Update decode_time_units()

- Rename to `decode_non_cf_time()` and remove logic for cf unit decoding
- Fix incorrect start date for pandas date range by adding offset from first coordinate point

Refactor function and variable names
- Update `xcdat_infer` attr from None to "None" since `to_netcdf` does not support None type
- Update fixtures for non cf time bounds and `generate_dataset()`

Update `decode_non_cf_time()` to use offsets correctly
- Using pd.date_range() is incorrect because it assumes no gaps or missing data. It also resets offsets to beginning of the month and year and does not consider leap years
- pd.DataOffset considers relative arithmetic operations based on the calendar

Update `decode_non_cf_time()` to handle time bounds
- Update tests for `decode_non_cf_time()`

Add functions to `__init__.py`
- Update time fixtures with correct attributes

Add `_preprocess_non_cf_dataset()`
- Add test for callable in `_preprocess_non_cf_dataset`

docs/api.rst

@@ -11,8 +11,9 @@ Top-level API
 
     dataset.open_dataset
     dataset.open_mfdataset
+    dataset.has_cf_compliant_time
+    dataset.decode_non_cf_time
     dataset.infer_or_keep_var
-    dataset.decode_time_units
     dataset.get_inferred_var
 
 .. currentmodule:: xarray

tests/fixtures.py

@@ -32,18 +32,20 @@
     ],
     dims=["time"],
     attrs={
+        "axis": "T",
         "long_name": "time",
         "standard_name": "time",
-        "axis": "T",
     },
 )
 time_non_cf = xr.DataArray(
     data=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11],
     dims=["time"],
     attrs={
+        "units": "months since 2000-01-01",
+        "calendar": "standard",
+        "axis": "T",
         "long_name": "time",
         "standard_name": "time",
-        "axis": "T",
     },
 )
 
@@ -72,18 +74,18 @@
 time_bnds_non_cf = xr.DataArray(
     name="time_bnds",
     data=[
-        [datetime(1999, 12, 16, 12), datetime(2000, 1, 16, 12)],
-        [datetime(2000, 1, 16, 12), datetime(2000, 2, 15, 12)],
-        [datetime(2000, 2, 15, 12), datetime(2000, 3, 16, 12)],
-        [datetime(2000, 3, 16, 12), datetime(2000, 4, 16)],
-        [datetime(2000, 4, 16), datetime(2000, 5, 16, 12)],
-        [datetime(2000, 5, 16, 12), datetime(2000, 6, 16)],
-        [datetime(2000, 6, 16), datetime(2000, 7, 16, 12)],
-        [datetime(2000, 7, 16, 12), datetime(2000, 8, 16, 12)],
-        [datetime(2000, 8, 16, 12), datetime(2000, 9, 16)],
-        [datetime(2000, 9, 16), datetime(2000, 10, 16, 12)],
-        [datetime(2000, 10, 16, 12), datetime(2000, 11, 16)],
-        [datetime(2000, 11, 16), datetime(2000, 12, 16)],
+        [-1, 0],
+        [0, 1],
+        [1, 2],
+        [2, 3],
+        [3, 4],
+        [4, 5],
+        [5, 6],
+        [6, 7],
+        [7, 8],
+        [8, 9],
+        [9, 10],
+        [10, 11],
     ],
     coords={"time": time_non_cf},
     dims=["time", "bnds"],
@@ -172,19 +174,18 @@ def generate_dataset(cf_compliant: bool, has_bounds: bool) -> xr.Dataset:
         )
 
         if cf_compliant:
-            ds = ds.assign({"time_bnds": time_bnds.copy()})
-            ds = ds.assign_coords({"time": time_cf.copy()})
+            ds.coords["time"] = time_cf.copy()
+            ds["time_bnds"] = time_bnds.copy()
         elif not cf_compliant:
-            ds = ds.assign({"time_bnds": time_bnds_non_cf.copy()})
-            ds = ds.assign_coords({"time": time_non_cf.copy()})
-            ds["time"] = ds.time.assign_attrs(units="months since 2000-01-01")
+            ds.coords["time"] = time_non_cf.copy()
+            ds["time_bnds"] = time_bnds_non_cf.copy()
 
         # If the "bounds" attribute is included in an existing DataArray and
         # added to a new Dataset, it will get dropped. Therefore, it needs to be
         # assigned to the DataArrays after they are added to Dataset.
-        ds["lat"] = ds.lat.assign_attrs(bounds="lat_bnds")
-        ds["lon"] = ds.lon.assign_attrs(bounds="lon_bnds")
-        ds["time"] = ds.time.assign_attrs(bounds="time_bnds")
+        ds["lat"].attrs["bounds"] = "lat_bnds"
+        ds["lon"].attrs["bounds"] = "lon_bnds"
+        ds["time"].attrs["bounds"] = "time_bnds"
 
     elif not has_bounds:
         ds = xr.Dataset(
@@ -193,9 +194,8 @@ def generate_dataset(cf_compliant: bool, has_bounds: bool) -> xr.Dataset:
         )
 
         if cf_compliant:
-            ds = ds.assign_coords({"time": time_cf.copy()})
+            ds.coords["time"] = time_cf.copy()
         elif not cf_compliant:
-            ds = ds.assign_coords({"time": time_non_cf.copy()})
-            ds["time"] = ds.time.assign_attrs(units="months since 2000-01-01")
+            ds.coords["time"] = time_non_cf.copy()
 
     return ds