type safety

meteor/scripts/common.py

@@ -111,7 +111,7 @@ def __init__(self, *args: Any, **kwargs: Any) -> None:
             "--structure",
             type=Path,
             required=True,
-            help="Specify CIF or PDB file path to compute phases from (usually a native model).",
+            help="Specify CIF or PDB file path, for phases (usually a native model). Required.",
         )
 
         self.add_argument(
@@ -136,7 +136,7 @@ def __init__(self, *args: Any, **kwargs: Any) -> None:
             type=WeightMode,
             default=WeightMode.optimize,
             choices=list(WeightMode),
-            help="Choose the k-weighting behavior.",
+            help="How to pick the k-parameter. Optimize means max negentropy. Default: `optimize`.",
         )
 
         self.add_argument(

meteor/scripts/compute_difference_map.py

@@ -33,7 +33,10 @@ def __init__(self, *args: Any, **kwargs: Any) -> None:
             type=WeightMode,
             default=WeightMode.optimize,
             choices=list(WeightMode),
-            help="Choose how to find a TV denoising regularization weight (lambda) parameter.",
+            help=(
+                "How to find a TV regularization weight (lambda). Optimize means pick maximum "
+                "negentropy. Default: `optimize`."
+            ) 
         )
         self.add_argument(
             "-l",

meteor/tv.py

@@ -39,8 +39,8 @@ def json(self) -> dict:
         json_payload.pop("negentropy_at_weights")
         json_payload[self._scan_name] = [
             {
-                self._weight_name: self.weights_scanned[idx],
-                self._negentropy_name: self.negentropy_at_weights[idx],
+                self._weight_name: float(self.weights_scanned[idx]),
+                self._negentropy_name: float(self.negentropy_at_weights[idx]),
             }
             for idx in range(len(self.weights_scanned))
         ]

meteor/validate.py

@@ -57,7 +57,7 @@ def negentropy(samples: np.ndarray, tolerance: float = 0.1) -> float:
         )
         raise ValueError(msg)
 
-    return neg_e
+    return float(neg_e)
 
 
 class ScalarMaximizer:
@@ -103,7 +103,7 @@ def _update_optima(self, argument_test_value: float) -> float:
         if objective_value > self.objective_maximum:
             self.argument_optimum = argument_test_value
             self.objective_maximum = objective_value
-        return objective_value
+        return float(objective_value)
 
     def optimize_over_explicit_values(
         self, *, arguments_to_scan: Sequence[float] | np.ndarray
@@ -117,6 +117,7 @@ def optimize_over_explicit_values(
             A list or array of argument values to evaluate.
         """
         for argument_test_value in arguments_to_scan:
+            argument_test_value = float(argument_test_value)
             objective_value = self._update_optima(argument_test_value)
             self.values_evaluated.append(argument_test_value)
             self.objective_at_values.append(objective_value)