Update for ultimate

preprocessing/sports/space_data/ultimate/ultimate_space_class.py

@@ -95,3 +95,84 @@ def preprocessing(self):
                 )
 
         return event_data_dict, home_tracking_dict, away_tracking_dict
+
+    def detect_initiations(
+        self,
+        velocity_threshold=3.0,
+        acceleration_threshold=4.0,
+        distance_threshold=5.0,
+        player_threshold=2,
+    ):
+        """
+        Detect play initiations in tracking data.
+
+        This method processes raw tracking data (2_1.csv or 1_1_1.csv format),
+        calculates required motion features, detects play initiations, and optionally
+        extracts individual play segments.
+
+        Args:
+            velocity_threshold (float): Threshold for velocity (default: 3.0).
+            acceleration_threshold (float): Threshold for acceleration (default: 4.0).
+            distance_threshold (float): Threshold for distance (default: 5.0).
+            player_threshold (int): Threshold for player proximity (default: 2).
+            output_detected_dir (str): Optional directory to save detected play data.
+            output_extracted_dir (str): Optional directory to save extracted play segments.
+
+        Returns:
+            tuple: (detected_plays_dict, extracted_plays_dict)
+                - detected_plays_dict: Dictionary mapping match_id to detected play DataFrame
+                - extracted_plays_dict: Dictionary mapping (match_id, offense_id, play_num) to extracted play DataFrame
+        """
+        tracking_files = self.get_files()
+        if self.testing_mode:
+            tracking_files = tracking_files[:2]
+            print("Running in testing mode. Limited files will be processed.")
+
+        from .ultimate_space_detect_initiation import detect_play, extract_play
+        from .ultimate_space_preprocessing import create_intermediate_file
+
+        detected_plays_dict = {}
+        for tracking_path_i in tqdm(
+            tracking_files, total=len(tracking_files), desc="Processing tracking files"
+        ):
+            match_i = os.path.splitext(
+                os.path.splitext(os.path.basename(tracking_path_i))[0]
+            )[0]
+
+            # Read raw tracking data
+            match_tracking_df = pd.read_csv(tracking_path_i)
+
+            # Prepare data with required columns
+            intermediate_df = create_intermediate_file(match_tracking_df)
+
+            # Detect play initiations
+            detected_df = detect_play(
+                intermediate_df,
+                velocity_threshold=velocity_threshold,
+                acceleration_threshold=acceleration_threshold,
+                distance_threshold=distance_threshold,
+                player_threshold=player_threshold,
+            )
+
+            # Extract individual play segments
+
+            plays = extract_play(detected_df)
+
+            for play_key, play_df in plays.items():
+                detected_plays_dict[len(detected_plays_dict)] = (
+                    f"{match_i}-id{play_key[1]}-play{play_key[2]}"
+                )
+
+            if self.out_path:
+                os.makedirs(self.out_path + "/initiation/plays", exist_ok=True)
+
+                for play_key, df in plays.items():
+                    df.to_csv(
+                        os.path.join(
+                            self.out_path,
+                            "initiation/plays",
+                            f"{match_i}-id{play_key[1]}-play{play_key[2]}.csv",
+                        ),
+                        index=False,
+                    )
+        return detected_plays_dict

pyproject.toml

@@ -6,7 +6,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "openstarlab_preprocessing"
-version = "0.1.54"
+version = "0.1.55"
 description = "openstarlab preprocessing package"
 readme = "README.md"
 requires-python = ">=3.8"