Implementing Carl's suggestions

Awaiting regex error kickback to adjust the test files.

flepimop/gempyor_pkg/src/gempyor/model_info.py

@@ -19,7 +19,7 @@ def __init__(self, config: confuse.ConfigView):
         self.tf = config["end_date"].as_date()
         if self.tf <= self.ti:
             raise ValueError(
-                f"Time to finish ('{self.tf}') is less than or equal to time to start ('{self.ti}')."
+                f"Final time ('{self.tf}') is less than or equal to initial time('{self.ti}')."
             )
         self.n_days = (self.tf - self.ti).days + 1
         self.dates = pd.date_range(start=self.ti, end=self.tf, freq="D")
@@ -76,7 +76,7 @@ def __init__(
         # Auto-detect old config
         if config["interventions"].exists():
             raise ValueError(
-                "This config has an intervention section, which is only compatible with a previous version (1.1) of flepiMoP."
+                "This config has an intervention section, which is only compatible with a previous version (v1.1) of flepiMoP. "
             )
 
         # 1. Create a setup name that contains every scenario.
@@ -172,14 +172,14 @@ def __init__(
                     )  # TODO create a Stacked from all
             elif self.seir_modifiers_scenario is not None:
                 raise ValueError(
-                    "A seir modifiers scenario was provided to `ModelInfo` but there is no `seir_modifiers` section in the config."
+                    "A `seir_modifiers_scenario` argument was provided to `ModelInfo` but there is no `seir_modifiers` section in the config."
                 )
             else:
                 logging.info("Running `ModelInfo` with seir but without SEIR Modifiers")
 
         elif self.seir_modifiers_scenario is not None:
             raise ValueError(
-                "A seir modifiers scenario was provided to `ModelInfo` but there is no `seir_modifiers` section in the config."
+                "A `seir_modifiers_scenario` argument was provided to `ModelInfo` but there is no `seir` section in the config."
             )
         else:
             logging.critical("Running ModelInfo without SEIR")
@@ -208,7 +208,7 @@ def __init__(
             elif self.outcome_modifiers_scenario is not None:
                 if config["outcome_modifiers"].exists():
                     raise ValueError(
-                        "An outcome modifiers scenario was provided to `ModelInfo` but there is no `outcome_modifiers` section in the config."
+                        "A `outcome_modifiers_scenario` argument was provided to `ModelInfo` but there is no `outcome_modifiers` section in the config."
                     )
                 else:
                     self.outcome_modifiers_scenario = None
@@ -218,7 +218,7 @@ def __init__(
                 )
         elif self.outcome_modifiers_scenario is not None:
             raise ValueError(
-                "An outcome modifiers scenario was provided to `ModelInfo` but there is no `outcome_modifiers` section in the config."
+                "A `outcome_modifiers_scenario` argument was provided to `ModelInfo` but there is no `outcomes` section in the config."
             )
         else:
             logging.info("Running `ModelInfo` without outcomes.")

flepimop/gempyor_pkg/src/gempyor/seeding.py

@@ -158,8 +158,6 @@ def get_from_config(self, sim_id: int, modinf) -> nb.typed.Dict:
             )
         elif method == "FolderDraw" or method == "FromFile":
             amounts = seeding["amount"]
-        else:
-            raise ValueError(f"Unknown seeding method given, '{method}'.")
 
         return _DataFrame2NumbaDict(df=seeding, amounts=amounts, modinf=modinf)
 

flepimop/gempyor_pkg/src/gempyor/statistics.py

@@ -257,8 +257,8 @@ def llik(self, model_data: xr.DataArray, gt_data: xr.DataArray) -> xr.DataArray:
         }
         if self.dist not in dist_map:
             raise ValueError(
-                f"Invalid distribution specified [received: '{self.dist}']. "
-                f"Valid distributions: '{dist_map}'."
+                f"Invalid distribution specified: '{self.dist}'. "
+                f"Valid distributions: '{dist_map.keys()}'."
             )
         if self.dist in ["pois", "nbinom"]:
             model_data = model_data.astype(int)