adrn / one-datum

What can we infer about an orbit from the RV or astrometric jitter?

one-datum

What can we infer about an orbit from the Gaia RV or astrometric jitter?

Usage

This project is meant to be run containerized (although it'll probably also work fine with a standard scientific Python installation). These instructions will use Singularity, but this should also all work using Docker.

To start, clone the project:

git clone https://github.com/dfm/one-datum.git
cd one-datum

Downloading the data

To download all the necessary data files (using wget), run

scripts/download /path/to/data

where /path/to/data is the local path where you want to store the data files.

Running the container

You can run the container using Singularity as follows (on a module system, you might need to module load singularity first):

alias singularity_exec="singularity exec --bind /path/to/data:/data docker://ghcr.io/dfm/one-datum:main"
singularity_exec python

where /path/to/data is the data path that you used above, and on the first line we're defining an alias that we'll continue using below. This isn't necessary, but it'll make the demos easier to parse. The above command should drop you into a Python instance with a correctly configured environment.

Exploratory analysis with Jupyterlab

This container comes with Jupyterlab installed and you can start a server in this environment with:

singularity_exec jupyter lab --ip='*'

Since I'm normally running this on a remote machine, I would generally add a specific port (--port=8898, for example) and then forward that port via SSH to my development machine.

Inferring per-transit RV uncertainty

This is done on a grid in BP-RP color and apparent G-magnitude. Use

singularity_exec scripts/infer-per-transit-uncert

to run with the default arguments or add the --help flag to see the available parameters. This will save a file rv_uncertainty_grid.fits in your data directory.