Preserve time_bnds Variable in Ocean CMORisation Workflow (#144)

* update ocean cmoriser unit test

* create supergrid unit test

* trim test

* add test for time_bnds in unit test

* add var and dim check in ocean test

* add variables check in ocean cmoriser to ensure time_bnds in the ds and generate corresponding test

* pre-commit fix

* solve some conflicts

* add calculation time_bnds mechanism if there is no time_bnds in raw-data, and add cooresponding tests

* pre-commit fix

* unit test for ultilities.py

* unit test for ultilities.py

* fix test_ocean.py after made a slightly change on generate unit in  time_bnds

* pre-commit fix

* add warning for missing bounds

* add warning for missing bounds

* solve conflict

Author: rhaegar325

src/access_moppy/ocean.py

@@ -1,3 +1,4 @@
+import warnings
 from pathlib import Path
 from typing import Any, Dict, List, Optional, Union
 
@@ -7,6 +8,7 @@
 from access_moppy.base import CMIP6_CMORiser
 from access_moppy.derivations import custom_functions, evaluate_expression
 from access_moppy.ocean_supergrid import Supergrid
+from access_moppy.utilities import calculate_time_bounds
 from access_moppy.vocabulary_processors import CMIP6Vocabulary
 
 
@@ -60,10 +62,10 @@ def _get_dim_rename(self):
     def select_and_process_variables(self):
         """Select and process variables for the CMOR output."""
         input_vars = self.mapping[self.cmor_name]["model_variables"]
-        time_bnds = ["time_bnds"]
+        bnds_required = ["time_bnds"]
         calc = self.mapping[self.cmor_name]["calculation"]
 
-        required_vars = set(input_vars + time_bnds)
+        required_vars = set(input_vars + bnds_required)
         self.load_dataset(required_vars=required_vars)
 
         dim_rename = self._get_dim_rename()
@@ -93,14 +95,33 @@ def select_and_process_variables(self):
             )
 
         self.grid_type, self.symmetric = self.infer_grid_type()
-        # Drop all other data variables except the CMOR variable
-        self.ds = self.ds[[self.cmor_name, time_bnds[0]]]
+
+        # Check and calculate time_bnds if missing
+        if bnds_required[0] not in self.ds:
+            # Warn user that bounds are missing and will be calculated automatically
+            warnings.warn(
+                f"'{bnds_required[0]}' not found in raw data. Automatically calculating bounds for '{bnds_required[0]}' coordinate.",
+                UserWarning,
+                stacklevel=2,
+            )
+            try:
+                calculated_bnds = calculate_time_bounds(
+                    self.ds, time_coord="time", bnds_name="nv"
+                )
+                self.ds[bnds_required[0]] = calculated_bnds
+            except Exception as e:
+                raise ValueError(
+                    f"time_bnds is required for CMIP6 compliance but was not found "
+                    f"in the dataset and could not be calculated: {e}"
+                )
+
+        self.ds = self.ds[[self.cmor_name, bnds_required[0]]]
 
         # Drop unused coordinates
         used_coords = set()
         dims = list(self.ds[self.cmor_name].dims)
-        if time_bnds[0] in self.ds:
-            dims = list(dict.fromkeys(dims + list(self.ds[time_bnds[0]].dims)))
+        if bnds_required[0] in self.ds:
+            dims = list(dict.fromkeys(dims + list(self.ds[bnds_required[0]].dims)))
         for dim in dims:
             if dim in self.ds.coords:
                 used_coords.add(dim)

