Added cluster estimation output logging

modules/cluster_estimation/cluster_estimation.py

@@ -11,6 +11,7 @@
 
 from .. import object_in_world
 from .. import detection_in_world
+from ..common.modules.logger import logger
 
 
 class ClusterEstimation:
@@ -70,7 +71,11 @@ class ClusterEstimation:
 
     @classmethod
     def create(
-        cls, min_activation_threshold: int, min_new_points_to_run: int, random_state: int
+        cls,
+        min_activation_threshold: int,
+        min_new_points_to_run: int,
+        random_state: int,
+        local_logger: logger.Logger,
     ) -> "tuple[bool, ClusterEstimation | None]":
         """
         Data requirement conditions for estimation model to run.
@@ -88,6 +93,7 @@ def create(
             min_activation_threshold,
             min_new_points_to_run,
             random_state,
+            local_logger,
         )
 
     def __init__(
@@ -96,6 +102,7 @@ def __init__(
         min_activation_threshold: int,
         min_new_points_to_run: int,
         random_state: int,
+        local_logger: logger.Logger,
     ) -> None:
         """
         Private constructor, use create() method.
@@ -121,6 +128,7 @@ def __init__(
         self.__min_activation_threshold = min_activation_threshold
         self.__min_new_points_to_run = min_new_points_to_run
         self.__has_ran_once = False
+        self.__logger = local_logger
 
     def run(
         self, detections: "list[detection_in_world.DetectionInWorld]", run_override: bool
@@ -160,6 +168,7 @@ def run(
 
         # Check convergence
         if not self.__vgmm.converged_:
+            self.__logger.warning("Model failed to converge")
             return False, None
 
         # Get predictions from cluster model
@@ -201,7 +210,10 @@ def run(
 
             if result:
                 detections_in_world.append(landing_pad)
+            else:
+                self.__logger.warning("Failed to create ObjectInWorld object")
 
+        self.__logger.info(detections_in_world)
         return True, detections_in_world
 
     def __decide_to_run(self, run_override: bool) -> bool:

modules/cluster_estimation/cluster_estimation_worker.py

@@ -2,9 +2,13 @@
 Gets detections in world space and outputs estimations of objects.
 """
 
+import os
+import pathlib
+
 from utilities.workers import queue_proxy_wrapper
 from utilities.workers import worker_controller
 from . import cluster_estimation
+from ..common.modules.logger import logger
 
 
 def cluster_estimation_worker(
@@ -38,13 +42,25 @@ def cluster_estimation_worker(
     worker_controller: worker_controller.WorkerController
         How the main process communicates to this worker process.
     """
+    worker_name = pathlib.Path(__file__).stem
+    process_id = os.getpid()
+    result, local_logger = logger.Logger.create(f"{worker_name}_{process_id}", True)
+    if not result:
+        print("ERROR: Worker failed to create logger")
+        return
+
+    assert local_logger is not None
+
+    local_logger.info("Logger initialized")
+
     result, estimator = cluster_estimation.ClusterEstimation.create(
         min_activation_threshold,
         min_new_points_to_run,
         random_state,
+        local_logger,
     )
     if not result:
-        print("ERROR: Worker failed to create class object")
+        local_logger.error("Worker failed to create class object", True)
         return
 
     # Get Pylance to stop complaining

modules/object_in_world.py

@@ -38,3 +38,15 @@ def __init__(
         self.location_x = location_x
         self.location_y = location_y
         self.spherical_variance = spherical_variance
+
+    def __str__(self) -> str:
+        """
+        To string.
+        """
+        return f"{self.__class__}, location_x: {self.location_x}, location_y: {self.location_y}, spherical_variance: {self.spherical_variance}"
+
+    def __repr__(self) -> str:
+        """
+        For collections (e.g. list).
+        """
+        return str(self)

tests/unit/test_cluster_detection.py

@@ -7,6 +7,7 @@
 import sklearn.datasets
 
 from modules.cluster_estimation import cluster_estimation
+from modules.common.modules.logger import logger
 from modules import detection_in_world
 
 
@@ -26,10 +27,15 @@ def cluster_model() -> cluster_estimation.ClusterEstimation:  # type: ignore
     """
     Cluster estimation object.
     """
+    result, test_logger = logger.Logger.create("test_logger", False)
+    assert result
+    assert test_logger is not None
+
     result, model = cluster_estimation.ClusterEstimation.create(
         MIN_TOTAL_POINTS_THRESHOLD,
         MIN_NEW_POINTS_TO_RUN,
         RNG_SEED,
+        test_logger,
     )
     assert result
     assert model is not None