260 Update QualityControlPipeline to consume the QualityMetric.compute() dictionary-based API

[jeipollack]

Summary

This PR updates the pipeline and configuration handling to support the dictionary-based metric API, allowing a rejection policy to select a specific diagnostic produced by a quality metric.

Context: There was an incompatibility between the QualityMetric.compute() API introduced in PR #258 and the quality-control pipeline, which previously assumed that a metric returned a single array rather than multiple diagnostics in a dictionary.

closes #260

Several files were modified in this PR to resolve the bug, but all are within the scope of the changes. I recommend taking a look at the referenced issue and the Notes section at the bottom, where I tried to provide some additional context on the changes and where to start the review. If you have questions about the processing flow, I'm happy to set up a meeting to go through it together.

What’s changed

• Update QualityControlPipeline to consume the QualityMetric.compute() dictionary-based API.
• Allow quality metrics to expose multiple diagnostics through the QualityMetric.diagnostics class attribute.
• Add a diagnostic field to RejectionPolicyConfig to select which metric diagnostic is used by a rejection policy.
• Add configuration validation for metric resource requirements and rejection-policy/metric relationships, including validation of configured diagnostics.
• Separate configuration parsing from cross-section quality-control validation.
• Update resource handling and pipeline execution to validate required resources after they have been resolved.
• Update the pixel-mask and goodness-of-fit metrics to provide multiple diagnostics.
• Move cross-section validation tests from config_test.py to pipeline_test.py.
• Extend pipeline and configuration test coverage for the updated processing flow and rejection-policy behaviour.
• Removed deprecated YAML fixtures and tests

How to test / verify

• Review the updated configuration tests in config_test.py, particularly parsing and validation of rejection-policy configuration.
• Review the pipeline tests in pipeline_test.py for configuration validation and the updated metric → diagnostic → rejection-policy processing flow.
• Check the tests covering disabled, single, and multiple rejection policies.
• Confirm that the CI test suite passes.

Scope

• Feature
• Bug fix
• Hotfix
• Documentation / process change
• Internal / refactor
• Release

This PR is part of the quality-control/outlier-rejection work.

Changelog

The changes are internal and do not introduce a user-visible change to the public product behaviour.

• Changelog fragment added (if applicable)

Reviewer Checklist

• The PR targets the correct base branch (develop, or main for release PRs)
• The PR is assigned to the developer
• Appropriate labels are applied
• The PR is included in relevant projects and/or milestones
• Description clearly explains what has changed
• Issue references included, if applicable
• Code and documentation adhere to current standards (ruff)
• Documentation updates included, if relevant
• CI tests are passing
• All reviewer comments have been addressed

Next Steps / Notes (if applicable)

The recommended review path is to start with quality_control/pipeline.py to understand the end-to-end processing flow, then trace the corresponding changes into config.py, metrics/base.py, the concrete metrics, and the tests.

The main processing flow is:

configuration → pipeline → resource resolution → metric computation → diagnostics → rejection policy → validity masks → QualityControlResult

The associated issue contains the more detailed architectural discussion and validation results.