Added a custom sampling option to aggregate over the full time series time span.

tests/test_metrics/test_interface.py

@@ -74,7 +74,7 @@ def test_calculate_statistics_run_all_statistics_with_all_samplings(
         elif statistic == "percentiles1":
             expected_num_columns *= 3
         elif statistic == "percentiles2":
-            if sampling in {"daily", "monthly", "seasonal", "vegseason"}:
+            if sampling not in {"summer", "xsummer", "annual"}:
                 expected_num_columns *= 7
             else:
                 expected_num_columns *= 9

toarstats/metrics/constants.py

@@ -16,7 +16,8 @@ ALLOWED_SAMPLING_VALUES = [
     "vegseason",
     "summer",
     "xsummer",
-    "annual"
+    "annual",
+    "custom"
 ]
 
 STATISTICS_LIST = [
@@ -156,5 +157,6 @@ RSTAGS = {
     "seasonal": "AS",
     "summer": "AS",
     "xsummer": "AS",
-    "annual": "AS"
+    "annual": "AS",
+    "custom": "100AS"
 }

toarstats/metrics/interface.py

@@ -26,7 +26,7 @@ def calculate_statistics(
 
     :param sampling: temporal aggregation, one of ``daily``,
                      ``monthly``, ``seasonal``, ``vegseason``,
-                     ``summer``, ``xsummer``, or ``annual``;
+                     ``summer``, ``xsummer``, ``annual``, or ``custom``;
                      ``summer`` will pick the 6-months summer season in
                      the hemisphere where the station is located;
                      ``xsummer`` does the same for a 7-months summer

toarstats/metrics/stats.py

@@ -484,7 +484,7 @@ def diurnal_cycle(ser, ref, mtype, metadata, seasons, min_data_capture):
     # data record.
     nyears = ser.index.year.unique().size
     vmax = {"daily": 1, "monthly": 29, "seasonal": 91, "summer": 182,
-            "xsummer": 212, "annual": 365}
+            "xsummer": 212, "annual": 365, "custom": 365}
     dcap = int(min_data_capture * vmax[mtype] * nyears)
     for r in res:
         grouped = r["ser"].groupby(r["ser"].index.hour)