Merged main

Author: tomdurrant

.github/workflows/build-docs.yml

@@ -24,6 +24,8 @@ jobs:
                   path: ~/.cache
                   restore-keys: |
                       mkdocs-material-
+            - name: Install GDAL
+              run: sudo apt-get update && sudo apt-get install -y gdal-bin libgdal-dev
             - run: cd /tmp && git clone --depth 1 https://github.com/rom-py/rompy-notebooks.git
             - run: cp -r /tmp/rompy-notebooks/notebooks docs/
             - run: pip install .[docs]

Makefile

@@ -76,9 +76,6 @@ servedocs: ## serve documentation with MkDocs, watching for changes
 apidocs: ## generate API documentation using mkdocstrings
 	mkdocs build
 
-servedocs: docs ## compile the docs watching for changes
-	watchmedo shell-command -p '*.rst' -c '$(MAKE) -C docs html' -R -D .
-
 release: dist ## package and upload a release
 	twine upload dist/*
 

README.md

@@ -1,6 +1,4 @@
----
-title: "Relocatable Ocean Modelling in PYthon (rompy)"
----
+# "Relocatable Ocean Modelling in PYthon (rompy)"
 
 [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.15093426.svg)](https://doi.org/10.5281/zenodo.15093426)
 [![GitHub Pages](https://github.com/rom-py/rompy/actions/workflows/sphinx_docs_to_gh_pages.yaml/badge.svg)](https://rom-py.github.io/rompy/)
@@ -15,6 +13,7 @@ title: "Relocatable Ocean Modelling in PYthon (rompy)"
 Relocatable Ocean Modelling in PYthon (rompy) is a modular Python library that aims to streamline the setup, configuration, execution, and analysis of coastal ocean models. Rompy combines templated model configuration with powerful xarray-based data handling and pydantic validation, enabling users to efficiently generate model control files and input datasets for a variety of ocean and wave models. The architecture centers on high-level execution control (`ModelRun`) and flexible configuration objects, supporting both persistent scientific model state and runtime backend selection. Rompy provides unified interfaces for grids, data sources, boundary conditions, and spectra, with extensible plugin support for new models and execution environments. Comprehensive documentation, example Jupyter notebooks, and a robust logging/formatting framework make rompy accessible for both research and operational workflows. Current model support includes SWAN and SCHISM, with ongoing development for additional models and cloud/HPC backends.
 
 Key Features:
+
 - Modular architecture with clear separation of configuration and execution logic
 - Templated, reproducible model configuration using pydantic and xarray
 - Unified interfaces for grids, data, boundaries, and spectra
@@ -27,7 +26,7 @@ rompy is under active development—features, model support, and documentation a
 
 # Documentation
 
-See https://rom-py.github.io/rompy/
+See <https://rom-py.github.io/rompy/>
 
 # Code Formatting and Pre-commit Hooks
 

pyproject.toml

@@ -101,6 +101,10 @@ docs = [
 notebooks = [
     "rompy-notebooks @ git+https://github.com/rom-py/rompy-notebooks.git@package",
 ]
+test = ["pytest", "envyaml", "coverage"]
+extra = ["gcsfs", "zarr", "cloudpathlib[s3,gs,azure]"]
+dev = ["pytest", "envyaml", "coverage", "ruff", "black"]
+docs = ["mkdocs", "mkdocs-material", "mkdocstrings[python]"]
 
 [tool.setuptools.packages.find]
 where = ["src"]

src/rompy/core/source.py

@@ -191,6 +191,7 @@ class SourceDatamesh(SourceBase):
         description="The id of the datasource on Datamesh",
     )
     token: Optional[str] = Field(
+        default=None,
         description="Datamesh API token, taken from the environment if not provided",
     )
     kwargs: dict = Field(
@@ -246,6 +247,7 @@ def _open(self, variables: list, geofilter: dict, timefilter: dict) -> xr.Datase
             geofilter=geofilter,
             timefilter=timefilter,
         )
+        logger.debug(f"Datamesh query: {query}")
         return self.connector.query(query)
 
     def open(