From e0222d0391d232cd9990b1ad3bb911de69c7cfb7 Mon Sep 17 00:00:00 2001 From: Wietze Date: Wed, 20 Aug 2025 15:36:09 -0400 Subject: [PATCH 1/9] feat: log convert metrics to benchmark local runs --- .gitignore | 6 + Dockerfile | 76 + Makefile | 156 + docs/pipeline-integration-recommendations.md | 74 + geozarr-convert-template.yaml | 172 + geozarr-convert.yaml | 45 + params.json | 17 + pyproject.toml | 3 + src/eopf_geozarr/cli.py | 397 +- src/eopf_geozarr/metrics.py | 247 ++ tools/metrics_summary.py | 81 + uv.lock | 4052 ++++++++++++------ 12 files changed, 3946 insertions(+), 1380 deletions(-) create mode 100644 Dockerfile create mode 100644 Makefile create mode 100644 docs/pipeline-integration-recommendations.md create mode 100644 geozarr-convert-template.yaml create mode 100644 geozarr-convert.yaml create mode 100644 params.json create mode 100644 src/eopf_geozarr/metrics.py create mode 100644 tools/metrics_summary.py diff --git a/.gitignore b/.gitignore index 3e018a2..810b639 100644 --- a/.gitignore +++ b/.gitignore @@ -211,3 +211,9 @@ tests-output/ # uv uv.lock .vscode/settings.json + +*.zarr +.DS_§tore +out +runs +*.gz \ No newline at end of file diff --git a/Dockerfile b/Dockerfile new file mode 100644 index 0000000..7d21e19 --- /dev/null +++ b/Dockerfile @@ -0,0 +1,76 @@ +# ========= stage: builder ========= +FROM python:3.11-slim AS builder + +ENV PYTHONUNBUFFERED="1" \ + PIP_NO_CACHE_DIR="1" + +# Build arg: 0=wheel-only (small; build as linux/amd64), 1=portable (adds GDAL/PROJ & toolchain) +ARG PORTABLE_BUILD=0 + +# Base OS deps +RUN apt-get update && DEBIAN_FRONTEND=noninteractive apt-get install -y --no-install-recommends \ + ca-certificates curl git \ + && rm -rf /var/lib/apt/lists/* + +WORKDIR /app + +# uv + modern pip +COPY pyproject.toml uv.lock ./ +RUN pip install --no-cache-dir -U pip \ + && pip install --no-cache-dir uv \ + && uv --version + +# ---- Export ONLY third-party runtime deps (no hashes). We filter out any editable/self lines. +# NOTE: We export BEFORE copying src/ to avoid uv deciding this is a local edit; still filter to be safe. +RUN set -euo pipefail; \ + uv export --no-group dev --no-group test --format=requirements-txt --no-hashes -o /tmp/req.raw.txt; \ + awk ' \ + BEGIN{IGNORECASE=1} \ + # drop editable flags + /^-e[[:space:]]/ || /^--editable[[:space:]]/ {next} \ + # drop local/self refs + /@ file:/ || /file:\/\// {next} \ + # drop our own package if present + /^eopf-geozarr([[:space:]]|==|$)/ {next} \ + # drop comments/blank lines + /^[[:space:]]*#/ || /^[[:space:]]*$/ {next} \ + {print} \ + ' /tmp/req.raw.txt > /tmp/requirements.txt; \ + echo "----- filtered requirements (head) -----"; \ + sed -n '1,80p' /tmp/requirements.txt + +# ---- If PORTABLE, add toolchain + GDAL/PROJ for sdist builds (arm64 etc.) +RUN if [ "$PORTABLE_BUILD" = "1" ]; then \ + apt-get update && DEBIAN_FRONTEND=noninteractive apt-get install -y --no-install-recommends \ + build-essential gdal-bin libgdal-dev proj-bin libproj-dev \ + && rm -rf /var/lib/apt/lists/* ; \ + fi + +# ---- Install third-party deps, then your package (no re-resolve of deps) +RUN if [ "$PORTABLE_BUILD" = "1" ]; then \ + pip install --no-cache-dir -r /tmp/requirements.txt ; \ + else \ + PIP_ONLY_BINARY=":all:" pip install --no-cache-dir --prefer-binary -r /tmp/requirements.txt ; \ + fi + +# Now copy source and install the project itself without deps (pure python install) +COPY src ./src +RUN pip install --no-cache-dir --no-deps . + +# Optional: byte-compile +RUN python -m compileall -q /usr/local/lib/python3.11/site-packages || true + +# ========= stage: runtime ========= +FROM python:3.11-slim AS runtime +ENV PYTHONUNBUFFERED="1" + +# Tiny libs manylinux wheels often need +RUN apt-get update && DEBIAN_FRONTEND=noninteractive apt-get install -y --no-install-recommends \ + libstdc++6 libgomp1 \ + && rm -rf /var/lib/apt/lists/* + +WORKDIR /app +COPY --from=builder /usr/local /usr/local + +# Argo Script template supplies the command +CMD ["python", "-V"] diff --git a/Makefile b/Makefile new file mode 100644 index 0000000..f326be7 --- /dev/null +++ b/Makefile @@ -0,0 +1,156 @@ +# ===== Config ===== +IMAGE ?= eopf-geozarr:dev +NAMESPACE ?= argo # Kubernetes namespace where Argo runs +TPL ?= geozarr-convert-template.yaml +PARAMS ?= params.json +CLUSTER ?= k3s-default + +# Runtime param overrides (env > PARAMS file) +STAC_URL ?= +OUTPUT_ZARR ?= +GROUPS ?= + +# Abbrev: WF = Workflow name; PVC = PersistentVolumeClaim (-outpvc) + +.PHONY: build load-k3d load-minikube argo-install template apply \ + submit submit-cli submit-api status latest logs-save clean \ + _ensure-dirs fetch-tar run clean-pvc + +# Build the image locally +# make build -> WHEEL mode (small), builds linux/amd64 +# make build PORTABLE=1 -> PORTABLE mode (bigger), builds for native arch +build: + @if [ "$(PORTABLE)" = "1" ]; then \ + echo "==> Building PORTABLE image for native platform (allows source builds)"; \ + docker build \ + --build-arg PORTABLE_BUILD=1 \ + -t $(IMAGE) . ; \ + else \ + echo "==> Building WHEEL image for linux/amd64 (prebuilt wheels)"; \ + docker buildx build --platform=linux/amd64 \ + --build-arg PORTABLE_BUILD=0 \ + -t $(IMAGE) --load . ; \ + fi + +# Load image into k3d’s containerd (dev clusters) +load-k3d: + k3d image import $(IMAGE) --cluster $(CLUSTER) || \ + (docker save $(IMAGE) | docker exec -i $$(docker ps --format '{{.Names}}' | grep $(CLUSTER)-server-0) ctr -n k8s.io images import -) + +# Build the image inside minikube’s Docker +load-minikube: + eval "$$(minikube docker-env)"; docker build -t $(IMAGE) . + +# Install Argo Workflows (v3.7.1) into $(NAMESPACE) +argo-install: + kubectl create ns $(NAMESPACE) 2>/dev/null || true + kubectl apply -n $(NAMESPACE) -f https://github.com/argoproj/argo-workflows/releases/download/v3.7.1/install.yaml + kubectl -n $(NAMESPACE) rollout status deploy/workflow-controller + kubectl -n $(NAMESPACE) rollout status deploy/argo-server + +# Apply (or update) the WorkflowTemplate +template: + kubectl -n $(NAMESPACE) apply -f $(TPL) + kubectl -n $(NAMESPACE) get workflowtemplate geozarr-convert + +# Build + load + install + template (one shot) +apply: build load-k3d argo-install template + +# Submit via CLI (uses env overrides, else PARAMS file) +submit: _ensure-dirs + @STAC="$${STAC_URL:-$$(jq -r '.arguments.parameters[] | select(.name=="stac_url").value' $(PARAMS))}"; \ + OUT="$${OUTPUT_ZARR:-$$(jq -r '.arguments.parameters[] | select(.name=="output_zarr").value' $(PARAMS))}"; \ + GRP="$${GROUPS:-$$(jq -r '.arguments.parameters[] | select(.name=="groups").value' $(PARAMS))}"; \ + echo "Submitting:"; echo " stac_url=$$STAC"; echo " output_zarr=$$OUT"; echo " groups=$$GRP"; \ + WF=$$(argo submit -n $(NAMESPACE) --from workflowtemplate/geozarr-convert \ + -p stac_url="$$STAC" -p output_zarr="$$OUT" -p groups="$$GRP" -o name); \ + TSTAMP=$$(date +%Y%m%d-%H%M%S); \ + argo get -n $(NAMESPACE) $$WF -o json > runs/$${TSTAMP}-$${WF##*/}.json; \ + argo get -n $(NAMESPACE) $$WF --output wide | tee runs/$${TSTAMP}-$${WF##*/}.summary.txt; \ + echo "Workflow: $$WF" + +# Submit via CLI (PARAMS file only, no env overrides) +submit-cli: _ensure-dirs + @WF=$$(argo submit -n $(NAMESPACE) --from workflowtemplate/geozarr-convert \ + -p stac_url="$$(jq -r '.arguments.parameters[] | select(.name=="stac_url").value' $(PARAMS))" \ + -p output_zarr="$$(jq -r '.arguments.parameters[] | select(.name=="output_zarr").value' $(PARAMS))" \ + -p groups="$$(jq -r '.arguments.parameters[] | select(.name=="groups").value' $(PARAMS))" \ + -o name); \ + TSTAMP=$$(date +%Y%m%d-%H%M%S); \ + argo get -n $(NAMESPACE) $$WF -o json > runs/$${TSTAMP}-$${WF##*/}.json; \ + argo get -n $(NAMESPACE) $$WF --output wide | tee runs/$${TSTAMP}-$${WF##*/}.summary.txt; \ + echo "Workflow: $$WF" + +# Submit via Argo Server HTTP (dev port-forward, no token) +submit-api: _ensure-dirs + kubectl -n $(NAMESPACE) port-forward svc/argo-server 2746:2746 >/dev/null 2>&1 & echo $$! > .pf.pid + sleep 1 + curl -s -H 'Content-Type: application/json' \ + --data-binary @$(PARAMS) \ + http://localhost:2746/api/v1/workflows/$(NAMESPACE)/submit \ + | tee runs/submit-response.json | jq . >/dev/null || \ + (echo "Non-JSON response (see runs/submit-response.json)"; exit 1) + -@[ -f .pf.pid ] && kill $$(cat .pf.pid) 2>/dev/null || true + -@rm -f .pf.pid + +# Inspect +status: + argo list -n $(NAMESPACE); echo; kubectl -n $(NAMESPACE) get wf + +latest: + argo get -n $(NAMESPACE) @latest --output wide + +logs-save: _ensure-dirs + @WF=$$(argo list -n $(NAMESPACE) --output name | tail -1); \ + TSTAMP=$$(date +%Y%m%d-%H%M%S); \ + argo logs -n $(NAMESPACE) $$WF -c main > logs/$${TSTAMP}-$${WF##*/}.log; \ + echo "Wrote logs/$${TSTAMP}-$${WF##*/}.log" + +# Delete all workflows + completed pods +clean: + argo delete -n $(NAMESPACE) --all || true + kubectl -n $(NAMESPACE) delete pod -l workflows.argoproj.io/completed=true --force --grace-period=0 || true + +_ensure-dirs: + @mkdir -p runs logs + +# Fetch from PVC: copy tarball, unpack into runs//, pull any extra files on /outputs +fetch-tar: _ensure-dirs + @WF=$$(argo list -n $(NAMESPACE) --output name | tail -1 | sed 's#.*/##'); \ + PVC="$$WF-outpvc"; OUTDIR="runs/$$WF"; \ + echo "Workflow: $$WF"; echo "PVC: $$PVC"; mkdir -p $$OUTDIR; \ + kubectl -n $(NAMESPACE) delete pod fetch-$$WF --ignore-not-found >/dev/null 2>&1 || true; \ + cat <<'YAML' | sed "s/{{WF}}/$$WF/g" | sed "s/{{PVC}}/$$PVC/g" | kubectl -n $(NAMESPACE) apply -f - + apiVersion: v1 + kind: Pod + metadata: + name: fetch-{{WF}} + spec: + restartPolicy: Never + containers: + - name: fetch + image: busybox:1.36 + command: ["sh","-lc","sleep 600"] + volumeMounts: + - name: out + mountPath: /mnt/out + volumes: + - name: out + persistentVolumeClaim: + claimName: {{PVC}} + YAML + kubectl -n $(NAMESPACE) wait --for=condition=Ready pod/fetch-$$WF --timeout=60s + # Main artifact + kubectl -n $(NAMESPACE) cp fetch-$$WF:/mnt/out/geozarr.tar.gz $$OUTDIR/geozarr.tar.gz + tar -xzf $$OUTDIR/geozarr.tar.gz -C $$OUTDIR + # Copy any other files (e.g., dask-report.html) + kubectl -n $(NAMESPACE) cp fetch-$$WF:/mnt/out/. $$OUTDIR/ || true + kubectl -n $(NAMESPACE) delete pod fetch-$$WF --wait=false + @echo "Unpacked into $$OUTDIR/" + +# Convenience: build + load + template + submit + fetch +run: apply submit fetch-tar + +# Cleanup stray per-run PVCs (removes stored artifacts) +clean-pvc: + kubectl -n $(NAMESPACE) delete pvc -l workflows.argoproj.io/workflow 2>/dev/null || true diff --git a/docs/pipeline-integration-recommendations.md b/docs/pipeline-integration-recommendations.md new file mode 100644 index 0000000..dfb264c --- /dev/null +++ b/docs/pipeline-integration-recommendations.md @@ -0,0 +1,74 @@ +# Pipeline Integration Recommendations + +This document lists enhancements observed while integrating `eopf-geozarr` into an Argo-based batch conversion pipeline (data-model-pipeline). They are candidates for upstream inclusion or API refinement. + +## 1. Output Prefix Expansion +**Current (pipeline)**: Wrapper detects `output_zarr` ending with `/` and appends `_geozarr.zarr` derived from the input STAC/Zarr URL. +**Recommendation**: Support `--output-prefix` OR accept a trailing slash on positional `output_path` and perform the expansion internally (emitting the resolved final path). Add a log line: `Resolved output store: s3://.../S2A_..._geozarr.zarr`. + +## 2. Group Existence Validation (Pre-flight) +**Current (pipeline)**: `validate_params_groups.py` inspects filesystem structure (presence of `.zarray` / `.zgroup`). +**Recommendation**: Native CLI flag `--validate-groups` to prune or fail fast when groups don’t exist. Modes: +- `--validate-groups=warn` (default): drop missing, report. +- `--validate-groups=error`: abort if any missing. +Emit JSON or structured summary when `--verbose`. + +## 3. Profiles (WOZ Profiles) +**Current (pipeline)**: External JSON profile expansion before calling CLI. +**Recommendation**: Provide `--profile ` in CLI mapping to preset groups + chunk params. Add `eopf-geozarr profile list` / `profile show ` subcommands. Keep external mechanism as fallback. + +## 4. Compressor Handling +**Current**: Template attempted to pass `--compressor`; CLI does not expose codec choice. +**Recommendation**: If codec selection is desired, add `--compressor ` now (zstd, lz4, blosc) with validation; else document fixed default explicitly to avoid confusion. + +## 5. CRS Groups Convenience +**Current**: `--crs-groups` optional list. +**Recommendation**: Discover candidate groups automatically (search for geometry-like datasets) unless `--crs-groups` provided (override). Provide `eopf-geozarr info --crs-scan` to preview. + +## 6. Dask Cluster Ergonomics +**Current**: `--dask-cluster` toggles local cluster with no feedback. +**Recommendation**: Print cluster dashboard URL (if available) and add `--dask-workers N` for quick scaling. + +## 7. Structured Logging / Run Metadata +**Current**: Plain prints; pipeline scrapes logs. +**Recommendation**: Optional `--run-metadata ` to write machine-readable summary: inputs, resolved groups, timings, warnings. Eases automation and reproducibility. + +## 8. Validation Command Enhancements +**Current**: `validate` skeleton present but incomplete. +**Recommendation**: +- Implement spec checks (multiscales, attributes, chunk shape policy). +- Exit code non-zero on *hard* failures, zero with warnings for soft issues. +- `--format json` for programmatic consumption. + +## 9. HTML Tree Generation +**Current**: `_generate_html_output` scaffold incomplete. +**Recommendation**: Finish implementation; integrate with `info --html-output`. Provide minimal inline CSS (already drafted) and optional `--open-browser` flag. + +## 10. Progress Reporting +**Recommendation**: Emit periodic progress per group: `group=/measurements/r10m scale=2 written=...MB elapsed=...s` to assist monitoring in batch workflows. + +## 11. Retry / Resumability +**Recommendation**: Add `--resume` to skip already existing multiscale levels if output store partially present. + +## 12. Exit Codes (Contract) +Document exit code meanings: +- 0 success +- 2 validation (input) error +- 3 group resolution failure +- 4 conversion runtime error + +## 13. Environment Variable Overrides +Allow `EOZ_DEFAULT_PROFILE`, `EOZ_OUTPUT_PREFIX` env vars as implicit defaults (still overridden by flags). + +## 14. Example Invocation Block in README +Provide ready-to-copy examples for Sentinel-2 & Sentinel-1 including polarization groups when Phase 1 logic is public. + +--- +**Next Steps (Suggested Order)** +1. Implement output prefix expansion (low risk, high UX win) +2. Group validation flag (prevents silent empty writes) +3. Finish validate + info HTML features +4. Add structured run metadata output +5. Introduce profiles subcommands then deprecate external expansion path over time + +Feedback welcome—pipeline experience will continue surfacing actionable deltas. diff --git a/geozarr-convert-template.yaml b/geozarr-convert-template.yaml new file mode 100644 index 0000000..417ea50 --- /dev/null +++ b/geozarr-convert-template.yaml @@ -0,0 +1,172 @@ +apiVersion: argoproj.io/v1alpha1 +kind: WorkflowTemplate +metadata: + name: geozarr-convert + namespace: argo +spec: + entrypoint: run-convert + + # Per-workflow PVC. Argo creates -outpvc and injects a volume named "outpvc". + volumeClaimTemplates: + - metadata: + name: outpvc + spec: + accessModes: ["ReadWriteOnce"] + resources: + requests: + storage: 10Gi + + arguments: + parameters: + - name: stac_url + value: "https://objectstore.eodc.eu:2222/e05ab01a9d56408d82ac32d69a5aae2a:202505-s02msil2a/18/products/cpm_v256/S2B_MSIL2A_20250518T112119_N0511_R037_T29RLL_20250518T140519.zarr" + - name: output_zarr + value: "./S2B_MSIL2A_20250518_T29RLL_geozarr.zarr" + # Space-separated groups; script normalizes to absolute paths. + - name: groups + value: "/measurements/reflectance/r10m /measurements/reflectance/r20m /measurements/reflectance/r60m" + - name: dask_perf_html + value: "out/debug/dask-report-threads.html" + + templates: + - name: run-convert + volumes: + - name: tmp + emptyDir: {} + + inputs: + parameters: + - name: stac_url + - name: output_zarr + - name: groups + - name: dask_perf_html + + script: + image: eopf-geozarr:dev + imagePullPolicy: IfNotPresent + command: ["sh", "-lc"] + volumeMounts: + - name: tmp + mountPath: /tmp + - name: outpvc + mountPath: /outputs + source: | + set -euo pipefail + + STAC="{{inputs.parameters.stac_url}}" + OUT="{{inputs.parameters.output_zarr}}" + GROUPS_RAW="{{inputs.parameters.groups}}" + PERF="{{inputs.parameters.dask_perf_html}}" + OUTDIR="/outputs" + + case "$STAC" in *…*|*...*) echo "ERROR: stac_url contains ellipsis"; exit 64;; esac + + mkdir -p "$OUTDIR" "$(dirname "$PERF")" + + echo "=== preflight ===" + echo "stac_url=$STAC" + if command -v curl >/dev/null 2>&1; then + (curl -sI --max-time 8 "$STAC" | head -n1) || true + elif command -v wget >/dev/null 2>&1; then + (wget -qS --spider "$STAC" 2>&1 | head -n1) || true + else + echo "(no curl/wget in image; skipping HEAD)" + fi + + # Normalize group paths + NORM_GROUPS="" + for g in $GROUPS_RAW; do + [ -z "$g" ] && continue + case "$g" in /*) NORM="$g";; *) NORM="/$g";; esac + NORM="$(printf "%s" "$NORM" | sed 's#//*#/#g')" + NORM_GROUPS="$NORM_GROUPS $NORM" + done + GROUPS="$(printf "%s" "$NORM_GROUPS" | sed 's/^ *//')" + echo "groups(norm)=$GROUPS" + + # Validate groups early with xarray (robust to API differences) + python - "$STAC" $GROUPS <<'PY' + import sys, xarray as xr + + def iter_children(node): + """Return iterable of (name, child) for DataTree children + across xarray versions where .groups may be dict/list/tuple.""" + groups = getattr(node, "groups", None) + if isinstance(groups, dict): + yield from groups.items() + elif isinstance(groups, (list, tuple)): + for name in groups: + try: + yield name, node[name] + except Exception: + pass + else: + # try .children (older/alt APIs) + children = getattr(node, "children", None) + if isinstance(children, dict): + yield from children.items() + + if len(sys.argv) < 2: + print("no args passed to validation; skipping"); sys.exit(0) + stac, *groups = sys.argv[1:] + dt = xr.open_datatree(stac, engine="zarr", consolidated=None) + + print("=== available groups ===") + def walk(node, prefix=""): + path = prefix or "/" + print(path) + for name, child in iter_children(node): + newp = f"{prefix}/{name}" if prefix else f"/{name}" + walk(child, newp) + walk(dt) + + bad = [] + for g in groups: + key = g.strip("/") + try: + _ = dt[key] + except Exception as e: + print(f"[error] {g}: {e}") + bad.append(g) + if bad: + try: + meas = dt["measurements"] + print("candidates under /measurements:") + for name, _child in iter_children(meas): + print(" /measurements/" + name) + except Exception: + pass + sys.exit(64) + PY + + echo "=== convert ===" + set -x + eopf-geozarr convert \ + --dask-cluster \ + --dask-perf-html "$PERF" \ + "$STAC" \ + "$OUT" \ + --verbose \ + $(for g in $GROUPS; do printf -- ' --groups %s' "$g"; done) + set +x + + # Persist extras on PVC for fetch-tar convenience + cp -f "$PERF" "$OUTDIR/dask-report.html" 2>/dev/null || true + + echo "=== package artifact ===" + tar -czf "$OUTDIR/geozarr.tar.gz" -C "$(dirname "$OUT")" "$(basename "$OUT")" + + resources: + requests: + cpu: "1" + memory: "2Gi" + limits: + cpu: "4" + memory: "8Gi" + + outputs: + artifacts: + - name: dask-report + path: "{{inputs.parameters.dask_perf_html}}" + - name: geozarr-tar + path: /outputs/geozarr.tar.gz diff --git a/geozarr-convert.yaml b/geozarr-convert.yaml new file mode 100644 index 0000000..ee24e41 --- /dev/null +++ b/geozarr-convert.yaml @@ -0,0 +1,45 @@ +apiVersion: argoproj.io/v1alpha1 +kind: Workflow +metadata: + generateName: geozarr-convert- +spec: + entrypoint: run-convert + templates: + - name: run-convert + inputs: + parameters: + - name: stac_url + value: "https://objectstore.eodc.eu:2222/e05ab01a9d56408d82ac32d69a5aae2a:202505-s02msil2a/18/products/cpm_v256/S2B_MSIL2A_20250518T112119_N0511_R037_T29RLL_20250518T140519.zarr" + - name: output_zarr + value: "./S2B_MSIL2A_20250518_T29RLL_geozarr.zarr" + - name: groups + value: "measurements/reflectance/r20m" + - name: dask_perf_html + value: "out/debug/dask-report-threads.html" + script: + image: eopf-geozarr:dev + command: ["sh"] + source: | + set -eu + mkdir -p "$(dirname "{{inputs.parameters.dask_perf_html}}")" + eopf-geozarr convert \ + --dask-cluster \ + --dask-perf-html "{{inputs.parameters.dask_perf_html}}" \ + "{{inputs.parameters.stac_url}}" \ + "{{inputs.parameters.output_zarr}}" \ + --verbose \ + --groups "{{inputs.parameters.groups}}" + tar -czf geozarr.tar.gz -C "$(dirname "{{inputs.parameters.output_zarr}}")" "$(basename "{{inputs.parameters.output_zarr}}")" + resources: + requests: + cpu: "2" + memory: "4Gi" + limits: + cpu: "4" + memory: "8Gi" + outputs: + artifacts: + - name: dask-report + path: "{{inputs.parameters.dask_perf_html}}" + - name: geozarr-tar + path: geozarr.tar.gz diff --git a/params.json b/params.json new file mode 100644 index 0000000..0e050a6 --- /dev/null +++ b/params.json @@ -0,0 +1,17 @@ +{ + "namespace": "argo", + "resourceKind": "Workflow", + "resourceName": "", + "submitOptions": {}, + "workflowTemplateRef": { "name": "geozarr-convert" }, + "arguments": { + "parameters": [ + { + "name": "stac_url", + "value": "https://objectstore.eodc.eu:2222/e05ab01a9d56408d82ac32d69a5aae2a:202505-s02msil2a/18/products/cpm_v256/S2B_MSIL2A_20250518T112119_N0511_R037_T29RLL_20250518T140519.zarr" + }, + { "name": "output_zarr", "value": "./S2B_MSIL2A_20250518_T29RLL_geozarr.zarr" }, + { "name": "groups", "value": "measurements/reflectance/r20m" } + ] + } +} diff --git a/pyproject.toml b/pyproject.toml index 56c483e..0cc63c4 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -39,6 +39,9 @@ dependencies = [ "s3fs>=2024.6.0", "boto3>=1.34.0", "pyproj>=3.7.0", + "ipykernel>=6.30.1", + "jupyter>=1.1.1", + "bokeh>=3.7.3", ] [dependency-groups] diff --git a/src/eopf_geozarr/cli.py b/src/eopf_geozarr/cli.py index 78ff3cc..29d82a9 100644 --- a/src/eopf_geozarr/cli.py +++ b/src/eopf_geozarr/cli.py @@ -1,13 +1,15 @@ #!/usr/bin/env python3 -""" -Command-line interface for eopf-geozarr. +"""Command-line interface for eopf-geozarr. This module provides CLI commands for converting EOPF datasets to GeoZarr compliant format. """ import argparse import sys +from contextlib import nullcontext +from datetime import datetime from pathlib import Path +from time import perf_counter from typing import Any, Optional import xarray as xr @@ -19,9 +21,16 @@ is_s3_path, validate_s3_access, ) +from .metrics import MetricsRecorder -def setup_dask_cluster(enable_dask: bool, verbose: bool = False) -> Optional[Any]: +def setup_dask_cluster( + enable_dask: bool, + verbose: bool = False, + mode: str = "threads", # threads | processes | single-threaded + n_workers: int = 4, + threads_per_worker: int = 1, +) -> Optional[Any]: """ Set up a dask cluster for parallel processing. @@ -32,7 +41,7 @@ def setup_dask_cluster(enable_dask: bool, verbose: bool = False) -> Optional[Any verbose : bool, default False Enable verbose output - Returns + Returns: ------- dask.distributed.Client or None Dask client if enabled, None otherwise @@ -41,18 +50,35 @@ def setup_dask_cluster(enable_dask: bool, verbose: bool = False) -> Optional[Any return None try: - from dask.distributed import Client + from dask.distributed import Client, LocalCluster + + if mode not in {"threads", "processes", "single-threaded"}: + raise ValueError(f"Unsupported --dask-mode: {mode}") + + processes = mode == "processes" + # For single-threaded, use one worker, one thread, processes=False + if mode == "single-threaded": + n_workers = 1 + threads_per_worker = 1 + processes = False # Set up local cluster - client = Client() # set up local cluster + cluster = LocalCluster( + n_workers=n_workers, + threads_per_worker=threads_per_worker, + processes=processes, + ) + client = Client(cluster) if verbose: print(f"🚀 Dask cluster started: {client}") print(f" Dashboard: {client.dashboard_link}") print(f" Workers: {len(client.scheduler_info()['workers'])}") else: - print("🚀 Dask cluster started for parallel processing") - + print( + f"🚀 Dask cluster started " + f"({mode}; workers={n_workers}, threads/worker={threads_per_worker})" + ) return client except ImportError: @@ -65,9 +91,92 @@ def setup_dask_cluster(enable_dask: bool, verbose: bool = False) -> Optional[Any sys.exit(1) +def _duration_from_startstops(ev: dict, action: str) -> float: + """Best-effort duration extractor from task_stream 'startstops'.""" + total = 0.0 + for ss in ev.get("startstops", []) or []: + if ss.get("action") == action: + s, t = ss.get("start"), ss.get("stop") + if s is not None and t is not None: + total += max(0.0, float(t) - float(s)) + return total + + +def _parse_task_events(task_events: Optional[list]) -> dict: + """Normalize task_stream events across Dask versions into aggregate numbers.""" + if not task_events: + return { + "tasks_observed": 0, + "compute_time_s_sum": 0.0, + "transfer_time_s_sum": 0.0, + } + comp_sum = 0.0 + xfer_sum = 0.0 + for e in task_events: + # compute time + cd = e.get("compute_duration") + if cd is None: + cs = e.get("compute_start") or e.get("start") + ce = e.get("compute_stop") or e.get("stop") + if cs is not None and ce is not None: + cd = max(0.0, float(ce) - float(cs)) + else: + cd = _duration_from_startstops(e, "compute") + comp_sum += float(cd or 0.0) + # transfer time + td = e.get("transfer_duration") + if td is None: + ts = e.get("transfer_start") + te = e.get("transfer_stop") + if ts is not None and te is not None: + td = max(0.0, float(te) - float(ts)) + else: + td = _duration_from_startstops(e, "transfer") + xfer_sum += float(td or 0.0) + return { + "tasks_observed": len(task_events), + "compute_time_s_sum": comp_sum, + "transfer_time_s_sum": xfer_sum, + } + + +def _summarize_dask_metrics( + client: Any, task_events: Optional[list], wall_clock_s: float +) -> dict: + info = client.scheduler_info() + workers = info.get("workers", {}) + total_threads = sum(int(w.get("nthreads", 0) or 0) for w in workers.values()) + memory_limit = sum(int(w.get("memory_limit", 0) or 0) for w in workers.values()) + memory_used = sum( + int(w.get("metrics", {}).get("memory", 0) or 0) for w in workers.values() + ) + spilled = sum( + int(w.get("metrics", {}).get("spilled_nbytes", 0) or 0) + for w in workers.values() + ) + + parsed = _parse_task_events(task_events) + tasks = parsed["tasks_observed"] + base = { + "wall_clock_s": wall_clock_s, + "workers": len(workers), + "threads_total": total_threads, + "tasks_observed": tasks, + "tasks_per_sec": (tasks / wall_clock_s) if tasks and wall_clock_s else 0.0, + "compute_time_s_sum": parsed["compute_time_s_sum"], + "transfer_time_s_sum": parsed["transfer_time_s_sum"], + "memory_used_bytes": memory_used, + "memory_limit_bytes": memory_limit, + } + if spilled: + base["spilled_nbytes"] = spilled + if getattr(client, "dashboard_link", None): + base["dashboard_link"] = client.dashboard_link + return base + + def convert_command(args: argparse.Namespace) -> None: - """ - Convert EOPF dataset to GeoZarr compliant format. + """Convert EOPF dataset to GeoZarr compliant format. Parameters ---------- @@ -76,9 +185,20 @@ def convert_command(args: argparse.Namespace) -> None: """ # Set up dask cluster if requested dask_client = setup_dask_cluster( - enable_dask=getattr(args, "dask_cluster", False), verbose=args.verbose + enable_dask=getattr(args, "dask_cluster", False), + verbose=args.verbose, + mode=getattr(args, "dask_mode", "threads"), + n_workers=getattr(args, "dask_workers", 4), + threads_per_worker=getattr(args, "dask_threads_per_worker", 1), ) + # Prepare for metrics writing even on failures (local outputs only) + debug_dir: Optional[Path] = None + run_id = datetime.now().strftime("%Y%m%d-%H%M%S") + status = "unknown" + error_msg = None + metrics = None + try: # Validate input path (handle both local paths and URLs) input_path_str = args.input_path @@ -129,6 +249,12 @@ def convert_command(args: argparse.Namespace) -> None: output_path = Path(output_path_str) output_path.parent.mkdir(parents=True, exist_ok=True) output_path = str(output_path) + # Prepare debug dir for metrics + debug_dir = Path(output_path) / "debug" + try: + debug_dir.mkdir(parents=True, exist_ok=True) + except Exception: + debug_dir = None if args.verbose: print(f"Loading EOPF dataset from: {input_path}") @@ -142,12 +268,31 @@ def convert_command(args: argparse.Namespace) -> None: # Load the EOPF DataTree with appropriate storage options print("Loading EOPF dataset...") storage_options = get_storage_options(input_path) - dt = xr.open_datatree( - str(input_path), - engine="zarr", - chunks="auto", - storage_options=storage_options, - ) + # Metrics setup + if getattr(args, "metrics", True): + metrics = MetricsRecorder() + metrics.set_environment() + metrics.set_input( + source_uri=str(input_path), + profile=None, + groups=args.groups, + dask={ + "enabled": bool(dask_client is not None), + "mode": getattr(args, "dask_mode", None), + "workers": getattr(args, "dask_workers", None), + "threads_per_worker": getattr( + args, "dask_threads_per_worker", None + ), + }, + ) + + with metrics.time_step("open_input") if metrics else nullcontext(): + dt = xr.open_datatree( + str(input_path), + engine="zarr", + chunks="auto", + storage_options=storage_options, + ) if args.verbose: print(f"Loaded DataTree with {len(dt.children)} groups") @@ -157,19 +302,106 @@ def convert_command(args: argparse.Namespace) -> None: # Convert to GeoZarr compliant format print("Converting to GeoZarr compliant format...") - dt_geozarr = create_geozarr_dataset( - dt_input=dt, - groups=args.groups, - output_path=output_path, - spatial_chunk=args.spatial_chunk, - min_dimension=args.min_dimension, - tile_width=args.tile_width, - max_retries=args.max_retries, - crs_groups=args.crs_groups, - ) + t0 = perf_counter() + task_events = None + perf_ctx = nullcontext() + if dask_client is not None and getattr(args, "dask_perf_html", None): + from dask.distributed import performance_report + + perf_path = args.dask_perf_html + if not (perf_path.startswith(("s3://", "gs://", "http://", "https://"))): + Path(perf_path).parent.mkdir(parents=True, exist_ok=True) + perf_ctx = performance_report(filename=perf_path) + + capture_tasks = False + if dask_client is not None: + try: + from dask.distributed import get_task_stream + + capture_tasks = True + except Exception: + pass + + if capture_tasks: + with perf_ctx: + from dask.distributed import get_task_stream + + with get_task_stream(client=dask_client, plot=False) as ts: + with metrics.time_step("convert") if metrics else nullcontext(): + dt_geozarr = create_geozarr_dataset( + dt_input=dt, + groups=args.groups, + output_path=output_path, + spatial_chunk=args.spatial_chunk, + min_dimension=args.min_dimension, + tile_width=args.tile_width, + max_retries=args.max_retries, + crs_groups=args.crs_groups, + ) + task_events = ts.data + else: + with perf_ctx: + with metrics.time_step("convert") if metrics else nullcontext(): + dt_geozarr = create_geozarr_dataset( + dt_input=dt, + groups=args.groups, + output_path=output_path, + spatial_chunk=args.spatial_chunk, + min_dimension=args.min_dimension, + tile_width=args.tile_width, + max_retries=args.max_retries, + crs_groups=args.crs_groups, + ) + + wall_clock = perf_counter() - t0 + if ( + dask_client is not None + and getattr(args, "verbose", False) + and getattr(args, "dask_perf_html", None) + ): + print(f"📊 Dask performance report: {args.dask_perf_html}") print("✅ Successfully converted EOPF dataset to GeoZarr format") print(f"Output saved to: {output_path}") + status = "ok" + + # Drop a JSON run summary (local paths only) + try: + if debug_dir is not None: + summary_path = Path(output_path) / "debug" / "dask_run_summary.json" + summary_path.parent.mkdir(parents=True, exist_ok=True) + from json import dumps as _dumps + + summary = { + "dask_enabled": bool(dask_client is not None), + "mode": getattr(args, "dask_mode", None), + "workers": getattr(args, "dask_workers", None), + "threads_per_worker": getattr( + args, "dask_threads_per_worker", None + ), + "perf_report": getattr(args, "dask_perf_html", None), + "wall_clock_s": wall_clock if dask_client is not None else None, + "groups": args.groups, + "spatial_chunk": args.spatial_chunk, + "min_dimension": args.min_dimension, + "tile_width": args.tile_width, + } + summary_path.write_text(_dumps(summary, indent=2)) + if args.verbose: + print(f"🧾 Wrote run summary: {summary_path}") + # New metrics: run_summary.json and chunk_report.json + if metrics and debug_dir is not None and getattr(args, "metrics", True): + # Build output summary best-effort + with metrics.time_step("summarize_output"): + metrics.build_output_summary(output_path) + payload = metrics.finalize(status="ok") + run_summary = Path(output_path) / "debug" / "run_summary.json" + MetricsRecorder.write_json(run_summary, payload) + if args.verbose: + print(f"🧾 Wrote metrics: {run_summary}") + except Exception as _exc: + if args.verbose: + print(f"(debug) could not write run summary: {_exc}") if args.verbose: # Check if dt_geozarr is a DataTree or Dataset @@ -183,10 +415,21 @@ def convert_command(args: argparse.Namespace) -> None: except Exception as e: print(f"❌ Error during conversion: {e}") + error_msg = str(e) if args.verbose: import traceback traceback.print_exc() + # Write failure metrics if possible + try: + if metrics and debug_dir is not None and getattr(args, "metrics", True): + payload = metrics.finalize(status="error", exception=error_msg) + run_summary = Path(args.output_path) / "debug" / "run_summary.json" + MetricsRecorder.write_json(run_summary, payload) + if args.verbose: + print(f"🧾 Wrote failure metrics: {run_summary}") + except Exception: + pass sys.exit(1) finally: # Clean up dask client if it was created @@ -200,10 +443,54 @@ def convert_command(args: argparse.Namespace) -> None: if args.verbose: print(f"Warning: Error closing dask cluster: {e}") + # Best-effort metrics write (works for success or failure, local only) + try: + if debug_dir is not None: + attempt = 1 + try: + attempt = 1 + len(list(debug_dir.glob("dask_metrics_*.json"))) + except Exception: + pass + wall = None + if "t0" in locals(): + wall = perf_counter() - t0 + metrics_doc = { + "status": status, + "run_id": run_id, + "attempt": attempt, + "dask_enabled": bool(dask_client is not None), + "mode": getattr(args, "dask_mode", None), + } + if dask_client is not None and wall is not None: + try: + metrics_doc.update( + _summarize_dask_metrics( + dask_client, locals().get("task_events"), wall + ) + ) + except Exception: + metrics_doc["wall_clock_s"] = wall + elif wall is not None: + metrics_doc["wall_clock_s"] = wall + if error_msg: + metrics_doc["error"] = error_msg + + path_ts = debug_dir / f"dask_metrics_{run_id}_attempt{attempt}.json" + path_latest = debug_dir / "dask_metrics.json" + from json import dumps as _dumps + + txt = _dumps(metrics_doc, indent=2) + path_ts.write_text(txt) + path_latest.write_text(txt) + if getattr(args, "verbose", False): + print(f"📈 Wrote metrics: {path_ts}") + except Exception as _mexc: + if getattr(args, "verbose", False): + print(f"(debug) could not write metrics snapshot: {_mexc}") + def info_command(args: argparse.Namespace) -> None: - """ - Display information about an EOPF dataset. + """Display information about an EOPF dataset. Parameters ---------- @@ -253,8 +540,7 @@ def info_command(args: argparse.Namespace) -> None: def _generate_optimized_tree_html(dt: xr.DataTree) -> str: - """ - Generate an optimized, condensed tree HTML representation. + """Generate an optimized, condensed tree HTML representation. This function creates a clean tree view that: - Hides empty nodes by default @@ -266,7 +552,7 @@ def _generate_optimized_tree_html(dt: xr.DataTree) -> str: dt : xr.DataTree DataTree to visualize - Returns + Returns: ------- str HTML representation of the optimized tree @@ -609,8 +895,7 @@ def render_node(node: Any, path: str = "", level: int = 0) -> str: def _generate_html_output( dt: xr.DataTree, output_path: str, input_path: str, verbose: bool = False ) -> None: - """ - Generate HTML output for DataTree visualization. + """Generate HTML output for DataTree visualization. Parameters ---------- @@ -926,8 +1211,7 @@ def _generate_html_output( def validate_command(args: argparse.Namespace) -> None: - """ - Validate GeoZarr compliance of a dataset. + """Validate GeoZarr compliance of a dataset. Parameters ---------- @@ -1021,10 +1305,9 @@ def validate_command(args: argparse.Namespace) -> None: def create_parser() -> argparse.ArgumentParser: - """ - Create the argument parser for the CLI. + """Create the argument parser for the CLI. - Returns + Returns: ------- argparse.ArgumentParser Configured argument parser @@ -1095,6 +1378,42 @@ def create_parser() -> argparse.ArgumentParser: action="store_true", help="Start a local dask cluster for parallel processing of chunks", ) + convert_parser.add_argument( + "--dask-mode", + type=str, + default="threads", + choices=["threads", "processes", "single-threaded"], + help="Local Dask execution mode (default: threads).", + ) + convert_parser.add_argument( + "--dask-workers", + type=int, + default=4, + help="Number of Dask workers (default: 4).", + ) + convert_parser.add_argument( + "--dask-threads-per-worker", + type=int, + default=1, + help="Threads per worker (default: 1).", + ) + convert_parser.add_argument( + "--dask-perf-html", + type=str, + help="Write a Dask performance report HTML to this path (local only).", + ) + convert_parser.add_argument( + "--metrics", + action="store_true", + default=True, + help="Emit structured conversion metrics JSON (default: on). Use --no-metrics to disable.", + ) + convert_parser.add_argument( + "--no-metrics", + dest="metrics", + action="store_false", + help="Disable metrics emission.", + ) convert_parser.set_defaults(func=convert_command) # Info command diff --git a/src/eopf_geozarr/metrics.py b/src/eopf_geozarr/metrics.py new file mode 100644 index 0000000..fc4ae73 --- /dev/null +++ b/src/eopf_geozarr/metrics.py @@ -0,0 +1,247 @@ +from __future__ import annotations + +import json +import os +import platform +import sys +from contextlib import contextmanager +from dataclasses import dataclass, field +from datetime import datetime, timezone +from pathlib import Path +from time import perf_counter +from typing import Any, Dict, Iterator, List, Optional + +import xarray as xr + + +@dataclass +class MetricsRecorder: + schema_version: str = "convert_metrics/v1" + run_id: str = field( + default_factory=lambda: datetime.now(timezone.utc).strftime("%Y%m%d-%H%M%S") + ) + attempt: int = 1 + started_at: str = field( + default_factory=lambda: datetime.now(timezone.utc) + .isoformat() + .replace("+00:00", "Z") + ) + steps: List[Dict[str, Any]] = field(default_factory=list) + input_info: Dict[str, Any] = field(default_factory=dict) + output_info: Dict[str, Any] = field(default_factory=dict) + environment: Dict[str, Any] = field(default_factory=dict) + ended_at: Optional[str] = None + + def set_input( + self, + *, + source_uri: str, + profile: Optional[str], + groups: List[str], + dask: Dict[str, Any], + ) -> None: + self.input_info = { + "source_uri": source_uri, + "profile": profile, + "groups": groups, + "dask": dask, + } + + def set_environment(self) -> None: + def _get_version(pkg: str) -> Optional[str]: + try: + import importlib.metadata as importlib_metadata + + return importlib_metadata.version(pkg) + except Exception: + return None + + self.environment = { + "python": sys.version.split()[0], + "platform": platform.platform(), + "packages": { + "eopf_geozarr": _get_version("eopf-geozarr") + or _get_version("eopf_geozarr"), + "xarray": _get_version("xarray"), + "zarr": _get_version("zarr"), + "dask": _get_version("dask"), + "fsspec": _get_version("fsspec"), + }, + "num_cpus": os.cpu_count(), + } + + @contextmanager + def time_step(self, name: str) -> Iterator[None]: + t0 = perf_counter() + try: + yield + finally: + dt = perf_counter() - t0 + self.steps.append({"name": name, "duration_s": dt}) + + def build_output_summary(self, output_path: str) -> None: + info: Dict[str, Any] = { + "store_uri": output_path, + "store_total_bytes": None, + "per_variable": [], + "multiscales": [], + "metadata": {"consolidated": None}, + } + + # Size (local only) + try: + op = Path(output_path) + if op.exists(): + total = 0 + for p in op.rglob("*"): + if p.is_file(): + try: + total += p.stat().st_size + except Exception: + pass + info["store_total_bytes"] = total + except Exception: + pass + + # Structure via xarray datatree (works for local and remote) + try: + dt = xr.open_datatree(str(output_path), engine="zarr") + # consolidated metadata flag (best effort) + try: + info["metadata"]["consolidated"] = getattr(dt, "_consolidated", None) + except Exception: + info["metadata"]["consolidated"] = None + + # walk groups and variables + def _walk(tree: xr.DataTree, prefix: str = "") -> None: + if hasattr(tree, "data_vars") and tree.data_vars: + for var_name, da in tree.data_vars.items(): + chunks = None + try: + enc = getattr(da, "encoding", {}) or {} + ch = enc.get("chunks") + if isinstance(ch, (list, tuple)): + chunks = list(ch) + except Exception: + pass + info["per_variable"].append( + { + "path": f"{prefix}/{var_name}" if prefix else var_name, + "shape": list(da.shape), + "chunks": chunks, + "dtype": str(da.dtype), + "nchunks": None, # unknown without scanning zarr metadata + "compressor": None, + } + ) + for name, child in (tree.children or {}).items(): + new_prefix = f"{prefix}/{name}" if prefix else name + _walk(child, new_prefix) + + _walk(dt) + except Exception: + pass + + # Enrich compressor/chunk info via zarr (best-effort; works with local and remote stores) + try: + import zarr + + def _walk_z(g: Any, prefix: str = "") -> None: + try: + # arrays() + for name, arr in g.arrays(): + path = f"{prefix}/{name}" if prefix else name + comp_name = None + comp_cfg = None + try: + comp = getattr(arr, "compressor", None) + if comp is not None: + # numcodecs or blosc-like compressors + comp_name = getattr(comp, "codec_id", None) or getattr( + comp, "id", None + ) + comp_cfg = getattr(comp, "get_config", lambda: None)() + except Exception: + pass + # merge + merged = False + for rec in info["per_variable"]: + if rec.get("path") == path: + rec["compressor"] = { + "name": comp_name, + "config": comp_cfg, + } + try: + rec["chunks"] = ( + list(arr.chunks) + if getattr(arr, "chunks", None) + else rec.get("chunks") + ) + except Exception: + pass + try: + rec["dtype"] = str(arr.dtype) + except Exception: + pass + merged = True + break + if not merged: + try: + info["per_variable"].append( + { + "path": path, + "shape": list(arr.shape), + "chunks": list(arr.chunks) + if getattr(arr, "chunks", None) + else None, + "dtype": str(arr.dtype), + "nchunks": None, + "compressor": { + "name": comp_name, + "config": comp_cfg, + }, + } + ) + except Exception: + pass + # groups() + for name, grp in g.groups(): + new_prefix = f"{prefix}/{name}" if prefix else name + _walk_z(grp, new_prefix) + except Exception: + pass + + z = zarr.open_group(str(output_path), mode="r") + _walk_z(z) + except Exception: + pass + + self.output_info = info + + def finalize(self, status: str, exception: Optional[str] = None) -> Dict[str, Any]: + self.ended_at = datetime.now(timezone.utc).isoformat().replace("+00:00", "Z") + wall = 0.0 + try: + wall = sum(float(s.get("duration_s", 0.0)) for s in self.steps) + except Exception: + wall = 0.0 + return { + "schema_version": self.schema_version, + "run": { + "run_id": self.run_id, + "attempt": self.attempt, + "started_at": self.started_at, + "ended_at": self.ended_at, + "status": status, + "exception": exception, + }, + "input": self.input_info, + "output": self.output_info, + "performance": {"wall_clock_s": wall, "steps": self.steps}, + "environment": self.environment, + } + + @staticmethod + def write_json(path: Path, payload: Dict[str, Any]) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + path.write_text(json.dumps(payload, indent=2)) diff --git a/tools/metrics_summary.py b/tools/metrics_summary.py new file mode 100644 index 0000000..6782bae --- /dev/null +++ b/tools/metrics_summary.py @@ -0,0 +1,81 @@ +#!/usr/bin/env python3 +""" +Aggregate conversion run summaries into a CSV for quick benchmarking. + +Usage: + uv run python tools/metrics_summary.py --root ./out --output ./out/metrics.csv +""" + +from __future__ import annotations + +import argparse +import csv +import json +from pathlib import Path +from typing import Any, Dict, List + + +def find_run_summaries(root: Path) -> List[Path]: + return list(root.rglob("run_summary.json")) + + +def flatten_record(path: Path, doc: Dict[str, Any]) -> Dict[str, Any]: + run = doc.get("run", {}) + inp = doc.get("input", {}) + out = doc.get("output", {}) + perf = doc.get("performance", {}) + env = doc.get("environment", {}) + + return { + "file": str(path), + "run_id": run.get("run_id"), + "status": run.get("status"), + "source_uri": inp.get("source_uri"), + "groups": ",".join(inp.get("groups", []) or []), + "dask_mode": (inp.get("dask") or {}).get("mode"), + "dask_workers": (inp.get("dask") or {}).get("workers"), + "dask_threads_per_worker": (inp.get("dask") or {}).get("threads_per_worker"), + "store_uri": out.get("store_uri"), + "store_total_bytes": out.get("store_total_bytes"), + "wall_clock_s": perf.get("wall_clock_s"), + "python": env.get("python"), + "xarray": (env.get("packages") or {}).get("xarray"), + "zarr": (env.get("packages") or {}).get("zarr"), + "dask": (env.get("packages") or {}).get("dask"), + } + + 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a/docs/pipeline-integration-recommendations.md +++ /dev/null @@ -1,74 +0,0 @@ -# Pipeline Integration Recommendations - -This document lists enhancements observed while integrating `eopf-geozarr` into an Argo-based batch conversion pipeline (data-model-pipeline). They are candidates for upstream inclusion or API refinement. - -## 1. Output Prefix Expansion -**Current (pipeline)**: Wrapper detects `output_zarr` ending with `/` and appends `_geozarr.zarr` derived from the input STAC/Zarr URL. -**Recommendation**: Support `--output-prefix` OR accept a trailing slash on positional `output_path` and perform the expansion internally (emitting the resolved final path). Add a log line: `Resolved output store: s3://.../S2A_..._geozarr.zarr`. - -## 2. Group Existence Validation (Pre-flight) -**Current (pipeline)**: `validate_params_groups.py` inspects filesystem structure (presence of `.zarray` / `.zgroup`). -**Recommendation**: Native CLI flag `--validate-groups` to prune or fail fast when groups don’t exist. Modes: -- `--validate-groups=warn` (default): drop missing, report. -- `--validate-groups=error`: abort if any missing. -Emit JSON or structured summary when `--verbose`. - -## 3. Profiles (WOZ Profiles) -**Current (pipeline)**: External JSON profile expansion before calling CLI. -**Recommendation**: Provide `--profile ` in CLI mapping to preset groups + chunk params. Add `eopf-geozarr profile list` / `profile show ` subcommands. Keep external mechanism as fallback. - -## 4. Compressor Handling -**Current**: Template attempted to pass `--compressor`; CLI does not expose codec choice. -**Recommendation**: If codec selection is desired, add `--compressor ` now (zstd, lz4, blosc) with validation; else document fixed default explicitly to avoid confusion. - -## 5. CRS Groups Convenience -**Current**: `--crs-groups` optional list. -**Recommendation**: Discover candidate groups automatically (search for geometry-like datasets) unless `--crs-groups` provided (override). Provide `eopf-geozarr info --crs-scan` to preview. - -## 6. Dask Cluster Ergonomics -**Current**: `--dask-cluster` toggles local cluster with no feedback. -**Recommendation**: Print cluster dashboard URL (if available) and add `--dask-workers N` for quick scaling. - -## 7. Structured Logging / Run Metadata -**Current**: Plain prints; pipeline scrapes logs. -**Recommendation**: Optional `--run-metadata ` to write machine-readable summary: inputs, resolved groups, timings, warnings. Eases automation and reproducibility. - -## 8. Validation Command Enhancements -**Current**: `validate` skeleton present but incomplete. -**Recommendation**: -- Implement spec checks (multiscales, attributes, chunk shape policy). -- Exit code non-zero on *hard* failures, zero with warnings for soft issues. -- `--format json` for programmatic consumption. - -## 9. HTML Tree Generation -**Current**: `_generate_html_output` scaffold incomplete. -**Recommendation**: Finish implementation; integrate with `info --html-output`. Provide minimal inline CSS (already drafted) and optional `--open-browser` flag. - -## 10. Progress Reporting -**Recommendation**: Emit periodic progress per group: `group=/measurements/r10m scale=2 written=...MB elapsed=...s` to assist monitoring in batch workflows. - -## 11. Retry / Resumability -**Recommendation**: Add `--resume` to skip already existing multiscale levels if output store partially present. - -## 12. Exit Codes (Contract) -Document exit code meanings: -- 0 success -- 2 validation (input) error -- 3 group resolution failure -- 4 conversion runtime error - -## 13. Environment Variable Overrides -Allow `EOZ_DEFAULT_PROFILE`, `EOZ_OUTPUT_PREFIX` env vars as implicit defaults (still overridden by flags). - -## 14. Example Invocation Block in README -Provide ready-to-copy examples for Sentinel-2 & Sentinel-1 including polarization groups when Phase 1 logic is public. - ---- -**Next Steps (Suggested Order)** -1. Implement output prefix expansion (low risk, high UX win) -2. Group validation flag (prevents silent empty writes) -3. Finish validate + info HTML features -4. Add structured run metadata output -5. Introduce profiles subcommands then deprecate external expansion path over time - -Feedback welcome—pipeline experience will continue surfacing actionable deltas. diff --git a/src/eopf_geozarr/metrics.py b/src/eopf_geozarr/metrics.py index fc4ae73..45bc318 100644 --- a/src/eopf_geozarr/metrics.py +++ b/src/eopf_geozarr/metrics.py @@ -88,19 +88,41 @@ def build_output_summary(self, output_path: str) -> None: "metadata": {"consolidated": None}, } - # Size (local only) + # Size (local or remote via fsspec) try: + total = None op = Path(output_path) if op.exists(): - total = 0 + acc = 0 for p in op.rglob("*"): if p.is_file(): try: - total += p.stat().st_size + acc += p.stat().st_size except Exception: pass - info["store_total_bytes"] = total + total = acc + else: + try: + import fsspec + + fs, root = fsspec.core.url_to_fs(output_path) + # find() returns all file-like entries under the root + acc = 0 + for path in fs.find(root): + try: + info_dict = fs.info(path) + size = info_dict.get("size") + if isinstance(size, (int, float)): + acc += int(size) + except Exception: + # ignore paths that fail info() + pass + total = acc + except Exception: + total = None + info["store_total_bytes"] = total except Exception: + # best-effort only pass # Structure via xarray datatree (works for local and remote) @@ -218,6 +240,54 @@ def _walk_z(g: Any, prefix: str = "") -> None: self.output_info = info + @staticmethod + def _schema() -> Dict[str, Any]: + return { + "$schema": "http://json-schema.org/draft-07/schema#", + "type": "object", + "required": [ + "schema_version", + "run", + "input", + "output", + "performance", + "environment", + ], + "properties": { + "schema_version": {"type": "string"}, + "run": { + "type": "object", + "required": ["run_id", "status", "started_at"], + "properties": { + "run_id": {"type": "string"}, + "attempt": {"type": ["integer", "null"]}, + "started_at": {"type": "string"}, + "ended_at": {"type": ["string", "null"]}, + "status": {"type": "string"}, + "exception": {"type": ["string", "null"]}, + }, + }, + "input": {"type": "object"}, + "output": {"type": "object"}, + "performance": {"type": "object"}, + "environment": {"type": "object"}, + }, + "additionalProperties": True, + } + + @staticmethod + def validate_payload(payload: Dict[str, Any]) -> List[str]: + try: + # Optional validation if jsonschema is available + import jsonschema + + validator = jsonschema.Draft7Validator(MetricsRecorder._schema()) + errors = [e.message for e in validator.iter_errors(payload)] + return errors + except Exception: + # If jsonschema not installed or validation fails unexpectedly, skip + return [] + def finalize(self, status: str, exception: Optional[str] = None) -> Dict[str, Any]: self.ended_at = datetime.now(timezone.utc).isoformat().replace("+00:00", "Z") wall = 0.0 From 7331bcb0f6be7bb191319d1b3aa520e6642c3094 Mon Sep 17 00:00:00 2001 From: Wietze Date: Mon, 15 Sep 2025 13:09:20 -0400 Subject: [PATCH 3/9] feat(cli): output prefix expansion, group validation, optional run-metadata writing --- src/eopf_geozarr/cli.py | 251 ++++++++++++++++++++++++++++++++++++---- 1 file changed, 228 insertions(+), 23 deletions(-) diff --git a/src/eopf_geozarr/cli.py b/src/eopf_geozarr/cli.py index 29d82a9..a00e7d2 100644 --- a/src/eopf_geozarr/cli.py +++ b/src/eopf_geozarr/cli.py @@ -11,6 +11,7 @@ from pathlib import Path from time import perf_counter from typing import Any, Optional +from urllib.parse import urlparse import xarray as xr @@ -175,6 +176,62 @@ def _summarize_dask_metrics( return base +def _derive_item_id_from_input_path(path: str) -> str: + """Best-effort item id from local path or URL.""" + try: + if path.startswith(("http://", "https://", "s3://", "gs://")): + parsed = urlparse(path) + name = Path(parsed.path.rstrip("/")).name + else: + name = Path(str(path).rstrip("/")).name + if name.endswith(".zarr"): + name = name[: -len(".zarr")] + return name or "dataset" + except Exception: + return "dataset" + + +def _resolve_output_prefix(prefix: str, item_id: str) -> str: + target_name = f"{item_id}_geozarr.zarr" + if prefix.startswith(("http://", "https://", "s3://", "gs://")): + if not prefix.endswith("/"): + prefix = prefix + "/" + return prefix + target_name + return str(Path(prefix) / target_name) + + +def _has_group(tree: xr.DataTree, path: str) -> bool: + try: + parts = [p for p in str(path).strip("/").split("/") if p] + node = tree + for seg in parts: + children = getattr(node, "children", {}) or {} + if seg not in children: + return False + node = children[seg] + return True + except Exception: + return False + + +def _local_path_exists(p: str) -> bool: + try: + return Path(p).exists() + except Exception: + return False + + +def _remove_local_tree(p: str) -> bool: + try: + import shutil + + if Path(p).exists(): + shutil.rmtree(p) + return True + except Exception: + return False + + def convert_command(args: argparse.Namespace) -> None: """Convert EOPF dataset to GeoZarr compliant format. @@ -213,8 +270,27 @@ def convert_command(args: argparse.Namespace) -> None: sys.exit(1) input_path = str(input_path) + # Fast path: just list groups and exit + if getattr(args, "list_groups", False): + storage_options = get_storage_options(input_path) + dt = xr.open_datatree( + str(input_path), + engine="zarr", + chunks="auto", + storage_options=storage_options, + ) + print("Available groups:") + for group_name in dt.children: + print(f" - {group_name}") + return + # Handle output path validation output_path_str = args.output_path + # Expand trailing-slash prefix to a concrete store + if output_path_str.endswith("/"): + item_id = _derive_item_id_from_input_path(str(input_path)) + output_path_str = _resolve_output_prefix(output_path_str, item_id) + print(f"Resolved output store: {output_path_str}") if is_s3_path(output_path_str): # S3 path - validate S3 access print("🔍 Validating S3 access...") @@ -247,14 +323,17 @@ def convert_command(args: argparse.Namespace) -> None: else: # Local path - create directory if it doesn't exist output_path = Path(output_path_str) - output_path.parent.mkdir(parents=True, exist_ok=True) + # In dry-run mode, don't create anything + if not getattr(args, "dry_run", False): + output_path.parent.mkdir(parents=True, exist_ok=True) output_path = str(output_path) - # Prepare debug dir for metrics - debug_dir = Path(output_path) / "debug" - try: - debug_dir.mkdir(parents=True, exist_ok=True) - except Exception: - debug_dir = None + # Prepare debug dir for metrics (skip in dry-run) + if not getattr(args, "dry_run", False): + debug_dir = Path(output_path) / "debug" + try: + debug_dir.mkdir(parents=True, exist_ok=True) + except Exception: + debug_dir = None if args.verbose: print(f"Loading EOPF dataset from: {input_path}") @@ -268,14 +347,48 @@ def convert_command(args: argparse.Namespace) -> None: # Load the EOPF DataTree with appropriate storage options print("Loading EOPF dataset...") storage_options = get_storage_options(input_path) - # Metrics setup + # Metrics setup (environment first; set_input after group validation) if getattr(args, "metrics", True): metrics = MetricsRecorder() metrics.set_environment() + + with metrics.time_step("open_input") if metrics else nullcontext(): + dt = xr.open_datatree( + str(input_path), + engine="zarr", + chunks="auto", + storage_options=storage_options, + ) + + # Validate/prune groups if requested + groups_effective = list(getattr(args, "groups", []) or []) + validate_mode = getattr(args, "validate_groups", None) + missing: list[str] = [] + if validate_mode in {"warn", "error"} and groups_effective: + existing: list[str] = [] + for g in groups_effective: + if _has_group(dt, g): + existing.append(g) + else: + missing.append(g) + if missing: + msg = f"Groups not found: {', '.join(missing)}" + if validate_mode == "error": + print(f"❌ {msg}") + sys.exit(3) + else: + print(f"⚠️ {msg}; proceeding with remaining groups") + groups_effective = existing + if not groups_effective: + print("❌ No valid groups to convert after validation") + sys.exit(3) + + # Now that groups are finalized, set input metadata for metrics + if metrics is not None: metrics.set_input( source_uri=str(input_path), profile=None, - groups=args.groups, + groups=groups_effective, dask={ "enabled": bool(dask_client is not None), "mode": getattr(args, "dask_mode", None), @@ -286,19 +399,60 @@ def convert_command(args: argparse.Namespace) -> None: }, ) - with metrics.time_step("open_input") if metrics else nullcontext(): - dt = xr.open_datatree( - str(input_path), - engine="zarr", - chunks="auto", - storage_options=storage_options, - ) + # Overwrite policy handling (local only) + overwrite = getattr(args, "overwrite", "fail") + is_remote = is_s3_path(output_path) + if not is_remote: + exists = _local_path_exists(output_path) + if exists: + if overwrite == "fail": + print( + f"❌ Output already exists and overwrite policy is 'fail': {output_path}" + ) + sys.exit(2) + if overwrite == "skip": + print(f"⏭️ Output exists; skipping as per policy: {output_path}") + return + if overwrite == "replace": + if getattr(args, "dry_run", False): + print( + f"🧪 Dry-run: would remove existing output: {output_path}" + ) + else: + ok = _remove_local_tree(output_path) + if not ok: + print(f"❌ Failed to remove existing output: {output_path}") + sys.exit(2) + # merge: do nothing + else: + if overwrite == "fail": + print( + "ℹ️ Remote output existence not checked; 'fail' policy may not prevent overwrite." + ) + elif overwrite == "replace": + print( + "⚠️ 'replace' is not implemented for remote outputs; proceeding may overwrite keys." + ) + + # Print dry-run plan and exit early + if getattr(args, "dry_run", False): + print("\nDry-run plan:") + print("============") + print(f"Input: {input_path}") + print(f"Output: {output_path}") + print(f"Groups: {groups_effective}") + print(f"Overwrite policy: {overwrite}") + print(f"Dask: {'on' if dask_client is not None else 'off'}") + print("No data will be written.") + return if args.verbose: print(f"Loaded DataTree with {len(dt.children)} groups") print("Available groups:") for group_name in dt.children: print(f" - {group_name}") + if missing: + print(f"After validation, converting groups: {groups_effective}") # Convert to GeoZarr compliant format print("Converting to GeoZarr compliant format...") @@ -330,7 +484,7 @@ def convert_command(args: argparse.Namespace) -> None: with metrics.time_step("convert") if metrics else nullcontext(): dt_geozarr = create_geozarr_dataset( dt_input=dt, - groups=args.groups, + groups=groups_effective, output_path=output_path, spatial_chunk=args.spatial_chunk, min_dimension=args.min_dimension, @@ -344,7 +498,7 @@ def convert_command(args: argparse.Namespace) -> None: with metrics.time_step("convert") if metrics else nullcontext(): dt_geozarr = create_geozarr_dataset( dt_input=dt, - groups=args.groups, + groups=groups_effective, output_path=output_path, spatial_chunk=args.spatial_chunk, min_dimension=args.min_dimension, @@ -381,7 +535,7 @@ def convert_command(args: argparse.Namespace) -> None: ), "perf_report": getattr(args, "dask_perf_html", None), "wall_clock_s": wall_clock if dask_client is not None else None, - "groups": args.groups, + "groups": groups_effective, "spatial_chunk": args.spatial_chunk, "min_dimension": args.min_dimension, "tile_width": args.tile_width, @@ -399,6 +553,17 @@ def convert_command(args: argparse.Namespace) -> None: MetricsRecorder.write_json(run_summary, payload) if args.verbose: print(f"🧾 Wrote metrics: {run_summary}") + # Optional external run-metadata path + if getattr(args, "run_metadata", None): + try: + outp = Path(args.run_metadata) + outp.parent.mkdir(parents=True, exist_ok=True) + MetricsRecorder.write_json(outp, payload) + if args.verbose: + print(f"🧾 Wrote run metadata: {outp}") + except Exception as _e: + if args.verbose: + print(f"(debug) could not write run-metadata: {_e}") except Exception as _exc: if args.verbose: print(f"(debug) could not write run summary: {_exc}") @@ -424,10 +589,22 @@ def convert_command(args: argparse.Namespace) -> None: try: if metrics and debug_dir is not None and getattr(args, "metrics", True): payload = metrics.finalize(status="error", exception=error_msg) - run_summary = Path(args.output_path) / "debug" / "run_summary.json" - MetricsRecorder.write_json(run_summary, payload) - if args.verbose: - print(f"🧾 Wrote failure metrics: {run_summary}") + run_summary = Path(output_path) / "debug" / "run_summary.json" + try: + MetricsRecorder.write_json(run_summary, payload) + if args.verbose: + print(f"🧾 Wrote failure metrics: {run_summary}") + except Exception: + pass + if getattr(args, "run_metadata", None): + try: + outp = Path(args.run_metadata) + outp.parent.mkdir(parents=True, exist_ok=True) + MetricsRecorder.write_json(outp, payload) + if args.verbose: + print(f"🧾 Wrote run metadata (error): {outp}") + except Exception: + pass except Exception: pass sys.exit(1) @@ -1414,6 +1591,34 @@ def create_parser() -> argparse.ArgumentParser: action="store_false", help="Disable metrics emission.", ) + convert_parser.add_argument( + "--list-groups", + action="store_true", + help="List available groups in the input and exit.", + ) + convert_parser.add_argument( + "--dry-run", + action="store_true", + help="Validate I/O, resolve groups and output, and print the plan without writing.", + ) + convert_parser.add_argument( + "--overwrite", + type=str, + choices=["fail", "skip", "replace", "merge"], + default="fail", + help="Behavior when output exists (local): fail, skip, replace, or merge (default: fail).", + ) + convert_parser.add_argument( + "--validate-groups", + type=str, + choices=["warn", "error"], + help="Validate requested groups against input; warn to prune, error to abort.", + ) + convert_parser.add_argument( + "--run-metadata", + type=str, + help="Also write finalized metrics payload to this JSON path.", + ) convert_parser.set_defaults(func=convert_command) # Info command From ffd2dce3b04e0941c99ceaedfa9ef7a5d424988f Mon Sep 17 00:00:00 2001 From: Wietze Date: Tue, 16 Sep 2025 17:20:26 -0400 Subject: [PATCH 4/9] chore: remove legacy prototype Makefile and obsolete Argo conversion manifests --- .gitignore | 3 +- Dockerfile | 76 ------- Makefile | 156 -------------- README.md | 395 +++++++--------------------------- docs/installation.md | 12 +- geozarr-convert-template.yaml | 172 --------------- geozarr-convert.yaml | 45 ---- pyproject.toml | 8 +- 8 files changed, 88 insertions(+), 779 deletions(-) delete mode 100644 Dockerfile delete mode 100644 Makefile delete mode 100644 geozarr-convert-template.yaml delete mode 100644 geozarr-convert.yaml diff --git a/.gitignore b/.gitignore index 810b639..66559ab 100644 --- a/.gitignore +++ b/.gitignore @@ -216,4 +216,5 @@ uv.lock .DS_§tore out runs -*.gz \ No newline at end of file +*.gz +.cbuild/ \ No newline at end of file diff --git a/Dockerfile b/Dockerfile deleted file mode 100644 index 7d21e19..0000000 --- a/Dockerfile +++ /dev/null @@ -1,76 +0,0 @@ -# ========= stage: builder ========= -FROM python:3.11-slim AS builder - -ENV PYTHONUNBUFFERED="1" \ - PIP_NO_CACHE_DIR="1" - -# Build arg: 0=wheel-only (small; build as linux/amd64), 1=portable (adds GDAL/PROJ & toolchain) -ARG PORTABLE_BUILD=0 - -# Base OS deps -RUN apt-get update && DEBIAN_FRONTEND=noninteractive apt-get install -y --no-install-recommends \ - ca-certificates curl git \ - && rm -rf /var/lib/apt/lists/* - -WORKDIR /app - -# uv + modern pip -COPY pyproject.toml uv.lock ./ -RUN pip install --no-cache-dir -U pip \ - && pip install --no-cache-dir uv \ - && uv --version - -# ---- Export ONLY third-party runtime deps (no hashes). We filter out any editable/self lines. -# NOTE: We export BEFORE copying src/ to avoid uv deciding this is a local edit; still filter to be safe. -RUN set -euo pipefail; \ - uv export --no-group dev --no-group test --format=requirements-txt --no-hashes -o /tmp/req.raw.txt; \ - awk ' \ - BEGIN{IGNORECASE=1} \ - # drop editable flags - /^-e[[:space:]]/ || /^--editable[[:space:]]/ {next} \ - # drop local/self refs - /@ file:/ || /file:\/\// {next} \ - # drop our own package if present - /^eopf-geozarr([[:space:]]|==|$)/ {next} \ - # drop comments/blank lines - /^[[:space:]]*#/ || /^[[:space:]]*$/ {next} \ - {print} \ - ' /tmp/req.raw.txt > /tmp/requirements.txt; \ - echo "----- filtered requirements (head) -----"; \ - sed -n '1,80p' /tmp/requirements.txt - -# ---- If PORTABLE, add toolchain + GDAL/PROJ for sdist builds (arm64 etc.) -RUN if [ "$PORTABLE_BUILD" = "1" ]; then \ - apt-get update && DEBIAN_FRONTEND=noninteractive apt-get install -y --no-install-recommends \ - build-essential gdal-bin libgdal-dev proj-bin libproj-dev \ - && rm -rf /var/lib/apt/lists/* ; \ - fi - -# ---- Install third-party deps, then your package (no re-resolve of deps) -RUN if [ "$PORTABLE_BUILD" = "1" ]; then \ - pip install --no-cache-dir -r /tmp/requirements.txt ; \ - else \ - PIP_ONLY_BINARY=":all:" pip install --no-cache-dir --prefer-binary -r /tmp/requirements.txt ; \ - fi - -# Now copy source and install the project itself without deps (pure python install) -COPY src ./src -RUN pip install --no-cache-dir --no-deps . - -# Optional: byte-compile -RUN python -m compileall -q /usr/local/lib/python3.11/site-packages || true - -# ========= stage: runtime ========= -FROM python:3.11-slim AS runtime -ENV PYTHONUNBUFFERED="1" - -# Tiny libs manylinux wheels often need -RUN apt-get update && DEBIAN_FRONTEND=noninteractive apt-get install -y --no-install-recommends \ - libstdc++6 libgomp1 \ - && rm -rf /var/lib/apt/lists/* - -WORKDIR /app -COPY --from=builder /usr/local /usr/local - -# Argo Script template supplies the command -CMD ["python", "-V"] diff --git a/Makefile b/Makefile deleted file mode 100644 index f326be7..0000000 --- a/Makefile +++ /dev/null @@ -1,156 +0,0 @@ -# ===== Config ===== -IMAGE ?= eopf-geozarr:dev -NAMESPACE ?= argo # Kubernetes namespace where Argo runs -TPL ?= geozarr-convert-template.yaml -PARAMS ?= params.json -CLUSTER ?= k3s-default - -# Runtime param overrides (env > PARAMS file) -STAC_URL ?= -OUTPUT_ZARR ?= -GROUPS ?= - -# Abbrev: WF = Workflow name; PVC = PersistentVolumeClaim (-outpvc) - -.PHONY: build load-k3d load-minikube argo-install template apply \ - submit submit-cli submit-api status latest logs-save clean \ - _ensure-dirs fetch-tar run clean-pvc - -# Build the image locally -# make build -> WHEEL mode (small), builds linux/amd64 -# make build PORTABLE=1 -> PORTABLE mode (bigger), builds for native arch -build: - @if [ "$(PORTABLE)" = "1" ]; then \ - echo "==> Building PORTABLE image for native platform (allows source builds)"; \ - docker build \ - --build-arg PORTABLE_BUILD=1 \ - -t $(IMAGE) . ; \ - else \ - echo "==> Building WHEEL image for linux/amd64 (prebuilt wheels)"; \ - docker buildx build --platform=linux/amd64 \ - --build-arg PORTABLE_BUILD=0 \ - -t $(IMAGE) --load . ; \ - fi - -# Load image into k3d’s containerd (dev clusters) -load-k3d: - k3d image import $(IMAGE) --cluster $(CLUSTER) || \ - (docker save $(IMAGE) | docker exec -i $$(docker ps --format '{{.Names}}' | grep $(CLUSTER)-server-0) ctr -n k8s.io images import -) - -# Build the image inside minikube’s Docker -load-minikube: - eval "$$(minikube docker-env)"; docker build -t $(IMAGE) . - -# Install Argo Workflows (v3.7.1) into $(NAMESPACE) -argo-install: - kubectl create ns $(NAMESPACE) 2>/dev/null || true - kubectl apply -n $(NAMESPACE) -f https://github.com/argoproj/argo-workflows/releases/download/v3.7.1/install.yaml - kubectl -n $(NAMESPACE) rollout status deploy/workflow-controller - kubectl -n $(NAMESPACE) rollout status deploy/argo-server - -# Apply (or update) the WorkflowTemplate -template: - kubectl -n $(NAMESPACE) apply -f $(TPL) - kubectl -n $(NAMESPACE) get workflowtemplate geozarr-convert - -# Build + load + install + template (one shot) -apply: build load-k3d argo-install template - -# Submit via CLI (uses env overrides, else PARAMS file) -submit: _ensure-dirs - @STAC="$${STAC_URL:-$$(jq -r '.arguments.parameters[] | select(.name=="stac_url").value' $(PARAMS))}"; \ - OUT="$${OUTPUT_ZARR:-$$(jq -r '.arguments.parameters[] | select(.name=="output_zarr").value' $(PARAMS))}"; \ - GRP="$${GROUPS:-$$(jq -r '.arguments.parameters[] | select(.name=="groups").value' $(PARAMS))}"; \ - echo "Submitting:"; echo " stac_url=$$STAC"; echo " output_zarr=$$OUT"; echo " groups=$$GRP"; \ - WF=$$(argo submit -n $(NAMESPACE) --from workflowtemplate/geozarr-convert \ - -p stac_url="$$STAC" -p output_zarr="$$OUT" -p groups="$$GRP" -o name); \ - TSTAMP=$$(date +%Y%m%d-%H%M%S); \ - argo get -n $(NAMESPACE) $$WF -o json > runs/$${TSTAMP}-$${WF##*/}.json; \ - argo get -n $(NAMESPACE) $$WF --output wide | tee runs/$${TSTAMP}-$${WF##*/}.summary.txt; \ - echo "Workflow: $$WF" - -# Submit via CLI (PARAMS file only, no env overrides) -submit-cli: _ensure-dirs - @WF=$$(argo submit -n $(NAMESPACE) --from workflowtemplate/geozarr-convert \ - -p stac_url="$$(jq -r '.arguments.parameters[] | select(.name=="stac_url").value' $(PARAMS))" \ - -p output_zarr="$$(jq -r '.arguments.parameters[] | select(.name=="output_zarr").value' $(PARAMS))" \ - -p groups="$$(jq -r '.arguments.parameters[] | select(.name=="groups").value' $(PARAMS))" \ - -o name); \ - TSTAMP=$$(date +%Y%m%d-%H%M%S); \ - argo get -n $(NAMESPACE) $$WF -o json > runs/$${TSTAMP}-$${WF##*/}.json; \ - argo get -n $(NAMESPACE) $$WF --output wide | tee runs/$${TSTAMP}-$${WF##*/}.summary.txt; \ - echo "Workflow: $$WF" - -# Submit via Argo Server HTTP (dev port-forward, no token) -submit-api: _ensure-dirs - kubectl -n $(NAMESPACE) port-forward svc/argo-server 2746:2746 >/dev/null 2>&1 & echo $$! > .pf.pid - sleep 1 - curl -s -H 'Content-Type: application/json' \ - --data-binary @$(PARAMS) \ - http://localhost:2746/api/v1/workflows/$(NAMESPACE)/submit \ - | tee runs/submit-response.json | jq . >/dev/null || \ - (echo "Non-JSON response (see runs/submit-response.json)"; exit 1) - -@[ -f .pf.pid ] && kill $$(cat .pf.pid) 2>/dev/null || true - -@rm -f .pf.pid - -# Inspect -status: - argo list -n $(NAMESPACE); echo; kubectl -n $(NAMESPACE) get wf - -latest: - argo get -n $(NAMESPACE) @latest --output wide - -logs-save: _ensure-dirs - @WF=$$(argo list -n $(NAMESPACE) --output name | tail -1); \ - TSTAMP=$$(date +%Y%m%d-%H%M%S); \ - argo logs -n $(NAMESPACE) $$WF -c main > logs/$${TSTAMP}-$${WF##*/}.log; \ - echo "Wrote logs/$${TSTAMP}-$${WF##*/}.log" - -# Delete all workflows + completed pods -clean: - argo delete -n $(NAMESPACE) --all || true - kubectl -n $(NAMESPACE) delete pod -l workflows.argoproj.io/completed=true --force --grace-period=0 || true - -_ensure-dirs: - @mkdir -p runs logs - -# Fetch from PVC: copy tarball, unpack into runs//, pull any extra files on /outputs -fetch-tar: _ensure-dirs - @WF=$$(argo list -n $(NAMESPACE) --output name | tail -1 | sed 's#.*/##'); \ - PVC="$$WF-outpvc"; OUTDIR="runs/$$WF"; \ - echo "Workflow: $$WF"; echo "PVC: $$PVC"; mkdir -p $$OUTDIR; \ - kubectl -n $(NAMESPACE) delete pod fetch-$$WF --ignore-not-found >/dev/null 2>&1 || true; \ - cat <<'YAML' | sed "s/{{WF}}/$$WF/g" | sed "s/{{PVC}}/$$PVC/g" | kubectl -n $(NAMESPACE) apply -f - - apiVersion: v1 - kind: Pod - metadata: - name: fetch-{{WF}} - spec: - restartPolicy: Never - containers: - - name: fetch - image: busybox:1.36 - command: ["sh","-lc","sleep 600"] - volumeMounts: - - name: out - mountPath: /mnt/out - volumes: - - name: out - persistentVolumeClaim: - claimName: {{PVC}} - YAML - kubectl -n $(NAMESPACE) wait --for=condition=Ready pod/fetch-$$WF --timeout=60s - # Main artifact - kubectl -n $(NAMESPACE) cp fetch-$$WF:/mnt/out/geozarr.tar.gz $$OUTDIR/geozarr.tar.gz - tar -xzf $$OUTDIR/geozarr.tar.gz -C $$OUTDIR - # Copy any other files (e.g., dask-report.html) - kubectl -n $(NAMESPACE) cp fetch-$$WF:/mnt/out/. $$OUTDIR/ || true - kubectl -n $(NAMESPACE) delete pod fetch-$$WF --wait=false - @echo "Unpacked into $$OUTDIR/" - -# Convenience: build + load + template + submit + fetch -run: apply submit fetch-tar - -# Cleanup stray per-run PVCs (removes stored artifacts) -clean-pvc: - kubectl -n $(NAMESPACE) delete pvc -l workflows.argoproj.io/workflow 2>/dev/null || true diff --git a/README.md b/README.md index 44f3f6e..cca76db 100644 --- a/README.md +++ b/README.md @@ -1,380 +1,137 @@ # EOPF GeoZarr -GeoZarr compliant data model for EOPF (Earth Observation Processing Framework) datasets. +Turn EOPF datasets into a GeoZarr-style Zarr v3 store. Keep the data values intact and add standard geospatial metadata, multiscale overviews, and per-variable dimensions. -## Overview - -This library provides tools to convert EOPF datasets to GeoZarr-spec 0.4 compliant format while maintaining native projections and using /2 downsampling logic for multiscale support. - -## Key Features - -- **GeoZarr Specification Compliance**: Full compliance with GeoZarr spec 0.4 -- **Native CRS Preservation**: No reprojection to TMS, maintains original coordinate reference systems -- **Multiscale Support**: COG-style /2 downsampling with overview levels as children groups -- **CF Conventions**: Proper CF standard names and grid_mapping attributes -- **Robust Processing**: Band-by-band writing with validation and retry logic -- **S3 Support**: Direct output to Amazon S3 buckets with automatic credential validation -- **Parallel Processing**: Optional dask cluster support for parallel chunk processing -- **Chunk Alignment**: Automatic chunk alignment to prevent data corruption with dask - -## GeoZarr Compliance Features - -- `_ARRAY_DIMENSIONS` attributes on all arrays -- CF standard names for all variables -- `grid_mapping` attributes referencing CF grid_mapping variables -- `GeoTransform` attributes in grid_mapping variables -- Proper multiscales metadata structure -- Native CRS tile matrix sets +## Quick Start -## Installation +Install (uv): ```bash -pip install eopf-geozarr +uv sync --frozen +uv run eopf-geozarr --help ``` -For development: +Or pip: ```bash -git clone -cd eopf-geozarr -pip install -e ".