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

John Hunter jdhunter at ace.bsd.uchicago.edu
Fri Jan 19 11:10:12 CST 2007


>>>>> "Gabriel" == Gabriel Gellner <ggellner at uoguelph.ca> writes:
    Gabriel> Re 1): I have never understood why this is the
    Gabriel> case. GTK+, gcc stack, and many other libraries are LGPL
    Gabriel> and are used by companies for there products (like
    Gabriel> acrobat for linux, and OS X as far as I know (gcc
    Gabriel> stack)), and I have never heard of their code being
    Gabriel> 'infected'.

    Gabriel> If so then could it be that the problem is not point 3,
    Gabriel> but rather that companies do not understand that the LGPL
    Gabriel> does not take their code, and stop them from making
    Gabriel> proprietary products with it (you would only have to

I think this is broadly correct -- the LGPL should be safe for these
companies and should not threaten their code.  Part of what is going
on here is that companies that distribute software don't want to risk
it.  The FSF is fairly fanatical, and as we are seeing with GPL3, may
change the rules as time goes on.  For small companies who want to
distribute source code with their software but cannot afford lawyers
and legal fights, I think the preference is to not get involved with
the GPL, L or otherwise.  As Eric Jones has said about licenses, the
fewer words the better.  His point is, I think, that the larger and
more complex the license, the more likely it is that lawyers can
disagree over it.

And LGPL can hinder code reuse.  If someone releases a large LGPL
package that defines some small but nifty scientific algorithm, we may
prefer to cut that algorithm out and insert it into our own code base
w/o making the entire package a dependency at link time.  This makes
it much easier to include and reuse just the bits you want.  Of
course, the package authors may not want this and may want us to
depend on their package, which is understandable and is their choice,
but it certainly restricts our choice in building a set of tools that
we can distribute with minimal size and dependencies as we see fit.

JDH


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