Merge branch 'fix-2751-windowed-arrays' into update_CROCO_advection

erikvansebille · erikvansebille · commit 14feecb45430 · 2026-07-14T17:28:51.000+02:00
diff --git a/docs/user_guide/examples/explanation_kernelloop.md b/docs/user_guide/examples/explanation_kernelloop.md
@@ -57,7 +57,6 @@ import parcels.tutorial
 
 # Load the CopernicusMarine data in the Agulhas region from the example_datasets
 ds_fields = parcels.tutorial.open_dataset("CopernicusMarine_data_for_Argo_tutorial/data")
-ds_fields.load()  # load the dataset into memory
 
 # Create an idealised wind field and add it to the dataset
 tdim, ydim, xdim = (len(ds_fields.time),len(ds_fields.latitude), len(ds_fields.longitude))
@@ -83,6 +82,9 @@ fieldset = parcels.FieldSet.from_sgrid_conventions(
         "Wind": ("UWind", "VWind")
     }
 )
+
+# Convert the FieldSet to windowed arrays for better performance
+fieldset = fieldset.to_windowed_arrays()
 ```
 
 Now we define a wind Kernel that uses a forward Euler method to apply the wind forcing. Note that we update the `particles.dx` and `particles.dy` variables, rather than `particles.x` and `particles.y` directly.
diff --git a/docs/user_guide/examples/tutorial_delaystart.ipynb b/docs/user_guide/examples/tutorial_delaystart.ipynb
@@ -56,12 +56,14 @@
     "ds_fields = parcels.tutorial.open_dataset(\n",
     "    \"CopernicusMarine_data_for_Argo_tutorial/data\"\n",
     ")\n",
-    "ds_fields.load()  # load the dataset into memory\n",
     "\n",
     "# Convert to SGRID-compliant dataset and create FieldSet\n",
     "fields = {\"U\": ds_fields[\"uo\"], \"V\": ds_fields[\"vo\"]}\n",
     "ds_fset = parcels.convert.copernicusmarine_to_sgrid(fields=fields)\n",
-    "fieldset = parcels.FieldSet.from_sgrid_conventions(ds_fset)"
+    "fieldset = parcels.FieldSet.from_sgrid_conventions(ds_fset)\n",
+    "\n",
+    "# Convert the FieldSet to windowed arrays for better performance\n",
+    "fieldset = fieldset.to_windowed_arrays()"
    ]
   },
   {
@@ -398,7 +400,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Parcels:docs (3.14.6)",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
diff --git a/docs/user_guide/examples/tutorial_diffusion.ipynb b/docs/user_guide/examples/tutorial_diffusion.ipynb
@@ -472,8 +472,7 @@
     "# Load the CopernicusMarine data in the Agulhas region from the example_datasets\n",
     "ds_fields = parcels.tutorial.open_dataset(\n",
     "    \"CopernicusMarine_data_for_Argo_tutorial/data\"\n",
-    ").isel(depth=slice(0, 1))\n",
-    "ds_fields.load()  # load the dataset into memory"
+    ").isel(depth=slice(0, 1))"
    ]
   },
   {
@@ -528,7 +527,10 @@
     "ds_cell_areas[\"cell_areas\"] = ([\"lat\", \"lon\"], calc_cell_areas(ds_fields))\n",
     "fset2 = parcels.FieldSet.from_sgrid_conventions(ds_cell_areas)\n",
     "\n",
-    "fieldset += fset2"
+    "fieldset += fset2\n",
+    "\n",
+    "# Convert the FieldSet to windowed arrays for better performance\n",
+    "fieldset = fieldset.to_windowed_arrays()"
    ]
   },
   {
diff --git a/docs/user_guide/examples/tutorial_dt_integrators.ipynb b/docs/user_guide/examples/tutorial_dt_integrators.ipynb
@@ -67,7 +67,6 @@
     "ds_fields = parcels.tutorial.open_dataset(\n",
     "    \"CopernicusMarine_data_for_Argo_tutorial/data\"\n",
     ")\n",
-    "ds_fields.load()  # load the dataset into memory\n",
