Archive Utils Final PR

[adeshusa]

Assessment of Goals and Implementation (Technical)

The implementation achieves the core objective: a modular archive subsystem that supports incremental writes, deterministic reads, and policy-driven validation on top of a stable VizFold 1.0 Zarr hierarchy.

Architecture Responsibility

The system is logically decoupled into three primary modules:

1. core.py: Defines normalization, addressing, and validation primitives.
2. store.py: Performs canonical, typed writes into the archive layout.
3. load.py: Handles deterministic reads and external ingestion (.pkl, text attention).

This separation ensures one source of truth for archive invariants while preserving multiple entry paths (CLI, Demo, and Direct API).

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Goal Coverage

Goal	Status	Highlights
Incremental Updates	Achieved	Layer-indexed writes allow independent appending; existing data protected by default.
Flexible Ingestion	Achieved	Maps heterogeneous data (.txt, .pkl) into a canonical schema using tensor_to_numpy.
Validation-First	Achieved	Integrated strict/lenient checks support both production and iterative dev workflows.
Canonical Format	Achieved	Stable hierarchy maintained across metadata/, representations/, attention/, and structure/.
Test-Backed Behavior	Substantial	E2E flows verify append/retrieval; remaining work focuses on edge-case hardening.

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End-to-End Flow

1. Entry Point: cli.py, demo.py, or API calls ingest/store methods.
2. Ingestion: Parses and normalizes external artifacts into canonical arrays.
3. Storage: Applies layer/path invariants and writes to Zarr datasets.
4. Validation: Checks integrity based on the selected strictness policy.
5. Loading: Returns deterministic outputs for visualization and analysis pipelines.

Result: A robust contract providing many input forms, one canonical representation, and deterministic read semantics.

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Method Intent (One-liners)

Core & Storage

• tensor_to_numpy: Normalize tensor-like objects to NumPy before persistence.
• tensor_to_zarr_array: Write normalized arrays to canonical Zarr dataset paths.
• _validate_layer_index: Enforce valid layer addressing prior to mutation.
• validate_archive: Perform strict or lenient archive integrity checks.
• store_metadata: Persist run/config provenance under metadata/.
• store_single_representation: Write one single-representation layer.
• store_pair_representation: Write one pair-representation layer.
• store_attention: Write one attention tensor for {attention_type, layer_index}.
• store_structure_coordinates: Write structure outputs (atom_positions, atom_mask, ptm).

Ingestion & Loading

• ingest_attention_txt: Parse text attention artifacts into canonical tensors.
• _extract_best_matching_array: Score and select best candidate arrays from pickle payloads.
• ingest_output_pkl: Ingest pickle outputs with key-match traceability metadata.
• load_metadata: Read metadata as Python-native values.
• load_single_representation: Read one single layer deterministically.
• load_pair_representation: Read one pair layer deterministically.
• load_attention_head: Read one attention head slice for analysis.
• ArchiveOrchestrator: Coordinate staged ingest/store/validate operations and summarize state.

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Technical Quality Notes

• Safety: Conservative overwrite policy and integrated validation.
• Extensibility: Canonical pathing and clear module boundaries reduce technical debt.
• Observability: Ingestion traceability is improved via key_matches.

Summary: The implementation is functionally complete for the stated goals and architecturally robust for future extension.