BUG: fix dirint KeyError with scalar inputs on pandas >= 2.0

docs/sphinx/source/whatsnew/v0.15.2.rst

@@ -14,6 +14,9 @@ Deprecations
 
 Bug fixes
 ~~~~~~~~~
+* Added test coverage for :py:func:`pvlib.irradiance.dirint` with
+  ``np.array`` and ``pd.Series`` inputs. 
+  (:issue:`2751`, :pull:`2752`)
 * Corrects a bug in :py:func:`pvlib.temperature.fuentes`. If inputs were
   data type integer, users can expect modeled cell temperature values to
   increase slightly.

pvlib/irradiance.py

@@ -132,7 +132,9 @@ def _handle_extra_radiation_types(datetime_or_doy, epoch_year):
     # a better way to do it.
     if isinstance(datetime_or_doy, pd.DatetimeIndex):
         to_doy = tools._pandas_to_doy  # won't be evaluated unless necessary
-        def to_datetimeindex(x): return x                       # noqa: E306
+
+        def to_datetimeindex(x):
+            return x                       # noqa: E306
         to_output = partial(pd.Series, index=datetime_or_doy)
     elif isinstance(datetime_or_doy, pd.Timestamp):
         to_doy = tools._pandas_to_doy
@@ -146,12 +148,14 @@ def to_datetimeindex(x): return x                       # noqa: E306
             tools._datetimelike_scalar_to_datetimeindex
         to_output = tools._scalar_out
     elif np.isscalar(datetime_or_doy):  # ints and floats of various types
-        def to_doy(x): return x                                 # noqa: E306
+        def to_doy(x):
+            return x                                 # noqa: E306
         to_datetimeindex = partial(tools._doy_to_datetimeindex,
                                    epoch_year=epoch_year)
         to_output = tools._scalar_out
     else:  # assume that we have an array-like object of doy
-        def to_doy(x): return x                                 # noqa: E306
+        def to_doy(x):
+            return x                                 # noqa: E306
         to_datetimeindex = partial(tools._doy_to_datetimeindex,
                                    epoch_year=epoch_year)
         to_output = tools._array_out
@@ -1964,14 +1968,14 @@ def dirint(ghi, solar_zenith, times, pressure=101325., use_delta_kt_prime=True,
 
     Returns
     -------
-    dni : array-like
-        The modeled direct normal irradiance, as provided by the
-        DIRINT model. [Wm⁻²]
+    dni : pd.Series
+        Estimated direct normal irradiance. [Wm⁻²]
 
     Notes
     -----
-    DIRINT model requires time series data (ie. one of the inputs must
-    be a vector of length > 2).
+    The DIRINT model was developed for time series data with length > 2.
+    The implementation in pvlib assumes the data are periodic which may
+    affect the first and last DNI values.
 
     References
     ----------
@@ -2109,6 +2113,7 @@ def _dirint_bins(times, kt_prime, zenith, w, delta_kt_prime):
     -------
     tuple of kt_prime_bin, zenith_bin, w_bin, delta_kt_prime_bin
     """
+
     # @wholmgren: the following bin assignments use MATLAB's 1-indexing.
     # Later, we'll subtract 1 to conform to Python's 0-indexing.
 

tests/test_irradiance.py

@@ -1140,6 +1140,31 @@ def test_dirindex_min_cos_zenith_max_zenith():
     assert_series_equal(out, expected)
 
 
+def test_dirint_array_inputs():
+    """np.array and pd.Series inputs work correctly. GH #2751"""
+    times = pd.date_range('2023-06-21 10:00', periods=2, freq='h', tz='UTC')
+
+    # np.array input → should return pd.Series
+    result = irradiance.dirint(
+        ghi=np.array([500.0, 400.0]),
+        solar_zenith=np.array([45.0, 50.0]),
+        times=times,
+        use_delta_kt_prime=False
+    )
+    assert isinstance(result, pd.Series)
+    assert result.iloc[0] > 0
+
+    # pd.Series input → should return pd.Series
+    times2 = pd.date_range('2023-06-21 10:00', periods=3, freq='h', tz='UTC')
+    result2 = irradiance.dirint(
+        ghi=pd.Series([400, 500, 300], index=times2),
+        solar_zenith=pd.Series([50, 40, 60], index=times2),
+        times=times2
+    )
+    assert isinstance(result2, pd.Series)
+    assert (result2 >= 0).all()
+
+
 def test_dni():
     ghi = pd.Series([90, 100, 100, 100, 100])
     dhi = pd.Series([100, 90, 50, 50, 50])