Fix issue #370 (energy_from_power returns incorrect index for shifted hourly data)

docs/sphinx/source/changelog/pending.rst

@@ -10,5 +10,5 @@ Enhancements
 
 Bug fixes
 ---------
-* Set marker linewidth to zero in :py:func:`~rdtools.plotting.degradation_summary_plots` (:pull:`433`)
-
+* Set marker linewidth to zero in `rdtools.plotting.degradation_summary_plots` (:pull:`433`)
+* Fix :py:func:`~rdtools.normalization.energy_from_power` returns incorrect index for shifted hourly data (:issue:`370`, :pull:`437`)

rdtools/normalization.py

@@ -478,9 +478,9 @@ def _aggregate(time_series, target_frequency, max_timedelta, series_type):
     '''
 
     # series that has same index as desired output
-    output_dummy = time_series.resample(target_frequency,
-                                        closed='right',
-                                        label='right').sum()
+    output_dummy = time_series.resample(
+        target_frequency, closed="right", label="right", origin="start"
+    ).sum()
 
     union_index = time_series.index.union(output_dummy.index)
     time_series = time_series.dropna()
@@ -521,13 +521,13 @@ def _aggregate(time_series, target_frequency, max_timedelta, series_type):
         raise ValueError("series_type must be either 'instantaneous' or 'right_labeled', "
                          "not '{}'".format(series_type))
 
-    series_sum = pd.Series(data=series_sum, index=time_series.index[1:])
+    series_sum = pd.Series(data=np.insert(series_sum, 0, np.nan), index=time_series.index)
 
-    aggregated = series_sum.resample(target_frequency,
-                                     closed='right',
-                                     label='right').sum(min_count=1)
+    aggregated = series_sum.resample(
+        target_frequency, closed="right", label="right", origin="start"
+    ).sum(min_count=1)
 
-    return aggregated
+    return aggregated[1:]
 
 
 def _interpolate_series(time_series, target_index, max_timedelta=None,

rdtools/test/analysis_chains_test.py

@@ -743,6 +743,24 @@ def test_plot_degradation_timeseries(sensor_analysis, clearsky_analysis):
     )
 
 
+def test_energy_from_power_hourly_data():
+
+    times = pd.date_range("2019-01-01 00:00:00", periods=3, freq="h")
+    pv = pd.Series([1.2, 2.8, 2.0], index=times)
+
+    energy = normalization.energy_from_power(pv)
+    pd.testing.assert_series_equal(energy, pv[1:], check_names=False)
+
+
+def test_energy_from_power_shifted_hourly_data():
+
+    times = pd.date_range("2019-01-01 00:30:00", periods=3, freq="h")
+    pv = pd.Series([1.2, 2.8, 2.0], index=times)
+
+    energy = normalization.energy_from_power(pv)
+    pd.testing.assert_series_equal(energy, pv[1:], check_names=False)
+
+
 def test_validated_filter_dict_initialization():
     valid_keys = ["key1", "key2"]
     filter_dict = ValidatedFilterDict(valid_keys, key1="value1", key2="value2")