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BUG: suppress RuntimeWarning in snow.categorize and outliers.zscore #238

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BUG: suppress RuntimeWarning in snow.categorize and outliers.zscore #238

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7c57a17
Fix divide-by-zero RuntimeWarning in quartic_restricted_r2 when y is …
Omesh37 Jul 11, 2026
e311ef2
BUG: suppress RuntimeWarning in snow.categorize and outliers.zscore G…
Omesh37 Jul 31, 2026
7327835
DOC: add whatsnew entry for RuntimeWarning fixes GH#XXX
Omesh37 Jul 31, 2026
3eaa7e5
FLAKE8
Omesh37 Jul 31, 2026
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5 changes: 5 additions & 0 deletions docs/whatsnew/v0.2.3.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -15,6 +15,10 @@ Bug Fixes
* Remove freq parameter from :py:func:`pvanalytics.quality.gaps.completeness`
and :py:func:`pvanalytics.quality.gaps.completeness_score`. Frequency is now
always calculated from the input data's DatetimeIndex. (:pull:`236`)
* Suppress divide-by-zero warnings in
:mod:`pvanalytics.features.snow` and precision-loss warnings in
:mod:`pvanalytics.quality.outliers` by extending ``np.errstate`` to
include ``invalid='ignore'``. (:issue:`#237`, :pull:`YYY`)

Requirements
~~~~~~~~~~~~
Expand All @@ -39,3 +43,4 @@ Testing
Contributors
~~~~~~~~~~~~
* Cliff Hansen (:ghuser:`cwhanse`)
* Omesh Chandure (:ghuser: `Omesh37`)
2 changes: 1 addition & 1 deletion pvanalytics/features/snow.py
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Expand Up @@ -224,7 +224,7 @@ def categorize(transmission, measured_voltage,
modeled_voltage_with_snow_copy = np.where(
transmission == 0, 0, modeled_voltage_with_snow)

with np.errstate(divide='ignore'):
with np.errstate(divide='ignore', invalid='ignore'):
vmp_ratio =\
measured_voltage /\
modeled_voltage_with_snow_copy
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4 changes: 3 additions & 1 deletion pvanalytics/quality/outliers.py
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@@ -1,5 +1,6 @@
"""Functions for identifying and labeling outliers."""
import pandas as pd
import numpy as np
from scipy import stats
from statsmodels import robust

Expand Down Expand Up @@ -75,7 +76,8 @@ def zscore(data, zmax=1.5, nan_policy='raise'):
"nan_policy. Expected 'raise' or 'omit'.")

is_outlier = pd.Series(False, index=data.index)
is_outlier.loc[~nan_mask] = abs(stats.zscore(data[~nan_mask])) > zmax
with np.errstate(invalid='ignore'):
is_outlier.loc[~nan_mask] = abs(stats.zscore(data[~nan_mask])) > zmax
return is_outlier


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5 changes: 4 additions & 1 deletion pvanalytics/util/_fit.py
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Expand Up @@ -127,4 +127,7 @@ def _quartic(x, a, b, c, e):
)
model = _quartic(x, params[0], params[1], params[2], params[3])
residuals = y - model
return 1 - (np.sum(residuals**2) / np.sum((y - np.mean(y))**2))
ss_tot = np.sum((y - np.mean(y))**2)
if ss_tot == 0:
return 0.0
return 1 - (np.sum(residuals**2) / ss_tot)
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