Recipes for MeerKAT data interaction and processing presented in CASA-Jupyter notebook format for easy
Usage and installation instruction for CASA-Jupyter can be found in Github
MeerKAT data is stored in a flexible format called MeerKAT Visibility Format (MVF), and accessed/processed as needed.
Easy access and software specifically developed to handle MeerKAT large data sizes are provided through the MeerKAT archive and
If you are using the docker container to run the CASA-Jupyter notebooks, you will need to install
- run/start the docker container
- enter the docker container as root: docker exec -tiu root
katdal: pip install katdal
- for active notebooks, restart the notebook kernel (using the
- running a cell with
import katdalshould now work
Note to reader
MeerKAT data files are large and combining the data for an observation using the full array into single files cause sizes of Giga- to Tera bytes. These files are to big for standard io-operations.
All MeerKAT data is accessed via the SARAO archive
- MeerKAT archive is access restricted, requiring registration and login
- To access data in the MeerKAT archive a token is required.
User guideline to register, access and retrieve data from the archive are provided in the Archive Interface User Guide
Example notebooks showing data interaction and extraction methods can be found in the archive folder
- Using tokens for
katdalprocessing: Accessing MeerKAT observation data
katdalprovides a script to convert these data sets to CASA MeasurementSets. Using tokens to convert MVF files to CASA MeasurementSet: Convert MVF dataset(s) to MeasurementSet
If you are following standard interferometric imaging data reduction using CASA measurement sets, you can also use the Direct Download Link to create and download a measurement set instead.
See Archive Interface User Guide for detail.
Interacting with any of the MeerKAT observation files is made easy by the
katdal python library
katdal repository Open source library available from PyPI
pip search katdal katdal (0.13) - Karoo Array Telescope data access library for interacting with data sets in the MeerKAT Visibility Format (MVF)
pip install katdal
Positional astronomy calculation use the PyEphem library
katdal documentation with user guide instructions can be found on the
katdal is specifically developed to allow efficient access to MeerKAT Visibility Format (MVF).
It is fully integrated to access data via the
katarchive line, optimised for large file data access and memory usage.
Introductory Jupyter notebooks illustrating some example data interaction and inspection using
Cutting edge functionality can be obtained by installing
katdal directly from the GitHub repository
pip install git+https://github.com/ska-sa/katdal.git
Import Note Care should be taken since installing the master from GitHub might not be as stable as PyPI.
CASA MeasurementSet data tables can be created using a convenient helper script
mvftoms.py available from
Measurement sets can be downloaded directly from the MeerKAT archive using some sensible defaults when created.
Examples on how to create measurement sets from a user control environment using tokens from the archive are given in example notebooks in the utils folder.
Standard recipes for flagging and calibration are provided in the casa folder.
Plotting and planning tools make extensive use of the
astropy python libraries
pip install matplotlib pip install astropy
Plotting the antenna location makes use of the
mpl_toolkits.basemap functionality, which may be a little tricky to install.
Basemap installation requires
libgeos. The following worked for the author
sudo apt-get install libgeos-3.6.2 libgeos-c1v5 libgeos-dev git clone https://github.com/matplotlib/basemap.git cd basemap/ pip install .