Charm Baryon Data This repository contains the data and plot-input files required to reproduce the figures in the charm parity paper Conda instructions: Install Environment conda env create -f environment.yml Activate/Use conda activate charm Update (w. new packages) Edit environment.yml Deactivate conda environment with conda deactivate Update conda environment with conda env update -f=environment.yml Run Instructions: change the scriptDir variable in main.sh and parityRatioRun.sho to where you have cloned the script repository https://github.com/RJaBi/charmBaryonScripts ensure that main.sh is executable (i.e. chmod o+x main.sh) This is neccessary for the subscript that runs the fits to work activate the charm conda environment Run Figures 1, 5-14 via ./main.sh This will likely take up to ~12 hours as it does all the fits Once the fits are made, the process is much quicker as the fits are saved Run Figures 2-4 via running parityRatioRun.sh This will likely take 12+ hours running the parityRatio plots off is slow, the inflection points are quick If you are doing this without running main.sh, you will need to make sure parityRatioRun unzip's the correlators The output will be in output/ but the figures actually used in the paper will be copied into paperPlots/ The input files for the fits are in baseToml. They are copied and modified by the run_fits script such that all combinations of operator, quarks and temperatures are done from the smaller subset of template toml files. Other input files are complete, based upon the relative paths defined. These are in completeToml Figure Details Figure 1 Plots the correlators plotG.py Figures 2-4 Does the parity-doubling R-ratio analysis gvarParityRatio.py plotInflectionPoints.py Figures 5-8 Does fits to the correlators and model averages simpleFit.py Figure 9 Plots the spectrum of masses against experiment plotSpectrum.py Figures 10-13 Plots the spectrum of masses as a function of temperature simplePlotEMP.py Figure 14 Considers the Gell-Mann-Okubo relation GellMannOkubo.py