From emmanuelle.gouillart at normalesup.org  Mon Mar  7 17:09:56 2016
From: emmanuelle.gouillart at normalesup.org (Emmanuelle Gouillart)
Date: Mon, 7 Mar 2016 23:09:56 +0100
Subject: [SciPy-User] ANN: release of scikit-image 0.12
Message-ID: <20160307220956.GA1366532@phare.normalesup.org>

Announcement: scikit-image 0.12
===============================

The scikit-image team is very pleased to announce the release of version
0.12 of scikit-image.

scikit-image is an image processing toolbox for Python and SciPy, that
includes algorithms for segmentation, geometric transformations, color
space manipulation, analysis, filtering, morphology, feature detection,
and more, for 2-D and 3-D (sometimes n-D) images.

For more information, examples, and documentation, please visit our
website:

http://scikit-image.org

and our example gallery

http://scikit-image.org/docs/dev/auto_examples/

This release includes new features such as image inpainting, seam
carving, image comparison metrics, and a parallelization framework based
on dask. The release has also improved the support of 3-D images, with
3-D skeletonization, 3-D phantom data and partial support of
measure.regionprops for 3-D images. Also note that the handling of
background pixels by measure.label, a function labeling connected
components, has changed to be consistent with scipy.ndimage.label.

For this release, we merged over 200 pull requests from 64 contributors,
with bug fixes, cleanups, improved documentation and new features.
Release notes are available on
http://scikit-image.org/docs/0.12.x/release_notes_and_installation.html#release-notes
and include a more detailed list of changes, and the complete list of
contributors to this release.

The release can be downloaded on PyPi
https://pypi.python.org/pypi/scikit-image or directly installed using pip

pip install --upgrade scikit-image

Please let us know any issues you might have on the issue tracker
https://github.com/scikit-image/scikit-image/issues

Happy image processing!
The scikit-image team



From lutz.maibaum at gmail.com  Mon Mar  7 20:00:57 2016
From: lutz.maibaum at gmail.com (Lutz Maibaum)
Date: Mon, 7 Mar 2016 17:00:57 -0800
Subject: [SciPy-User] Using special functions with keyword arguments
Message-ID: <54649B00-F9B6-4792-B512-59B565F2A973@gmail.com>

I am having difficulties calling the spherical harmonics function with keyword arguments. This works in older versions (for example Numpy 1.8 / SciPy 0.14):

>>> from scipy.special import sph_harm
>>> sph_harm(m=0,n=0,theta=0,phi=0)
array((0.28209479177387814+0j))

In newer versions, such as NumPy 1.10 / SciPy 0.15, I get

>>> from scipy.special import sph_harm
>>> sph_harm(m=0,n=0,theta=0,phi=0)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
ValueError: invalid number of arguments

Is it still possible to call sph_harm (and presumably other special functions) with keyword arguments? The reason I am trying to do this is that I would like to wrap sph_harm in a functools.partial object that takes only a single argument, and that I can then pass to multiprocessing.Pool.map.

Thanks,

Lutz



From jeffreback at gmail.com  Wed Mar  9 10:05:39 2016
From: jeffreback at gmail.com (Jeff Reback)
Date: Wed, 9 Mar 2016 10:05:39 -0500
Subject: [SciPy-User] ANN: pandas v0.18.0rc2 - RELEASE CANDIDATE
Message-ID: <CAHMnJKiGrM9DpE8b_9O=HKFXD5YrJTxdUK60bweqY9GDt1yvNw@mail.gmail.com>

Hi,

I'm pleased to announce the availability of the second release candidate of
Pandas 0.18.0.
Please try this RC and report any issues here: Pandas Issues
<https://github.com/pydata/pandas/issues>. Compared to RC1, we have added
updated read_sas and fixed float indexing. We will be releasing officially
very shortly.

THIS IS NOT A PRODUCTION RELEASE

This is a major release from 0.17.1 and includes a small number of API
changes, several new features, enhancements,
and performance improvements along with a large number of bug fixes. We
recommend that all users upgrade to this version.

Highlights include:

   - pandas >= 0.18.0 will no longer support compatibility with Python
   version 2.6 GH7718 <https://github.com/pydata/pandas/issues/7718> or
   version 3.3 GH11273 <https://github.com/pydata/pandas/issues/11273>
   - Moving and expanding window functions are now methods on Series and
   DataFrame similar to .groupby like objects, see here
   <http://pandas-docs.github.io/pandas-docs-travis/whatsnew.html#whatsnew-0180-enhancements-moments>
   .
   - Adding support for a RangeIndex as a specialized form of the
Int64Index for
   memory savings, see here
   <http://pandas-docs.github.io/pandas-docs-travis/whatsnew.html#whatsnew-0180-enhancements-rangeindex>
   .
   - API breaking .resample changes to make it more .groupby like, see here
   <http://pandas-docs.github.io/pandas-docs-travis/whatsnew.html#whatsnew-0180-breaking-resample>
   - Removal of support for positional indexing with floats, which was
   deprecated since 0.14.0. This will now raise a TypeError, see here
   <http://pandas-docs.github.io/pandas-docs-travis/whatsnew.html#whatsnew-0180-float-indexers>
   - The .to_xarray() function has been added for compatibility with the xarray
   package <http://xarray.pydata.org/en/stable/> see here
   <http://pandas-docs.github.io/pandas-docs-travis/whatsnew.html#whatsnew-0180-enhancements-xarray>
   .
   - The read_sas() function has been enhanced to read sas7bdat files, see
   here
   <http://pandas-docs.github.io/pandas-docs-travis/whatsnew.html#whatsnew-0180-enhancements-sas>
   - Addition of the .str.extractall() method
   <http://pandas-docs.github.io/pandas-docs-travis/whatsnew.html#whatsnew-0180-enhancements-extractall>,
   and API changes to the the .str.extract() method
   <http://pandas-docs.github.io/pandas-docs-travis/whatsnew.html#whatsnew-0180-enhancements-extract>,
   and the .str.cat() method
   <http://pandas-docs.github.io/pandas-docs-travis/whatsnew.html#whatsnew-0180-enhancements-strcat>
   - pd.test() top-level nose test runner is available GH4327
   <https://github.com/pydata/pandas/issues/4327>

See the Whatsnew
<http://pandas-docs.github.io/pandas-docs-travis/whatsnew.html> for much
more information.

