Merge branch 'max_version_0.6.7' into 'master'

fixed statistic output format, updated toarstats to 0.6.7

See merge request !19

CHANGELOG.md

 # Changelog
 All notable changes to this project will be documented in this file.
 
+## v0.6.7 - 2025-02-21 - fixed output format
+
+### general:
+* changed statistic output format to cover the whole daterange
+
 ## v0.6.6 - 2025-02-10 - fixed osdma8 statistic
 
 ### general:

setup.cfg

 [metadata]
 name = toarstats
-version = 0.6.6
+version = 0.6.7
 author = Niklas Selke, Martin Schultz, Max Lensing
 author_email = n.selke@fz-juelich.de, m.schultz@fz-juelich.de, m.lensing@fz-juelich.de
 description = Collection of statistics for the TOAR community

toarstats/metrics/interface.py

@@ -146,13 +146,8 @@ def calculate_statistics(
             )
         )
 
-    if resample_rule == "custom":
-        custom_index = pd.DatetimeIndex([ref.index.min().strftime("%Y-%m-%d")])
-        for res in results:
-            res["ser"].index = custom_index
-    else:
     results = harmonize_time(
-            results, resample_rule,
+        results, ref, resample_rule,
         "H" if "diurnal_cycle" in input_parameters.statistics else None
     )
     return pd.DataFrame({res["name"]: res["ser"] for res in results})

toarstats/metrics/stats_utils.py

@@ -140,11 +140,11 @@ def create_reference_series(index, sampling, daterange=None):
     min_date = index.min()
     max_date = index.max()
     if daterange is not None:
-        # adjust start and end-date by the minute-offset of the timeseries
+        # adjust start and end-date by the minute/second offset of the timeseries
         start_date = pd.to_datetime(daterange.split(",")[0])
-        start_date = start_date.replace(minute=min_date.minute)
+        start_date = start_date.replace(minute=min_date.minute, second=min_date.second)
         end_date = pd.to_datetime(daterange.split(",")[1])
-        end_date = end_date.replace(minute=max_date.minute)
+        end_date = end_date.replace(minute=max_date.minute, second=max_date.second)
 
         if sampling in ["annual", "seasonal", "summer", "xsummer"]:
             start_date = start_date.replace(month=1, day=1, hour=0)
@@ -290,19 +290,26 @@ def get_seasons(sampling, statistics, metadata, seasons, crops,
             else DEFAULT_SEASONS for stat in statistics]
 
 
-def harmonize_time(ser_list, mtype, rsfreq=None):
+def harmonize_time(ser_list, ref, mtype, rsfreq=None):
     """Harmonize the datetime index of all given series.
 
     :param ser_list: a list of series
+    :param ref: reference timeseries
     :param mtype: aggregation type
     :param rsfreq: resampling frequency for the datetime index
 
     :return: A list of series with harmonized datetime indices
     """
+    if mtype == "custom":
+        custom_index = pd.DatetimeIndex([ref.index.min().strftime("%Y-%m-%d")])
+        for record in ser_list:
+            record["ser"].index = custom_index
+    else:
         if rsfreq is None:
             rsfreq = RSTAGS[mtype]
-    tmin = min(record["ser"].index.min() for record in ser_list)
-    tmax = max(record["ser"].index.max() for record in ser_list)
+        tmin = ref.index.min()
+        tmax = ref.index.max()
+
         try:
             newindex = pd.date_range(tmin, tmax, freq=rsfreq)
         except ValueError: