ideas for clim and ano calculations using groupby

enacts/calc.py

@@ -619,6 +619,14 @@ def daily_tobegroupedby_season(
     return daily_tobegroupedby_season
 
 
+def dayofyear366(time_coord):
+    """maps dates into 1-to-366 integers day-of-year"""
+    return xr.where(
+        (~time_coord.dt.is_leap_year) & (time_coord.dt.dayofyear >= (31+29)),
+        time_coord.dt.dayofyear + 1,
+        time_coord.dt.dayofyear,
+    )
+
 # Seasonal Functions
 
 def seasonal_onset_date(

enacts/onset/tests/test_calc.py

@@ -277,6 +277,33 @@ def test_water_balance_et_has_T_reduce_True():
     assert np.array_equal(wb.soil_moisture, expected)
 
 
+def test_dayofyear366():
+
+    t = pd.date_range(start="2000-01-01", end="2005-02-28", freq="1D")
+    data = xr.DataArray(range(t.size), dims=["T"], coords={"T": t})
+    data_clim = (data
+        .groupby(calc.dayofyear366(data["T"]))
+        .mean()
+        .rename({"group": "T_doy"})
+    )
+    print(data_clim)
+    #Let's have anomalies computed on a random other slice of the data
+    data_ano = data.sel(T=slice("2000-02-28", "2005-02-28"))
+    data_ano = (data_ano
+        .groupby(calc.dayofyear366(data_ano["T"]))
+        #can still do non-reducing transformation if feel like it
+        .cumsum()
+        #compute ano
+        .groupby(calc.dayofyear366(data_ano["T"]))
+        #I like the idea of having a reserved name for daily clim dim
+        #other than "group" but maybe not necessary...
+        - data_clim.rename({"T_doy": "group"})
+    )
+    print(data_ano)
+
+    assert True
+
+
 def test_daily_tobegroupedby_season_cuts_on_days():
 
     precip = data_test_calc.multi_year_data_sample()