[SciPy-user] Any Books on SciPy?
Wed Feb 28 08:42:14 CST 2007
I agree that there is a huge void in proper Scipy documentation which
is essential for broader adoption. I know you can always go to the
source files but this is not something everybody will do. Although the
Documentation has been growing over the past year we're still missing
some central 'official' documentation or at least tutorial.
While I'm on the subject, I bought Travis' book early last year. I
understood that we should have received updates as they became
available. I never heard or received anything. Have there been any
updates in the past year or was I left out of the loop? The reason I
ask this is because I understand there were substantial changes to
numpy in the road to 1.0. Are these changes documented anywhere other
than the developers mailing lists?
Thank you very much,
On 2/28/07, email@example.com <firstname.lastname@example.org> wrote:
> Message: 8
> Date: Tue, 27 Feb 2007 23:19:51 -0700
> From: "Fernando Perez" <email@example.com>
> Subject: Re: [SciPy-user] Any Books on SciPy?
> To: "SciPy Users List" <firstname.lastname@example.org>
> Content-Type: text/plain; charset=ISO-8859-1; format=flowed
> On 2/27/07, John Hunter <email@example.com> wrote:
> > On 2/27/07, Robert Love <firstname.lastname@example.org> wrote:
> > > Are there any good, up to date books that people recommend for
> > > numerical work with Python?
> > >
> > > I see the book
> > >
> > > Python Scripting for Computational Science
> > > Hans Petter Langtangen
> > >
> > > Does anyone have opinions on this? Is it current? Are there better
> > > books?
> > I specifically do not recommend this book -- I own it but in my
> > opinion it is outdated and is more a collection of the author's
> > personal idioms than the current common practice in the scientific
> > python community. For numerical work in python most people use
> I happen to share John's opinion, and I also have a copy of this book.
> While it's technically correct, well written and fairly comprehensive
> (probably /too/ much, since it's a bit all over the map), I strongly
> dislike his approach. Much of the book uses his custom, home-made
> collection of scripts and tools, which you can only download if you go
> to a site and type a word from a certain page in the book (a simple
> 'protection' system).
> Now you have an unmaintained, unreleased (publicly), set of tools to
> learn from that don't have any licensing explicitly specified.
> Oh, and a good chunk of the tools in his distribution (since I have
> the book, I have the code) use Perl. Go figure (there's also a tcl
> directory thrown in for good measure).
> One of Python's main strengths for scientific work is precisely the
> openness and interoperability of the various tools, and we all do our
> part to help that be the case. The fact that this book follows an
> approach more or less orthogonal to those ideas makes me very much
> uninterested in using it.
> > There is a lot more, particularly for domain specific stiff, but these
> > links are good starting points. Unfortunately, there is no
> > one-stop-shop for a guide to scientific computing in python - Travis'
> > documentation is the closest thing we have but it pretty much just
> > covers numpy which is *the* core package. Fernando Perez and I have a
> > very brief and limited started guide covering multiple packages
> > (ipython, numpy, matplotlib, scipy, VTK) but I don't have the PDF
> > handy (Fernando, do you have the roadshow doc handy?).
> Well, you asked for it :)
> It's worth stressing, in the strongest possible terms, that this
> should NOT, in any way, shape or form, be considered anything beyond a
> pre-pre-alpha, pre-draft of a project for a possible book :) Besides,
> it's already outdated in several important places (numpy, mayavi, no
> After all I said about the Langtangen book, at least it's a real one.
> Our pdf draft is most certainly not. So if you need a book, with all
> of its limitations, Langtangen's is currently the only game in town
> that covers the whole spectrum of python for scientific computing. If
> John and I ever end up stranded on a desert island for 3 months with
> great internet access and poor diving gear, we might actually finish
> ours, but don't hold your breath.
> Honestly, I think that today your best bet is:
> 1. Buy Travis' book. It's fantastic, has everything you need to know
> about numpy, and you'll be supporting numpy itself.
> 2. Print Perry Greenfield's tutorial
> (http://new.scipy.org/wikis/topical_software/Tutorial). I think he's
> updating it for numpy now.
> 3. Have a look at some of the other info in
> http://new.scipy.org/Documentation, in particular D. Kuhlman's course
> is very nice.
Biofluid Mechanics Lab
Department of Mechanical Engineering
University of Maryland, Baltimore County
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