Best way to get this is to install via conda
<http://pandas-docs.github.io/pandas-docs-travis/install.html#installing-pandas-with-anaconda>
from
our development channel. Builds for osx-64,linux-64,win-64 for Python 2.7
and Python 3.5 are all available.

conda install pandas=v0.18.0rc2 -c pandas

Thanks to all who made this release happen. It is a very large release!

Jeff
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From pav at iki.fi  Thu Mar 10 05:21:22 2016
From: pav at iki.fi (Pauli Virtanen)
Date: Thu, 10 Mar 2016 10:21:22 +0000 (UTC)
Subject: [SciPy-User] Using special functions with keyword arguments
References: <54649B00-F9B6-4792-B512-59B565F2A973@gmail.com>
Message-ID: <nbrhr2$d8$1@ger.gmane.org>

Mon, 07 Mar 2016 17:00:57 -0800, Lutz Maibaum kirjoitti:
[clip]
> Is it still possible to call sph_harm (and presumably other special
> functions) with keyword arguments? 

Ufuncs in general don't support keyword arguments, so no.

> The reason I am trying to do this is
> that I would like to wrap sph_harm in a functools.partial object that
> takes only a single argument, and that I can then pass to
> multiprocessing.Pool.map.

You can probably still achieve that in some way.

-- 
Pauli Virtanen



From dave.hirschfeld at gmail.com  Thu Mar 10 15:29:18 2016
From: dave.hirschfeld at gmail.com (Dave)
Date: Thu, 10 Mar 2016 20:29:18 +0000 (UTC)
Subject: [SciPy-User] Using special functions with keyword arguments
References: <54649B00-F9B6-4792-B512-59B565F2A973@gmail.com>
 <nbrhr2$d8$1@ger.gmane.org>
Message-ID: <loom.20160310T212417-851@post.gmane.org>

Pauli Virtanen <pav <at> iki.fi> writes:

> 
> Mon, 07 Mar 2016 17:00:57 -0800, Lutz 
Maibaum kirjoitti:
> [clip]
> > Is it still possible to call sph_harm 
(and presumably other special
> > functions) with keyword arguments? 
> 
> Ufuncs in general don't support keyword 
arguments, so no.
> 
> > The reason I am trying to do this is
> > that I would like to wrap sph_harm in 
a functools.partial object that
> > takes only a single argument, and that 
I can then pass to
> > multiprocessing.Pool.map.
> 
> You can probably still achieve that in 
some way.
> 

I've run into this restriction before in 
the context of numba generated ufuncs. Is 
there some fundamental problem supporting 
kwargs in ufuncs or is it technically 
possible to do?

Thanks,
Dave



From gary.ruben at gmail.com  Thu Mar 10 16:49:42 2016
From: gary.ruben at gmail.com (gary ruben)
Date: Fri, 11 Mar 2016 08:49:42 +1100
Subject: [SciPy-User] Using special functions with keyword arguments
In-Reply-To: <loom.20160310T212417-851@post.gmane.org>
References: <54649B00-F9B6-4792-B512-59B565F2A973@gmail.com>
 <nbrhr2$d8$1@ger.gmane.org>
 <loom.20160310T212417-851@post.gmane.org>
Message-ID: <CAExsOdZKHe3uoywDOih_-ZGpUrzxwHE=xhOm0uxPAOq4EdzrVA@mail.gmail.com>

I haven't tried but I'd look at numpy.vectorize and functools.partial as
possible ways to do this.

Gary
On 11 Mar 2016 7:30 am, "Dave" <dave.hirschfeld at gmail.com> wrote:

> Pauli Virtanen <pav <at> iki.fi> writes:
>
> >
> > Mon, 07 Mar 2016 17:00:57 -0800, Lutz
> Maibaum kirjoitti:
> > [clip]
> > > Is it still possible to call sph_harm
> (and presumably other special
> > > functions) with keyword arguments?
> >
> > Ufuncs in general don't support keyword
> arguments, so no.
> >
> > > The reason I am trying to do this is
> > > that I would like to wrap sph_harm in
> a functools.partial object that
> > > takes only a single argument, and that
> I can then pass to
> > > multiprocessing.Pool.map.
> >
> > You can probably still achieve that in
> some way.
> >
>
> I've run into this restriction before in
> the context of numba generated ufuncs. Is
> there some fundamental problem supporting
> kwargs in ufuncs or is it technically
> possible to do?
>
> Thanks,
> Dave
>
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> https://mail.scipy.org/mailman/listinfo/scipy-user
>
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From gary.ruben at gmail.com  Fri Mar 11 00:08:00 2016
From: gary.ruben at gmail.com (gary ruben)
Date: Fri, 11 Mar 2016 16:08:00 +1100
Subject: [SciPy-User] Using special functions with keyword arguments
In-Reply-To: <CAExsOdZKHe3uoywDOih_-ZGpUrzxwHE=xhOm0uxPAOq4EdzrVA@mail.gmail.com>
References: <54649B00-F9B6-4792-B512-59B565F2A973@gmail.com>
 <nbrhr2$d8$1@ger.gmane.org> <loom.20160310T212417-851@post.gmane.org>
 <CAExsOdZKHe3uoywDOih_-ZGpUrzxwHE=xhOm0uxPAOq4EdzrVA@mail.gmail.com>
Message-ID: <CAExsOdZmOA2_ggJmh3vrQc=MPDuczK+ad1QPO-WwmXWpQKTXjg@mail.gmail.com>

Please ignore my previous comment; I hadn't read the earlier thread
messages.

On 11 March 2016 at 08:49, gary ruben <gary.ruben at gmail.com> wrote:

> I haven't tried but I'd look at numpy.vectorize and functools.partial as
> possible ways to do this.
>
> Gary
> On 11 Mar 2016 7:30 am, "Dave" <dave.hirschfeld at gmail.com> wrote:
>
>> Pauli Virtanen <pav <at> iki.fi> writes:
>>
>> >
>> > Mon, 07 Mar 2016 17:00:57 -0800, Lutz
>> Maibaum kirjoitti:
>> > [clip]
>> > > Is it still possible to call sph_harm
>> (and presumably other special
>> > > functions) with keyword arguments?
>> >
>> > Ufuncs in general don't support keyword
>> arguments, so no.
>> >
>> > > The reason I am trying to do this is
>> > > that I would like to wrap sph_harm in
>> a functools.partial object that
>> > > takes only a single argument, and that
>> I can then pass to
>> > > multiprocessing.Pool.map.
>> >
>> > You can probably still achieve that in
>> some way.
>> >
>>
>> I've run into this restriction before in
>> the context of numba generated ufuncs. Is
>> there some fundamental problem supporting
>> kwargs in ufuncs or is it technically
>> possible to do?
>>
>> Thanks,
>> Dave
>>
>> _______________________________________________
>> SciPy-User mailing list
>> SciPy-User at scipy.org
>> https://mail.scipy.org/mailman/listinfo/scipy-user
>>
>
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From lutz.maibaum at gmail.com  Fri Mar 11 14:37:56 2016
From: lutz.maibaum at gmail.com (Lutz Maibaum)
Date: Fri, 11 Mar 2016 11:37:56 -0800
Subject: [SciPy-User] Using special functions with keyword arguments
In-Reply-To: <nbrhr2$d8$1@ger.gmane.org>
References: <54649B00-F9B6-4792-B512-59B565F2A973@gmail.com>
 <nbrhr2$d8$1@ger.gmane.org>
Message-ID: <FAF09EB9-3751-4EAA-B6F8-CE31C5729728@gmail.com>


> On Mar 10, 2016, at 2:21 AM, Pauli Virtanen <pav at iki.fi> wrote:
> 
> Mon, 07 Mar 2016 17:00:57 -0800, Lutz Maibaum kirjoitti:
> [clip]
>> Is it still possible to call sph_harm (and presumably other special
>> functions) with keyword arguments? 
> 
> Ufuncs in general don't support keyword arguments, so no.

Thanks -- I thought so. I was mainly surprised that this used to work in older versions of SciPy; probably because sph_harm wasn?t a ufunc then.

>> The reason I am trying to do this is
>> that I would like to wrap sph_harm in a functools.partial object that
>> takes only a single argument, and that I can then pass to
>> multiprocessing.Pool.map.
> 
> You can probably still achieve that in some way.

Yes, certainly. This would have been the simplest way to parallelize my code. On the plus side, the sph_harm function got so much faster in the newer versions that evaluating it in parallel isn?t even that important anymore in my application.

Best,

Lutz




From jeffreback at gmail.com  Sat Mar 12 11:13:49 2016
From: jeffreback at gmail.com (Jeff Reback)
Date: Sat, 12 Mar 2016 11:13:49 -0500
Subject: [SciPy-User] ANN: pandas v0.18.0 Final released
Message-ID: <CAHMnJKgbR3qbwhRo6fPhe=usmtUPtPPYvQE5wSWCzADGgYDBBw@mail.gmail.com>

Hi,

This is a major release from 0.17.1 and includes a small number of API
changes,
several new features, enhancements, and performance improvements along with
a
large number of bug fixes. We recommend that all users upgrade to this
version.

This was a release of 3.5 months with 381 commits by 100 authors
encompassing 465 issues and 290 pull-requests.

*What is it:*

*pandas* is a Python package providing fast, flexible, and expressive data
structures designed to make working with ?relational? or ?labeled? data both
easy and intuitive. It aims to be the fundamental high-level building block
for
doing practical, real world data analysis in Python. Additionally, it has
the
broader goal of becoming the most powerful and flexible open source data
analysis / manipulation tool available in any language.

*Highlights*:

   - pandas >= 0.18.0 will no longer support compatibility with Python
   version 2.6 GH7718 <https://github.com/pydata/pandas/issues/7718> or
   version 3.3 GH11273 <https://github.com/pydata/pandas/issues/11273>
   - Moving and expanding window functions are now methods on Series and
   DataFrame similar to .groupby like objects, see here
   <http://pandas.pydata.org/pandas-docs/version/0.18.0/whatsnew.html#whatsnew-0180-enhancements-moments>
   .
   - Adding support for a RangeIndex as a specialized form of the
Int64Index for
   memory savings, see here
   <http://pandas.pydata.org/pandas-docs/version/0.18.0/whatsnew.html#whatsnew-0180-enhancements-rangeindex>
   .
   - API breaking .resample changes to make it more .groupby like, see here
   <http://pandas.pydata.org/pandas-docs/version/0.18.0/whatsnew.html#whatsnew-0180-breaking-resample>
   - Removal of support for positional indexing with floats, which was
   deprecated since 0.14.0. This will now raise a TypeError, see here
   <http://pandas.pydata.org/pandas-docs/version/0.18.0/whatsnew.html#whatsnew-0180-float-indexers>
   - The .to_xarray() function has been added for compatibility with the xarray
   package <http://xarray.pydata.org/en/stable/> see here
   <http://pandas.pydata.org/pandas-docs/version/0.18.0/whatsnew.html#whatsnew-0180-enhancements-xarray>
   .
   - The read_sas() function has been enhanced to read sas7bdat files, see
   here
   <http://pandas.pydata.org/pandas-docs/version/0.18.0/whatsnew.html#whatsnew-0180-enhancements-sas>
   - Addition of the .str.extractall() method
   <http://pandas.pydata.org/pandas-docs/version/0.18.0/whatsnew.html#whatsnew-0180-enhancements-extractall>,
   and API changes to the the .str.extract() method
   <http://pandas.pydata.org/pandas-docs/version/0.18.0/whatsnew.html#whatsnew-0180-enhancements-extract>,
   and the .str.cat() method
   <http://pandas.pydata.org/pandas-docs/version/0.18.0/whatsnew.html#whatsnew-0180-enhancements-strcat>
   - pd.test() top-level nose test runner is available GH4327
   <https://github.com/pydata/pandas/issues/4327>

See the Whatsnew
<http://pandas.pydata.org/pandas-docs/version/0.18.0/whatsnew.html> for
much more information and the full Documentation
<http://pandas.pydata.org/pandas-docs/version/0.18.0/> link.

*How to get it:*

Source tarballs, windows wheels, and macosx wheels are available on PyPI
<https://pypi.python.org/pypi/pandas>

Installation via conda is:

   - conda install pandas

windows wheels are courtesy of Christoph Gohlke and are built on Numpy 1.10
macosx wheels are courtesy of Matthew Brett.