     "\n",
     "OUTPUT_FOLDER = Path(\"output\")\n",
     "OUTPUT_FOLDER.mkdir(exist_ok=True)\n",
@@ -77,6 +76,9 @@
     "ds_fset = parcels.convert.copernicusmarine_to_sgrid(fields=fields)\n",
     "fieldset = parcels.FieldSet.from_sgrid_conventions(ds_fset)\n",
     "\n",
+    "# Convert the FieldSet to windowed arrays for better performance\n",
+    "fieldset = fieldset.to_windowed_arrays()\n",
+    "\n",
     "# Check field resolution in time and space\n",
     "print(\n",
     "    f\"dt = {np.unique(np.diff(ds_fields.time.values).astype('timedelta64[h]'))} hours\"\n",
diff --git a/docs/user_guide/examples/tutorial_gsw_density.ipynb b/docs/user_guide/examples/tutorial_gsw_density.ipynb
@@ -43,9 +43,6 @@
     "    \"CopernicusMarine_data_for_Argo_tutorial/data\"\n",
     ")\n",
     "\n",
-    "# TODO check how we can get good performance without loading full dataset in memory\n",
-    "ds_fields.load()  # load the dataset into memory\n",
-    "\n",
     "# Convert to SGRID-compliant dataset and create FieldSet\n",
     "fields = {\n",
     "    \"U\": ds_fields[\"uo\"],\n",
@@ -54,7 +51,10 @@
     "    \"so\": ds_fields[\"so\"],\n",
     "}\n",
     "ds_fset = parcels.convert.copernicusmarine_to_sgrid(fields=fields)\n",
-    "fieldset = parcels.FieldSet.from_sgrid_conventions(ds_fset)"
+    "fieldset = parcels.FieldSet.from_sgrid_conventions(ds_fset)\n",
+    "\n",
+    "# Convert the FieldSet to windowed arrays for better performance\n",
+    "fieldset = fieldset.to_windowed_arrays()"
    ]
   },
   {
diff --git a/docs/user_guide/examples/tutorial_manipulating_field_data.ipynb b/docs/user_guide/examples/tutorial_manipulating_field_data.ipynb
@@ -49,7 +49,6 @@
     "ds_fields = parcels.tutorial.open_dataset(\n",
     "    \"CopernicusMarine_data_for_Argo_tutorial/data\"\n",
     ")\n",
-    "ds_fields.load()  # load the dataset into memory\n",
     "\n",
     "# Create an idealised wind field and add it to the dataset\n",
     "ydim, xdim = len(ds_fields.latitude), len(ds_fields.longitude)\n",
@@ -122,6 +121,9 @@
     "ds_fset = parcels.convert.copernicusmarine_to_sgrid(fields=fields)\n",
     "fieldset = parcels.FieldSet.from_sgrid_conventions(ds_fset)\n",
     "\n",
+    "# Convert the FieldSet to windowed arrays for better performance\n",
+    "fieldset = fieldset.to_windowed_arrays()\n",
+    "\n",
     "npart = 10\n",
     "z = np.repeat(ds_fields.depth[0].values, npart)\n",
     "lons = np.repeat(32.2, npart)\n",
diff --git a/docs/user_guide/examples/tutorial_nemo.ipynb b/docs/user_guide/examples/tutorial_nemo.ipynb
@@ -307,7 +307,10 @@
     "    fields={\"U\": ds_u[\"uo\"], \"V\": ds_v[\"vo\"], \"W\": ds_w[\"wo\"]},\n",
     "    coords=ds_coords,\n",
     ")\n",
-    "fieldset = parcels.FieldSet.from_sgrid_conventions(ds_fset)"
+    "fieldset = parcels.FieldSet.from_sgrid_conventions(ds_fset)\n",
+    "\n",
+    "fieldset = fieldset.to_windowed_arrays()\n",
+    "fieldset.describe()"
    ]
   },
   {
@@ -327,12 +330,12 @@
    },
    "outputs": [],
    "source": [
-    "npart = 10\n",
+    "npart = 3\n",
     "pset = parcels.ParticleSet(\n",
     "    fieldset=fieldset,\n",
     "    x=np.linspace(1.9, 3.4, npart),\n",
-    "    y=np.linspace(65, 51.6, npart),\n",
-    "    z=100 * np.ones(npart),\n",
+    "    y=np.linspace(60, 58, npart),\n",
