refactor: alignment

README.md

@@ -4,45 +4,42 @@
 
 ## What is this?
 
-`mxalign` is an `xarray`-based package designed for the alignment and verification of meteorological datasets. It standardizes operations across datasets by attaching properties along three main axes:
-- **Space:** Grid or point-based data
-- **Time:** Forecasts, observations, or climatology
-- **Uncertainty:** Deterministic, ensemble, or quantile forecasts
+`mxalign` is an `xarray`-based package for aligning meteorological datasets. It operates on datasets that carry **traits** — metadata attributes that describe the nature of a dataset along three axes:
 
-Currently, `mxalign` also acts as a full execution engine. It can load datasets (e.g., Anemoi inference outputs, observation datasets), apply transformations, align datasets in both space and time to match a reference, safely broadcast NaNs, and execute verification metrics on scaled Dask clusters (Local or Slurm).
+`mxalign` is an `xarray`-based package for aligning meteorological datasets. It operates on datasets that carry **traits** — metadata attributes that describe the nature of a dataset along three axes:
+- **Space:** `grid` or `point`
+- **Time:** `forecast`, `observation`, or `climatology`
+- **Uncertainty:** `deterministic`, `ensemble`, or `quantile`
 
-> ⚠️ **Roadmap & Future Architecture Changes (planned for v0.2.0):**
-> Currently, `mxalign` handles both alignment and the execution of the verification tooling pipeline, including loading and validation. In the upcoming `v0.2.0` release, this architecture will be refactored:
-> - **Loading** will be split out into [`mlwp-data-loaders`](https://github.com/mlwp-tools/mlwp-data-loaders).
-> - **Validation** of loaded `xr.Dataset`s will be moved to [`mlwp-data-specs`](https://github.com/mlwp-tools/mlwp-data-specs) (which will contain the requirements for each of the dataset traits and the validation logic).
-> - **Execution** of the full verification pipeline (loading, transformations, alignment, and verification) from configuration files may be moved to a separate package in future releases.
-> - **Tests** will be added to `mxalign` (building on test datasets already integrated into `mlwp-data-loaders`) that ensure that all alignment operations work correctly (Testing notebook execution inside `mxalign` is explicitly excluded from the current roadmap).
+These traits are defined and validated by [`mlwp-data-specs`](https://github.com/mlwp-tools/mlwp-data-specs) and attached to datasets by [`mlwp-data-loaders`](https://github.com/mlwp-tools/mlwp-data-loaders). `mxalign` reads them to infer how datasets should be aligned, without needing to know how they were loaded.
+
+`mxalign` currently supports alignment in **space** and **time**. Alignment along the **uncertainty** axis (e.g. ensemble to deterministic) is planned for a future release.
 
 ## Python API
 
-`mxalign` provides building blocks for manual alignment, transformations, and interpolations of `xarray` datasets. This is ideal for interactive use in Jupyter notebooks or custom Python scripts.
+`mxalign` provides building blocks for spatial and temporal alignment of `xarray` datasets. This is ideal for interactive use in Jupyter notebooks or custom Python scripts.
 
 ```python
-import xarray as xr
-from mxalign import load, align_space, align_time, transform
+import mlwp_data_loaders as dl
+import mxalign as mx
 
-# Load datasets (using registered loaders)
-ds_obs = load(name="observations_loader", files=["obs.nc"])
-ds_fcst = load(name="anemoi_inference", files=["forecast.nc"])
+# Load datasets — traits are attached by the loader
+ds_obs = dl.load("observations_loader", files=["obs.nc"])
+ds_fcst = dl.load("anemoi_inference", files=["forecast.nc"])
 
 # Align the forecast spatially to match the observation reference
-ds_fcst_aligned_space = align_space(ds_fcst, reference=ds_obs, method="interpolation")
+ds_fcst_aligned = mx.align_space(ds_fcst, reference=ds_obs, method="interpolation")
 
 # Align datasets temporally
-datasets = {"obs": ds_obs, "fcst": ds_fcst_aligned_space}
-aligned_datasets = align_time(datasets, method="intersection")
+datasets = {"obs": ds_obs, "fcst": ds_fcst_aligned}
+aligned_datasets = mx.align_time(datasets, method="intersection")
 ```
 
 For a more comprehensive interactive example, check out the [introductory notebook](./examples/introduction.ipynb).
 
