Merge pull request #1873 from cta-observatory/modifier_switch

Add switch to ImageProcessor to enable/disable ImageModifier

ctapipe/image/image_processor.py

@@ -12,7 +12,7 @@
     PeakTimeStatisticsContainer,
 )
 from ..core import QualityQuery, TelescopeComponent
-from ..core.traits import Bool, List, create_class_enum_trait
+from ..core.traits import Bool, BoolTelescopeParameter, List, create_class_enum_trait
 from ..instrument import SubarrayDescription
 from . import (
     ImageCleaner,
@@ -61,11 +61,17 @@ class ImageProcessor(TelescopeComponent):
     image_cleaner_type = create_class_enum_trait(
         base_class=ImageCleaner, default_value="TailcutsImageCleaner"
     )
+
     use_telescope_frame = Bool(
         default_value=True,
         help="Whether to calculate parameters in the telescope or camera frame",
     ).tag(config=True)
 
+    apply_image_modifier = BoolTelescopeParameter(
+        default_value=False,
+        help="If true, apply ImageModifier to dl1 images"
+    ).tag(config=True)
+
     def __init__(
         self, subarray: SubarrayDescription, config=None, parent=None, **kwargs
     ):
@@ -196,8 +202,10 @@ def _process_telescope_event(self, event):
                 geometry = self.telescope_frame_geometries[tel_id]
             else:
                 geometry = self.subarray.tel[tel_id].camera.geometry
-            # compute image parameters only if requested to write them
-            dl1_camera.image = self.modify(tel_id=tel_id, image=dl1_camera.image)
+
+            if self.apply_image_modifier.tel[tel_id]:
+                dl1_camera.image = self.modify(tel_id=tel_id, image=dl1_camera.image)
+
             dl1_camera.image_mask = self.clean(
                 tel_id=tel_id,
                 image=dl1_camera.image,

ctapipe/image/modifications.py

@@ -160,30 +160,36 @@ def __init__(
         self.rng = np.random.default_rng(self.rng_seed)
 
     def __call__(self, tel_id, image, rng=None):
-        geom = self.subarray.tel[tel_id].camera.geometry
-        smeared_image = _smear_psf_randomly(
-            image=image,
-            fraction=self.psf_smear_factor.tel[tel_id],
-            indices=geom.neighbor_matrix_sparse.indices,
-            indptr=geom.neighbor_matrix_sparse.indptr,
-            smear_probabilities=np.full(geom.max_neighbors, 1 / geom.max_neighbors),
-            seed=self.rng.integers(0, np.iinfo(np.int64).max),
-        )
-
-        noise = np.where(
-            image > self.noise_transition_charge.tel[tel_id],
-            self.noise_level_bright_pixels.tel[tel_id],
-            self.noise_level_dim_pixels.tel[tel_id],
-        )
-        image_with_noise = _add_noise(
-            smeared_image,
-            noise,
-            rng=self.rng,
-            correct_bias=self.noise_correct_bias.tel[tel_id],
-        )
-        bias = np.where(
-            image > self.noise_transition_charge.tel[tel_id],
-            0,
-            self.noise_bias_dim_pixels.tel[tel_id],
-        )
-        return (image_with_noise + bias).astype(np.float32)
+        dtype = image.dtype
+
+        if self.psf_smear_factor.tel[tel_id] > 0:
+            geom = self.subarray.tel[tel_id].camera.geometry
+            image = _smear_psf_randomly(
+                image=image,
+                fraction=self.psf_smear_factor.tel[tel_id],
+                indices=geom.neighbor_matrix_sparse.indices,
+                indptr=geom.neighbor_matrix_sparse.indptr,
+                smear_probabilities=np.full(geom.max_neighbors, 1 / geom.max_neighbors),
+                seed=self.rng.integers(0, np.iinfo(np.int64).max),
+            )
+
+        if (
+            self.noise_level_dim_pixels.tel[tel_id] > 0
+            or self.noise_level_bright_pixels.tel[tel_id] > 0
+        ):
+            bright_pixels = image > self.noise_transition_charge.tel[tel_id]
+            noise = np.where(
+                bright_pixels,
+                self.noise_level_bright_pixels.tel[tel_id],
+                self.noise_level_dim_pixels.tel[tel_id],
+            )
+            image = _add_noise(
+                image,
+                noise,
+                rng=self.rng,
+                correct_bias=self.noise_correct_bias.tel[tel_id],
+            )
+
+            image[~bright_pixels] += self.noise_bias_dim_pixels.tel[tel_id]
+
+        return image.astype(dtype, copy=False)

ctapipe/image/tests/test_modifications.py

@@ -70,3 +70,16 @@ def test_smear_image():
             # drawn from the poissonian (especially with signal value 1)
             if fraction > 0:
                 assert not ((image > 0) == (smeared > 0)).all()
+
+
+def test_defaults_no_change(example_subarray):
+    """Test that the default settings do not change the input image"""
+    rng = np.random.default_rng(0)
+
+    modifier = modifications.ImageModifier(example_subarray)
+    tel_id = 1
+    n_pixels = example_subarray.tel[tel_id].camera.geometry.n_pixels
+    image = rng.normal(50, 15, size=n_pixels).astype(np.float32)
+
+    new_image = modifier(tel_id=tel_id, image=image)
+    assert np.all(image == new_image)

ctapipe/tools/tests/resources/image_modification_config.json

@@ -18,6 +18,7 @@
     },
     "ImageProcessor": {
         "image_cleaner_type": "TailcutsImageCleaner",
+        "apply_image_modifier": [["type", "*", true]],
         "TailcutsImageCleaner": {
             "picture_threshold_pe": [
                 ["type", "*", 10.0],