+    "    z=20 * np.ones(npart),\n",
     ")\n",
     "\n",
     "pfile = parcels.ParticleFile(\"output_nemo3D.parquet\", outputdt=np.timedelta64(1, \"D\"))\n",
@@ -358,16 +361,30 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "field = fieldset.U.data[0, 0, :, :]\n",
+    "fig, ax = plt.subplots(figsize=(8, 4))\n",
+    "\n",
+    "field = fieldset.U.data.isel(time=0, depth_center=0).compute()\n",
     "field = field.where(field != 0, np.nan)  # Mask land values for better plotting\n",
-    "plt.pcolormesh(fieldset.U.grid.lon, fieldset.U.grid.lat, field, cmap=\"RdBu\")\n",
+    "ax.pcolormesh(fieldset.U.grid.lon, fieldset.U.grid.lat, field, cmap=\"RdBu\")\n",
     "\n",
     "df = parcels.read_particlefile(\"output_nemo3D.parquet\")\n",
     "vmin = df[\"z\"].min()\n",
     "vmax = df[\"z\"].max()\n",
     "for traj in df.partition_by(\"particle_id\", maintain_order=True):\n",
-    "    plt.scatter(traj[\"x\"], traj[\"y\"], c=-traj[\"z\"], marker=\".\", vmin=-vmax, vmax=-vmin)\n",
-    "plt.colorbar(label=\"Depth (m)\")\n",
+    "    ax.plot(traj[\"x\"], traj[\"y\"], \"-\", color=\"k\", alpha=0.5)\n",
+    "    scat = ax.scatter(\n",
+    "        traj[\"x\"],\n",
+    "        traj[\"y\"],\n",
+    "        c=-traj[\"z\"],\n",
+    "        marker=\".\",\n",
+    "        vmin=-vmax,\n",
+    "        vmax=-vmin,\n",
+    "        zorder=10,\n",
+    "    )\n",
+    "plt.colorbar(scat, label=\"Depth (m)\", ax=ax)\n",
+    "ax.set_xlim([1, 9])\n",
+    "ax.set_ylim([56, 61])\n",
+    "ax.set_aspect(\"equal\")\n",
     "plt.show()"
    ]
   }
diff --git a/docs/user_guide/examples/tutorial_sampling.ipynb b/docs/user_guide/examples/tutorial_sampling.ipynb
@@ -60,12 +60,14 @@
     "ds_fields = parcels.tutorial.open_dataset(\n",
     "    \"CopernicusMarine_data_for_Argo_tutorial/data\"\n",
     ")\n",
-    "ds_fields.load()  # load the dataset into memory\n",
     "\n",
     "# Convert to SGRID-compliant dataset and create FieldSet\n",
     "fields = {\"U\": ds_fields[\"uo\"], \"V\": ds_fields[\"vo\"], \"thetao\": ds_fields[\"thetao\"]}\n",
     "ds_fset = parcels.convert.copernicusmarine_to_sgrid(fields=fields)\n",
-    "fieldset = parcels.FieldSet.from_sgrid_conventions(ds_fset)"
+    "fieldset = parcels.FieldSet.from_sgrid_conventions(ds_fset)\n",
+    "\n",
+    "# Convert the FieldSet to windowed arrays for better performance\n",
+    "fieldset = fieldset.to_windowed_arrays()"
    ]
   },
   {
diff --git a/docs/user_guide/examples/tutorial_write_in_kernel.ipynb b/docs/user_guide/examples/tutorial_write_in_kernel.ipynb
@@ -122,12 +122,12 @@
     "ds_fields = parcels.tutorial.open_dataset(\n",
     "    \"CopernicusMarine_data_for_Argo_tutorial/data\"\n",
     ")\n",
-    "ds_fields.load()  # load the dataset into memory\n",
     "\n",
     "# Convert to SGRID-compliant dataset and create FieldSet\n",
     "fields = {\"U\": ds_fields[\"uo\"], \"V\": ds_fields[\"vo\"]}\n",
     "ds_fset = parcels.convert.copernicusmarine_to_sgrid(fields=fields)\n",
     "fieldset = parcels.FieldSet.from_sgrid_conventions(ds_fset)\n",
+    "fieldset = fieldset.to_windowed_arrays()\n",
     "\n",
     "pset = parcels.ParticleSet(fieldset=fieldset, x=[31, 32], y=[-32, -31], z=[1, 1])\n",
     "\n",
diff --git a/docs/user_guide/getting_started/tutorial_output.ipynb b/docs/user_guide/getting_started/tutorial_output.ipynb
