generic_check
    assert_array_equal(actual,desired)
  File
"/home/nils/local/lib64/python2.7/site-packages/numpy/testing/utils.py",
line 734, in assert_array_equal
    verbose=verbose, header='Arrays are not equal')
  File
"/home/nils/local/lib64/python2.7/site-packages/numpy/testing/utils.py",
line 587, in assert_array_compare
    x = array(x, copy=False, subok=True)
ValueError: setting an array element with a sequence.

======================================================================
FAIL: test_trim (test_mstats_basic.TestTrimming)
----------------------------------------------------------------------
Traceback (most recent call last):
  File
"/home/nils/local/lib64/python2.7/site-packages/scipy/stats/tests/test_mstats_basic.py",
line 277, in test_trim
    assert_equal(trimx._mask.ravel(), expected)
  File
"/home/nils/local/lib64/python2.7/site-packages/numpy/ma/testutils.py",
line 123, in assert_equal
    return assert_array_equal(actual, desired, err_msg)
  File
"/home/nils/local/lib64/python2.7/site-packages/numpy/ma/testutils.py",
line 196, in assert_array_equal
    header='Arrays are not equal')
  File
"/home/nils/local/lib64/python2.7/site-packages/numpy/ma/testutils.py",
line 189, in assert_array_compare
    verbose=verbose, header=header)
  File
"/home/nils/local/lib64/python2.7/site-packages/numpy/testing/utils.py",
line 660, in assert_array_compare
    raise AssertionError(msg)
AssertionError:
Arrays are not equal

(mismatch 9.09090909091%)
 x: array([ True,  True,  True,  True,  True,  True,  True,  True,  True,
        True,  True,  True,  True,  True,  True,  True,  True,  True,
        True,  True, False, False, False, False, False, False, False,...
 y: array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0,
0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0,...

----------------------------------------------------------------------
Ran 16585 tests in 654.884s

FAILED (KNOWNFAIL=277, SKIP=904, errors=10, failures=1)
<nose.result.TextTestResult run=16585 errors=10 failures=1>

>>> scipy.__version__
'0.15.0.dev-15fbaa8'



On Mon, Apr 21, 2014 at 9:11 PM, Ralf Gommers <ralf.gommers at gmail.com>wrote:

>
>
>
> On Tue, Apr 15, 2014 at 9:19 PM, Nils Wagner <nils106 at googlemail.com>wrote:
>
>> >>> from scipy import stats
>> >>> stats.test()
>> Running unit tests for scipy.stats
>> NumPy version 1.9.0.dev-61c699e
>> NumPy is installed in /home/nils/local/lib64/python2.7/site-packages/numpy
>> SciPy version 0.15.0.dev-87df1db
>> SciPy is installed in /home/nils/local/lib64/python2.7/site-packages/scipy
>> Python version 2.7.3 (default, Apr 14 2012, 08:58:41) [GCC]
>> nose version 1.3.1
>>
>> ...
>>
>> ======================================================================
>> ERROR: test_gmean (test_mstats_basic.TestCompareWithStats)
>>
>> ----------------------------------------------------------------------
>> Traceback (most recent call last):
>>   File
>> "/home/nils/local/lib64/python2.7/site-packages/scipy/stats/tests/test_mstats_basic.py",
>> line 763, in test_gmean
>>     rm = stats.mstats.gmean(abs(xm))
>>   File
>> "/home/nils/local/lib64/python2.7/site-packages/scipy/stats/stats.py", line
>> 516, in gmean
>>     log_a = np.log(a)
>>   File "/home/nils/local/lib64/python2.7/site-packages/numpy/ma/core.py",
>> line 2837, in __array_wrap__
>>     d = filled(domain(*args), True)
>>   File "/home/nils/local/lib64/python2.7/site-packages/numpy/ma/core.py",
>> line 795, in __call__
>>     return umath.less_equal(x, self.critical_value)
>> RuntimeWarning: invalid value encountered in less_equal
>>
>> ======================================================================
>> ERROR: test_hmean (test_mstats_basic.TestCompareWithStats)
>>
>> ----------------------------------------------------------------------
>> Traceback (most recent call last):
>>   File
>> "/home/nils/local/lib64/python2.7/site-packages/scipy/stats/tests/test_mstats_basic.py",
>> line 775, in test_hmean
>>     rm = stats.mstats.hmean(abs(xm))
>>   File
>> "/home/nils/local/lib64/python2.7/site-packages/scipy/stats/stats.py", line
>> 562, in hmean
>>     if np.all(a > 0):  # Harmonic mean only defined if greater than zero
>> RuntimeWarning: invalid value encountered in greater
>>
>> ======================================================================
>> ERROR: test_tmax (test_mstats_basic.TestCompareWithStats)
>>
>> ----------------------------------------------------------------------
>> Traceback (most recent call last):
>>   File
>> "/home/nils/local/lib64/python2.7/site-packages/scipy/stats/tests/test_mstats_basic.py",
>> line 908, in test_tmax
>>     stats.mstats.tmax(xm,2.), 10)
>>   File
>> "/home/nils/local/lib64/python2.7/site-packages/scipy/stats/mstats_basic.py",
>> line 1383, in tmax
>>     am = trima(a, (None, upperlimit), (False, inclusive))
>>   File
>> "/home/nils/local/lib64/python2.7/site-packages/scipy/stats/mstats_basic.py",
>> line 1018, in trima
>>     condition |= (a > upper_lim)
>> RuntimeWarning: invalid value encountered in greater
>>
>> ======================================================================
>> ERROR: test_tmin (test_mstats_basic.TestCompareWithStats)
>>
>> ----------------------------------------------------------------------
>> Traceback (most recent call last):
>>   File
>> "/home/nils/local/lib64/python2.7/site-packages/scipy/stats/tests/test_mstats_basic.py",
>> line 919, in test_tmin
>>     stats.mstats.tmin(xm,lowerlimit=-1.), 10)
>>   File
>> "/home/nils/local/lib64/python2.7/site-packages/scipy/stats/mstats_basic.py",
>> line 1376, in tmin
>>     am = trima(a, (lowerlimit, None), (inclusive, False))
>>   File
>> "/home/nils/local/lib64/python2.7/site-packages/scipy/stats/mstats_basic.py",
>> line 1013, in trima
>>     condition |= (a < lower_lim)
>> RuntimeWarning: invalid value encountered in less
>>
>> ======================================================================
>> ERROR: test_tsem (test_mstats_basic.TestCompareWithStats)
>>
>> ----------------------------------------------------------------------
>> Traceback (most recent call last):
>>   File
>> "/home/nils/local/lib64/python2.7/site-packages/scipy/stats/tests/test_mstats_basic.py",
>> line 958, in test_tsem
>>     stats.mstats.tsem(xm,limits=(-2.,2.)),
>>   File
>> "/home/nils/local/lib64/python2.7/site-packages/scipy/stats/mstats_basic.py",
>> line 1393, in tsem
>>     am = trima(a.ravel(), limits, inclusive)
>>   File
>> "/home/nils/local/lib64/python2.7/site-packages/scipy/stats/mstats_basic.py",
>> line 1013, in trima
>>     condition |= (a < lower_lim)
>> RuntimeWarning: invalid value encountered in less
>>
>> ======================================================================
>> ERROR: test_zmap (test_mstats_basic.TestCompareWithStats)
>>
>> ----------------------------------------------------------------------
>> Traceback (most recent call last):
>>   File
>> "/home/nils/local/lib64/python2.7/site-packages/scipy/stats/tests/test_mstats_basic.py",
>> line 927, in test_zmap
>>     zm = stats.mstats.zmap(xm,ym)
>>   File
>> "/home/nils/local/lib64/python2.7/site-packages/scipy/stats/stats.py", line
>> 2063, in zmap
>>     return (scores - mns) / sstd
>>   File "/home/nils/local/lib64/python2.7/site-packages/numpy/ma/core.py",
>> line 3735, in __truediv__
>>     return true_divide(self, other)
>>   File "/home/nils/local/lib64/python2.7/site-packages/numpy/ma/core.py",
>> line 1089, in __call__
>>     m |= filled(domain(da, db), True)
>>   File "/home/nils/local/lib64/python2.7/site-packages/numpy/ma/core.py",
>> line 783, in __call__
>>     return umath.absolute(a) * self.tolerance >= umath.absolute(b)
>> RuntimeWarning: invalid value encountered in greater_equal
>>
>> ======================================================================
>> FAIL: test_example1a (test_morestats.TestAndersonKSamp)
>>
>> ----------------------------------------------------------------------
>> Traceback (most recent call last):
>>   File
>> "/home/nils/local/lib64/python2.7/site-packages/scipy/stats/tests/test_morestats.py",
>> line 97, in test_example1a
>>     midrank=False)
>>   File
>> "/home/nils/local/lib64/python2.7/site-packages/numpy/testing/utils.py",
>> line 1593, in assert_warns
>>     % func.__name__)
>> AssertionError: No warning raised when calling anderson_ksamp
>>
>> ======================================================================
>> FAIL: test_tmean (test_mstats_basic.TestCompareWithStats)
>>
>> ----------------------------------------------------------------------
>> Traceback (most recent call last):
>>   File
>> "/home/nils/local/lib64/python2.7/site-packages/scipy/stats/tests/test_mstats_basic.py",
>> line 901, in test_tmean
>>     assert_equal(stats.tmean(x),stats.mstats.tmean(xm))
>>   File
>> "/home/nils/local/lib64/python2.7/site-packages/numpy/ma/testutils.py",
>> line 100, in assert_equal
>>     raise AssertionError(msg)
>> AssertionError:
>> Items are not equal:
>>  ACTUAL: 0.040175456835823721
>>  DESIRED: 0.040175456835823714
>>
>>
>> ======================================================================
>> FAIL: test_trim (test_mstats_basic.TestTrimming)
>> ----------------------------------------------------------------------
>> Traceback (most recent call last):
>>   File
>> "/home/nils/local/lib64/python2.7/site-packages/scipy/stats/tests/test_mstats_basic.py",
>> line 274, in test_trim
>>
>>     assert_equal(trimx._mask.ravel(),[1]*20+[0]*70+[1]*20)
>>   File
>> "/home/nils/local/lib64/python2.7/site-packages/numpy/ma/testutils.py",
>> line 123, in assert_equal
>>     return assert_array_equal(actual, desired, err_msg)
>>   File
>> "/home/nils/local/lib64/python2.7/site-packages/numpy/ma/testutils.py",
>> line 196, in assert_array_equal
>>
>>     header='Arrays are not equal')
>>   File
>> "/home/nils/local/lib64/python2.7/site-packages/numpy/ma/testutils.py",
>> line 189, in assert_array_compare
>>     verbose=verbose, header=header)
>>   File
>> "/home/nils/local/lib64/python2.7/site-packages/numpy/testing/utils.py",
>> line 660, in assert_array_compare
>>
>>     raise AssertionError(msg)
>> AssertionError:
>> Arrays are not equal
>>
>> (mismatch 9.09090909091%)
>>  x: array([ True,  True,  True,  True,  True,  True,  True,  True,  True,
>>         True,  True,  True,  True,  True,  True,  True,  True,  True,
>>         True,  True, False, False, False, False, False, False, False,...
>>  y: array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0,
>> 0, 0,
>>        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
>> 0,
>>        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
>> 0,...
>>
>> ----------------------------------------------------------------------
>> Ran 1957 tests in 50.047s
>>
>> FAILED (KNOWNFAIL=4, SKIP=3, errors=6, failures=3)
>> <nose.result.TextTestResult run=1957 errors=6 failures=3>
>>
>>
>>
>> On Sun, Apr 13, 2014 at 10:58 PM, Ralf Gommers <ralf.gommers at gmail.com>wrote:
>>
>>>
>>>
>>>
>>> On Sun, Apr 13, 2014 at 7:42 AM, Charles R Harris <
>>> charlesr.harris at gmail.com> wrote:
>>>
>>>> Hi All,
>>>>
>>>> I get 75 errors and 3 failures when testing against current numpy on my
>>>> machine. Most of the errors are due to either the deprecation of the binary
>>>> '-' operator for booleans or to the deprecation of double ellipsis for
>>>> indexing, i.e., '(..., ...)' . The remainder look like two numerical
>>>> precision problems and one I can't immediately identify.
>>>>
>>>> The main question I have is what is the best way to deal with the
>>>> deprecations?
>>>>
>>>
>>> Fix them? It's not that much work; I'd like to still get that into
>>> 0.14.0 if possible. Will have a look.
>>>
>>> The QHull test I've seen fail randomly on various platforms, it has a
>>> real issue. The other two look like the tests need adapting.
>>>
>>> Ralf
>>>
>>>
>>>>
>>>> FAIL: test_lsmr.TestLSMR.testBidiagonalA
>>>> ----------------------------------------------------------------------
>>>> Traceback (most recent call last):
>>>>   File "/usr/lib/python2.7/site-packages/nose/case.py", line 197, in
>>>> runTest
>>>>     self.test(*self.arg)
>>>>   File
>>>> "/home/charris/.local/lib/python2.7/site-packages/scipy/sparse/linalg/isolve/tests/test_lsmr.py",
>>>> line 60, in testBidiagonalA
>>>>     self.assertCompatibleSystem(A,xtrue)
>>>>   File
>>>> "/home/charris/.local/lib/python2.7/site-packages/scipy/sparse/linalg/isolve/tests/test_lsmr.py",
>>>> line 40, in assertCompatibleSystem
>>>>     assert_almost_equal(norm(x - xtrue), 0, 6)
>>>>   File
>>>> "/home/charris/.local/lib/python2.7/site-packages/numpy/testing/utils.py",
>>>> line 486, in assert_almost_equal
>>>>     raise AssertionError(_build_err_msg())
>>>> AssertionError:
>>>> Arrays are not almost equal to 6 decimals
>>>>  ACTUAL: 6.048630163037888e-07
>>>>  DESIRED: 0
>>>>
>>>> ======================================================================
>>>> FAIL: test_qhull.TestUtilities.test_degenerate_barycentric_transforms
>>>> ----------------------------------------------------------------------
>>>> Traceback (most recent call last):
>>>>   File "/usr/lib/python2.7/site-packages/nose/case.py", line 197, in
>>>> runTest
>>>>     self.test(*self.arg)
>>>>   File
>>>> "/home/charris/.local/lib/python2.7/site-packages/numpy/testing/decorators.py",
>>>> line 146, in skipper_func
>>>>     return f(*args, **kwargs)
>>>>   File
>>>> "/home/charris/.local/lib/python2.7/site-packages/scipy/spatial/tests/test_qhull.py",
>>>> line 296, in test_degenerate_barycentric_transforms
>>>>     assert_(bad_count < 20, bad_count)
>>>>   File
>>>> "/home/charris/.local/lib/python2.7/site-packages/numpy/testing/utils.py",
>>>> line 50, in assert_
>>>>     raise AssertionError(smsg)
>>>> AssertionError: 20
>>>>
>>>> ======================================================================
>>>> FAIL: test_trim (test_mstats_basic.TestTrimming)
>>>> ----------------------------------------------------------------------
>>>> Traceback (most recent call last):
>>>>   File
>>>> "/home/charris/.local/lib/python2.7/site-packages/scipy/stats/tests/test_mstats_basic.py",
>>>> line 270, in test_trim
>>>>     assert_equal(trimx._mask.ravel(),[1]*20+[0]*70+[1]*20)
>>>>   File
>>>> "/home/charris/.local/lib/python2.7/site-packages/numpy/ma/testutils.py",
>>>> line 123, in assert_equal
>>>>     return assert_array_equal(actual, desired, err_msg)
>>>>   File
>>>> "/home/charris/.local/lib/python2.7/site-packages/numpy/ma/testutils.py",
>>>> line 196, in assert_array_equal
>>>>     header='Arrays are not equal')
>>>>   File
>>>> "/home/charris/.local/lib/python2.7/site-packages/numpy/ma/testutils.py",
>>>> line 189, in assert_array_compare
>>>>     verbose=verbose, header=header)
>>>>   File
>>>> "/home/charris/.local/lib/python2.7/site-packages/numpy/testing/utils.py",
>>>> line 660, in assert_array_compare
>>>>     raise AssertionError(msg)
>>>> AssertionError:
>>>> Arrays are not equal
>>>>
>>>> (mismatch 9.09090909091%)
>>>>  x: array([ True,  True,  True,  True,  True,  True,  True,  True,
>>>> True,
>>>>         True,  True,  True,  True,  True,  True,  True,  True,  True,
>>>>         True,  True, False, False, False, False, False, False, False,...
>>>>  y: array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
>>>> 0, 0, 0,
>>>>        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
>>>> 0, 0,
>>>>        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
>>>> 0, 0,...
>>>>
>>>>
>>>>
>
> Except for the Qhull one, all of these should be fixed by
> https://github.com/scipy/scipy/pull/3564.
>
> Ralf
>
>
> _______________________________________________
> SciPy-Dev mailing list
> SciPy-Dev at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-dev
>
>
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From ralf.gommers at gmail.com  Mon Apr 28 14:25:18 2014
From: ralf.gommers at gmail.com (Ralf Gommers)
Date: Mon, 28 Apr 2014 20:25:18 +0200
Subject: [SciPy-Dev] Errors with numpy-devel
In-Reply-To: <CAE7vhgMh4Vy-_U+FcL_17ytYOSVhwpWNEAbPk5fvdZunLMsrTw@mail.gmail.com>
References: <CAB6mnx+o8E3cV-N7HVhxH-dtxih+zKQNKotF9rJkaKfYuAjr1Q@mail.gmail.com>
	<CABL7CQiig9r6RdDyvUrc_Z=+D6noHkZUdM18NG9BwCVNE2_iQA@mail.gmail.com>
	<CAE7vhgOqjL_6wFjKQfSXoFXVHyES9X-CnCxu5oUkh5=HhkgEfA@mail.gmail.com>
	<CABL7CQiZZ6p4_L_cGQakoaKFb4xquAFNcRvVFbLmPrO0uf+iTw@mail.gmail.com>
	<CAE7vhgMh4Vy-_U+FcL_17ytYOSVhwpWNEAbPk5fvdZunLMsrTw@mail.gmail.com>
Message-ID: <CABL7CQg-sg4=PsDrHCLxEeU2rQxd8YSBGxeLjCFe5iw-ovmjNA@mail.gmail.com>

On Mon, Apr 28, 2014 at 8:16 PM, Nils Wagner <nils106 at googlemail.com> wrote:

> ======================================================================
> ERROR: test_scalar (test_size_check.TestDummyArray)
>
> ----------------------------------------------------------------------
> Traceback (most recent call last):
>   File
> "/home/nils/local/lib64/python2.7/site-packages/scipy/weave/tests/test_size_check.py",
> line 60, in test_scalar
>     self.generic_check(x,y,desired)
>   File
> "/home/nils/local/lib64/python2.7/site-packages/scipy/weave/tests/test_size_check.py",
> line 128, in generic_check
>     assert_array_equal(actual,desired)
>   File
> "/home/nils/local/lib64/python2.7/site-packages/numpy/testing/utils.py",
> line 734, in assert_array_equal
>     verbose=verbose, header='Arrays are not equal')
>   File
> "/home/nils/local/lib64/python2.7/site-packages/numpy/testing/utils.py",
> line 587, in assert_array_compare
>     x = array(x, copy=False, subok=True)
> ValueError: setting an array element with a sequence.<snip>
>
> ----------------------------------------------------------------------
> Ran 16585 tests in 654.884s
>
> FAILED (KNOWNFAIL=277, SKIP=904, errors=10, failures=1)
> <nose.result.TextTestResult run=16585 errors=10 failures=1>
>
> >>> scipy.__version__
> '0.15.0.dev-15fbaa8'
>

I can't reproduce these with current numpy master (7bab957) and the exact
same scipy version. You didn't report these before, so did they appear only
in the last few days?