*Issues:*

Please report any issues on our issue tracker
<https://github.com/pydata/pandas/issues>:

Jeff

*Thanks to all of the contributors*


   - ARF
   - Alex Alekseyev
   - Andrew McPherson
   - Andrew Rosenfeld
   - Anthonios Partheniou
   - Anton I. Sipos
   - Ben
   - Ben North
   - Bran Yang
   - Chris
   - Chris Carroux
   - Christopher C. Aycock
   - Christopher Scanlin
   - Cody
   - Da Wang
   - Daniel Grady
   - Dorozhko Anton
   - Dr-Irv
   - Erik M. Bray
   - Evan Wright
   - Francis T. O'Donovan
   - Frank Cleary
   - Gianluca Rossi
   - Graham Jeffries
   - Guillaume Horel
   - Henry Hammond
   - Isaac Schwabacher
   - Jean-Mathieu Deschenes
   - Jeff Reback
   - Joe Jevnik
   - John Freeman
   - John Fremlin
   - Jonas Hoersch
   - Joris Van den Bossche
   - Joris Vankerschaver
   - Justin Lecher
   - Justin Lin
   - Ka Wo Chen
   - Keming Zhang
   - Kerby Shedden
   - Kyle
   - Marco Farrugia
   - MasonGallo
   - MattRijk
   - Matthew Lurie
   - Maximilian Roos
   - Mayank Asthana
   - Mortada Mehyar
   - Moussa Taifi
   - Navreet Gill
   - Nicolas Bonnotte
   - Paul Reiners
   - Philip Gura
   - Pietro Battiston
   - RahulHP
   - Randy Carnevale
   - Rinoc Johnson
   - Rishipuri
   - Sangmin Park
   - Scott E Lasley
   - Sereger13
   - Shannon Wang
   - Skipper Seabold
   - Thierry Moisan
   - Thomas A Caswell
   - Toby Dylan Hocking
   - Tom Augspurger
   - Travis
   - Trent Hauck
   - Tux1
   - Varun
   - Wes McKinney
   - Will Thompson
   - Yoav Ram
   - Yoong Kang Lim
   - Yoshiki V?zquez Baeza
   - Young Joong Kim
   - Younggun Kim
   - Yuval Langer
   - alex argunov
   - behzad nouri
   - boombard
   - ian-pantano
   - chromy
   - daniel
   - dgram0
   - gfyoung
   - hack-c
   - hcontrast
   - jfoo
   - kaustuv deolal
   - llllllllll
   - ranarag
   - rockg
   - scls19fr
   - seales
   - sinhrks
   - srib
   - surveymedia.ca
   - tworec
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From grantwp3 at gmail.com  Sun Mar 13 20:24:28 2016
From: grantwp3 at gmail.com (Grant W. Petty)
Date: Sun, 13 Mar 2016 19:24:28 -0500
Subject: [SciPy-User] New book: Python Programming and Visualization for
	Scientists
Message-ID: <CAOwjK3uDUgzGiKziQbZqE-aOvBf-j7KhXmwKz0UcE-KD6SkBDw@mail.gmail.com>

A new book has just been published that may be of interest to some members
of this list, especially those relatively new to Python:  *Python
Programming and Visualization for Scientists*, by Alex DeCaria. Alex has
been a regular instructor of short courses in Python at meetings of the
American Meteorological Society.

This book is designed to be a concise, well-organized tutorial and
reference that encompasses both the Python language itself and the most
commonly used elements of selected modules, including matplotlib, numpy,
time, and others.

Additional information can be found on the attached flyer and at the book's
website
<http://www.sundogpublishing.com/shop/python-programming-and-visualization-for-scientists-alex-decaria/>.
Note that the website currently offers only the spiral-bound version for
sale; a conventional 7x10 softcover version will soon be available through
other outlets.

Sundog Publishing is a very small independent publisher that relies
entirely on reviews and word-of-mouth as its primary marketing tools.  If
you get a copy of Alex's book and think it is useful, either for yourself
or for new users of Python, I hope you will help spread the word, including
perhaps forwarding the attached flyer and/or above link.

Thanks!
Grant Petty
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From charlesr.harris at gmail.com  Sat Mar 19 19:27:03 2016
From: charlesr.harris at gmail.com (Charles R Harris)
Date: Sat, 19 Mar 2016 17:27:03 -0600
Subject: [SciPy-User] Numpy 1.11.0rc2 released.
Message-ID: <CAB6mnx+qOO-Uu5iL43p+FeOoF1gdc3QbSzQ8qg3DGR3UDxyExw@mail.gmail.com>

Hi All,

Numpy 1.11.0rc2 has been released. There have been a few reversions and
fixes since 1.11.0rc1, but by and large things have been pretty stable and
unless something terrible happens, I will make the final next weekend.
Source files and documentation can be found on Sourceforge
<https://sourceforge.net/projects/numpy/files/NumPy/1.11.0rc2/>, while
source files and OS X wheels for Python 2.7, 3.3, 3.4, and 3.5 can be
installed from Pypi. Please test and yell loudly if there is a problem.

Chuck
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From david.mikolas1 at gmail.com  Mon Mar 21 01:22:04 2016
From: david.mikolas1 at gmail.com (David Mikolas)
Date: Mon, 21 Mar 2016 13:22:04 +0800
Subject: [SciPy-User] (no subject)
Message-ID: <CAFYhK+6zDH_cGhKgasy=9-nSrvY7e2XJdhbKvMeEH0gw5eAnTA@mail.gmail.com>

I have a situation where scipy.optimize.minimize returns the initial
starting value as the answer without really iterating, and returns
"successful" status, using default method.

It works fine if I specify Nelder-Mead, but my question is "why does
minimize return successful when it isn's, and what can I do (if anything)
to flag this behavior?

The question is also here http://stackoverflow.com/q/36110998  and if
someone can also leave an answer there as well, that would be great! but
not necessary.

One particular - I'm using calls to Skyfield, which may or may not have
something to do with it.   (http://rhodesmill.org/skyfield/)

FULL OUTPUT using DEFAULT METHOD:
   status: 0
  success: True
     njev: 1
     nfev: 3
 hess_inv: array([[1]])
      fun: 1694.98753895812
        x: array([ 10000.])
  message: 'Optimization terminated successfully.'
      jac: array([ 0.])
      nit: 0

FULL OUTPUT using Nelder-Mead METHOD:
  status: 0
    nfev: 63
 success: True
     fun: 3.2179306044608054
       x: array([ 13053.81011963])
 message: 'Optimization terminated successfully.'
     nit: 28
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From johannesradinger at gmail.com  Mon Mar 21 04:42:33 2016
From: johannesradinger at gmail.com (Johannes Radinger)
Date: Mon, 21 Mar 2016 09:42:33 +0100
Subject: [SciPy-User] Scientific citation numpy.random.multinomial
Message-ID: <CABsGe_x-PgQ3yvpyZunQ3Tfq1YzLw-fOUWYDpeazYi5OuS+JqQ@mail.gmail.com>

Hi all,

I am using the function numpy.random.multinomial in my code and want
to explicitly cite this function/method in a scientific (peer reviewed)
article.
What is the appropriate way to cite numpy.random.multinomial?

