DOC: clarify ineichen and lookup_linke_turbidity usage

pvlib/clearsky.py

@@ -28,7 +28,11 @@ def ineichen(apparent_zenith, airmass_absolute, linke_turbidity,
     report on clear sky models found the Ineichen/Perez model to have
     excellent performance with a minimal input data set [3]_.
 
-    Default values for monthly Linke turbidity provided by SoDa [4]_, [5]_.
+    Default monthly Linke turbidity values are available via
+    :py:func:`pvlib.clearsky.lookup_linke_turbidity`, which uses data
+    provided by SoDa [4]_, [5]_. Users must supply Linke turbidity values
+    explicitly unless providing their own turbidity data.
+
 
     Parameters
     -----------
@@ -147,9 +151,13 @@ def ineichen(apparent_zenith, airmass_absolute, linke_turbidity,
 def lookup_linke_turbidity(time, latitude, longitude, filepath=None,
                            interp_turbidity=True):
     """
-    Look up the Linke Turibidity from the ``LinkeTurbidities.h5``
+    Look up the Linke turbidity from the ``LinkeTurbidities.h5`
     data file supplied with pvlib.
 
+    The Linke turbidity climatology used by this function is sourced from
+    SoDa (Solar Radiation Data) and corresponds to the references cited in
+    :py:func:`pvlib.clearsky.ineichen`.
+
     Parameters
     ----------
     time : pandas.DatetimeIndex
@@ -175,6 +183,19 @@ def lookup_linke_turbidity(time, latitude, longitude, filepath=None,
     The returned value for each time is either the monthly value or an
     interpolated value to smooth the transition between months.
     Interpolation is done on the day of year as determined by UTC.
+
+    Examples
+    --------
+    >>> from pvlib.clearsky import lookup_linke_turbidity, ineichen
+    >>> from pvlib.location import Location
+    >>> import pandas as pd
+    >>>
+    >>> times = pd.date_range('2024-06-01', freq='1H', periods=24, tz='UTC')
+    >>> loc = Location(35, -110)
+    >>>
+    >>> tl = lookup_linke_turbidity(times, loc.latitude, loc.longitude)
+    >>> cs = ineichen(times, loc.latitude, loc.longitude,
+    ...               linke_turbidity=tl)
     """
 
     # The .h5 file 'LinkeTurbidities.h5' contains a single 2160 x 4320 x 12

pvlib/transformer.py

@@ -27,12 +27,11 @@ def simple_efficiency(
     input_power : numeric
         The real AC power input to the transformer. [W]
 
-    no_load_loss : numeric
+    no_load_loss : float
         The constant losses experienced by a transformer, even
-        when the transformer is not under load. Fraction of transformer rating,
-        value from 0 to 1. [unitless]
+        when the transformer is not under load. Fraction of transformer rating, value from 0 to 1. [unitless]
 
-    load_loss:  numeric
+    load_loss:  float
         The load dependent losses experienced by the transformer.
         Fraction of transformer rating, value from 0 to 1. [unitless]
 

tests/test_transformer.py

@@ -1,7 +1,9 @@
 import pandas as pd
+import numpy as np
+import pytest
 
 from numpy.testing import assert_allclose
-
+from pvlib.transformer import simple_efficiency
 from pvlib import transformer
 
 
@@ -48,13 +50,47 @@ def test_simple_efficiency_known_values():
 
     # verify correct behavior at no-load condition
     assert_allclose(
-        transformer.simple_efficiency(no_load_loss*rating, *args),
+        transformer.simple_efficiency(no_load_loss * rating, *args),
         0.0
     )
 
     # verify correct behavior at rated condition
     assert_allclose(
-        transformer.simple_efficiency(rating*(1 + no_load_loss + load_loss),
-                                      *args),
+        transformer.simple_efficiency(
+            rating * (1 + no_load_loss + load_loss),
+            *args
+        ),
         rating,
     )
+
+
+# =========================
+# NEW TESTS (Issue #2649)
+# =========================
+
+def test_simple_efficiency_numpy_input_power():
+    input_power = np.array([500, 1000, 1500])
+    no_load_loss = 0.01
+    load_loss = 0.02
+    transformer_rating = 2000
+
+    output = simple_efficiency(
+        input_power, no_load_loss, load_loss, transformer_rating
+    )
+
+    assert isinstance(output, np.ndarray)
+    assert output.shape == input_power.shape
+
+
+def test_simple_efficiency_zero_load_loss():
+    input_power = np.array([500, 1000])
+    no_load_loss = 0.01
+    load_loss = 0.0
+    transformer_rating = 2000
+
+    with pytest.warns(RuntimeWarning):
+        output = simple_efficiency(
+            input_power, no_load_loss, load_loss, transformer_rating
+        )
+
+    assert np.all(np.isnan(output))