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This repository contains the analysis pipeline used in https://arxiv.org/abs/1909.09102. Please contact the authors if you have any questions.

Running the pipeline

1. Unpack and download all necessary data by running the bash script dwl_data.sh. This will unpack data.tar.gz, download all necessary Planck maps and compute the SZ masks using mk_mask_sz.py.
2. Compute all power spectra and covariance matrices running python pipeline.py params.yml.
3. Run the likelihood sampler by running python mcmc.py params.yaml.
4. Most of the paper plots can be generated by running the plots/plot_***.py scripts. Some hard-coded paths may need to be modified.

Source code

• dwl_data.sh downloads all the data needed for the analysis.
• All the data analysis modules can be found in analysis.
• The theory prediction modules can be found in model.
• The likelihood modules are in likelihood.
• pipeline.py contains the power spectrum measurement pipeline.
• mcmc.py contains the likelihood pipeline.
• plot_stuff.py contains a few plotting routines.

Theory notes

Theory notes can be found in notes, where you can also find the paper submodule containing the .tex source of the paper. Read the relevant README in notes if you want to obtain the relevant files.

Credit

We ask that you cite https://arxiv.org/abs/1909.09102 if you use this pipeline for any of your work.