#======================================================================================
# Now call the function with the relevant arguments.
# There are many models to test here. A brief definition is given for each, but the actual
# models are defined in the Models_2D.py script. The first 15 were implemented in Portik
# et al. 2016 (doi: 10.1111/mec.14266), the following 9 are newer for various projects.
# Here it is set up to call each model one by one sequentially, which could finish relatively quickly.
# If it takes too long, create multiple verisions of this script, block out some models (use hashes or delete),
# and execute one version for every core you have available. It will greatly speed up these steps,
# and sometimes if extrapolations fail the script will crash too and this could prevent it from
# happening too many times.
# Standard neutral model, populations never diverge
Optimize_Functions.Optimize_Round1(pts, fs, outfile, reps, maxiter,
"no_divergence")
# Split into two populations, no migration.
Optimize_Functions.Optimize_Round1(pts, fs, outfile, reps, maxiter, "no_mig")
# Split into two populations, with continuous symmetric migration.
Optimize_Functions.Optimize_Round1(pts, fs, outfile, reps, maxiter, "sym_mig")
# Split into two populations, with continuous asymmetric migration.
Optimize_Functions.Optimize_Round1(pts, fs, outfile, reps, maxiter, "asym_mig")
# Split with continuous symmetric migration, followed by isolation.
Optimize_Functions.Optimize_Round1(pts, fs, outfile, reps, maxiter,
"anc_sym_mig")
# Split with continuous asymmetric migration, followed by isolation.
pts = [30, 40, 50]
#prefix for output file naming
outfile = "C-O"
#spectrum object name (we defined this above)
fs = fs_1
#integer to control number of replicates per model
reps = int(30)
#max number of iterations per optimization step (though see dadi user group for explanation)
maxiter = int(20)
#======================================================================================
# Call the function with the relevant arguments.
# 1 Standard neutral model, populations never diverge
Optimize_Functions.Optimize_Round1(pts, fs, outfile, reps, maxiter,
"no_divergence")
# 2 Split into two populations, no migration.
Optimize_Functions.Optimize_Round1(pts, fs, outfile, reps, maxiter, "no_mig")
# 3 Split into two populations, with continuous symmetric migration.
Optimize_Functions.Optimize_Round1(pts, fs, outfile, reps, maxiter, "sym_mig")
# 4 Split into two populations, with continuous asymmetric migration.
Optimize_Functions.Optimize_Round1(pts, fs, outfile, reps, maxiter, "asym_mig")
#===========================================================================
#clock the amount of time to complete the script
t_finish = datetime.now()
elapsed = t_finish - t_begin
print '\n', '\n', "-----------------------------------------------------------------------------------------------------"
# Prepare settings to run the optimization round 2 function defined in the
# script 'Optimize_Functions.py'.
#**************
#spectrum object name (as defined above)
fs = fs_1
#integer to control number of replicates per model
reps = int(30)
#max number of iterations per optimization step (though see dadi user group for explanation)
maxiter = int(20)
#======================================================================================
# Call the function with the relevant arguments.
# Divergence with no migration
Optimize_Functions.Optimize_Round1(pts, fs, outfile, reps, maxiter, "no_mig",
no_mig_params)
# Split into two populations, with continuous symmetric migration.
Optimize_Functions.Optimize_Round1(pts, fs, outfile, reps, maxiter, "sym_mig",
sym_mig_params)
# Split into two populations, with continuous asymmetric migration.
Optimize_Functions.Optimize_Round1(pts, fs, outfile, reps, maxiter, "asym_mig",
asym_mig_params)
#===========================================================================
#clock the amount of time to complete the script
t_finish = datetime.now()
elapsed = t_finish - t_begin
print '\n', '\n', "-----------------------------------------------------------------------------------------------------"
print "Finished all analyses!"