[dev]" +pip install -e . ``` -## Quick Start - -### Command Line Interface - -After installation, you can use the `eopf-geozarr` command: - -```bash -# Convert EOPF dataset to GeoZarr format (local output) -eopf-geozarr convert input.zarr output.zarr - -# Convert EOPF dataset to GeoZarr format (S3 output) -eopf-geozarr convert input.zarr s3://my-bucket/path/to/output.zarr +## Workflows -# Convert with parallel processing using dask cluster -eopf-geozarr convert input.zarr output.zarr --dask-cluster +For Argo / batch orchestration use: https://github.com/EOPF-Explorer/data-model-pipeline -# Convert with dask cluster and verbose output -eopf-geozarr convert input.zarr output.zarr --dask-cluster --verbose +## Convert -# Get information about a dataset -eopf-geozarr info input.zarr - -# Validate GeoZarr compliance -eopf-geozarr validate output.zarr - -# Get help -eopf-geozarr --help -``` - -### S3 Support - -The library supports direct output to S3-compatible storage, including custom providers like OVH Cloud. Simply provide an S3 URL as the output path: +Remote → local: ```bash -# Convert to S3 -eopf-geozarr convert local_input.zarr s3://my-bucket/geozarr-data/output.zarr --verbose +uv run eopf-geozarr convert \ + "https://.../S2B_MSIL2A_... .zarr" \ + "/tmp/S2B_MSIL2A_..._geozarr.zarr" \ + --groups /measurements/reflectance \ + --verbose ``` -#### S3 Configuration +Notes: +- Parent groups auto-expand to leaf datasets. +- Overviews use /2 coarsening; multiscales live on parent groups. +- Defaults: Blosc Zstd, conservative chunking, metadata consolidation after write. -Before using S3 output, ensure your S3 credentials are configured: +## S3 -**For AWS S3:** +Env for S3/S3-compatible storage: ```bash -export AWS_ACCESS_KEY_ID=your_access_key -export AWS_SECRET_ACCESS_KEY=your_secret_key -export AWS_DEFAULT_REGION=us-east-1 +export AWS_ACCESS_KEY_ID=... +export AWS_SECRET_ACCESS_KEY=... +export AWS_REGION=eu-west-1 +# Custom endpoint (OVH, MinIO, etc.) +export AWS_ENDPOINT_URL=https://s3.your-endpoint.example ``` -**For OVH Cloud Object Storage:** +Write to S3: ```bash -export AWS_ACCESS_KEY_ID=your_ovh_access_key -export AWS_SECRET_ACCESS_KEY=your_ovh_secret_key -export AWS_DEFAULT_REGION=gra # or other OVH region -export AWS_ENDPOINT_URL=https://s3.gra.cloud.ovh.net # OVH endpoint +uv run eopf-geozarr convert \ + "https://.../S2B_MSIL2A_... .zarr" \ + "s3://your-bucket/path/S2B_MSIL2A_..._geozarr.zarr" \ + --groups /measurements/reflectance \ + --verbose ``` -**For other S3-compatible providers:** +## Info & Validate -```bash -export AWS_ACCESS_KEY_ID=your_access_key -export AWS_SECRET_ACCESS_KEY=your_secret_key -export AWS_DEFAULT_REGION=your_region -export AWS_ENDPOINT_URL=https://your-s3-endpoint.com -``` - -**Alternative: AWS CLI Configuration** +Summary: ```bash -aws configure -# Note: For custom endpoints, you'll still need to set AWS_ENDPOINT_URL +uv run eopf-geozarr info "/tmp/S2B_MSIL2A_..._geozarr.zarr" ``` -#### S3 Features - -- **Custom Endpoints**: Support for any S3-compatible storage (AWS, OVH Cloud, MinIO, etc.) -- **Automatic Validation**: The tool validates S3 access before starting conversion -- **Credential Detection**: Automatically detects and validates S3 credentials -- **Error Handling**: Provides helpful error messages for S3 configuration issues -- **Performance**: Optimized for S3 with proper chunking and retry logic - -### Parallel Processing with Dask - -The library supports parallel processing using dask clusters for improved performance on large datasets: +HTML report: ```bash -# Enable dask cluster for parallel processing -eopf-geozarr convert input.zarr output.zarr --dask-cluster - -# With verbose output to see cluster information -eopf-geozarr convert input.zarr output.zarr --dask-cluster --verbose -``` - -#### Dask Features - -- **Local Cluster**: Automatically starts a local dask cluster with multiple workers -- **Dashboard Access**: Provides access to the dask dashboard for monitoring (shown in verbose mode) -- **Automatic Cleanup**: Properly closes the cluster even if errors occur during processing -- **Chunk Alignment**: Automatically aligns Zarr chunks with dask chunks to prevent data corruption -- **Memory Efficiency**: Better memory management through parallel chunk processing -- **Error Handling**: Graceful handling of dask import errors with helpful installation instructions - -#### Chunk Alignment - -The library includes advanced chunk alignment logic to prevent the common issue of overlapping chunks when using dask: - -- **Smart Detection**: Automatically detects if data is dask-backed and uses existing chunk structure -- **Aligned Calculation**: Uses `calculate_aligned_chunk_size()` to find optimal chunk sizes that divide evenly into data dimensions -- **Proper Rechunking**: Ensures datasets are rechunked to match encoding before writing -- **Fallback Logic**: For non-dask arrays, uses reasonable chunk sizes that don't exceed data dimensions - -This prevents errors like: - -``` -❌ Failed to write tci after 2 attempts: Specified Zarr chunks encoding['chunks']=(1, 3660, 3660) -for variable named 'tci' would overlap multiple Dask chunks -``` - -#### S3 Python API - -```python -import os -import xarray as xr -from eopf_geozarr import create_geozarr_dataset - -# Configure for OVH Cloud (example) -os.environ['AWS_ACCESS_KEY_ID'] = 'your_ovh_access_key' -os.environ['AWS_SECRET_ACCESS_KEY'] = 'your_ovh_secret_key' -os.environ['AWS_DEFAULT_REGION'] = 'gra' -os.environ['AWS_ENDPOINT_URL'] = 'https://s3.gra.cloud.ovh.net' - -# Load your EOPF DataTree -dt = xr.open_datatree("path/to/eopf/dataset.zarr", engine="zarr") - -# Convert directly to S3 -dt_geozarr = create_geozarr_dataset( - dt_input=dt, - groups=["/measurements/r10m", "/measurements/r20m", "/measurements/r60m"], - output_path="s3://my-bucket/geozarr-data/output.zarr", - spatial_chunk=4096, - min_dimension=256, - tile_width=256, - max_retries=3 -) +uv run eopf-geozarr info "/tmp/S2B_MSIL2A_..._geozarr.zarr" --html /tmp/summary.html ``` -### Python API - -```python -import xarray as xr -from eopf_geozarr import create_geozarr_dataset - -# Load your EOPF DataTree -dt = xr.open_datatree("path/to/eopf/dataset.zarr", engine="zarr") - -# Define groups to convert (e.g., resolution groups) -groups = ["/measurements/r10m", "/measurements/r20m", "/measurements/r60m"] - -# Convert to GeoZarr compliant format -dt_geozarr = create_geozarr_dataset( - dt_input=dt, - groups=groups, - output_path="path/to/output/geozarr.zarr", - spatial_chunk=4096, - min_dimension=256, - tile_width=256, - max_retries=3 -) -``` - -## API Reference - -### Main Functions - -#### `create_geozarr_dataset` - -Create a GeoZarr-spec 0.4 compliant dataset from EOPF data. - -**Parameters:** +Validate (counts only real data vars, skips `spatial_ref`/`crs`): -- `dt_input` (xr.DataTree): Input EOPF DataTree -- `groups` (List[str]): List of group names to process as Geozarr datasets -- `output_path` (str): Output path for the Zarr store -- `spatial_chunk` (int, default=4096): Spatial chunk size for encoding -- `min_dimension` (int, default=256): Minimum dimension for overview levels -- `tile_width` (int, default=256): Tile width for TMS compatibility -- `max_retries` (int, default=3): Maximum number of retries for network operations - -**Returns:** - -- `xr.DataTree`: DataTree containing the GeoZarr compliant data - -#### `setup_datatree_metadata_geozarr_spec_compliant` - -Set up GeoZarr-spec compliant CF standard names and CRS information. - -**Parameters:** - -- `dt` (xr.DataTree): The data tree containing the datasets to process -- `groups` (List[str]): List of group names to process as Geozarr datasets - -**Returns:** - -- `Dict[str, xr.Dataset]`: Dictionary of datasets with GeoZarr compliance applied - -### Utility Functions - -#### `downsample_2d_array` - -Downsample a 2D array using block averaging. - -#### `calculate_aligned_chunk_size` - -Calculate a chunk size that divides evenly into the dimension size. This ensures that Zarr chunks align properly with the data dimensions, preventing chunk overlap issues when writing with Dask. - -**Parameters:** - -- `dimension_size` (int): Size of the dimension to chunk -- `target_chunk_size` (int): Desired chunk size - -**Returns:** - -- `int`: Aligned chunk size that divides evenly into dimension_size - -**Example:** - -```python -from eopf_geozarr.conversion.utils import calculate_aligned_chunk_size - -# For a dimension of size 5490 with target chunk size 3660 -aligned_size = calculate_aligned_chunk_size(5490, 3660) # Returns 2745 +```bash +uv run eopf-geozarr validate "/tmp/S2B_MSIL2A_..._geozarr.zarr" ``` -#### `is_grid_mapping_variable` - -Check if a variable is a grid_mapping variable by looking for references to it. - -#### `validate_existing_band_data` - -Validate that a specific band exists and is complete in the dataset. - -## Architecture - -The library is organized into the following modules: - -- **`conversion`**: Core conversion tools for EOPF to GeoZarr transformation - - `geozarr.py`: Main conversion functions and GeoZarr spec compliance - - `utils.py`: Utility functions for data processing and validation -- **`data_api`**: Data access API (future development with pydantic-zarr) - -## GeoZarr Specification Compliance - -This library implements the GeoZarr specification 0.4 with the following key requirements: - -1. **Array Dimensions**: All arrays must have `_ARRAY_DIMENSIONS` attributes -2. **CF Standard Names**: All variables must have CF-compliant `standard_name` attributes -3. **Grid Mapping**: Data variables must reference CF grid_mapping variables via `grid_mapping` attributes -4. **Multiscales Structure**: Overview levels are stored as children groups with proper tile matrix metadata -5. **Native CRS**: Coordinate reference systems are preserved without reprojection - -## Contributing to GeoZarr Specification - -Our implementation has contributed valuable feedback to the GeoZarr specification development process. Based on our real-world experience with Earth observation data, we have identified and reported several areas for improvement: - -### Key Contributions - -- **[Arbitrary Coordinate Systems Support](https://github.com/zarr-developers/geozarr-spec/issues/81)**: Advocating for native CRS preservation instead of web mapping bias -- **[Chunking Performance Optimization](https://github.com/zarr-developers/geozarr-spec/issues/82)**: Proposing flexible chunking strategies for optimal performance -- **[Multiscale Hierarchy Clarification](https://github.com/zarr-developers/geozarr-spec/issues/83)**: Providing clear structure definitions for multiscale implementations - -Our implementation demonstrates that scientific accuracy and performance can be maintained while working with arbitrary coordinate systems, not just web mapping projections. This is particularly important for Earth observation data that often comes in UTM zones, polar stereographic, or other scientific projections. - -For detailed information about our contributions, see our [GeoZarr Specification Contribution documentation](docs/geozarr-specification-contribution.md). - -## Development - -### Setting up Development Environment +## Benchmark (optional) ```bash -# Clone the repository -git clone -cd eopf-geozarr - -# Install in development mode with all dependencies -pip install -e ".[dev,docs,all]" - -# Install pre-commit hooks -pre-commit install +uv run eopf-geozarr benchmark "/tmp/..._geozarr.zarr" --samples 8 --window 1024 1024 ``` -### Running Tests +## STAC ```bash -pytest +uv run eopf-geozarr stac \ + "/tmp/..._geozarr.zarr" \ + "/tmp/..._collection.json" \ + --bbox "minx miny maxx maxy" \ + --start "YYYY-MM-DDTHH:MM:SSZ" \ + --end "YYYY-MM-DDTHH:MM:SSZ" ``` -### Code Quality +## Python API -The project uses: +```python +from eopf_geozarr.conversion.geozarr import GeoZarrWriter +from eopf_geozarr.validation.validate import validate_store +from eopf_geozarr.info.summary import summarize -- **Black** for code formatting -- **isort** for import sorting -- **flake8** for linting -- **mypy** for type checking -- **pre-commit** for automated checks +src = "https://.../S2B_MSIL2A_... .zarr" +dst = "/tmp/S2B_MSIL2A_..._geozarr.zarr" -### Building Documentation +writer = GeoZarrWriter(src, dst, storage_options={}) +writer.write(groups=["/measurements/reflectance"], verbose=True) -```bash -cd docs -make html -``` +report = validate_store(dst) +print(report.ok) -## Contributing +tree = summarize(dst) +print(tree["summary"]) # or write HTML via CLI +``` -1. Fork the repository -2. Create a feature branch (`git checkout -b feature/amazing-feature`) -3. Make your changes -4. Run tests and ensure code quality checks pass -5. Commit your changes (`git commit -m 'Add amazing feature'`) -6. Push to the branch (`git push origin feature/amazing-feature`) -7. Open a Pull Request +## What it writes -## License +- `_ARRAY_DIMENSIONS` per variable (correct axis order). +- `grid_mapping = "spatial_ref"` per variable; `spatial_ref` holds CRS/georeferencing. +- Multiscales on parent groups; /2 overviews. +- Blosc Zstd compression; conservative chunking; consolidated metadata. +- Overviews keep per-band attributes (grid_mapping reattached across levels). -This project is licensed under the Apache License 2.0 - see the LICENSE file for details. +## Consolidated metadata -## Acknowledgments +Speeds up reads. Some tools note it isn’t in the core Zarr v3 spec yet; data stays valid. You can disable consolidation during writes or remove the index if preferred. -- Built on top of the excellent [xarray](https://xarray.pydata.org/) and [zarr](https://zarr.readthedocs.io/) libraries -- Follows the [GeoZarr specification](https://github.com/zarr-developers/geozarr-spec) for geospatial data in Zarr -- Designed for compatibility with [EOPF](https://eopf.readthedocs.io/) datasets +## Troubleshooting -## Support +- Parent group shows no data vars: select leaves (CLI auto-expands). +- S3 errors: check env vars and `AWS_ENDPOINT_URL` for custom endpoints. +- HTML path is a directory: a default filename is created inside. -For questions, issues, or contributions, please visit the [GitHub repository](https://github.com/developmentseed/eopf-geozarr). diff --git a/docs/installation.md b/docs/installation.md index 836f409..bc8fc06 100644 --- a/docs/installation.md +++ b/docs/installation.md @@ -25,14 +25,20 @@ If you have [uv](https://docs.astral.sh/uv/) installed: uv add eopf-geozarr ``` -### Development Installation +### Development Installation (uv preferred) -For development or to get the latest features: +For development or to get the latest features using uv: ```bash git clone https://github.com/eopf-explorer/data-model.git cd data-model -pip install -e . +uv sync --all-extras --dev +``` + +To run tests quickly: + +```bash +uv run env PYTHONPATH=src python -m pytest -q ``` ## Dependencies diff --git a/geozarr-convert-template.yaml b/geozarr-convert-template.yaml deleted file mode 100644 index 417ea50..0000000 --- a/geozarr-convert-template.yaml +++ /dev/null @@ -1,172 +0,0 @@ -apiVersion: argoproj.io/v1alpha1 -kind: WorkflowTemplate -metadata: - name: geozarr-convert - namespace: argo -spec: - entrypoint: run-convert - - # Per-workflow PVC. Argo creates -outpvc and injects a volume named "outpvc". - volumeClaimTemplates: - - metadata: - name: outpvc - spec: - accessModes: ["ReadWriteOnce"] - resources: - requests: - storage: 10Gi - - arguments: - parameters: - - name: stac_url - value: "https://objectstore.eodc.eu:2222/e05ab01a9d56408d82ac32d69a5aae2a:202505-s02msil2a/18/products/cpm_v256/S2B_MSIL2A_20250518T112119_N0511_R037_T29RLL_20250518T140519.zarr" - - name: output_zarr - value: "./S2B_MSIL2A_20250518_T29RLL_geozarr.zarr" - # Space-separated groups; script normalizes to absolute paths. - - name: groups - value: "/measurements/reflectance/r10m /measurements/reflectance/r20m /measurements/reflectance/r60m" - - name: dask_perf_html - value: "out/debug/dask-report-threads.html" - - templates: - - name: run-convert - volumes: - - name: tmp - emptyDir: {} - - inputs: - parameters: - - name: stac_url - - name: output_zarr - - name: groups - - name: dask_perf_html - - script: - image: eopf-geozarr:dev - imagePullPolicy: IfNotPresent - command: ["sh", "-lc"] - volumeMounts: - - name: tmp - mountPath: /tmp - - name: outpvc - mountPath: /outputs - source: | - set -euo pipefail - - STAC="{{inputs.parameters.stac_url}}" - OUT="{{inputs.parameters.output_zarr}}" - GROUPS_RAW="{{inputs.parameters.groups}}" - PERF="{{inputs.parameters.dask_perf_html}}" - OUTDIR="/outputs" - - case "$STAC" in *…*|*...*) echo "ERROR: stac_url contains ellipsis"; exit 64;; esac - - mkdir -p "$OUTDIR" "$(dirname "$PERF")" - - echo "=== preflight ===" - echo "stac_url=$STAC" - if command -v curl >/dev/null 2>&1; then - (curl -sI --max-time 8 "$STAC" | head -n1) || true - elif command -v wget >/dev/null 2>&1; then - (wget -qS --spider "$STAC" 2>&1 | head -n1) || true - else - echo "(no curl/wget in image; skipping HEAD)" - fi - - # Normalize group paths - NORM_GROUPS="" - for g in $GROUPS_RAW; do - [ -z "$g" ] && continue - case "$g" in /*) NORM="$g";; *) NORM="/$g";; esac - NORM="$(printf "%s" "$NORM" | sed 's#//*#/#g')" - NORM_GROUPS="$NORM_GROUPS $NORM" - done - GROUPS="$(printf "%s" "$NORM_GROUPS" | sed 's/^ *//')" - echo "groups(norm)=$GROUPS" - - # Validate groups early with xarray (robust to API differences) - python - "$STAC" $GROUPS <<'PY' - import sys, xarray as xr - - def iter_children(node): - """Return iterable of (name, child) for DataTree children - across xarray versions where .groups may be dict/list/tuple.""" - groups = getattr(node, "groups", None) - if isinstance(groups, dict): - yield from groups.items() - elif isinstance(groups, (list, tuple)): - for name in groups: - try: - yield name, node[name] - except Exception: - pass - else: - # try .children (older/alt APIs) - children = getattr(node, "children", None) - if isinstance(children, dict): - yield from children.items() - - if len(sys.argv) < 2: - print("no args passed to validation; skipping"); sys.exit(0) - stac, *groups = sys.argv[1:] - dt = xr.open_datatree(stac, engine="zarr", consolidated=None) - - print("=== available groups ===") - def walk(node, prefix=""): - path = prefix or "/" - print(path) - for name, child in iter_children(node): - newp = f"{prefix}/{name}" if prefix else f"/{name}" - walk(child, newp) - walk(dt) - - bad = [] - for g in groups: - key = g.strip("/") - try: - _ = dt[key] - except Exception as e: - print(f"[error] {g}: {e}") - bad.append(g) - if bad: - try: - meas = dt["measurements"] - print("candidates under /measurements:") - for name, _child in iter_children(meas): - print(" /measurements/" + name) - except Exception: - pass - sys.exit(64) - PY - - echo "=== convert ===" - set -x - eopf-geozarr convert \ - --dask-cluster \ - --dask-perf-html "$PERF" \ - "$STAC" \ - "$OUT" \ - --verbose \ - $(for g in $GROUPS; do printf -- ' --groups %s' "$g"; done) - set +x - - # Persist extras on PVC for fetch-tar convenience - cp -f "$PERF" "$OUTDIR/dask-report.html" 2>/dev/null || true - - echo "=== package artifact ===" - tar -czf "$OUTDIR/geozarr.tar.gz" -C "$(dirname "$OUT")" "$(basename "$OUT")" - - resources: - requests: - cpu: "1" - memory: "2Gi" - limits: - cpu: "4" - memory: "8Gi" - - outputs: - artifacts: - - name: dask-report - path: "{{inputs.parameters.dask_perf_html}}" - - name: geozarr-tar - path: /outputs/geozarr.tar.gz diff --git a/geozarr-convert.yaml b/geozarr-convert.yaml deleted file mode 100644 index ee24e41..0000000 --- a/geozarr-convert.yaml +++ /dev/null @@ -1,45 +0,0 @@ -apiVersion: argoproj.io/v1alpha1 -kind: Workflow -metadata: - generateName: geozarr-convert- -spec: - entrypoint: run-convert - templates: - - name: run-convert - inputs: - parameters: - - name: stac_url - value: "https://objectstore.eodc.eu:2222/e05ab01a9d56408d82ac32d69a5aae2a:202505-s02msil2a/18/products/cpm_v256/S2B_MSIL2A_20250518T112119_N0511_R037_T29RLL_20250518T140519.zarr" - - name: output_zarr - value: "./S2B_MSIL2A_20250518_T29RLL_geozarr.zarr" - - name: groups - value: "measurements/reflectance/r20m" - - name: dask_perf_html - value: "out/debug/dask-report-threads.html" - script: - image: eopf-geozarr:dev - command: ["sh"] - source: | - set -eu - mkdir -p "$(dirname "{{inputs.parameters.dask_perf_html}}")" - eopf-geozarr convert \ - --dask-cluster \ - --dask-perf-html "{{inputs.parameters.dask_perf_html}}" \ - "{{inputs.parameters.stac_url}}" \ - "{{inputs.parameters.output_zarr}}" \ - --verbose \ - --groups "{{inputs.parameters.groups}}" - tar -czf geozarr.tar.gz -C "$(dirname "{{inputs.parameters.output_zarr}}")" "$(basename "{{inputs.parameters.output_zarr}}")" - resources: - requests: - cpu: "2" - memory: "4Gi" - limits: - cpu: "4" - memory: "8Gi" - outputs: - artifacts: - - name: dask-report - path: "{{inputs.parameters.dask_perf_html}}" - - name: geozarr-tar - path: geozarr.tar.gz diff --git a/pyproject.toml b/pyproject.toml index 0cc63c4..57bab3c 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -39,9 +39,6 @@ dependencies = [ "s3fs>=2024.6.0", "boto3>=1.34.0", "pyproj>=3.7.0", - "ipykernel>=6.30.1", - "jupyter>=1.1.1", - "bokeh>=3.7.3", ] [dependency-groups] @@ -144,10 +141,7 @@ markers = [ [tool.coverage.run] source = ["src"] -omit = [ - "tests/*", - "setup.py", -] +omit = ["tests/*", "setup.py"] [tool.coverage.report] exclude_lines = [ From 779e8125693b9ffe96f02030a6e45ec99c802667 Mon Sep 17 00:00:00 2001 From: Wietze Date: Tue, 16 Sep 2025 18:57:56 -0400 Subject: [PATCH 5/9] feat(conversion): introduce modular conversion pipeline and CLI command suite --- .pre-commit-config.yaml | 4 +- src/eopf_geozarr/__init__.py | 15 +- src/eopf_geozarr/cli.py | 1772 ++------------------ src/eopf_geozarr/commands/__init__.py | 13 + src/eopf_geozarr/commands/_shared.py | 46 + src/eopf_geozarr/commands/cmd_benchmark.py | 174 ++ src/eopf_geozarr/commands/cmd_convert.py | 275 +++ src/eopf_geozarr/commands/cmd_info.py | 34 + src/eopf_geozarr/commands/cmd_stac.py | 92 + src/eopf_geozarr/commands/cmd_validate.py | 91 + src/eopf_geozarr/commands/dask_utils.py | 26 + src/eopf_geozarr/commands/html_utils.py | 119 ++ src/eopf_geozarr/conversion/__init__.py | 12 +- src/eopf_geozarr/conversion/geozarr.py | 1542 +++++------------ src/eopf_geozarr/conversion/hierarchy.py | 43 + src/eopf_geozarr/conversion/io.py | 30 + src/eopf_geozarr/conversion/metadata.py | 77 + src/eopf_geozarr/conversion/multiscales.py | 411 +++++ src/eopf_geozarr/conversion/prepare.py | 147 ++ src/eopf_geozarr/conversion/utils.py | 200 ++- src/eopf_geozarr/conversion/zarr_write.py | 36 + 21 files changed, 2324 insertions(+), 2835 deletions(-) create mode 100644 src/eopf_geozarr/commands/__init__.py create mode 100644 src/eopf_geozarr/commands/_shared.py create mode 100644 src/eopf_geozarr/commands/cmd_benchmark.py create mode 100644 src/eopf_geozarr/commands/cmd_convert.py create mode 100644 src/eopf_geozarr/commands/cmd_info.py create mode 100644 src/eopf_geozarr/commands/cmd_stac.py create mode 100644 src/eopf_geozarr/commands/cmd_validate.py create mode 100644 src/eopf_geozarr/commands/dask_utils.py create mode 100644 src/eopf_geozarr/commands/html_utils.py create mode 100644 src/eopf_geozarr/conversion/hierarchy.py create mode 100644 src/eopf_geozarr/conversion/io.py create mode 100644 src/eopf_geozarr/conversion/metadata.py create mode 100644 src/eopf_geozarr/conversion/multiscales.py create mode 100644 src/eopf_geozarr/conversion/prepare.py create mode 100644 src/eopf_geozarr/conversion/zarr_write.py diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index d544d9a..df62d05 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -9,13 +9,15 @@ repos: hooks: - id: isort language_version: python + files: ^(src/|tests/) - repo: https://github.com/astral-sh/ruff-pre-commit rev: v0.8.4 hooks: - id: ruff - args: ["--fix"] + args: ["--fix", "--line-length=100"] - id: ruff-format + args: ["--line-length=100"] - repo: https://github.com/pre-commit/mirrors-mypy rev: v1.11.2 diff --git a/src/eopf_geozarr/__init__.py b/src/eopf_geozarr/__init__.py index bb366e0..cff588f 100644 --- a/src/eopf_geozarr/__init__.py +++ b/src/eopf_geozarr/__init__.py @@ -1,26 +1,29 @@ """EOPF GeoZarr - GeoZarr compliant data model for EOPF datasets.""" -from importlib.metadata import version +try: + from importlib.metadata import version as _load_version +except Exception: # pragma: no cover - extremely unlikely + _load_version = None # type: ignore from .conversion import ( - async_consolidate_metadata, calculate_aligned_chunk_size, - consolidate_metadata, create_geozarr_dataset, downsample_2d_array, is_grid_mapping_variable, - iterative_copy, setup_datatree_metadata_geozarr_spec_compliant, validate_existing_band_data, ) +from .conversion.metadata import async_consolidate_metadata, consolidate_metadata -__version__ = version("eopf-geozarr") +try: + __version__ = _load_version("eopf-geozarr") if _load_version is not None else "0.0.0+local" +except Exception: # pragma: no cover + __version__ = "0.0.0+local" __all__ = [ "__version__", "create_geozarr_dataset", "setup_datatree_metadata_geozarr_spec_compliant", - "iterative_copy", "consolidate_metadata", "async_consolidate_metadata", "downsample_2d_array", diff --git a/src/eopf_geozarr/cli.py b/src/eopf_geozarr/cli.py index a00e7d2..b738f5b 100644 --- a/src/eopf_geozarr/cli.py +++ b/src/eopf_geozarr/cli.py @@ -1,1672 +1,156 @@ -#!/usr/bin/env python3 -"""Command-line interface for eopf-geozarr. - -This module provides CLI commands for converting EOPF datasets to GeoZarr compliant format. -""" +"""Minimal CLI entry point wiring subcommands from commands package.""" import argparse import sys -from contextlib import nullcontext -from datetime import datetime -from pathlib import Path -from time import perf_counter -from typing import Any, Optional -from urllib.parse import urlparse - -import xarray as xr - -from . import create_geozarr_dataset -from .conversion.fs_utils import ( - get_s3_credentials_info, - get_storage_options, - is_s3_path, - validate_s3_access, +from typing import Any, Dict, List + +from .commands import ( + benchmark_command, + convert_command, + info_command, + stac_command, + validate_command, ) -from .metrics import MetricsRecorder - - -def setup_dask_cluster( - enable_dask: bool, - verbose: bool = False, - mode: str = "threads", # threads | processes | single-threaded - n_workers: int = 4, - threads_per_worker: int = 1, -) -> Optional[Any]: - """ - Set up a dask cluster for parallel processing. - - Parameters - ---------- - enable_dask : bool - Whether to enable dask cluster - verbose : bool, default False - Enable verbose output - - Returns: - ------- - dask.distributed.Client or None - Dask client if enabled, None otherwise - """ - if not enable_dask: - return None - - try: - from dask.distributed import Client, LocalCluster - - if mode not in {"threads", "processes", "single-threaded"}: - raise ValueError(f"Unsupported --dask-mode: {mode}") - - processes = mode == "processes" - # For single-threaded, use one worker, one thread, processes=False - if mode == "single-threaded": - n_workers = 1 - threads_per_worker = 1 - processes = False - - # Set up local cluster - cluster = LocalCluster( - n_workers=n_workers, - threads_per_worker=threads_per_worker, - processes=processes, - ) - client = Client(cluster) - - if verbose: - print(f"🚀 Dask cluster started: {client}") - print(f" Dashboard: {client.dashboard_link}") - print(f" Workers: {len(client.scheduler_info()['workers'])}") - else: - print( - f"🚀 Dask cluster started " - f"({mode}; workers={n_workers}, threads/worker={threads_per_worker})" - ) - return client - - except ImportError: - print( - "❌ Error: dask.distributed not available. Install with: pip install 'dask[distributed]'" - ) - sys.exit(1) - except Exception as e: - print(f"❌ Error starting dask cluster: {e}") - sys.exit(1) - - -def _duration_from_startstops(ev: dict, action: str) -> float: - """Best-effort duration extractor from task_stream 'startstops'.""" - total = 0.0 - for ss in ev.get("startstops", []) or []: - if ss.get("action") == action: - s, t = ss.get("start"), ss.get("stop") - if s is not None and t is not None: - total += max(0.0, float(t) - float(s)) - return total - - -def _parse_task_events(task_events: Optional[list]) -> dict: - """Normalize task_stream events across Dask versions into aggregate numbers.""" - if not task_events: - return { - "tasks_observed": 0, - "compute_time_s_sum": 0.0, - "transfer_time_s_sum": 0.0, - } - comp_sum = 0.0 - xfer_sum = 0.0 - for e in task_events: - # compute time - cd = e.get("compute_duration") - if cd is None: - cs = e.get("compute_start") or e.get("start") - ce = e.get("compute_stop") or e.get("stop") - if cs is not None and ce is not None: - cd = max(0.0, float(ce) - float(cs)) - else: - cd = _duration_from_startstops(e, "compute") - comp_sum += float(cd or 0.0) - # transfer time - td = e.get("transfer_duration") - if td is None: - ts = e.get("transfer_start") - te = e.get("transfer_stop") - if ts is not None and te is not None: - td = max(0.0, float(te) - float(ts)) - else: - td = _duration_from_startstops(e, "transfer") - xfer_sum += float(td or 0.0) - return { - "tasks_observed": len(task_events), - "compute_time_s_sum": comp_sum, - "transfer_time_s_sum": xfer_sum, - } - - -def _summarize_dask_metrics( - client: Any, task_events: Optional[list], wall_clock_s: float -) -> dict: - info = client.scheduler_info() - workers = info.get("workers", {}) - total_threads = sum(int(w.get("nthreads", 0) or 0) for w in workers.values()) - memory_limit = sum(int(w.get("memory_limit", 0) or 0) for w in workers.values()) - memory_used = sum( - int(w.get("metrics", {}).get("memory", 0) or 0) for w in workers.values() - ) - spilled = sum( - int(w.get("metrics", {}).get("spilled_nbytes", 0) or 0) - for w in workers.values() - ) - - parsed = _parse_task_events(task_events) - tasks = parsed["tasks_observed"] - base = { - "wall_clock_s": wall_clock_s, - "workers": len(workers), - "threads_total": total_threads, - "tasks_observed": tasks, - "tasks_per_sec": (tasks / wall_clock_s) if tasks and wall_clock_s else 0.0, - "compute_time_s_sum": parsed["compute_time_s_sum"], - "transfer_time_s_sum": parsed["transfer_time_s_sum"], - "memory_used_bytes": memory_used, - "memory_limit_bytes": memory_limit, - } - if spilled: - base["spilled_nbytes"] = spilled - if getattr(client, "dashboard_link", None): - base["dashboard_link"] = client.dashboard_link - return base - - -def _derive_item_id_from_input_path(path: str) -> str: - """Best-effort item id from local path or URL.""" - try: - if path.startswith(("http://", "https://", "s3://", "gs://")): - parsed = urlparse(path) - name = Path(parsed.path.rstrip("/")).name - else: - name = Path(str(path).rstrip("/")).name - if name.endswith(".zarr"): - name = name[: -len(".zarr")] - return name or "dataset" - except Exception: - return "dataset" - - -def _resolve_output_prefix(prefix: str, item_id: str) -> str: - target_name = f"{item_id}_geozarr.zarr" - if prefix.startswith(("http://", "https://", "s3://", "gs://")): - if not prefix.endswith("/"): - prefix = prefix + "/" - return prefix + target_name - return str(Path(prefix) / target_name) - - -def _has_group(tree: xr.DataTree, path: str) -> bool: - try: - parts = [p for p in str(path).strip("/").split("/") if p] - node = tree - for seg in parts: - children = getattr(node, "children", {}) or {} - if seg not in children: - return False - node = children[seg] - return True - except Exception: - return False - - -def _local_path_exists(p: str) -> bool: - try: - return Path(p).exists() - except Exception: - return False - - -def _remove_local_tree(p: str) -> bool: - try: - import shutil - - if Path(p).exists(): - shutil.rmtree(p) - return True - except Exception: - return False - - -def convert_command(args: argparse.Namespace) -> None: - """Convert EOPF dataset to GeoZarr compliant format. - - Parameters - ---------- - args : argparse.Namespace - Command line arguments - """ - # Set up dask cluster if requested - dask_client = setup_dask_cluster( - enable_dask=getattr(args, "dask_cluster", False), - verbose=args.verbose, - mode=getattr(args, "dask_mode", "threads"), - n_workers=getattr(args, "dask_workers", 4), - threads_per_worker=getattr(args, "dask_threads_per_worker", 1), - ) - - # Prepare for metrics writing even on failures (local outputs only) - debug_dir: Optional[Path] = None - run_id = datetime.now().strftime("%Y%m%d-%H%M%S") - status = "unknown" - error_msg = None - metrics = None - - try: - # Validate input path (handle both local paths and URLs) - input_path_str = args.input_path - if input_path_str.startswith(("http://", "https://", "s3://", "gs://")): - # URL - no local validation needed - input_path = input_path_str - else: - # Local path - validate existence - input_path = Path(input_path_str) - if not input_path.exists(): - print(f"Error: Input path {input_path} does not exist") - sys.exit(1) - input_path = str(input_path) - - # Fast path: just list groups and exit - if getattr(args, "list_groups", False): - storage_options = get_storage_options(input_path) - dt = xr.open_datatree( - str(input_path), - engine="zarr", - chunks="auto", - storage_options=storage_options, - ) - print("Available groups:") - for group_name in dt.children: - print(f" - {group_name}") - return - - # Handle output path validation - output_path_str = args.output_path - # Expand trailing-slash prefix to a concrete store - if output_path_str.endswith("/"): - item_id = _derive_item_id_from_input_path(str(input_path)) - output_path_str = _resolve_output_prefix(output_path_str, item_id) - print(f"Resolved output store: {output_path_str}") - if is_s3_path(output_path_str): - # S3 path - validate S3 access - print("🔍 Validating S3 access...") - success, error_msg = validate_s3_access(output_path_str) - if not success: - print(f"❌ Error: Cannot access S3 path {output_path_str}") - print(f" Reason: {error_msg}") - print("\n💡 S3 Configuration Help:") - print(" Make sure you have S3 credentials configured:") - print( - " - Set AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY environment variables" - ) - print(" - Set AWS_DEFAULT_REGION (default: us-east-1)") - print( - " - For custom S3 providers (e.g., OVH Cloud), set AWS_ENDPOINT_URL" - ) - print(" - Or configure AWS CLI with 'aws configure'") - print(" - Or use IAM roles if running on EC2") - - if args.verbose: - creds_info = get_s3_credentials_info() - print("\n🔧 Current AWS configuration:") - for key, value in creds_info.items(): - print(f" {key}: {value or 'Not set'}") - - sys.exit(1) - - print("✅ S3 access validated successfully") - output_path = output_path_str - else: - # Local path - create directory if it doesn't exist - output_path = Path(output_path_str) - # In dry-run mode, don't create anything - if not getattr(args, "dry_run", False): - output_path.parent.mkdir(parents=True, exist_ok=True) - output_path = str(output_path) - # Prepare debug dir for metrics (skip in dry-run) - if not getattr(args, "dry_run", False): - debug_dir = Path(output_path) / "debug" - try: - debug_dir.mkdir(parents=True, exist_ok=True) - except Exception: - debug_dir = None - - if args.verbose: - print(f"Loading EOPF dataset from: {input_path}") - print(f"Groups to convert: {args.groups}") - print(f"CRS groups: {args.crs_groups}") - print(f"Output path: {output_path}") - print(f"Spatial chunk size: {args.spatial_chunk}") - print(f"Min dimension: {args.min_dimension}") - print(f"Tile width: {args.tile_width}") - - # Load the EOPF DataTree with appropriate storage options - print("Loading EOPF dataset...") - storage_options = get_storage_options(input_path) - # Metrics setup (environment first; set_input after group validation) - if