Ralf
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From nils106 at googlemail.com  Mon Apr 28 15:00:11 2014
From: nils106 at googlemail.com (Nils Wagner)
Date: Mon, 28 Apr 2014 21:00:11 +0200
Subject: [SciPy-Dev] Errors with numpy-devel
In-Reply-To: <CABL7CQg-sg4=PsDrHCLxEeU2rQxd8YSBGxeLjCFe5iw-ovmjNA@mail.gmail.com>
References: <CAB6mnx+o8E3cV-N7HVhxH-dtxih+zKQNKotF9rJkaKfYuAjr1Q@mail.gmail.com>
	<CABL7CQiig9r6RdDyvUrc_Z=+D6noHkZUdM18NG9BwCVNE2_iQA@mail.gmail.com>
	<CAE7vhgOqjL_6wFjKQfSXoFXVHyES9X-CnCxu5oUkh5=HhkgEfA@mail.gmail.com>
	<CABL7CQiZZ6p4_L_cGQakoaKFb4xquAFNcRvVFbLmPrO0uf+iTw@mail.gmail.com>
	<CAE7vhgMh4Vy-_U+FcL_17ytYOSVhwpWNEAbPk5fvdZunLMsrTw@mail.gmail.com>
	<CABL7CQg-sg4=PsDrHCLxEeU2rQxd8YSBGxeLjCFe5iw-ovmjNA@mail.gmail.com>
Message-ID: <CAE7vhgNZEy5GKmWWmSjayPiD_e_YY1Z7tsfXhV=hZeRW7fv5Sg@mail.gmail.com>

I am using

>>> numpy.__version__
'1.9.0.dev-7bab957'



On Mon, Apr 28, 2014 at 8:25 PM, Ralf Gommers <ralf.gommers at gmail.com>wrote:

>
>
>
> On Mon, Apr 28, 2014 at 8:16 PM, Nils Wagner <nils106 at googlemail.com>wrote:
>
>> ======================================================================
>> ERROR: test_scalar (test_size_check.TestDummyArray)
>>
>> ----------------------------------------------------------------------
>> Traceback (most recent call last):
>>   File
>> "/home/nils/local/lib64/python2.7/site-packages/scipy/weave/tests/test_size_check.py",
>> line 60, in test_scalar
>>     self.generic_check(x,y,desired)
>>   File
>> "/home/nils/local/lib64/python2.7/site-packages/scipy/weave/tests/test_size_check.py",
>> line 128, in generic_check
>>     assert_array_equal(actual,desired)
>>   File
>> "/home/nils/local/lib64/python2.7/site-packages/numpy/testing/utils.py",
>> line 734, in assert_array_equal
>>     verbose=verbose, header='Arrays are not equal')
>>   File
>> "/home/nils/local/lib64/python2.7/site-packages/numpy/testing/utils.py",
>> line 587, in assert_array_compare
>>     x = array(x, copy=False, subok=True)
>> ValueError: setting an array element with a sequence.<snip>
>>
>>
>> ----------------------------------------------------------------------
>> Ran 16585 tests in 654.884s
>>
>> FAILED (KNOWNFAIL=277, SKIP=904, errors=10, failures=1)
>> <nose.result.TextTestResult run=16585 errors=10 failures=1>
>>
>> >>> scipy.__version__
>> '0.15.0.dev-15fbaa8'
>>
>
> I can't reproduce these with current numpy master (7bab957) and the exact
> same scipy version. You didn't report these before, so did they appear only
> in the last few days?
>
> Ralf
>
>
> _______________________________________________
> SciPy-Dev mailing list
> SciPy-Dev at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-dev
>
>
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From robert.kern at gmail.com  Mon Apr 28 15:23:01 2014
From: robert.kern at gmail.com (Robert Kern)
Date: Mon, 28 Apr 2014 20:23:01 +0100
Subject: [SciPy-Dev] wheels on PyPI?
In-Reply-To: <CABL7CQhObA0ZQjWQGTXyopwztUu=zfSY1v2ggU+F15xPnq4prw@mail.gmail.com>
References: <6B9AFD6A2B9E477CBB6D979CF46A5DC9@edx.org>
	<9B1D27822EB44B9CA1F947948EB3E85E@edx.org>
	<CAF6FJits9i_6Eq49-SaUYBH_enMR-hwDq1kB6zNQrPBdoaenEQ@mail.gmail.com>
	<CABL7CQhObA0ZQjWQGTXyopwztUu=zfSY1v2ggU+F15xPnq4prw@mail.gmail.com>
Message-ID: <CAF6FJisqmdpu03RRA9eBnFUeGtwpBOmPZXSXHixF8Dsmq7aObQ@mail.gmail.com>

On Mon, Apr 28, 2014 at 6:04 PM, Ralf Gommers <ralf.gommers at gmail.com> wrote:
>
> On Mon, Apr 28, 2014 at 6:08 PM, Robert Kern <robert.kern at gmail.com> wrote:

>> It looks like you only have Linux and OS X wheels for numpy and scipy.
>> The general consensus, Python-community-wide, is that only noarch and
>> Windows wheels should be put up on PyPI. The PyPI server even enforces
>> this, at this present time. Linux and even OS X environments are a
>> little too variable.
>
> For Linux that's correct, but for OS X we are going to put those on PyPi.
> Actually, for Numpy those are already there thanks to Matthew's efforts:
> https://pypi.python.org/pypi/numpy. For Scipy we plan to do the same for
> 0.14.0.

Great! I didn't realize PyPI had expanded their policy to allow OS X
uploads too.

-- 
Robert Kern


From ralf.gommers at gmail.com  Mon Apr 28 15:28:54 2014
From: ralf.gommers at gmail.com (Ralf Gommers)
Date: Mon, 28 Apr 2014 21:28:54 +0200
Subject: [SciPy-Dev] Errors with numpy-devel
In-Reply-To: <CAE7vhgNZEy5GKmWWmSjayPiD_e_YY1Z7tsfXhV=hZeRW7fv5Sg@mail.gmail.com>
References: <CAB6mnx+o8E3cV-N7HVhxH-dtxih+zKQNKotF9rJkaKfYuAjr1Q@mail.gmail.com>
	<CABL7CQiig9r6RdDyvUrc_Z=+D6noHkZUdM18NG9BwCVNE2_iQA@mail.gmail.com>
	<CAE7vhgOqjL_6wFjKQfSXoFXVHyES9X-CnCxu5oUkh5=HhkgEfA@mail.gmail.com>
	<CABL7CQiZZ6p4_L_cGQakoaKFb4xquAFNcRvVFbLmPrO0uf+iTw@mail.gmail.com>
	<CAE7vhgMh4Vy-_U+FcL_17ytYOSVhwpWNEAbPk5fvdZunLMsrTw@mail.gmail.com>
	<CABL7CQg-sg4=PsDrHCLxEeU2rQxd8YSBGxeLjCFe5iw-ovmjNA@mail.gmail.com>
	<CAE7vhgNZEy5GKmWWmSjayPiD_e_YY1Z7tsfXhV=hZeRW7fv5Sg@mail.gmail.com>
Message-ID: <CABL7CQiBheViaQVJAN3ft_nyHbD6e2_RiWx_yBYv+_EP1G4=wA@mail.gmail.com>

On Mon, Apr 28, 2014 at 9:00 PM, Nils Wagner <nils106 at googlemail.com> wrote:

> I am using
>
> >>> numpy.__version__
> '1.9.0.dev-7bab957'
>

Me too, but can't reproduce the errors. Would you be able to figure out
what numpy commit caused this?

