WITCH (WHERE IS THAT CLUSTER HIDING)

This repository contains tools for modeling and fitting SZ data of galaxy clusters. While this code was originally written for MUSTANG-2 it is largely generic enough to work with data for other telescopes.

The WITCH Library

The core of this repository is the WITCH library. While the name contains minkasi it does not actually require the minkasi mapmaker to be used; rather, it produces models of galaxy clusters and their gradients in a format that can be consumed by minkasi's fitting code.

The core concept of WITCH is to model the cluster as a 3D pressure profile and then apply modifications to that profile to represent substructure. For example, a cavity can be modeled as a multiplicative suppression of the pressure within a certain region. The profile is then integrated along the line of sight to produce a signal like we would observe via the SZ effect. To produce gradients of the clusters and JIT expensive computations we employ jax.

This framework makes it very easy to add new types of models, see the Contributing section for more.

The fitter.py Script

The other main part of this repository is the fitter.py script. It is generically a script to perform fit models and make maps using minkasi, but there are a two key points that make it nice to use:

1. A flexible configuration system that allows the user to control mapmaking and fitting parameters, model specification, IO, etc. via yaml files.
2. First class support for models from the WITCH library.

For the most part the config files are easy to make by using one of the files in the configs folder as a base. However there are some subtleties and advanced configurations that will eventually get documented properly.

Installation

To install the WITCH library first clone this repository and from within it run:

pip install .

Note that this will only install WITCH and its dependencies, to also install dependencies for fitter.py do:

pip install .[fitter]

If you are going to be actively working on the WITCH library you probably want to include the -e flag.

All the dependencies should be installed by pip with the one exception being minkasi itself (only needed for fitter.py). Instructions on installing minkasi can be found here.

Contributing

All are welcome to contribute to this repository, be it code or config files. In general contributions other than minor changes should follow the branch/fork -> PR -> merge workflow. If you are going to contribute regularly, contact one of us to get push access to the repository.

Style and Standards

In general contributions should be PEP8 with commits in the conventional commits format. This library follows semantic versioning, so changes that bump the version should do so by editing pyproject.toml.

In order to make following these rules easier this repository is setup to work with commitizen and pre-commit. It is recommended that you make use of these tools to save time.

Getting Started

1. Install both tools with pip install commitizen pre-commit.
2. cd into the WITCH repository it you aren't already in it.
3. (Optional) Setup commitizen to automatically run when you run git commit. Follow instruction here.
4. Make sure the pre-commit hook is installed by running pre-commit install.

Example Workflow

1. Make a branch for the edits you want to make.
2. Code.
3. Commit your code with a conventional commit message.

• cz c gives you a wizard that will do this for you, if you followed Step 3 above then git commit will also do this (but not git commit -m).

4. Repeat step 3 and 4 until the goal if your branch has been completed.
5. Put in a PR.
6. Once the PR is merged the repo version and tag will update automatically.

Adding New Models

When adding new models to WITCH, be they profiles or substructure, there are some changes that need to be made to core.py to expose them properly.

1. A variable N_PAR_{MODEL} needs to be defined with the number of fittable parameters in the model. Do not include parameters like the grid here.
2. A parameter n_{model} needs to be added to the functions helper, model, and model_grad. Remember to update the static_argnums for model and model_grad. In helper set a default value of 0 for backwards compatibility.
3. A block grabbing the parameters for the model needs to be added to model. This can largely be copied from the other models, just remember to swap out the relevant variables.
4. A block applying model needs to be added to model. Pressure profiles should come first then substructure. This can largely be copied from the other models, just remember to swap out the relevant variables.

Adding a new model also (usually) means you should bump the minor version in the version number.

Profiling Code

The script scratch/profile.py uses jax profiling tools to benchmark the library. It outputs a trace file understandable perfetto as well as a text file containing metadata about the software and hardware used while profiling. To use non default settings use python profile.py --help but in most cases the default settings are fine.

The profiling script has some additional dependencies. To install them run:

pip install .[profile]