Skip to main content

Create a tracking beam from ARTS tied-array beam data

Reason this release was yanked:

Calculation of TAB indices is wrong in this version, please update to 1.3+

Project description

ARTS tracking beams

DOI PyPI version Build Status codecov

The Apertif Radio Transient System (ARTS) archive contains tied-array beam (TAB) data. The TABs have a time-dependent and frequency-dependent pointing. This tool is able to convert the TAB data to a tracking beam (TB), which tracks a fixed point on the sky over the course of an observation.

Dependencies

  • python >= 3.6
  • numpy >= 1.17
  • astropy
  • tqdm

Installation

To install the latest release:

pip install arts_tracking_beams

To install the latest master branch:

pip install git+https://github.com/loostrum/arts_tracking_beams

Usage

Input data

First download the data set of interest from the Apertif Long-Term Archive (ALTA). Tools to find which pulsars are in the field-of-view of a given Apertif pointing and to download the data are available as a separate python package.

A data file from the archive is identified by three parameters: the task ID, compound beam (CB) index, and TAB index. The file ARTS200102003_CB00_TAB00.fits would be the observation identified by task ID 200102003 (that is, the third observation on January 2nd, 2020), CB zero, TAB zero. A TB is created from the TABs of a single CB.

Creating a tracking beam

The TB is created from the TAB data with arts_create_tracking_beam.

The simplest use case is to create a tracking beam from a folder which contains only one data set (i.e. the TABs of one CB of one observation), for a source with known coordinates. For example, to create a tracking beam towards the Crab pulsar:

arts_create_tracking_beam --input_folder /path/to/data/ --source 'PSR B0531+21'

If there are multiple data sets in the input data folder, specify the task ID and/or CB index. Instead of the source name, it is also possible to provide a RA and Dec. The name of the output FITS file is determined automatically from the input source name or RA/Dec, but can also be specified manually. Using all of these options, an example command is:

arts_create_tracking_beam --input_folder /path/to/data/ --taskid 200102003 --cb 0 --ra 05:34:32 --dec 22:00:52 --output tracking_beam.fits

The TB creation consists of two steps:

  1. Calculate the required TABs at each frequency and time
  2. Reorder the data from the input TAB FITS files and create a new FITS file containing the TB.

The results of step 1 can be saved to disk with --save_tab_indices. To only calculate the TAB indices and disable step 2 completely, use --no_fits_output. To generate the FITS output from a TAB indices file on disk, use--load_tab_indices /path/to/tab/index/file.txt. The script then loads the TAB indices and immediately goes to step 2.

There are a few more settings that can be customized. Run arts_create_tracking_beam -h for an overview of all options.

Creating a synthesised beam

A synthesised beam (SB) is a type of beam that reorders the TABs as function of frequency, but not as function of time. A single CB is covered by 71 SBs. Each SB is always made out of the same TABs. The SBs are used in the real-time transient search that ARTS runs. The brightest transients may also be detectable in the archival data, so we here include a tool to create the synthesised beams as well.

The synthesised beam tool, arts_create_synthesised_beam, works in a very similar fashion as the tracking beam tool. An example command:

arts_create_synthesised_beam --input_folder /path/to/data --sb 35

Run arts_create_synthesised_beam -h for more options.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

arts_tracking_beams-1.2.tar.gz (21.3 kB view details)

Uploaded Source

Built Distribution

arts_tracking_beams-1.2-py3-none-any.whl (30.4 kB view details)

Uploaded Python 3

File details

Details for the file arts_tracking_beams-1.2.tar.gz.

File metadata

  • Download URL: arts_tracking_beams-1.2.tar.gz
  • Upload date:
  • Size: 21.3 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.2.0 pkginfo/1.5.0.1 requests/2.24.0 setuptools/50.3.0 requests-toolbelt/0.9.1 tqdm/4.49.0 CPython/3.6.7

File hashes

Hashes for arts_tracking_beams-1.2.tar.gz
Algorithm Hash digest
SHA256 20997fef749e9609420026da4f9ac9172f7fd667e176d8e5e97cdcf301eecef2
MD5 c796ccbb3d9454a29495bdb8bdde71f5
BLAKE2b-256 513f4946f83b8400cd8915fa09001645e9bc567fd14f5d55908c867bf5030033

See more details on using hashes here.

File details

Details for the file arts_tracking_beams-1.2-py3-none-any.whl.

File metadata

  • Download URL: arts_tracking_beams-1.2-py3-none-any.whl
  • Upload date:
  • Size: 30.4 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.3.0 pkginfo/1.7.0 requests/2.25.1 setuptools/51.1.2 requests-toolbelt/0.9.1 tqdm/4.56.0 CPython/3.8.7

File hashes

Hashes for arts_tracking_beams-1.2-py3-none-any.whl
Algorithm Hash digest
SHA256 03e98b77a3b37f716bf6f214552424725061eb5fc857a05326df1fb7ac7a6393
MD5 7bea4a58aea59aba39d7493ba8f6c19c
BLAKE2b-256 25065846fd0f54b8fc16cbba63faf3a0816795a155f694eee48603b3afef6e84

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page