Adopt ghi_clear variable name for clearsky GHI

pvlib/clearsky.py

@@ -327,13 +327,13 @@ def haurwitz(apparent_zenith):
     '''
 
     cos_zenith = tools.cosd(apparent_zenith.values)
-    clearsky_ghi = np.zeros_like(apparent_zenith.values)
+    ghi_clear = np.zeros_like(apparent_zenith.values)
     cos_zen_gte_0 = cos_zenith > 0
-    clearsky_ghi[cos_zen_gte_0] = (1098.0 * cos_zenith[cos_zen_gte_0] *
-                                   np.exp(-0.059/cos_zenith[cos_zen_gte_0]))
+    ghi_clear[cos_zen_gte_0] = (1098.0 * cos_zenith[cos_zen_gte_0] *
+                                np.exp(-0.059/cos_zenith[cos_zen_gte_0]))
 
     df_out = pd.DataFrame(index=apparent_zenith.index,
-                          data=clearsky_ghi,
+                          data=ghi_clear,
                           columns=['ghi'])
 
     return df_out

pvlib/irradiance.py

@@ -16,7 +16,7 @@
 from pvlib import atmosphere, solarposition, tools
 import pvlib  # used to avoid dni name collision in complete_irradiance
 
-from pvlib._deprecation import pvlibDeprecationWarning
+from pvlib._deprecation import pvlibDeprecationWarning, renamed_kwarg_warning
 import warnings
 
 
@@ -1614,7 +1614,12 @@ def ghi_from_poa_driesse_2023(surface_tilt, surface_azimuth,
         return ghi
 
 
-def clearsky_index(ghi, clearsky_ghi, max_clearsky_index=2.0):
+@renamed_kwarg_warning(
+    since='11.2',
+    old_param_name='clearsky_ghi',
+    new_param_name='ghi_clear',
+    removal="12.0")
+def clearsky_index(ghi, ghi_clear, max_clearsky_index=2.0):
     """
     Calculate the clearsky index.
 
@@ -1626,7 +1631,7 @@ def clearsky_index(ghi, clearsky_ghi, max_clearsky_index=2.0):
     ghi : numeric
         Global horizontal irradiance. [Wm⁻²]
 
-    clearsky_ghi : numeric
+    ghi_clear : numeric
         Modeled clearsky GHI
 
     max_clearsky_index : numeric, default 2.0
@@ -1638,12 +1643,12 @@ def clearsky_index(ghi, clearsky_ghi, max_clearsky_index=2.0):
     clearsky_index : numeric
         Clearsky index
     """
-    clearsky_index = ghi / clearsky_ghi
+    clearsky_index = ghi / ghi_clear
     # set +inf, -inf, and nans to zero
     clearsky_index = np.where(~np.isfinite(clearsky_index), 0,
                               clearsky_index)
     # but preserve nans in the input arrays
-    input_is_nan = ~np.isfinite(ghi) | ~np.isfinite(clearsky_ghi)
+    input_is_nan = ~np.isfinite(ghi) | ~np.isfinite(ghi_clear)
     clearsky_index = np.where(input_is_nan, np.nan, clearsky_index)
 
     clearsky_index = np.maximum(clearsky_index, 0)
@@ -2151,15 +2156,20 @@ def _dirint_bins(times, kt_prime, zenith, w, delta_kt_prime):
     return kt_prime_bin, zenith_bin, w_bin, delta_kt_prime_bin
 
 
-def dirindex(ghi, ghi_clearsky, dni_clearsky, zenith, times, pressure=101325.,
+@renamed_kwarg_warning(
+    since='11.2',
+    old_param_name='ghi_clearsky',
+    new_param_name='ghi_clear',
+    removal="12.0")
+def dirindex(ghi, ghi_clear, dni_clearsky, zenith, times, pressure=101325.,
              use_delta_kt_prime=True, temp_dew=None, min_cos_zenith=0.065,
              max_zenith=87):
     """
     Determine DNI from GHI using the DIRINDEX model.
 
     The DIRINDEX model [1]_ modifies the DIRINT model implemented in
     :py:func:`pvlib.irradiance.dirint` by taking into account information
-    from a clear sky model. It is recommended that ``ghi_clearsky`` be
+    from a clear sky model. It is recommended that ``ghi_clear`` be
     calculated using the Ineichen clear sky model
     :py:func:`pvlib.clearsky.ineichen` with ``perez_enhancement=True``.
 
@@ -2170,7 +2180,7 @@ def dirindex(ghi, ghi_clearsky, dni_clearsky, zenith, times, pressure=101325.,
     ghi : array-like
         Global horizontal irradiance. [Wm⁻²]
 
-    ghi_clearsky : array-like
+    ghi_clear : array-like
         Global horizontal irradiance from clear sky model. [Wm⁻²]
 
     dni_clearsky : array-like
@@ -2232,7 +2242,7 @@ def dirindex(ghi, ghi_clearsky, dni_clearsky, zenith, times, pressure=101325.,
                         temp_dew=temp_dew, min_cos_zenith=min_cos_zenith,
                         max_zenith=max_zenith)
 
-    dni_dirint_clearsky = dirint(ghi_clearsky, zenith, times,
+    dni_dirint_clearsky = dirint(ghi_clear, zenith, times,
                                  pressure=pressure,
                                  use_delta_kt_prime=use_delta_kt_prime,
                                  temp_dew=temp_dew,

pvlib/tests/test_irradiance.py

@@ -15,7 +15,8 @@
     assert_frame_equal,
     assert_series_equal,
     requires_ephem,
-    requires_numba
+    requires_numba,
+    fail_on_pvlib_version
 )
 
 from pvlib._deprecation import pvlibDeprecationWarning
@@ -1094,6 +1095,29 @@ def test_dirindex(times):
         equal_nan=True)
 
 
+@fail_on_pvlib_version("0.12")
+def test_dirindex_ghi_clearsky_deprecation():
+    with pytest.warns(pvlibDeprecationWarning, match='ghi_clear'):
+        times = pd.DatetimeIndex(['2014-06-24T18-1200'])
+        ghi = pd.Series([1038.62], index=times)
+        ghi_clearsky = pd.Series(
+            np.array([1042.48031487]),
+            index=times
+        )
+        dni_clearsky = pd.Series(
+            np.array([939.95469881]),
+            index=times
+        )
+        zenith = pd.Series(
+            np.array([10.56413562]),
+            index=times
+        )
+        pressure, tdew = 93193, 10
+        irradiance.dirindex(
+            ghi=ghi, ghi_clearsky=ghi_clearsky, dni_clearsky=dni_clearsky,
+            zenith=zenith, times=times, pressure=pressure, temp_dew=tdew)
+
+
 def test_dirindex_min_cos_zenith_max_zenith():
     # map out behavior under difficult conditions with various
     # limiting kwargs settings
@@ -1235,6 +1259,13 @@ def test_clearsky_index():
     assert_series_equal(out, expected)
 
 
+@fail_on_pvlib_version("0.12")
+def test_clearsky_index_clearsky_ghi_deprecation():
+    with pytest.warns(pvlibDeprecationWarning, match='ghi_clear'):
+        ghi, clearsky_ghi = 200, 300
+        irradiance.clearsky_index(ghi, clearsky_ghi=clearsky_ghi)
+
+
 def test_clearness_index():
     ghi = np.array([-1, 0, 1, 1000])
     solar_zenith = np.array([180, 90, 89.999, 0])