Facet calibration for LOFAR
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README.md

Factor: Facet Calibration for LOFAR

Factor is a tool for producing low-noise, high-resolution wide-field images from LOFAR HBA data. Factor has been designed to use as few free parameters as possible in order to mitigate the effects of over-fitting and thus maximize image fidelity. Factor runs well on single machines or on compute clusters with multiple nodes (with a shared file system). It requires only modest resources (at least 32 GB of memory and 1 TB of disk space).

What's New Since v1.3

  • Options have been added to allow the scales used by WSClean during clean to be specified (selfcal_multiscale_scales_pixel and facet_multiscale_scales_pixel under [imaging])

  • The atrous_do column in the directions file has been renamed to mscale_selfcal_do, as this name better describes its purpose

  • Improved handling of flagged solutions during smoothing of the amplitude solutions

What's New in v1.3

  • Updated to use WSClean 2.4. Earlier versions are no longer supported
  • Preaveraging is now done in frequency as well as in time. This preaveraging generally improves the S/N of the CS slow-gain solutions for fainter sources by a factor of ~ 2
  • An option (min_fraction_per_band) has been added that sets the minimum allowed unflagged fraction per band
  • WSClean's automasking feature is now used during imaging. The old image-mask-image sequence is no longer used during self calibration, but can still be used during the final, full-bandwidth facet imaging if automask_facet_image = False under the [imaging] section of the parset. The option update_selfcal_clean_regions has been removed, as it no longer applies (PyBDSF masking has been removed from selfcal)
  • The combination of flagging ranges specified by the flag_abstime, flag_baseline, and flag_freqrange options can now be set with the flag_expr option

What's New in v1.2

  • An unarchiving tool (unarchivefactor) has been added that can unarchive an archive made with archivefactor
  • An option (update_selfcal_clean_regions) has been added that controls whether user-supplied clean masks are updated during selfcal
  • Intersections in user-supplied clean masks are now detected and an error raised
  • An archiving tool (archivefactor) has been added that can archive the subtracted datasets, the sky models, the instrument tables, the selfcal plots, and the calibrated data for one or more directions
  • Polynomial sky models generated directly by WSClean during imaging are used for prediction, resulting in improved and faster subtraction of extended sources. Due to this change, Factor now requires WSClean v2.3 or higher

Installation

To install Factor, follow the instructions below.

Note: Factor is currently installed on the LOFAR CEP3 cluster. Users on CEP3 should run the following command before using Factor:

source ~rafferty/init_factor

Dependencies

Factor requires the following:

  • The LOFAR offline trunk from the LOFAR software repository (a version of the trunk after 02/11/2016 is required)
  • WSClean (version 2.4 or later)
  • LSMTool (version 1.2.0 or later)
  • jinja2
  • Shapely
  • APLpy (version 1.0 or later)
  • pyds9 (optional, to allow checkfactor to interface with ds9)
  • Dysco (optional, to allow compression of the visibilities and weights)

Downloading and Installing

Get the latest developer version by cloning the git repository:

git clone https://github.com/lofar-astron/factor.git

Then install with:

cd factor
python setup.py install

Usage

The Factor executable (named runfactor) can be used from the command line with a parset that defines the parameters of the run. E.g.:

$ runfactor factor.parset

You can check the progress of a run with checkfactor:

$ checkfactor factor.parset

The parset defines the data and working directories, various options, etc. Factor handles all the initialization and sets up the directories, pipeline parsets, etc.

For details on the usage, please see the full documentation and the examples in the examples directory.