tests/mocks/mock_data.py

@@ -524,6 +524,175 @@ def create_mock_3d_ocean_dataset(
     return ds
 
 
+def create_mock_om2_dataset(nt=12, ny=300, nx=360):
+    """
+    Create a mock ACCESS-OM2 ocean dataset with B-grid coordinates.
+    Uses xt_ocean/yt_ocean for T-grid points.
+    """
+    import cftime
+
+    xt_ocean = np.linspace(0.5, 359.5, nx)
+    yt_ocean = np.linspace(-89.5, 89.5, ny)
+
+    time = [
+        cftime.DatetimeProlepticGregorian(1850, month + 1, 15) for month in range(nt)
+    ]
+
+    data = np.random.rand(nt, ny, nx).astype(np.float32)
+
+    # Time bounds
+    days_per_month = [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31]
+    base_days = (1850 - 1) * 365
+    time_bnds = np.zeros((nt, 2))
+    cumulative = base_days
+    for i in range(nt):
+        time_bnds[i, 0] = cumulative
+        time_bnds[i, 1] = cumulative + days_per_month[i % 12]
+        cumulative += days_per_month[i % 12]
+
+    ds = xr.Dataset(
+        data_vars={
+            "surface_temp": (
+                ["time", "yt_ocean", "xt_ocean"],
+                data,
+                {
+                    "long_name": "Conservative temperature",
+                    "units": "K",
+                    "_FillValue": np.float32(-1e20),
+                    "standard_name": "sea_surface_temperature",
+                },
+            ),
+            "time_bnds": (["time", "nv"], time_bnds),
+        },
+        coords={
+            "xt_ocean": (
+                "xt_ocean",
+                xt_ocean,
+                {"long_name": "tcell longitude", "units": "degrees_E"},
+            ),
+            "yt_ocean": (
+                "yt_ocean",
+                yt_ocean,
+                {"long_name": "tcell latitude", "units": "degrees_N"},
+            ),
+            "time": (
+                "time",
+                time,
+                {
+                    "units": "days since 0001-01-01 00:00:00",
+                    "calendar": "proleptic_gregorian",
+                    "bounds": "time_bnds",
+                },
+            ),
+            "nv": ("nv", [1.0, 2.0]),
+        },
+        attrs={
+            "title": "ACCESS-OM2",
+            "grid_type": "mosaic",
+        },
+    )
+    return ds
+
+
+def create_mock_om3_dataset(nt=12, ny=300, nx=360):
+    """
+    Create a mock ACCESS-OM3 ocean dataset with C-grid coordinates.
+    Uses xh/yh for T-grid (tracer) points.
+    """
+    import cftime
+
+    xh = np.linspace(0.5, 359.5, nx)
+    yh = np.linspace(-89.5, 89.5, ny)
+
+    time = [
+        cftime.DatetimeProlepticGregorian(1850, month + 1, 15) for month in range(nt)
+    ]
+
+    data = np.random.rand(nt, ny, nx).astype(np.float32)
+
+    ds = xr.Dataset(
+        data_vars={
+            "tos": (
+                ["time", "yh", "xh"],
+                data,
+                {
+                    "long_name": "Sea Surface Temperature",
+                    "units": "degC",
+                    "_FillValue": np.float32(-1e20),
+                },
+            ),
+        },
+        coords={
+            "xh": (
+                "xh",
+                xh,
+                {"long_name": "h point nominal longitude", "units": "degrees_E"},
+            ),
+            "yh": (
+                "yh",
+                yh,
+                {"long_name": "h point nominal latitude", "units": "degrees_N"},
+            ),
+            "time": (
+                "time",
+                time,
+                {
+                    "units": "days since 0001-01-01 00:00:00",
+                    "calendar": "proleptic_gregorian",
+                },
+            ),
+        },
+        attrs={"title": "ACCESS-OM3"},
+    )
+    return ds
+
+
+def create_mock_supergrid_dataset(ny=7, nx=9):
+    """
+    Create a minimal mock supergrid dataset for testing.
+
+    The supergrid has dimensions (2*ny+1, 2*nx+1) to represent
+    both cell centers and corners on a staggered grid.
+
+    Parameters
+    ----------
+    ny : int
+        Number of tracer cells in y direction
+    nx : int
+        Number of tracer cells in x direction
+
+    Returns
+    -------
+    xr.Dataset
+        Mock supergrid with x and y coordinates
+    """
+    # Supergrid dimensions
+    sg_ny = 2 * ny + 1
+    sg_nx = 2 * nx + 1
+
+    # Create simple regular lat/lon grid for testing
+    # x ranges from 0 to 360, y from -90 to 90
+    x_1d = np.linspace(0, 360, sg_nx)
+    y_1d = np.linspace(-90, 90, sg_ny)
+
+    x, y = np.meshgrid(x_1d, y_1d)
+
+    ds = xr.Dataset(
+        {
+            "x": (["nyp", "nxp"], x),
+            "y": (["nyp", "nxp"], y),
+        },
+        coords={
+            "nyp": np.arange(sg_ny),
+            "nxp": np.arange(sg_nx),
+        },
+        attrs={
+            "title": "Mock Supergrid for Testing",
+        },
+    )
+    return ds
+
+
 def create_chunked_dataset(chunks=None, **kwargs):
     """Create a chunked dataset for testing dask operations."""
     if chunks is None: