[SciPy-user] SciPy 2005 - thanks, accolades and suggestions
simon.j.hook at jpl.nasa.gov
Sun Sep 25 12:45:50 CDT 2005
Many thanks to Enthought and all the speakers/developers for putting on
an excellent SciPy 2005. As a someone who is fairly new to both Python
and SciPy, I found this a tremendous opportunity to learn more about
what can be done with Python for scientific computing (just about
At the meeting there was a request for suggestions on how increase the
usability and audience. Here are more thoughts:
1. The Enthought distribution of Python (Enthon) which includes many of
the scientific packages, tested to make sure they work together is
extremely valuable to someone like me who does not know what tools are
out there and does not want to do dozens of downloads and installs only
to find that package "a" conflicts with "b". I realize that such a
distribution is a significant contribution and I would be happy to pay a
modest sum for such a package. If someone puts together such a package
please make the payment easy - i..e. credit card over the net. If you
work in/with government there are certain methods that are
straighforward to pay for stuff and others that require much more
paperwork, e.g. check, paypal, ebay. The best method is credit card over
the net. Given the rapid rate of improvement, another approach might be
to buy the first distribution, then get free new distributions for a
year and then buy the next distribution. Note setting up service
contracts is also a hassle so better to have a new distribution to buy
once per year or so.
2. I would like to suggest the following packages be added to the Enthon
inotebook (shown at scipy - not sure where to download)
3. Many of the packages have excellent tutorials but they are package
centric. What I mean by this is instead of taking a scientific problem
and showing how you solve it, each package carefully describes its
capabilities largely in isolation. It would be great if there was a
simple tutorial(s) that tried to show how the tools could be used
together. For example, say you want to read in some air temperature
data, produce a plot of the data, create and fit a model to the data,
generate an inotebook and then create a vision type model to interact
with the data - you would have to know about all the packages and have
read all the tutorials for each of the packages - too much time would be
spent on the tools and not enough on the problem. Incidentally there is
an example of something headed along these lines in the scipy tutorial.
I am happy to help try and put something like this together, perhaps it
could be a tutorial, perhaps a book - it would benefit from help from
each of the people developing the different packages. On that note it
would be nice if the package tutorials were on the scipy site under docs
and if package developers called their guides "package-name_users_guide"
rather than just "users_guide".
4. More examples. I find it much easier to follow examples than read
manuals. It would be great if there were more user contributed examples
in scipy. Matplotlib does an excellent job with examples. I just sent an
example in on using read_array for scipy.
5. If there were some tutorials that showed how the tools could be
used to solve scientific problems it may be worthwhile trying to have a
morning at scipy for new users that worked them through the tutorials.
Alternatively these could be at science conferences. In my research
area, before a conference starts there are usually classes which people
pay for that teach them new tools. The classes tend to be geared
towards a particular research area, in my case geologic remote sensing.
I have run a few classes and it would be good way to introduce students
to scipy+ in the context of what they need to do.
Again many thanks again to the organizers, developers and presenters.
Simon J. Hook, MSc, PhD
Jet Propulsion Laboratory
Pasadena, CA 91109
Email: simon.j.hook at jpl.nasa.gov
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