 ## Executing via a Configuration
 
-For full verification pipeline execution, `mxalign` uses a YAML configuration file. This allows you to declaratively define how datasets are loaded, transformed, aligned, and verified.
+`mxalign` can drive a full verification pipeline from a YAML configuration file, orchestrating dataset loading (via `mlwp-data-loaders`), transformations, alignment, and verification.
 
 ### Configuration Contents
 

examples/config.yaml

@@ -7,17 +7,17 @@ dates:
 
 datasets:
   la-1024-ea-00:
-    loader: anemoi-inference
+    loader: mlwp_data_loaders.loaders.anemoi.anemoi_inference
     files: /scratch/project_465000527/vandenbl/sg-la-comparison/inference-out/la-1024-01-ea-00/{reference_time:strftime(%Y%m%d%H)}.nc
     #grid_mapping: CERRA
     variables: ["2t","10u","10v","msl"]
   cerra:
-    loader: anemoi-datasets
+    loader: mlwp_data_loaders.loaders.anemoi.anemoi_datasets
     files: /scratch/project_465002133/datasets/cerra-rr-an-oper-0001-mars-5p5km-1984-2020-3h-v2-rmi.zarr
     #grid_mapping: CERRA
     variables: ["2t_2", "10u_10", "10v_10", "msl_0"]
   # synop:
-  #   loader: mxalign
+  #   loader: ./myloader.py
   #   files: /scratch/project_465002133/datasets/observations/cerra_synops_2020.nc
   #   variables: ["2t","10si","msl"]
 transformations:

examples/myloader.py

@@ -0,0 +1,21 @@
+import xarray as xr
+
+
+def load_dataset(file):
+    ds = xr.open_mfdataset([file], engine="h5netcdf")
+
+    ds = ds.rename_dims(code="point_index")
+
+    ds.attrs["mlwp_time_trait"] = "observation"
+    ds.attrs["mlwp_space_trait"] = "point"
+    ds.attrs["mlwp_uncertainty_trait"] = "deterministic"
+
+    ds.coords["valid_time"].attrs["standard_name"] = "time"
+    ds.coords["latitude"].attrs.update(
+        {"standard_name": "latitude", "units": "degrees_north"}
+    )
+    ds.coords["longitude"].attrs.update(
+        {"standard_name": "longitude", "units": "degrees_east"}
+    )
+
+    return ds

pyproject.toml

@@ -12,7 +12,8 @@ dependencies = [
     "dask>=2026.1.2",
     "earthkit-data>=0.19.0",
     "h5netcdf>=1.8.1",
-    "h5py>=3.15.1",
+    "h5py<3.15",
+    "mlwp-data-loaders",
     "netcdf4>=1.7.4",
     "pyyaml>=6.0.3",
     "scipy>=1.17.0",
@@ -40,7 +41,14 @@ jobqueue = [
 requires = ["hatchling"]
 build-backend = "hatchling.build"
 
+[tool.pytest.ini_options]
+testpaths = ["tests"]
+
 [dependency-groups]
 dev = [
     "ipykernel>=7.2.0",
+    "pytest>=8.0",
 ]
+
+[tool.uv.sources]
+mlwp-data-loaders = { git = "https://github.com/mlwp-tools/mlwp-data-loaders", branch = "main" }

src/mxalign/__init__.py

@@ -1,6 +1,3 @@
-from .properties.properties import Properties, Time, Space, Uncertainty
-from .loaders.loader import load
-from .loaders.registry import available_loaders, register_loader
 from .transformations.transform import transform
 from .transformations.registry import available_transformations, register_transformation
 from .interpolations.interpolate import interpolate
@@ -9,18 +6,10 @@
 from .align.space import align_space
 
 from . import accessors
-from . import loaders
 from . import transformations
 from . import interpolations
 
 __all__ = [
-    "Properties",
-    "Time",
-    "Space",
-    "Uncertainty",
-    "load",
-    "available_loaders",
-    "register_loader",
     "transform",
     "available_transformations",
     "register_transformation",
@@ -30,7 +19,6 @@
     "align_time",
     "align_space",
     "accessors",
-    "loaders",
     "transformations",
     "interpolations",
 ]

src/mxalign/accessors/__init__.py

@@ -1,7 +1,3 @@
-from . import space
-from . import time
+from . import mx
 
-__all__ = [
-    "space",
-    "time",
-]
+__all__ = ["mx"]