Add custom error types in uxarray

test/core/test_dataarray.py

@@ -1,5 +1,6 @@
 import numpy as np
 import uxarray as ux
+from uxarray.errors import DimensionError
 from uxarray.grid.geometry import _build_polygon_shells, _build_corrected_polygon_shells
 from uxarray.core.dataset import UxDataset, UxDataArray
 import pytest
@@ -124,7 +125,7 @@ def test_isel_invalid_dim(gridpath, datasetpath):
     uxda = UxDataArray(data, dims=["time", "n_face"], uxgrid=uxds.uxgrid)
 
     with pytest.raises(
-        ValueError,
+        DimensionError,
         match=r"Dimensions \{'invalid_dim'\} do not exist\..*Available dimensions: \('time', 'n_face'\)",
     ):
         uxda.isel(invalid_dim=0)

test/grid/integrate/test_zonal.py

@@ -8,6 +8,7 @@
 
 import uxarray as ux
 from uxarray.constants import ERROR_TOLERANCE
+from uxarray.errors import DataCenteringError
 
 class TestZonalCSne30:
 
@@ -380,7 +381,7 @@ def test_non_face_centered_raises(self, gridpath, datasetpath):
         uxda = ux.UxDataArray(
             np.zeros(uxgrid.n_node), dims=["n_node"], uxgrid=uxgrid
         )
-        with pytest.raises(ValueError, match="face-centered"):
+        with pytest.raises(DataCenteringError, match="face-centered"):
             uxda.zonal_anomaly()
 
     def test_invalid_lat_input_raises(self):

test/test_remap_yac.py

@@ -2,7 +2,8 @@
 import pytest
 
 import uxarray as ux
-from uxarray.remap.yac import YacNotAvailableError, _import_yac
+from uxarray.errors import YacNotAvailableError
+from uxarray.remap.yac import _import_yac
 
 
 try:

uxarray/core/aggregation.py

@@ -1,6 +1,7 @@
 import numpy as np
 
 import uxarray.core.dataarray
+from uxarray.errors import DataCenteringError
 from uxarray.grid.connectivity import get_face_node_partitions
 
 NUMPY_AGGREGATIONS = {
@@ -32,7 +33,7 @@ def _uxda_grid_aggregate(uxda, destination, aggregation, **kwargs):
         elif destination == "edge":
             return _node_to_edge_aggregation(uxda, aggregation, kwargs)
         else:
-            raise ValueError(
+            raise DataCenteringError(
                 f"Invalid destination for a node-centered data variable. Expected"
                 f"one of ['face', 'edge' but received {destination}"
             )
@@ -62,7 +63,7 @@ def _uxda_grid_aggregate(uxda, destination, aggregation, **kwargs):
         #     raise ValueError("TODO: ")
 
     else:
-        raise ValueError(
+        raise DataCenteringError(
             "Invalid data mapping. Data variable is expected to be mapped to either the "
             "nodes, faces, or edges of the source grid."
         )
@@ -73,7 +74,7 @@ def _node_to_face_aggregation(uxda, aggregation, aggregation_func_kwargs):
     import dask.array as da
 
     if not uxda._node_centered():
-        raise ValueError(
+        raise DataCenteringError(
             f"Data Variable must be mapped to the corner nodes of each face, with dimension "
             f"{uxda.uxgrid.n_face}."
         )
@@ -141,7 +142,7 @@ def _node_to_edge_aggregation(uxda, aggregation, aggregation_func_kwargs):
     import dask.array as da
 
     if not uxda._node_centered():
-        raise ValueError(
+        raise DataCenteringError(
             f"Data Variable must be mapped to the corner nodes of each face, with dimension "
             f"{uxda.uxgrid.n_face}."
         )

uxarray/core/api.py

@@ -14,6 +14,7 @@
     _open_dataset_with_fallback,
     match_chunks_to_ugrid,
 )
+from uxarray.errors import GridInvalidError
 from uxarray.grid import Grid
 from uxarray.io._scrip import (
     _detect_multigrid,
@@ -248,7 +249,7 @@ def _active_mask_values_for_grid(grid_name: str) -> np.ndarray:
             }
 
