Update inference subcommands for LowPass

dadi_cli/InferDFE.py

@@ -19,6 +19,8 @@ def infer_dfe(
     lower_bounds: list[float],
     fixed_params: list[float],
     misid: bool,
+    cov_args: list,
+    cov_inbreeding: list,
     cuda: bool,
     maxeval: int,
     maxtime: int,
@@ -109,6 +111,19 @@ def infer_dfe(
     if misid:
         func = dadi.Numerics.make_anc_state_misid_func(func)
 
+    if cov_args != []:
+        try:
+            from dadi.LowPass.LowPass import make_low_pass_func_GATK_multisample as func_cov
+        except ModuleNotFoundError:
+            raise ImportError("ERROR:\nCurrent dadi version does not support coverage model\n")
+        nseq = [int(ele) for ele in cov_args[1:]]
+        if cov_inbreeding == []:
+            Fx = None
+        else:
+            Fx = cov_inbreeding
+
+        func = func_cov(func, cov_args[0], fs.pop_ids, nseq, fs.sample_sizes, Fx=Fx)
+
     p0_len = len(p0)
     lower_bounds = convert_to_None(lower_bounds, p0_len)
     upper_bounds = convert_to_None(upper_bounds, p0_len)

dadi_cli/InferDM.py

@@ -88,17 +88,15 @@ def infer_demography(
     if cov_args != []:
         try:
             from dadi.LowPass.LowPass import make_low_pass_func_GATK_multisample as func_cov
-            import pickle
         except ModuleNotFoundError:
             raise ImportError("ERROR:\nCurrent dadi version does not support coverage model\n")
-        cov_dd = cov_args[0]
         nseq = [int(ele) for ele in cov_args[1:]]
         if cov_inbreeding == []:
             Fx = None
+        else:
+            Fx = cov_inbreeding
 
-        cov_dd = pickle.load(open(cov_dd, 'rb'))
-        func_ex = func_cov(func_ex, cov_dd, fs.pop_ids, nseq, fs.sample_sizes, Fx=Fx)
-        print(func_cov)
+        func_ex = func_cov(func_ex, cov_args[0], fs.pop_ids, nseq, fs.sample_sizes, Fx=Fx)
 
     p0_len = len(p0)
     lower_bounds = convert_to_None(lower_bounds, p0_len)
@@ -217,13 +215,13 @@ def infer_global_opt(
             import pickle
         except ModuleNotFoundError:
             raise ImportError("ERROR:\nCurrent dadi version does not support coverage model\n")
-        cov_dd = cov_args[0]
         nseq = [int(ele) for ele in cov_args[1:]]
         if cov_inbreeding == []:
             Fx = None
+        else:
+            Fx = cov_inbreeding
 
-        cov_dd = pickle.load(open(cov_dd, 'rb'))
-        func_ex = func_cov(func_ex, cov_dd, fs.pop_ids, nseq, fs.sample_sizes, Fx=Fx)
+        func_ex = func_cov(func_ex, cov_args[0], fs.pop_ids, nseq, fs.sample_sizes, Fx=Fx)
 
     p0_len = len(p0)
     lower_bounds = convert_to_None(lower_bounds, p0_len)

dadi_cli/parsers/infer_dfe_parsers.py

@@ -92,6 +92,9 @@ def _run_infer_dfe(args: argparse.Namespace) -> None:
     # Due to development history, much of the code expects a args.misid variable, so create it.
     args.misid = not (fs.folded or args.nomisid)
 
+    if args.cov_args != []:
+        args.cov_args[0] = pickle.load(open(args.cov_args[0], 'rb'))
+
     make_dir(args.output_prefix)
 
     # # Things need to be updated for these to work

dadi_cli/parsers/infer_dm_parsers.py

@@ -91,6 +91,10 @@ def _run_infer_dm(args: argparse.Namespace) -> None:
 
     make_dir(args.output_prefix)
 
+    if args.cov_args != []:
+        import pickle
+        args.cov_args[0] = pickle.load(open(args.cov_args[0], 'rb'))
+
     # Because basic standard neutral models do not need to optimized
     # we can calculate the log-likelihood and theta
     if args.model in ['snm_1d', 'snm_2d'] and args.p0 == -1 and args.lbounds == None and args.ubounds == None: