diff --git a/src/anemoi/graphs/nodes/weights.py b/src/anemoi/graphs/nodes/weights.py
deleted file mode 100644
index 25419cc..0000000
--- a/src/anemoi/graphs/nodes/weights.py
+++ /dev/null
@@ -1,61 +0,0 @@
-import logging
-from abc import ABC
-from abc import abstractmethod
-from typing import Optional
-
-import numpy as np
-import torch
-from scipy.spatial import SphericalVoronoi
-from torch_geometric.data.storage import NodeStorage
-
-from anemoi.graphs.generate.transforms import to_sphere_xyz
-from anemoi.graphs.normalizer import NormalizerMixin
-
-logger = logging.getLogger(__name__)
-
-
-class BaseWeights(ABC, NormalizerMixin):
-    """Base class for the weights of the nodes."""
-
-    def __init__(self, norm: Optional[str] = None):
-        self.norm = norm
-
-    @abstractmethod
-    def compute(self, nodes: NodeStorage, *args, **kwargs): ...
-
-    def get_weights(self, *args, **kwargs) -> torch.Tensor:
-        weights = self.compute(*args, **kwargs)
-        if weights.ndim == 1:
-            weights = weights[:, np.newaxis]
-        norm_weights = self.normalize(weights)
-        return torch.tensor(norm_weights, dtype=torch.float32)
-
-
-class UniformWeights(BaseWeights):
-    """Implements a uniform weight for the nodes."""
-
-    def compute(self, nodes: NodeStorage) -> np.ndarray:
-        return np.ones(nodes.num_nodes)
-
-
-class AreaWeights(BaseWeights):
-    """Implements the area of the nodes as the weights."""
-
-    def __init__(self, norm: str = "unit-max", radius: float = 1.0, centre: np.ndarray = np.array([0, 0, 0])):
-        super().__init__(norm=norm)
-
-        # Weighting of the nodes
-        self.radius = radius
-        self.centre = centre
-
-    def compute(self, nodes: NodeStorage, *args, **kwargs) -> np.ndarray:
-        latitudes, longitudes = nodes.x[:, 0], nodes.x[:, 1]
-        points = to_sphere_xyz((latitudes, longitudes))
-        sv = SphericalVoronoi(points, self.radius, self.centre)
-        area_weights = sv.calculate_areas()
-        logger.debug(
-            "There are %d of weights, which (unscaled) add up a total weight of %.2f.",
-            len(area_weights),
-            np.array(area_weights).sum(),
-        )
-        return area_weights