Some more cleaning up

cta-observatory · Oct 2, 2024 · aa4819f · aa4819f
1 parent 583b408
commit aa4819f
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Showing 7 changed files with 10 additions and 24 deletions.
diff --git a/docs/api-reference/monitoring/calculator.rst b/docs/api-reference/monitoring/calculator.rst
diff --git a/docs/api-reference/monitoring/index.rst b/docs/api-reference/monitoring/index.rst
@@ -10,7 +10,7 @@ Monitoring data are time-series used to monitor the status or quality of hardwar
 
 This module provides some code to help to generate monitoring data from processed event data, particularly for the purposes of calibration and data quality assessment.
 
-Code related to :ref:`stats_aggregator`, :ref:`calibration_calculator`, and :ref:`outlier_detector` is implemented here.
+Currently, only code related to :ref:`stats_aggregator` and :ref:`outlier_detector` is implemented here.
 
 
 Submodules

diff --git a/docs/changes/2554.feature.rst b/docs/changes/2554.feature.rst
diff --git a/docs/changes/2609.features.rst b/docs/changes/2609.features.rst
diff --git a/src/ctapipe/io/__init__.py b/src/ctapipe/io/__init__.py
@@ -18,7 +18,6 @@
 
 from .datawriter import DATA_MODEL_VERSION, DataWriter
 
-
 __all__ = [
     "HDF5TableWriter",
     "HDF5TableReader",

diff --git a/src/ctapipe/monitoring/aggregator.py b/src/ctapipe/monitoring/aggregator.py
@@ -57,16 +57,16 @@ def __call__(
         and call the relevant function of the particular aggregator to compute aggregated statistic values.
         The chunks are generated in a way that ensures they do not overflow the bounds of the table.
         - If ``chunk_shift`` is None, chunks will not overlap, but the last chunk is ensured to be
-        of size ``chunk_size``, even if it means the last two chunks will overlap.
+        of size `chunk_size`, even if it means the last two chunks will overlap.
         - If ``chunk_shift`` is provided, it will determine the number of samples to shift between the start
         of consecutive chunks resulting in an overlap of chunks. Chunks that overflows the bounds
         of the table are not considered.
 
         Parameters
         ----------
         table : astropy.table.Table
-            table with images of shape (n_images, n_channels, n_pix), event IDs and
-            timestamps of shape (n_images, )
+            table with images of shape (n_images, n_channels, n_pix)
+            and timestamps of shape (n_images, )
         masked_pixels_of_sample : ndarray, optional
             boolean array of masked pixels of shape (n_pix, ) that are not available for processing
         chunk_shift : int, optional

diff --git a/src/ctapipe/monitoring/outlier.py b/src/ctapipe/monitoring/outlier.py
@@ -81,7 +81,7 @@ class MedianOutlierDetector(OutlierDetector):
     the configurable factors and the camera median of the statistic values.
     """
 
-    validity_range = List(
+    median_range_factors = List(
         trait=Float(),
         default_value=[-1.0, 1.0],
         help=(
@@ -98,8 +98,8 @@ def __call__(self, column):
         # Detect outliers based on the deviation of the median distribution
         deviation = column - camera_median[:, :, np.newaxis]
         outliers = np.logical_or(
-            deviation < self.validity_range[0] * camera_median[:, :, np.newaxis],
-            deviation > self.validity_range[1] * camera_median[:, :, np.newaxis],
+            deviation < self.median_range_factors[0] * camera_median[:, :, np.newaxis],
+            deviation > self.median_range_factors[1] * camera_median[:, :, np.newaxis],
         )
         return outliers
 
@@ -112,7 +112,7 @@ class StdOutlierDetector(OutlierDetector):
     the configurable factors and the camera standard deviation of the statistic values.
     """
 
-    validity_range = List(
+    std_range_factors = List(
         trait=Float(),
         default_value=[-1.0, 1.0],
         help=(
@@ -131,7 +131,7 @@ def __call__(self, column):
         # Detect outliers based on the deviation of the standard deviation distribution
         deviation = column - camera_median[:, :, np.newaxis]
         outliers = np.logical_or(
-            deviation < self.validity_range[0] * camera_std[:, :, np.newaxis],
-            deviation > self.validity_range[1] * camera_std[:, :, np.newaxis],
+            deviation < self.std_range_factors[0] * camera_std[:, :, np.newaxis],
+            deviation > self.std_range_factors[1] * camera_std[:, :, np.newaxis],
         )
         return outliers