New Features in PSRCHIVE 9.1.1

This minor release fixes a fairly fundamental bug in the code that finds the on-pulse region, which is commonly used to compute the signal-to-noise ratio in pdv.

New Features in PSRCHIVE 9.1

Virtual Memory Management: To support large files, a custom virtual memory manager can be activated. See Large File Support for more details.

Data formats: If the plugin for a specific file format does not implement an unload routine, then the data will be automatically converted to an archive type that does.

Polarimetric Calibration: The pcm can model the parallactic angle variation of an X-Y (Meridian) mount, like that of the Mount Pleasant 26m antenna.

Bugs Fixed:

  • memory leak patched up in TextInterface class
  • PERIOD column supported in PSRFITS output
  • circularly polarized receivers can be calibrated again
  • matrix template matching features repaired in pcm

New Features in PSRCHIVE 9.0

Python: When dynamic library support is enabled, and both Python development libraries and SWIG are available, a psrchive python module will be compiled and installed. This module enables a limited set of psrchive functionality to be integrated into python scripts.

Data formats: The PRESTO Prepfold file format extension has been added by Paul Demorest. See the file format-specific release notes for known issues and more information about new formats.

Polarimetric Calibration: The pcm program implements a new polarimetric calibration model that combines the use of matrix template matching (van Straten 2006) with other constraints such as reference source observations and temporal variations (van Straten 2004).

Least Squares Minimization: The pcm program can make use of the gsl_multifit_fdfsolver_lmsder least squares minimization algorithm implemented by the GNU Scientific Library (GSL). This algorithm uses a scaling matrix derived from "the column norms of the Jacobian to estimate the sensitivity of the residual to each [of the model parameters]. This improves the behaviour of the algorithm for badly scaled functions."