ragavi 0.3.6

Radio Astronomy Gain and Visibility Inspector

ragavi

Radio Astronomy Gain and Visibility Inspector

Introduction

This library mainly requires

1. Bokeh

2. Python casacore

3. Daskms

4. Datashader

5. Nodejs>=8

- Install build dependencies:

** Python casacore comes as a dependency of Daskms ** Nodejs is a requirement for Bokeh and can be installed using the commands

$ sudo apt-get install curl
$ curl -sL https://deb.nodesource.com/setup_8.x | bash -
$ apt-get install -y nodejs

All python requirements are found in requirements.txt

or

To install nodejs in the virtual environment, use: nodeenv, a nodejs virtual environment. More info can be found here

Create nodejs virtual environment with:

$ nodeenv envName

and

$ . envName/bin/activate

to switch to environment.

Installation

Installation from source, working directory where source is checked out

$ pip install .

This package is available on PYPI via

$ pip install ragavi

Usage

Ragavi currently has two segements:

1. Gain plotter

2. Visibility plotter

For the gains plotter, the name-space ragavi-vis is used. To get help for this

Note

ragavi namespace will soon change to ragavi-vis

$ ragavi-gains -h

To use ragavi gain plotter

$ ragavi-gains -t /path/to/your/table -g table_type (K / B/ F/ G/ D)

Multiple tables can be plotted on the same document simply by adding them in a space separated list to the -t / --table switch. They must however be accompanied by their respective gain table type in the -g switch. e.g

$ ragavi-gains -t delay/table/1/ bandpass/table/2 flux/table/3 -g K B F

For the visibility plotter, the name-space ragavi-vis is used. Help can be obtained by running

$ ragavi-vis -h

To run ragavi-vis, the arguments --table, --xaxis and --yaxis are basic requirements e.g.

$ ragavi-vis --ms /my/measurement/set --xaxis time --yaxis amplitude

For large datasets, it is advisable to supply at least --ymin and --ymax values to avoid an extra pass over the data.

Change the size (resolution) of the output aggregated image – and resulting html file size – by specifying --canvas-width and --canvas-height options.

The xova package is required for Averaging. It is not available on PyPi yet and therefore can be installed via:

$ pip install git+git://github.com/ska-sa/xova.git@master

License

This project is licensed under the MIT License - see license for details.

Contribute

Contributions are always welcome! Please ensure that you adhere to our coding standards pep8.