Note I am assuming you have at least julia 1.6 installed

Additionally you will require ehtim and dynesty to be installed in a system aware python. Currently I am just using the build in fft so pip install ehtim pip install dynesty should be enough.

If for some reason Julia is looking at the wrong python installation please open a Julia REPL and type

ENV["PYTHON"] = "/path/to/python/binary"
using Pkg; Pkg.build()

This should allow you to use the custom python installation.

This code base is using the Julia Language and DrWatson to make a reproducible scientific project named

EHTGRMHDCal

To (locally) reproduce this project, do the following:

0. Download this code base. Notice that raw data are typically not included in the git-history and may need to be downloaded independently.
1. Open a Julia console and do:

   julia> using Pkg
   julia> Pkg.add("DrWatson") # install globally, for using `quickactivate`
   julia> Pkg.activate("path/to/this/project")
   julia> Pkg.instantiate()
   

This will install all necessary packages for you to be able to run the scripts and everything should work out of the box, including correctly finding local paths.

The main script you should look at in this repo is src/main.jl To use it do something like

julia -p 2 main.jl filelist --out test.csv --pa "[0.0, 45.0, 90.0, 135.0]"  --stride 200

Note that by default the script will load the data in data/"snapshot_fitting_scans_120s_noisefrac0.05"

The only argument that isn't optional is filelist. This is a file that contains the paths of all the hdf5 file you would like to analyze. For the other options please see the docstring of the main function.

Note this will seem to hang at the begining. This is because Julia uses a JIT compiler which means it is called just before its first called

If you are using a single node then the -p option will work great. If you are using multiple nodes then it will fail! For a multiple node job there are two options.

For a slurm cluster create a batch submission using

#!/bin/sh

#SBATCH .... #insert usual sbatch stuff

# create a host/machine file
srun hostname -s > hostfile

# now pass this machine file to julia
julia --machine-file ./hostfile main.jl filelist --out test.csv --pa "[0.0, 45.0, 90.0, 135.0]"  --stride 200

On Harvard's hydra cluster I have had some issues getting everything to work. The best way forward I found was to use the ClusterManagers.jl package to directly ask for resources. This is partly automated using src/main_hydra.jl. You will need to change one thing in this script for your resources. Specifically

addprocs_sge(80; qsub_flags=`-l s_cpu=24:00:00 -l mres=4G,h_data=4G,h_vmem=4G`, wd=pwd())

This will ask hydra for 80 cores throughout the cluster. Please change 80 to however many cores you want. You may want to ask for a single node to lower communication.