This repository hosts self-contained examples demonstrating MPoL functionality. More info on each example can be found in the README.md within each example folder.
This repository is not continuously integrated with the rest of the codebase, because the computational demands are too significant. If you do encounter an error, please log it as a GitHub issue.
Each example assumes you have activated a (virtual) python environment to which you've successfully installed the MPoL package, and that your version of python is at least the minimum current version supported by MPoL.
To run an example, cd into that subfolder.
The requirements.txt file lists the additional python packages necessary for the analysis specific to the example in that folder. You can install them with
pip install -r requirements.txt
Snakefile is a snakemake file setting up the workflow. Within each example directory, you can run
$ snakemake -c 1 all
and you should see all scripts execute in order.
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01 - Intro: Setup Mock Image and Baselines | Generate a mock sky image
$I_\nu(l,m)$ and interferometer baselines$(u,v)$ (but not visibilities $\mathcal{V}(u,v)$). These products are used as input for the other examples. - 02 - Intro: Stochastic Gradient Descent | A complete end-to-end example using MPoL to image mock data.
- 03 - Advanced: Visibility Inference with Pyro | Use MPoL with Pyro to sample parametric visibility plane models.
- 04 - Advanced: IM Lup protoplanetary disk | Use MPoL to image the ALMA DSHARP observations of the IM Lup protoplanetary disk, taking into account alignment and weight-scaling adjustments for a multi-execution block dataset.