[SciPy-user] Re-releasing Python Equations under a new license?

Fernando Perez fperez.net at gmail.com
Thu Jan 18 15:47:53 CST 2007


On 1/18/07, zunzun at zunzun.com <zunzun at zunzun.com> wrote:
> Can I get any advice on which software license to distribute
> my code?  The sourceforge URL, which I forgot to post before, is
>
> http://sourceforge.net/projects/pythonequations
>
> I'm just interested in feedback from users, whether someone uses
> the code in a commercial application is not particulary important
> to me personally.  I would like people to know where to get the
> source code itself if someone does redistribute, again so that
> the chance of feedback on the source code is increased.
>
> I'm not chained to the GPL, I'm just naive and inexperienced.
> Any advice would be greatly appreciated, and my thanks to
> Glen Mabey for his thoughts below.  Re-releasing would happen
> this weekend if I knew which license to use other than the GPL.

John Hunter has a standardized and extremely well-written response to
this question.  Rather than poorly paraphrasing him, I just hit 'jdh
bsd pitch' in my gmail search box, and I'll let his words tell the
rest.

This should (with John's permission) probably go into the scipy FAQ...

Cheers,

f

###### Written by John Hunter in response to this same question

Would you consider licensing your code under a more permissive
license?  Most of the essential scientific computing tools in python
(scipy, numpy, matplotlib, ipython, vtk, enthought tool suite, ....)
are licensed under a BSD-ish style license, and cannot reuse GPLd
code.

My standard "licensing pitch" is included below::

I'll start by summarizing what many of you already know about open
source licenses. I believe this discussion is broadly correct, though
it is not a legal document and if you want legally precise statements
you should reference the original licenses cited here. The
Open-Source-Initiative is a clearing house for OS licenses, so you can
read more there.

The two dominant license variants in the wild are GPL-style and
BSD-style. There are countless other licenses that place specific
restrictions on code reuse, but the purpose of this document is to
discuss the differences between the GPL and BSD variants, specifically
in regards to my experience developing matplotlib and in my
discussions with other developers about licensing issues.

The best known and perhaps most widely used license is the GPL, which
in addition to granting you full rights to the source code including
redistribution, carries with it an extra obligation. If you use GPL
code in your own code, or link with it, your product must be released
under a GPL compatible license. I.e., you are required to give the source
code to other people and give them the right to redistribute it as
well. Many of the most famous and widely used open source projects are
released under the GPL, including linux, gcc and emacs.

The second major class are the BSD-style licenses (which includes MIT
and the python PSF license). These basically allow you to do whatever
you want with the code: ignore it, include it in your own open source
project, include it in your proprietary product, sell it,
whatever. python itself is released under a BSD compatible license, in
the sense that, quoting from the PSF license page

 There is no GPL-like "copyleft" restriction. Distributing
 binary-only versions of Python, modified or not, is allowed. There
 is no requirement to release any of your source code. You can also
 write extension modules for Python and provide them only in binary
 form.

Famous projects released under a BSD-style license in the permissive
sense of the last paragraph are the BSD operating system, python and
TeX.

I believe the choice of license is an important one, and I advocate a
BSD-style license. In my experience, the most important commodity an
open source project needs to succeed is users. Of course, doing
something useful is a prerequisite to getting users, but I also
believe users are something of a prerequisite to doing something
useful. It is very difficult to design in a vacuum, and users drive
good software by suggesting features and finding bugs. If you satisfy
the needs of some users, you will inadvertently end up satisfying the
needs of a large class of users. And users become developers,
especially if they have some skills and find a feature they need
implemented, or if they have a thesis to write. Once you have a lot of
users and a number of developers, a network effect kicks in,
exponentially increasing your users and developers. In open source
parlance, this is sometimes called competing for mind share.

So I believe the number one (or at least number two) commodity an open
source project can possess is mind share, which means you want as many
damned users using your software as you can get. Even though you are
giving it away for free, you have to market your software, promote it,
and support it as if you were getting paid for it. Now, how does this
relate to licensing, you are asking?

Many software companies will not use GPL code in their own software,
even those that are highly committed to open source development, such
as enthought, out of legitimate concern that use of the GPL will
"infect" their code base by its viral nature. In effect, they want to
retain the right to release some proprietary code. And in my
experience, companies make for some of the best developers, because
they have the resources to get a job done, even a boring one, if they
need it in their code. Two of the matplotlib backends (FLTK and WX)
were contributed by private sector companies who are using matplotlib
either internally or in a commercial product -- I doubt these
companies would have been using matplotlib if the code were GPL. In my
experience, the benefits of collaborating with the private sector are
real, whereas the fear that some private company will "steal" your
product and sell it in a proprietary application leaving you with
nothing is not.

There is a lot of GPL code in the world, and it is a constant reality
in the development of matplotlib that when we want to reuse some
algorithm, we have to go on a hunt for a non-GPL version. Most
recently this occurred in a search for a good contouring algorithm. I
worry that the "license wars", the effect of which are starting to be
felt on many projects, have a potential to do real harm to open source
software development. There are two unpalatable options. 1) Go with
GPL and lose the mind-share of the private sector 2) Forgo GPL code
and retain the contribution of the private sector. This is a very
tough decision because their is a lot of very high quality software
that is GPL and we need to use it; they don't call the license viral
for nothing.

The third option, which is what is motivating me to write this, is to
convince people who have released code under the GPL to re-release it
under a BSD compatible license. Package authors retain the copyright
to their software and have discretion to re-release it under a license
of their choosing. Many people choose the GPL when releasing a package
because it is the most famous open source license, and did not
consider issues such as those raised here when choosing a
license. When asked, these developers will often be amenable to
re-releasing their code under a more permissive license. Fernando
Perez did this with ipython, which was released under the LGPL and
then re-released under a BSD license to ease integration with
matplotlib, scipy and enthought code. The LGPL is more permissive than
the GPL, allowing you to link with it non-virally, but many companies
are still loath to use it out of legal concerns, and you cannot reuse
LGPL code in a proprietary product.

So I encourage you to release your code under a BSD compatible
license, and when you encounter an open source developer whose code
you want to use, encourage them to do the same. Feel free to forward
this document on them.

Comments, suggestions for improvements, corrections, etc, should be
sent to jdhunter at ace.bsd.uchicago.edu


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