I am aware that SciPy can be cited as:
Jones E, Oliphant E, Peterson P, et al. SciPy: Open Source Scientific Tools
for Python, 2001-,
http://www.scipy.org/ [Online; accessed 2016-03-21].

However, this is not specifically for the "random" library of numpy. Is
there any other reference one can use?

Best regards,
Johannes
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From david.mikolas1 at gmail.com  Mon Mar 21 05:09:25 2016
From: david.mikolas1 at gmail.com (David Mikolas)
Date: Mon, 21 Mar 2016 17:09:25 +0800
Subject: [SciPy-User] minimize reporting successful but not searching (with
	default method)
Message-ID: <CAFYhK+6=5dEfFzeo8nU4whjE9=Qps+=DSej+KV0MANQQV39ecg@mail.gmail.com>

I have a situation where scipy.optimize.minimize returns the initial
starting value as the answer without really iterating, and returns
"successful" status, using default method.

It works fine if I specify Nelder-Mead, but my question is "why does
minimize return successful when it isn's, and what can I do (if anything)
to flag this behavior?

The question is also here http://stackoverflow.com/q/36110998  and if
someone can also leave an answer there as well, that would be great! but
not necessary.

One particular - I'm using calls to Skyfield, which may or may not have
something to do with it.   (http://rhodesmill.org/skyfield/)

FULL OUTPUT using DEFAULT METHOD:
   status: 0
  success: True
     njev: 1
     nfev: 3
 hess_inv: array([[1]])
      fun: 1694.98753895812
        x: array([ 10000.])
  message: 'Optimization terminated successfully.'
      jac: array([ 0.])
      nit: 0

FULL OUTPUT using Nelder-Mead METHOD:
  status: 0
    nfev: 63
 success: True
     fun: 3.2179306044608054
       x: array([ 13053.81011963])
 message: 'Optimization terminated successfully.'
     nit: 28
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From nelle.varoquaux at gmail.com  Mon Mar 21 10:32:16 2016
From: nelle.varoquaux at gmail.com (Nelle Varoquaux)
Date: Mon, 21 Mar 2016 15:32:16 +0100
Subject: [SciPy-User] Scipy2016: call for proposals
In-Reply-To: <CAE-UAvQm2Sja7LXVnOkm6ETQwokeGXT-WsRbHNkYd639i450yQ@mail.gmail.com>
References: <CAE-UAvQm2Sja7LXVnOkm6ETQwokeGXT-WsRbHNkYd639i450yQ@mail.gmail.com>
Message-ID: <CAE-UAvS3kT8cSW8yBSPJi4MGt4Z_3M8DMo6h+_aOa2AD3JsWYw@mail.gmail.com>

Dear all,

This is a quick reminder that the deadline for submitting talks and posters
proposal is next friday.

Thanks,
Nelle

On 22 February 2016 at 10:15, Nelle Varoquaux <nelle.varoquaux at gmail.com>
wrote:

>
> Dear all,
>
> SciPy 2016, the Fifteenth Annual Conference on Python in Science, takes
> place in Austin, TX on July, 11th to 17th. The conference features two days
> of tutorials by followed by three days of presentations, and concludes with
> two days of developer sprints on projects of interest to attendees. .
>
> The topics presented at SciPy are very diverse, with a focus on advanced
> software engineering and original uses of Python and its scientific
> libraries, either in theoretical or experimental research, from both
> academia and the industry. This year we are happy to announce two
> specialized tracks that run in parallel to the general conference (Data
> Science , High Performance Computing) and 8 mini-symposia (Earth and Space
> Science, Biology and Medicine, Engineering, Social Sciences, Special
> Purpose Databases, Case Studies in Industry, Education, Reproducibility)
>
> Submissions for talks and posters are welcome on our website (
> http://scipy2016.scipy.org). In your abstract, please provide details on
> what Python tools are being employed, and how. The talk and poster
> submission deadline is March 25th, 2016, while the tutorial submission
> deadline is March, 21st, 2016.
>
>
> Important dates:
>
> Mar 21: Tutorial Proposals Due
> Mar 25: Talk and Poster Proposals Due
> May 11: Plotting Contest Submissions Due
> Apr 22: Tutorials Announced
> Apr 22: Financial Aid Submissions Due
> May 4: Talk and Posters Announced
> May 11: Financial Aid Recipients Notified
> May 22: Early Bird Registration Deadline
> Jul 11-12: SciPy 2016 Tutorials
> Jul 13-15: SciPy 2016 General Conference
> Jul 16-17: SciPy 2016 Sprints
>
> We look forward to an exciting conference and hope to see you in Austin in
> July!
>
>
> The Scipy 2016
> http://scipy2016.scipy.org/
>
> Conference Chairs: Aric Hagberg, Prabhu Ramachandran
> Tutorial Chairs: Justin Vincent, Ben Root
> Program Chair: Serge Rey, Nelle Varoquaux
> Proceeding Chairs: Sebastian Benthall
>
>
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From nelle.varoquaux at gmail.com  Mon Mar 21 13:32:29 2016
From: nelle.varoquaux at gmail.com (Nelle Varoquaux)
Date: Mon, 21 Mar 2016 18:32:29 +0100
Subject: [SciPy-User] Scipy2016: call for proposals
In-Reply-To: <CAE-UAvS3kT8cSW8yBSPJi4MGt4Z_3M8DMo6h+_aOa2AD3JsWYw@mail.gmail.com>
References: <CAE-UAvQm2Sja7LXVnOkm6ETQwokeGXT-WsRbHNkYd639i450yQ@mail.gmail.com>
 <CAE-UAvS3kT8cSW8yBSPJi4MGt4Z_3M8DMo6h+_aOa2AD3JsWYw@mail.gmail.com>
Message-ID: <CAE-UAvRZZPc8YT_s6CWper8cFUZQP3_9ts5hrKhJghd5ygddtA@mail.gmail.com>

I'm terribly sorry about this second email.
The deadline for submitting talks and posters for scipy 2016 is this friday
(friday 25th), and not next friday (april fools day).