getattr(args, "metrics", True): - metrics = MetricsRecorder() - metrics.set_environment() - - with metrics.time_step("open_input") if metrics else nullcontext(): - dt = xr.open_datatree( - str(input_path), - engine="zarr", - chunks="auto", - storage_options=storage_options, - ) - - # Validate/prune groups if requested - groups_effective = list(getattr(args, "groups", []) or []) - validate_mode = getattr(args, "validate_groups", None) - missing: list[str] = [] - if validate_mode in {"warn", "error"} and groups_effective: - existing: list[str] = [] - for g in groups_effective: - if _has_group(dt, g): - existing.append(g) - else: - missing.append(g) - if missing: - msg = f"Groups not found: {', '.join(missing)}" - if validate_mode == "error": - print(f"❌ {msg}") - sys.exit(3) - else: - print(f"⚠️ {msg}; proceeding with remaining groups") - groups_effective = existing - if not groups_effective: - print("❌ No valid groups to convert after validation") - sys.exit(3) - - # Now that groups are finalized, set input metadata for metrics - if metrics is not None: - metrics.set_input( - source_uri=str(input_path), - profile=None, - groups=groups_effective, - dask={ - "enabled": bool(dask_client is not None), - "mode": getattr(args, "dask_mode", None), - "workers": getattr(args, "dask_workers", None), - "threads_per_worker": getattr( - args, "dask_threads_per_worker", None - ), - }, - ) - - # Overwrite policy handling (local only) - overwrite = getattr(args, "overwrite", "fail") - is_remote = is_s3_path(output_path) - if not is_remote: - exists = _local_path_exists(output_path) - if exists: - if overwrite == "fail": - print( - f"❌ Output already exists and overwrite policy is 'fail': {output_path}" - ) - sys.exit(2) - if overwrite == "skip": - print(f"⏭️ Output exists; skipping as per policy: {output_path}") - return - if overwrite == "replace": - if getattr(args, "dry_run", False): - print( - f"🧪 Dry-run: would remove existing output: {output_path}" - ) - else: - ok = _remove_local_tree(output_path) - if not ok: - print(f"❌ Failed to remove existing output: {output_path}") - sys.exit(2) - # merge: do nothing - else: - if overwrite == "fail": - print( - "ℹ️ Remote output existence not checked; 'fail' policy may not prevent overwrite." - ) - elif overwrite == "replace": - print( - "⚠️ 'replace' is not implemented for remote outputs; proceeding may overwrite keys." - ) - - # Print dry-run plan and exit early - if getattr(args, "dry_run", False): - print("\nDry-run plan:") - print("============") - print(f"Input: {input_path}") - print(f"Output: {output_path}") - print(f"Groups: {groups_effective}") - print(f"Overwrite policy: {overwrite}") - print(f"Dask: {'on' if dask_client is not None else 'off'}") - print("No data will be written.") - return - - if args.verbose: - print(f"Loaded DataTree with {len(dt.children)} groups") - print("Available groups:") - for group_name in dt.children: - print(f" - {group_name}") - if missing: - print(f"After validation, converting groups: {groups_effective}") - - # Convert to GeoZarr compliant format - print("Converting to GeoZarr compliant format...") - t0 = perf_counter() - task_events = None - perf_ctx = nullcontext() - if dask_client is not None and getattr(args, "dask_perf_html", None): - from dask.distributed import performance_report - - perf_path = args.dask_perf_html - if not (perf_path.startswith(("s3://", "gs://", "http://", "https://"))): - Path(perf_path).parent.mkdir(parents=True, exist_ok=True) - perf_ctx = performance_report(filename=perf_path) - - capture_tasks = False - if dask_client is not None: - try: - from dask.distributed import get_task_stream - - capture_tasks = True - except Exception: - pass - - if capture_tasks: - with perf_ctx: - from dask.distributed import get_task_stream - - with get_task_stream(client=dask_client, plot=False) as ts: - with metrics.time_step("convert") if metrics else nullcontext(): - dt_geozarr = create_geozarr_dataset( - dt_input=dt, - groups=groups_effective, - output_path=output_path, - spatial_chunk=args.spatial_chunk, - min_dimension=args.min_dimension, - tile_width=args.tile_width, - max_retries=args.max_retries, - crs_groups=args.crs_groups, - ) - task_events = ts.data - else: - with perf_ctx: - with metrics.time_step("convert") if metrics else nullcontext(): - dt_geozarr = create_geozarr_dataset( - dt_input=dt, - groups=groups_effective, - output_path=output_path, - spatial_chunk=args.spatial_chunk, - min_dimension=args.min_dimension, - tile_width=args.tile_width, - max_retries=args.max_retries, - crs_groups=args.crs_groups, - ) - - wall_clock = perf_counter() - t0 - if ( - dask_client is not None - and getattr(args, "verbose", False) - and getattr(args, "dask_perf_html", None) - ): - print(f"📊 Dask performance report: {args.dask_perf_html}") - - print("✅ Successfully converted EOPF dataset to GeoZarr format") - print(f"Output saved to: {output_path}") - status = "ok" - - # Drop a JSON run summary (local paths only) - try: - if debug_dir is not None: - summary_path = Path(output_path) / "debug" / "dask_run_summary.json" - summary_path.parent.mkdir(parents=True, exist_ok=True) - from json import dumps as _dumps - - summary = { - "dask_enabled": bool(dask_client is not None), - "mode": getattr(args, "dask_mode", None), - "workers": getattr(args, "dask_workers", None), - "threads_per_worker": getattr( - args, "dask_threads_per_worker", None - ), - "perf_report": getattr(args, "dask_perf_html", None), - "wall_clock_s": wall_clock if dask_client is not None else None, - "groups": groups_effective, - "spatial_chunk": args.spatial_chunk, - "min_dimension": args.min_dimension, - "tile_width": args.tile_width, - } - summary_path.write_text(_dumps(summary, indent=2)) - if args.verbose: - print(f"🧾 Wrote run summary: {summary_path}") - # New metrics: run_summary.json and chunk_report.json - if metrics and debug_dir is not None and getattr(args, "metrics", True): - # Build output summary best-effort - with metrics.time_step("summarize_output"): - metrics.build_output_summary(output_path) - payload = metrics.finalize(status="ok") - run_summary = Path(output_path) / "debug" / "run_summary.json" - MetricsRecorder.write_json(run_summary, payload) - if args.verbose: - print(f"🧾 Wrote metrics: {run_summary}") - # Optional external run-metadata path - if getattr(args, "run_metadata", None): - try: - outp = Path(args.run_metadata) - outp.parent.mkdir(parents=True, exist_ok=True) - MetricsRecorder.write_json(outp, payload) - if args.verbose: - print(f"🧾 Wrote run metadata: {outp}") - except Exception as _e: - if args.verbose: - print(f"(debug) could not write run-metadata: {_e}") - except Exception as _exc: - if args.verbose: - print(f"(debug) could not write run summary: {_exc}") - - if args.verbose: - # Check if dt_geozarr is a DataTree or Dataset - if hasattr(dt_geozarr, "children"): - print(f"Converted DataTree has {len(dt_geozarr.children)} groups") - print("Converted groups:") - for group_name in dt_geozarr.children: - print(f" - {group_name}") - else: - print("Converted dataset (single group)") - - except Exception as e: - print(f"❌ Error during conversion: {e}") - error_msg = str(e) - if args.verbose: - import traceback - - traceback.print_exc() - # Write failure metrics if possible - try: - if metrics and debug_dir is not None and getattr(args, "metrics", True): - payload = metrics.finalize(status="error", exception=error_msg) - run_summary = Path(output_path) / "debug" / "run_summary.json" - try: - MetricsRecorder.write_json(run_summary, payload) - if args.verbose: - print(f"🧾 Wrote failure metrics: {run_summary}") - except Exception: - pass - if getattr(args, "run_metadata", None): - try: - outp = Path(args.run_metadata) - outp.parent.mkdir(parents=True, exist_ok=True) - MetricsRecorder.write_json(outp, payload) - if args.verbose: - print(f"🧾 Wrote run metadata (error): {outp}") - except Exception: - pass - except Exception: - pass - sys.exit(1) - finally: - # Clean up dask client if it was created - if dask_client is not None: - try: - if hasattr(dask_client, "close"): - dask_client.close() - if args.verbose: - print("🔄 Dask cluster closed") - except Exception as e: - if args.verbose: - print(f"Warning: Error closing dask cluster: {e}") - - # Best-effort metrics write (works for success or failure, local only) - try: - if debug_dir is not None: - attempt = 1 - try: - attempt = 1 + len(list(debug_dir.glob("dask_metrics_*.json"))) - except Exception: - pass - wall = None - if "t0" in locals(): - wall = perf_counter() - t0 - metrics_doc = { - "status": status, - "run_id": run_id, - "attempt": attempt, - "dask_enabled": bool(dask_client is not None), - "mode": getattr(args, "dask_mode", None), - } - if dask_client is not None and wall is not None: - try: - metrics_doc.update( - _summarize_dask_metrics( - dask_client, locals().get("task_events"), wall - ) - ) - except Exception: - metrics_doc["wall_clock_s"] = wall - elif wall is not None: - metrics_doc["wall_clock_s"] = wall - if error_msg: - metrics_doc["error"] = error_msg - - path_ts = debug_dir / f"dask_metrics_{run_id}_attempt{attempt}.json" - path_latest = debug_dir / "dask_metrics.json" - from json import dumps as _dumps - - txt = _dumps(metrics_doc, indent=2) - path_ts.write_text(txt) - path_latest.write_text(txt) - if getattr(args, "verbose", False): - print(f"📈 Wrote metrics: {path_ts}") - except Exception as _mexc: - if getattr(args, "verbose", False): - print(f"(debug) could not write metrics snapshot: {_mexc}") - - -def info_command(args: argparse.Namespace) -> None: - """Display information about an EOPF dataset. - - Parameters - ---------- - args : argparse.Namespace - Command line arguments - """ - # Handle both local paths and URLs - input_path_str = args.input_path - if input_path_str.startswith(("http://", "https://", "s3://", "gs://")): - # URL - no local validation needed - input_path = input_path_str - else: - # Local path - validate existence - input_path = Path(input_path_str) - if not input_path.exists(): - print(f"Error: Input path {input_path} does not exist") - sys.exit(1) - input_path = str(input_path) - - try: - print(f"Loading dataset from: {input_path}") - # Use unified storage options for S3 support - storage_options = get_storage_options(input_path) - dt = xr.open_datatree( - input_path, engine="zarr", chunks="auto", storage_options=storage_options - ) - - if hasattr(args, "html_output") and args.html_output: - # Generate HTML output - _generate_html_output(dt, args.html_output, input_path, args.verbose) - else: - # Standard console output - print("\nDataset Information:") - print("==================") - print(f"Total groups: {len(dt.children)}") - - print("\nGroup structure:") - print(dt) - - except Exception as e: - print(f"❌ Error reading dataset: {e}") - if args.verbose: - import traceback - - traceback.print_exc() - sys.exit(1) - - -def _generate_optimized_tree_html(dt: xr.DataTree) -> str: - """Generate an optimized, condensed tree HTML representation. - - This function creates a clean tree view that: - - Hides empty nodes by default - - Shows only nodes with data variables or meaningful content - - Provides a more condensed, focused view - - Parameters - ---------- - dt : xr.DataTree - DataTree to visualize - - Returns: - ------- - str - HTML representation of the optimized tree - """ - - def has_meaningful_content(node: Any) -> bool: - """Check if a node has meaningful content (data variables, attributes, or meaningful children).""" - if hasattr(node, "ds") and node.ds is not None: - # Has data variables - if hasattr(node.ds, "data_vars") and len(node.ds.data_vars) > 0: - return True - # Has meaningful attributes (more than just empty metadata) - if hasattr(node.ds, "attrs") and node.ds.attrs: - return True - - # Check if any children have meaningful content - if hasattr(node, "children") and node.children: - return any( - has_meaningful_content(child) for child in node.children.values() - ) - - return False - - def format_dimensions(dims: Any) -> str: - """Format dimensions in a compact way.""" - if not dims: - return "" - return f"({', '.join(f'{k}: {v}' for k, v in dims.items())})" - - def format_data_vars(data_vars: Any) -> str: - """Format data variables using xarray's rich HTML representation.""" - if not data_vars: - return "" - - # Create a temporary dataset with just these variables to get xarray's HTML - import xarray as xr - - temp_ds = xr.Dataset(data_vars) - - # Get xarray's HTML representation and extract just the variables section - try: - html_repr = temp_ds._repr_html_() - # Extract the variables section from xarray's HTML - # This gives us the rich, interactive variable display - return f'
{html_repr}
' - except Exception: - # Fallback to simple format if xarray HTML fails - vars_html = [] - for name, var in data_vars.items(): - dims_str = format_dimensions(dict(zip(var.dims, var.shape))) - dtype_str = str(var.dtype) - vars_html.append( - f""" -
- {name} - {dims_str} - {dtype_str} -
- """ - ) - return "".join(vars_html) - - def format_attributes(attrs: Any) -> str: - """Format attributes in a compact way.""" - if not attrs: - return "" - - # Show only first few attributes to keep it condensed - items = list(attrs.items())[:5] # Show max 5 attributes - attrs_html = [] - for key, value in items: - # Truncate long values - value_str = str(value) - if len(value_str) > 50: - value_str = value_str[:47] + "..." - attrs_html.append( - f""" -
- {key}: - {value_str} -
- """ - ) - - if len(attrs) > 5: - attrs_html.append( - f'
... and {len(attrs) - 5} more
' - ) - - return "".join(attrs_html) - - def render_node(node: Any, path: str = "", level: int = 0) -> str: - """Render a single node and its children.""" - if not has_meaningful_content(node): - return "" # Skip empty nodes - - node_name = path.split("/")[-1] if path else "root" - if not node_name: - node_name = "root" - - # Determine node type and content - has_data = hasattr(node, "ds") and node.ds is not None - data_vars_count = ( - len(node.ds.data_vars) if has_data and hasattr(node.ds, "data_vars") else 0 - ) - attrs_count = ( - len(node.ds.attrs) if has_data and hasattr(node.ds, "attrs") else 0 - ) - children_count = ( - len( - [ - child - for child in node.children.values() - if has_meaningful_content(child) - ] - ) - if hasattr(node, "children") - else 0 - ) - - # Create node summary - summary_parts = [] - if data_vars_count > 0: - summary_parts.append(f"{data_vars_count} variables") - if attrs_count > 0: - summary_parts.append(f"{attrs_count} attributes") - if children_count > 0: - summary_parts.append(f"{children_count} subgroups") - - summary = " • ".join(summary_parts) if summary_parts else "empty group" - - # Generate HTML for this node - node_html = f""" -
-
- - {'📁' if children_count > 0 else '📄'} - {node_name} - ({summary}) - -
- """ - - # Add data variables if present - if has_data and hasattr(node.ds, "data_vars") and node.ds.data_vars: - node_html += f""" -
-

Variables

-
- {format_data_vars(node.ds.data_vars)} -
-
- """ - - # Add attributes if present - if has_data and hasattr(node.ds, "attrs") and node.ds.attrs: - node_html += f""" -
-

Attributes

-
- {format_attributes(node.ds.attrs)} -
-
- """ - - # Add children - if hasattr(node, "children") and node.children: - children_html = [] - for child_name, child_node in node.children.items(): - child_path = f"{path}/{child_name}" if path else child_name - child_html = render_node(child_node, child_path, level + 1) - if child_html: # Only add if not empty - children_html.append(child_html) - - if children_html: - node_html += f""" -
-

Subgroups

-
- {"".join(children_html)} -
-
- """ - - node_html += """ -
-
-
- """ - - return node_html - - # Generate the complete tree - tree_content = render_node(dt) - - # Wrap in container with custom styles - return f""" -
- - {tree_content} -
- """ - - -def _generate_html_output( - dt: xr.DataTree, output_path: str, input_path: str, verbose: bool = False -) -> None: - """Generate HTML output for DataTree visualization. - - Parameters - ---------- - dt : xr.DataTree - DataTree to visualize - output_path : str - Path for HTML output file - input_path : str - Original input path for reference - verbose : bool, default False - Enable verbose output - """ - try: - # Generate optimized tree structure - tree_html = _generate_optimized_tree_html(dt) - - # Create a complete HTML document with EOPF-style formatting - html_content = f""" - - - - - - DataTree Visualization - {Path(input_path).name} - - - -
-
-

{Path(input_path).name}

-
-
-
Dataset Path
-
{input_path}
-
-
-
Total Groups
-
{len(dt.children)}
-
-
-
Generated
-
{__import__('datetime').datetime.now().strftime('%Y-%m-%d %H:%M:%S')}
-
-
-
- -
-
- {tree_html} -
-
- - -
- - -""" - - # Write HTML file - output_file = Path(output_path) - output_file.parent.mkdir(parents=True, exist_ok=True) - - with open(output_file, "w", encoding="utf-8") as f: - f.write(html_content) - - print(f"✅ HTML visualization generated: {output_file}") - - if verbose: - print(f" File size: {output_file.stat().st_size / 1024:.1f} KB") - print(f" Groups included: {len(dt.children)}") - - # Try to open in browser if possible - try: - import webbrowser - - webbrowser.open(f"file://{output_file.absolute()}") - print("🌐 Opening in default browser...") - except Exception as e: - if verbose: - print(f" Note: Could not auto-open browser: {e}") - print(f" You can open the file manually: {output_file.absolute()}") - - except Exception as e: - print(f"❌ Error generating HTML output: {e}") - if verbose: - import traceback - - traceback.print_exc() - sys.exit(1) - - -def validate_command(args: argparse.Namespace) -> None: - """Validate GeoZarr compliance of a dataset. - - Parameters - ---------- - args : argparse.Namespace - Command line arguments - """ - # Handle both local paths and URLs - input_path_str = args.input_path - if input_path_str.startswith(("http://", "https://", "s3://", "gs://")): - # URL - no local validation needed - input_path = input_path_str - else: - # Local path - validate existence - input_path = Path(input_path_str) - if not input_path.exists(): - print(f"Error: Input path {input_path} does not exist") - sys.exit(1) - input_path = str(input_path) - - try: - print(f"Validating GeoZarr compliance for: {input_path}") - # Use unified storage options for S3 support - storage_options = get_storage_options(input_path) - dt = xr.open_datatree( - input_path, engine="zarr", chunks="auto", storage_options=storage_options - ) - - compliance_issues = [] - total_variables = 0 - compliant_variables = 0 - - print("\nValidation Results:") - print("==================") - - for group_name, group in dt.children.items(): - print(f"\nGroup: {group_name}") - - if not hasattr(group, "data_vars") or not group.data_vars: - print(" ⚠️ No data variables found") - continue - - for var_name, var in group.data_vars.items(): - total_variables += 1 - issues = [] - - # Check for _ARRAY_DIMENSIONS - if "_ARRAY_DIMENSIONS" not in var.attrs: - issues.append("Missing _ARRAY_DIMENSIONS attribute") - - # Check for standard_name - if "standard_name" not in var.attrs: - issues.append("Missing standard_name attribute") - - # Check for grid_mapping (for data variables, not grid_mapping variables) - if ( - "grid_mapping" not in var.attrs - and "grid_mapping_name" not in var.attrs - ): - issues.append("Missing grid_mapping attribute") - - if issues: - print(f" ❌ {var_name}: {', '.join(issues)}") - compliance_issues.extend(issues) - else: - print(f" ✅ {var_name}: Compliant") - compliant_variables += 1 - - print("\nSummary:") - print("========") - print(f"Total variables checked: {total_variables}") - print(f"Compliant variables: {compliant_variables}") - print(f"Non-compliant variables: {total_variables - compliant_variables}") - - if compliance_issues: - print("\n❌ Dataset is NOT GeoZarr compliant") - print(f"Issues found: {len(compliance_issues)}") - if args.verbose: - print("Detailed issues:") - for issue in set(compliance_issues): - print(f" - {issue}") - else: - print("\n✅ Dataset appears to be GeoZarr compliant") - - except Exception as e: - print(f"❌ Error validating dataset: {e}") - if args.verbose: - import traceback - - traceback.print_exc() - sys.exit(1) def create_parser() -> argparse.ArgumentParser: - """Create the argument parser for the CLI. - - Returns: - ------- - argparse.ArgumentParser - Configured argument parser - """ parser = argparse.ArgumentParser( prog="eopf-geozarr", description="Convert EOPF datasets to GeoZarr compliant format", ) - parser.add_argument("--version", action="version", version="%(prog)s 0.1.0") - - subparsers = parser.add_subparsers(dest="command", help="Available commands") - - # Convert command - convert_parser = subparsers.add_parser( - "convert", help="Convert EOPF dataset to GeoZarr compliant format" - ) - convert_parser.add_argument( - "input_path", type=str, help="Path to input EOPF dataset (Zarr format)" - ) - convert_parser.add_argument( - "output_path", - type=str, - help="Path for output GeoZarr dataset (local path or S3 URL like s3://bucket/path)", - ) - convert_parser.add_argument( - "--groups", - type=str, - nargs="+", - default=["/measurements/r10m", "/measurements/r20m", "/measurements/r60m"], - help="Groups to convert (default: Sentinel-2 resolution groups)", - ) - convert_parser.add_argument( - "--spatial-chunk", - type=int, - default=4096, - help="Spatial chunk size for encoding (default: 4096)", - ) - convert_parser.add_argument( - "--min-dimension", - type=int, - default=256, - help="Minimum dimension for overview levels (default: 256)", - ) - convert_parser.add_argument( - "--tile-width", - type=int, - default=256, - help="Tile width for TMS compatibility (default: 256)", - ) - convert_parser.add_argument( - "--max-retries", - type=int, - default=3, - help="Maximum number of retries for network operations (default: 3)", - ) - convert_parser.add_argument( - "--crs-groups", - type=str, - nargs="*", - help="Groups that need CRS information added on best-effort basis (e.g., /conditions/geometry)", - ) - convert_parser.add_argument( - "--verbose", action="store_true", help="Enable verbose output" - ) - convert_parser.add_argument( - "--dask-cluster", - action="store_true", - help="Start a local dask cluster for parallel processing of chunks", - ) - convert_parser.add_argument( - "--dask-mode", - type=str, - default="threads", - choices=["threads", "processes", "single-threaded"], - help="Local Dask execution mode (default: threads).", - ) - convert_parser.add_argument( - "--dask-workers", - type=int, - default=4, - help="Number of Dask workers (default: 4).", - ) - convert_parser.add_argument( - "--dask-threads-per-worker", - type=int, - default=1, - help="Threads per worker (default: 1).", - ) - convert_parser.add_argument( - "--dask-perf-html", - type=str, - help="Write a Dask performance report HTML to this path (local only).", - ) - convert_parser.add_argument( - "--metrics", - action="store_true", - default=True, - help="Emit structured conversion metrics JSON (default: on). Use --no-metrics to disable.", - ) - convert_parser.add_argument( - "--no-metrics", - dest="metrics", - action="store_false", - help="Disable metrics emission.", - ) - convert_parser.add_argument( - "--list-groups", - action="store_true", - help="List available groups in the input and exit.", - ) - convert_parser.add_argument( - "--dry-run", - action="store_true", - help="Validate I/O, resolve groups and output, and print the plan without writing.", - ) - convert_parser.add_argument( - "--overwrite", - type=str, - choices=["fail", "skip", "replace", "merge"], - default="fail", - help="Behavior when output exists (local): fail, skip, replace, or merge (default: fail).", - ) - convert_parser.add_argument( - "--validate-groups", - type=str, - choices=["warn", "error"], - help="Validate requested groups against input; warn to prune, error to abort.", - ) - convert_parser.add_argument( - "--run-metadata", - type=str, - help="Also write finalized metrics payload to this JSON path.", - ) - convert_parser.set_defaults(func=convert_command) - - # Info command - info_parser = subparsers.add_parser( - "info", help="Display information about an EOPF dataset" - ) - info_parser.add_argument("input_path", type=str, help="Path to EOPF dataset") - info_parser.add_argument( - "--verbose", action="store_true", help="Enable verbose output" - ) - info_parser.add_argument( - "--html-output", - type=str, - help="Generate HTML visualization and save to specified file (e.g., dataset_info.html)", - ) - info_parser.set_defaults(func=info_command) - - # Validate command - validate_parser = subparsers.add_parser( - "validate", help="Validate GeoZarr compliance of a dataset" - ) - validate_parser.add_argument( - "input_path", type=str, help="Path to dataset to validate" - ) - validate_parser.add_argument( - "--verbose", action="store_true", help="Enable verbose output" - ) - validate_parser.set_defaults(func=validate_command) - + subs = parser.add_subparsers(dest="command", help="Commands") + + command_specs: List[Dict[str, Any]] = [ + { + "name": "convert", + "func": convert_command, + "help": "Convert dataset to GeoZarr", + "positional": [ + ("input_path", {"type": str}), + ("output_path", {"type": str}), + ], + "options": [ + ( + "--groups", + { + "type": str, + "nargs": "+", + "default": [ + "/measurements/r10m", + "/measurements/r20m", + "/measurements/r60m", + ], + }, + ), + ("--spatial-chunk", {"type": int, "default": 4096}), + ("--min-dimension", {"type": int, "default": 256}), + ("--tile-width", {"type": int, "default": 256}), + ("--max-retries", {"type": int, "default": 3}), + ( + "--crs-groups", + { + "type": str, + "nargs": "*", + "help": "Groups that need CRS information added", + }, + ), + ( + "--overwrite", + { + "choices": ["fail", "skip", "merge", "replace"], + "default": "fail", + "help": "Overwrite policy for existing output groups", + }, + ), + ("--verbose", {"action": "store_true"}), + ("--dask-cluster", {"action": "store_true"}), + ( + "--metrics-out", + {"type": str, "help": "Write metrics JSON (file or dir ending /)"}, + ), + ], + }, + { + "name": "info", + "func": info_command, + "help": "Show dataset info", + "positional": [("input_path", {"type": str})], + "options": [ + ("--verbose", {"action": "store_true"}), + ("--html-output", {"type": str, "help": "Write HTML visualization"}), + ], + }, + { + "name": "validate", + "func": validate_command, + "help": "Validate GeoZarr compliance", + "positional": [("input_path", {"type": str})], + "options": [("--verbose", {"action": "store_true"})], + }, + { + "name": "benchmark", + "func": benchmark_command, + "help": "Benchmark random window reads", + "positional": [("input_path", {"type": str})], + "options": [ + ("--compare-with", {"type": str}), + ("--groups", {"type": str, "nargs": "*"}), + ("--variables", {"type": str, "nargs": "*"}), + ("--window-size", {"type": int, "default": 512}), + ("--windows", {"type": int, "default": 5}), + ("--seed", {"type": int, "default": 42}), + ("--verbose", {"action": "store_true"}), + ("--dask-cluster", {"action": "store_true"}), + ], + }, + { + "name": "stac", + "func": stac_command, + "help": "Generate minimal STAC JSON", + "positional": [("input_path", {"type": str}), ("output", {"type": str})], + "options": [ + ("--type", {"choices": ["item", "collection"], "default": "item"}), + ("--id", {"type": str}), + ("--title", {"type": str}), + ( + "--bbox", + { + "type": float, + "nargs": 4, + "metavar": ("MINX", "MINY", "MAXX", "MAXY"), + }, + ), + ("--datetime", {"type": str}), + ("--collection-id", {"type": str}), + ("--license", {"type": str}), + ("--start-datetime", {"type": str}), + ("--end-datetime", {"type": str}), + ], + }, + ] + + for spec in command_specs: + p = subs.add_parser(spec["name"], help=spec["help"]) + for arg, kwargs in spec.get("positional", []): + p.add_argument(arg, **kwargs) + for arg, kwargs in spec.get("options", []): + p.add_argument(arg, **kwargs) + p.set_defaults(func=spec["func"]) return parser def main() -> None: - """Execute main entry point for the CLI.""" parser = create_parser() - if len(sys.argv) == 1: - # Show help if no arguments provided parser.print_help() return - args = parser.parse_args() - if hasattr(args, "func"): args.func(args) else: parser.print_help() -if __name__ == "__main__": +if __name__ == "__main__": # pragma: no cover main() diff --git a/src/eopf_geozarr/commands/__init__.py b/src/eopf_geozarr/commands/__init__.py new file mode 100644 index 0000000..b54ca27 --- /dev/null +++ b/src/eopf_geozarr/commands/__init__.py @@ -0,0 +1,13 @@ +from .cmd_benchmark import benchmark_command +from .cmd_convert import convert_command +from .cmd_info import info_command +from .cmd_stac import stac_command +from .cmd_validate import validate_command + +__all__ = [ + "convert_command", + "info_command", + "validate_command", + "benchmark_command", + "stac_command", +] diff --git a/src/eopf_geozarr/commands/_shared.py b/src/eopf_geozarr/commands/_shared.py new file mode 100644 index 0000000..fb0ce23 --- /dev/null +++ b/src/eopf_geozarr/commands/_shared.py @@ -0,0 +1,46 @@ +from __future__ import annotations + +import sys +from pathlib import Path +from typing import Callable, Dict, Optional, Tuple + +REMOTE_PREFIXES = ("http://", "https://", "s3://", "gs://") + + +def resolve_input_path(path_str: str) -> str: + if path_str.startswith(REMOTE_PREFIXES): + return path_str + p = Path(path_str) + if not p.exists(): + print(f"Error: Input path {p} does not exist") + sys.exit(1) + return str(p) + + +def prepare_output_path( + path_str: str, + is_s3_path: Callable[[str], bool], + validate_s3_access: Callable[[str], Tuple[bool, Optional[str]]], + get_s3_credentials_info: Callable[[], Dict[str, Optional[str]]], + verbose: bool = False, +) -> str: + if is_s3_path(path_str): + print("Validating S3 access...") + success, error_msg = validate_s3_access(path_str) + if not success: + print(f"Error: Cannot access S3 path {path_str}") + print(f" Reason: {error_msg}") + print("\nS3 configuration help:") + print(" Set AWS_ACCESS_KEY_ID / AWS_SECRET_ACCESS_KEY / AWS_DEFAULT_REGION") + print(" Set AWS_ENDPOINT_URL for custom providers or configure AWS CLI") + if verbose: + creds_info = get_s3_credentials_info() + print("\nCurrent AWS configuration:") + for key, value in creds_info.items(): + print(f" {key}: {value or 'Not set'}") + sys.exit(1) + print("S3 access validated successfully") + return path_str + out = Path(path_str) + out.parent.mkdir(parents=True, exist_ok=True) + return str(out) diff --git a/src/eopf_geozarr/commands/cmd_benchmark.py b/src/eopf_geozarr/commands/cmd_benchmark.py new file mode 100644 index 0000000..fc47920 --- /dev/null +++ b/src/eopf_geozarr/commands/cmd_benchmark.py @@ -0,0 +1,174 @@ +import argparse +import json +import sys +import time +from typing import Any, Dict, List, Optional, Tuple, cast + +import xarray as xr + +from ..conversion.fs_utils import get_storage_options +from .dask_utils import setup_dask_cluster + + +def _open_datatree(path: str, chunks: Optional[str] = None) -> xr.DataTree: + storage_options = get_storage_options(path) + return xr.open_datatree(path, engine="zarr", chunks=chunks, storage_options=storage_options) + + +def _detect_spatial_dims(var: xr.DataArray) -> Optional[Tuple[str, str]]: + spatial_y = {"y", "lat", "latitude", "rows"} + spatial_x = {"x", "lon", "longitude", "cols", "col"} + dims: List[str] = [str(d) for d in var.dims] + y_dim = next((d for d in dims if d in spatial_y), None) + x_dim = next((d for d in dims if d in spatial_x), None) + if y_dim is not None and x_dim is not None: + return y_dim, x_dim + if len(dims) >= 2: + return dims[-2], dims[-1] + return None + + +def _first_non_spatial_isel(var: xr.DataArray, yx: Tuple[str, str]) -> Dict[str, int]: + sel: Dict[str, int] = {} + for d_any in var.dims: + d = str(d_any) + if d not in yx: + sel[d] = 0 + return sel + + +def benchmark_command(args: argparse.Namespace) -> None: + dask_client = setup_dask_cluster( + enable_dask=getattr(args, "dask_cluster", False), verbose=args.verbose + ) + try: + dt_a = _open_datatree(args.input_path, chunks=None) + dt_b = ( + _open_datatree(args.compare_with, chunks=None) + if getattr(args, "compare_with", None) + else None + ) + groups: List[str] = ( + cast(List[str], args.groups) if args.groups else list(dt_a.children.keys()) + ) + variables: Optional[List[str]] = args.variables + rng = __import__("random") + rng.seed(args.seed) + + def run_one(dt: xr.DataTree, tag: str) -> Dict[str, Any]: + timings: List[float] = [] + bytes_read: List[int] = [] + samples = 0 + + def iter_leaf_nodes(path: str, node: xr.DataTree) -> List[Tuple[str, xr.DataTree]]: + out: List[Tuple[str, xr.DataTree]] = [] + try: + ds_local = node.to_dataset() + except Exception: + return out + if len(ds_local.data_vars) > 0: + out.append((path, node)) + for child_name, child in node.children.items(): + try: + child_path = f"{path.rstrip('/')}/{child_name}" if path else str(child_name) + out.extend(iter_leaf_nodes(child_path, cast(xr.DataTree, child))) + except Exception: + continue + return out + + for g in groups: + try: + node = cast(xr.DataTree, dt[g]) + except Exception: + if args.verbose: + print(f"Skipping missing group {g} in {tag}") + continue + leaves = iter_leaf_nodes(g, node) + if not leaves and args.verbose: + print(f"No data variables found under group {g} in {tag}") + for leaf_path, leaf_node in leaves: + ds = leaf_node.to_dataset() + var_names = variables or [str(n) for n in ds.data_vars] + for name in var_names: + if name not in ds.data_vars: + if args.verbose: + print(f"Variable {name} not in {leaf_path} for {tag}") + continue + var = ds[name] + yx = _detect_spatial_dims(var) + if not yx: + if args.verbose: + print( + f"Could not detect spatial dims for {leaf_path}/{name} in {tag}" + ) + continue + y_dim, x_dim = yx + ny, nx = int(var.sizes[y_dim]), int(var.sizes[x_dim]) + wy, wx = min(args.window_size, ny), min(args.window_size, nx) + if wy <= 0 or wx <= 0: + continue + for _ in range(args.windows): + sy = 0 if ny == wy else rng.randint(0, ny - wy) + sx = 0 if nx == wx else rng.randint(0, nx - wx) + base_isel = _first_non_spatial_isel(var, (y_dim, x_dim)) + sel = { + **base_isel, + y_dim: slice(sy, sy + wy), + x_dim: slice(sx, sx + wx), + } + t0 = time.perf_counter() + # Selection mapping keys/values constrained to str -> (int|slice) + arr = var.isel(**{k: v for k, v in sel.items()}).values + dt_sec = time.perf_counter() - t0 + timings.append(dt_sec) + try: + nbytes = int(getattr(arr, "nbytes", arr.size * arr.itemsize)) + except Exception: + nbytes = wy * wx + bytes_read.append(nbytes) + samples += 1 + if samples == 0: + return {"tag": tag, "samples": 0} + timings_sorted = sorted(timings) + med = timings_sorted[len(timings_sorted) // 2] + avg = sum(timings) / len(timings) + avg_mb = (sum(bytes_read) / len(bytes_read)) / (1024 * 1024) + return { + "tag": tag, + "samples": samples, + "median_s": med, + "mean_s": avg, + "mean_window_mb": avg_mb, + } + + res_a = run_one(dt_a, "A") + res_b = run_one(dt_b, "B") if dt_b is not None else None + print("Benchmark summary") + print("=================") + print(json.dumps({k: v for k, v in res_a.items() if k != "tag"}, indent=2)) + if res_b: + print("\nComparison") + print("-----------") + print(json.dumps({k: v for k, v in res_b.items() if k != "tag"}, indent=2)) + if res_a.get("samples") and res_b.get("samples"): + try: + speedup = res_a["median_s"] / res_b["median_s"] + print(f"\nSpeedup (A/B median): {speedup:.2f}x") + except Exception: + pass + except Exception as e: + print(f"Error during benchmark: {e}") + if args.verbose: + import traceback + + traceback.print_exc() + sys.exit(1) + finally: + if dask_client is not None: + try: + if hasattr(dask_client, "close"): + dask_client.close() + if args.verbose: + print("Dask cluster closed") + except Exception: + pass diff --git a/src/eopf_geozarr/commands/cmd_convert.py b/src/eopf_geozarr/commands/cmd_convert.py new file mode 100644 index 0000000..edd9825 --- /dev/null +++ b/src/eopf_geozarr/commands/cmd_convert.py @@ -0,0 +1,275 @@ +import argparse +import sys +from pathlib import Path +from typing import Any +from typing import Any as _Any +from typing import Dict, List, cast + +import xarray as xr + +from .. import create_geozarr_dataset +from ..conversion.fs_utils import ( + get_s3_credentials_info, + get_storage_options, + is_s3_path, + validate_s3_access, +) +from ..conversion.helpers import normalize_group_path +from ._shared import prepare_output_path, resolve_input_path +from .dask_utils import setup_dask_cluster + +MetricsRecorder: _Any +try: # Optional metrics + from ..metrics import MetricsRecorder as MetricsRecorderCls + + MetricsRecorder = MetricsRecorderCls +except Exception: # pragma: no cover + MetricsRecorder = None + + +def convert_command(args: argparse.Namespace) -> None: + dask_client = setup_dask_cluster( + enable_dask=getattr(args, "dask_cluster", False), verbose=args.verbose + ) + + rec = None + metrics_path = None + if getattr(args, "metrics_out", None) and MetricsRecorder is not None: + try: + rec = MetricsRecorder() + rec.set_environment() + except Exception: + rec = None + + try: + input_path = resolve_input_path(args.input_path) + output_path = prepare_output_path( + args.output_path, + is_s3_path=is_s3_path, + validate_s3_access=validate_s3_access, + get_s3_credentials_info=get_s3_credentials_info, + verbose=args.verbose, + ) + + # Normalize empty --crs-groups flag (present but with no values) -> [] + if getattr(args, "crs_groups", None) is None and "--crs-groups" in sys.argv: + # Normalize explicit empty list + args.crs_groups = [] + + if args.verbose: + print(f"Loading EOPF dataset from: {input_path}") + print(f"Requested groups: {args.groups}") + print(f"CRS groups: {args.crs_groups}") + print(f"Output path: {output_path}") + print(f"Spatial chunk size: {args.spatial_chunk}") + print(f"Min dimension: {args.min_dimension}") + print(f"Tile width: {args.tile_width}") + + print("Loading EOPF dataset...") + storage_options = get_storage_options(input_path) + if rec: + with rec.time_step("open_input"): + dt = xr.open_datatree( + str(input_path), + engine="zarr", + chunks="auto", + storage_options=storage_options, + ) + else: + dt = xr.open_datatree( + str(input_path), + engine="zarr", + chunks="auto", + storage_options=storage_options, + ) + + if args.verbose: + print(f"Loaded DataTree with {len(dt.children)} groups") + print("Available top-level groups:") + for group_name in dt.children: + print(f" - {group_name}") + + def iter_leaf_groups(path: str, node: xr.DataTree) -> List[str]: + out: List[str] = [] + try: + ds_local = node.to_dataset() + if len(ds_local.data_vars) > 0: + return [normalize_group_path(path)] + except Exception: + return out + for child_name, child in getattr(node, "children", {}).items(): + child_path = f"{path.rstrip('/')}/{child_name}" if path else child_name + out.extend(iter_leaf_groups(child_path, cast(xr.DataTree, child))) + return out + + groups_requested: List[str] = args.groups + groups_expanded: List[str] = [] + for g in groups_requested: + g_norm = normalize_group_path(g) + if g_norm in {"/", "//"}: + try: + if len(dt.to_dataset().data_vars) > 0: + groups_expanded.append("/") + except Exception: + pass + for child_name, child in dt.children.items(): + groups_expanded.extend(iter_leaf_groups(child_name, child)) + continue + try: + node = dt[g_norm] + except Exception: + try: + node = dt[g_norm.lstrip("/")] + except Exception: + if args.verbose: + print(f"Warning: Requested group not found: {g_norm}; skipping") + continue + from typing import cast as _cast + + leaves = iter_leaf_groups(g_norm, _cast(xr.DataTree, node)) + if leaves: + groups_expanded.extend(leaves) + elif args.verbose: + print(f"Warning: No data variables found under {g_norm}; skipping") + + if not groups_expanded: + print("No valid groups with data variables were found to convert. Nothing to do.") + sys.exit(1) + + if args.verbose: + print("Groups to convert (expanded to leaves):") + for g in groups_expanded: + print(f" - {g}") + + if rec: + try: + dask_meta: Dict[str, Any] = {} + if dask_client is not None: + try: + sched = dask_client.scheduler_info() + workers = sched.get("workers", {}) + any_worker: Dict[str, Any] = ( + cast(Dict[str, Any], next(iter(workers.values()))) if workers else {} + ) + dask_meta = { + "mode": "local", + "workers": len(workers), + "threads_per_worker": any_worker.get("nthreads"), + } + except Exception: + dask_meta = {"mode": "local"} + rec.set_input( + source_uri=input_path, + profile=None, + groups=groups_expanded, + dask=dask_meta, + ) + except Exception: + pass + + print("Converting to GeoZarr compliant format...") + if rec: + with rec.time_step("convert"): + dt_geozarr = create_geozarr_dataset( + dt_input=dt, + groups=groups_expanded, + output_path=output_path, + spatial_chunk=args.spatial_chunk, + min_dimension=args.min_dimension, + tile_width=args.tile_width, + max_retries=args.max_retries, + crs_groups=args.crs_groups, + overwrite=getattr(args, "overwrite", "fail"), + ) + else: + dt_geozarr = create_geozarr_dataset( + dt_input=dt, + groups=groups_expanded, + output_path=output_path, + spatial_chunk=args.spatial_chunk, + min_dimension=args.min_dimension, + tile_width=args.tile_width, + max_retries=args.max_retries, + crs_groups=args.crs_groups, + overwrite=getattr(args, "overwrite", "fail"), + ) + + print("Conversion completed") + print(f"Output saved to: {output_path}") + + if rec: + try: + with rec.time_step("output_summary"): + rec.build_output_summary(output_path) + payload = rec.finalize("success") + metrics_path = Path(args.metrics_out) + if str(metrics_path).endswith("/") or metrics_path.is_dir(): + metrics_path = metrics_path / "run_summary.json" + if not metrics_path.suffix: + metrics_path = metrics_path.with_name(metrics_path.name + ".json") + from ..metrics import MetricsRecorder as _MR + + _MR.write_json(metrics_path, payload) + if args.verbose: + print(f"Metrics written to: {metrics_path}") + except Exception as me: # pragma: no cover + if args.verbose: + print(f"Warning: failed to write metrics: {me}") + + if args.verbose: + if hasattr(dt_geozarr, "children"): + print(f"Converted DataTree has {len(dt_geozarr.children)} groups") + print("Converted groups:") + for group_name in dt_geozarr.children: + print(f" - {group_name}") + else: + print("Converted dataset (single group)") + + except Exception as e: + err_msg = str(e) + print(f"Error during conversion: {err_msg}") + if ( + "already exists, but encoding was provided" in err_msg + and getattr(args, "overwrite", "fail") == "fail" + ): + print( + "Hint: Existing arrays detected in output. Re-run with --overwrite replace to force a clean write, " + "or use --overwrite skip/merge for incremental behavior (if supported)." + ) + if rec: + try: + if not rec.output_info: + rec.build_output_summary(output_path) + payload = rec.finalize("error", exception=str(e)[:500]) + metrics_path = metrics_path or ( + Path(getattr(args, "metrics_out")) + if getattr(args, "metrics_out", None) + else None + ) + if metrics_path is not None: + if str(metrics_path).endswith("/"): + metrics_path = metrics_path / "run_summary.json" + if not metrics_path.suffix: + metrics_path = metrics_path.with_name(metrics_path.name + ".json") + from ..metrics import MetricsRecorder as _MR + + _MR.write_json(metrics_path, payload) + if args.verbose: + print(f"Error metrics written to: {metrics_path}") + except Exception: + pass + if args.verbose: + import traceback + + traceback.print_exc() + sys.exit(1) + finally: + if dask_client is not None: + try: + if hasattr(dask_client, "close"): + dask_client.close() + if args.verbose: + print("Dask cluster closed") + except Exception: + if args.verbose: + print("Warning: Error closing dask cluster") diff --git a/src/eopf_geozarr/commands/cmd_info.py b/src/eopf_geozarr/commands/cmd_info.py new file mode 100644 index 0000000..a9621d6 --- /dev/null +++ b/src/eopf_geozarr/commands/cmd_info.py @@ -0,0 +1,34 @@ +import argparse +import sys + +import xarray as xr + +from ..conversion.fs_utils import get_storage_options +from ._shared import resolve_input_path +from .html_utils import generate_html_output + + +def info_command(args: argparse.Namespace) -> None: + input_path = resolve_input_path(args.input_path) + + try: + print(f"Loading dataset from: {input_path}") + storage_options = get_storage_options(input_path) + dt = xr.open_datatree( + input_path, engine="zarr", chunks="auto", storage_options=storage_options + ) + if getattr(args, "html_output", None): + generate_html_output(dt, args.html_output, input_path, args.verbose) + else: + print("\nDataset Information:") + print("==================") + print(f"Total groups: {len(dt.children)}") + print("\nGroup structure:") + print(dt) + except Exception as e: + print(f"Error reading dataset: {e}") + if args.verbose: + import traceback + + traceback.print_exc() + sys.exit(1) diff --git a/src/eopf_geozarr/commands/cmd_stac.py b/src/eopf_geozarr/commands/cmd_stac.py new file mode 100644 index 0000000..13bf623 --- /dev/null +++ b/src/eopf_geozarr/commands/cmd_stac.py @@ -0,0 +1,92 @@ +import argparse +import json +from datetime import datetime +from pathlib import Path +from typing import Any, Dict, List, Optional + +import fsspec + +from ..conversion.fs_utils import get_storage_options, is_s3_path + + +def _bbox_to_geometry(bbox: List[float]) -> Dict[str, Any]: + minx, miny, maxx, maxy = bbox + return { + "type": "Polygon", + "coordinates": [ + [ + [minx, miny], + [maxx, miny], + [maxx, maxy], + [minx, maxy], + [minx, miny], + ] + ], + } + + +def _write_json(path: str, obj: Dict[str, Any]) -> None: + out_path = path + "stac.json" if path.endswith("/") else path + if not is_s3_path(out_path) and not out_path.startswith(("http://", "https://")): + Path(out_path).parent.mkdir(parents=True, exist_ok=True) + storage_options = get_storage_options(out_path) or {} + open_opts = {"compression": None} + open_opts.update(storage_options) + with fsspec.open(out_path, "w", **open_opts) as f: + f.write(json.dumps(obj, indent=2)) + print(f"STAC written to: {out_path}") + + +def stac_command(args: argparse.Namespace) -> None: + ds_id = args.id or Path(args.input_path).stem + link_href = args.input_path + now_iso = ( + datetime.now(tz=__import__("datetime").timezone.utc) + .replace(microsecond=0) + .isoformat() + .replace("+00:00", "Z") + ) + bbox: Optional[List[float]] = None + if args.bbox and len(args.bbox) == 4: + bbox = [float(x) for x in args.bbox] + if args.type == "item": + item: Dict[str, Any] = { + "type": "Feature", + "stac_version": "1.0.0", + "id": ds_id, + "properties": {"datetime": args.datetime or now_iso}, + "links": [], + "assets": { + "geozarr": { + "href": link_href, + "type": "application/vnd+zarr", + "roles": ["data"], + "title": args.title or "GeoZarr dataset", + } + }, + } + if bbox: + item["bbox"] = bbox + item["geometry"] = _bbox_to_geometry(bbox) + if args.collection_id: + item["collection"] = args.collection_id + _write_json(args.output, item) + else: + if not bbox: + print("Warning: Collection without bbox provided; extent will be minimal placeholder.") + bbox = [0.0, 0.0, 0.0, 0.0] + collection: Dict[str, Any] = { + "type": "Collection", + "stac_version": "1.0.0", + "id": ds_id, + "description": args.title or ds_id, + "license": args.license or "proprietary", + "extent": { + "spatial": {"bbox": [bbox]}, + "temporal": { + "interval": [[args.start_datetime or now_iso, args.end_datetime or None]] + }, + }, + "links": [], + } + _write_json(args.output, collection) diff --git a/src/eopf_geozarr/commands/cmd_validate.py b/src/eopf_geozarr/commands/cmd_validate.py new file mode 100644 index 0000000..d65ae37 --- /dev/null +++ b/src/eopf_geozarr/commands/cmd_validate.py @@ -0,0 +1,91 @@ +import argparse +import sys +from typing import Iterable, List, Tuple + +import xarray as xr + +from ..conversion.fs_utils import get_storage_options +from ._shared import resolve_input_path + + +def validate_command(args: argparse.Namespace) -> None: + input_path = resolve_input_path(args.input_path) + + try: + print(f"Validating GeoZarr compliance for: {input_path}") + storage_options = get_storage_options(input_path) + dt = xr.open_datatree( + input_path, engine="zarr", chunks="auto", storage_options=storage_options + ) + compliance_issues: List[str] = [] + total_variables = 0 + compliant_variables = 0 + print("\nValidation Results:") + print("==================") + + def walk(node: xr.DataTree, path: str = "") -> Iterable[Tuple[str, xr.DataTree]]: + # Depth-first traversal yielding (path, node) + yield path, node + children = getattr(node, "children", None) + if children: + for child_name, child in children.items(): + child_path = f"{path}/{child_name}" if path else child_name + yield from walk(child, child_path) + + any_reported = False + + def check_var(var: xr.DataArray) -> List[str]: + issues: List[str] = [] + attrs = var.attrs + if "_ARRAY_DIMENSIONS" not in attrs: + issues.append("Missing _ARRAY_DIMENSIONS attribute") + if "standard_name" not in attrs: + issues.append("Missing standard_name attribute") + if "grid_mapping" not in attrs and "grid_mapping_name" not in attrs: + issues.append("Missing grid_mapping attribute") + return issues + + for path, node in walk(dt): + try: + ds = node.to_dataset() + except Exception: + continue + if not len(ds.data_vars): + continue + any_reported = True + print(f"\nGroup: /{path.lstrip('/')}") + for var_name, var in ds.data_vars.items(): + if var_name in {"spatial_ref", "crs"}: + continue + total_variables += 1 + issues = check_var(var) + if issues: + print(f" FAIL {var_name}: {', '.join(issues)}") + compliance_issues.extend(issues) + else: + print(f" OK {var_name}: Compliant") + compliant_variables += 1 + + print("\nSummary:") + print("========") + if not any_reported: + print("No data variables found in any groups. Dataset may be metadata-only or empty.") + print(f"Total variables checked: {total_variables}") + print(f"Compliant variables: {compliant_variables}") + print(f"Non-compliant variables: {total_variables - compliant_variables}") + if compliance_issues: + print("\nDataset is NOT GeoZarr compliant") + print(f"Issues found: {len(compliance_issues)}") + if args.verbose: + print("Detailed issues:") + for issue in set(compliance_issues): + print(f" - {issue}") + else: + print("\nDataset appears to be GeoZarr compliant") + except Exception as e: + print(f"Error validating dataset: {e}") + if args.verbose: + import traceback + + traceback.print_exc() + sys.exit(1) diff --git a/src/eopf_geozarr/commands/dask_utils.py b/src/eopf_geozarr/commands/dask_utils.py new file mode 100644 index 0000000..0c09d3b --- /dev/null +++ b/src/eopf_geozarr/commands/dask_utils.py @@ -0,0 +1,26 @@ +import sys +from typing import Any, Optional + + +def setup_dask_cluster(enable_dask: bool, verbose: bool = False) -> Optional[Any]: + if not enable_dask: + return None + try: + from dask.distributed import Client + + client = Client() + if verbose: + print(f"Dask cluster started: {client}") + print(f" Dashboard: {client.dashboard_link}") + print(f" Workers: {len(client.scheduler_info()['workers'])}") + else: + print("Dask cluster started for parallel processing") + return client + except ImportError: + print( + "Error: dask.distributed not available. Install with: pip install 'dask[distributed]'" + ) + sys.exit(1) + except Exception as e: # pragma: no cover + print(f"Error starting dask cluster: {e}") + sys.exit(1) diff --git a/src/eopf_geozarr/commands/html_utils.py b/src/eopf_geozarr/commands/html_utils.py new file mode 100644 index 0000000..2a4a4a2 --- /dev/null +++ b/src/eopf_geozarr/commands/html_utils.py @@ -0,0 +1,119 @@ +import sys +from pathlib import Path +from typing import Any, Iterable, Mapping, MutableMapping, Sequence + +import xarray as xr + + +def _optimized_tree_html(dt: xr.DataTree) -> str: # condensed from original + def has_content(node: Any) -> bool: + ds: xr.Dataset | None = getattr(node, "ds", None) + if ds is not None: + if getattr(ds, "data_vars", None) and len(ds.data_vars) > 0: + return True + if getattr(ds, "attrs", None): + return True + children: MutableMapping[str, Any] | None = getattr(node, "children", None) + if children: + return any(has_content(c) for c in children.values()) + return False + + def format_data_vars(data_vars: Mapping[str, Any]) -> str: + if not data_vars: + return "" + try: + html_repr = xr.Dataset(data_vars)._repr_html_() + return f'
{html_repr}
' + except Exception: + return "".join( + f"
{name}
" + for name in data_vars + ) + + def format_attrs(attrs: Mapping[str, Any]) -> str: + if not attrs: + return "" + items: Sequence[tuple[str, Any]] = list(attrs.items())[:5] + parts: list[str] = [] + for k, v in items: + vstr = str(v) + if len(vstr) > 50: + vstr = vstr[:47] + "..." + parts.append( + f"
{k}:{vstr}
" + ) + if len(attrs) > 5: + parts.append(f"
... and {len(attrs) - 5} more
") + return "".join(parts) + + def render(node: Any, path: str = "", level: int = 0) -> str: + if not has_content(node): + return "" + node_name = path.split("/")[-1] if path else "root" + has_data = getattr(node, "ds", None) is not None and getattr(node.ds, "data_vars", None) + data_vars_count = len(node.ds.data_vars) if has_data else 0 + attrs_count = len(node.ds.attrs) if has_data and node.ds.attrs else 0 + children: Iterable[Any] = [ + c for c in getattr(node, "children", {}).values() if has_content(c) + ] + summary: list[str] = [] + if data_vars_count: + summary.append(f"{data_vars_count} variables") + if attrs_count: + summary.append(f"{attrs_count} attributes") + if children: + child_list = list(children) + summary.append(f"{len(child_list)} subgroups") + summary_txt = " • ".join(summary) if summary else "empty group" + html: list[str] = [ + f"
", + f"
{'📁' if list(children) else '📄'}{node_name}({summary_txt})
", + ] + if has_data: + html.append( + "

Variables

" + + format_data_vars(getattr(node.ds, "data_vars")) + + "
" + ) + if getattr(node.ds, "attrs"): + html.append( + "

Attributes

" + + format_attrs(getattr(node.ds, "attrs")) + + "
" + ) + if children: + html.append( + "

Subgroups

" + ) + for cname, cnode in getattr(node, "children", {}).items(): + cpath = f"{path}/{cname}" if path else cname + ch = render(cnode, cpath, level + 1) + if ch: + html.append(ch) + html.append("
") + html.append("
") + return "".join(html) + + tree_content = render(dt) + return f"
{tree_content}
" + + +def generate_html_output( + dt: xr.DataTree, output_path: str, input_path: str, verbose: bool = False +) -> None: + try: + tree_html = _optimized_tree_html(dt) + html_content = f"DataTree - {Path(input_path).name}{tree_html}" + out = Path(output_path) + out.parent.mkdir(parents=True, exist_ok=True) + out.write_text(html_content, encoding="utf-8") + print(f"HTML visualization generated: {out}") + if verbose: + print(f" File size: {out.stat().st_size / 1024:.1f} KB") + except Exception as e: + print(f"Error generating HTML output: {e}") + if verbose: + import traceback + + traceback.print_exc() + sys.exit(1) diff --git a/src/eopf_geozarr/conversion/__init__.py b/src/eopf_geozarr/conversion/__init__.py index 9f72087..4286323 100644 --- a/src/eopf_geozarr/conversion/__init__.py +++ b/src/eopf_geozarr/conversion/__init__.py @@ -10,14 +10,9 @@ validate_s3_access, write_s3_json_metadata, ) -from .geozarr import ( - async_consolidate_metadata, - calculate_overview_levels, - consolidate_metadata, - create_geozarr_dataset, - iterative_copy, - setup_datatree_metadata_geozarr_spec_compliant, -) +from .geozarr import create_geozarr_dataset, setup_datatree_metadata_geozarr_spec_compliant +from .metadata import async_consolidate_metadata, consolidate_metadata +from .multiscales import calculate_overview_levels from .utils import ( calculate_aligned_chunk_size, downsample_2d_array, @@ -28,7 +23,6 @@ __all__ = [ "create_geozarr_dataset", "setup_datatree_metadata_geozarr_spec_compliant", - "iterative_copy", "consolidate_metadata", "async_consolidate_metadata", "calculate_overview_levels", diff --git a/src/eopf_geozarr/conversion/geozarr.py b/src/eopf_geozarr/conversion/geozarr.py index 7becea3..f172333 100644 --- a/src/eopf_geozarr/conversion/geozarr.py +++ b/src/eopf_geozarr/conversion/geozarr.py @@ -13,20 +13,51 @@ - Proper multiscales metadata structure """ -import dataclasses +import logging import os -import shutil import time from typing import Any, Dict, List, Optional, Tuple -import numpy as np import xarray as xr -import zarr -from zarr.core.sync import sync -from zarr.storage import StoreLike -from zarr.storage._common import make_store_path from . import fs_utils, utils +from .helpers import build_var_encodings, inject_crs_if_requested, maybe_step, normalize_group_path +from .hierarchy import ensure_group_hierarchy as _ensure_group_hierarchy +from .hierarchy import safe_rmtree as _safe_rmtree +from .io import load_existing_dataset + +# encoding creation for level-0 is now minimal and done inline +from .metadata import consolidate_metadata +from .multiscales import create_geozarr_compliant_multiscales +from .prepare import ( + _find_reference_crs, + prepare_dataset_with_crs_info, + setup_datatree_metadata_geozarr_spec_compliant, +) +from .zarr_write import write_dataset_zarr + +log = logging.getLogger(__name__) + + +def _normalize_spatial_dim_order(ds: xr.Dataset) -> xr.Dataset: + """Ensure spatial dims appear in the order ('y','x') for all data vars. + + Keeps any leading non-spatial dims (e.g., 'band', 'time') unchanged and appends + 'y','x' at the end when both are present. Grid-mapping variables are ignored. + """ + try: + for var in list(ds.data_vars): + if utils.is_grid_mapping_variable(ds, var): + continue + dims = tuple(ds[var].dims) + if "y" in dims and "x" in dims: + leading = [d for d in dims if d not in ("y", "x")] + new_order = tuple(leading + ["y", "x"]) + if new_order != dims: + ds[var] = ds[var].transpose(*new_order) + return ds + except Exception: + return ds def create_geozarr_dataset( @@ -38,6 +69,12 @@ def create_geozarr_dataset( tile_width: int = 256, max_retries: int = 3, crs_groups: Optional[List[str]] = None, + overwrite: str = "fail", + max_overview_levels: Optional[int] = None, + skip_overviews: bool = False, + base_write_mode: str = "single", + copy_non_selected_groups: bool = False, + crs_injection_groups: Optional[List[str]] = None, ) -> xr.DataTree: """ Create a GeoZarr-spec 0.4 compliant dataset from EOPF data. @@ -47,7 +84,7 @@ def create_geozarr_dataset( dt_input : xr.DataTree Input EOPF DataTree groups : list[str] - List of group names to process as Geozarr datasets + List of group names to process as GeoZarr datasets output_path : str Output path for the Zarr store spatial_chunk : int, default 4096 @@ -59,268 +96,159 @@ def create_geozarr_dataset( max_retries : int, default 3 Maximum number of retries for network operations crs_groups : list[str], optional - List of group names that need CRS information added on best-effort basis + List of group names that need CRS information added on best-effort basis (no-op here) + overwrite : str, default "fail" + Overwrite policy: one of {"fail", "skip", "merge", "replace"} Returns ------- xr.DataTree DataTree containing the GeoZarr compliant data """ - from zarr.codecs import BloscCodec - - dt = dt_input.copy() - compressor = BloscCodec(cname="zstd", clevel=3, shuffle="shuffle", blocksize=0) - - # Get the measurements datasets prepared for GeoZarr compliance - geozarr_groups = setup_datatree_metadata_geozarr_spec_compliant(dt, groups) - - # Create the GeoZarr compliant store through iterative processing - dt_geozarr = iterative_copy( - dt, - geozarr_groups, - output_path, - compressor, - spatial_chunk, - min_dimension, - tile_width, - max_retries, - crs_groups, - ) - - # Consolidate metadata at the root level AFTER all groups are written - print("Consolidating metadata at root level for consistent zarr access...") - try: - zarr_group = fs_utils.open_zarr_group(output_path, mode="r+") - consolidate_metadata(zarr_group.store) - print("✅ Root level metadata consolidation completed") - except Exception as e: - print(f"⚠️ Warning: Root level consolidation failed: {e}") - - return dt_geozarr - - -def setup_datatree_metadata_geozarr_spec_compliant( - dt: xr.DataTree, groups: List[str] -) -> Dict[str, xr.Dataset]: - """ - Set up GeoZarr-spec compliant CF standard names and CRS information. - - Parameters - ---------- - dt : xr.DataTree - The data tree containing the datasets to process - groups : list[str] - List of group names to process as Geozarr datasets - - Returns - ------- - dict[str, xr.Dataset] - Dictionary of datasets with GeoZarr compliance applied - """ - geozarr_groups = {} - grid_mapping_var_name = "spatial_ref" - - for key in groups: - if not dt[key].data_vars: - continue - - print(f"Processing group for GeoZarr compliance: {key}") - ds = dt[key].to_dataset().copy() - - # Process all bands in the group - for band in ds.data_vars: - print(f" Processing band: {band}") - - # Set CF standard name and _ARRAY_DIMENSIONS - ds[band].attrs["standard_name"] = "toa_bidirectional_reflectance" - if hasattr(ds[band], "dims"): - ds[band].attrs["_ARRAY_DIMENSIONS"] = list(ds[band].dims) - ds[band].attrs["grid_mapping"] = grid_mapping_var_name - - # Set CRS if available - if "proj:epsg" in ds[band].attrs: - epsg = ds[band].attrs["proj:epsg"] - print(f" Setting CRS for {band} to EPSG:{epsg}") - ds = ds.rio.write_crs(f"epsg:{epsg}") - - # Add _ARRAY_DIMENSIONS to coordinate variables - _add_coordinate_metadata(ds) - - # Set up spatial_ref variable with GeoZarr required attributes - _setup_grid_mapping(ds, grid_mapping_var_name) - - geozarr_groups[key] = ds - - return geozarr_groups - - -def iterative_copy( - dt_input: xr.DataTree, - geozarr_groups: Dict[str, xr.Dataset], - output_path: str, - compressor: Any, - spatial_chunk: int = 4096, - min_dimension: int = 256, - tile_width: int = 256, - max_retries: int = 3, - crs_groups: Optional[List[str]] = None, -) -> xr.DataTree: - """ - Iteratively copy groups from original DataTree to GeoZarr DataTree. - - Parameters - ---------- - dt_input : xarray.DataTree - Input DataTree to copy from - geozarr_groups : dict[str, xr.Dataset] - Dictionary of GeoZarr groups to process - output_path : str - Output path for the Zarr store - compressor : Any - Compressor to use for encoding - spatial_chunk : int, default 4096 - Spatial chunk size for encoding - min_dimension : int, default 256 - Minimum dimension for overview levels - tile_width : int, default 256 - Tile width for TMS compatibility - max_retries : int, default 3 - Maximum number of retries for network operations - crs_groups : list[str], optional - List of group names that need CRS information added on best-effort basis - - Returns - ------- - xarray.DataTree - Updated GeoZarr DataTree with copied groups and variables including multiscale children - """ - # Create result DataTree and initialize storage + # Initialize an empty result and create the root store dt_result = xr.DataTree() storage_options = fs_utils.get_storage_options(output_path) - dt_result.to_zarr( + write_dataset_zarr( + dt_result.to_dataset() if hasattr(dt_result, "to_dataset") else xr.Dataset(), output_path, mode="a", consolidated=True, - compute=True, + safe_chunks=False, storage_options=storage_options, ) - written_groups = set() - reference_crs = None + # Prepare GeoZarr-compliant datasets per ADR (CF attrs, _ARRAY_DIMENSIONS, grid_mapping) + with maybe_step(None, "prepare_metadata"): + geozarr_groups = setup_datatree_metadata_geozarr_spec_compliant(dt_input, groups) - # Process all groups in the tree using iterative approach - for relative_path, node in dt_input.subtree_with_keys: - if relative_path == ".": - continue + # ADR compression: Blosc zstd level 3 with shuffle + try: + from zarr.codecs import BloscCodec + + compressor = BloscCodec(cname="zstd", clevel=3, shuffle="shuffle", blocksize=0) + except Exception: + compressor = None + + # Orchestrator uses ADR compressor + writer = GeoZarrWriter( + output_path=output_path, + compressor=compressor, + spatial_chunk=spatial_chunk, + min_dimension=min_dimension, + tile_width=tile_width, + max_retries=max_retries, + overwrite=overwrite, + max_overview_levels=max_overview_levels, + skip_overviews=skip_overviews, + base_write_mode=base_write_mode, + ) - current_group_path = "/" + relative_path - print(f"Processing group '{current_group_path}' in iterative copy") + requested = {normalize_group_path(g) for g in groups} - # Handle GeoZarr groups with special processing - if current_group_path in geozarr_groups: - print(f"Processing '{current_group_path}' as GeoZarr group") - write_geozarr_group( - dt_result, - current_group_path, - geozarr_groups[current_group_path], - output_path, - spatial_chunk=spatial_chunk, - compressor=compressor, - max_retries=max_retries, - min_dimension=min_dimension, - tile_width=tile_width, - ) - written_groups.add(current_group_path) - continue - - # Get dataset from the node - ds = node.to_dataset().drop_encoding() - - # Add CRS information if needed - if crs_groups and current_group_path in crs_groups: - print(f"Adding CRS information for group '{current_group_path}'") - if reference_crs is None: - reference_crs = _find_reference_crs(geozarr_groups) - ds = prepare_dataset_with_crs_info(ds, reference_crs=reference_crs) - - # Process groups with data variables - if node.data_vars: - print( - f"Writing group '{current_group_path}' with data variables to GeoZarr DataTree" - ) + # Write only the requested target groups + crs_requested = {normalize_group_path(g) for g in (crs_groups or [])} if crs_groups else None - # Set up encoding - encoding = _create_encoding(ds, compressor, spatial_chunk) + def ref_crs_func() -> Any: + return _find_reference_crs(geozarr_groups) - # Write the dataset - group_param = current_group_path.lstrip("/") if current_group_path else None - ds.to_zarr( + for key, ds in geozarr_groups.items(): + current_group = normalize_group_path(key) + ds = inject_crs_if_requested( + current_group, + ds, + crs_requested, + ref_crs_func, + prepare_dataset_with_crs_info, + verbose=True if crs_requested else False, + ) + with maybe_step(None, "write_group"): + dt_result = writer.write_group(dt_result, current_group, ds) + + # Report any explicitly requested crs_groups that were not found + if crs_groups: + existing = {normalize_group_path(g) for g in geozarr_groups.keys()} + for g in crs_groups: + g_norm = normalize_group_path(g) + if g_norm not in existing: + print(f"CRS group {g_norm} not found in DataTree (best-effort skip)") + + # Optionally copy non-selected groups verbatim (no overviews) for parity + if copy_non_selected_groups: + storage_options = fs_utils.get_storage_options(output_path) + for rel_path, node in dt_input.subtree_with_keys: + if rel_path == ".": + continue + grp = "/" + rel_path + if grp in requested: + continue + try: + ds = node.to_dataset().drop_encoding() + except Exception: + continue + if len(ds.data_vars) == 0 and len(ds.coords) == 0: + continue + + # Optional CRS injection for specified groups + if crs_injection_groups and ( + grp in crs_injection_groups or rel_path in crs_injection_groups + ): + try: + ref_crs = _find_reference_crs(geozarr_groups) + ds = prepare_dataset_with_crs_info(ds, reference_crs=ref_crs) + except Exception as e: + log.debug(f"CRS injection skipped for {grp}: {e}") + + # Minimal encoding: reuse helper + encoding: Dict[str, Any] = build_var_encodings(ds, compressor) + + # Clear legacy encodings to avoid zarr v2 artifacts + try: + for name in list(ds.variables): + enc = getattr(ds[name], "encoding", None) + if isinstance(enc, dict): + enc.clear() + except Exception: + pass + + # Ensure hierarchy exists and write dataset directly (no multiscales) + _ensure_group_hierarchy(output_path, grp) + mode = "a" + target_path = fs_utils.normalize_path(f"{output_path}/{rel_path}") + try: + exists = fs_utils.path_exists(target_path) + except Exception: + exists = False + if overwrite == "replace" and exists: + try: + _safe_rmtree(target_path) + exists = False + except Exception as e: + log.warning(f"Failed to clean existing group at {target_path}: {e}") + if not exists: + mode = "w" + + write_dataset_zarr( + ds, output_path, - group=group_param, - mode="w", - consolidated=False, + group=rel_path, + mode=mode, + consolidated=True, zarr_format=3, - encoding=encoding, + encoding=encoding if encoding else None, + align_chunks=True, + safe_chunks=False, storage_options=storage_options, ) - dt_result[relative_path] = xr.DataTree(ds) - - written_groups.add(current_group_path) - - return dt_result if isinstance(dt_result, xr.DataTree) else xr.DataTree(dt_result) - - -def prepare_dataset_with_crs_info( - ds: xr.Dataset, reference_crs: Optional[str] = None -) -> xr.Dataset: - """ - Prepare a dataset with CRS information without writing it to disk. - - Parameters - ---------- - ds : xr.Dataset - Dataset to prepare with CRS information - reference_crs : str, optional - Reference CRS to use (e.g., "epsg:4326") - - Returns - ------- - xr.Dataset - Dataset with CRS information added - """ - ds = ds.copy() - - # Set up coordinate variables with proper attributes - _add_coordinate_metadata(ds) - - # Add CRS information if we have spatial coordinates and a reference CRS - if "x" in ds.coords and "y" in ds.coords and reference_crs: - print(f" Adding CRS information: {reference_crs}") - ds = ds.rio.write_crs(reference_crs) - ds.attrs["grid_mapping"] = "spatial_ref" - - # Ensure spatial_ref variable has proper attributes - if "spatial_ref" in ds: - _add_geotransform(ds, "spatial_ref") - - # Set up data variables with proper attributes - for var_name in ds.data_vars: - if "_ARRAY_DIMENSIONS" not in ds[var_name].attrs and hasattr( - ds[var_name], "dims" - ): - ds[var_name].attrs["_ARRAY_DIMENSIONS"] = list(ds[var_name].dims) - - # Add grid_mapping reference if spatial coordinates are present - if "x" in ds[var_name].coords and "y" in ds[var_name].coords and reference_crs: - ds[var_name].attrs["grid_mapping"] = "spatial_ref" - ds[var_name].attrs["proj:epsg"] = reference_crs.split(":")[-1] - if "spatial_ref" in ds and "GeoTransform" in ds["spatial_ref"].attrs: - ds[var_name].attrs["proj:transform"] = ds["spatial_ref"].attrs[ - "GeoTransform" - ] + # Final root-level metadata consolidation per ADR + try: + with maybe_step(None, "root_consolidate"): + zgroup = fs_utils.open_zarr_group(fs_utils.normalize_path(output_path), mode="r+") + consolidate_metadata(zgroup.store) + except Exception as e: + log.warning(f"Root metadata consolidation skipped: {e}") - return ds + return dt_result def write_geozarr_group( @@ -333,511 +261,19 @@ def write_geozarr_group( max_retries: int = 3, min_dimension: int = 256, tile_width: int = 256, + overwrite: str = "fail", ) -> xr.DataTree: - """ - Write a group to a GeoZarr dataset with multiscales support. - - Parameters - ---------- - dt_result : xr.DataTree - Result DataTree to update - group_name : str - Name of the group to write - ds : xarray.Dataset - Dataset to write - output_path : str - Output path for the GeoZarr dataset - spatial_chunk : int, default 4096 - Spatial chunk size - compressor : Any, optional - Compressor to use for encoding - max_retries : int, default 3 - Maximum number of retries for writing - min_dimension : int, default 256 - Minimum dimension for overview levels - tile_width : int, default 256 - Tile width for TMS compatibility - - Returns - ------- - xarray.DataTree - The written GeoZarr DataTree with multiscale groups as children - """ - print(f"\n=== Processing {group_name} with GeoZarr-spec compliance ===") - - # Create a new container for the group - dt = xr.DataTree() - dt_result[group_name.lstrip("/")] = dt - dt.attrs = ds.attrs.copy() - - # Create encoding for all variables - encoding = _create_geozarr_encoding(ds, compressor, spatial_chunk) - - # Write native data in the group 0 (overview level 0) - native_dataset_group_name = f"{group_name}/0" - native_dataset_path = f"{output_path}/{native_dataset_group_name.lstrip('/')}" - - # Check for existing dataset - existing_native_dataset = _load_existing_dataset(native_dataset_path) - - # Write native data band by band - success, ds = write_dataset_band_by_band_with_validation( - ds, - existing_native_dataset, - output_path, - encoding, - max_retries, - native_dataset_group_name, - False, - ) - if not success: - raise RuntimeError(f"Failed to write all bands for {group_name}") - - # Create GeoZarr-spec compliant multiscales - try: - print(f"Creating GeoZarr-spec compliant multiscales for {group_name}") - create_geozarr_compliant_multiscales( - ds=ds, - output_path=output_path, - group_name=group_name, - min_dimension=min_dimension, - tile_width=tile_width, - spatial_chunk=spatial_chunk, - ) - except Exception as e: - print( - f"Warning: Failed to create GeoZarr-spec compliant multiscales for {group_name}: {e}" - ) - print("Continuing with next group...") - - # Consolidate metadata - print(f" Consolidating metadata for group {group_name}...") - group_path = fs_utils.normalize_path(f"{output_path}/{group_name.lstrip('/')}") - zarr_group = fs_utils.open_zarr_group(group_path, mode="r+") - consolidate_metadata(zarr_group.store) - print(" ✅ Metadata consolidated") - - return dt - - -def create_geozarr_compliant_multiscales( - ds: xr.Dataset, - output_path: str, - group_name: str, - min_dimension: int = 256, - tile_width: int = 256, - spatial_chunk: int = 4096, -) -> Dict[str, Any]: - """ - Create GeoZarr-spec compliant multiscales following the specification exactly. - - Parameters - ---------- - ds : xarray.Dataset - Source dataset with all variables - output_path : str - Output path for the Zarr store - group_name : str - Name of the resolution group - min_dimension : int, default 256 - Minimum dimension for overview levels - tile_width : int, default 256 - Tile width for TMS compatibility - spatial_chunk : int, default 4096 - Spatial chunk size for encoding - - Returns - ------- - dict - Dictionary with overview levels information - """ - from zarr.codecs import BloscCodec - - compressor = BloscCodec(cname="zstd", clevel=3, shuffle="shuffle") - - # Get spatial information from the first data variable - data_vars = [ - var for var in ds.data_vars if not utils.is_grid_mapping_variable(ds, var) - ] - if not data_vars: - return {} - - first_var = data_vars[0] - native_height, native_width = ds[first_var].shape[-2:] - native_crs = ds.rio.crs - native_bounds = ds.rio.bounds() - - print(f"Creating GeoZarr-compliant multiscales for {group_name}") - print(f"Native resolution: {native_width} x {native_height}") - print(f"Native CRS: {native_crs}") - - # Calculate overview levels - overview_levels = calculate_overview_levels( - native_width, native_height, min_dimension, tile_width - ) - - print(f"Total overview levels: {len(overview_levels)}") - for ol in overview_levels: - print( - f"Overview level {ol['level']}: {ol['width']} x {ol['height']} " - f"(scale factor: {ol['scale_factor']})" - ) - - # Create native CRS tile matrix set - tile_matrix_set = create_native_crs_tile_matrix_set( - native_crs, native_bounds, overview_levels, None - ) - - # Create tile matrix limits - tile_matrix_limits = _create_tile_matrix_limits(overview_levels, tile_width) - - # Add multiscales metadata to the group - zarr_json_path = fs_utils.normalize_path(f"{output_path}/{group_name}/zarr.json") - zarr_json = fs_utils.read_json_metadata(zarr_json_path) - zarr_json_attributes = zarr_json.get("attributes", {}) - zarr_json_attributes["multiscales"] = { - "tile_matrix_set": tile_matrix_set, - "resampling_method": "average", - "tile_matrix_limits": tile_matrix_limits, - } - fs_utils.write_json_metadata(zarr_json_path, zarr_json) - - print(f"Added multiscales metadata to {group_name}") - - # Create overview levels as children groups - timing_data = [] - previous_level_ds = ds - overview_datasets = {} - - for overview in overview_levels: - level = overview["level"] - - # Skip level 0 - native resolution is already in group 0 - if level == 0: - print("Skipping level 0 - native resolution is already in group 0") - continue - - width = overview["width"] - height = overview["height"] - scale_factor = overview["scale_factor"] - - print(f"\nCreating overview level {level} (1:{scale_factor} scale)...") - print(f"Target dimensions: {width} x {height}") - print( - f" Using pyramid approach: creating level {level} from level {level - 1}" - ) - - # Create overview dataset - overview_ds = create_overview_dataset_all_vars( - previous_level_ds, - level, - width, - height, - native_crs, - native_bounds, - data_vars, - ) - - # Create encoding for this overview level - encoding = _create_geozarr_encoding(overview_ds, compressor, spatial_chunk) - - # Write overview level - overview_path = fs_utils.normalize_path(f"{output_path}/{group_name}/{level}") - start_time = time.time() - - storage_options = fs_utils.get_storage_options(overview_path) - print(f"Writing overview level {level} at {overview_path}") - - # Ensure the directory exists for local paths - if not fs_utils.is_s3_path(overview_path): - os.makedirs(os.path.dirname(overview_path), exist_ok=True) - - # Write the overview dataset - overview_group = f"{group_name}/{level}" - overview_ds.to_zarr( - output_path, - group=overview_group, - mode="w", - consolidated=True, - zarr_format=3, - encoding=encoding, - align_chunks=True, - storage_options=storage_options, - ) - - overview_datasets[level] = overview_ds - proc_time = time.time() - start_time - - timing_data.append( - { - "level": level, - "time": proc_time, - "pixels": width * height, - "width": width, - "height": height, - "scale_factor": scale_factor, - } - ) - - print(f"Level {level}: Successfully created in {proc_time:.2f}s") - - # Consolidate metadata - group_path = fs_utils.normalize_path( - f"{output_path}/{overview_group.lstrip('/')}" - ) - zarr_group = fs_utils.open_zarr_group(group_path, mode="r+") - consolidate_metadata(zarr_group.store) - print(f" ✅ Metadata consolidated for overview level {level}") - - # Update previous_level_ds for the next iteration - previous_level_ds = overview_ds - - print( - f"\n✅ Created {len(overview_levels)} GeoZarr-compliant overview levels using pyramid approach" - ) - - return { - "overview_datasets": overview_datasets, - "levels": overview_levels, - "timing": timing_data, - "tile_matrix_set": tile_matrix_set, - "tile_matrix_limits": tile_matrix_limits, - } - - -def calculate_overview_levels( - native_width: int, - native_height: int, - min_dimension: int = 256, - tile_width: int = 256, -) -> List[Dict[str, Any]]: - """ - Calculate overview levels following COG /2 downsampling logic. - - Parameters - ---------- - native_width : int - Width of the native resolution data - native_height : int - Height of the native resolution data - min_dimension : int, default 256 - Stop creating overviews when dimension is smaller than this - tile_width : int, default 256 - Tile width for TMS compatibility calculations - - Returns - ------- - list - List of overview level dictionaries - """ - overview_levels = [] - level = 0 - current_width = native_width - current_height = native_height - - while min(current_width, current_height) >= min_dimension: - # Calculate zoom level for TMS compatibility - zoom_for_width = max(0, int(np.ceil(np.log2(current_width / tile_width)))) - zoom_for_height = max(0, int(np.ceil(np.log2(current_height / tile_width)))) - zoom = max(zoom_for_width, zoom_for_height) - - overview_levels.append( - { - "level": level, - "zoom": zoom, - "width": current_width, - "height": current_height, - "scale_factor": 2**level, - } - ) - - level += 1 - current_width = native_width // (2**level) - current_height = native_height // (2**level) - - return overview_levels - - -def create_native_crs_tile_matrix_set( - native_crs: Any, - native_bounds: Tuple[float, float, float, float], - overview_levels: List[Dict[str, Any]], - group_prefix: Optional[str] = "", -) -> Dict[str, Any]: - """ - Create a custom Tile Matrix Set for the native CRS following GeoZarr spec. - - Parameters - ---------- - native_crs : rasterio.crs.CRS - Native CRS of the data - native_bounds : tuple - Native bounds (left, bottom, right, top) - overview_levels : list - List of overview level dictionaries - group_prefix : str, optional - Group prefix for the tile matrix IDs - - Returns - ------- - dict - Tile Matrix Set definition following OGC standard - """ - left, bottom, right, top = native_bounds - tile_matrices = [] - - for overview in overview_levels: - level = overview["level"] - width = overview["width"] - height = overview["height"] - - # Calculate cell size - cell_size_x = (right - left) / width - cell_size_y = (top - bottom) / height - cell_size = max(cell_size_x, cell_size_y) - - # Calculate scale denominator - scale_denominator = cell_size * 3779.5275 - - # Calculate matrix dimensions - tile_width = 256 - tile_height = 256 - matrix_width = int(np.ceil(width / tile_width)) - matrix_height = int(np.ceil(height / tile_height)) - - matrix_id = f"{group_prefix}/{level}" if group_prefix else str(level) - - tile_matrices.append( - { - "id": matrix_id, - "scaleDenominator": scale_denominator, - "cellSize": cell_size, - "pointOfOrigin": [left, top], - "tileWidth": tile_width, - "tileHeight": tile_height, - "matrixWidth": matrix_width, - "matrixHeight": matrix_height, - } - ) - - # Create the complete Tile Matrix Set - epsg_code = native_crs.to_epsg() if native_crs else None - crs_uri = ( - f"http://www.opengis.net/def/crs/EPSG/0/{epsg_code}" - if epsg_code - else (native_crs.to_wkt() if native_crs else "") - ) - - return { - "id": f"Native_CRS_{epsg_code if epsg_code else 'Custom'}", - "title": f"Native CRS Tile Matrix Set ({native_crs})", - "crs": crs_uri, - "supportedCRS": crs_uri, - "orderedAxes": ["X", "Y"], - "tileMatrices": tile_matrices, - } - - -def create_overview_dataset_all_vars( - ds: xr.Dataset, - level: int, - width: int, - height: int, - native_crs: Any, - native_bounds: Tuple[float, float, float, float], - data_vars: List[str], -) -> xr.Dataset: - """ - Create an overview dataset containing all variables for a specific level. - - Parameters - ---------- - ds : xarray.Dataset - Source dataset - level : int - Overview level number - width : int - Width of this overview level - height : int - Height of this overview level - native_crs : rasterio.crs.CRS - Native CRS of the data - native_bounds : tuple - Native bounds (left, bottom, right, top) - data_vars : list - List of data variable names to include - - Returns - ------- - xarray.Dataset - Overview dataset with all variables - """ - import rasterio.transform - - # Calculate the transform for this overview level - overview_transform = rasterio.transform.from_bounds(*native_bounds, width, height) - - # Create coordinate arrays - left, bottom, right, top = native_bounds - x_coords = np.linspace(left, right, width, endpoint=False) - y_coords = np.linspace(top, bottom, height, endpoint=False) - - overview_coords = { - "x": (["x"], x_coords, _get_x_coord_attrs()), - "y": (["y"], y_coords, _get_y_coord_attrs()), - } - - # Find the grid_mapping variable name - grid_mapping_var_name = _find_grid_mapping_var_name(ds, data_vars) - - # Downsample all data variables - overview_data_vars = {} - for var in data_vars: - print(f" Downsampling {var}...") - - source_data = ds[var].values - - # Create downsampled data - if source_data.ndim == 3: - downsampled_data = np.zeros( - (source_data.shape[0], height, width), dtype=source_data.dtype - ) - for i in range(source_data.shape[0]): - downsampled_data[i] = utils.downsample_2d_array( - source_data[i], height, width - ) - dims = ( - ["time", "y", "x"] - if "time" in ds[var].dims - else [ds[var].dims[0], "y", "x"] - ) - else: - downsampled_data = utils.downsample_2d_array(source_data, height, width) - dims = ["y", "x"] - - attrs = { - "standard_name": ds[var].attrs.get( - "standard_name", "toa_bidirectional_reflectance" - ), - "_ARRAY_DIMENSIONS": dims, - "grid_mapping": grid_mapping_var_name, - } - - overview_data_vars[var] = (dims, downsampled_data, attrs) - - # Create overview dataset - overview_ds = xr.Dataset(overview_data_vars, coords=overview_coords) - - # Add grid_mapping variable - _add_grid_mapping_variable( - overview_ds, ds, grid_mapping_var_name, overview_transform, native_crs + """Thin wrapper delegating to GeoZarrWriter for backward compatibility.""" + writer = GeoZarrWriter( + output_path=output_path, + compressor=compressor, + spatial_chunk=spatial_chunk, + min_dimension=min_dimension, + tile_width=tile_width, + max_retries=max_retries, + overwrite=overwrite, ) - - # Set CRS using rioxarray - overview_ds = overview_ds.rio.write_crs(native_crs) - overview_ds.attrs["grid_mapping"] = grid_mapping_var_name - - return overview_ds + return writer.write_group(dt_result, group_name, ds) def write_dataset_band_by_band_with_validation( @@ -874,13 +310,11 @@ def write_dataset_band_by_band_with_validation( tuple[bool, xarray.Dataset] (True if all bands were written successfully, updated dataset) """ - print( - f"Writing GeoZarr-spec compliant base resolution for {group_name} band by band with validation" - ) + log.info(f"Writing base resolution for {group_name} band-by-band with validation") # Get data variables - data_vars = [ - var for var in ds.data_vars if not utils.is_grid_mapping_variable(ds, var) + data_vars: List[str] = [ + str(var) for var in ds.data_vars if not utils.is_grid_mapping_variable(ds, str(var)) ] successful_vars = [] @@ -893,17 +327,41 @@ def write_dataset_band_by_band_with_validation( for var in data_vars: # Check if this band already exists and is valid if not force_overwrite and store_exists: - if utils.validate_existing_band_data(existing_dataset, var, ds): - ds.drop_vars(var) - ds[var] = existing_dataset[var] # type: ignore - print(f" ✅ Band {var} already exists and is valid, skipping") - skipped_vars.append(var) - successful_vars.append(var) - continue - if os.path.exists(os.path.join(output_path, group_name, var)): - shutil.rmtree(os.path.join(output_path, group_name, var)) + if existing_dataset is not None and utils.validate_existing_band_data( + existing_dataset, var, ds + ): + # Ensure the on-disk array looks complete before skipping + try: + array_dir = fs_utils.normalize_path( + f"{output_path}/{group_name.lstrip('/')}" + f"/{var}" + ) + ok, _rep = utils.audit_group_completeness(array_dir, [var]) + except Exception as e: + log.warning(f"Audit failed for {var} ({e}); will rewrite") + ok = False + if ok: + ds.drop_vars(var) + ds[var] = existing_dataset[var] + log.info(f"Band {var} already exists and is valid; skipping") + skipped_vars.append(var) + successful_vars.append(var) + continue + else: + # Remove incomplete array directory and proceed to write + try: + if os.path.exists(array_dir): + _safe_rmtree(array_dir) + log.info(f"Removed incomplete array for {var}; will rewrite") + except Exception as e: + log.warning(f"Failed to clean incomplete array for {var}: {e}") + try: + _arr_dir = fs_utils.normalize_path(f"{output_path}/{group_name.lstrip('/')}/{var}") + if os.path.exists(_arr_dir): + _safe_rmtree(_arr_dir) + except Exception: + pass - print(f" Writing data variable {var}...") + log.info(f"Writing data variable {var}...") # Create a single-variable dataset with its coordinates single_var_ds = ds[[var]] @@ -912,6 +370,9 @@ def write_dataset_band_by_band_with_validation( var_encoding = {} if var in encoding: var_encoding[var] = encoding[var] + # Avoid grid_mapping duplication in encoding dict + if isinstance(var_encoding[var], dict): + var_encoding[var].pop("grid_mapping", None) # Add coordinate encoding if not already present for coord in single_var_ds.coords: @@ -919,11 +380,16 @@ def write_dataset_band_by_band_with_validation( existing_dataset is None or coord not in existing_dataset.coords ): var_encoding[coord] = encoding[coord] + if isinstance(var_encoding[coord], dict): + var_encoding[coord].pop("grid_mapping", None) # Try to write this variable with retries success = False for attempt in range(max_retries): try: + # Do not supply encoding for grid-mapping variable even if present + if "spatial_ref" in var_encoding: + var_encoding.pop("spatial_ref", None) # Ensure the dataset is properly chunked to align with encoding if var in var_encoding and "chunks" in var_encoding[var]: target_chunks = var_encoding[var]["chunks"] @@ -942,21 +408,21 @@ def write_dataset_band_by_band_with_validation( storage_options = fs_utils.get_storage_options(output_path) single_var_ds.to_zarr( output_path, - group=group_name, + group=group_name.lstrip("/"), mode="a", consolidated=False, zarr_format=3, encoding=var_encoding, + align_chunks=True, + safe_chunks=False, storage_options=storage_options, ) - print(f" ✅ Successfully wrote {var}") + log.info(f"Successfully wrote {var}") successful_vars.append(var) success = True if existing_dataset is None: - group_path = fs_utils.normalize_path( - f"{output_path}/{group_name.lstrip('/')}" - ) + group_path = fs_utils.normalize_path(f"{output_path}/{group_name.lstrip('/')}") storage_options = fs_utils.get_storage_options(output_path) existing_dataset = xr.open_dataset( group_path, @@ -974,429 +440,189 @@ def write_dataset_band_by_band_with_validation( if os.path.exists( os.path.join(output_path, group_name.lstrip("/"), written_var) ): - shutil.rmtree( - os.path.join( - output_path, group_name.lstrip("/"), written_var - ) - ) + _safe_rmtree(os.path.join(output_path, group_name.lstrip("/"), written_var)) if attempt < max_retries - 1: - print( - f" ⚠️ Attempt {attempt + 1} failed for {var}: {e}, retrying in 2 seconds..." - ) + log.warning(f"Attempt {attempt + 1} failed for {var}: {e}; retrying in 2s") time.sleep(2) else: - print( - f" ❌ Failed to write {var} after {max_retries} attempts: {e}" - ) + log.error(f"Failed to write {var} after {max_retries} attempts: {e}") failed_vars.append(var) break if not success: - print(f" Failed to write data variable {var}") + log.error(f"Failed to write data variable {var}") # Consolidate metadata group_path = fs_utils.normalize_path(f"{output_path}/{group_name.lstrip('/')}") zarr_group = fs_utils.open_zarr_group(group_path, mode="r+") consolidate_metadata(zarr_group.store) - print(f" ✅ Metadata consolidated for {len(successful_vars)} variables") + log.info(f"Metadata consolidated for {len(successful_vars)} variables") # Report results if failed_vars: - print( - f"❌ Failed to write {len(failed_vars)} variables for {group_name}: {failed_vars}" - ) - print( - f"✅ Successfully wrote {len(successful_vars) - len(skipped_vars)} new variables" - ) - print( - f"⏭️ Skipped {len(skipped_vars)} existing valid variables: {skipped_vars}" - ) + log.error(f"Failed to write {len(failed_vars)} variables for {group_name}: {failed_vars}") + log.info(f"Successfully wrote {len(successful_vars) - len(skipped_vars)} new variables") + log.info(f"Skipped {len(skipped_vars)} existing valid variables: {skipped_vars}") return False, ds else: - print( - f"✅ Successfully processed all {len(successful_vars)} variables for {group_name}" - ) + log.info(f"Successfully processed all {len(successful_vars)} variables for {group_name}") if skipped_vars: - print( - f" - Wrote {len(successful_vars) - len(skipped_vars)} new variables" - ) - print(f" - Skipped {len(skipped_vars)} existing valid variables") + log.info(f"- Wrote {len(successful_vars) - len(skipped_vars)} new variables") + log.info(f"- Skipped {len(skipped_vars)} existing valid variables") return True, ds -def consolidate_metadata( - store: StoreLike, - path: Optional[str] = None, - zarr_format: Optional[zarr.core.common.ZarrFormat] = None, -) -> zarr.Group: - """ - Consolidate metadata of all nodes in a hierarchy. - - Parameters - ---------- - store : StoreLike - The store-like object whose metadata to consolidate - path : str, optional - Path to a group in the store to consolidate at - zarr_format : {2, 3, None}, optional - The zarr format of the hierarchy - - Returns - ------- - zarr.Group - The group with consolidated metadata - """ - return zarr.Group( - sync(async_consolidate_metadata(store, path=path, zarr_format=zarr_format)) - ) - - -async def async_consolidate_metadata( - store: StoreLike, - path: Optional[str] = None, - zarr_format: Optional[zarr.core.common.ZarrFormat] = None, -) -> zarr.core.group.AsyncGroup: - """ - Consolidate metadata of all nodes in a hierarchy asynchronously. - - Parameters - ---------- - store : StoreLike - The store-like object whose metadata to consolidate - path : str, optional - Path to a group in the store to consolidate at - zarr_format : {2, 3, None}, optional - The zarr format of the hierarchy - - Returns - ------- - zarr.core.group.AsyncGroup - The group with consolidated metadata - """ - store_path = await make_store_path(store, path=path) - - if not store_path.store.supports_consolidated_metadata: - store_name = type(store_path.store).__name__ - raise TypeError( - f"The Zarr Store in use ({store_name}) doesn't support consolidated metadata", +class GeoZarrWriter: + """Orchestrates GeoZarr group writes, including level-0, overviews, and metadata.""" + + def __init__( + self, + *, + output_path: str, + compressor: Any, + spatial_chunk: int, + min_dimension: int, + tile_width: int, + max_retries: int, + overwrite: str, + _load_existing_dataset: Any = load_existing_dataset, + max_overview_levels: Optional[int] = None, + skip_overviews: bool = False, + base_write_mode: str = "single", + ) -> None: + self.output_path = output_path + self.compressor = compressor + self.spatial_chunk = spatial_chunk + self.min_dimension = min_dimension + self.tile_width = tile_width + self.max_retries = max_retries + self.overwrite = overwrite + self._load_existing_dataset = _load_existing_dataset + self.max_overview_levels = max_overview_levels + self.skip_overviews = skip_overviews + self.base_write_mode = ( + base_write_mode if base_write_mode in {"single", "band-validated"} else "single" ) - group = await zarr.core.group.AsyncGroup.open( - store_path, zarr_format=zarr_format, use_consolidated=False - ) - group.store_path.store._check_writable() - - members_metadata = { - k: v.metadata - async for k, v in group.members( - max_depth=None, use_consolidated_for_children=False + def write_group(self, dt_result: xr.DataTree, group_name: str, ds: xr.Dataset) -> xr.DataTree: + from .helpers import ( # local import to avoid cycles + build_var_encodings, + normalize_group_path, ) - } - zarr.core.group.ConsolidatedMetadata._flat_to_nested(members_metadata) + norm_group = normalize_group_path(group_name) + _ensure_group_hierarchy(self.output_path, norm_group) - consolidated_metadata = zarr.core.group.ConsolidatedMetadata( - metadata=members_metadata - ) - metadata = dataclasses.replace( - group.metadata, consolidated_metadata=consolidated_metadata - ) - group = dataclasses.replace( - group, - metadata=metadata, - ) + # Prepare minimal encoding to preserve source layout and avoid coord conflicts + # Only set compressors and dtype for data variables; do not force chunks or encode coords + encoding: Dict[str, Any] = build_var_encodings(ds, self.compressor) - await group._save_metadata() - return group - - -# Helper functions -def _add_coordinate_metadata(ds: xr.Dataset) -> None: - """Add proper metadata to coordinate variables.""" - for coord_name in ds.coords: - if coord_name == "x": - ds[coord_name].attrs.update( - { - "_ARRAY_DIMENSIONS": ["x"], - "standard_name": "projection_x_coordinate", - "units": "m", - "long_name": "x coordinate of projection", - } - ) - elif coord_name == "y": - ds[coord_name].attrs.update( - { - "_ARRAY_DIMENSIONS": ["y"], - "standard_name": "projection_y_coordinate", - "units": "m", - "long_name": "y coordinate of projection", - } - ) - elif coord_name == "time": - ds[coord_name].attrs.update( - {"_ARRAY_DIMENSIONS": ["time"], "standard_name": "time"} - ) - elif coord_name == "angle": - ds[coord_name].attrs.update( - { - "_ARRAY_DIMENSIONS": ["angle"], - "standard_name": "angle", - "long_name": "angle coordinate", - } - ) - elif coord_name == "band": - ds[coord_name].attrs.update( - { - "_ARRAY_DIMENSIONS": ["band"], - "standard_name": "band", - "long_name": "spectral band identifier", - } - ) - elif coord_name == "detector": - ds[coord_name].attrs.update( - { - "_ARRAY_DIMENSIONS": ["detector"], - "standard_name": "detector", - "long_name": "detector identifier", - } + level0_group = f"{norm_group}/0" + level0_path = fs_utils.normalize_path(f"{self.output_path}/{level0_group.lstrip('/')}") + + # Handle overwrite policy for base level + mode = "a" + try: + exists = fs_utils.path_exists(level0_path) + except Exception: + exists = False + if self.overwrite == "replace" and exists: + try: + _safe_rmtree(level0_path) + exists = False + except Exception as e: + log.warning(f"Failed to clean existing level-0 group at {level0_path}: {e}") + if not exists: + mode = "w" + + # Clear any legacy encodings (e.g., numcodecs 'compressor') from source variables + try: + for name in list(ds.variables): + enc = getattr(ds[name], "encoding", None) + if isinstance(enc, dict): + enc.clear() + except Exception: + pass + + storage_options = fs_utils.get_storage_options(self.output_path) + + # Decide write strategy for level-0 + if self.base_write_mode == "band-validated": + # Load existing dataset if present to enable skip/merge + existing = None + try: + if fs_utils.path_exists(level0_path): + existing = self._load_existing_dataset(level0_path) + except Exception: + existing = None + + # If overwrite=replace and exists, clean and reset + if self.overwrite == "replace" and fs_utils.path_exists(level0_path): + try: + _safe_rmtree(level0_path) + existing = None + except Exception as e: + log.warning(f"Failed to clean existing level-0 group at {level0_path}: {e}") + + # Use band-by-band validated writing + ok, _ = write_dataset_band_by_band_with_validation( + ds, + existing, + self.output_path, + encoding, + self.max_retries, + level0_group, + force_overwrite=(self.overwrite == "replace"), ) + if not ok and self.overwrite == "fail": + raise RuntimeError(f"Level-0 write failed for {level0_group}") else: - # Generic coordinate - if "_ARRAY_DIMENSIONS" not in ds[coord_name].attrs: - ds[coord_name].attrs["_ARRAY_DIMENSIONS"] = [coord_name] - - -def _setup_grid_mapping(ds: xr.Dataset, grid_mapping_var_name: str) -> None: - """Set up spatial_ref variable with GeoZarr required attributes.""" - if ds.rio.crs and "spatial_ref" in ds: - ds["spatial_ref"].attrs["_ARRAY_DIMENSIONS"] = [] - - # Add GeoTransform if available - if ds.rio.transform(): - transform_gdal = ds.rio.transform().to_gdal() - transform_str = " ".join([str(i) for i in transform_gdal]) - ds["spatial_ref"].attrs["GeoTransform"] = transform_str - - # Update all data variables to reference the grid_mapping - ds.attrs["grid_mapping"] = grid_mapping_var_name - for band in ds.data_vars: - if band != "spatial_ref": - ds[band].attrs["grid_mapping"] = grid_mapping_var_name - - -def _add_geotransform(ds: xr.Dataset, grid_mapping_var: str) -> None: - """Add GeoTransform to grid_mapping variable.""" - ds[grid_mapping_var].attrs["_ARRAY_DIMENSIONS"] = [] + # Single-shot write of the full dataset at level 0 (preserve values as-is) + write_dataset_zarr( + ds, + self.output_path, + group=level0_group.lstrip("/"), + mode=mode, + consolidated=True, + zarr_format=3, + encoding=encoding if encoding else None, + align_chunks=True, + safe_chunks=False, + storage_options=storage_options, + ) - if len(ds.coords["x"]) > 1 and len(ds.coords["y"]) > 1: - x_coords = ds.coords["x"].values - y_coords = ds.coords["y"].values + # Consolidate metadata at level 0 + try: + zarr_group = fs_utils.open_zarr_group(level0_path, mode="r+") + consolidate_metadata(zarr_group.store) + except Exception as e: + log.warning(f"Level-0 metadata consolidation skipped for {level0_group}: {e}") - pixel_size_x = float(x_coords[1] - x_coords[0]) - pixel_size_y = float(y_coords[0] - y_coords[1]) + ds_updated = ds - transform_str = ( - f"{x_coords[0]} {pixel_size_x} 0.0 {y_coords[0]} 0.0 {pixel_size_y}" - ) - ds[grid_mapping_var].attrs["GeoTransform"] = transform_str - - -def _find_reference_crs(geozarr_groups: Dict[str, xr.Dataset]) -> Optional[str]: - """Find the reference CRS in the geozarr groups.""" - for key, group in geozarr_groups.items(): - if group.rio.crs: - crs_string: str = group.rio.crs.to_string() - return crs_string - return None - - -def _create_encoding( - ds: xr.Dataset, compressor: Any, spatial_chunk: int -) -> Dict[str, Any]: - """Create encoding for dataset variables.""" - encoding: Dict[str, Any] = {} - for var in ds.data_vars: - if hasattr(ds[var].data, "chunks"): - current_chunks = ds[var].chunks - if len(current_chunks) >= 2: - chunking = tuple( - current_chunks[i][0] - if len(current_chunks[i]) > 0 - else ds[var].shape[i] - for i in range(len(current_chunks)) - ) - else: - chunking = ( - current_chunks[0][0] - if len(current_chunks[0]) > 0 - else ds[var].shape[0], + # Generate GeoZarr multiscales and write overviews + if not self.skip_overviews: + try: + create_geozarr_compliant_multiscales( + ds_updated, + self.output_path, + norm_group, + min_dimension=self.min_dimension, + tile_width=self.tile_width, + spatial_chunk=self.spatial_chunk, + overwrite=self.overwrite, + max_retries=self.max_retries, + max_overview_levels=self.max_overview_levels, ) - else: - data_shape = ds[var].shape - if len(data_shape) >= 2: - chunk_y = min(spatial_chunk, data_shape[-2]) - chunk_x = min(spatial_chunk, data_shape[-1]) - if len(data_shape) == 3: - chunking = (1, chunk_y, chunk_x) - else: - chunking = (chunk_y, chunk_x) - else: - chunking = (min(spatial_chunk, data_shape[-1]),) - - encoding[var] = {"compressors": [compressor], "chunks": chunking} - - # Add coordinate encoding - for coord in ds.coords: - encoding[coord] = {"compressors": None} - - return encoding - + except Exception as e: + print(f"Warning: skipping overviews for {norm_group} due to error: {e}") + log.warning(f"Skipping overviews for {norm_group}: {e}") -def _create_geozarr_encoding( - ds: xr.Dataset, compressor: Any, spatial_chunk: int -) -> Dict[str, Any]: - """Create encoding for GeoZarr dataset variables.""" - encoding: Dict[str, Any] = {} - for var in ds.data_vars: - if utils.is_grid_mapping_variable(ds, var): - encoding[var] = {"compressors": None} + # Update result tree + if norm_group == "/": + # Assign dataset to root of the DataTree + dt_result = xr.DataTree(ds_updated) else: - data_shape = ds[var].shape - if len(data_shape) >= 2: - height, width = data_shape[-2:] - spatial_chunk_aligned = min( - spatial_chunk, - utils.calculate_aligned_chunk_size(width, spatial_chunk), - utils.calculate_aligned_chunk_size(height, spatial_chunk), - ) - else: - spatial_chunk_aligned = spatial_chunk - - encoding[var] = { - "chunks": (spatial_chunk_aligned, spatial_chunk_aligned), - "compressors": compressor, - } - - # Add coordinate encoding - for coord in ds.coords: - encoding[coord] = {"compressors": None} - - return encoding - - -def _load_existing_dataset(path: str) -> Optional[xr.Dataset]: - """Load existing dataset if it exists.""" - try: - if fs_utils.path_exists(path): - storage_options = fs_utils.get_storage_options(path) - return xr.open_dataset( - path, - zarr_format=3, - storage_options=storage_options, - engine="zarr", - chunks="auto", - decode_coords="all", - ) - except Exception as e: - print(f"Warning: Could not open existing dataset at {path}: {e}") - return None - - -def _create_tile_matrix_limits( - overview_levels: List[Dict[str, Any]], tile_width: int -) -> Dict[str, Any]: - """Create tile matrix limits for overview levels.""" - tile_matrix_limits = {} - for ol in overview_levels: - level_str = str(ol["level"]) - max_tile_col = int(np.ceil(ol["width"] / tile_width)) - 1 - max_tile_row = int(np.ceil(ol["height"] / tile_width)) - 1 - - tile_matrix_limits[level_str] = { - "tileMatrix": level_str, - "minTileCol": 0, - "maxTileCol": max_tile_col, - "minTileRow": 0, - "maxTileRow": max_tile_row, - } - return tile_matrix_limits - - -def _get_x_coord_attrs() -> Dict[str, Any]: - """Get standard attributes for x coordinate.""" - return { - "units": "m", - "long_name": "x coordinate of projection", - "standard_name": "projection_x_coordinate", - "_ARRAY_DIMENSIONS": ["x"], - } - - -def _get_y_coord_attrs() -> Dict[str, Any]: - """Get standard attributes for y coordinate.""" - return { - "units": "m", - "long_name": "y coordinate of projection", - "standard_name": "projection_y_coordinate", - "_ARRAY_DIMENSIONS": ["y"], - } - - -def _find_grid_mapping_var_name(ds: xr.Dataset, data_vars: List[str]) -> str: - """Find the grid_mapping variable name from the dataset.""" - grid_mapping_var_name = ds.attrs.get("grid_mapping", None) - if not grid_mapping_var_name and data_vars: - first_var = data_vars[0] - if first_var in ds and "grid_mapping" in ds[first_var].attrs: - grid_mapping_var_name = ds[first_var].attrs["grid_mapping"] - - if not grid_mapping_var_name: - grid_mapping_var_name = "spatial_ref" - - return str(grid_mapping_var_name) - - -def _add_grid_mapping_variable( - overview_ds: xr.Dataset, - ds: xr.Dataset, - grid_mapping_var_name: str, - overview_transform: Any, - native_crs: Any, -) -> None: - """Add grid_mapping variable to overview dataset.""" - if grid_mapping_var_name in ds: - grid_mapping_attrs = ds[grid_mapping_var_name].attrs.copy() - - transform_gdal = overview_transform.to_gdal() - transform_str = " ".join([str(i) for i in transform_gdal]) - grid_mapping_attrs["GeoTransform"] = transform_str - grid_mapping_attrs["_ARRAY_DIMENSIONS"] = [] - - overview_ds[grid_mapping_var_name] = xr.DataArray( - data=ds[grid_mapping_var_name].values, - attrs=grid_mapping_attrs, - ) - else: - print(f" Creating new grid_mapping variable '{grid_mapping_var_name}'") - - transform_gdal = overview_transform.to_gdal() - transform_str = " ".join([str(i) for i in transform_gdal]) - - grid_mapping_attrs = { - "_ARRAY_DIMENSIONS": [], - "GeoTransform": transform_str, - } - - if native_crs: - if native_crs.to_epsg(): - grid_mapping_attrs["spatial_ref"] = native_crs.to_wkt() - grid_mapping_attrs["crs_wkt"] = native_crs.to_wkt() - else: - grid_mapping_attrs["spatial_ref"] = native_crs.to_wkt() - grid_mapping_attrs["crs_wkt"] = native_crs.to_wkt() - - overview_ds[grid_mapping_var_name] = xr.DataArray( - data=np.array(b"", dtype="S1"), - attrs=grid_mapping_attrs, - ) + dt_result[norm_group.lstrip("/")] = xr.DataTree(ds_updated) + return dt_result diff --git a/src/eopf_geozarr/conversion/hierarchy.py b/src/eopf_geozarr/conversion/hierarchy.py new file mode 100644 index 0000000..cb6274a --- /dev/null +++ b/src/eopf_geozarr/conversion/hierarchy.py @@ -0,0 +1,43 @@ +import logging +import os +import shutil +import time +from typing import Optional + +from . import fs_utils + +log = logging.getLogger(__name__) + + +def safe_rmtree(path: str, retries: int = 3, delay: float = 0.2) -> None: + last_err: Optional[Exception] = None + for _ in range(retries): + try: + if os.path.exists(path): + shutil.rmtree(path) + return + except Exception as e: # pragma: no cover + last_err = e + time.sleep(delay) + if last_err: + raise last_err + + +def ensure_group_hierarchy(output_path: str, group_name: str) -> None: + try: + base = fs_utils.normalize_path(f"{output_path}/{group_name.lstrip('/')}") + lvl0 = fs_utils.normalize_path(f"{base}/0") + zpath = fs_utils.normalize_path(f"{base}/zarr.json") + zpath0 = fs_utils.normalize_path(f"{lvl0}/zarr.json") + if not fs_utils.path_exists(zpath): + log.debug(f"Ensuring base group: {zpath}") + fs_utils.write_json_metadata( + zpath, {"zarr_format": 3, "node_type": "group", "attributes": {}} + ) + if not fs_utils.path_exists(zpath0): + log.debug(f"Ensuring level-0 group: {zpath0}") + fs_utils.write_json_metadata( + zpath0, {"zarr_format": 3, "node_type": "group", "attributes": {}} + ) + except Exception as e: + log.warning(f"Could not ensure group hierarchy for {group_name}: {e}") diff --git a/src/eopf_geozarr/conversion/io.py b/src/eopf_geozarr/conversion/io.py new file mode 100644 index 0000000..18dcf6f --- /dev/null +++ b/src/eopf_geozarr/conversion/io.py @@ -0,0 +1,30 @@ +from __future__ import annotations + +from typing import Optional + +import xarray as xr + +from . import fs_utils + + +def load_existing_dataset(path: str) -> Optional[xr.Dataset]: + """Load existing dataset if it exists. + + Mirrors previous _load_existing_dataset behavior but with a public name. + """ + try: + if fs_utils.path_exists(path): + storage_options = fs_utils.get_storage_options(path) + return xr.open_dataset( + path, + zarr_format=3, + storage_options=storage_options, + engine="zarr", + chunks="auto", + decode_coords="all", + mask_and_scale=False, + decode_cf=False, + ) + except Exception as e: + print(f"Warning: Could not open existing dataset at {path}: {e}") + return None diff --git a/src/eopf_geozarr/conversion/metadata.py b/src/eopf_geozarr/conversion/metadata.py new file mode 100644 index 0000000..ffc7f4c --- /dev/null +++ b/src/eopf_geozarr/conversion/metadata.py @@ -0,0 +1,77 @@ +from __future__ import annotations + +import asyncio +from typing import Any, Optional, Union + +import zarr +from zarr.storage import StoreLike + +from . import fs_utils + + +def consolidate_metadata( + store: StoreLike, + path: Optional[str] = None, + zarr_format: Optional[Any] = None, +) -> zarr.Group: + """Consolidate metadata using public Zarr APIs (v2/v3 friendly). + + Attempts to use zarr.convenience.consolidate_metadata or zarr.consolidate_metadata. + If unavailable or failing, logs a warning and returns an opened group. + """ + try: + _consolidate: Any = None + try: + from zarr.convenience import consolidate_metadata as _cm + + _consolidate = _cm + except Exception: + _consolidate = getattr(zarr, "consolidate_metadata", None) + if callable(_consolidate): + try: + _consolidate(store, path=path) + except TypeError: + _consolidate(store) + else: + print("⚠️ Consolidation API not available; skipping consolidation") + except Exception as e: # pragma: no cover + print(f"⚠️ Consolidation failed: {e}") + + # Return a group handle for callers expecting it + try: + return zarr.open_group(store, mode="r+", zarr_format=3) + except Exception: + return zarr.open_group(store, mode="r+") + + +async def async_consolidate_metadata( + target: Union[str, StoreLike, zarr.Group], + path: Optional[str] = None, + zarr_format: Optional[Any] = None, + **kwargs: Any, +) -> zarr.Group: + """Async wrapper for consolidate_metadata (backwards compatible). + + Accepts a path string, a StoreLike, or a zarr.Group. Any extra kwargs are ignored + for compatibility with previous signatures that accepted storage options. + """ + + def _resolve_store() -> StoreLike: + if hasattr(target, "store"): + return getattr(target, "store") + if isinstance(target, str): + group_path = ( + fs_utils.normalize_path(target) + if not path + else fs_utils.normalize_path(f"{target.rstrip('/')}/{path.lstrip('/')}") + ) + zgroup = fs_utils.open_zarr_group(group_path, mode="r+") + return zgroup.store + return target # assume StoreLike + + store_like = _resolve_store() + loop = asyncio.get_running_loop() + return await loop.run_in_executor( + None, + lambda: consolidate_metadata(store_like, path=path, zarr_format=zarr_format), + ) diff --git a/src/eopf_geozarr/conversion/multiscales.py b/src/eopf_geozarr/conversion/multiscales.py new file mode 100644 index 0000000..0f2f3bb --- /dev/null +++ b/src/eopf_geozarr/conversion/multiscales.py @@ -0,0 +1,411 @@ +"""Multiscales overview generation and metadata helpers. + +Downsampling relies on numpy which may lack full type stubs in minimal +environments; suppress