[SciPy-Dev] SciPy Goal
Thu Jan 5 00:02:19 CST 2012
On Wed, Jan 4, 2012 at 9:29 PM, Travis Oliphant <firstname.lastname@example.org> wrote:
> On Jan 4, 2012, at 8:22 PM, Fernando Perez wrote:
> > Hi all,
> > On Wed, Jan 4, 2012 at 5:43 PM, Travis Oliphant <email@example.com>
> >> What do others think is missing? Off the top of my head: basic
> >> (dwt primarily) and more complete interpolation strategies (I'd like to
> >> finish the basic interpolation approaches I started a while ago).
> >> Originally, I used GAMS as an "overview" of the kinds of things needed
> >> SciPy. Are there other relevant taxonomies these days?
> > Well, probably not something that fits these ideas for scipy
> > one-to-one, but the Berkeley 'thirteen dwarves' list from the 'View
> > from Berkeley' paper on parallel computing is not a bad starting
> > point; summarized here they are:
> > Dense Linear Algebra
> > Sparse Linear Algebra 
> > Spectral Methods
> > N-Body Methods
> > Structured Grids
> > Unstructured Grids
> > MapReduce
> > Combinational Logic
> > Graph Traversal
> > Dynamic Programming
> > Backtrack and Branch-and-Bound
> > Graphical Models
> > Finite State Machines
> This is a nice list, thanks!
> > Descriptions of each can be found here:
> > http://view.eecs.berkeley.edu/wiki/Dwarf_Mine and the full study is
> > here:
> > http://www.eecs.berkeley.edu/Pubs/TechRpts/2006/EECS-2006-183.html
> > That list is biased towards the classes of codes used in
> > supercomputing environments, and some of the topics are probably
> > beyond the scope of scipy (say structured/unstructured grids, at least
> > for now).
> > But it can be a decent guiding outline to reason about what are the
> > 'big areas' of scientific computing, so that scipy at least provides
> > building blocks that would be useful in these directions.
> Thanks for the links.
> > One area that hasn't been directly mentioned too much is the situation
> > with statistical tools. On the one hand, we have the phenomenal work
> > of pandas, statsmodels and sklearn, which together are helping turn
> > python into a great tool for statistical data analysis (understood in
> > a broad sense). But it would probably be valuable to have enough of a
> > statistical base directly in numpy/scipy so that the 'out of the box'
> > experience for statistical work is improved. I know we have
> > scipy.stats, but it seems like it needs some love.
> It seems like scipy stats has received quite a bit of attention. There
> is always more to do, of course, but I'm not sure what specifically you
> think is missing or needs work.
Test coverage, for example. I recently fixed several wildly incorrect
skewness and kurtosis formulas for some distributions, and I now have very
little confidence that any of the other distributions are correct. Of
course, most of them probably *are* correct, but without tests, all are in
A big question to me is the impact of data-frames as the underlying
> data-representation of the algorithms and the relationship between the
> data-frame and a NumPy array.
> > Cheers,
> > f
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