fix traits for outlier detectors

src/ctapipe/monitoring/outlier.py

@@ -13,7 +13,7 @@
 
 import numpy as np
 
-from ctapipe.core import TelescopeComponent
+from ctapipe.core import TelescopeComponent, traits
 from ctapipe.core.traits import List
 
 
@@ -55,11 +55,14 @@ class RangeOutlierDetector(OutlierDetector):
     """
 
     validity_range = List(
-        [1.0, 2.0],
+        trait=traits.Float(),
+        default_value=[1.0, 2.0],
         help=(
             "Range of valid statistic values (in units of the image value)."
             "Values outside the range are identified as outliers."
         ),
+        minlen=2,
+        maxlen=2,
     ).tag(config=True)
 
     def __call__(self, column):
@@ -80,12 +83,15 @@ class MedianOutlierDetector(OutlierDetector):
     """
 
     median_range_factors = List(
-        [-1.0, 1.0],
+        trait=traits.Float(),
+        default_value=[-1.0, 1.0],
         help=(
             "Multiplicative factors (unitless) applied to the camera median"
             "of the provided statistic values to define a valid range based on the"
             "deviation of the values to its camera median."
         ),
+        minlen=2,
+        maxlen=2,
     ).tag(config=True)
 
     def __call__(self, column):
@@ -109,12 +115,15 @@ class StdOutlierDetector(OutlierDetector):
     """
 
     std_range_factors = List(
-        [-1.0, 1.0],
+        trait=traits.Float(),
+        default_value=[-1.0, 1.0],
         help=(
             "Multiplicative factors (unitless) applied to the camera standard deviation"
             "of the provided statistic values to define a valid range based on the"
             "deviation of the values to its camera median."
         ),
+        minlen=2,
+        maxlen=2,
     ).tag(config=True)
 
     def __call__(self, column):

src/ctapipe/monitoring/tests/test_outlier.py

@@ -27,7 +27,9 @@ def test_range_detection(example_subarray):
     # Initialize the outlier detector based on the range of valid values
     # In this test, the interval [15, 25] corresponds to the range (in waveform samples)
     # of accepted mean (or median) values of peak times of flat-field events
-    detector = RangeOutlierDetector(subarray=example_subarray, validity_range=[15, 25])
+    detector = RangeOutlierDetector(
+        subarray=example_subarray, validity_range=[15.0, 25.0]
+    )
     # Detect outliers
     outliers = detector(table["mean"])
     # Construct the expected outliers
@@ -54,7 +56,7 @@ def test_median_detection(example_subarray):
     # In this test, the interval [-0.9, 8] corresponds to multiplication factors
     # typical used for the median values of charge images of flat-field events
     detector = MedianOutlierDetector(
-        subarray=example_subarray, median_range_factors=[-0.9, 8]
+        subarray=example_subarray, median_range_factors=[-0.9, 8.0]
     )
     # Detect outliers
     outliers = detector(table["median"])
@@ -87,7 +89,7 @@ def test_std_detection(example_subarray):
     # typical used for the std values of charge images of flat-field events
     # and median (and std) values of charge images of pedestal events
     detector = StdOutlierDetector(
-        subarray=example_subarray, std_range_factors=[-15, 15]
+        subarray=example_subarray, std_range_factors=[-15.0, 15.0]
     )
     ff_outliers = detector(ff_table["std"])
     ped_outliers = detector(ped_table["median"])