conversion refactor with resumable multiscales + JSON metrics

[wietzesuijker]

Modular refactor for clearer CLI, safer/idempotent writes, resilient multiscales, optional metrics; preserves public high-level API (create_geozarr_dataset).

Core Changes

• CLI split: convert, validate, info (--html), benchmark, stac; adds --overwrite {fail,skip,merge,replace}, --metrics-out, --crs-groups.
• Orchestration: GeoZarrWriter centralizes base write, multiscales, consolidation, audits.
• Multiscales: resumable, per-level completeness + retry, optional max level cap, unchanged external metadata shape.
• Integrity: band/group completeness audit (missing/zero-byte chunks), skip intact arrays, rewrite incomplete ones.
• Encoding: unified create_geozarr_encoding (excludes CRS/grid mapping vars, env soft cap EOPF_MAX_CHUNK_BYTES).
• CRS: best-effort injection for supplemental groups (warn, don’t fail).
• Metrics (optional): per-step timing + structural summary JSON.
• Validation & consolidation: tolerant fallbacks (warnings instead of hard errors).
• Extras: benchmark random access, minimal STAC draft, HTML structural report.

Reliability / Safety

• Default overwrite still fail.
• Defensive cleanup before rewrites of partial arrays.
• Multiscale code was hardened (safer, resumable, validated) without changing the API, metadata, or on-disk layout.

Dev & Docs

• Restored richer README (trimmed tone, added uv workflow).
• Targeted new tests (writer, encoding, helpers, multiscales path).
• .gitignore uv.lock to condense changes. Happy to revert if preferred.

Backward Compatibility

• External entry (create_geozarr_dataset) intact; legacy unused internals removed.
• Re-exports maintain import paths.

Performance Notes

• Skips already-valid data/multiscales on reruns.
• Chunk size soft caps memory usage.

Follow-Up Suggestions

• Builder Pattern Core API (pipeline-friendly): geo = GeoZarrBuilder(config).with_input(dataset_or_url).select(groups_or_vars).inject_crs(crs_groups) .write(output_path).
  Benefits: composability, clearer intent sequencing, easier partial reuse (e.g., build → metrics → dry-run).
• Structured logging (JSON) + log levels.
• Pluggable metrics sinks (stdout, file, HTTP).
• Multiscales schema version tag & optional validation hook.
• Extended dtype/compression strategy matrix & autotuning.
• Incremental resume mode (true “merge” semantics with per-var diff).

(inherits some feats from early-stage metrics exploration https://github.com/wietzesuijker/data-model/tree/feat/convert-metrics)