Thanks,
Nelle

On 21 March 2016 at 15:32, Nelle Varoquaux <nelle.varoquaux at gmail.com>
wrote:

> Dear all,
>
> This is a quick reminder that the deadline for submitting talks and
> posters proposal is next friday.
>
> Thanks,
> Nelle
>
> On 22 February 2016 at 10:15, Nelle Varoquaux <nelle.varoquaux at gmail.com>
> wrote:
>
>>
>> Dear all,
>>
>> SciPy 2016, the Fifteenth Annual Conference on Python in Science, takes
>> place in Austin, TX on July, 11th to 17th. The conference features two days
>> of tutorials by followed by three days of presentations, and concludes with
>> two days of developer sprints on projects of interest to attendees. .
>>
>> The topics presented at SciPy are very diverse, with a focus on advanced
>> software engineering and original uses of Python and its scientific
>> libraries, either in theoretical or experimental research, from both
>> academia and the industry. This year we are happy to announce two
>> specialized tracks that run in parallel to the general conference (Data
>> Science , High Performance Computing) and 8 mini-symposia (Earth and Space
>> Science, Biology and Medicine, Engineering, Social Sciences, Special
>> Purpose Databases, Case Studies in Industry, Education, Reproducibility)
>>
>> Submissions for talks and posters are welcome on our website (
>> http://scipy2016.scipy.org). In your abstract, please provide details on
>> what Python tools are being employed, and how. The talk and poster
>> submission deadline is March 25th, 2016, while the tutorial submission
>> deadline is March, 21st, 2016.
>>
>>
>> Important dates:
>>
>> Mar 21: Tutorial Proposals Due
>> Mar 25: Talk and Poster Proposals Due
>> May 11: Plotting Contest Submissions Due
>> Apr 22: Tutorials Announced
>> Apr 22: Financial Aid Submissions Due
>> May 4: Talk and Posters Announced
>> May 11: Financial Aid Recipients Notified
>> May 22: Early Bird Registration Deadline
>> Jul 11-12: SciPy 2016 Tutorials
>> Jul 13-15: SciPy 2016 General Conference
>> Jul 16-17: SciPy 2016 Sprints
>>
>> We look forward to an exciting conference and hope to see you in Austin
>> in July!
>>
>>
>> The Scipy 2016
>> http://scipy2016.scipy.org/
>>
>> Conference Chairs: Aric Hagberg, Prabhu Ramachandran
>> Tutorial Chairs: Justin Vincent, Ben Root
>> Program Chair: Serge Rey, Nelle Varoquaux
>> Proceeding Chairs: Sebastian Benthall
>>
>>
>
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From shoyer at gmail.com  Mon Mar 21 17:12:25 2016
From: shoyer at gmail.com (Stephan Hoyer)
Date: Mon, 21 Mar 2016 14:12:25 -0700
Subject: [SciPy-User] Scientific citation numpy.random.multinomial
In-Reply-To: <CABsGe_x-PgQ3yvpyZunQ3Tfq1YzLw-fOUWYDpeazYi5OuS+JqQ@mail.gmail.com>
References: <CABsGe_x-PgQ3yvpyZunQ3Tfq1YzLw-fOUWYDpeazYi5OuS+JqQ@mail.gmail.com>
Message-ID: <CAEQ_TveMN_r_2g73z9yNszmx=KSOYJbNQaLj5_FKT0LdkLV15A@mail.gmail.com>

Hi Johannes,

If you delve into NumPy's source code, you'll discover that it uses the
Box-Muller transform:
https://github.com/numpy/numpy/blob/v1.10.4/numpy/random/mtrand/randomkit.c#L377

Apparently this is known as the "Marsaglia polar method":
https://en.wikipedia.org/wiki/Marsaglia_polar_method

Cheers,
Stephan

On Mon, Mar 21, 2016 at 1:42 AM, Johannes Radinger <
johannesradinger at gmail.com> wrote:

> Hi all,
>
> I am using the function numpy.random.multinomial in my code and want
> to explicitly cite this function/method in a scientific (peer reviewed)
> article.
> What is the appropriate way to cite numpy.random.multinomial?
>
> I am aware that SciPy can be cited as:
> Jones E, Oliphant E, Peterson P, et al. SciPy: Open Source Scientific
> Tools for Python, 2001-,
> http://www.scipy.org/ [Online; accessed 2016-03-21].
>
> However, this is not specifically for the "random" library of numpy. Is
> there any other reference one can use?
>
> Best regards,
> Johannes
>
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> https://mail.scipy.org/mailman/listinfo/scipy-user
>
>
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From shoyer at gmail.com  Mon Mar 21 17:13:49 2016
From: shoyer at gmail.com (Stephan Hoyer)
Date: Mon, 21 Mar 2016 14:13:49 -0700
Subject: [SciPy-User] Scientific citation numpy.random.multinomial
In-Reply-To: <CAEQ_TveMN_r_2g73z9yNszmx=KSOYJbNQaLj5_FKT0LdkLV15A@mail.gmail.com>
References: <CABsGe_x-PgQ3yvpyZunQ3Tfq1YzLw-fOUWYDpeazYi5OuS+JqQ@mail.gmail.com>
 <CAEQ_TveMN_r_2g73z9yNszmx=KSOYJbNQaLj5_FKT0LdkLV15A@mail.gmail.com>
Message-ID: <CAEQ_Tve8vsFJ_RN-zHFNSXG6pCyUBx8wq8wvFe0QTCcuWTBo1g@mail.gmail.com>

And... apparently I have poor reading comprehension -- Johannes asked about
the multinomial distribution, not the normal distribution.

That said, the same method should work -- look in the source code!

