MIT/CXC S-Lang Packages

This page provides links to S-Lang programs developed to support various research applications. These may have dependencies on S-Lang modules, or upon ISIS. See the individual links for explicit dependencies.

(The primary difference between a package and a module is that the packages here are entirely written in S-Lang source, whereas modules depend on a binary library.)

Currently available packages are:

• SITAR: S-lang/ISIS Timing Analysis Routines (includes Baysian Blocks functions).

• aglc: ACIS-Grating Light Curve (also computes exposure-corrected phase curves). (2007.06.15 UPATE to v1.3.5: bug fixes in time computation, affects short period phasing)

• Nonthermal spectral models for ISIS

• add_pha: Add multiple counts spectra, grating or imaging spectra, multiple observations or orders from one observation.

• findzo: Accurately centroid the zero order using the intersection of the ACIS CCD frame-shift streak and a dispersed arm (for the many cases when the zero order image is distorted by pileup or excluded from telemetry).

• Pileup Color: Pileup in the ACIS detector, when used with or without a grating, is a "local" phenomenon, that is it varies from pixel to pixel in an image depending on the local event rate. Hence for an extended source (or dispersed grating image) it is useful to make an image calibrated to indicate the observed "island rate" of events. The pileup_color.sl generates such a color-coded image from a FITS event file.

• TGCat: the Chandra Transmission Grating Catalog and Archive, data reprocessing scripts.

• Density-dependent APED plasma models for the He-like triplets: Data files and ISIS code to provide density-dependent APED plasma models for the X-ray band He-like triplet lines (for collisionally and photoexcited plasmas).

• XSTARDB: ISIS interfaces to warmabs and photemis output: ISIS code to provide interfaces for atomic data optionally output by the XSTAR Xspec models (warmabs, photemis), for line/edge identification, or access to feature strengths vs model parameters.