Set index to be the middle of the period

Author: AdamRJensen

pvlib/iotools/meteonorm.py

@@ -91,7 +91,7 @@ def get_meteonorm_forecast_basic(
     Returns
     -------
     data : pd.DataFrame
-        Time series data. The index corresponds to the start (left) of the
+        Time series data. The index corresponds to the middle of the
         interval unless ``interval_index`` is set to True.
     meta : dict
         Metadata.
@@ -181,7 +181,7 @@ def get_meteonorm_forecast_precision(
     Returns
     -------
     data : pd.DataFrame
-        Time series data. The index corresponds to the start (left) of the
+        Time series data. The index corresponds to the middle of the
         interval unless ``interval_index`` is set to True.
     meta : dict
         Metadata.
@@ -270,7 +270,7 @@ def get_meteonorm_observation_realtime(
     Returns
     -------
     data : pd.DataFrame
-        Time series data. The index corresponds to the start (left) of the
+        Time series data. The index corresponds to the middle of the
         interval unless ``interval_index`` is set to True.
     meta : dict
         Metadata.
@@ -358,7 +358,7 @@ def get_meteonorm_observation_training(
     Returns
     -------
     data : pd.DataFrame
-        Time series data. The index corresponds to the start (left) of the
+        Time series data. The index corresponds to the middle of the
         interval unless ``interval_index`` is set to True.
     meta : dict
         Metadata.
@@ -473,7 +473,7 @@ def get_meteonorm_tmy(
     Returns
     -------
     data : pd.DataFrame
-        Time series data. The index corresponds to the start (left) of the
+        Time series data. The index corresponds to the middle of the
         interval unless ``interval_index`` is set to True.
     meta : dict
         Metadata.
@@ -513,12 +513,12 @@ def get_meteonorm_tmy(
     start, end = None, None
 
     data, meta = _get_meteonorm(
-            latitude, longitude, start, end,
-            api_key, parameters,
-            surface_tilt, surface_azimuth,
-            time_step, horizon,
-            interval_index, map_variables,
-            url, endpoint, **additional_params)
+        latitude, longitude, start, end,
+        api_key, parameters,
+        surface_tilt, surface_azimuth,
+        time_step, horizon,
+        interval_index, map_variables,
+        url, endpoint, **additional_params)
     return data, meta
 
 
@@ -602,14 +602,14 @@ def _parse_meteonorm(response, interval_index, map_variables):
     data = pd.DataFrame(data_json)
 
     # xxx: experimental feature - see parameter description
-    if interval_index:
-        data.index = pd.IntervalIndex.from_arrays(
-            left=pd.to_datetime(response.json()["start_times"]),
-            right=pd.to_datetime(response.json()["end_times"]),
-            closed="left",
-        )
-    else:
-        data.index = pd.to_datetime(response.json()["start_times"])
+    data.index = pd.IntervalIndex.from_arrays(
+        left=pd.to_datetime(response.json()["start_times"]),
+        right=pd.to_datetime(response.json()["end_times"]),
+        closed="left",
+    )
+
+    if not interval_index:
+        data.index = data.index.mid
 
     meta = response.json()["meta"]
 

tests/iotools/test_meteonorm.py

@@ -51,7 +51,8 @@ def expected_meta():
 @pytest.fixture
 def expected_meteonorm_index():
     expected_meteonorm_index = \
-        pd.date_range('2023-01-01', '2024-12-31 23:59', freq='1h', tz='UTC')
+        pd.date_range('2023-01-01', '2024-12-31 23:59', freq='1h', tz='UTC') \
+        + pd.Timedelta(minutes=30)
     expected_meteonorm_index.freq = None
     return expected_meteonorm_index
 
@@ -74,7 +75,7 @@ def expected_meteonorm_data():
         [210.75, 210.7458778],
         [221.0, 220.99278214],
     ]
-    index = pd.date_range('2023-01-01', periods=12, freq='1h', tz='UTC')
+    index = pd.date_range('2023-01-01 00:30', periods=12, freq='1h', tz='UTC')
     index.freq = None
     expected = pd.DataFrame(expected, index=index, columns=columns)
     return expected
@@ -183,7 +184,7 @@ def test_get_meteonorm_forecast_precision(demo_api_key, demo_url):
         url=demo_url)
 
     assert data.index[1] - data.index[0] == pd.Timedelta(minutes=15)
-    assert data.shape == (5, 1)
+    assert data.shape == (60/15*3+1, 1)
     assert meta['frequency'] == '15_minutes'
 
 
@@ -249,16 +250,6 @@ def expected_meteonorm_tmy_meta():
     return meta
 
 
-@pytest.fixture
-def expected_meteonorm_tmy_interval_index():
-    index = pd.date_range(
-        '2005-01-01', periods=8760, freq='1h', tz=3600)
-    index.freq = None
-    interval_index = pd.IntervalIndex.from_arrays(
-        index, index + pd.Timedelta(hours=1), closed='left')
-    return interval_index
-
-
 @pytest.fixture
 def expected_meteonorm_tmy_data():
     # The first 12 rows of data
@@ -290,7 +281,7 @@ def expected_meteonorm_tmy_data():
 @pytest.mark.flaky(reruns=RERUNS, reruns_delay=RERUNS_DELAY)
 def test_get_meteonorm_tmy(
         demo_api_key, demo_url, expected_meteonorm_tmy_meta,
-        expected_meteonorm_tmy_data, expected_meteonorm_tmy_interval_index):
+        expected_meteonorm_tmy_data):
     data, meta = pvlib.iotools.get_meteonorm_tmy(
         latitude=50, longitude=10,
         api_key=demo_api_key,
@@ -312,5 +303,3 @@ def test_get_meteonorm_tmy(
         url=demo_url)
     assert meta == expected_meteonorm_tmy_meta
     pd.testing.assert_frame_equal(data.iloc[:12], expected_meteonorm_tmy_data)
-    pd.testing.assert_index_equal(
-        data.index, expected_meteonorm_tmy_interval_index)