On Mon, Mar 21, 2016 at 2:12 PM, Stephan Hoyer <shoyer at gmail.com> wrote:

> Hi Johannes,
>
> If you delve into NumPy's source code, you'll discover that it uses the
> Box-Muller transform:
>
> https://github.com/numpy/numpy/blob/v1.10.4/numpy/random/mtrand/randomkit.c#L377
>
> Apparently this is known as the "Marsaglia polar method":
> https://en.wikipedia.org/wiki/Marsaglia_polar_method
>
> Cheers,
> Stephan
>
> On Mon, Mar 21, 2016 at 1:42 AM, Johannes Radinger <
> johannesradinger at gmail.com> wrote:
>
>> Hi all,
>>
>> I am using the function numpy.random.multinomial in my code and want
>> to explicitly cite this function/method in a scientific (peer reviewed)
>> article.
>> What is the appropriate way to cite numpy.random.multinomial?
>>
>> I am aware that SciPy can be cited as:
>> Jones E, Oliphant E, Peterson P, et al. SciPy: Open Source Scientific
>> Tools for Python, 2001-,
>> http://www.scipy.org/ [Online; accessed 2016-03-21].
>>
>> However, this is not specifically for the "random" library of numpy. Is
>> there any other reference one can use?
>>
>> Best regards,
>> Johannes
>>
>> _______________________________________________
>> SciPy-User mailing list
>> SciPy-User at scipy.org
>> https://mail.scipy.org/mailman/listinfo/scipy-user
>>
>>
>
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From charlesr.harris at gmail.com  Sun Mar 27 19:32:46 2016
From: charlesr.harris at gmail.com (Charles R Harris)
Date: Sun, 27 Mar 2016 17:32:46 -0600
Subject: [SciPy-User] Numpy 1.11.0 released
Message-ID: <CAB6mnxJTxO_ZHDZxjcnsS0VAdw8ECwYZFtMLb_b2cPydy0t4sQ@mail.gmail.com>

Hi All,

I'm pleased to announce the release of Numpy 1.11.0. This release is the
result of six months of work comprising 371 merged pull requests submitted
by 112 authors containing many bug fixes and improvements. Highlights are:

   - The datetime64 type is now timezone naive.
   - A dtype parameter has been added to ``randint``.
   - Improved detection of two arrays possibly sharing memory.
   - Automatic bin size estimation for ``np.histogram``.
   - Speed optimization of A @ A.T and dot(A, A.T).
   - New function ``np.moveaxis`` for reordering array axes.

Source files and documentation can be found on Sourceforge
<https://sourceforge.net/projects/numpy/files/NumPy/1.11.0/>, while source
files and OS X wheels for Python 2.7, 3.3, 3.4, and 3.5 can be installed
from Pypi. Note that this is the last release to support Python 2.6, 3.2,
and 3.3.

Contributors are listed below in alphabetical order with the names of new
contributors starred.

Abdullah Alrasheed*
Aditya Panchal*
Alain*
Alex Griffing
Alex Rogozhnikov*
Alex Willmer
Alexander Heger*
Allan Haldane
Anatoly Techtonik*
Andrew Nelson
Anne Archibald
Antoine Pitrou
Antony Lee*
Behzad Nouri
Bertrand Lefebvre
Blake Griffith
Boxiang Sun*
Brigitta Sipocz*
Carl Kleffner
Charles Harris
Chris Hogan*
Christoph Gohlke
Colin Jermain*
Cong Ma*
Daniel
David Freese
David Sanders*
Dmitry Odzerikho*
Dmitry Zagorny*
Eric Larson*
Eric Moore
Ethan Kruse*
Eugene Krokhalev*
Evgeni Burovski*
Francis T. O'Donovan*
Fran?ois Boulogne*
Gabi Davar*
Gerrit Holl
Gopal Singh Meena*
Greg Yang*
Greg Young*
Gregory R. Lee
Griffin Hosseinzadeh*
Hassan Kibirige*
Holger Kohr*
Ian Henriksen
Iceman9*
Jaime Fernandez
James Camel*
Jason King*
John Bjorn Nelson*
John Kirkham
Jonathan Helmus
Jonathan Underwood*
Joseph Fox-Rabinovitz*
Julian Taylor
Julien Dubois*
Julien Lhermitte*
Julien Schueller*
J?rn Hees*
Konstantinos Psychas*
Lars Buitinck
Luke Zoltan Kelley*
MaPePeR*
Mad Physicist*
Mark Wiebe
Marten van Kerkwijk
Matthew Brett
Matthias Geier*
Maximilian Trescher*
Michael  K. Tran*
Michael Behrisch*
Michael Currie*
Michael L?ffler*
Nathaniel Hellabyte*
Nathaniel J. Smith
Nick Papior*
Nicolas Calle*
Nicol?s Della Penna*
Olivier Grisel
Pauli Virtanen
Peter Iannucci
Phaiax*
Ralf Gommers
Rehas Sachdeva*
Ronan Lamy
Ruediger Meier*
Ryan Grout*
Ryosuke Okuta*
R?my L?one*
Sam Radhakrishnan*
Samuel St-Jean*
Sebastian Berg
Simon Conseil*
Stephan Hoyer
Stuart Archibald*
Stuart Berg
Sumith*
Tapasweni Pathak*
Thomas Robitaille
Tobias Megies*
Tushar Gautam*
Varun Nayyar*
Vincent Legoll*
Warren Weckesser
Wendell Smith
Yash Mehrotra*
Yifan Li*
endolith
floatingpointstack*
ldoddema*
yolanda15*

Thanks to all who worked on this release.

Chuck
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From andyfaff at gmail.com  Tue Mar 29 19:10:03 2016
From: andyfaff at gmail.com (Andrew Nelson)
Date: Wed, 30 Mar 2016 10:10:03 +1100
Subject: [SciPy-User] Sampling from an arbitrary distribution.
Message-ID: <CAAbtOZe8e3-sQdRx5JYw+WLG1PhLTCJjdiMZP_kP_TUEFKT_ZA@mail.gmail.com>

Hi all,
I wish to sample from an arbitrary probability density distribution. At the
moment I don't have a functional form for the density distribution (so I'm
not sure if I can subclass scipy.stats.rv_continuous)

What I do have is an array containing (finely) histogrammed samples of the
distribution. I found some pre-existing code (
http://www.nehalemlabs.net/prototype/blog/2013/12/16/how-to-do-inverse-transformation-sampling-in-scipy-and-numpy/)
which I successfully modified to deal with pre-histogrammed data.

Q1: Can I still subclass scipy.stats.rv_continuous to sample from my
arbitrary distribution? e.g. use the histogram to construct the object and
refer to this histogram when overriding _pdf.
Q2: If not, then is it worth adding functionality for this kind of
sampling? Where would it belong scipy.stats or numpy.random?