         if not grids_dict:
-            raise ValueError(f"No grids detected in file: {grid_filename_or_obj}")
+            raise GridInvalidError(f"No grids detected in file: {grid_filename_or_obj}")
 
         available_grids = list(grids_dict.keys())
 

uxarray/core/dataarray.py

@@ -27,6 +27,7 @@
     _compute_zonal_anomaly,
 )
 from uxarray.cross_sections import UxDataArrayCrossSectionAccessor
+from uxarray.errors import DataCenteringError, DimensionError, GridsMismatchError
 from uxarray.formatting_html import array_repr
 from uxarray.grid import Grid
 from uxarray.grid.dual import construct_dual
@@ -247,7 +248,7 @@ def to_geodataframe(
 
         if self.values.ndim > 1:
             # data is multidimensional, must be a 1D slice
-            raise ValueError(
+            raise DimensionError(
                 f"Data Variable must be 1-dimensional, with shape {self.uxgrid.n_face} "
                 f"for face-centered data."
             )
@@ -290,19 +291,19 @@ def to_geodataframe(
             gdf[var_name] = _data
 
         elif self.values.size == self.uxgrid.n_node:
-            raise ValueError(
+            raise DataCenteringError(
                 f"Data Variable with size {self.values.size} does not match the number of faces "
                 f"({self.uxgrid.n_face}. Current size matches the number of nodes. Consider running "
                 f"``UxDataArray.topological_mean(destination='face') to aggregate the data onto the faces."
             )
         elif self.values.size == self.uxgrid.n_edge:
-            raise ValueError(
+            raise DataCenteringError(
                 f"Data Variable with size {self.values.size} does not match the number of faces "
                 f"({self.uxgrid.n_face}. Current size matches the number of edges."
             )
         else:
             # data is not mapped to
-            raise ValueError(
+            raise DimensionError(
                 f"Data Variable with size {self.values.size} does not match the number of faces "
                 f"({self.uxgrid.n_face}."
             )
@@ -340,7 +341,7 @@ def to_polycollection(
         """
         # data is multidimensional, must be a 1D slice
         if self.values.ndim > 1:
-            raise ValueError(
+            raise DimensionError(
                 f"Data Variable must be 1-dimensional, with shape {self.uxgrid.n_face} "
                 f"for face-centered data."
             )
@@ -391,7 +392,7 @@ def to_polycollection(
             else:
                 return poly_collection
         else:
-            raise ValueError("Data variable must be face centered.")
+            raise DataCenteringError("Data variable must be face centered.")
 
     def to_raster(
         self,
@@ -616,13 +617,17 @@ def integrate(
             integral = np.einsum("i,...i", face_areas, self.values)
 
         elif self.values.shape[-1] == self.uxgrid.n_node:
-            raise ValueError("Integrating data mapped to each node not yet supported.")
+            raise DataCenteringError(
+                "Integrating data mapped to each node not yet supported."
+            )
 
         elif self.values.shape[-1] == self.uxgrid.n_edge:
-            raise ValueError("Integrating data mapped to each edge not yet supported.")
+            raise DataCenteringError(
+                "Integrating data mapped to each edge not yet supported."
+            )
 
         else:
-            raise ValueError(
+            raise DimensionError(
                 f"The final dimension of the data variable does not match the number of nodes, edges, "
                 f"or faces. Expected one of "
                 f"{self.uxgrid.n_node}, {self.uxgrid.n_edge}, or {self.uxgrid.n_face}, "
@@ -684,7 +689,7 @@ def zonal_mean(self, lat=(-90, 90, 10), conservative: bool = False, **kwargs):
         physical analysis. Non-conservative averaging samples at latitude lines.
         """
         if not self._face_centered():
-            raise ValueError(
+            raise DataCenteringError(
                 "Zonal mean computations are currently only supported for face-centered data variables."
             )
 
@@ -765,7 +770,7 @@ def zonal_mean(self, lat=(-90, 90, 10), conservative: bool = False, **kwargs):
                 )
 
             if edges.ndim != 1 or edges.size < 2:
-                raise ValueError("Band edges must be 1D with at least two values")
+                raise DimensionError("Band edges must be 1D with at least two values")
 
             res = _compute_conservative_zonal_mean_bands(self, edges)
 
@@ -832,7 +837,7 @@ def zonal_anomaly(self, lat=(-90, 90, 10), conservative: bool = False):
         >>> uxds["var"].zonal_anomaly(lat=(-60, 60, 5), conservative=True)
         """
         if not self._face_centered():
-            raise ValueError(
+            raise DataCenteringError(
                 "Zonal anomaly is only supported for face-centered data variables."
             )
 
@@ -851,7 +856,7 @@ def zonal_anomaly(self, lat=(-90, 90, 10), conservative: bool = False):
             )
 
         if edges.ndim != 1 or edges.size < 2:
-            raise ValueError("Band edges must be 1D with at least two values.")
+            raise DimensionError("Band edges must be 1D with at least two values.")
 
         res = _compute_zonal_anomaly(self, edges, conservative=conservative)
 
@@ -911,7 +916,7 @@ def azimuthal_mean(
         from uxarray.grid.coordinates import _lonlat_rad_to_xyz
 
         if not self._face_centered():
-            raise ValueError(
+            raise DataCenteringError(
                 "Azimuthal mean computations are currently only supported for face-centered data variables."
             )
 
@@ -1630,13 +1635,13 @@ def curl(
             raise TypeError("other must be a UxDataArray")
 
         if self.uxgrid != other.uxgrid:
-            raise ValueError("Both vector components must be on the same grid")
+            raise GridsMismatchError("Both vector components must be on the same grid")
 
         if self.dims != other.dims:
-            raise ValueError("Both vector components must have the same dimensions")
+            raise DimensionError("Both vector components must have the same dimensions")
 
         if len(self.dims) != 1:
-            raise ValueError(
+            raise DimensionError(
                 "Curl computation currently only supports 1-dimensional data. "
                 "Use .isel() to select a single time slice or level."
             )
@@ -1721,20 +1726,20 @@ def divergence(
             raise TypeError("other must be a UxDataArray")
 
         if self.uxgrid != other.uxgrid:
-            raise ValueError("Both UxDataArrays must have the same grid")
+            raise GridsMismatchError("Both UxDataArrays must have the same grid")
 
         if self.dims != other.dims:
-            raise ValueError("Both UxDataArrays must have the same dimensions")
+            raise DimensionError("Both UxDataArrays must have the same dimensions")
 
         if self.ndim > 1:
-            raise ValueError(
+            raise DimensionError(
                 "Divergence currently requires 1D face-centered data. Consider "
                 "reducing the dimension by selecting data across leading dimensions (e.g., `.isel(time=0)`, "
                 "`.sel(lev=500)`, or `.mean('time')`)."
             )
 
         if not (self._face_centered() and other._face_centered()):
-            raise ValueError(
+            raise DataCenteringError(
                 "Computing the divergence is only supported for face-centered data variables."
             )
 
@@ -1801,19 +1806,19 @@ def scalardotgradient(self, v: "UxDataArray", q: "UxDataArray") -> "UxDataArray"
             raise TypeError("q must be a UxDataArray")
 
         if self.uxgrid != v.uxgrid or self.uxgrid != q.uxgrid:
-            raise ValueError("All UxDataArrays must have the same grid")
+            raise GridsMismatchError("All UxDataArrays must have the same grid")
 
         if self.dims != v.dims or self.dims != q.dims:
-            raise ValueError("All UxDataArrays must have the same dimensions")
+            raise DimensionError("All UxDataArrays must have the same dimensions")
 
         if self.ndim > 1:
-            raise ValueError(
+            raise DimensionError(
                 "Scalar dot gradient currently requires 1D face-centered data. "
                 "Consider selecting a single slice before computing."
             )
 
         if not (self._face_centered() and v._face_centered() and q._face_centered()):
-            raise ValueError(
+            raise DataCenteringError(
                 "Computing the scalar dot gradient is only supported for face-centered data variables."
             )
 
