The AstroStat Slog

Sherpa is a fitting environment in which Chandra data (and really, X-ray data from any observatory) can be analyzed. It has just undergone a major update and now runs on python. Or allows python to run. Something like that. It is a very powerful tool, but I can never remember how to use it, and I have an amazing knack for not finding what I need in the documentation. So here is a little cheat sheet (which I will keep updating as and when if I learn more): Continue reading ‘Everybody needs crampons’ »

I was told to stay away from python and I’ve obeyed the order sincerely. However, I collected the following stuffs several months back at the instance of hearing about import inference and I hate to see them getting obsolete. At that time, collecting these modules and getting through them could help me complete the first step toward the quest Learning Python (the first posting of this slog). Continue reading ‘some python modules’ »

X={ primer, tutorial, cookbook, Introduction, guidebook, 101, for dummies, …}

I’ve heard many times about the lack of documentation of this extensive data analysis system, ciao. I saw people still using ciao 3.4 although the new version 4 has been available for many months. Although ciao is not the only tool for Chandra data analysis, it was specifically designed for it. Therefore, I expect it being used frequently with popularity. But the reality is against my expectation. Whatever (fierce) discussion I’ve heard, it has been irrelevant to me because ciao is not intended for statistical analysis. Then, out of sudden, after many months, a realization hit me. ciao is different from other data analysis systems and softwares. This difference has been a hampering factor of introducing ciao outside the Chandra scientist community and of gaining popularity. This difference was the reason I often got lost in finding suitable documentations. Continue reading ‘Where is ciao X ?’ »

Almost 100 years ago, A.S. Eddington stated in his book Stellar Movements (1914) that

…in calculating the mean error of a series of observations it is preferable to use the simple mean residual irrespective of sign rather than the mean square residual

Such eminent astronomer said already least absolute deviation over chi-square, if I match simple mean residual and mean square residual to relevant methodologies, in order. Continue reading ‘a century ago’ »

Astronomers tend to think in Bayesian way, but their Bayesian implementation is very limited. OpenBUGS, WinBUGS, GeoBUGS (BUGS for geostatistics; for example, modeling spatial distribution), R2WinBUGS (R BUGS wrapper) or PyBUGS (Python BUGS wrapper) could boost their Bayesian eagerness. Oh, by the way, BUGS stands for Bayesian inference Using Gibbs Sampling. Continue reading ‘BUGS’ »

PyIMSL is a collection of Python wrappers to the math and statistical algorithms in the IMSL C Numerical Library^[1]. I recall the days of digging in IMSL (International Mathematics and Statistics Library) user manuals and learning Fortran and C to use this vast library (Splus was to slow at that time). Upon knowing that Python is very favored among astronomers (click here to see the slog posts about Python) and that limits exist in Numerical Recipes (I didn’t check the latest version published last year, though), probably IMSL is useful for mathematical and statistical analysis for astronomers.

To know more, Continue reading ‘PyIMSL’ »

1. cited from http://en.wikipedia.org/wiki/IMSL[↩]

The brackets could be filled with other languages but two are introduced today: Perl (perl.org) and Python (python.org). These two are widely used among astronomers and can be empowered by R (r-project.org). Continue reading ‘R-[{Perl,Python}] Interface’ »

Both in astronomy and statistics, python is recognized as a versatile programming language. I asked python tutorials to Alanna. The following is her answer, which looks very useful for those who wish to learn python.
Continue reading ‘Learning Python’ »