Add GlobalDummyDatastore for global domain testing

[RajdeepKushwaha5]

Describe your changes

Adds a GlobalDummyDatastore test variant that simulates a global domain (no lateral boundaries) by returning an all-zero boundary mask. This enables testing the model's behavior when there is no boundary forcing — a prerequisite for global weather forecasting support.

Changes:

• tests/dummy_datastore.py: Added GlobalDummyDatastore subclass of DummyDatastore with SHORT_NAME = "dummydata_global" that overrides boundary_mask to return all zeros.
• tests/conftest.py: Registered GlobalDummyDatastore in DATASTORES and DATASTORES_EXAMPLES so all parametrized datastore tests automatically cover it.
• tests/test_datastores.py: Relaxed test_boundary_mask to accept all-zero masks (global domains). Changed set(da_mask.values) == {0, 1} to set(da_mask.values).issubset({0, 1}) and replaced rigid sum checks with conditional logic for LAM vs global datastores.
• tests/test_training.py: Added test_training_global that verifies training works end-to-end with an all-zero boundary mask (i.e., unroll_prediction uses only model predictions with no boundary forcing).

Motivation: As part of enabling global forecasting (#63), the test infrastructure needs to accommodate datastores without lateral boundaries. Currently test_boundary_mask rejects valid all-zero masks, blocking any global datastore from passing the existing test suite. This PR fixes that gap and adds dedicated global training coverage.

Issue Link

Closes #445

Part of global forecasting support: #63

Type of change

• 🐛 Bug fix (non-breaking change that fixes an issue)
• ✨ New feature (non-breaking change that adds functionality)
• 💥 Breaking change (fix or feature that would cause existing functionality to not work as expected)
• 📖 Documentation (Addition or improvements to documentation)

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[Copilot]

Pull request overview

Adds a new global-domain dummy datastore variant to enable testing scenarios with no lateral boundary forcing, and updates tests to accept/cover all-zero boundary masks as a valid “global” case.

Changes:

• Introduces GlobalDummyDatastore (boundary mask = all zeros) and registers it in the test datastore registry.
• Updates test_boundary_mask to allow global (all-zero) masks in addition to LAM-style masks.
• Adds an explicit global training test that runs end-to-end training using the new datastore.

Reviewed changes

Copilot reviewed 4 out of 4 changed files in this pull request and generated 3 comments.

File	Description
tests/dummy_datastore.py	Adds GlobalDummyDatastore overriding boundary_mask to represent a global domain.
tests/conftest.py	Registers the new datastore kind so existing parametrized tests cover it.
tests/test_datastores.py	Relaxes boundary mask assertions to allow all-zero masks for global domains.
tests/test_training.py	Adds a dedicated global training test and imports GlobalDummyDatastore.

---

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[Copilot]

GlobalDummyDatastore.boundary_mask returns a new DataArray without the grid_index coordinates (MultiIndex) and without a name, unlike DummyDatastore.boundary_mask and the real datastores. This can break xarray alignment if the mask is ever combined with other datastore DataArrays by coordinates. Consider constructing the all-zero mask via xr.zeros_like(self.ds["boundary_mask"]).astype(int) (or explicitly passing coords={"grid_index": self.ds.grid_index} and name="boundary_mask").

Suggested change

	The boundary mask with all zeros, with dimensions
	``('grid_index',)``.
	"""
	return xr.DataArray(
	np.zeros(self.num_grid_points, dtype=int),
	dims=["grid_index"],
	)
	The boundary mask with all zeros, preserving the coordinates and
	name of the underlying datastore's ``boundary_mask`` variable.
	"""
	return xr.zeros_like(self.ds["boundary_mask"]).astype(int)

[Copilot]

This new test runs a full run_simple_training() again, but dummydata_global is already included in DATASTORES and therefore covered by the existing parametrized test_training. This duplicates the most expensive part of the test suite and can significantly increase CI runtime. Consider converting this to a lightweight assertion-only test (boundary mask is all zeros), or folding the assertion into the existing parametrized test_training branch for dummydata_global so training is executed only once.

Suggested change

	"""Test that training works with a global-style datastore (boundary mask
	all zeros). In a global domain there are no lateral boundaries, so the
	model's predictions should be used everywhere without any boundary
	forcing."""
	datastore = GlobalDummyDatastore()
	# Verify the global property: boundary mask should be all zeros
	assert datastore.boundary_mask.sum() == 0, (
	"GlobalDummyDatastore boundary_mask should be all zeros"
	)
	run_simple_training(datastore, set_output_std=False)
	"""Test global-style datastore boundary behavior (boundary mask all zeros).
	Training with this datastore is already exercised via the parametrized
	test_training, so this test only verifies the global boundary mask
	property to avoid duplicate expensive training runs.
	"""
	datastore = init_datastore_example("dummydata_global")
	# Verify the global property: boundary mask should be all zeros
	assert datastore.boundary_mask.sum() == 0, (
	"GlobalDummyDatastore boundary_mask should be all zeros"
	)

[Copilot]

da_mask.sum() is computed multiple times and used in both an if and asserts. To make the intent clearer and avoid repeated reductions, consider computing a single scalar once (e.g., mask_sum = int(da_mask.sum())) and using that for the conditional and assertions.

Suggested change

	if da_mask.sum() > 0:
	# LAM-style datastore: has both boundary and interior points
	assert da_mask.sum() < da_mask.size
	else:
	# Global-style datastore: no boundary points (all interior)
	assert da_mask.sum() == 0
	mask_sum = int(da_mask.sum())
	if mask_sum > 0:
	# LAM-style datastore: has both boundary and interior points
	assert mask_sum < da_mask.size
	else:
	# Global-style datastore: no boundary points (all interior)
	assert mask_sum == 0

[RajdeepKushwaha5]

Closes #445