@@ -58,12 +58,14 @@
     "ds_fields = parcels.tutorial.open_dataset(\n",
     "    \"CopernicusMarine_data_for_Argo_tutorial/data\"\n",
     ")\n",
-    "ds_fields.load()  # load the dataset into memory\n",
     "\n",
     "# Convert to SGRID-compliant dataset and create FieldSet\n",
     "fields = {\"U\": ds_fields[\"uo\"], \"V\": ds_fields[\"vo\"]}\n",
     "ds_fset = parcels.convert.copernicusmarine_to_sgrid(fields=fields)\n",
-    "fieldset = parcels.FieldSet.from_sgrid_conventions(ds_fset)"
+    "fieldset = parcels.FieldSet.from_sgrid_conventions(ds_fset)\n",
+    "\n",
+    "# Convert the FieldSet to windowed arrays for better performance\n",
+    "fieldset = fieldset.to_windowed_arrays()"
    ]
   },
   {
diff --git a/docs/user_guide/getting_started/tutorial_quickstart.md b/docs/user_guide/getting_started/tutorial_quickstart.md
@@ -31,8 +31,6 @@ hydrodynamics fields in which the particles are tracked. Here we provide an exam
 
 ```{code-cell}
 ds_fields = parcels.tutorial.open_dataset("CopernicusMarine_data_for_Argo_tutorial/data")
-
-ds_fields.load()  # load the dataset into memory
 ds_fields
 ```
 
@@ -52,6 +50,15 @@ ds_fset = parcels.convert.copernicusmarine_to_sgrid(fields=fields)
 fieldset = parcels.FieldSet.from_sgrid_conventions(ds_fset)
 ```
 
+Now, in order to improve performance, we can convert the `parcels.FieldSet` to windowed arrays. This is especially useful for large datasets with many timeslices, as it allows Parcels to load only the necessary timeslices into memory during the simulation.
+
+#TODO add link to performance explanation notebook here.
+
+```{code-cell}
+# Convert the FieldSet to windowed arrays for better performance
+fieldset = fieldset.to_windowed_arrays()
+```
+
 You can inspect the `parcels.FieldSet` object with the `describe` method in order to see which `parcels.Field`s are included, and which grid and interpolation method is used for each field. This also gives information on the type of mesh and the time interval of the `parcels.FieldSet`:
 
 ```{code-cell}
diff --git a/src/parcels/_core/_windowed_array.py b/src/parcels/_core/_windowed_array.py
@@ -108,4 +108,6 @@ def maybe_windowed(data: xr.DataArray, max_levels: int | None = None):
         return data
     if data.dims and data.dims[0] == "time" and is_dask_collection(data.data):
         return WindowedArray(data, max_levels=max_levels)
+    elif data.dims and data.dims[0] == "mockT" and is_dask_collection(data.data):
+        return data.compute()
     return data
diff --git a/src/parcels/_core/xgrid.py b/src/parcels/_core/xgrid.py
@@ -7,6 +7,7 @@
 import numpy.typing as npt
 import xarray as xr
 import xgcm
+from dask import is_dask_collection
 
 import parcels._sgrid as sgrid
 import parcels._typing as ptyping
@@ -207,6 +208,9 @@ def lon(self):
             _ = self.xgcm_grid.axes["X"]
         except KeyError:
             return np.zeros(1)
+        # ensure lon is loaded into memory for dask-backed datasets, as it is used in the search method
+        if is_dask_collection(self._ds["lon"].data):
+            self._ds["lon"].load()
         return self._ds["lon"].values
 
     @property
@@ -221,6 +225,9 @@ def lat(self):
             _ = self.xgcm_grid.axes["Y"]
         except KeyError:
             return np.zeros(1)