@@ -1876,12 +1881,12 @@ def difference(self, destination: str | None = "edge"):
                 dims[-1] = "n_edge"
                 name = f"{var_name}edge_face_difference"
             elif destination == "face":
-                raise ValueError(
+                raise DataCenteringError(
                     "Invalid destination 'face' for a face-centered data variable, computing"
                     "the difference and storing it on each face is not possible"
                 )
             elif destination == "node":
-                raise ValueError(
+                raise DataCenteringError(
                     "Support for computing the difference of a face-centered data variable and storing"
                     "the result on each node not yet supported."
                 )
@@ -1894,13 +1899,13 @@ def difference(self, destination: str | None = "edge"):
                 dims[-1] = "n_edge"
                 name = f"{var_name}edge_node_difference"
             elif destination == "node":
-                raise ValueError(
+                raise DataCenteringError(
                     "Invalid destination 'node' for a node-centered data variable, computing"
                     "the difference and storing it on each node is not possible"
                 )
 
             elif destination == "face":
-                raise ValueError(
+                raise DataCenteringError(
                     "Support for computing the difference of a node-centered data variable and storing"
                     "the result on each face not yet supported."
                 )
@@ -1911,7 +1916,7 @@ def difference(self, destination: str | None = "edge"):
             )
 
         else:
-            raise ValueError("TODO: ")
+            raise DataCenteringError("TODO: ")
 
         uxda = UxDataArray(
             _difference,
@@ -1983,7 +1988,7 @@ def isel(
 
         Raises
         ------
-        ValueError
+        DimensionError (subclass of ValueError)
             If more than one grid dimension is selected and `ignore_grid=False`.
         """
         from uxarray.core.utils import _validate_indexers
@@ -2037,7 +2042,7 @@ def isel(
                 # e.g. "Dimensions {'level'} do not exist. Expected one of ('n_face', 'time', 'lev')"
                 # Let's just append the available dimensions.
                 original_error_msg = str(e)
-                raise ValueError(
+                raise DimensionError(
                     f"{original_error_msg}. Available dimensions: {self.dims}"
                 ) from e
             else:
@@ -2102,7 +2107,7 @@ def from_healpix(
             raise ValueError("`da` must be a xr.DataArray")
 
         if face_dim not in da.dims:
-            raise ValueError(
+            raise DimensionError(
                 f"The provided face dimension '{face_dim}' is present in the provided healpix data array."
                 f"Please set 'face_dim' to the dimension corresponding to the healpix face dimension."
             )
@@ -2136,7 +2141,7 @@ def _slice_from_grid(self, sliced_grid):
             )
 
         else:
-            raise ValueError(
+            raise DataCenteringError(
                 "Data variable must be either node, edge, or face centered."
             )
 

uxarray/core/dataset.py

@@ -15,6 +15,7 @@
 import uxarray
 from uxarray.core.dataarray import UxDataArray
 from uxarray.core.utils import _map_dims_to_ugrid, _open_dataset_with_fallback
+from uxarray.errors import DimensionError
 from uxarray.formatting_html import dataset_repr
 from uxarray.grid import Grid
 from uxarray.grid.dual import construct_dual
@@ -347,7 +348,7 @@ def from_healpix(
             ds = _open_dataset_with_fallback(ds, **kwargs)
 
         if face_dim not in ds.dims:
-            raise ValueError(
+            raise DimensionError(
                 f"The provided face dimension '{face_dim}' is not present in the provided healpix dataset."
                 f"Please set 'face_dim' to the dimension corresponding to the healpix face dimension."
             )