feat: fix documentation to refer to anemoi datasets instead of zarr d…

…atasets

graphs/docs/graphs/node_attributes/boolean_operations.rst

@@ -6,7 +6,7 @@
 of boolean opearations to support these operations when defining node
 attributes. Below, an attribute `mask` is computed as the intersection
 of two other masks, that are generated as the non-missing values in 2
-different variables in a Zarr dataset.
+different variables in a anemoi dataset.
 
 .. literalinclude:: ../yaml/attributes_boolean_operation.yaml
    :language: yaml

graphs/docs/graphs/node_attributes/zarr_attribute.rst

@@ -1,6 +1,6 @@
-###################
- From Zarr dataset
-###################
+#####################
+ From anemoi dataset
+#####################
 
 Zarr datasets are the standard format to define data nodes in
 :ref:`anemoi-graphs <anemoi-graphs:index-page>`. The user can define

graphs/docs/graphs/node_coordinates/zarr_dataset.rst

@@ -1,15 +1,15 @@
 .. _zarr-file:
 
-###################
- From Zarr dataset
-###################
+#####################
+ From anemoi dataset
+#####################
 
-This class builds a set of nodes from a Zarr dataset. The nodes are
+This class builds a set of nodes from a anemoi dataset. The nodes are
 defined by the coordinates of the dataset. The ZarrDataset class
 supports operations compatible with :ref:`anemoi-datasets
 <anemoi-datasets:index-page>`.
 
-To define the `node coordinates` based on a Zarr dataset, you can use
+To define the `node coordinates` based on a anemoi dataset, you can use
 the following YAML configuration:
 
 .. code:: yaml
@@ -21,13 +21,13 @@ the following YAML configuration:
          dataset: /path/to/dataset.zarr
        attributes: ...
 
-where `dataset` is the path to the Zarr dataset.
+where `dataset` is the path to the anemoi dataset.
 
 The ``ZarrDatasetNodes`` class supports operations over multiple
 datasets. For example, the `cutout` operation supports combining a
 regional dataset and a global dataset to enable both limited area and
 stretched grids. To define the `node coordinates` that combine multiple
-Zarr datasets, you can use the following YAML configuration:
+anemoi datasets, you can use the following YAML configuration:
 
 .. code:: yaml
 

graphs/docs/index.rst

@@ -41,8 +41,8 @@ recipe file, which can be used to build graphs for the input, hidden and
 output layers. For each layer, the package allows you to:
 
 -  :ref:`Define graph nodes <graphs-node_coordinates>` based on
-   coordinates defined in a dataset (Zarr and NPZ) or via algorithmic
-   approaches such as the triangular refined icosahedron.
+   coordinates defined in a dataset (anemoi dataset and NPZ) or via
+   algorithmic approaches such as the triangular refined icosahedron.
 
 -  :ref:`Define edges <graphs-edges>` (connections between nodes) based
    on methods such as the cut-off radius or K nearest-neighbours.

graphs/docs/overview.rst

@@ -34,15 +34,15 @@ categories:
 data nodes
    A set of nodes representing one or multiple datasets. The `data
    nodes` may correspond to the input/output of our data-driven model.
-   They can be defined from Zarr datasets and this method supports all
+   They can be defined from anemoi datasets and this method supports all
    :ref:`anemoi-datasets <anemoi-datasets:index-page>` operations such
    as `cutout` or `thinning`.
 
 hidden nodes
    The `hidden nodes` capture intermediate representations of the model,
    which are used to learn the dynamics of the system considered
    (atmosphere, ocean, etc, ...). These nodes can be generated from
-   existing locations (Zarr datasets or NPZ files) or algorithmically
+   existing locations (Anemoi datasets or NPZ files) or algorithmically
    from iterative refinements of polygons over the globe.
 
 Another important term that can refer to both data and hidden nodes is

graphs/src/anemoi/graphs/nodes/attributes.py

@@ -229,14 +229,14 @@ def __init__(self) -> None:
 
 
 class NonmissingZarrVariable(BooleanBaseNodeAttribute):
-    """Mask of valid (not missing) values of a Zarr dataset variable.
+    """Mask of valid (not missing) values of a Anemoi dataset variable.
 
-    It reads a variable from a Zarr dataset and returns a boolean mask of nonmissing values in the first timestep.
+    It reads a variable from a Anemoi dataset and returns a boolean mask of nonmissing values in the first timestep.
 
     Attributes
     ----------
     variable : str
-        Variable to read from the Zarr dataset.
+        Variable to read from the Anemoi dataset.
 
     Methods
     -------

graphs/src/anemoi/graphs/nodes/builders/from_file.py

@@ -26,7 +26,7 @@
 
 
 class ZarrDatasetNodes(BaseNodeBuilder):
-    """Nodes from Zarr dataset.
+    """Nodes from an anemoi dataset.
 
     Attributes
     ----------

models/docs/modules/data_indices.rst

@@ -59,7 +59,7 @@ remapper-preprocessor.
 
 There are two main Index-levels:
 
--  Data: The data at "Zarr"-level provided by Anemoi-Datasets
+-  Data: The data at "anemoi-datasets"-level provided by Anemoi-Datasets
 -  Model: The "squeezed" tensors with irrelevant parts missing.
 
 Additionally, there are two internal model levels (After preprocessor

training/src/anemoi/training/data/dataset.py

@@ -51,7 +51,7 @@ def __init__(
         Parameters
         ----------
         data_reader : Callable
-            user function that opens and returns the zarr array data
+            user function that opens and returns the anemoi-datasets array data
         grid_indices : Type[BaseGridIndices]
             indices of the grid to keep. Defaults to None, which keeps all spatial indices.
         rollout : int, optional
@@ -246,7 +246,7 @@ def per_worker_init(self, n_workers: int, worker_id: int) -> None:
     def __iter__(self) -> torch.Tensor:
         """Return an iterator over the dataset.
 
-        The datasets are retrieved by Anemoi Datasets from zarr files. This iterator yields
+        The datasets are retrieved by anemoi.datasets from anemoi datasets. This iterator yields
         chunked batches for DDP and sharded training.
 
         Currently it receives data with an ensemble dimension, which is discarded for

training/src/anemoi/training/schemas/graphs/node_attributes_schemas.py

@@ -47,9 +47,12 @@ class CutOutMaskSchema(BaseModel):
 
 class NonmissingZarrVariableSchema(BaseModel):
     target_: Literal["anemoi.graphs.nodes.attributes.NonmissingZarrVariable"] = Field(..., alias="_target_")
-    "Implementation of a mask from the nonmissing values of a Zarr variable from anemoi.graphs.nodes.attributes."
+    (
+        "Implementation of a mask from the nonmissing values of a anemoi-datasets variable "
+        "from anemoi.graphs.nodes.attributes."
+    )
     variable: str
-    "The Zarr variable to use."
+    "The anemoi-datasets variable to use."
 
 
 class BooleanOperationSchema(BaseModel):

training/src/anemoi/training/schemas/graphs/node_schemas.py

@@ -27,7 +27,7 @@
 
 class ZarrNodeSchema(BaseModel):
     target_: Literal["anemoi.graphs.nodes.ZarrDatasetNodes"] = Field(..., alias="_target_")
-    "Nodes from Zarr dataset class implementation from anemoi.graphs.nodes."
+    "Nodes from Anemoi dataset class implementation from anemoi.graphs.nodes."
     dataset: Union[str, dict]  # TODO(Helen): Discuss schema with Baudouin
     "The dataset containing the nodes."