+        # ensure lat is loaded into memory for dask-backed datasets, as it is used in the search method
+        if is_dask_collection(self._ds["lat"].data):
+            self._ds["lat"].load()
         return self._ds["lat"].values
 
     @property
diff --git a/src/parcels/_datasets/remote.py b/src/parcels/_datasets/remote.py
@@ -74,21 +74,21 @@ def _get_data_home() -> Path:
         "data/NemoNorthSeaORCA025-N006_data/ORCA025-N06_20000104d05U.nc",
         "data/NemoNorthSeaORCA025-N006_data/ORCA025-N06_20000109d05U.nc",
         "data/NemoNorthSeaORCA025-N006_data/ORCA025-N06_20000114d05U.nc",
-        "data/NemoNorthSeaORCA025-N006_data/ORCA025-N06_20000119d05U.nc",
-        "data/NemoNorthSeaORCA025-N006_data/ORCA025-N06_20000124d05U.nc",
-        "data/NemoNorthSeaORCA025-N006_data/ORCA025-N06_20000129d05U.nc",
+        # "data/NemoNorthSeaORCA025-N006_data/ORCA025-N06_20000119d05U.nc",
+        # "data/NemoNorthSeaORCA025-N006_data/ORCA025-N06_20000124d05U.nc",
+        # "data/NemoNorthSeaORCA025-N006_data/ORCA025-N06_20000129d05U.nc",
         "data/NemoNorthSeaORCA025-N006_data/ORCA025-N06_20000104d05V.nc",
         "data/NemoNorthSeaORCA025-N006_data/ORCA025-N06_20000109d05V.nc",
         "data/NemoNorthSeaORCA025-N006_data/ORCA025-N06_20000114d05V.nc",
-        "data/NemoNorthSeaORCA025-N006_data/ORCA025-N06_20000119d05V.nc",
-        "data/NemoNorthSeaORCA025-N006_data/ORCA025-N06_20000124d05V.nc",
-        "data/NemoNorthSeaORCA025-N006_data/ORCA025-N06_20000129d05V.nc",
+        # "data/NemoNorthSeaORCA025-N006_data/ORCA025-N06_20000119d05V.nc",
+        # "data/NemoNorthSeaORCA025-N006_data/ORCA025-N06_20000124d05V.nc",
+        # "data/NemoNorthSeaORCA025-N006_data/ORCA025-N06_20000129d05V.nc",
         "data/NemoNorthSeaORCA025-N006_data/ORCA025-N06_20000104d05W.nc",
         "data/NemoNorthSeaORCA025-N006_data/ORCA025-N06_20000109d05W.nc",
         "data/NemoNorthSeaORCA025-N006_data/ORCA025-N06_20000114d05W.nc",
-        "data/NemoNorthSeaORCA025-N006_data/ORCA025-N06_20000119d05W.nc",
-        "data/NemoNorthSeaORCA025-N006_data/ORCA025-N06_20000124d05W.nc",
-        "data/NemoNorthSeaORCA025-N006_data/ORCA025-N06_20000129d05W.nc",
+        # "data/NemoNorthSeaORCA025-N006_data/ORCA025-N06_20000119d05W.nc",
+        # "data/NemoNorthSeaORCA025-N006_data/ORCA025-N06_20000124d05W.nc",
+        # "data/NemoNorthSeaORCA025-N006_data/ORCA025-N06_20000129d05W.nc",
         "data/NemoNorthSeaORCA025-N006_data/coordinates.nc",
     ]
     + [
@@ -107,7 +107,7 @@ def _get_data_home() -> Path:
         "data/SWASH_data/field_0065552.nc",
         "data/SWASH_data/field_0065557.nc",
     ]
-    + [f"data/WOA_data/woa18_decav_t{m:02d}_04.nc" for m in range(1, 13)]
+    # + [f"data/WOA_data/woa18_decav_t{m:02d}_04.nc" for m in range(1, 13)]
     + ["data/CROCOidealized_data/CROCO_idealized.nc"]
     # These datasets are from v4 of Parcels where we're opting for Zipped zarr datasets
     + [
@@ -238,7 +238,7 @@ class _Purpose(enum.Enum):
     ("NemoNorthSeaORCA025-N006_data/mesh_mask", (_V3Dataset(_ODIE,"data/NemoNorthSeaORCA025-N006_data/coordinates.nc", _preprocess_drop_time_from_mesh1), _Purpose.TUTORIAL)),
     # "POPSouthernOcean_data/t.x1_SAMOC_flux.16900*.nc", # TODO v4: In v3 but should not be in v4 https://github.com/Parcels-code/Parcels/issues/2571#issuecomment-4214476973
