more intuitive overlap between handling of genetic data and individual metadata about pyslim HOT 3 CLOSED

Yeah that covers a lot of it. I'd add (in bold):

1. Simulate data with SLiM
2. Recapitate, mutate, etcetera
3. Take a subsample of the modern individuals
   or historical individuals(as remembered in the SLiM model)
4. Get these individual locations and make a map
5. Compute divergences between individuals using tskit, and plot against geographic distance
   and write out divergences for use by other programs
6. Write out a VCF file of these individual genotypes for use by other programs
7. Write out locations/times/metadata of these individuals for use by other programs in a way that makes it easy to match up to genetic data (divergences or genotypes) to metadata, either because they have the same order, or because both sets of output can be associated with individual IDs

Does that make more sense? Thanks Peter!

from pyslim.

Thanks! I'm a little unclear on what exactly the example workflow should be, though. How's this?

1. Simulate data with SLiM
2. Recapitate, mutate, etcetera
3. Take a subsample of the modern individuals
4. Get these individual locations and make a map
5. Compute divergences between individuals using tskit, and plot against geographic distance
6. Write out a VCF file of these individual genotypes for use by other programs

... would that cover things? Want to suggest any other steps?

from pyslim.

This got done in #48, I think.

from pyslim.