fix rebase mistake

Author: jbuces

dl1_data_handler/image_mapper.py

@@ -11,8 +11,8 @@
 import astropy.units as u
 
 from ctapipe.instrument.camera import PixelShape
-from ctapipe.core import TelescopeComponent
-from ctapipe.core.traits import Bool, Int
+from ctapipe.core import Component
+from ctapipe.core.traits import Bool, Int, Float
 
 __all__ = [
     "ImageMapper",
@@ -27,7 +27,7 @@
     "HexagonalPatchMapper",
 ]
 
-class ImageMapper(TelescopeComponent):
+class ImageMapper(Component):
     """
     Base component for mapping raw 1D vectors into 2D mapped images.
 
@@ -90,8 +90,23 @@ def __init__(
         parent : ctapipe.core.Component or ctapipe.core.Tool
             Parent of this component in the configuration hierarchy,
             this is mutually exclusive with passing ``config``
+        **kwargs
+            Additional keyword arguments for traitlets. Non-traitlet kwargs
+            (like 'subarray') are filtered out for compatibility.
         """
 
+        # Filter out non-traitlet kwargs before passing to Component
+        # This allows compatibility with ctapipe's reader which may pass extra kwargs
+        component_kwargs = {
+            key: value for key, value in kwargs.items()
+            if self.class_own_traits().get(key) is not None
+        }
+
+        super().__init__(
+            config=config,
+            parent=parent,
+            **component_kwargs,
+        )
         # Camera types
         self.geometry = geometry
         self.camera_type = self.geometry.name
@@ -1259,6 +1274,16 @@ class RebinMapper(ImageMapper):
         ),
     ).tag(config=True)
 
+    max_memory_gb = Float(
+        default_value=10,
+        allow_none=True,
+        help=(
+            "Maximum memory in GB that RebinMapper is allowed to allocate. "
+            "Set to None to disable memory checks. Default is 10 GB. "
+            "Note: RebinMapper uses approximately (image_shape * 10)^2 * image_shape^2 * 4 bytes."
+        ),
+    ).tag(config=True)
+
     def __init__(
         self,
         geometry,
@@ -1298,6 +1323,26 @@ def __init__(
             self.image_shape = self.interpolation_image_shape
         self.internal_shape = self.image_shape + self.internal_pad * 2
         self.rebinning_mult_factor = 10
+
+        # Validate memory requirements before proceeding (if max_memory_gb is set)
+        if self.max_memory_gb is not None:
+            # RebinMapper uses a fine grid (internal_shape * rebinning_mult_factor)^2
+            # and creates a mapping matrix of shape (fine_grid_size, internal_shape, internal_shape)
+            fine_grid_size = (self.internal_shape * self.rebinning_mult_factor) ** 2
+            estimated_memory_gb = (
+                fine_grid_size * self.internal_shape * self.internal_shape * 4
+            ) / (1024**3)  # 4 bytes per float32
+
+            if estimated_memory_gb > self.max_memory_gb:
+                raise ValueError(
+                    f"RebinMapper with image_shape={self.image_shape} would require "
+                    f"approximately {estimated_memory_gb:.1f} GB of memory, which exceeds "
+                    f"the limit of {self.max_memory_gb:.1f} GB. "
+                    f"To allow this allocation, set max_memory_gb to a higher value or None. "
+                    f"Alternatively, consider using a smaller interpolation_image_shape (recommended < 60) "
+                    f"or use BilinearMapper or BicubicMapper instead, which are more memory-efficient."
+                )
+
         # Creating the hexagonal and the output grid for the conversion methods.
         input_grid, output_grid = super()._get_grids_for_interpolation()
         # Calculate the mapping table

dl1_data_handler/reader.py

@@ -668,7 +668,7 @@ def _construct_mono_example_identifiers(self):
             self.n_signal_events = np.count_nonzero(
                 self.example_identifiers[class_column] == 1
             )
-            if self.input_url_background:
+            if self.input_url_background or (isinstance(self, DLTriggerReader) and not self.one_class):
                 self.n_bkg_events = np.count_nonzero(
                     self.example_identifiers[class_column] == 0
                 )
@@ -2206,10 +2206,11 @@ def _add_trigger_table(self, batch):
         # Packing info
         with tables.open_file(self.input_trigger_files[0], 'r') as h5file:
             node = h5file.get_node('/table')
-            self._trigger_mask_shape = tuple(node._v_attrs.trigger_mask_shape)
-            self._trigger_mask_bits = int(node._v_attrs.trigger_mask_bits)
-            self._trigger_mask_packed_len = int(node._v_attrs.trigger_mask_packed_len)
-            self._trigger_mask_bitorder = str(node._v_attrs.trigger_mask_bitorder)
+            if hasattr(node._v_attrs, 'trigger_mask_shape'):
+                self._trigger_mask_shape = tuple(node._v_attrs.trigger_mask_shape)
+                self._trigger_mask_bits = int(node._v_attrs.trigger_mask_bits)
+                self._trigger_mask_packed_len = int(node._v_attrs.trigger_mask_packed_len)
+                self._trigger_mask_bitorder = str(node._v_attrs.trigger_mask_bitorder)
 
         for file_idx, trigger_file in enumerate(self.input_trigger_files):
             tdscan_table = read_table(trigger_file, "/table")