     # ("SWASH_data/data", (_V3Dataset(_ODIE,"data/SWASH_data/field_00655*.nc"), _Purpose.TUTORIAL)),
-    ("WOA_data/data", (_V3Dataset(_ODIE,"data/WOA_data/woa18_decav_t*_04.nc", _preprocess_set_cf_calendar_360_day), _Purpose.TUTORIAL)),
+    # ("WOA_data/data", (_V3Dataset(_ODIE,"data/WOA_data/woa18_decav_t*_04.nc", _preprocess_set_cf_calendar_360_day), _Purpose.TUTORIAL)),
     ("CROCOidealized_data/data", (_V3Dataset(_ODIE,"data/CROCOidealized_data/CROCO_idealized.nc"), _Purpose.TUTORIAL)),
 ] + [
     ("Benchmarks_FESOM2-baroclinic-gyre/data", (_ZarrZipDataset(_ODIE, 'data-zarr/Benchmarks_FESOM2-baroclinic-gyre/data.zip', zarr_format=2), _Purpose.TESTING)),
diff --git a/tests/test_sigmagrids.py b/tests/test_sigmagrids.py
@@ -46,7 +46,6 @@ def test_conversion_3DCROCO():
 
 def test_advection_3DCROCO():
     ds_fields = parcels.tutorial.open_dataset("CROCOidealized_data/data")
-    ds_fields.load()
 
     fields = {
         "U": ds_fields["u"],
@@ -61,6 +60,7 @@ def test_advection_3DCROCO():
     ds_fset = parcels.convert.croco_to_sgrid(fields=fields, coords=ds_fields)
 
     fieldset = parcels.FieldSet.from_sgrid_conventions(ds_fset)
+    fieldset = fieldset.to_windowed_arrays()
     fieldset.add_context("hc", ds_fields.hc.item())
 
     runtime = 10_000

Original file line number	Diff line number	Diff line change
`@@ -57,7 +57,6 @@ import parcels.tutorial`
`57`	`57`
`58`	`58`	`# Load the CopernicusMarine data in the Agulhas region from the example_datasets`
`59`	`59`	`ds_fields = parcels.tutorial.open_dataset("CopernicusMarine_data_for_Argo_tutorial/data")`
`60`		`-ds_fields.load() # load the dataset into memory`
`61`	`60`
`62`	`61`	`# Create an idealised wind field and add it to the dataset`
`63`	`62`	`tdim, ydim, xdim = (len(ds_fields.time),len(ds_fields.latitude), len(ds_fields.longitude))`
`@@ -83,6 +82,9 @@ fieldset = parcels.FieldSet.from_sgrid_conventions(`
`83`	`82`	`"Wind": ("UWind", "VWind")`
`84`	`83`	`}`
`85`	`84`	`)`
	`85`	`+`
	`86`	`+# Convert the FieldSet to windowed arrays for better performance`
	`87`	`+fieldset = fieldset.to_windowed_arrays()`
`86`	`88`	```
`87`	`89`
`88`	`90`	Now we define a wind Kernel that uses a forward Euler method to apply the wind forcing. Note that we update the `particles.dx` and `particles.dy` variables, rather than `particles.x` and `particles.y` directly.
Original file line number	Diff line number	Diff line change
`@@ -472,8 +472,7 @@`
`472`	`472`	`"# Load the CopernicusMarine data in the Agulhas region from the example_datasets\n",`
`473`	`473`	`"ds_fields = parcels.tutorial.open_dataset(\n",`
`474`	`474`	`" \"CopernicusMarine_data_for_Argo_tutorial/data\"\n",`
`475`		`- ").isel(depth=slice(0, 1))\n",`
`476`		`- "ds_fields.load() # load the dataset into memory"`
	`475`	`+ ").isel(depth=slice(0, 1))"`
`477`	`476`	`]`
`478`	`477`	`},`
`479`	`478`	`{`
`@@ -528,7 +527,10 @@`
`528`	`527`	`"ds_cell_areas[\"cell_areas\"] = ([\"lat\", \"lon\"], calc_cell_areas(ds_fields))\n",`
`529`	`528`	`"fset2 = parcels.FieldSet.from_sgrid_conventions(ds_cell_areas)\n",`
`530`	`529`	`"\n",`
`531`		`- "fieldset += fset2"`
	`530`	`+ "fieldset += fset2\n",`
	`531`	`+ "\n",`
	`532`	`+ "# Convert the FieldSet to windowed arrays for better performance\n",`
	`533`	`+ "fieldset = fieldset.to_windowed_arrays()"`
`532`	`534`	`]`
`533`	`535`	`},`
`534`	`536`	`{`