Merge pull request #247 from NREL/patch_development_with_2.0.4

Fix leading nans bug in _aggregate (#245)
NREL · Dec 5, 2020 · 39993fc · 39993fc
2 parents a4987a1 + 2ed33f8
commit 39993fc
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diff --git a/docs/degradation_and_soiling_example.ipynb b/docs/degradation_and_soiling_example.ipynb
diff --git a/docs/sphinx/source/changelog.rst b/docs/sphinx/source/changelog.rst
@@ -1,11 +1,10 @@
 RdTools Change Log
 ==================
 
+.. include:: changelog/v2.0.4.rst
 .. include:: changelog/v2.1.0-beta.0.rst
 .. include:: changelog/v2.0.3.rst
 .. include:: changelog/v2.0.2.rst
 .. include:: changelog/v2.0.1.rst
 .. include:: changelog/v2.0.0.rst
 .. include:: changelog/pre_2.0.0.rst
-
-
diff --git a/docs/sphinx/source/changelog/v2.0.4.rst b/docs/sphinx/source/changelog/v2.0.4.rst
@@ -0,0 +1,14 @@
+*******************************************
+v2.0.4 and v2.1.0-beta.1 (December 4, 2020)
+*******************************************
+
+Bug Fixes
+---------
+* Fix bug related to leading NaN values with
+  :py:func:`~rdtools.normalization.energy_from_power`. This fixed a small
+  normalization error in ``degradation_and_soiling_example.ipynb`` and
+  slightly changed the clear-sky degradation results (:issue:`244`, :pull:`245`)
+
+Contributors
+------------
+* Kevin Anderson (:ghuser:`kanderso-nrel`)
diff --git a/rdtools/normalization.py b/rdtools/normalization.py
@@ -620,6 +620,9 @@ def _aggregate(time_series, target_frequency, max_timedelta, series_type):
     max_interval_nanoseconds = max_timedelta.total_seconds() * 10.0**9
 
     gap_mask = t_diffs > max_interval_nanoseconds
+    if time_series.index[0] != union_index[0]:
+        # mask leading NaNs
+        gap_mask[:time_series.index[0]] = True
 
     time_series = time_series.reindex(union_index)
     t_diffs = np.diff(time_series.index.astype('int64').values)

diff --git a/rdtools/test/energy_from_power_test.py b/rdtools/test/energy_from_power_test.py
@@ -99,3 +99,12 @@ def test_energy_from_power_single_value_with_target():
     expected_result = pd.Series([100.], index=times, name='energy_Wh')
     result = energy_from_power(power, target_frequency='H')
     pd.testing.assert_series_equal(result, expected_result)
+
+def test_energy_from_power_leading_nans():
+    # GH 244
+    power = pd.Series(1, pd.date_range('2019-01-01', freq='15min', periods=5))
+    power.iloc[:2] = np.nan
+    expected_result = pd.Series([np.nan, np.nan, 0.25, 0.25],
+                                index=power.index[1:], name='energy_Wh')
+    result = energy_from_power(power)
+    pd.testing.assert_series_equal(result, expected_result)