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shading: use morphology.footprint_rectangle((1, 3)) for 1x3 structuring element #233

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Merged
cwhanse merged 6 commits into from
Jul 8, 2026
Merged

shading: use morphology.footprint_rectangle((1, 3)) for 1x3 structuring element #233

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893f8ca
shading: use morphology.footprint_rectangle((1, 3)) for 1x3 structuri…
Omesh37 Jul 2, 2026
cfd31c9
Fix read-only array error in _prepare_images by copying to_numpy() ou…
Omesh37 Jul 4, 2026
553b9d5
Move footprint_rectangle import to module level with version fallback…
Omesh37 Jul 8, 2026
394267b
Apply suggestion from @cwhanse
cwhanse Jul 8, 2026
e976c1f
fixed flake8 error
Omesh37 Jul 8, 2026
f3e8713
Fix DatetimeIndex.diff() AttributeError in completeness_score
Omesh37 Jul 8, 2026
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5 changes: 3 additions & 2 deletions pvanalytics/features/shading.py
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Original file line number Diff line number Diff line change
Expand Up @@ -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):
Expand Down Expand Up @@ -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 (
Expand Down Expand Up @@ -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,
Expand Down
2 changes: 1 addition & 1 deletion pvanalytics/quality/gaps.py
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Original file line number Diff line number Diff line change
Expand Up @@ -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:
Expand Down
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