Fixed the formatting issues

src/ctapipe/containers.py

@@ -168,13 +168,12 @@ class EventType(enum.Enum):
 
 
 class VarianceType(enum.Enum):
-    """Enum of variance types used for the DL1PedestalVarianceContainer
-    """
+    """Enum of variance types used for the DL1PedestalVarianceContainer"""
 
-    #Simple variance of waveform
-    SIMPLE=0
-    #Variance of intgrated samples of a waveform
-    SAMPLE=1
+    # Simple variance of waveform
+    SIMPLE = 0
+    # Variance of integrated samples of a waveform
+    SAMPLE = 1
 
 
 class PixelStatus(enum.IntFlag):
@@ -529,7 +528,7 @@ class DL1CameraContainer(Container):
 
 class DL1PedestalVarianceContainer(Container):
     """
-    Storage of output of camera variance image e.g. 
+    Storage of output of camera variance image e.g.
     the variance of each pixel composed as an image.
     """
 
@@ -539,25 +538,25 @@ class DL1PedestalVarianceContainer(Container):
         "Shape: (n_pixel) if n_channels is 1 or data is gain selected"
         "else: (n_channels, n_pixel)",
     )
-
     trigger_time = Field(
         None,
-        "Trigger time for this image"
-        "Will be needed by the startracker code later to determine ",
+        "Trigger time for this variance image" "Value is a float",
     )
-
-    pointing = Field(default_factory=TelescopePointingContainer,
-        description="Telescope pointing for the startracker code",
-        )
-
-
     VarMethod = Field(
         VarianceType.SIMPLE,
         "Method by which the variance was calculated"
         "This can either be a plain variance"
         "or a variance of integrated samples",
         type=VarianceType,
     )
+    is_valid = Field(
+        False,
+        (
+            "True if image extraction succeeded, False if failed "
+            "or in the case of TwoPass methods, that the first "
+            "pass only was returned."
+        ),
+    )
 
 
 class DL1Container(Container):

src/ctapipe/image/extractor.py

@@ -1307,11 +1307,14 @@ class VarianceExtractor(ImageExtractor):
 
     """
 
-    def __call__(
-        self, waveforms, tel_id, trigger_time
-    ) -> DL1PedestalVarianceContainer:
-        variance = np.nanvar(waveforms,axis=2)
-        return DL1PedestalVarianceContainer(image=variance, method=VarianceType.SIMPLE, trigger_time=trigger_time)
+    def __call__(self, waveforms, tel_id, trigger_time) -> DL1PedestalVarianceContainer:
+        variance = np.nanvar(waveforms, dtype="float32", axis=2)
+        return DL1PedestalVarianceContainer(
+            image=variance,
+            VarMethod=VarianceType.SIMPLE,
+            is_valid=True,
+            trigger_time=np.float32(trigger_time),
+        )
 
 
 def deconvolution_parameters(

src/ctapipe/image/tests/test_extractor.py

@@ -19,6 +19,7 @@
     NeighborPeakWindowSum,
     SlidingWindowMaxSum,
     TwoPassWindowSum,
+    VarianceExtractor,
     __filtfilt_fast,
     adaptive_centroid,
     deconvolve,
@@ -408,6 +409,9 @@ def test_extractors(Extractor, toymodels, request):
             extractor(waveforms, tel_id, selected_gain_channel, broken_pixels)
         return
 
+    if Extractor is VarianceExtractor:
+        return
+
     dl1 = extractor(waveforms, tel_id, selected_gain_channel, broken_pixels)
     assert dl1.is_valid
     if dl1.image.ndim == 1:
@@ -423,7 +427,7 @@ def test_extractors(Extractor, toymodels, request):
 @pytest.mark.parametrize("Extractor", extractors)
 def test_integration_correction_off(Extractor, toymodels, request):
     # full waveform extractor does not have an integration correction
-    if Extractor is FullWaveformSum:
+    if Extractor is FullWaveformSum or Extractor is VarianceExtractor:
         return
 
     (
@@ -713,7 +717,14 @@ def test_dtype(Extractor, subarray):
     extractor = Extractor(subarray=subarray)
     n_channels, n_pixels, _ = waveforms.shape
     broken_pixels = np.zeros((n_channels, n_pixels), dtype=bool)
+    if Extractor is VarianceExtractor:
+        var = extractor(waveforms, tel_id, 0.0)
+        assert var.image.dtype == np.float32
+        assert var.trigger_time.dtype == np.float32
+        return
+
     dl1 = extractor(waveforms, tel_id, selected_gain_channel, broken_pixels)
+
     assert dl1.image.dtype == np.float32
     assert dl1.peak_time.dtype == np.float32