diff --git a/pvanalytics/features/shading.py b/pvanalytics/features/shading.py
index 08ea73ed..74c82971 100644
--- a/pvanalytics/features/shading.py
+++ b/pvanalytics/features/shading.py
@@ -5,6 +5,7 @@
 from skimage import morphology, measure
 import pvlib
 from pvanalytics import util
+from skimage.morphology import rectangle as footprint_rectangle
 
 
 def _to_image(data, width):
@@ -113,7 +114,7 @@ def _prepare_images(ghi, clearsky, daytime, interval):
     ghi_image = pd.DataFrame(cloudless_image).interpolate(
         axis=0,
         limit_direction='both'
-    ).to_numpy()
+    ).to_numpy().copy()
     # set night to nan
     ghi_image[~_to_image(daytime.to_numpy(), image_width)] = np.nan
     return (
@@ -365,7 +366,7 @@ def fixed(ghi, daytime, clearsky, interval=None, min_gradient=2):
     threshold = gradient > min_gradient  # binary image of wire candidates
 
     # From here we CAN use skimage because we are working with binary images.
-    three_minute_mask = morphology.rectangle(1, 3)
+    three_minute_mask = footprint_rectangle(1, 3)
     wires = morphology.remove_small_objects(
         morphology.binary_closing(threshold, three_minute_mask),
         min_size=200,
diff --git a/pvanalytics/quality/gaps.py b/pvanalytics/quality/gaps.py
index 2e16d83e..4441149f 100644
--- a/pvanalytics/quality/gaps.py
+++ b/pvanalytics/quality/gaps.py
@@ -274,7 +274,7 @@ def completeness_score(series, keep_index=True):
         (fraction of the day for which `series` has data).
 
     """
-    seconds_per_sample = series.index.diff().total_seconds()[1]
+    seconds_per_sample = (series.index[1] - series.index[0]).total_seconds()
     daily_counts = series.resample('D').count()
     daily_completeness = (daily_counts * seconds_per_sample) / (1440*60)
     if keep_index: