Remove type casting of field data

parcels/field.py

@@ -3,7 +3,7 @@
 import warnings
 from collections.abc import Iterable
 from pathlib import Path
-from typing import TYPE_CHECKING, Literal
+from typing import TYPE_CHECKING, cast
 
 import dask.array as da
 import numpy as np
@@ -20,6 +20,7 @@
 from parcels._typing import (
     GridIndexingType,
     InterpMethod,
+    InterpMethodOption,
     Mesh,
     VectorType,
     assert_valid_gridindexingtype,
@@ -140,8 +141,6 @@ class Field:
         Minimum allowed value on the field. Data below this value are set to zero
     vmax : float
         Maximum allowed value on the field. Data above this value are set to zero
-    cast_data_dtype : str
-        Cast Field data to dtype. Supported dtypes are "float32" (np.float32 (default)) and "float64 (np.float64).
     time_origin : parcels.tools.converters.TimeConverter
         Time origin of the time axis (only if grid is None)
     interp_method : str
@@ -162,7 +161,6 @@ class Field:
     """
 
     allow_time_extrapolation: bool
-    _cast_data_dtype: type[np.float32] | type[np.float64]
 
     def __init__(
         self,
@@ -179,7 +177,6 @@ def __init__(
         transpose: bool = False,
         vmin: float | None = None,
         vmax: float | None = None,
-        cast_data_dtype: type[np.float32] | type[np.float64] | Literal["float32", "float64"] = "float32",
         time_origin: TimeConverter | None = None,
         interp_method: InterpMethod = "linear",
         allow_time_extrapolation: bool | None = None,
@@ -246,19 +243,6 @@ def __init__(
         self.vmin = vmin
         self.vmax = vmax
 
-        match cast_data_dtype:
-            case "float32":
-                self._cast_data_dtype = np.float32
-            case "float64":
-                self._cast_data_dtype = np.float64
-            case _:
-                self._cast_data_dtype = cast_data_dtype
-
-        if self.cast_data_dtype not in [np.float32, np.float64]:
-            raise ValueError(
-                f"Unsupported cast_data_dtype {self.cast_data_dtype!r}. Choose either: 'float32' or 'float64'"
-            )
-
         if not self.grid.defer_load:
             self.data = self._reshape(self.data, transpose)
             self._loaded_time_indices = range(self.grid.tdim)
@@ -332,10 +316,6 @@ def interp_method(self, value):
     def gridindexingtype(self):
         return self._gridindexingtype
 
-    @property
-    def cast_data_dtype(self):
-        return self._cast_data_dtype
-
     @property
     def netcdf_engine(self):
         return self._netcdf_engine
@@ -522,6 +502,7 @@ def from_netcdf(
                 interp_method = interp_method[variable[0]]
             else:
                 raise RuntimeError(f"interp_method is a dictionary but {variable[0]} is not in it")
+        interp_method = cast(InterpMethodOption, interp_method)
 
         if "lon" in dimensions and "lat" in dimensions:
             with NetcdfFileBuffer(
@@ -719,10 +700,6 @@ def _reshape(self, data, transpose=False):
         # Ensure that field data is the right data type
         if not isinstance(data, (np.ndarray)):
             data = np.array(data)
-        if (self.cast_data_dtype == np.float32) and (data.dtype != np.float32):
-            data = data.astype(np.float32)
-        elif (self.cast_data_dtype == np.float64) and (data.dtype != np.float64):
-            data = data.astype(np.float64)
         if transpose:
             data = np.transpose(data)
         if self.grid._lat_flipped:
@@ -1059,7 +1036,6 @@ def computeTimeChunk(self, data, tindex):
             timestamp=timestamp,
             interp_method=self.interp_method,
             data_full_zdim=self.data_full_zdim,
-            cast_data_dtype=self.cast_data_dtype,
         )
         filebuffer.__enter__()
         time_data = filebuffer.time

parcels/fieldfilebuffer.py

@@ -19,7 +19,6 @@
         timestamp=None,
         interp_method: InterpMethodOption = "linear",
         data_full_zdim=None,
-        cast_data_dtype=np.float32,
         gridindexingtype="nemo",
         **kwargs,
     ):
@@ -28,7 +27,6 @@
         self.indices = indices
         self.dataset = None
         self.timestamp = timestamp
-        self.cast_data_dtype = cast_data_dtype
         self.ti = None
         self.interp_method = interp_method
         self.gridindexingtype = gridindexingtype
@@ -140,10 +138,10 @@
             else:
                 return np.empty((0, len(self.indices["depth"]), len(self.indices["lat"]), len(self.indices["lon"])))
 
-    def _check_extend_depth(self, data, di):
+    def _check_extend_depth(self, data, dim):
         return (
             self.indices["depth"][-1] == self.data_full_zdim - 1
-            and data.shape[di] == self.data_full_zdim - 1
+            and data.shape[dim] == self.data_full_zdim - 1
             and self.interp_method in ["bgrid_velocity", "bgrid_w_velocity", "bgrid_tracer"]
         )
 
@@ -192,8 +190,7 @@
     def data_access(self):
         data = self.dataset[self.name]
         ti = range(data.shape[0]) if self.ti is None else self.ti
-        data = self._apply_indices(data, ti)
-        return np.array(data, dtype=self.cast_data_dtype)
+        return np.array(self._apply_indices(data, ti))
 
     @property
     def time(self):

parcels/fieldset.py

@@ -279,9 +279,6 @@ def check_velocityfields(U, V, W):
             if V.gridindexingtype != U.gridindexingtype or (W and W.gridindexingtype != U.gridindexingtype):
                 raise ValueError("Not all velocity Fields have the same gridindexingtype")
 
-            if U.cast_data_dtype != V.cast_data_dtype or (W and W.cast_data_dtype != U.cast_data_dtype):
-                raise ValueError("Not all velocity Fields have the same dtype")
-
         if isinstance(self.U, NestedField):
             w = self.W if hasattr(self, "W") else [None] * len(self.U)
             for U, V, W in zip(self.U, self.V, w, strict=True):

tests/test_fieldset.py

@@ -192,38 +192,6 @@ def test_fieldset_from_cgrid_interpmethod():
         FieldSet.from_c_grid_dataset(filenames, variable, dimensions, interp_method="partialslip")
 
 
-@pytest.mark.parametrize("cast_data_dtype", ["float32", "float64"])
-def test_fieldset_float64(cast_data_dtype, tmpdir):
-    xdim, ydim = 10, 5
-    lon = np.linspace(0.0, 10.0, xdim, dtype=np.float64)
-    lat = np.linspace(0.0, 10.0, ydim, dtype=np.float64)
-    U, V = np.meshgrid(lon, lat)
-    dimensions = {"lat": lat, "lon": lon}
-    data = {"U": np.array(U, dtype=np.float64), "V": np.array(V, dtype=np.float64)}
-
-    fieldset = FieldSet.from_data(data, dimensions, mesh="flat", cast_data_dtype=cast_data_dtype)
-    if cast_data_dtype == "float32":
-        assert fieldset.U.data.dtype == np.float32
-    else:
-        assert fieldset.U.data.dtype == np.float64
-    pset = ParticleSet(fieldset, Particle, lon=1, lat=2)
-
-    failed = False
-    try:
-        pset.execute(AdvectionRK4, runtime=2)
-    except RuntimeError:
-        failed = True  # noqa
-    assert np.isclose(pset[0].lon, 2.70833)
-    assert np.isclose(pset[0].lat, 5.41667)
-    filepath = tmpdir.join("test_fieldset_float64")
-    fieldset.U.write(filepath)
-    da = xr.open_dataset(str(filepath) + "U.nc")
-    if cast_data_dtype == "float32":
-        assert da["U"].dtype == np.float32
-    else:
-        assert da["U"].dtype == np.float64
-
-
 @pytest.mark.parametrize("indslon", [range(10, 20), [1]])
 @pytest.mark.parametrize("indslat", [range(30, 60), [22]])
 def test_fieldset_from_file_subsets(indslon, indslat, tmpdir):