Remove unused n_tel parameter

src/eventdisplay_ml/config.py

@@ -34,15 +34,14 @@ def configure_training(analysis_type):
     parser.add_argument(
         "--model_prefix",
         required=True,
-        help=("Path to directory for writing XGBoost models (without n_tel / energy bin suffix)."),
+        help=("Path to directory for writing XGBoost models (energy bin suffix)."),
     )
     parser.add_argument(
         "--hyperparameter_config",
         help="Path to JSON file with hyperparameter configuration.",
         default=None,
         type=str,
     )
-    parser.add_argument("--n_tel", type=int, help="Telescope multiplicity (2, 3, or 4).")
     parser.add_argument(
         "--train_test_fraction",
         type=float,
@@ -111,7 +110,6 @@ def configure_training(analysis_type):
 
     _logger.info(f"--- XGBoost {analysis_type} training ---")
     _logger.info(f"Observatory: {model_configs.get('observatory')}")
-    _logger.info(f"Telescope multiplicity: {model_configs.get('n_tel')}")
     _logger.info(f"Model output prefix: {model_configs.get('model_prefix')}")
     _logger.info(f"Train vs test fraction: {model_configs['train_test_fraction']}")
     _logger.info(f"Random state: {model_configs['random_state']}")
@@ -131,15 +129,14 @@ def configure_training(analysis_type):
 
     if analysis_type == "stereo_analysis":
         model_configs["pre_cuts"] = pre_cuts_regression(
-            model_configs.get("n_tel"), min_images=model_configs.get("min_images", 2)
+            min_images=model_configs.get("min_images", 2)
         )
     elif analysis_type == "classification":
         _logger.info(f"Energy bin {model_configs['energy_bin_number']}")
         model_parameters = utils.load_model_parameters(
             model_configs["model_parameters"], model_configs["energy_bin_number"]
         )
         model_configs["pre_cuts"] = pre_cuts_classification(
-            model_configs.get("n_tel"),
             e_min=np.power(10.0, model_parameters.get("energy_bins_log10_tev", []).get("E_min")),
             e_max=np.power(10.0, model_parameters.get("energy_bins_log10_tev", []).get("E_max")),
         )
@@ -165,7 +162,7 @@ def configure_apply(analysis_type):
         "--model_prefix",
         required=True,
         metavar="MODEL_PREFIX",
-        help=("Path to directory containing XGBoost models (without n_tel / energy bin suffix)."),
+        help=("Path to directory containing XGBoost models (without energy bin suffix)."),
     )
     parser.add_argument(
         "--model_name",

src/eventdisplay_ml/hyper_parameters.py

@@ -88,14 +88,12 @@ def _load_hyper_parameters_from_file(config_file):
     return hyperparameters
 
 
-def pre_cuts_regression(n_tel, min_images=2):
+def pre_cuts_regression(min_images=2):
     """
     Get pre-cuts for regression analysis.
 
     Parameters
     ----------
-    n_tel : int or None
-        Number of telescopes (not currently used).
     min_images : int
         Minimum number of images (DispNImages) for quality cut (default: 2).
 
@@ -112,7 +110,7 @@ def pre_cuts_regression(n_tel, min_images=2):
     return event_cut if event_cut else None
 
 
-def pre_cuts_classification(n_tel, e_min, e_max):
+def pre_cuts_classification(e_min, e_max):
     """Get pre-cuts for classification analysis (no multiplicity filter)."""
     event_cut = f"(Erec >= {e_min}) & (Erec < {e_max})"
     event_cut += " & " + " & ".join(f"({c})" for c in PRE_CUTS_CLASSIFICATION)