Implement KSB methods for determining cosmic shear and confirm the emergence of bias. Explore Bayesian methods of inferring shear.

Do not use the -p option E.g. python toy.py -n -1 <shear component 1=-0.01> -2 <shear component 2=0.02> -e <sigma_e=0.05> -s <sigma_pr=0.3> -t -i Add -d if you want to display average time per galaxy

This will save a file "gi.csv" to "data/" where is a time stamp and is the index you specified above. Make sure you provide unique indices to each cluster job so that the jobs don't write over each other and make a mess.

The CSV files are comma separated with the format P,Q1,Q2,R11,R12,R22.

Use the -p option but don't specify -1 or -2 E.g. python toy.py -p -n -e <sigma_e=0.05> -s <sigma_pr=0.3> -t -i

This will draw n pairs (g,h) from generate_pairs.py. It will calculate P,Q,R for these pairs.

And it will save files "gi.csv" and "hi.csv" to "data/" where is a time stamp and is the index you specified above. Make sure you provide unique indices to each cluster job so that the jobs don't write over each other and make a mess.

The CSV files are comma separated with the format P,Q1,Q2,R11,R12,R22.

This will process every CSV file in a specified directory assuming they contain P,Q,R for uncorrelated galaxies of a specified shear. And it will infer that shear. No command line options yet.

This will process every CSV file in a specified directory assuming they contain P,Q,R for correlated pairs. And it will try to infer the covmat. No command line options yet.