Ralf



>
> On Mon, Apr 28, 2014 at 8:25 PM, Ralf Gommers <ralf.gommers at gmail.com>wrote:
>
>>
>>
>>
>> On Mon, Apr 28, 2014 at 8:16 PM, Nils Wagner <nils106 at googlemail.com>wrote:
>>
>>> ======================================================================
>>> ERROR: test_scalar (test_size_check.TestDummyArray)
>>>
>>> ----------------------------------------------------------------------
>>> Traceback (most recent call last):
>>>   File
>>> "/home/nils/local/lib64/python2.7/site-packages/scipy/weave/tests/test_size_check.py",
>>> line 60, in test_scalar
>>>     self.generic_check(x,y,desired)
>>>   File
>>> "/home/nils/local/lib64/python2.7/site-packages/scipy/weave/tests/test_size_check.py",
>>> line 128, in generic_check
>>>     assert_array_equal(actual,desired)
>>>   File
>>> "/home/nils/local/lib64/python2.7/site-packages/numpy/testing/utils.py",
>>> line 734, in assert_array_equal
>>>     verbose=verbose, header='Arrays are not equal')
>>>   File
>>> "/home/nils/local/lib64/python2.7/site-packages/numpy/testing/utils.py",
>>> line 587, in assert_array_compare
>>>     x = array(x, copy=False, subok=True)
>>> ValueError: setting an array element with a sequence.<snip>
>>>
>>>
>>> ----------------------------------------------------------------------
>>> Ran 16585 tests in 654.884s
>>>
>>> FAILED (KNOWNFAIL=277, SKIP=904, errors=10, failures=1)
>>> <nose.result.TextTestResult run=16585 errors=10 failures=1>
>>>
>>> >>> scipy.__version__
>>> '0.15.0.dev-15fbaa8'
>>>
>>
>> I can't reproduce these with current numpy master (7bab957) and the exact
>> same scipy version. You didn't report these before, so did they appear only
>> in the last few days?
>>
>> Ralf
>>
>>
>> _______________________________________________
>> SciPy-Dev mailing list
>> SciPy-Dev at scipy.org
>> http://mail.scipy.org/mailman/listinfo/scipy-dev
>>
>>
>
> _______________________________________________
> SciPy-Dev mailing list
> SciPy-Dev at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-dev
>
>
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From matthew.brett at gmail.com  Mon Apr 28 15:32:08 2014
From: matthew.brett at gmail.com (Matthew Brett)
Date: Mon, 28 Apr 2014 12:32:08 -0700
Subject: [SciPy-Dev] wheels on PyPI?
In-Reply-To: <CAF6FJisqmdpu03RRA9eBnFUeGtwpBOmPZXSXHixF8Dsmq7aObQ@mail.gmail.com>
References: <6B9AFD6A2B9E477CBB6D979CF46A5DC9@edx.org>
	<9B1D27822EB44B9CA1F947948EB3E85E@edx.org>
	<CAF6FJits9i_6Eq49-SaUYBH_enMR-hwDq1kB6zNQrPBdoaenEQ@mail.gmail.com>
	<CABL7CQhObA0ZQjWQGTXyopwztUu=zfSY1v2ggU+F15xPnq4prw@mail.gmail.com>
	<CAF6FJisqmdpu03RRA9eBnFUeGtwpBOmPZXSXHixF8Dsmq7aObQ@mail.gmail.com>
Message-ID: <CAH6Pt5qSxgqaMPvGSAHyiSbOe7U1pbEPnpw57M4w4oAyUTDj9Q@mail.gmail.com>

Hi,

On Mon, Apr 28, 2014 at 12:23 PM, Robert Kern <robert.kern at gmail.com> wrote:
> On Mon, Apr 28, 2014 at 6:04 PM, Ralf Gommers <ralf.gommers at gmail.com> wrote:
>>
>> On Mon, Apr 28, 2014 at 6:08 PM, Robert Kern <robert.kern at gmail.com> wrote:
>
>>> It looks like you only have Linux and OS X wheels for numpy and scipy.
>>> The general consensus, Python-community-wide, is that only noarch and
>>> Windows wheels should be put up on PyPI. The PyPI server even enforces
>>> this, at this present time. Linux and even OS X environments are a
>>> little too variable.
>>
>> For Linux that's correct, but for OS X we are going to put those on PyPi.
>> Actually, for Numpy those are already there thanks to Matthew's efforts:
>> https://pypi.python.org/pypi/numpy. For Scipy we plan to do the same for
>> 0.14.0.
>
> Great! I didn't realize PyPI had expanded their policy to allow OS X
> uploads too.

Yes, that happened in January. There's a summary of current OSX wheel
status here:

https://github.com/MacPython/wiki/wiki/Spinning-wheels

Cheers,

Matthew


From travis at continuum.io  Mon Apr 28 15:47:46 2014
From: travis at continuum.io (Travis Oliphant)
Date: Mon, 28 Apr 2014 14:47:46 -0500
Subject: [SciPy-Dev] wheels on PyPI?
In-Reply-To: <9B1D27822EB44B9CA1F947948EB3E85E@edx.org>
References: <6B9AFD6A2B9E477CBB6D979CF46A5DC9@edx.org>
	<9B1D27822EB44B9CA1F947948EB3E85E@edx.org>
Message-ID: <CAMcnTE47kWjT6UpdHTFg=JDUY_PAq0-fwCGQzB1Woa7-yZOAQA@mail.gmail.com>

I've brought this up before, but it is probably worth repeating.

conda provides a completely free and accessible method to manage binaries
including NumPy and SciPy.    We are working with the PyPA to make as many
tools work with conda packages as possible, and get official recognition as
a PyPA-approved approach to managing the integration problem with Python
binaries.     I had many discussions with Nick Coghlan from the PyPA at
PyCon to help people understand the benefits / trade-offs of conda and
other approaches.

We built this system, and made it completely free to solve the exact
problem you are having.     It is another mechanism that works completely
today and anyone can use it to manage your binary installations of NumPy
and SciPy.

You are free to grab the Anaconda-built NumPy or SciPy conda packages and
host them on a server of your choosing -- or rely on our free server at
repo.continuum.io.   If you want to build your own NumPy and SciPy packages
(and avoid the attribution requirement of Anaconda binaries), then building
a conda package is very straight-forward and quite easy.    We are working
on improved documentation, but if you can build a "tree of binaries," you
can package it in a conda .tar.gz file.     In fact, the "conda pipbuild"
command will take anything installable via pip and build a conda package
from it.

