further tweaks to push production scale sims through

workflows/config/snakemake/production_config_HomSap.yml

@@ -1,7 +1,7 @@
 # snakemake settings
 "seed": 12345
 "replicates": 3
-"output_dir": "results"
+"output_dir": "HomSap_results"
 
 # species-specific settings
 "species": "HomSap"

workflows/dfe.snake

@@ -159,11 +159,11 @@ rule dadi_infer_dfe:
         opts = 100,
         prefix = output_dir + "/inference/{demog}/dadi/{dfes}/{annots}/{seeds}/pop{ids}/pop{ids}.two_epoch.gamma"
     resources:
-        time=2000
+        time=4000
     threads: 8
     shell:
         """
-        dadi-cli InferDFE --fs {input[0]} --cache1d {input[1]} --demo-popt {input[2]} --output-prefix {params.prefix} --pdf1d {params.dfe} --p0 {params.dfe_p0} --ubounds {params.dfe_ubounds} --lbounds {params.dfe_lbounds} --ratio {params.ratio} --optimizations {params.opts} --cpus {threads} --nomisid --force-convergence 1
+        dadi-cli InferDFE --fs {input[0]} --cache1d {input[1]} --demo-popt {input[2]} --output-prefix {params.prefix} --pdf1d {params.dfe} --p0 {params.dfe_p0} --ubounds {params.dfe_ubounds} --lbounds {params.dfe_lbounds} --ratio {params.ratio} --optimizations {params.opts} --cpus {threads} --nomisid --force-convergence 0
 
         """
 
@@ -437,7 +437,7 @@ rule plot_results:
 
         if wildcards.demog == 'Constant': 
             model = stdpopsim.PiecewiseConstantSize(species.population_size)
-            mutation_rate = 1.29e-08
+            mutation_rate = species.genome.mean_mutation_rate
 
             dadi_bestfits = [ b for b in dadi_bestfits if 'pop0' in b ]
             polydfe_bestfits = [ b for b in polydfe_bestfits if 'pop0' in b ]
@@ -446,6 +446,8 @@ rule plot_results:
         else: 
             model = species.get_demographic_model(wildcards.demog)
             mutation_rate = model.mutation_rate
+            if mutation_rate is None:
+                mutation_rate = species.genome.mean_mutation_rate
 
         pop_names = [ model.populations[i].name for i in range(len(model.populations)) ]
         plots.plot_all_dfe_results([dadi_bestfits, polydfe_bestfits, dfe_alpha_bestfits, grapes_bestfits], output, mutation_rate, seq_len, 0.7, pop_names)

workflows/n_t.snake

@@ -160,6 +160,8 @@ def mutation_rate_helper(demog, species):
         mutation_rate = species.genome.mean_mutation_rate
     else:
         mutation_rate = species.get_demographic_model(demog).mutation_rate
+        if mutation_rate is None:
+                mutation_rate = species.genome.mean_mutation_rate
     return mutation_rate
 
 rule write_bdd:
@@ -345,7 +347,7 @@ rule run_msmc:
         output_dir + "/inference/{demog}/msmc/{dfes}/{annots}/{seeds}/{pops}/{samps}.trees.multihep.txt.final.txt"
     threads: 8
     resources:
-        mem_mb=48000
+        mem_mb = lambda wildcards, attempt: attempt * 64000
     run:
         inputs = expand(output_dir + "/inference/{demog}/msmc/{dfes}/{annots}/{seeds}/{pops}/{chrms}.trees.multihep.txt",
                     demog=wildcards.demog,
@@ -459,6 +461,8 @@ rule gone_prep_inputs:
         output_dir + "/inference/{demog}/gone/{dfes}/{annots}/{seeds}/{pops}/gone.ped",
         output_dir + "/inference/{demog}/gone/{dfes}/{annots}/{seeds}/{pops}/gone.map",
     threads: 1
+    resources:
+        mem_mb=36000
     run:
         inputs = expand(
             output_dir

workflows/simulation.snake

@@ -60,14 +60,19 @@ rule simulation:
     input:
     output:
         output_dir + "/simulated_data/{demog}/{dfes}/{annots}/{seeds}/sim_{chrms}.trees"
-    resources: time=3000, mem_mb=10000
+    resources: 
+        time = lambda wildcards, attempt: attempt * 3000, 
+        mem_mb = lambda wildcards, attempt: attempt * 50000
     run:
         if wildcards.demog == 'Constant': 
             model = stdpopsim.PiecewiseConstantSize(species.population_size)
             mutation_rate = species.genome.mean_mutation_rate
         else: 
             model = species.get_demographic_model(wildcards.demog)
             mutation_rate = model.mutation_rate
+            if mutation_rate is None:
+                mutation_rate = species.genome.mean_mutation_rate
+
         samples = {f"{model.populations[i].name}": m for i, m in enumerate(demo_sample_size_dict[wildcards.demog])}
         genetic_map_id = config["genetic_map"]
         contig = species.get_contig(wildcards.chrms, genetic_map=genetic_map_id)