Open source pulsar search and analysis toolkit
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README.md

PRESTO

http://www.cv.nrao.edu/~sransom/presto/

PRESTO is a large suite of pulsar search and analysis software developed by Scott Ransom mostly from scratch, and released under the GPL (v2). It was primarily designed to efficiently search for binary millisecond pulsars from long observations of globular clusters (although it has since been used in several surveys with short integrations and to process a lot of X-ray data as well). It is written primarily in ANSI C, with many of the recent routines in Python. According to Steve Eikenberry, PRESTO stands for: PulsaR Exploration and Search TOolkit!

PRESTO has discovered over 600 pulsars, including more than 230 recycled and/or binary pulsars!

New in Version 2.1:

  • accelsearch now has a "jerk" search capability (thanks to UVA undergrad Bridget Andersen for help with this!). This makes searches take a lot longer, but definitely improves sensitivity when the observation duration is 5-15% of the duration of the orbital period. Typically -wmax should be set to 3-5x -zmax (and you probably never need to set -zmax to anything larger than 300).
  • Ability to ignore bad channels on the command line (-ignorechan) (see rfifind_stats.py and weights_to_ignorechan.py)
  • Lots of new python utilities (such as for handling RFI, showing bandpasses, making waterfall plots, ...)
  • New wrappers for the python interface (will make the transition to Python 3.X much smoother later this year)
  • Many bug fixes and minor improvements

About PRESTO:

PRESTO is written with portability, ease-of-use, and memory efficiency in mind, it can currently handle raw data from the following pulsar machines or formats:

  • PSRFITS search-format data (as from GUPPI at the GBT, PUPPI and the Mock Spectrometers at Arecibo, and much new and archived data from Parkes)
  • 1-, 2-, 4-, 8-, and 32-bit (float) filterbank format from SIGPROC
  • A time series composed of single precision (i.e. 4-byte) floating point data (with a text ".inf" file describing it)
  • Photon arrival times (or events) in ASCII or double-precision binary formats

Notice that the following formats which used to be supported are not:

  • Wideband Arecibo Pulsar Processor (WAPP) at Arecibo
  • The Parkes and Jodrell Bank 1-bit filterbank formats
  • SPIGOT at the GBT
  • Berkeley-Caltech Pulsar Machine (BCPM) at the GBT

If you need to process them, you can either checkout the "classic" branch of PRESTO (see below), which is not being actively developed. Or you can use DSPSR to convert those formats into SIGPROC filterbank format (and/or maybe someday soon, PSRFITS search format). You can grab DSPSR here. If you really need to get one of these machines working in PRESTO v2, let me know and we can probably make it happen. It will take a day or two of porting for each backend.

The software is composed of numerous routines designed to handle three main areas of pulsar analysis:

  1. Data Preparation: Interference detection (rfifind) and removal (zapbirds) , de-dispersion (prepdata, prepsubband, and mpiprepsubband), barycentering (via TEMPO).
  2. Searching: Fourier-domain acceleration (accelsearch), single-pulse (single_pulse_search.py), and phase-modulation or sideband searches (search_bin).
  3. Folding: Candidate optimization (prepfold) and Time-of-Arrival (TOA) generation (get_TOAs.py).
  4. Misc: Data exploration (readfile, exploredat, explorefft), de-dispersion planning (DDplan.py), date conversion (mjd2cal, cal2mjd), tons of python pulsar/astro libraries, average pulse creation, flux density estimation, and more...
  5. Post Single Pulse Searching Tools: Grouping algorithm (rrattrap.py), Production and of single pulse diagnostic plots (make_spd.py, plot_spd.py, and waterfaller.py).

Many additional utilities are provided for various tasks that are often required when working with pulsar data such as time conversions, Fourier transforms, time series and FFT exploration, byte-swapping, etc.

The Fourier-Domain acceleration search technique that PRESTO uses in the routine accelsearch is described in Ransom, Eikenberry, and Middleditch (2002), and the phase-modulation search technique used by search_bin is described in Ransom, Cordes, and Eikenberry (2003). Some other basic information about PRESTO can be found in my thesis. I will eventually get around to finishing the documentation for PRESTO, but until then you should know that each routine returns its basic usage when you call it with no arguments. I am also willing to provide limited support via email or telephone (434-296-0320).

Tutorial: Note that in the "docs" directory there is a tutorial which walks you through all the main steps of finding pulsars using PRESTO.

Getting it:

The PRESTO source code is released under the GPL and can be browsed or gotten from here in many different ways (including zipped or tar'd or via git). If you are too lazy to read how to get it but have git on your system do:

git clone git://github.com/scottransom/presto.git

To update it on a regular basis do

cd $PRESTO
git pull

and then re-make things in $PRESTO/src.

For more detailed installation instructions, see INSTALL.

If you don't want to mess with git (which means that you will need to re-install a tarball whenever there are updates) you can get it from the "Download Source" link on the github page.

If you want the "classic" branch, do the following:

git clone git://github.com/scottransom/presto.git
cd presto
git remote add classic origin/classic 
git checkout -b classic origin/classic

then build as per the (old) INSTALL file.

Development:

If you plan to tweak the code, I highly suggest that you use git and clone the directory (or fork it using an account on github). And if you want to contribute your changes back, please give me a "pull request"!

Code contributions and/or patches to fix bugs are most welcome!

Final Thoughts:

Please let me know if you decide to use PRESTO for any "real" searches. And if you find anything with it, it would be great if you would cite either my thesis or whichever of the two papers listed above is appropriate. Thanks!

Acknowledgements:

Big thanks go to Steve Eikenberry for his help developing the algorithms, Dunc Lorimer and David Kaplan for help with (retired) code to process BCPM, SCAMP, and Spigot data, Jason Hessels for many contributions to the Python routines, and (alphabetical): Bridget Andersen, Anne Archibald, Cees Bassa, Matteo Bachetti, Slavko Bogdanov, Fernando Camilo, Paul Demorest, Paulo Freire, Chen Karako, Mike Keith, Patrick Lazarus, Maggie Livingstone, Chitrang Patel, Paul Ray, Paul Scholz, Ingrid Stairs, Kevin Stovall, Joeri van Leeuwen for many comments, suggestions and patches!

Scott Ransom sransom@nrao.edu