You can also use the freely available binstar.org service to host your
binaries.   As long as the binaries are freely available to everyone, then
there will never be a charge for this service.    Currently binstar.org is
still in beta, but request an invite and you will get access pretty
quickly.    You can also host .whl packages at binstar.org (look for
pypi.binstar.org).

This system works very well, and it works for Windows, Mac, and Linux.  If
someone else would like to host a conda repository, the conda index command
and a webserver is all you need.    All of these tools are BSD licensed and
developed on Github.

It really is worth your while to check it out.    It would be easy for edX
to have their own distro that they define which could either be a channel
on binstar.org or your own distribution hosted from your site.   Either
way.

I don't have as much time as I'd like to participate on this mailing list,
but I am still very interested in making sure *everyone* can get the
numpy-stack installed as easily as possible and take advantage of all the
work we have done in building binaries and building frameworks that solve
the exact problems this community has.

Best,

-Travis




On Mon, Apr 28, 2014 at 10:55 AM, David Baumgold <db at edx.org> wrote:

> Hey all, I work for edX <http://code.edx.org>, and we include NumPy and
> SciPy in our software platform. We have a lot of tests for our code, and
> running them requires making a virtual environment and installing all of
> our dependencies each time -- but NumPy and SciPy take a long time to
> compile. We've actually built Python wheel <http://pythonwheels.com> files
> for all of our dependencies, which we host on an AWS server:
> http://edx-wheels.s3-website-us-east-1.amazonaws.com It would be super
> convenient if we could get wheel files for NumPy and SciPy on PyPI<https://pypi.python.org/pypi>, so
> that its easier for us to install them in our test environment -- and I
> imagine it would help a whole lot of other people, as well! Who has the
> authorization to upload new packages to the NumPy and SciPy projects on
> PyPI, and how can I contribute our wheel files back to the community?
> Alternatively, how can I help the SciPy project build and distribute wheel
> files automatically, as part of the standard release process? Thanks for
> the info!
>  -David Baumgold
>
>
> _______________________________________________
> SciPy-Dev mailing list
> SciPy-Dev at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-dev
>
>


-- 

Travis Oliphant
CEO
Continuum Analytics, Inc.
http://www.continuum.io
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From charlesr.harris at gmail.com  Mon Apr 28 17:53:18 2014
From: charlesr.harris at gmail.com (Charles R Harris)
Date: Mon, 28 Apr 2014 15:53:18 -0600
Subject: [SciPy-Dev] requesting code review: added close matches to
	constants.codata.value()
In-Reply-To: <535E862E.8070209@gmail.com>
References: <535CB410.1010803@gmail.com>
	<CAPJVwBkae3Z83EW376XCT4h1DCjEFivYkyWGD9f9tW4BiMCNGA@mail.gmail.com>
	<535D51AF.1090305@gmail.com> <535E4E13.6080006@crans.org>
	<CAF6FJivEnLcOwUVSWr3F6dci-fzXE2SeVDWEGgB+Y8pj0x36sg@mail.gmail.com>
	<535E862E.8070209@gmail.com>
Message-ID: <CAB6mnx+EJtGKSmKWTX+xOfMMfhKO8uBh0Rbm4iST7q2cjD5+sA@mail.gmail.com>

On Mon, Apr 28, 2014 at 10:47 AM, Kevin Davies <kdavies4 at gmail.com> wrote:

> Thanks for everyone's help.  It sounds like the best option is to add
> this  recommendation system to constants.codata.find().  Please take a
> look:
> https://github.com/kdavies4/scipy/compare/codata-find
>
>
> On 04/28/2014 02:53 AM, Robert Kern wrote:
> > On Mon, Apr 28, 2014 at 1:48 PM, Pierre Haessig
> > <pierre.haessig at crans.org> wrote:
> >> Hi Kevin,
> >>
> >> Just a random thought:  what about putting your recommendation system in
> >> a separate "search" function ?
> >> The KeyError raised by value(...) could be one-line message pointing to
> >> this search function.
> > The argument to KeyError should be the key that is causing the
> > problem, not a natural language message. That's why his attempt to use
> > a multi-line string got printed as its repr().
> >
> >> That function, meant to be used interactively unlike value(), could use
> >> prints I guess.
> > But yes, I agree that adding a function specifically documented to be
> > used interactively would be a better place for this functionality.
> >
>

Maybe just add something like a `suggestion` function which returns a list
of suggested keys. No need for a print statement, as the list will be
displayed on the terminal.

Chuck
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From matthew.brett at gmail.com  Mon Apr 28 22:34:45 2014
From: matthew.brett at gmail.com (Matthew Brett)
Date: Mon, 28 Apr 2014 19:34:45 -0700
Subject: [SciPy-Dev] wheels on PyPI?
In-Reply-To: <9B1D27822EB44B9CA1F947948EB3E85E@edx.org>
References: <6B9AFD6A2B9E477CBB6D979CF46A5DC9@edx.org>
	<9B1D27822EB44B9CA1F947948EB3E85E@edx.org>
Message-ID: <CAH6Pt5p3Na1ooA_8MuCiP17pN3cm65MRzdrmKt1D4b=42qvjNg@mail.gmail.com>

Hi,

On Mon, Apr 28, 2014 at 8:55 AM, David Baumgold <db at edx.org> wrote:
> Hey all, I work for edX, and we include NumPy and SciPy in our software
> platform. We have a lot of tests for our code, and running them requires
> making a virtual environment and installing all of our dependencies each
> time ? but NumPy and SciPy take a long time to compile. We?ve actually built
> Python wheel files for all of our dependencies, which we host on an AWS
> server: http://edx-wheels.s3-website-us-east-1.amazonaws.com It would be
> super convenient if we could get wheel files for NumPy and SciPy on PyPI, so
> that its easier for us to install them in our test environment ? and I
> imagine it would help a whole lot of other people, as well! Who has the
> authorization to upload new packages to the NumPy and SciPy projects on
> PyPI, and how can I contribute our wheel files back to the community?
> Alternatively, how can I help the SciPy project build and distribute wheel
> files automatically, as part of the standard release process? Thanks for the
> info!

As promised some time ago, I've put up scipy wheels for 0.13.3 on pypi.

Assuming you've got Python.org Python, you should now be able to do:

pip install numpy scipy matplotlib

from the OSX command line (after upgrading pip to latest).