A.
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From josef.pktd at gmail.com  Tue Mar 29 19:30:25 2016
From: josef.pktd at gmail.com (josef.pktd at gmail.com)
Date: Tue, 29 Mar 2016 19:30:25 -0400
Subject: [SciPy-User] Sampling from an arbitrary distribution.
In-Reply-To: <CAAbtOZe8e3-sQdRx5JYw+WLG1PhLTCJjdiMZP_kP_TUEFKT_ZA@mail.gmail.com>
References: <CAAbtOZe8e3-sQdRx5JYw+WLG1PhLTCJjdiMZP_kP_TUEFKT_ZA@mail.gmail.com>
Message-ID: <CAMMTP+Bv5V5BBrFsNTaXbnTu8Cz+-dOPGztREg4tPZtXb2sXwQ@mail.gmail.com>

On Tue, Mar 29, 2016 at 7:10 PM, Andrew Nelson <andyfaff at gmail.com> wrote:
> Hi all,
> I wish to sample from an arbitrary probability density distribution. At the
> moment I don't have a functional form for the density distribution (so I'm
> not sure if I can subclass scipy.stats.rv_continuous)
>
> What I do have is an array containing (finely) histogrammed samples of the
> distribution. I found some pre-existing code
> (http://www.nehalemlabs.net/prototype/blog/2013/12/16/how-to-do-inverse-transformation-sampling-in-scipy-and-numpy/)
> which I successfully modified to deal with pre-histogrammed data.

I was very successful using linear interpolation for the ppf to
generate random samples, which corresponds to a histogram distribution
http://jpktd.blogspot.ca/2012/12/visual-inspection-of-random-numbers.html


>
> Q1: Can I still subclass scipy.stats.rv_continuous to sample from my
> arbitrary distribution? e.g. use the histogram to construct the object and
> refer to this histogram when overriding _pdf.

It's very inefficient to generate random numbers if only the pdf is
defined. The default generic approach uses ppf which needs cdf which
needs. So you would better also define cdf and ppf.


> Q2: If not, then is it worth adding functionality for this kind of sampling?
> Where would it belong scipy.stats or numpy.random?

It might not fit well into the current scipy.distribution setup
because that is mostly stateless. In this case it could be an
advantage to store the interpolation arrays. Or maybe the advantage is
not so large if creating the interpolator is cheap

A Histogram distribution would be the continuous analog to the
arbitrary finite number of points discrete distribution, so might fit
as well.

Josef

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


From ndbecker2 at gmail.com  Wed Mar 30 13:28:53 2016
From: ndbecker2 at gmail.com (Neal Becker)
Date: Wed, 30 Mar 2016 13:28:53 -0400
Subject: [SciPy-User] Sampling from an arbitrary distribution.
References: <CAAbtOZe8e3-sQdRx5JYw+WLG1PhLTCJjdiMZP_kP_TUEFKT_ZA@mail.gmail.com>
 <CAMMTP+Bv5V5BBrFsNTaXbnTu8Cz+-dOPGztREg4tPZtXb2sXwQ@mail.gmail.com>
Message-ID: <ndh2cl$u39$1@ger.gmane.org>

josef.pktd at gmail.com wrote:

> On Tue, Mar 29, 2016 at 7:10 PM, Andrew Nelson <andyfaff at gmail.com> wrote:
>> Hi all,
>> I wish to sample from an arbitrary probability density distribution. At
>> the moment I don't have a functional form for the density distribution
>> (so I'm not sure if I can subclass scipy.stats.rv_continuous)
>>
>> What I do have is an array containing (finely) histogrammed samples of
>> the distribution. I found some pre-existing code
>> (http://www.nehalemlabs.net/prototype/blog/2013/12/16/how-to-do-inverse-transformation-sampling-in-scipy-and-numpy/)
>> which I successfully modified to deal with pre-histogrammed data.
> 
> I was very successful using linear interpolation for the ppf to
> generate random samples, which corresponds to a histogram distribution
> http://jpktd.blogspot.ca/2012/12/visual-inspection-of-random-numbers.html
> 
> 
>>
>> Q1: Can I still subclass scipy.stats.rv_continuous to sample from my
>> arbitrary distribution? e.g. use the histogram to construct the object
>> and refer to this histogram when overriding _pdf.
> 
> It's very inefficient to generate random numbers if only the pdf is
> defined. The default generic approach uses ppf which needs cdf which
> needs. So you would better also define cdf and ppf.
> 
> 
>> Q2: If not, then is it worth adding functionality for this kind of
>> sampling? Where would it belong scipy.stats or numpy.random?
> 
> It might not fit well into the current scipy.distribution setup
> because that is mostly stateless. In this case it could be an
> advantage to store the interpolation arrays. Or maybe the advantage is
> not so large if creating the interpolator is cheap
> 
> A Histogram distribution would be the continuous analog to the
> arbitrary finite number of points discrete distribution, so might fit
> as well.
> 
> Josef
> 
>>
>> A.
>>
>>
>> _______________________________________________
>> SciPy-User mailing list
>> SciPy-User at scipy.org
>> https://mail.scipy.org/mailman/listinfo/scipy-user
>>

You might want to look at unuran.



From rays at blue-cove.com  Thu Mar 31 13:13:49 2016
From: rays at blue-cove.com (R Schumacher)
Date: Thu, 31 Mar 2016 10:13:49 -0700
Subject: [SciPy-User] inverse filter design, remez?
Message-ID: <201603311713.u2VHDqMS020388@blue-cove.com>

We have sets of ADC data collected with different front-end filters 
at acquisition; given an empirically determined roll off in dB/octave 
or dB/Hz, I'd like to perform equalization (in this case the low 
frequencies) and I know from sweeps that roll off begins under ~200Hz 
and -3dB is ~15Hz.

One general example is Bank's algorithm:
<http://nullege.com/codes/show/src at p@o at porc-HEAD@porc.py>http://nullege.com/codes/show/src at p@o at porc-HEAD@porc.py
where people correct sound for their room acoustics.
There is also the Nelson-Kirkeby inverse filter methodology.

Our specific case is flattening the response due to a single-pole RC 
high-pass filter so I think remez() might be just fine; can anyone 
here supply links or examples in scipy to this application of remez()?
I'm guessing
scipy.signal.remez(
2,
range(.625, 512, .625),
desired= <inverse of measured gain, range(.625, 256, .625)>,
weight=<unsure of this...>,
Hz=1024,
type=<unsure, what to use for high-pass?>,
maxiter=25,
grid_density=16
)

- Ray 
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