583 research outputs found
Py2Cy: A Genetic Improvement Tool To Speed Up Python
Due to its ease of use and wide range of custom libraries, Python has
quickly gained popularity and is used by a wide range of developers
all over the world. While Python allows for fast writing of source
code, the resulting programs are slow to execute when compared
to programs written in other programming languages like C. One
of the reasons for its slow execution time is the dynamic typing
of variables. Cython is an extension to Python, which can achieve
execution speed-ups by compiler optimization. One possibility for
improvements is the use of static typing, which can be added to
Python scripts by developers. To alleviate the need for manual effort,
we create Py2Cy, a Genetic Improvement tool for automatically
converting Python scripts to statically typed Cython scripts. To
show the feasibility of improving runtime with Py2Cy, we optimize
a Python script for generating Fibonacci numbers. The results show
that Py2Cy is able to speed up the execution time by up to a factor
of 18
Nonparametric Methods in Astronomy: Think, Regress, Observe -- Pick Any Three
Telescopes are much more expensive than astronomers, so it is essential to
minimize required sample sizes by using the most data-efficient statistical
methods possible. However, the most commonly used model-independent techniques
for finding the relationship between two variables in astronomy are flawed. In
the worst case they can lead without warning to subtly yet catastrophically
wrong results, and even in the best case they require more data than necessary.
Unfortunately, there is no single best technique for nonparametric regression.
Instead, we provide a guide for how astronomers can choose the best method for
their specific problem and provide a python library with both wrappers for the
most useful existing algorithms and implementations of two new algorithms
developed here.Comment: 19 pages, PAS
Beyond XSPEC: Towards Highly Configurable Analysis
We present a quantitative comparison between software features of the defacto
standard X-ray spectral analysis tool, XSPEC, and ISIS, the Interactive
Spectral Interpretation System. Our emphasis is on customized analysis, with
ISIS offered as a strong example of configurable software. While noting that
XSPEC has been of immense value to astronomers, and that its scientific core is
moderately extensible--most commonly via the inclusion of user contributed
"local models"--we identify a series of limitations with its use beyond
conventional spectral modeling. We argue that from the viewpoint of the
astronomical user, the XSPEC internal structure presents a Black Box Problem,
with many of its important features hidden from the top-level interface, thus
discouraging user customization. Drawing from examples in custom modeling,
numerical analysis, parallel computation, visualization, data management, and
automated code generation, we show how a numerically scriptable, modular, and
extensible analysis platform such as ISIS facilitates many forms of advanced
astrophysical inquiry.Comment: Accepted by PASP, for July 2008 (15 pages
MaLeS: A Framework for Automatic Tuning of Automated Theorem Provers
MaLeS is an automatic tuning framework for automated theorem provers. It
provides solutions for both the strategy finding as well as the strategy
scheduling problem. This paper describes the tool and the methods used in it,
and evaluates its performance on three automated theorem provers: E, LEO-II and
Satallax. An evaluation on a subset of the TPTP library problems shows that on
average a MaLeS-tuned prover solves 8.67% more problems than the prover with
its default settings
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