Adjust TrendAnalysis interpolations for index mismatch

rdtools/analysis_chains.py

@@ -54,8 +54,8 @@ class TrendAnalysis():
         normalized in the normalization step based on it's 95th percentile
         (see TrendAnalysis._pvwatts_norm() source).
     interp_freq : str or pandas.tseries.offsets.DateOffset
-        Pandas frequency specification used to interpolate all pandas.Series
-        passed at instantiation. We recommend using the natural frequency of the
+        Pandas frequency specification used to interpolate the input PV power
+        or energy. We recommend using the natural frequency of the
         data, rather than up or down sampling. Analysis requires regular time series.
         For more information see
         https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#dateoffset-objects
@@ -86,21 +86,22 @@ def __init__(self, pv, poa_global=None, temperature_cell=None, temperature_ambie
 
         if interp_freq is not None:
             pv = normalization.interpolate(pv, interp_freq, max_timedelta)
-            if poa_global is not None:
-                poa_global = normalization.interpolate(
-                    poa_global, pv.index, max_timedelta)
-            if temperature_cell is not None:
-                temperature_cell = normalization.interpolate(
-                    temperature_cell, pv.index, max_timedelta)
-            if temperature_ambient is not None:
-                temperature_ambient = normalization.interpolate(
-                    temperature_ambient, pv.index, max_timedelta)
-            if power_expected is not None:
-                power_expected = normalization.interpolate(
-                    power_expected, pv.index, max_timedelta)
-            if isinstance(windspeed, pd.Series):
-                windspeed = normalization.interpolate(
-                    windspeed, pv.index, max_timedelta)
+
+        if poa_global is not None:
+            poa_global = normalization.interpolate(
+                poa_global, pv.index, max_timedelta)
+        if temperature_cell is not None:
+            temperature_cell = normalization.interpolate(
+                temperature_cell, pv.index, max_timedelta)
+        if temperature_ambient is not None:
+            temperature_ambient = normalization.interpolate(
+                temperature_ambient, pv.index, max_timedelta)
+        if power_expected is not None:
+            power_expected = normalization.interpolate(
+                power_expected, pv.index, max_timedelta)
+        if isinstance(windspeed, pd.Series):
+            windspeed = normalization.interpolate(
+                windspeed, pv.index, max_timedelta)
 
         if pv_input == 'power':
             self.pv_power = pv
@@ -171,22 +172,21 @@ def set_clearsky(self, pvlib_location=None, pv_azimuth=None, pv_tilt=None,
         '''
         max_timedelta = self.max_timedelta
 
-        if self.interp_freq is not None:
-            if poa_global_clearsky is not None:
-                poa_global_clearsky = normalization.interpolate(
-                    poa_global_clearsky, self.pv_energy.index, max_timedelta)
-            if temperature_cell_clearsky is not None:
-                temperature_cell_clearsky = normalization.interpolate(
-                    temperature_cell_clearsky, self.pv_energy.index, max_timedelta)
-            if temperature_ambient_clearsky is not None:
-                temperature_ambient_clearsky = normalization.interpolate(
-                    temperature_ambient_clearsky, self.pv_energy.index, max_timedelta)
-            if isinstance(pv_azimuth, (pd.Series, pd.DataFrame)):
-                pv_azimuth = normalization.interpolate(
-                    pv_azimuth, self.pv_energy.index, max_timedelta)
-            if isinstance(pv_tilt, (pd.Series, pd.DataFrame)):
-                pv_tilt = normalization.interpolate(
-                    pv_tilt, self.pv_energy.index, max_timedelta)
+        if poa_global_clearsky is not None:
+            poa_global_clearsky = normalization.interpolate(
+                poa_global_clearsky, self.pv_energy.index, max_timedelta)
+        if temperature_cell_clearsky is not None:
+            temperature_cell_clearsky = normalization.interpolate(
+                temperature_cell_clearsky, self.pv_energy.index, max_timedelta)
+        if temperature_ambient_clearsky is not None:
+            temperature_ambient_clearsky = normalization.interpolate(
+                temperature_ambient_clearsky, self.pv_energy.index, max_timedelta)
+        if isinstance(pv_azimuth, (pd.Series, pd.DataFrame)):
+            pv_azimuth = normalization.interpolate(
+                pv_azimuth, self.pv_energy.index, max_timedelta)
+        if isinstance(pv_tilt, (pd.Series, pd.DataFrame)):
+            pv_tilt = normalization.interpolate(
+                pv_tilt, self.pv_energy.index, max_timedelta)
 
         self.pvlib_location = pvlib_location
         self.pv_azimuth = pv_azimuth

rdtools/test/analysis_chains_test.py

@@ -336,6 +336,27 @@ def test_no_set_clearsky(clearsky_parameters):
         rd_analysis.clearsky_analysis()
 
 
+def test_index_mismatch():
+    # GH #277
+    times = pd.date_range('2019-01-01', '2022-01-01', freq='15min')
+    pv = pd.Series(1.0, index=times)
+    dummy_series = pd.Series(1.0, index=times[::4])  # low-frequency weather inputs
+    keys = ['poa_global', 'temperature_cell', 'temperature_ambient', 'power_expected', 'windspeed']
+    kwargs = {key: dummy_series.copy() for key in keys}
+    rd_analysis = TrendAnalysis(pv, **kwargs)
+    for key in keys:
+        interpolated_series = getattr(rd_analysis, key)
+        assert interpolated_series.index.equals(times)
+
+    cs_keys = ['poa_global_clearsky', 'temperature_cell_clearsky', 'temperature_ambient_clearsky',
+               'pv_azimuth', 'pv_tilt']
+    cs_kwargs = {key: dummy_series.copy() for key in cs_keys}
+    rd_analysis.set_clearsky(**cs_kwargs)
+    for key in cs_keys:
+        interpolated_series = getattr(rd_analysis, key)
+        assert interpolated_series.index.equals(times[1:])
+
+
 @pytest.fixture
 def soiling_parameters(basic_parameters, soiling_normalized_daily, cs_input):
     # parameters for soiling analysis with TrendAnalysis