Nikolay Mayorov | 22 May 09:14 2016

Boundary conditions for CubicSpline


There is some discussion going on about how to implement periodic boundary conditions for CubicSpline class, which to appear in 0.18.0 scipy release:

If it's of interest to you, please take a look and comment. Any other feedback is also welcome.


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SciPy 2016 Conference (Scientific Computing with Python): Tutorials and Talks Announced

**ANN: SciPy 2016 Conference (Scientific Computing with Python): Tutorials and Talks Announced**


We’re excited to announce this year’s accepted Talks & Posters and Tutorial Schedule!  This year’s 3 major talk tracks include Python in Data Science, High Performance Computing, and general Scientific Computing. Our six mini-symposia include: Earth and Space Science, Engineering, Medicine and Biology, Case Studies in Industry, Education, and Reproducibility. For tutorials, you can choose from 18 different SciPy tutorials, including a 1 day Software Carpentry Scientific Python course that assumes some programming experience but no Python knowledge, or a 2-day Software Carpentry Instructor Training.


We hope you’ll join us - early bird registration ENDS May 22, 2016. Register at:


About SciPy 2016

SciPy 2016, the 15th annual Scientific Computing with Python conference, will be held July 11-17, 2016 in Austin, Texas. SciPy is a community dedicated to the advancement of scientific computing through open source Python software for mathematics, science, and engineering. The annual SciPy Conference brings together over 650 participants from industry, academia, and government to showcase their latest projects, learn from skilled users and developers, and collaborate on code development. The full program will consist of 2 days of tutorials (July 11-12), 3 days of talks (July 13-15), and 2days of developer sprints (July 16-17). More info is available on the conference website at (where you can sign up for the mailing list); or follow <at> scipyconf on Twitter.  



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Charles R Harris | 18 May 23:09 2016

Scipy 2016 attending

Hi All,

Out of curiosity, who all here intends to be at Scipy 2016?

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Evgeni Burovski | 12 May 15:02 2016

scipy 0.17.1 release


On behalf of the scipy development team, I'm pleased to announce the
availability of scipy 0.17.1.

This is a bugfix release with no new features compared to 0.17.0.

Source tarballs and OS X wheels are available from PyPI or from GitHub
releases at

We recommend that all users upgrade from scipy 0.17.0.



SciPy 0.17.1 Release Notes

SciPy 0.17.1 is a bug-fix release with no new features compared to 0.17.0.

Issues closed for 0.17.1

- `#5817 <>`__: BUG: skew,
kurtosis return np.nan instead of "propagate"
- `#5850 <>`__: Test failed
with sgelsy
- `#5898 <>`__:
interpolate.interp1d crashes using float128
- `#5953 <>`__: Massive
performance regression in cKDTree.query with L_inf distance...
- `#6062 <>`__: mannwhitneyu
breaks backward compatibility in 0.17.0
- `#6134 <>`__: T test does
not handle nans

Pull requests for 0.17.1

- `#5902 <>`__: BUG:
interpolate: make interp1d handle np.float128 again
- `#5957 <>`__: BUG: slow down
with p=np.inf in 0.17 cKDTree.query
- `#5970 <>`__: Actually
propagate nans through stats functions with nan_policy="propagate"
- `#5971 <>`__: BUG: linalg:
fix lwork check in *gelsy
- `#6074 <>`__: BUG: special:
fixed violation of strict aliasing rules.
- `#6083 <>`__: BUG: Fix dtype
for sum of linear operators
- `#6100 <>`__: BUG: Fix
mannwhitneyu to be backward compatible
- `#6135 <>`__: Don't pass
null pointers to LAPACK, even during workspace queries.
- `#6148 <>`__: stats: fix
handling of nan values in T tests and kendalltau
Nathan Woods | 11 May 04:20 2016

(no subject)

There have been a number of problems (e.g. GitHub issues #5002, #4831) with the C interface of scipy.integrate.quad since work was done a couple of years ago to allow performance optimizations ( Specifically, the QuadPack Fortran library does not really accommodate multivariate integrand functions, which leads to all sorts of monkey business in the API to hide that multivariate-ness. This is further complicated by the fact that the QuadPack interface is one of the oldest parts of SciPy and uses the Python C API directly, which many (most?) of us are unfamiliar with. 

I'd really like to figure out some kind of "good" solution to this. I think that the requirements of the problem are fairly simple: connect the existing scipy.integrate.quad routine to the underlying Fortran code, while allowing users to use compiled function pointers as integrand functions, if they want to (and are willing to put in some effort). 

I guess that the question here is, what is the best way to go about this? Should we simply find a way to fix the existing interface code? Should we roll back the changes and disallow the use of function pointers in quad? Should we replace the existing API with something else, using Cython or f2py? Should we alter the QuadPack code itself (perhaps by adding a void* argument to the function) to make it more friendly to multivariate functions in general?

The current state of affairs bothers me, especially since I was involved in the PR that  caused the new problems, but I haven't been able to come up with a good way to fix things yet. I'd love to get some ideas and feedback on how this could be resolved.

Nathan Woods
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David Shi | 11 May 01:08 2016

Looking for an example of using PchipInterpolator

Hello, Friends,

I am looking for an example of using Scipy  PchipInterpolator.

However, when I used matplotlib to plot the data, the last section of the line is missing.  Only data points are present.

Looking forward to hearing from you.


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Eric Quintero | 4 May 23:12 2016

scipy.signal filtering API

Hi All,

I would like to bring the following Github issue to your attention, where I would like to start a discussion
on unifying the API of filter/LTI system design and analysis in scipy.signal:

I appreciate any and all feedback that users and developers may have!

Thanks for your time,
Eric Quintero
Pauli Virtanen | 24 Apr 22:38 2016

PR adding boundary value problem solver in scipy


I'd like to draw attention to a PR from Nikolay adding a BVP problem 
solver to Scipy. If this is interesting to you, maybe try it out:

WESLEY WANG | 19 Apr 00:47 2016

coding project

Hi All! 

My name is Wesley Wang and I am currently working on a project for our class which involves helping contribute to a project. While I was looking through a list of projects, I became very interested in SciPy, which combines coding with math and science. 

Is there anything which I can possibly contribute to in terms of code? (As a chemistry major, I'm looking to maybe add or change some things in the file involving constants commonly used in chemistry) 

Any feedback would be nice! 
Thanks and have a great day! 

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Boris Faleichik | 17 Apr 19:10 2016

On an example problem from optimize/

Dear all,

my question is addressed to the authors of

I am looking for more detailed description of the nonlinear integro-differential equation which is a test problem for Newton-Krylov iteration in the documentation:

\nabla^2 P = 10 \left(\int_0^1\int_0^1\cosh(P)\,dx\,dy\right)^2

As a computational scientist I would like to know the origin of this problem and possibly to get some references on related scientific papers.

Thank you very much!

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Tyler Reddy | 15 Apr 12:22 2016

Adding surface area calculation for spherical polygons


I've opened an issue ( that describes and demonstrates code for calculating the surface areas of spherical polygons. It would be helpful to have some input--I've put sample results there and linked to a github with Dockerfile, etc. for reproducing the results / modifying code if needed.

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