fix bug

modules/cluster_estimation/cluster_estimation.py

@@ -20,20 +20,6 @@ class ClusterEstimation:
     works by predicting 'cluster centres' from groups of closely placed landing pad
     detections.
 
-    ATTRIBUTES
-    ----------
-    min_activation_threshold: int
-        Minimum total data points before model runs.
-
-    min_new_points_to_run: int
-        Minimum number of new data points that must be collected before running model.
-
-    max_num_components: int
-        Max number of real landing pads.
-
-    random_state: int
-        Seed for randomizer, to get consistent results.
-
     METHODS
     -------
     run()
@@ -80,17 +66,35 @@ def create(
     ) -> "tuple[bool, ClusterEstimation | None]":
         """
         Data requirement conditions for estimation model to run.
+
+        PARAMETERS:
+        min_activation_threshold: int
+            Minimum total data points before model runs. Must be at least 1.
+
+        min_new_points_to_run: int
+            Minimum number of new data points that must be collected before running model. Must be at least 0.
+
+        max_num_components: int
+            Max number of real landing pads. Must be at least 1.
+
+        random_state: int
+            Seed for randomizer, to get consistent results. Must be at least 0.
+
+        local_logger: logger.Logger
+            The local logger to log this object's information.
+
+        RETURNS: The ClusterEstimation object if all conditions pass, otherwise False, None
         """
-        # These parameters must be positive
-        if min_new_points_to_run < 0 or random_state < 0:
+        if min_activation_threshold < 1:
             return False, None
 
-        # At least 1 point for model to fit
-        if min_activation_threshold < 1:
+        if min_new_points_to_run < 0:
+            return False, None
+
+        if max_num_components < 1:
             return False, None
 
-        # This must be greater than 0
-        if max_num_components < 0:
+        if random_state < 0:
             return False, None
 
         return True, ClusterEstimation(