Cheers,

Matthew


From charlesnwoods at gmail.com  Mon Apr 28 23:59:05 2014
From: charlesnwoods at gmail.com (Nathan Woods)
Date: Mon, 28 Apr 2014 21:59:05 -0600
Subject: [SciPy-Dev] Multivariate ctypes integration PR
In-Reply-To: <CAF6FJiu_=RWH71ys64rYn-Nmex+MTA+tLY1XPnf+vE7_3pLFzg@mail.gmail.com>
References: <CAEpsjT40tznXWYWN-eGUhUSg_ijKYrYREv0RS9XYezrD6eh6Nw@mail.gmail.com>
	<CAAYHtCQTCdtLe_ezw++-E+6F5MfyXLoiK8rOg0ydTOuX2ums-A@mail.gmail.com>
	<CAAYHtCR8307yXnzpZYuhLSdpepNuC3ZSMkzV6-anBjVpa+ok5A@mail.gmail.com>
	<CAAYHtCQaxx2H_JaXCQGXAX=8+_h-T=8rGa=6WNRxg2F+DmreWw@mail.gmail.com>
	<CAF6FJiu_=RWH71ys64rYn-Nmex+MTA+tLY1XPnf+vE7_3pLFzg@mail.gmail.com>
Message-ID: <E414886A-10E4-4F50-80D1-819A60FC3053@gmail.com>

This sounds promising, but I will have to have a much better understanding of distutils than I do just at the moment to try it. I?ll see what I can figure out.

N

On Apr 28, 2014, at 9:53 AM, Robert Kern <robert.kern at gmail.com> wrote:

> On Mon, Apr 28, 2014 at 4:46 PM, Nathan Woods <charlesnwoods at gmail.com> wrote:
>> Anyone? Anyone?
>> 
>> From the lack of response to my email two weeks ago, I guess that there
>> isn't really a good way to distribute a library function for testing. When
>> ctypes functionality was initially included, the test function used was part
>> of the standard library, to avoid this same problem. I propose that we
>> therefore do one of two things.
> 
> The stdlib tests ctypes by building a Python extension module
> `_ctypes_test` along with all of the other stdlib extension modules.
> It is careful to `EXPORT` all of the symbols that will be needed. We
> could do the same here. We will just have an extra _private extension
> module built along with all of the others while scipy is building. It
> will not be compiled during the unit test.
> 
> http://hg.python.org/cpython/file/a14012352f65/Modules/_ctypes/_ctypes_test.c
> http://hg.python.org/cpython/file/a14012352f65/Lib/ctypes/test/test_cfuncs.py#l7
> 
> -- 
> Robert Kern
> _______________________________________________
> SciPy-Dev mailing list
> SciPy-Dev at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-dev



From kasturi.surya at gmail.com  Tue Apr 29 00:17:53 2014
From: kasturi.surya at gmail.com (Surya)
Date: Tue, 29 Apr 2014 09:47:53 +0530
Subject: [SciPy-Dev] SciPy Central: Using Class based views,
 in-memory for creating repos, ajax previewing
Message-ID: <CAArdDCq-rLq1tyrTCi27Zz+Vo9xatfPo9NMEUP1iT5J6H5zjJA@mail.gmail.com>

Hello all,

Its been quite much time I emailed in the community.

Currently I am working on rewriting "scipy_central/submission" module
which seems to be quite messy.

The major changes to the present version of it are to be followed:

1. Rewrite `create new submission`, `edit submission` views using
django's class based views. They provide much API to automate several
things! [on the way]

2. The preview of restructured text is done by ajax [done but not committed]

3. The syntax highlight of written source code is done using
javascript (Ace editor) which technically eliminates the usage of
`Pygments` module
This editor provides much capabilities [in master]

4. This app also stores submitted data in file system as Hg
repositories apart from storing in database. The current module simply
handles operations directly on file system, and doesn't take care of
errors raised during the process which means there might be several
temporary, unused, broken folders in the file system being not used
anymore.

My idea is to first do all the process in-memory and only save at last
when no errors arise. It has to be noted that the following data to be
handled by in-memory

1. created Hg repo files
2. Submitted snippet file / zip-file (limit of 25 Mb)

I am doubtful whether using in-memory would be a good idea or not in
this case! It would be great if anyone suggests.

Looking forward your comments

Thanks


From robert.kern at gmail.com  Tue Apr 29 04:37:46 2014
From: robert.kern at gmail.com (Robert Kern)
Date: Tue, 29 Apr 2014 09:37:46 +0100
Subject: [SciPy-Dev] Multivariate ctypes integration PR
In-Reply-To: <E414886A-10E4-4F50-80D1-819A60FC3053@gmail.com>
References: <CAEpsjT40tznXWYWN-eGUhUSg_ijKYrYREv0RS9XYezrD6eh6Nw@mail.gmail.com>
	<CAAYHtCQTCdtLe_ezw++-E+6F5MfyXLoiK8rOg0ydTOuX2ums-A@mail.gmail.com>
	<CAAYHtCR8307yXnzpZYuhLSdpepNuC3ZSMkzV6-anBjVpa+ok5A@mail.gmail.com>
	<CAAYHtCQaxx2H_JaXCQGXAX=8+_h-T=8rGa=6WNRxg2F+DmreWw@mail.gmail.com>
	<CAF6FJiu_=RWH71ys64rYn-Nmex+MTA+tLY1XPnf+vE7_3pLFzg@mail.gmail.com>
	<E414886A-10E4-4F50-80D1-819A60FC3053@gmail.com>
Message-ID: <CAF6FJisv1GM8WTzJemnY9rRyUuSG+MGOSKmQJkq5nxS1dAkxJw@mail.gmail.com>

On Tue, Apr 29, 2014 at 4:59 AM, Nathan Woods <charlesnwoods at gmail.com> wrote:
> This sounds promising, but I will have to have a much better understanding of distutils than I do just at the moment to try it. I?ll see what I can figure out.

It's really easy. Let's say you put the test module as
scipy/integrate/_ctypes_test.c. Just take a look at
scipy/integrate/setup.py. Add a new line:

  config.add_extension('_ctypes_test', sources=['_ctypes_test.c'])

And in the scipy/integrate/bento.info:

  Extension:
      Sources: _ctypes_test.c

-- 
Robert Kern


From matthew.brett at gmail.com  Tue Apr 29 05:12:13 2014
From: matthew.brett at gmail.com (Matthew Brett)
Date: Tue, 29 Apr 2014 02:12:13 -0700
Subject: [SciPy-Dev] wheels on PyPI?
In-Reply-To: <CAH6Pt5p3Na1ooA_8MuCiP17pN3cm65MRzdrmKt1D4b=42qvjNg@mail.gmail.com>
References: <6B9AFD6A2B9E477CBB6D979CF46A5DC9@edx.org>
	<9B1D27822EB44B9CA1F947948EB3E85E@edx.org>
	<CAH6Pt5p3Na1ooA_8MuCiP17pN3cm65MRzdrmKt1D4b=42qvjNg@mail.gmail.com>
Message-ID: <CAH6Pt5pix7tZGej4R95o75u+_xrywiBNmoo59E0Wn0GC_G=cxQ@mail.gmail.com>

Hi,

On Mon, Apr 28, 2014 at 7:34 PM, Matthew Brett <matthew.brett at gmail.com> wrote:
> Hi,
>
> On Mon, Apr 28, 2014 at 8:55 AM, David Baumgold <db at edx.org> wrote:
>> Hey all, I work for edX, and we include NumPy and SciPy in our software
>> platform. We have a lot of tests for our code, and running them requires
>> making a virtual environment and installing all of our dependencies each
>> time ? but NumPy and SciPy take a long time to compile. We?ve actually built
>> Python wheel files for all of our dependencies, which we host on an AWS
>> server: http://edx-wheels.s3-website-us-east-1.amazonaws.com It would be
>> super convenient if we could get wheel files for NumPy and SciPy on PyPI, so
>> that its easier for us to install them in our test environment ? and I
>> imagine it would help a whole lot of other people, as well! Who has the
>> authorization to upload new packages to the NumPy and SciPy projects on
>> PyPI, and how can I contribute our wheel files back to the community?
>> Alternatively, how can I help the SciPy project build and distribute wheel
>> files automatically, as part of the standard release process? Thanks for the
>> info!
>
> As promised some time ago, I've put up scipy wheels for 0.13.3 on pypi.
>
> Assuming you've got Python.org Python, you should now be able to do:
>
> pip install numpy scipy matplotlib
>
> from the OSX command line (after upgrading pip to latest).

Automated tests on a clean 10.6 machine showing tests passing with pypi wheels:

http://nipy.bic.berkeley.edu/builders/scipy-2.7.6-wheel/builds/6
http://nipy.bic.berkeley.edu/builders/scipy-3.3.5-wheel/builds/0
http://nipy.bic.berkeley.edu/builders/scipy-3.4.0-wheel/builds/0

The tests start with nothing but the system python, install python.org
python, install wheels into a virtualenv and run the scipy tests.
The test machine has no compilers installed.

Enjoy,

Matthew


From charlesnwoods at gmail.com  Tue Apr 29 17:51:13 2014
From: charlesnwoods at gmail.com (Nathan Woods)
Date: Tue, 29 Apr 2014 15:51:13 -0600
Subject: [SciPy-Dev] Multivariate ctypes integration PR
In-Reply-To: <CAF6FJisv1GM8WTzJemnY9rRyUuSG+MGOSKmQJkq5nxS1dAkxJw@mail.gmail.com>
References: <CAEpsjT40tznXWYWN-eGUhUSg_ijKYrYREv0RS9XYezrD6eh6Nw@mail.gmail.com>
	<CAAYHtCQTCdtLe_ezw++-E+6F5MfyXLoiK8rOg0ydTOuX2ums-A@mail.gmail.com>
	<CAAYHtCR8307yXnzpZYuhLSdpepNuC3ZSMkzV6-anBjVpa+ok5A@mail.gmail.com>
	<CAAYHtCQaxx2H_JaXCQGXAX=8+_h-T=8rGa=6WNRxg2F+DmreWw@mail.gmail.com>
	<CAF6FJiu_=RWH71ys64rYn-Nmex+MTA+tLY1XPnf+vE7_3pLFzg@mail.gmail.com>
	<E414886A-10E4-4F50-80D1-819A60FC3053@gmail.com>
	<CAF6FJisv1GM8WTzJemnY9rRyUuSG+MGOSKmQJkq5nxS1dAkxJw@mail.gmail.com>
Message-ID: <E4BEA4BC-E617-4D1E-81B4-29B93E14552E@gmail.com>

All right, I think I was able to figure this out. An updated version has been added to the PR on GitHub, and it works on both Ubuntu 12.04 and OSX 10.9. Loading the library in ctypes SHOULD be straightforward in Windows as well, but we still need to check that. 

We?d love it if anyone could take a look at our implementation and offer comments, since it?s different from what?s done in the python standard library.

Nathan Woods

On Apr 29, 2014, at 2:37 AM, Robert Kern <robert.kern at gmail.com> wrote:

> On Tue, Apr 29, 2014 at 4:59 AM, Nathan Woods <charlesnwoods at gmail.com> wrote:
>> This sounds promising, but I will have to have a much better understanding of distutils than I do just at the moment to try it. I?ll see what I can figure out.
> 
> It's really easy. Let's say you put the test module as
> scipy/integrate/_ctypes_test.c. Just take a look at
> scipy/integrate/setup.py. Add a new line:
> 
>  config.add_extension('_ctypes_test', sources=['_ctypes_test.c'])
> 
> And in the scipy/integrate/bento.info:
> 
>  Extension:
>      Sources: _ctypes_test.c
> 
> -- 
> Robert Kern
> _______________________________________________
> SciPy-Dev mailing list
> SciPy-Dev at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-dev



From kdavies4 at gmail.com  Wed Apr 30 15:20:52 2014
From: kdavies4 at gmail.com (Kevin Davies)
Date: Wed, 30 Apr 2014 09:20:52 -1000
Subject: [SciPy-Dev] requesting code review: added close matches to
	constants.codata.value()
Message-ID: <53614D14.2090200@gmail.com>

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From npkuin at gmail.com  Wed Apr 30 16:07:59 2014
From: npkuin at gmail.com (Paul Kuin)
Date: Wed, 30 Apr 2014 21:07:59 +0100
Subject: [SciPy-Dev] requesting code review: added close matches to
	constants.codata.value()
In-Reply-To: <53614D14.2090200@gmail.com>
References: <53614D14.2090200@gmail.com>
Message-ID: <CANoQ6N3Q=bz0iW9P6JpQ_BKQs-JVBRpbm4kp1QMwrZT1B+M3EQ@mail.gmail.com>

Not being familiar with this module, I loaded constants.  It would be nice
if the doc would point to the more extensive documentation in
constants.codata.  Without this discussion I would be lost.


On Wed, Apr 30, 2014 at 8:20 PM, Kevin Davies <kdavies4 at gmail.com> wrote:

>  I addressed some coding issues (thanks to Robert Kern) and submitted a
> pull request:
> https://github.com/scipy/scipy/pull/3595
> I attached some screenshots.
>
> This change adds support for shell-style wildcards in codata.find().  If
> disp=True and no exact matches are found in _current_constants, then exact
> matches are shown from _obsolete_constants.  If there are no exact matches
> in _obsolete_constants, then close matches are shown from
> _current_constants.
>
> I hope this is a good compromise based on the discussion here.
>
>
>
> _______________________________________________
> SciPy-Dev mailing list
> SciPy-Dev at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-dev
>
>


-- 

* * * * * * * * http://www.mssl.ucl.ac.uk/~npmk/ * * * *
Dr. N.P.M. Kuin      (n.kuin at ucl.ac.uk)
phone +44-(0)1483 (prefix) -204927 (work)
mobile +44(0)7806985366  skype ID: npkuin
Mullard Space Science Laboratory  ? University College London  ?
Holmbury St Mary ? Dorking ? Surrey RH5 6NT?  U.K.
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