Set the 'seasons_per_statistic' to the correct value in all cases.

toarstats/interface.py

@@ -4,9 +4,9 @@ This module contains the following function:
 calculate_statistics - calculate the requested statistics
 """
 
-from toarstats.defaults import DEFAULT_DATA_CAPTURE
+from toarstats.defaults import DEFAULT_CROPS, DEFAULT_DATA_CAPTURE
 from toarstats.input_checks import check_input_parameters
-from toarstats.stats_utils import create_reference_data_frame
+from toarstats.stats_utils import create_reference_data_frame, get_seasons
 
 
 def calculate_statistics(
@@ -93,8 +93,10 @@ def calculate_statistics(
     :param station_climatic_zone: station's climatic zone, used if
                                   missing in the ``metadata`` argument
 
-    :raises KeyError: raised if any key in the metadata parameter is not
-                      recognized
+    :raises KeyError: raised if
+                        - any key in the metadata parameter is not
+                          recognized
+                        - a growing season is unknown
     :raises TypeError: raised if any parameter has the wrong type or any
                        required parameter is missing
     :raises ValueError: raised if
@@ -106,6 +108,13 @@ def calculate_statistics(
         sampling, statistics, data, metadata, seasons, crops, data_capture,
         datetimes, values, station_lat, station_lon, station_climatic_zone
     )
+    seasons_per_statistic = get_seasons(
+        input_parameters.sampling, input_parameters.statistics,
+        input_parameters.metadata, input_parameters.seasons,
+        input_parameters.crops if input_parameters.crops is not None
+        else DEFAULT_CROPS, input_parameters.required.seasons,
+        input_parameters.required.crops
+    )
     resample_rule = ("seasonal" if input_parameters.sampling == "vegseason"
                      else input_parameters.sampling)
     if input_parameters.data_capture is not None:

toarstats/stats_utils.py

@@ -7,6 +7,7 @@ create_reference_data_frame - create a reference data frame
 dma8_processor - calculate the daily maximum 8-hour running mean
 get_elevation_angle - calculate the solar elevation angle
 get_growing_season - get the correct season name
+get_seasons - get the correct seasons for each statistic
 harmonize_time - harmonize the datetime index of all given data frames
 kth_highest - calculate the kth highest value of the given data frame
 perc - calculate percentiles
@@ -24,7 +25,8 @@ import datetime as dt
 import numpy as np
 import pandas as pd
 
-from toarstats.constants import CLIMATIC_ZONE, RSTAGS, SEASON_DICT
+from toarstats.constants import RSTAGS, SEASON_DICT
+from toarstats.defaults import DEFAULT_SEASONS
 from toarstats.solar_position import calc_zenith
 
 
@@ -198,16 +200,7 @@ def get_growing_season(croptype, climatic_zone, latitude):
     :return: The growing season matching the crop type, climatic zone
              and latitude
     """
-    if isinstance(climatic_zone, str):
-        try:
-            myclimzone = climatic_zone[
-                climatic_zone.index("(")+1:climatic_zone.index(")")
-            ]
-        except ValueError:
-            myclimzone = climatic_zone
-    else:
-        myclimzone = CLIMATIC_ZONE[climatic_zone]
-    myclimzone = myclimzone.replace(" ", "_")
+    myclimzone = climatic_zone.replace(" ", "_")
     res = f"{croptype}-{myclimzone}-{'NH' if latitude >= 0. else 'SH'}"
     if (croptype == "wheat" and "cool_temperate" in myclimzone
             and latitude < 0.):
@@ -218,6 +211,43 @@ def get_growing_season(croptype, climatic_zone, latitude):
     return res
 
 
+def get_seasons(sampling, statistics, metadata, seasons, crops,
+                seasons_required, crops_required):
+    """Get the correct seasons for each statistic.
+
+    :param sampling: the given sampling parameter
+    :param statistics: the given statistics parameter
+    :param metadata: the given metadata parameter
+    :param seasons: the given seasons parameter
+    :param crops: the given crops parameter
+    :param seasons_required: boolean denoting whether the seasons
+                             parameter is required
+    :param crops_required: boolean denoting whether the crops parameter
+                           is required
+
+    :raises KeyError: raised if a growing season is unknown
+
+    :return: A list containing the seasons for each statistic
+    """
+    if sampling in ["summer", "xsummer"]:
+        return len(statistics)*[[
+            f"{'N' if metadata.station_lat > 0 else 'S'}H-"
+            f"{'X' if sampling == 'xsummer' else ''}Summer"
+        ]]
+    if not seasons_required:
+        return len(statistics)*[None]
+    if seasons is not None:
+        return len(statistics)*[seasons]
+    if not crops_required:
+        return len(statistics)*[DEFAULT_SEASONS]
+    crops_season = [get_growing_season(crop, metadata.station_climatic_zone,
+                                       metadata.station_lat) for crop in crops]
+    if sampling == "vegseason":
+        return len(statistics)*[crops_season]
+    return [crops_season if "aot40" in stat or "w126" in stat
+            else DEFAULT_SEASONS for stat in statistics]
+
+
 def harmonize_time(dflist, mtype, rsfreq=None):
     """Harmonize the datetime index of all given data frames.