aggregate_logger: fix bug with timestamp at beginning of the new year

@schaefed

The problem was, that a a aggregated year should start with the timestamp 00:30 when aggregated to 30 minutes and with the 01.01. when aggregated to a whole day.
The new solution seems to be a bit hacky.
In the writeData function a freq is needed (I renamed is to change_freq) for the iterDataFreq(data, change_freq) function in dacces.py Until now change_freq=y, that isn't even listed in freqs dictionary in the iterDataFreq function in dacces.py , but it works only for aggregation for 1d, not for the 30min aggregation.
For the 30min it works with change_freq=yearly.
Maybe you have an idea how to solve this better.

level1/do_aggregate_logger.py

@@ -14,6 +14,7 @@ from lib.logger import initLogger, exceptionLogged
 
 from lib.daccess import ConfigFields as F
 
+pd.options.mode.chained_assignment = None
 
 AGGREGATIONS = {
     "SUM": np.nansum,
@@ -73,8 +74,8 @@ def aggregatePeriod(df, pflags1, pflags2, aggregations):
     data = np.full(df_data.shape[1], NODATA, dtype="float64")
     with warnings.catch_warnings():
         warnings.simplefilter("ignore", category=RuntimeWarning)
-        for agg in np.unique(aggregations): #.drop_duplicates():
-            idx = (aggregations == agg) & np.isfinite(df_data).all(axis=0)
+        for agg in np.unique(aggregations):
+            idx = (aggregations == agg)
             if agg == "CIRCULAR_MAX_DIST":
                 for i, col in zip(np.where(idx)[0], columns[idx]):
                     wsi = columns.get_loc("WS" + col[2:])
@@ -96,8 +97,8 @@ def aggregatePeriod(df, pflags1, pflags2, aggregations):
 
 def aggregateData(df, freq, config):
     groups = (reindexTable(df)
-        .astype(float)
-        .groupby(pd.Grouper(freq=freq, closed="right", label="right")))
+              .astype(float)
+              .groupby(pd.Grouper(freq=freq, closed="right", label="right")))
 
     cols = df.columns.get_level_values("varnames").drop_duplicates()
     # insert potentially missing column labels into config
@@ -109,23 +110,19 @@ def aggregateData(df, freq, config):
     # that's still a long runinng call
     out = groups.apply(aggregatePeriod, pflags1.to_numpy(), pflags2.to_numpy(), aggregations.to_numpy())
     out.columns = df.columns
-    if freq == '1D':
-        out.index = out.index - pd.Timedelta(days=1)
     return out
 
 
 def writeData(logger, data, device, freq):
-    if freq == "1D":
-        change_freq = "Y"
-    else:
-        change_freq = "yearly"
-    for date, out in iterDataFreq(data, change_freq):
+    for date, out in iterDataFreq(data, "yearly"):
         fname = Path(
             device.derivedpath,
             freq,
             f"{device.station_key}_{device.logger_key}_{freq}_{date.year}.level2.csv"
         )
         logger.debug(fname)
+        if freq == '1D':
+            out.index = out.index - pd.Timedelta(days=1)
         writeTable(fname, out, make_path=True)
         
 
@@ -178,7 +175,6 @@ if __name__ == "__main__":
 
     parser = ChainParser("Aggregate logger data to a frequency of 30 minutes", {"ndays": 1, "freq": "30min"})
     parser._add_argument("--freq", type=str, help="target time frequency as a pandas offset string")
-    pd.options.mode.chained_assignment = None
     args = parser.parse_args()
 
     main(