Fix sample interval calculation on DST transitions and kimber on tz-naive data

docs/sphinx/source/whatsnew/v0.16.2.rst

@@ -21,6 +21,9 @@ Bug fixes
   is scalar (:issue:`2881`, :pull:`2882`)
 * :py:func:`pvlib.scaling.wvm` no longer returns all NaNs when ``positions``
   contains a single site (:issue:`2895`, :pull:`2896`)
+* Fix :py:func:`pvlib.clearsky.detect_clearsky` and :py:func:`pvlib.temperature.prilliman`
+  failing on Daylight Saving Time (DST) transition days, and :py:func:`pvlib.soiling.kimber`
+  failing on timezone-naive data. (:issue:`2935`)
 
 Enhancements
 ~~~~~~~~~~~~
@@ -80,3 +83,4 @@ Contributors
 * :ghuser:`bfs2021`
 * :ghuser:`xiajiahang`
 * Jakub Krasuski (:ghuser:`Blizzeq`)
+* S B Pranay (:ghuser:`pranaysb`)

pvlib/soiling.py

@@ -192,11 +192,9 @@ def kimber(rainfall, cleaning_threshold=6, soiling_loss_rate=0.0015,
     # convert grace_period to timedelta
     grace_period = datetime.timedelta(days=grace_period)
 
-    # get indices as np.datetime64 (UTC), calculate timestep as np.timedelta64,
-    # and convert timestep to fraction of days
-    rain_index_vals = rainfall.index.tz_convert(None).to_numpy()
-    timestep_interval = (rain_index_vals[1] - rain_index_vals[0])
-    day_fraction = timestep_interval / np.timedelta64(24, 'h')
+    # calculate timestep as timedelta and convert to fraction of days
+    timestep_interval = rainfall.index[1] - rainfall.index[0]
+    day_fraction = timestep_interval / pd.Timedelta(days=1)
 
     # accumulate rainfall
     accumulated_rainfall = rainfall.rolling(

pvlib/tools.py

@@ -429,18 +429,17 @@ def _get_sample_intervals(times, win_length):
     """ Calculates time interval and samples per window for Reno-style clear
     sky detection functions
     """
-    deltas = (
-        np.diff(times.tz_localize(None).to_numpy())  # tz-naive np.datetime64
-        / np.timedelta64(1, '60s')
-    )
+    if not isinstance(times, pd.DatetimeIndex):
+        times = pd.DatetimeIndex(times)
+
+    deltas = (times[1:] - times[:-1]) / pd.Timedelta('60s')
 
     # determine if we can proceed
     if (  # infer wrapper is None if no freq is determined
         _pandas_infer_freq_BaseOffset_transition_handler(times)
         and len(np.unique(deltas)) == 1
     ):
-        sample_interval = times[1] - times[0]
-        sample_interval = sample_interval.seconds / 60  # in minutes
+        sample_interval = (times[1] - times[0]).total_seconds() / 60
         samples_per_window = int(win_length / sample_interval)
         return sample_interval, samples_per_window
     else:

tests/test_clearsky.py

@@ -710,6 +710,17 @@ def test_detect_clearsky_optimizer_not_failed(
     assert isinstance(clear_samples, pd.Series)
 
 
+def test_detect_clearsky_dst():
+    # GH 2935
+    loc = Location(49.28, -123.12, tz='America/Vancouver')
+    times = pd.date_range('2026-03-07', '2026-03-10', freq='1min',
+                          tz=loc.tz, inclusive='left')
+    ghi = loc.get_clearsky(times)['ghi']
+    result = clearsky.detect_clearsky(ghi, ghi)
+    assert isinstance(result, pd.Series)
+    assert len(result) == len(times)
+
+
 @pytest.fixture
 def detect_clearsky_helper_data():
     samples_per_window = 3

tests/test_soiling.py

@@ -170,6 +170,14 @@ def test_kimber_nowash(greensboro_rain, expected_kimber_nowash):
     assert np.allclose(nowash.values, expected_kimber_nowash['soiling'].values)
 
 
+def test_kimber_tz_naive(greensboro_rain, expected_kimber_nowash):
+    # GH 2935
+    rain_naive = greensboro_rain.copy()
+    rain_naive.index = rain_naive.index.tz_localize(None)
+    actual = kimber(rain_naive)
+    assert np.allclose(actual.values, expected_kimber_nowash['soiling'].values)
+
+
 @pytest.fixture
 def expected_kimber_manwash():
     return pd.read_csv(

tests/test_temperature.py

@@ -425,6 +425,16 @@ def test_prilliman_nans():
     assert_series_equal(actual, expected)
 
 
+def test_prilliman_dst():
+    # GH 2935
+    times = pd.date_range('2026-03-07', '2026-03-10', freq='1min',
+                          tz='America/Vancouver', inclusive='left')
+    cell_temperature = pd.Series(25.0, index=times)
+    wind_speed = pd.Series(1.0, index=times)
+    actual = temperature.prilliman(cell_temperature, wind_speed)
+    assert_series_equal(cell_temperature, actual)
+
+
 def test_glm_conversions():
     # it is easiest and sufficient to test conversion from  & to the same model
     glm = temperature.GenericLinearModel(module_efficiency=0.1,

tests/test_tools.py

@@ -283,3 +283,27 @@ def test__file_context_manager():
     buffer = StringIO("test content")
     with tools._file_context_manager(buffer) as obj:
         assert obj.read() == "test content"
+
+
+def test_get_sample_intervals_dst():
+    # GH 2935
+    times = pd.date_range('2026-03-07', '2026-03-10', freq='1min',
+                          tz='America/Vancouver')
+    interval, count = tools._get_sample_intervals(times, 20)
+    assert interval == 1.0
+    assert count == 20
+
+
+def test_get_sample_intervals_naive():
+    # GH 2935
+    times = pd.date_range('2026-01-01', periods=60, freq='1min')
+    interval, count = tools._get_sample_intervals(times, 20)
+    assert interval == 1.0
+    assert count == 20
+
+
+def test_get_sample_intervals_unequal():
+    times = pd.DatetimeIndex(['2026-01-01 00:00', '2026-01-01 00:01',
+                              '2026-01-01 00:05'])
+    with pytest.raises(NotImplementedError, match='unequal time intervals'):
+        tools._get_sample_intervals(times, 20)