missing-import noise locally. +""" + +# mypy: disable-error-code=import-not-found + +import logging +import os +import time +from typing import Any, Dict, List, Optional, Tuple + +import numpy as np +import xarray as xr + +from . import fs_utils, utils +from .encoding import create_geozarr_encoding +from .metadata import consolidate_metadata +from .zarr_write import write_dataset_zarr + +log = logging.getLogger(__name__) + + +def calculate_overview_levels( + native_width: int, + native_height: int, + min_dimension: int = 256, + tile_width: int = 256, +) -> List[Dict[str, Any]]: + levels: List[Dict[str, Any]] = [] + level = 0 + while True: + width = native_width // (2**level) + height = native_height // (2**level) + if min(width, height) < min_dimension: + break + zoom_w = max(0, int(np.ceil(np.log2(width / tile_width)))) + zoom_h = max(0, int(np.ceil(np.log2(height / tile_width)))) + levels.append( + { + "level": level, + "zoom": max(zoom_w, zoom_h), + "width": width, + "height": height, + "scale_factor": 2**level, + } + ) + level += 1 + return levels + + +def create_native_crs_tile_matrix_set( + native_crs: Any, + native_bounds: Tuple[float, float, float, float], + overview_levels: List[Dict[str, Any]], + group_prefix: Optional[str] = "", +) -> Dict[str, Any]: + left, bottom, right, top = native_bounds + + def matrix_entry(ol: Dict[str, Any]) -> Dict[str, Any]: + width, height = ol["width"], ol["height"] + cell_size = max((right - left) / width, (top - bottom) / height) + scale_denominator = cell_size * 3779.5275 + matrix_width = int(np.ceil(width / 256)) + matrix_height = int(np.ceil(height / 256)) + mid = f"{group_prefix}/{ol['level']}" if group_prefix else str(ol["level"]) + return { + "id": mid, + "scaleDenominator": scale_denominator, + "cellSize": cell_size, + "pointOfOrigin": [left, top], + "tileWidth": 256, + "tileHeight": 256, + "matrixWidth": matrix_width, + "matrixHeight": matrix_height, + } + + epsg_code = native_crs.to_epsg() if native_crs else None + crs_uri = ( + f"http://www.opengis.net/def/crs/EPSG/0/{epsg_code}" + if epsg_code + else (native_crs.to_wkt() if native_crs else "") + ) + return { + "id": f"Native_CRS_{epsg_code if epsg_code else 'Custom'}", + "title": f"Native CRS Tile Matrix Set ({native_crs})", + "crs": crs_uri, + "supportedCRS": crs_uri, + "orderedAxes": ["X", "Y"], + "tileMatrices": [matrix_entry(ol) for ol in overview_levels], + } + + +def _get_x_coord_attrs() -> Dict[str, Any]: + return { + "units": "m", + "long_name": "x coordinate of projection", + "standard_name": "projection_x_coordinate", + "_ARRAY_DIMENSIONS": ["x"], + } + + +def _get_y_coord_attrs() -> Dict[str, Any]: + return { + "units": "m", + "long_name": "y coordinate of projection", + "standard_name": "projection_y_coordinate", + "_ARRAY_DIMENSIONS": ["y"], + } + + +def _find_grid_mapping_var_name(ds: xr.Dataset, data_vars: List[str]) -> str: + grid_mapping_var_name = ds.attrs.get("grid_mapping", None) + if not grid_mapping_var_name and data_vars: + first_var = data_vars[0] + if first_var in ds and "grid_mapping" in ds[first_var].attrs: + grid_mapping_var_name = ds[first_var].attrs["grid_mapping"] + if not grid_mapping_var_name: + grid_mapping_var_name = "spatial_ref" + return str(grid_mapping_var_name) + + +def _add_grid_mapping_variable( + overview_ds: xr.Dataset, + ds: xr.Dataset, + grid_mapping_var_name: str, + overview_transform: Any, + native_crs: Any, +) -> None: + if grid_mapping_var_name in ds: + grid_mapping_attrs = ds[grid_mapping_var_name].attrs.copy() + transform_gdal = overview_transform.to_gdal() + grid_mapping_attrs["GeoTransform"] = " ".join([str(i) for i in transform_gdal]) + grid_mapping_attrs["_ARRAY_DIMENSIONS"] = [] + overview_ds[grid_mapping_var_name] = xr.DataArray( + data=ds[grid_mapping_var_name].values, + attrs=grid_mapping_attrs, + ) + else: + log.debug(f"Creating new grid_mapping variable '{grid_mapping_var_name}'") + transform_gdal = overview_transform.to_gdal() + transform_str = " ".join([str(i) for i in transform_gdal]) + grid_mapping_new: Dict[str, Any] = { + "_ARRAY_DIMENSIONS": [], + "GeoTransform": transform_str, + } + if native_crs: + grid_mapping_new["spatial_ref"] = native_crs.to_wkt() + grid_mapping_new["crs_wkt"] = native_crs.to_wkt() + overview_ds[grid_mapping_var_name] = xr.DataArray( + data=np.array(b"", dtype="S1"), attrs=grid_mapping_new + ) + + +def create_overview_dataset_all_vars( + ds: xr.Dataset, + level: int, + width: int, + height: int, + native_crs: Any, + native_bounds: Tuple[float, float, float, float], + data_vars: List[str], +) -> xr.Dataset: + import rasterio.transform + + overview_transform = rasterio.transform.from_bounds(*native_bounds, width, height) + grid_mapping_var_name = _find_grid_mapping_var_name(ds, data_vars) + + overview_vars: Dict[str, xr.DataArray] = {} + for var in data_vars: + log.debug(f"Downsampling {var} with lazy coarsen") + arr = ds[var] + coarsen_kwargs: Dict[str, int] = {} + if "y" in arr.dims: + coarsen_kwargs["y"] = 2 + if "x" in arr.dims: + coarsen_kwargs["x"] = 2 + if not coarsen_kwargs: + overview_vars[var] = arr + continue + coarsener = arr.coarsen(boundary="trim", **coarsen_kwargs) + down = coarsener.mean() + attrs = dict(arr.attrs) + attrs.setdefault("standard_name", "toa_bidirectional_reflectance") + attrs["_ARRAY_DIMENSIONS"] = list(down.dims) + attrs["grid_mapping"] = grid_mapping_var_name + overview_vars[var] = down.assign_attrs(attrs) + + overview_ds: xr.Dataset = xr.Dataset(overview_vars) + left, bottom, right, top = native_bounds + x_coords = np.linspace(left, right, width, endpoint=False) + y_coords = np.linspace(top, bottom, height, endpoint=False) + overview_ds = overview_ds.assign_coords(x=("x", x_coords), y=("y", y_coords)) + if "x" in overview_ds.coords: + overview_ds["x"].attrs = _get_x_coord_attrs() + if "y" in overview_ds.coords: + overview_ds["y"].attrs = _get_y_coord_attrs() + + _add_grid_mapping_variable( + overview_ds, ds, grid_mapping_var_name, overview_transform, native_crs + ) + + # rioxarray can set dataset-level CRS/grid mapping and sometimes drop + # variable-level 'grid_mapping' attributes. Write CRS first, then + # re-inject per-variable attribute to guarantee persistence. + overview_ds = overview_ds.rio.write_crs(native_crs) + for var in list(overview_ds.data_vars): + if utils.is_grid_mapping_variable(overview_ds, var): + continue + try: + overview_ds[var].attrs["grid_mapping"] = grid_mapping_var_name + except Exception: + pass + overview_ds.attrs["grid_mapping"] = grid_mapping_var_name + return overview_ds + + +def _create_tile_matrix_limits( + overview_levels: List[Dict[str, Any]], tile_width: int +) -> Dict[str, Any]: + return { + str(ol["level"]): { + "tileMatrix": str(ol["level"]), + "minTileCol": 0, + "maxTileCol": int(np.ceil(ol["width"] / tile_width)) - 1, + "minTileRow": 0, + "maxTileRow": int(np.ceil(ol["height"] / tile_width)) - 1, + } + for ol in overview_levels + } + + +def create_geozarr_compliant_multiscales( + ds: xr.Dataset, + output_path: str, + group_name: str, + min_dimension: int = 256, + tile_width: int = 256, + spatial_chunk: int = 4096, + overwrite: str = "fail", + max_retries: int = 3, + max_overview_levels: Optional[int] = None, +) -> Dict[str, Any]: + from zarr.codecs import BloscCodec + + compressor = BloscCodec(cname="zstd", clevel=3, shuffle="shuffle") + + data_vars: List[str] = [ + str(var) for var in ds.data_vars if not utils.is_grid_mapping_variable(ds, str(var)) + ] + if not data_vars: + return {} + + first_var: str = data_vars[0] + native_height, native_width = ds[first_var].shape[-2:] + native_crs = ds.rio.crs + native_bounds = ds.rio.bounds() + + log.info(f"Creating GeoZarr-compliant multiscales for {group_name}") + log.debug(f"Native resolution: {native_width} x {native_height}") + log.debug(f"Native CRS: {native_crs}") + + overview_levels: List[Dict[str, Any]] = calculate_overview_levels( + native_width, native_height, min_dimension, tile_width + ) + if max_overview_levels is not None: + try: + # levels include 0; keep levels <= max_overview_levels + overview_levels = [ + ol for ol in overview_levels if int(ol.get("level", 0)) <= int(max_overview_levels) + ] + except Exception: + pass + log.info(f"Total overview levels: {len(overview_levels)}") + for ol in overview_levels: + log.debug( + "Overview level %s: %s x %s (scale: %s)", + ol["level"], + ol["width"], + ol["height"], + ol["scale_factor"], + ) + + tile_matrix_set: Dict[str, Any] = create_native_crs_tile_matrix_set( + native_crs, native_bounds, overview_levels, None + ) + tile_matrix_limits: Dict[str, Any] = _create_tile_matrix_limits(overview_levels, tile_width) + + zarr_json_path = fs_utils.normalize_path(f"{output_path}/{group_name.lstrip('/')}/zarr.json") + zarr_json = fs_utils.read_json_metadata(zarr_json_path) + zarr_json_attributes = zarr_json.get("attributes", {}) + zarr_json_attributes["multiscales"] = { + "tile_matrix_set": tile_matrix_set, + "resampling_method": "average", + "tile_matrix_limits": tile_matrix_limits, + } + fs_utils.write_json_metadata(zarr_json_path, zarr_json) + log.debug(f"Added multiscales metadata to {group_name}") + + timing_data: List[Dict[str, Any]] = [] + previous_level_ds = ds + overview_datasets: Dict[int, xr.Dataset] = {} + + for overview in overview_levels: + level = overview["level"] + if level == 0: + log.debug("Skipping level 0 - native resolution is already in group 0") + continue + + width = overview["width"] + height = overview["height"] + scale_factor = overview["scale_factor"] + log.info(f"Creating overview level {level} (1:{scale_factor})") + log.debug(f"Target dimensions: {width} x {height}") + log.debug(f"Creating level {level} from level {level - 1}") + + overview_ds = create_overview_dataset_all_vars( + previous_level_ds, + level, + width, + height, + native_crs, + native_bounds, + data_vars, + ) + encoding = create_geozarr_encoding(overview_ds, compressor, spatial_chunk) + overview_path = fs_utils.normalize_path(f"{output_path}/{group_name.lstrip('/')}/{level}") + start_time = time.time() + storage_options = fs_utils.get_storage_options(overview_path) + log.info(f"Writing overview level {level} at {overview_path}") + + if fs_utils.path_exists(overview_path) and overwrite in {"skip", "merge"}: + try: + ok, _rep = utils.audit_group_completeness( + overview_path, [v for v in data_vars if v in overview_ds.data_vars] + ) + except Exception as e: + log.warning(f"Overview audit failed ({e}); will rewrite level {level}") + ok = False + if ok: + log.info(f"Skipping overview level {level} (already exists and complete)") + overview_datasets[level] = overview_ds + previous_level_ds = overview_ds + continue + + if not fs_utils.is_s3_path(overview_path): + os.makedirs(os.path.dirname(overview_path), exist_ok=True) + + overview_group = f"{group_name}/{level}" + # Write with retries to mitigate transient network disconnects + last_err: Optional[Exception] = None + for attempt in range(int(max_retries) if max_retries else 1): + try: + write_dataset_zarr( + overview_ds, + output_path, + group=overview_group.lstrip("/"), + mode="w", + consolidated=True, + zarr_format=3, + encoding=encoding, + align_chunks=True, + safe_chunks=False, + storage_options=storage_options, + ) + last_err = None + break + except Exception as e: + last_err = e + if attempt < int(max_retries) - 1: + log.warning( + f"Overview level {level}: attempt {attempt + 1} failed ({e}); retrying in 2s" + ) + time.sleep(2) + else: + log.error(f"Overview level {level}: failed after {max_retries} attempts: {e}") + if last_err is not None: + raise last_err + + overview_datasets[level] = overview_ds + proc_time = time.time() - start_time + timing_data.append( + { + "level": level, + "time": proc_time, + "pixels": width * height, + "width": width, + "height": height, + "scale_factor": scale_factor, + } + ) + log.info(f"Level {level}: Successfully created in {proc_time:.2f}s") + + group_path = fs_utils.normalize_path(f"{output_path}/{overview_group.lstrip('/')}") + zarr_group = fs_utils.open_zarr_group(group_path, mode="r+") + consolidate_metadata(zarr_group.store) + log.debug(f"Metadata consolidated for overview level {level}") + + previous_level_ds = overview_ds + + log.info( + f"Created {len(overview_levels)} GeoZarr-compliant overview levels using pyramid approach" + ) + return { + "overview_datasets": overview_datasets, + "levels": overview_levels, + "timing": timing_data, + "tile_matrix_set": tile_matrix_set, + "tile_matrix_limits": tile_matrix_limits, + } diff --git a/src/eopf_geozarr/conversion/prepare.py b/src/eopf_geozarr/conversion/prepare.py new file mode 100644 index 0000000..e4d3f0b --- /dev/null +++ b/src/eopf_geozarr/conversion/prepare.py @@ -0,0 +1,147 @@ +from __future__ import annotations + +import logging +from typing import Dict, Optional + +import rioxarray # noqa: F401 # Registers the .rio accessor +import xarray as xr + + +def setup_datatree_metadata_geozarr_spec_compliant( + dt: xr.DataTree, groups: list[str] +) -> Dict[str, xr.Dataset]: + geozarr_groups: Dict[str, xr.Dataset] = {} + grid_mapping_var_name = "spatial_ref" + + for key in groups: + logging.getLogger(__name__).debug(f"Processing group for GeoZarr compliance: {key}") + ds = dt[key].to_dataset().copy() + + for band in ds.data_vars: + logging.getLogger(__name__).debug(f" Processing band: {band}") + ds[band].attrs["standard_name"] = "toa_bidirectional_reflectance" + if hasattr(ds[band], "dims"): + ds[band].attrs["_ARRAY_DIMENSIONS"] = list(ds[band].dims) + ds[band].attrs["grid_mapping"] = grid_mapping_var_name + + if "proj:epsg" in ds[band].attrs: + epsg = ds[band].attrs["proj:epsg"] + logging.getLogger(__name__).debug(f" Setting CRS for {band} to EPSG:{epsg}") + crs_str = f"epsg:{epsg}" + ds = ds.rio.write_crs(crs_str) + if "spatial_ref" not in ds: + ds["spatial_ref"] = xr.DataArray(0) + + _add_coordinate_metadata(ds) + _setup_grid_mapping(ds, grid_mapping_var_name) + geozarr_groups[key] = ds + + return geozarr_groups + + +def prepare_dataset_with_crs_info( + ds: xr.Dataset, reference_crs: Optional[str] = None +) -> xr.Dataset: + ds = ds.copy() + _add_coordinate_metadata(ds) + if "x" in ds.coords and "y" in ds.coords and reference_crs: + logging.getLogger(__name__).debug(f" Adding CRS information: {reference_crs}") + ds = ds.rio.write_crs(reference_crs) + if "spatial_ref" not in ds: + ds["spatial_ref"] = xr.DataArray(0) + _add_geotransform(ds, "spatial_ref") + + for var_name in ds.data_vars: + if "_ARRAY_DIMENSIONS" not in ds[var_name].attrs and hasattr(ds[var_name], "dims"): + ds[var_name].attrs["_ARRAY_DIMENSIONS"] = list(ds[var_name].dims) + if "x" in ds[var_name].coords and "y" in ds[var_name].coords and reference_crs: + ds[var_name].attrs["grid_mapping"] = "spatial_ref" + ds[var_name].attrs["proj:epsg"] = reference_crs.split(":")[-1] + if "spatial_ref" in ds and "GeoTransform" in ds["spatial_ref"].attrs: + ds[var_name].attrs["proj:transform"] = ds["spatial_ref"].attrs["GeoTransform"] + + return ds + + +def _add_coordinate_metadata(ds: xr.Dataset) -> None: + for coord_name in ds.coords: + if coord_name == "x": + ds[coord_name].attrs.update( + { + "_ARRAY_DIMENSIONS": ["x"], + "standard_name": "projection_x_coordinate", + "units": "m", + "long_name": "x coordinate of projection", + } + ) + elif coord_name == "y": + ds[coord_name].attrs.update( + { + "_ARRAY_DIMENSIONS": ["y"], + "standard_name": "projection_y_coordinate", + "units": "m", + "long_name": "y coordinate of projection", + } + ) + elif coord_name == "time": + ds[coord_name].attrs.update({"_ARRAY_DIMENSIONS": ["time"], "standard_name": "time"}) + elif coord_name == "angle": + ds[coord_name].attrs.update( + { + "_ARRAY_DIMENSIONS": ["angle"], + "standard_name": "angle", + "long_name": "angle coordinate", + } + ) + elif coord_name == "band": + ds[coord_name].attrs.update( + { + "_ARRAY_DIMENSIONS": ["band"], + "standard_name": "band", + "long_name": "spectral band identifier", + } + ) + elif coord_name == "detector": + ds[coord_name].attrs.update( + { + "_ARRAY_DIMENSIONS": ["detector"], + "standard_name": "detector", + "long_name": "detector identifier", + } + ) + else: + if "_ARRAY_DIMENSIONS" not in ds[coord_name].attrs: + ds[coord_name].attrs["_ARRAY_DIMENSIONS"] = [coord_name] + + +def _setup_grid_mapping(ds: xr.Dataset, grid_mapping_var_name: str) -> None: + if ds.rio.crs and "spatial_ref" in ds: + ds[grid_mapping_var_name].attrs["_ARRAY_DIMENSIONS"] = [] + if ds.rio.transform(): + transform_gdal = ds.rio.transform().to_gdal() + transform_str = " ".join([str(i) for i in transform_gdal]) + ds[grid_mapping_var_name].attrs["GeoTransform"] = transform_str + for band in ds.data_vars: + if band != grid_mapping_var_name: + ds[band].attrs["grid_mapping"] = grid_mapping_var_name + + +def _add_geotransform(ds: xr.Dataset, grid_mapping_var: str) -> None: + ds[grid_mapping_var].attrs["_ARRAY_DIMENSIONS"] = [] + if len(ds.coords["x"]) > 1 and len(ds.coords["y"]) > 1: + x_coords = ds.coords["x"].values + y_coords = ds.coords["y"].values + pixel_size_x = float(x_coords[1] - x_coords[0]) + pixel_size_y = float(y_coords[0] - y_coords[1]) + transform_str = f"{x_coords[0]} {pixel_size_x} 0.0 {y_coords[0]} 0.0 {pixel_size_y}" + ds[grid_mapping_var].attrs["GeoTransform"] = transform_str + + +def _find_reference_crs(geozarr_groups: Dict[str, xr.Dataset]) -> Optional[str]: + for _, group in geozarr_groups.items(): + if group.rio.crs: + return group.rio.crs.to_string() # type: ignore[no-any-return] + return None + + +# No manual CRS writers required; rioxarray is a hard dependency in this project. diff --git a/src/eopf_geozarr/conversion/utils.py b/src/eopf_geozarr/conversion/utils.py index e0e1350..7e1500d 100644 --- a/src/eopf_geozarr/conversion/utils.py +++ b/src/eopf_geozarr/conversion/utils.py @@ -1,9 +1,148 @@ """Utility functions for GeoZarr conversion.""" +# mypy: disable-error-code=import-not-found + +import os +from typing import Any, Dict, List, Optional, Tuple + import numpy as np import rasterio # noqa: F401 # Import to enable .rio accessor import xarray as xr +from . import fs_utils + +# Verbose logging flag controlled by environment variable +_VERBOSE = os.environ.get("EOPF_GEOZARR_VERBOSE", "").lower() in { + "1", + "true", + "yes", + "on", +} + + +def log(message: str) -> None: + """Print message when verbose mode is enabled via EOPF_GEOZARR_VERBOSE.""" + if _VERBOSE: + print(message) + + +def _safe_get_chunk_shape(meta: Dict[str, Any]) -> Optional[Tuple[int, ...]]: + """Extract chunk shape from Zarr v3 or v2 metadata.""" + cg = meta.get("chunk_grid") + if isinstance(cg, dict): + chunk_shape_val = cg.get("chunk_shape") + if isinstance(chunk_shape_val, list): + return tuple(int(x) for x in chunk_shape_val) + grid = cg.get("grid") + if isinstance(grid, dict): + grid_cs = grid.get("chunk_shape") + if isinstance(grid_cs, list): + return tuple(int(x) for x in grid_cs) + # Zarr v2 style + chunks_val = meta.get("chunks") + if isinstance(chunks_val, (list, tuple)): + return tuple(int(x) for x in chunks_val) + return None + + +def estimate_array_completeness(array_dir: str) -> Tuple[bool, Dict[str, Any]]: + """Estimate if a Zarr array directory appears complete. + + Heuristics: + - zarr.json exists and is readable + - shape present in metadata + - chunk directory exists and non-empty + - if chunk shape known: count of chunk files approximates expected grid size + - no zero-byte chunk files among a sample + """ + info: Dict[str, Any] = {"path": array_dir, "problems": []} + try: + meta_path = fs_utils.normalize_path(f"{array_dir}/zarr.json") + meta = fs_utils.read_json_metadata(meta_path) + except Exception as e: + info["problems"].append(f"metadata_unreadable:{e}") + return False, info + + shape = meta.get("shape") if isinstance(meta, dict) else None + if not isinstance(shape, (list, tuple)): + info["problems"].append("missing_shape") + return False, info + shape_t = tuple(int(x) for x in shape) + chunk_shape = _safe_get_chunk_shape(meta) + info["shape"] = shape_t + info["chunk_shape"] = chunk_shape + + fs = fs_utils.get_filesystem(array_dir) + chunks_base = fs_utils.normalize_path(f"{array_dir}/c") + if not fs.exists(chunks_base): + info["problems"].append("missing_chunk_dir") + return False, info + + # Find chunk files under c/ + try: + files = [p for p in fs.find(chunks_base) if not p.endswith("/")] + except Exception: + # Fallback to ls if find not available + try: + files = fs.ls(chunks_base, detail=False) + except Exception as e: + info["problems"].append(f"list_failed:{e}") + return False, info + + info["chunk_files_found"] = len(files) + + if len(files) == 0: + info["problems"].append("no_chunks") + return False, info + + # Zero-byte sample check + zero_bytes = 0 + sample = files[:5] + files[-5:] if len(files) > 10 else files + for p in sample: + try: + s = fs.size(p) + if s == 0: + zero_bytes += 1 + except Exception: + continue + if zero_bytes > 0: + info["problems"].append("zero_byte_chunks") + + # Expected chunk count estimate if chunk_shape known + if chunk_shape is not None: + import math + + dims = len(shape_t) + # align chunk_shape length with dims (prepend 1s if needed) + cs = (1,) * (dims - len(chunk_shape)) + tuple(chunk_shape) + expected = 1 + for i in range(dims): + expected *= math.ceil(shape_t[i] / max(1, cs[i])) + info["expected_chunk_count"] = expected + # allow some leeway; if far fewer files, likely incomplete + if len(files) < max(1, int(0.9 * expected)): + info["problems"].append("chunk_count_below_expected") + + is_complete = len(info["problems"]) == 0 + return is_complete, info + + +def audit_group_completeness(group_path: str, variables: List[str]) -> Tuple[bool, Dict[str, Any]]: + """Audit a Zarr group for variable completeness. + + Returns (ok, report). ok = False if any variable appears incomplete. + """ + report: Dict[str, Any] = {"group": group_path, "variables": {}} + all_ok = True + for v in variables: + array_dir = fs_utils.normalize_path(f"{group_path}/{v}") + ok, details = estimate_array_completeness(array_dir) + report["variables"][v] = details + if not ok: + all_ok = False + report["ok"] = all_ok + return all_ok, report + def downsample_2d_array( source_data: np.ndarray, target_height: int, target_width: int @@ -33,12 +172,8 @@ def downsample_2d_array( if block_size_y > 1 and block_size_x > 1: # Block averaging - reshaped = source_data[ - : target_height * block_size_y, : target_width * block_size_x - ] - reshaped = reshaped.reshape( - target_height, block_size_y, target_width, block_size_x - ) + reshaped = source_data[: target_height * block_size_y, : target_width * block_size_x] + reshaped = reshaped.reshape(target_height, block_size_y, target_width, block_size_x) downsampled = reshaped.mean(axis=(1, 3)) else: # Simple subsampling @@ -65,10 +200,13 @@ def is_grid_mapping_variable(ds: xr.Dataset, var_name: str) -> bool: bool True if this variable is referenced as a grid_mapping """ - for data_var in ds.data_vars: - if data_var != var_name and "grid_mapping" in ds[data_var].attrs: - if ds[data_var].attrs["grid_mapping"] == var_name: - return True + for data_var in list(ds.data_vars): + try: + if data_var != var_name and "grid_mapping" in ds[data_var].attrs: + if ds[data_var].attrs.get("grid_mapping") == var_name: + return True + except Exception: + continue return False @@ -125,10 +263,7 @@ def validate_existing_band_data( """ try: # Check if the variable exists - if ( - var_name not in existing_group.data_vars - and var_name not in existing_group.coords - ): + if var_name not in existing_group.data_vars and var_name not in existing_group.coords: return False # Check shape matches @@ -162,9 +297,7 @@ def validate_existing_band_data( if array_info.size == 0: return False # read a piece of data to ensure it's valid - test = array_info.isel( - {dim: 0 for dim in array_info.dims} - ).values.mean() + test = array_info.isel({dim: 0 for dim in array_info.dims}).values.mean() if np.isnan(test): return False except Exception as e: @@ -175,3 +308,36 @@ def validate_existing_band_data( except Exception: return False + + +def validate_existing_band_metadata( + existing_group: xr.Dataset, var_name: str, reference_ds: xr.Dataset +) -> bool: + """Fast validation without reading data values. + + Checks existence, shape, minimal attrs, and CRS equality only. + """ + try: + if var_name not in existing_group.data_vars and var_name not in existing_group.coords: + return False + + if var_name in reference_ds.data_vars: + expected_shape = reference_ds[var_name].shape + existing_shape = existing_group[var_name].shape + if expected_shape != existing_shape: + return False + + if var_name in reference_ds.data_vars and not is_grid_mapping_variable( + reference_ds, var_name + ): + for attr in ("_ARRAY_DIMENSIONS", "standard_name"): + if attr not in existing_group[var_name].attrs: + return False + + if getattr(existing_group, "rio", None) is not None: + if existing_group.rio.crs != reference_ds.rio.crs: + return False + + return True + except Exception: + return False diff --git a/src/eopf_geozarr/conversion/zarr_write.py b/src/eopf_geozarr/conversion/zarr_write.py new file mode 100644 index 0000000..dc6a72a --- /dev/null +++ b/src/eopf_geozarr/conversion/zarr_write.py @@ -0,0 +1,36 @@ +from __future__ import annotations + +from typing import Any, Mapping + +import xarray as xr + + +def write_dataset_zarr( + ds: xr.Dataset, + store_path: str, + *, + group: str | None = None, + mode: str = "a", + consolidated: bool = True, + zarr_format: int = 3, + encoding: Mapping[str, Mapping[str, Any]] | None = None, + align_chunks: bool = True, + safe_chunks: bool = False, + storage_options: Mapping[str, Any] | None = None, +) -> None: + """Typed thin wrapper around xarray.Dataset.to_zarr. + + Centralizes parameters and narrows types to avoid repeated mypy ignore noise + caused by incomplete upstream type stubs for xarray/zarr interactions. + """ + ds.to_zarr( + store_path, + group=group, + mode=mode, + consolidated=consolidated, + zarr_format=zarr_format, + encoding=encoding, # already filtered by caller + align_chunks=align_chunks, + safe_chunks=safe_chunks, + storage_options=storage_options, + ) From a70af48ff28070efe644261ebf5ceacc5aad9859 Mon Sep 17 00:00:00 2001 From: Wietze Date: Tue, 16 Sep 2025 18:58:15 -0400 Subject: [PATCH 6/9] feat(multiscales): add encoding utilities, helpers and filesystem abstractions --- src/eopf_geozarr/conversion/encoding.py | 68 +++++++++++++ src/eopf_geozarr/conversion/fs_utils.py | 115 +++++++++++++++------- src/eopf_geozarr/conversion/helpers.py | 124 ++++++++++++++++++++++++ 3 files changed, 273 insertions(+), 34 deletions(-) create mode 100644 src/eopf_geozarr/conversion/encoding.py create mode 100644 src/eopf_geozarr/conversion/helpers.py diff --git a/src/eopf_geozarr/conversion/encoding.py b/src/eopf_geozarr/conversion/encoding.py new file mode 100644 index 0000000..bd5a459 --- /dev/null +++ b/src/eopf_geozarr/conversion/encoding.py @@ -0,0 +1,68 @@ +import os +from typing import Any, Dict, Tuple + +import xarray as xr + +from . import utils +from .helpers import iter_str + + +def create_geozarr_encoding( + ds: xr.Dataset, compressor: Any, spatial_chunk: int +) -> Dict[str, Dict[str, Any]]: + """Create encoding for GeoZarr dataset variables (spec-aware).""" + encoding: Dict[str, Dict[str, Any]] = {} + # Optional safety cap for chunk bytes to avoid OOM. Default ~8 MiB unless overridden. + try: + max_chunk_bytes = int(os.environ.get("EOPF_MAX_CHUNK_BYTES", str(8 * 1024 * 1024))) + except Exception: + max_chunk_bytes = 8 * 1024 * 1024 + for var in iter_str(ds.data_vars): + if utils.is_grid_mapping_variable(ds, var): + encoding[var] = {"compressors": None} + else: + data_shape = ds[var].shape + dtype_size = getattr(ds[var].dtype, "itemsize", 1) or 1 + if len(data_shape) >= 2: + height, width = data_shape[-2:] + spatial_chunk_aligned = min( + spatial_chunk, + utils.calculate_aligned_chunk_size(width, spatial_chunk), + utils.calculate_aligned_chunk_size(height, spatial_chunk), + ) + else: + spatial_chunk_aligned = spatial_chunk + + # Build chunk tuple matching variable dimensionality. + # Use 1 for all leading (non-spatial) dims, and spatial_chunk_aligned for the last two. + if len(data_shape) == 1: + chunks: Tuple[int, ...] = (min(spatial_chunk_aligned, data_shape[0]),) + elif len(data_shape) == 2: + chunks = (spatial_chunk_aligned, spatial_chunk_aligned) + else: + leading: Tuple[int, ...] = tuple(1 for _ in range(len(data_shape) - 2)) + chunks = leading + (spatial_chunk_aligned, spatial_chunk_aligned) + + # Enforce max_chunk_bytes by reducing spatial chunk if needed + # Estimate total bytes per chunk as product(chunks) * dtype_size + from math import prod as _prod + + est_bytes = _prod(chunks) * dtype_size + if max_chunk_bytes and est_bytes > max_chunk_bytes and len(chunks) >= 1: + # Reduce spatial chunks proportionally (keep leading dims as-is) + lead = chunks[:-2] if len(chunks) > 2 else tuple() + yc, xc = (chunks[-2], chunks[-1]) if len(chunks) >= 2 else (chunks[-1], 1) + factor = (est_bytes / max_chunk_bytes) ** 0.5 + new_y = max(1, int(yc / factor)) + new_x = max(1, int(xc / factor)) + chunks = lead + (new_y, new_x) if len(chunks) >= 2 else (new_y,) + + encoding[var] = { + "chunks": chunks, + "compressors": ([compressor] if compressor is not None else None), + } + + for coord in iter_str(ds.coords): + encoding[coord] = {"compressors": None} + + return encoding diff --git a/src/eopf_geozarr/conversion/fs_utils.py b/src/eopf_geozarr/conversion/fs_utils.py index 5f908d8..2da4834 100644 --- a/src/eopf_geozarr/conversion/fs_utils.py +++ b/src/eopf_geozarr/conversion/fs_utils.py @@ -1,8 +1,14 @@ -"""S3 utilities for GeoZarr conversion.""" +"""S3 utilities for GeoZarr conversion. + +Note: Optional dependencies may lack type stubs; suppress their missing-import +noise locally to keep global mypy strictness intact. +""" + +# mypy: disable-error-code=import-not-found import json import os -from typing import Any, Dict, Optional +from typing import Any, Dict, Literal, Optional from urllib.parse import urlparse import s3fs @@ -108,9 +114,7 @@ def get_s3_storage_options(s3_path: str, **s3_kwargs: Any) -> Dict[str, Any]: default_s3_kwargs = { "anon": False, # Use credentials "use_ssl": True, - "client_kwargs": { - "region_name": os.environ.get("AWS_DEFAULT_REGION", "us-east-1") - }, + "client_kwargs": {"region_name": os.environ.get("AWS_DEFAULT_REGION", "us-east-1")}, } # Add custom endpoint support (e.g., for OVH Cloud) @@ -147,6 +151,36 @@ def get_storage_options(path: str, **kwargs: Any) -> Optional[Dict[str, Any]]: """ if is_s3_path(path): return get_s3_storage_options(path, **kwargs) + # For HTTP(S) paths, ensure servers don't apply content-encoding (e.g., gzip) + # to chunk responses which would corrupt codec bytes (e.g., Blosc) and + # trigger decompression errors. Force identity encoding and set a sane + # default block size for ranged requests. + if path.startswith(("http://", "https://")): + headers = {"Accept-Encoding": "identity"} + # Merge user headers if provided + user_headers = kwargs.get("headers") + if isinstance(user_headers, dict): + headers.update(user_headers) + http_opts: Dict[str, Any] = { + "headers": headers, + "block_size": kwargs.get("block_size", 0), + "simple_links": kwargs.get("simple_links", True), + } + # Add conservative aiohttp client settings to mitigate disconnects + try: + import aiohttp + + timeout = kwargs.get("timeout") or aiohttp.ClientTimeout(total=120) + connector = kwargs.get("connector") or aiohttp.TCPConnector(limit=8) + client_kwargs = kwargs.get("client_kwargs", {}) or {} + if not isinstance(client_kwargs, dict): + client_kwargs = {} + client_kwargs.setdefault("timeout", timeout) + client_kwargs.setdefault("connector", connector) + http_opts["client_kwargs"] = client_kwargs + except Exception: + pass + return http_opts # For local paths, return None (no storage options needed) # Future protocols (gcs://, azure://, etc.) can be added here return None @@ -201,9 +235,7 @@ def create_s3_store(s3_path: str, **s3_kwargs: Any) -> str: return s3_path -def write_s3_json_metadata( - s3_path: str, metadata: Dict[str, Any], **s3_kwargs: Any -) -> None: +def write_s3_json_metadata(s3_path: str, metadata: Dict[str, Any], **s3_kwargs: Any) -> None: """ Write JSON metadata directly to S3. @@ -224,9 +256,7 @@ def write_s3_json_metadata( "anon": False, "use_ssl": True, "asynchronous": False, # Force synchronous mode - "client_kwargs": { - "region_name": os.environ.get("AWS_DEFAULT_REGION", "us-east-1") - }, + "client_kwargs": {"region_name": os.environ.get("AWS_DEFAULT_REGION", "us-east-1")}, } # Add custom endpoint support (e.g., for OVH Cloud) @@ -266,9 +296,7 @@ def read_s3_json_metadata(s3_path: str, **s3_kwargs: Any) -> Dict[str, Any]: "anon": False, "use_ssl": True, "asynchronous": False, # Force synchronous mode - "client_kwargs": { - "region_name": os.environ.get("AWS_DEFAULT_REGION", "us-east-1") - }, + "client_kwargs": {"region_name": os.environ.get("AWS_DEFAULT_REGION", "us-east-1")}, } # Add custom endpoint support (e.g., for OVH Cloud) @@ -308,9 +336,7 @@ def s3_path_exists(s3_path: str, **s3_kwargs: Any) -> bool: "anon": False, "use_ssl": True, "asynchronous": False, # Force synchronous mode - "client_kwargs": { - "region_name": os.environ.get("AWS_DEFAULT_REGION", "us-east-1") - }, + "client_kwargs": {"region_name": os.environ.get("AWS_DEFAULT_REGION", "us-east-1")}, } # Add custom endpoint support (e.g., for OVH Cloud) @@ -327,7 +353,9 @@ def s3_path_exists(s3_path: str, **s3_kwargs: Any) -> bool: return result -def open_s3_zarr_group(s3_path: str, mode: str = "r", **s3_kwargs: Any) -> zarr.Group: +def open_s3_zarr_group( + s3_path: str, mode: Literal["r", "r+", "w", "a", "w-"] = "r", **s3_kwargs: Any +) -> zarr.Group: """ Open a Zarr group from S3 using storage_options. @@ -346,9 +374,7 @@ def open_s3_zarr_group(s3_path: str, mode: str = "r", **s3_kwargs: Any) -> zarr. Zarr group """ storage_options = get_s3_storage_options(s3_path, **s3_kwargs) - return zarr.open_group( - s3_path, mode=mode, zarr_format=3, storage_options=storage_options - ) + return zarr.open_group(s3_path, mode=mode, zarr_format=3, storage_options=storage_options) def get_s3_credentials_info() -> Dict[str, Optional[str]]: @@ -362,9 +388,7 @@ def get_s3_credentials_info() -> Dict[str, Optional[str]]: """ return { "aws_access_key_id": os.environ.get("AWS_ACCESS_KEY_ID"), - "aws_secret_access_key": "***" - if os.environ.get("AWS_SECRET_ACCESS_KEY") - else None, + "aws_secret_access_key": "***" if os.environ.get("AWS_SECRET_ACCESS_KEY") else None, "aws_session_token": "***" if os.environ.get("AWS_SESSION_TOKEN") else None, "aws_default_region": os.environ.get("AWS_DEFAULT_REGION", "us-east-1"), "aws_profile": os.environ.get("AWS_PROFILE"), @@ -395,9 +419,7 @@ def validate_s3_access(s3_path: str, **s3_kwargs: Any) -> tuple[bool, Optional[s "anon": False, "use_ssl": True, "asynchronous": False, # Force synchronous mode - "client_kwargs": { - "region_name": os.environ.get("AWS_DEFAULT_REGION", "us-east-1") - }, + "client_kwargs": {"region_name": os.environ.get("AWS_DEFAULT_REGION", "us-east-1")}, } # Add custom endpoint support (e.g., for OVH Cloud) @@ -441,9 +463,34 @@ def get_filesystem(path: str, **kwargs: Any) -> Any: # Get S3 storage options and use them for fsspec storage_options = get_s3_storage_options(path, **kwargs) return fsspec.filesystem("s3", **storage_options) - else: - # For local paths, use the local filesystem - return fsspec.filesystem("file") + if path.startswith(("http://", "https://")): + # Ensure identity encoding for raw chunk bytes over HTTP(S) + headers = {"Accept-Encoding": "identity"} + user_headers = kwargs.get("headers") + if isinstance(user_headers, dict): + headers.update(user_headers) + http_opts: Dict[str, Any] = { + "headers": headers, + "block_size": kwargs.get("block_size", 0), + "simple_links": kwargs.get("simple_links", True), + } + # Add conservative aiohttp client settings to mitigate disconnects + try: + import aiohttp + + timeout = kwargs.get("timeout") or aiohttp.ClientTimeout(total=120) + connector = kwargs.get("connector") or aiohttp.TCPConnector(limit=8) + client_kwargs = kwargs.get("client_kwargs", {}) or {} + if not isinstance(client_kwargs, dict): + client_kwargs = {} + client_kwargs.setdefault("timeout", timeout) + client_kwargs.setdefault("connector", connector) + http_opts["client_kwargs"] = client_kwargs + except Exception: + pass + return fsspec.filesystem("http", **http_opts) + # For local paths, use the local filesystem + return fsspec.filesystem("file") def write_json_metadata(path: str, metadata: Dict[str, Any], **kwargs: Any) -> None: @@ -519,7 +566,9 @@ def path_exists(path: str, **kwargs: Any) -> bool: return result -def open_zarr_group(path: str, mode: str = "r", **kwargs: Any) -> zarr.Group: +def open_zarr_group( + path: str, mode: Literal["r", "r+", "w", "a", "w-"] = "r", **kwargs: Any +) -> zarr.Group: """ Open a Zarr group from any path type using unified storage options. @@ -538,6 +587,4 @@ def open_zarr_group(path: str, mode: str = "r", **kwargs: Any) -> zarr.Group: Zarr group """ storage_options = get_storage_options(path, **kwargs) - return zarr.open_group( - path, mode=mode, zarr_format=3, storage_options=storage_options - ) + return zarr.open_group(path, mode=mode, zarr_format=3, storage_options=storage_options) diff --git a/src/eopf_geozarr/conversion/helpers.py b/src/eopf_geozarr/conversion/helpers.py new file mode 100644 index 0000000..e9803a7 --- /dev/null +++ b/src/eopf_geozarr/conversion/helpers.py @@ -0,0 +1,124 @@ +from __future__ import annotations + +from typing import Any, Callable, Dict, Optional, Protocol, Set + +import xarray as xr + +from . import utils + + +def iter_str(keys: Any) -> list[str]: + """Return list of string keys from an iterable of Hashable items. + + xarray exposes mapping-like objects whose iteration type is Hashable; we only + operate on string variable names, so cast/filter accordingly for type safety. + """ + out: list[str] = [] + try: + for k in keys: # runtime iteration; filter for str + if isinstance(k, str): + out.append(k) + except Exception: + pass + return out + + +def normalize_group_path(group: str) -> str: + if group in {"", "."}: + return "/" + return group if group.startswith("/") else f"/{group}" + + +def build_var_encodings(ds: xr.Dataset, compressor: Any | None) -> Dict[str, Dict[str, Any]]: + if compressor is None: + return {} + + def enc_for(var: str) -> Dict[str, Any]: + base: Dict[str, Any] = {"compressors": [compressor]} + try: + base["dtype"] = str(ds[var].dtype) + except Exception: + pass + return base + + return { + var: enc_for(var) + for var in iter_str(ds.data_vars) + if not utils.is_grid_mapping_variable(ds, var) + } + + +def safely( + label: str, + func: Callable[[], Any], + *, + verbose: bool = False, + swallow: bool = True, +) -> Any: + try: + return func() + except Exception as e: # pragma: no cover + if verbose: + print(f"Warning: {label} failed: {e}") + if not swallow: + raise + return None + + +class _CRSPreparer(Protocol): + def __call__(self, ds: xr.Dataset, *, reference_crs: Any) -> xr.Dataset: ... + + +def inject_crs_if_requested( + group: str, + ds: xr.Dataset, + requested: Optional[Set[str]], + reference_crs_finder: Callable[[], Any], + preparer: _CRSPreparer, + *, + verbose: bool = False, +) -> xr.Dataset: + if not requested: + return ds + norm = normalize_group_path(group) + if norm not in requested: + return ds + try: + if verbose: + print(f"Adding CRS information to group: {norm}") + ref_crs = reference_crs_finder() + if verbose: + print("Inferred reference CRS from measurements:", ref_crs) + return preparer(ds, reference_crs=ref_crs) + except Exception as e: # pragma: no cover + if verbose: + print(f"Warning: CRS preparation skipped for {norm}: {e}") + return ds + + +class MaybeStep: + def __init__(self, recorder: Any, name: str): # recorder is external metrics-like object + self.rec: Any = recorder + self.name: str = name + self.ctx: Any | None = None + + def __enter__(self) -> "MaybeStep": # pragma: no cover - trivial + if self.rec: + try: + self.ctx = self.rec.time_step(self.name) + self.ctx.__enter__() + except Exception: + self.ctx = None + return self + + def __exit__(self, exc_type: Any, exc: Any, tb: Any) -> None: # pragma: no cover - trivial + if self.ctx is not None: + try: + self.ctx.__exit__(exc_type, exc, tb) + except Exception: + pass + return None + + +def maybe_step(recorder: Any, name: str) -> MaybeStep: # factory + return MaybeStep(recorder, name) From 8cccc3c2d217a0d2e485c5e5aa30fc137a22031a Mon Sep 17 00:00:00 2001 From: Wietze Date: Tue, 16 Sep 2025 18:58:23 -0400 Subject: [PATCH 7/9] feat(metrics): add conversion metrics collection --- src/eopf_geozarr/metrics.py | 7 ++----- 1 file changed, 2 insertions(+), 5 deletions(-) diff --git a/src/eopf_geozarr/metrics.py b/src/eopf_geozarr/metrics.py index 45bc318..7a7a82e 100644 --- a/src/eopf_geozarr/metrics.py +++ b/src/eopf_geozarr/metrics.py @@ -22,9 +22,7 @@ class MetricsRecorder: ) attempt: int = 1 started_at: str = field( - default_factory=lambda: datetime.now(timezone.utc) - .isoformat() - .replace("+00:00", "Z") + default_factory=lambda: datetime.now(timezone.utc).isoformat().replace("+00:00", "Z") ) steps: List[Dict[str, Any]] = field(default_factory=list) input_info: Dict[str, Any] = field(default_factory=dict) @@ -60,8 +58,7 @@ def _get_version(pkg: str) -> Optional[str]: "python": sys.version.split()[0], "platform": platform.platform(), "packages": { - "eopf_geozarr": _get_version("eopf-geozarr") - or _get_version("eopf_geozarr"), + "eopf_geozarr": _get_version("eopf-geozarr") or _get_version("eopf_geozarr"), "xarray": _get_version("xarray"), "zarr": _get_version("zarr"), "dask": _get_version("dask"), From 1e27166054c3234b24e86af1ff51b1c42d94f8f1 Mon Sep 17 00:00:00 2001 From: Wietze Date: Tue, 16 Sep 2025 18:58:32 -0400 Subject: [PATCH 8/9] test: expand suite with helpers, encoding, writer and e2e cases --- tests/conftest.py | 15 ++++ tests/test_cli_e2e.py | 44 +++------- tests/test_conversion.py | 36 ++------ tests/test_encoding.py | 80 +++++++++++++++++ tests/test_helpers.py | 130 ++++++++++++++++++++++++++++ tests/test_integration_sentinel2.py | 73 ++++------------ tests/test_writer.py | 71 +++++++++++++++ 7 files changed, 336 insertions(+), 113 deletions(-) create mode 100644 tests/conftest.py create mode 100644 tests/test_encoding.py create mode 100644 tests/test_helpers.py create mode 100644 tests/test_writer.py diff --git a/tests/conftest.py b/tests/conftest.py new file mode 100644 index 0000000..d3e08f9 --- /dev/null +++ b/tests/conftest.py @@ -0,0 +1,15 @@ +import warnings + +# Filter noisy Zarr v3 consolidated metadata warning in tests +warnings.filterwarnings( + "ignore", + message="Consolidated metadata is currently not part in the Zarr format 3 specification", + category=UserWarning, +) + +# Future-proof xarray dims warning until upstream change lands +warnings.filterwarnings( + "ignore", + message="The return type of `Dataset.dims` will be changed", + category=FutureWarning, +) diff --git a/tests/test_cli_e2e.py b/tests/test_cli_e2e.py index 86148f3..52397d4 100644 --- a/tests/test_cli_e2e.py +++ b/tests/test_cli_e2e.py @@ -111,13 +111,9 @@ def test_cli_convert_real_sentinel2_data(self, temp_output_dir: str) -> None: cmd_info = ["python", "-m", "eopf_geozarr", "info", str(output_path)] - result_info = subprocess.run( - cmd_info, capture_output=True, text=True, timeout=60 - ) + result_info = subprocess.run(cmd_info, capture_output=True, text=True, timeout=60) - assert ( - result_info.returncode == 0 - ), f"CLI info command failed: {result_info.stderr}" + assert result_info.returncode == 0, f"CLI info command failed: {result_info.stderr}" print("✅ CLI info command succeeded") print(f"Info output: {result_info.stdout}") @@ -138,9 +134,7 @@ def test_cli_convert_real_sentinel2_data(self, temp_output_dir: str) -> None: str(output_path), ] - result_validate = subprocess.run( - cmd_validate, capture_output=True, text=True, timeout=60 - ) + result_validate = subprocess.run(cmd_validate, capture_output=True, text=True, timeout=60) assert ( result_validate.returncode == 0 @@ -151,7 +145,8 @@ def test_cli_convert_real_sentinel2_data(self, temp_output_dir: str) -> None: # Verify validation output validate_output = result_validate.stdout assert "Validation Results:" in validate_output, "Should show validation header" - assert "✅" in validate_output, "Should show successful validations" + success_ok = ("✅" in validate_output) or ("GeoZarr compliant" in validate_output) + assert success_ok, "Validation should indicate success" # Test 4: Verify data structure and compliance print("\n=== Verifying converted data structure ===") @@ -160,9 +155,7 @@ def test_cli_convert_real_sentinel2_data(self, temp_output_dir: str) -> None: print("✅ All CLI end-to-end tests passed!") - def _verify_converted_data_structure( - self, output_path: Path, groups: list[str] - ) -> None: + def _verify_converted_data_structure(self, output_path: Path, groups: list[str]) -> None: """Verify the structure and compliance of converted data.""" # Check each group was converted for group in groups: @@ -215,9 +208,7 @@ def _verify_converted_data_structure( ds.close() # Check for overview levels - level_dirs = [ - d for d in group_path.iterdir() if d.is_dir() and d.name.isdigit() - ] + level_dirs = [d for d in group_path.iterdir() if d.is_dir() and d.name.isdigit()] print(f" Overview levels: {sorted([d.name for d in level_dirs])}") if len(level_dirs) > 1: @@ -352,24 +343,15 @@ def test_cli_convert_with_crs_groups(self, temp_output_dir: str) -> None: # Check command succeeded if result.returncode != 0: - print( - f"CLI convert with --crs-groups failed with return code {result.returncode}" - ) + print(f"CLI convert with --crs-groups failed with return code {result.returncode}") print(f"STDOUT: {result.stdout}") print(f"STDERR: {result.stderr}") # Don't fail the test if CRS groups don't exist in the dataset # This is expected behavior for best-effort processing - if ( - "not found in DataTree" in result.stdout - or "not found in DataTree" in result.stderr - ): - print( - "✅ CLI handled missing CRS groups gracefully (expected behavior)" - ) + if "not found in DataTree" in result.stdout or "not found in DataTree" in result.stderr: + print("✅ CLI handled missing CRS groups gracefully (expected behavior)") return - pytest.fail( - f"CLI convert with --crs-groups command failed: {result.stderr}" - ) + pytest.fail(f"CLI convert with --crs-groups command failed: {result.stderr}") print("✅ CLI convert with --crs-groups command succeeded") @@ -434,9 +416,7 @@ def test_cli_crs_groups_empty_list(self, temp_output_dir: str) -> None: result = subprocess.run(cmd, capture_output=True, text=True, timeout=60) # Should succeed (empty crs_groups list is valid) - assert ( - result.returncode == 0 - ), f"CLI with empty --crs-groups failed: {result.stderr}" + assert result.returncode == 0, f"CLI with empty --crs-groups failed: {result.stderr}" assert "CRS groups: []" in result.stdout, "Should show empty CRS groups list" print("✅ CLI with empty --crs-groups list works correctly") diff --git a/tests/test_conversion.py b/tests/test_conversion.py index 2072860..95b1c85 100644 --- a/tests/test_conversion.py +++ b/tests/test_conversion.py @@ -15,10 +15,8 @@ setup_datatree_metadata_geozarr_spec_compliant, validate_existing_band_data, ) -from eopf_geozarr.conversion.geozarr import ( - create_overview_dataset_all_vars, - prepare_dataset_with_crs_info, -) +from eopf_geozarr.conversion.geozarr import prepare_dataset_with_crs_info +from eopf_geozarr.conversion.multiscales import create_overview_dataset_all_vars class TestUtilityFunctions: @@ -123,9 +121,7 @@ def test_validate_existing_band_data_missing(self) -> None: def test_calculate_overview_levels(self) -> None: """Test overview levels calculation.""" - levels = calculate_overview_levels( - 1024, 1024, min_dimension=256, tile_width=256 - ) + levels = calculate_overview_levels(1024, 1024, min_dimension=256, tile_width=256) # Should have levels 0, 1, 2 (1024 -> 512 -> 256) assert len(levels) == 3 @@ -184,10 +180,7 @@ def test_setup_datatree_metadata_geozarr_spec_compliant(self) -> None: assert "standard_name" in processed_ds[band].attrs assert "_ARRAY_DIMENSIONS" in processed_ds[band].attrs assert "grid_mapping" in processed_ds[band].attrs - assert ( - processed_ds[band].attrs["standard_name"] - == "toa_bidirectional_reflectance" - ) + assert processed_ds[band].attrs["standard_name"] == "toa_bidirectional_reflectance" # Check coordinate attributes for coord in ["x", "y"]: @@ -269,9 +262,7 @@ def test_prepare_dataset_with_crs_info_with_spatial_coordinates(self) -> None: dt["conditions/geometry"] = geometry_ds # Mock the output path and file operations - with patch( - "eopf_geozarr.conversion.geozarr.fs_utils.normalize_path" - ) as mock_normalize: + with patch("eopf_geozarr.conversion.geozarr.fs_utils.normalize_path") as mock_normalize: with patch( "eopf_geozarr.conversion.geozarr.fs_utils.get_storage_options" ) as mock_storage: @@ -437,15 +428,10 @@ def test_prepare_dataset_with_crs_info_data_variable_attributes(self) -> None: if var_name != "spatial_ref": # Skip grid mapping variable var_attrs = processed_ds[var_name].attrs assert "_ARRAY_DIMENSIONS" in var_attrs - assert var_attrs["_ARRAY_DIMENSIONS"] == list( - processed_ds[var_name].dims - ) + assert var_attrs["_ARRAY_DIMENSIONS"] == list(processed_ds[var_name].dims) # Variables with spatial coordinates should have grid_mapping - if ( - "x" in processed_ds[var_name].dims - and "y" in processed_ds[var_name].dims - ): + if "x" in processed_ds[var_name].dims and "y" in processed_ds[var_name].dims: assert "grid_mapping" in var_attrs assert var_attrs["grid_mapping"] == "spatial_ref" @@ -494,9 +480,7 @@ def test_prepare_dataset_with_crs_info_crs_inference(self) -> None: pixel_size_y = float(y_coords[0] - y_coords[1]) # Usually negative # Create GeoTransform (GDAL format) - transform_str = ( - f"{x_coords[0]} {pixel_size_x} 0.0 {y_coords[0]} 0.0 {pixel_size_y}" - ) + transform_str = f"{x_coords[0]} {pixel_size_x} 0.0 {y_coords[0]} 0.0 {pixel_size_y}" ds["spatial_ref"].attrs["GeoTransform"] = transform_str # Verify CRS was inferred and applied @@ -613,9 +597,7 @@ def test_prepare_dataset_with_crs_info_no_spatial_coordinates(self) -> None: # Set up data variables with proper attributes for var_name in ds.data_vars: # Add _ARRAY_DIMENSIONS attribute if missing - if "_ARRAY_DIMENSIONS" not in ds[var_name].attrs and hasattr( - ds[var_name], "dims" - ): + if "_ARRAY_DIMENSIONS" not in ds[var_name].attrs and hasattr(ds[var_name], "dims"): ds[var_name].attrs["_ARRAY_DIMENSIONS"] = list(ds[var_name].dims) # Verify the group was processed but no CRS was added diff --git a/tests/test_encoding.py b/tests/test_encoding.py new file mode 100644 index 0000000..069e77a --- /dev/null +++ b/tests/test_encoding.py @@ -0,0 +1,80 @@ +import numpy as np +import xarray as xr + +from eopf_geozarr.conversion.encoding import create_geozarr_encoding + + +def _make_ds(shape_2d=(1024, 2048), extra_dims=("band",), bands=3, dtype="uint16"): + h, w = shape_2d + coords = {"y": np.arange(h), "x": np.arange(w)} + data_vars = {} + if extra_dims: + coords[extra_dims[0]] = np.arange(bands) + data = np.random.randint(0, 1000, size=(bands, h, w), dtype=dtype) + data_vars["data"] = (extra_dims + ("y", "x"), data) + else: + data = np.random.randint(0, 1000, size=(h, w), dtype=dtype) + data_vars["data"] = (("y", "x"), data) + # Add a grid mapping var and reference it via a data variable's attrs + data_vars["spatial_ref"] = ((), np.array(0, dtype="int8")) + ds = xr.Dataset(data_vars=data_vars, coords=coords) + ds["data"].attrs["grid_mapping"] = "spatial_ref" + return ds + + +def test_grid_mapping_excluded_from_compression(): + ds = _make_ds() + enc = create_geozarr_encoding(ds, compressor="COMP", spatial_chunk=4096) + assert ( + enc["spatial_ref"].get("compressors") is None + ), "grid mapping var should not be compressed" + assert enc["data"]["compressors"], "data variable should have compressors list" + + +def test_chunk_shape_3d_leading_dims_collapsed(): + ds = _make_ds() + enc = create_geozarr_encoding(ds, compressor=None, spatial_chunk=512) + chunks = enc["data"]["chunks"] + # Expect (1, y_chunk, x_chunk) + assert len(chunks) == 3 and chunks[0] == 1, f"Leading dim should be 1-sized in chunks: {chunks}" + assert chunks[1] == chunks[2], "Spatial chunks should be square" + assert chunks[1] <= 512, "Spatial chunk should not exceed requested size" + + +def test_chunk_shape_2d(): + ds = _make_ds(extra_dims=None) + enc = create_geozarr_encoding(ds, compressor=None, spatial_chunk=256) + chunks = enc["data"]["chunks"] + assert len(chunks) == 2, "2D var should have 2 chunk dims" + assert chunks[0] == chunks[1] <= 256 + + +def test_max_chunk_bytes_enforced(monkeypatch): + ds = _make_ds() + # Force very low limit so logic must reduce + monkeypatch.setenv("EOPF_MAX_CHUNK_BYTES", "16384") # 16 KiB + enc = create_geozarr_encoding(ds, compressor=None, spatial_chunk=2048) + chunks = enc["data"]["chunks"] + # Compute approximate bytes + dtype_size = ds["data"].dtype.itemsize + try: + from math import prod + except Exception: + + def prod(vals): + out = 1 + for v in vals: + out *= v + return out + + est_bytes = prod(chunks) * dtype_size + assert est_bytes <= 16384, f"Chunk bytes {est_bytes} exceed limit with chunks {chunks}" + + +def test_coord_variables_no_compression(): + ds = _make_ds() + enc = create_geozarr_encoding(ds, compressor=None, spatial_chunk=512) + for coord in ds.coords: + assert ( + enc[coord].get("compressors") is None + ), f"Coordinate {coord} should not have compressors" diff --git a/tests/test_helpers.py b/tests/test_helpers.py new file mode 100644 index 0000000..8755d77 --- /dev/null +++ b/tests/test_helpers.py @@ -0,0 +1,130 @@ +import numpy as np +import pytest +import xarray as xr + +from eopf_geozarr.conversion.helpers import ( + build_var_encodings, + inject_crs_if_requested, + maybe_step, + normalize_group_path, + safely, +) + + +class DummyRecorder: + def __init__(self): + self.steps = [] + + class _Ctx: + def __init__(self, outer, name): + self.outer = outer + self.name = name + + def __enter__(self): + self.outer.steps.append((self.name, "start")) + return self + + def __exit__(self, exc_type, exc, tb): + self.outer.steps.append((self.name, "end")) + return False + + def time_step(self, name): # mimic recorder interface + return self._Ctx(self, name) + + +def test_normalize_group_path(): + assert normalize_group_path("") == "/" + assert normalize_group_path(".") == "/" + assert normalize_group_path("/a") == "/a" + assert normalize_group_path("a") == "/a" + + +def test_build_var_encodings_excludes_grid_mapping(): + ds = xr.Dataset( + { + "B01": ("y", np.arange(5).astype("uint16")), + "spatial_ref": ([], 0), + }, + coords={"y": ("y", np.arange(5))}, + ) + # Mark spatial_ref as grid mapping variable + ds["B01"].attrs["grid_mapping"] = "spatial_ref" + ds["spatial_ref"].attrs["crs_wkt"] = "WKT" + + class DummyCompressor: + codec_id = "dummy" + configuration = {} + + comp = DummyCompressor() + + enc = build_var_encodings(ds, comp) + assert "B01" in enc + assert "spatial_ref" not in enc # grid mapping excluded + assert enc["B01"]["compressors"][0] is comp + assert enc["B01"]["dtype"] == str(ds["B01"].dtype) + + +def test_build_var_encodings_none_compressor(): + ds = xr.Dataset({"B01": ("y", np.arange(3))}) + assert build_var_encodings(ds, None) == {} + + +def test_inject_crs_if_requested_applies_once(): + ds = xr.Dataset( + {"B01": (("y", "x"), np.random.rand(4, 4))}, + coords={"y": ("y", np.arange(4)), "x": ("x", np.arange(4))}, + ) + + def ref_crs(): + return "EPSG:4326" + + def preparer(ds_in, reference_crs): # mimic prepare_dataset_with_crs_info minimal + # attach spatial_ref variable and grid_mapping attrs + if "spatial_ref" not in ds_in: + ds_in["spatial_ref"] = xr.DataArray( + 0, attrs={"crs_wkt": reference_crs, "_ARRAY_DIMENSIONS": []} + ) + ds_in["B01"].attrs["grid_mapping"] = "spatial_ref" + return ds_in + + requested = {"/group"} + # Not in requested + out = inject_crs_if_requested("/other", ds.copy(), requested, ref_crs, preparer) + assert "spatial_ref" not in out + + # In requested + out2 = inject_crs_if_requested("group", ds.copy(), requested, ref_crs, preparer) + assert "spatial_ref" in out2 + assert out2["spatial_ref"].attrs["crs_wkt"] == "EPSG:4326" + assert out2["B01"].attrs["grid_mapping"] == "spatial_ref" + + +def test_safely_swallow_and_raise(): + calls = {"n": 0} + + def good(): + calls["n"] += 1 + return 42 + + def bad(): + raise RuntimeError("boom") + + assert safely("good", good) == 42 + assert calls["n"] == 1 + # swallow True returns None + assert safely("bad", bad, swallow=True) is None + with pytest.raises(RuntimeError): + safely("bad", bad, swallow=False) + + +def test_maybe_step_recorder(): + rec = DummyRecorder() + with maybe_step(rec, "encode"): + pass + assert rec.steps == [("encode", "start"), ("encode", "end")] + + +def test_maybe_step_no_recorder(): + # Should act as no-op + with maybe_step(None, "noop"): + pass # nothing to assert; just ensure no exception diff --git a/tests/test_integration_sentinel2.py b/tests/test_integration_sentinel2.py index 78794c5..06eba36 100644 --- a/tests/test_integration_sentinel2.py +++ b/tests/test_integration_sentinel2.py @@ -211,9 +211,7 @@ def sample_sentinel2_datatree(self) -> xr.DataTree: dt["quality"].attrs = {"description": "Quality assessment data"} - dt["quality/l1c_quicklook"].attrs = { - "description": "L1C quicklook RGB composite" - } + dt["quality/l1c_quicklook"].attrs = {"description": "L1C quicklook RGB composite"} return dt @@ -295,16 +293,12 @@ def _verify_basic_structure(self, output_path, groups) -> None: for group in groups: group_path = output_path / group.lstrip("/") assert group_path.exists(), f"Group {group} not found" - assert ( - group_path / "zarr.json" - ).exists(), f"Group {group} missing zarr.json" + assert (group_path / "zarr.json").exists(), f"Group {group} missing zarr.json" # Check that level 0 (native resolution) exists level_0_path = group_path / "0" assert level_0_path.exists(), f"Level 0 not found for {group}" - assert ( - level_0_path / "zarr.json" - ).exists(), f"Level 0 missing zarr.json for {group}" + assert (level_0_path / "zarr.json").exists(), f"Level 0 missing zarr.json for {group}" def _verify_geozarr_spec_compliance(self, output_path, group) -> None: """ @@ -335,9 +329,7 @@ def _verify_geozarr_spec_compliance(self, output_path, group) -> None: assert ds[var_name].attrs["_ARRAY_DIMENSIONS"] == list( ds[var_name].dims ), f"Incorrect _ARRAY_DIMENSIONS for {var_name} in {group}" - print( - f" ✅ _ARRAY_DIMENSIONS: {ds[var_name].attrs['_ARRAY_DIMENSIONS']}" - ) + print(f" ✅ _ARRAY_DIMENSIONS: {ds[var_name].attrs['_ARRAY_DIMENSIONS']}") # Check coordinates for coord_name in ds.coords: @@ -356,8 +348,7 @@ def _verify_geozarr_spec_compliance(self, output_path, group) -> None: "standard_name" in ds[var_name].attrs ), f"Missing standard_name for {var_name} in {group}" assert ( - ds[var_name].attrs["standard_name"] - == "toa_bidirectional_reflectance" + ds[var_name].attrs["standard_name"] == "toa_bidirectional_reflectance" ), f"Incorrect standard_name for {var_name} in {group}" print(f" ✅ standard_name: {ds[var_name].attrs['standard_name']}") @@ -401,16 +392,12 @@ def _verify_geozarr_spec_compliance(self, output_path, group) -> None: if coord in ds.coords: if "standard_name" in ds[coord].attrs: expected_name = ( - "projection_x_coordinate" - if coord == "x" - else "projection_y_coordinate" + "projection_x_coordinate" if coord == "x" else "projection_y_coordinate" ) assert ( ds[coord].attrs["standard_name"] == expected_name ), f"Incorrect standard_name for {coord} coordinate in {group}" - print( - f" ✅ {coord} standard_name: {ds[coord].attrs['standard_name']}" - ) + print(f" ✅ {coord} standard_name: {ds[coord].attrs['standard_name']}") ds.close() @@ -421,9 +408,7 @@ def _verify_multiscale_structure(self, output_path, group) -> None: group_path = output_path / group.lstrip("/") # Check that at least one level exists (level 0 is always created) - level_dirs = [ - d for d in group_path.iterdir() if d.is_dir() and d.name.isdigit() - ] + level_dirs = [d for d in group_path.iterdir() if d.is_dir() and d.name.isdigit()] assert ( len(level_dirs) >= 1 ), f"Expected at least 1 overview level for {group}, found {len(level_dirs)}" @@ -458,9 +443,7 @@ def _verify_multiscale_structure(self, output_path, group) -> None: assert len(ds.data_vars) > 0, f"No data variables in {level_path}" # Verify that spatial dimensions exist - assert ( - "x" in ds.dims and "y" in ds.dims - ), f"Missing spatial dimensions in {level_path}" + assert "x" in ds.dims and "y" in ds.dims, f"Missing spatial dimensions in {level_path}" # Store shape for progression verification level_shapes[level_num] = (ds.dims["y"], ds.dims["x"]) @@ -515,9 +498,7 @@ def _verify_rgb_data_access(self, output_path, groups) -> None: # Test access to different overview levels (as in notebook) group_path = output_path / group.lstrip("/") - level_dirs = [ - d for d in group_path.iterdir() if d.is_dir() and d.name.isdigit() - ] + level_dirs = [d for d in group_path.iterdir() if d.is_dir() and d.name.isdigit()] for level_dir in sorted(level_dirs, key=lambda x: int(x.name))[ :3 @@ -540,23 +521,15 @@ def _verify_rgb_data_access(self, output_path, groups) -> None: # Verify data is not empty assert red_data.size > 0, f"Empty red data in {group} level {level_num}" - assert ( - green_data.size > 0 - ), f"Empty green data in {group} level {level_num}" - assert ( - blue_data.size > 0 - ), f"Empty blue data in {group} level {level_num}" + assert green_data.size > 0, f"Empty green data in {group} level {level_num}" + assert blue_data.size > 0, f"Empty blue data in {group} level {level_num}" - print( - f" Level {level_num}: RGB access successful, shape {red_data.shape}" - ) + print(f" Level {level_num}: RGB access successful, shape {red_data.shape}") ds.close() @pytest.mark.slow - def test_performance_characteristics( - self, sample_sentinel2_datatree, temp_output_dir - ) -> None: + def test_performance_characteristics(self, sample_sentinel2_datatree, temp_output_dir) -> None: """ Test performance characteristics following notebook analysis. @@ -569,9 +542,7 @@ def test_performance_characteristics( output_path = Path(temp_output_dir) / "sentinel2_performance_test.zarr" # Convert with performance-focused parameters - groups = [ - "/measurements/reflectance/r10m" - ] # Focus on one group for performance testing + groups = ["/measurements/reflectance/r10m"] # Focus on one group for performance testing with patch("eopf_geozarr.conversion.geozarr.print"): create_geozarr_dataset( @@ -587,14 +558,10 @@ def test_performance_characteristics( # Test data access performance across overview levels group = groups[0] group_path = output_path / group.lstrip("/") - level_dirs = [ - d for d in group_path.iterdir() if d.is_dir() and d.name.isdigit() - ] + level_dirs = [d for d in group_path.iterdir() if d.is_dir() and d.name.isdigit()] timing_data = [] - for level_dir in sorted(level_dirs, key=lambda x: int(x.name))[ - :4 - ]: # Test first 4 levels + for level_dir in sorted(level_dirs, key=lambda x: int(x.name))[:4]: # Test first 4 levels level_num = int(level_dir.name) level_path = str(level_dir) @@ -620,9 +587,7 @@ def test_performance_characteristics( access_time = time.time() - start_time ds.close() - timing_data.append( - {"level": level_num, "time": access_time, "pixels": pixel_count} - ) + timing_data.append({"level": level_num, "time": access_time, "pixels": pixel_count}) print(f" Level {level_num}: {access_time:.3f}s, {pixel_count:,} pixels") @@ -636,7 +601,7 @@ def test_performance_characteristics( # Allow some flexibility, but generally expect fewer pixels at higher levels assert ( curr_pixels <= prev_pixels * 1.1 - ), f"Level {timing_data[i]['level']} has more pixels than level {timing_data[i-1]['level']}" + ), f"Level {timing_data[i]['level']} has more pixels than level {timing_data[i - 1]['level']}" print("✅ Performance characteristics verified!") diff --git a/tests/test_writer.py b/tests/test_writer.py new file mode 100644 index 0000000..643dabf --- /dev/null +++ b/tests/test_writer.py @@ -0,0 +1,71 @@ +from pathlib import Path + +import numpy as np +import xarray as xr + +from eopf_geozarr.conversion.geozarr import GeoZarrWriter +from eopf_geozarr.conversion.helpers import build_var_encodings + + +def test_writer_build_var_encodings_excludes_grid_mapping(tmp_path: Path): + ds = xr.Dataset( + { + "B01": ( + ("y", "x"), + np.arange(16, dtype="uint16").reshape(4, 4), + {"grid_mapping": "spatial_ref"}, + ), + "spatial_ref": ([], 0, {"crs_wkt": "EPSG:4326"}), + }, + coords={ + "y": ("y", np.arange(4)), + "x": ("x", np.arange(4)), + }, + ) + comp = None # rely on helper returning {} + enc = build_var_encodings(ds, comp) + assert enc == {} + + # Now simulate with a dummy compressor object minimal interface + class DummyCompressor: + codec_id = "dummy" + configuration = {} + + dcomp = DummyCompressor() + enc2 = build_var_encodings(ds, dcomp) + assert "B01" in enc2 + assert "spatial_ref" not in enc2 + + +def test_write_group_basic(tmp_path: Path): + ds = xr.Dataset( + { + "B01": (("y", "x"), np.ones((4, 4), dtype="uint16")), + "spatial_ref": ([], 0, {"crs_wkt": "EPSG:4326"}), + }, + coords={"y": ("y", np.arange(4)), "x": ("x", np.arange(4))}, + ) + # Add grid mapping attr + ds["B01"].attrs["grid_mapping"] = "spatial_ref" + + writer = GeoZarrWriter( + output_path=str(tmp_path / "out.zarr"), + compressor=None, + spatial_chunk=4096, + min_dimension=2, + tile_width=256, + max_retries=1, + overwrite="replace", + max_overview_levels=1, + skip_overviews=True, + base_write_mode="single", + ) + + dt = xr.DataTree() + dt2 = writer.write_group(dt, "/group", ds) + assert "group" in dt2.children + # Ensure spatial_ref not duplicated in data vars of on-disk store + # (implicit check: no exception and dataset can be reopened) + reopened = xr.open_dataset(str(tmp_path / "out.zarr/group/0"), engine="zarr", chunks="auto") + assert "B01" in reopened.data_vars + assert "spatial_ref" in reopened.variables From 5ecb022427574ddcc0cd53f46e8115b0400c6f34 Mon Sep 17 00:00:00 2001 From: Wietze Date: Tue, 16 Sep 2025 20:32:25 -0400 Subject: [PATCH 9/9] docs(readme): usage, API sections and quick start --- README.md | 280 +++++++++++++++++++++++++++++++++++++++--------------- 1 file changed, 203 insertions(+), 77 deletions(-) diff --git a/README.md b/README.md index cca76db..66b3d84 100644 --- a/README.md +++ b/README.md @@ -1,137 +1,263 @@ # EOPF GeoZarr -Turn EOPF datasets into a GeoZarr-style Zarr v3 store. Keep the data values intact and add standard geospatial metadata, multiscale overviews, and per-variable dimensions. +GeoZarr compliant data model for EOPF (Earth Observation Processing Framework) datasets. -## Quick Start +Turn EOPF datasets into a GeoZarr-style Zarr v3 store while: +- Preserving native CRS (no forced TMS reprojection) +- Adding CF + GeoZarr compliant metadata +- Building /2 multiscale overviews +- Writing robust, retry-aware band data with validation + +## Overview + +This library converts EOPF datatrees into GeoZarr-spec 0.4 aligned Zarr v3 stores without forcing web-mercator style tiling. It focuses on scientific fidelity (native CRS), robust metadata (CF + GeoZarr), and operational resilience (retry + completeness auditing) while supporting multiscale /2 overviews. + +## Key Features -Install (uv): +- **GeoZarr Specification Compliance** (0.4 features implemented) +- **Native CRS Preservation** (UTM, polar, arbitrary projections) +- **Multiscale /2 Overviews** (COG-style hierarchy as child groups) +- **CF Conventions** (`standard_name`, `grid_mapping`, `_ARRAY_DIMENSIONS`) +- **Resilient Writing** (band-by-band with retries & auditing) +- **S3 & S3-Compatible Support** (AWS, OVH, MinIO, custom endpoints) +- **Optional Parallel Processing** (local Dask cluster) +- **Automatic Chunk Alignment** (prevents overlapping Dask/Zarr chunks) +- **HTML Summary & Validation Tools** +- **STAC & Benchmark Commands** +- **Consolidated Metadata** (faster open) + +## GeoZarr Compliance Features + +- `_ARRAY_DIMENSIONS` attributes on all arrays +- CF standard names for all variables +- `grid_mapping` attributes referencing CF grid_mapping variables +- `GeoTransform` attributes in grid_mapping variables +- Proper multiscales metadata structure +- Native CRS tile matrix sets + +## Installation +```bash +pip install eopf-geozarr +``` + +Development (uv): ```bash uv sync --frozen uv run eopf-geozarr --help ``` -Or pip: - +Editable (pip): ```bash -pip install -e . +pip install -e .[dev] ``` -## Workflows - -For Argo / batch orchestration use: https://github.com/EOPF-Explorer/data-model-pipeline +## Quick Start -## Convert +### Command Line Interface -Remote → local: +After installation, you can use the `eopf-geozarr` command: ```bash -uv run eopf-geozarr convert \ - "https://.../S2B_MSIL2A_... .zarr" \ - "/tmp/S2B_MSIL2A_..._geozarr.zarr" \ - --groups /measurements/reflectance \ - --verbose -``` +# Convert EOPF dataset to GeoZarr format (local output) +eopf-geozarr convert input.zarr output.zarr + +# Convert specific groups (e.g. resolution groups) +eopf-geozarr convert input.zarr output.zarr --groups /measurements/r10m /measurements/r20m -Notes: -- Parent groups auto-expand to leaf datasets. -- Overviews use /2 coarsening; multiscales live on parent groups. -- Defaults: Blosc Zstd, conservative chunking, metadata consolidation after write. +# Convert EOPF dataset to GeoZarr format (S3 output) +eopf-geozarr convert input.zarr s3://my-bucket/path/to/output.zarr -## S3 +# Convert with parallel processing using dask cluster +eopf-geozarr convert input.zarr output.zarr --dask-cluster -Env for S3/S3-compatible storage: +# Convert with dask cluster and verbose output +eopf-geozarr convert input.zarr output.zarr --dask-cluster --verbose +# Generate an HTML summary while inspecting +eopf-geozarr info output.zarr --html report.html + +# Get information about a dataset +eopf-geozarr info input.zarr + +# Validate GeoZarr compliance +eopf-geozarr validate output.zarr + +# Benchmark access patterns (optional) +eopf-geozarr benchmark output.zarr --samples 8 --window 1024 1024 + +# Produce draft STAC artifacts +eopf-geozarr stac output.zarr stac_collection.json \ + --bbox "minx miny maxx maxy" --start 2025-01-01T00:00:00Z --end 2025-01-31T23:59:59Z + +# Get help +eopf-geozarr --help +``` + +#### Notes +- Parent groups auto-expand to leaf datasets when omitted. +- Multiscale overviews are generated with /2 coarsening and attached as child groups. +- Defaults: Blosc Zstd (level 3), conservative chunking, metadata consolidation enabled. +- Use `--groups` to limit processing or speed up experimentation. + +## S3 Support + +Environment vars: ```bash export AWS_ACCESS_KEY_ID=... export AWS_SECRET_ACCESS_KEY=... -export AWS_REGION=eu-west-1 -# Custom endpoint (OVH, MinIO, etc.) -export AWS_ENDPOINT_URL=https://s3.your-endpoint.example +export AWS_DEFAULT_REGION=eu-west-1 +export AWS_ENDPOINT_URL=https://s3.your-endpoint.example # optional custom endpoint ``` -Write to S3: - +Write directly: ```bash -uv run eopf-geozarr convert \ - "https://.../S2B_MSIL2A_... .zarr" \ - "s3://your-bucket/path/S2B_MSIL2A_..._geozarr.zarr" \ - --groups /measurements/reflectance \ - --verbose +eopf-geozarr convert input.zarr s3://my-bucket/path/output_geozarr.zarr --groups /measurements/r10m ``` -## Info & Validate +Features: +- Credential validation before write +- Custom endpoints (OVH, MinIO, etc.) +- Retry logic around object writes -Summary: +## Parallel Processing with Dask ```bash -uv run eopf-geozarr info "/tmp/S2B_MSIL2A_..._geozarr.zarr" +eopf-geozarr convert input.zarr out.zarr --dask-cluster --verbose ``` +Benefits: +- Local cluster auto-start & cleanup +- Chunk alignment to prevent overlapping writes +- Better memory distribution for large scenes -HTML report: +## Python API -```bash -uv run eopf-geozarr info "/tmp/S2B_MSIL2A_..._geozarr.zarr" --html /tmp/summary.html +High-level dataset conversion: +```python +import xarray as xr +from eopf_geozarr import create_geozarr_dataset + +dt = xr.open_datatree("path/to/eopf.zarr", engine="zarr") +out = create_geozarr_dataset( + dt_input=dt, + groups=["/measurements/r10m", "/measurements/r20m"], + output_path="/tmp/out_geozarr.zarr", + spatial_chunk=4096, + min_dimension=256, + tile_width=256, +) +``` + +Selective writer usage (advanced): +```python +from eopf_geozarr.conversion.geozarr import GeoZarrWriter +writer = GeoZarrWriter(output_path="/tmp/out.zarr", spatial_chunk=4096) +# writer.write_group(...) ``` -Validate (counts only real data vars, skips `spatial_ref`/`crs`): +## API Reference -```bash -uv run eopf-geozarr validate "/tmp/S2B_MSIL2A_..._geozarr.zarr" +`create_geozarr_dataset(dt_input, groups, output_path, spatial_chunk=4096, ...) -> xr.DataTree` +: Produce a GeoZarr-compliant hierarchy. + +`setup_datatree_metadata_geozarr_spec_compliant(dt, groups) -> dict[str, xr.Dataset]` +: Apply CF + GeoZarr metadata to selected groups. + +`downsample_2d_array(source_data, target_h, target_w) -> np.ndarray` +: Block-average /2 overview generation primitive. + +`calculate_aligned_chunk_size(dimension_size, target_chunk_size) -> int` +: Returns evenly dividing chunk to avoid overlap. + +## Architecture + +``` +eopf_geozarr/ + commands/ # CLI subcommands (convert, validate, info, stac, benchmark) + conversion/ # Core geozarr pipeline, helpers, multiscales, encodings + metrics.py # Lightweight metrics hooks (optional) ``` -## Benchmark (optional) +## Contributing to GeoZarr Specification + +Upstream issue discussions influenced: +- Arbitrary CRS preservation +- Chunking performance & strategies +- Multiscale hierarchy clarity + +## Benchmark & STAC Commands +Benchmark: ```bash -uv run eopf-geozarr benchmark "/tmp/..._geozarr.zarr" --samples 8 --window 1024 1024 +eopf-geozarr benchmark /tmp/out_geozarr.zarr --samples 8 --window 1024 1024 ``` -## STAC - +STAC draft artifacts: ```bash -uv run eopf-geozarr stac \ - "/tmp/..._geozarr.zarr" \ - "/tmp/..._collection.json" \ - --bbox "minx miny maxx maxy" \ - --start "YYYY-MM-DDTHH:MM:SSZ" \ - --end "YYYY-MM-DDTHH:MM:SSZ" +eopf-geozarr stac /tmp/out_geozarr.zarr /tmp/collection.json \ + --bbox "minx miny maxx maxy" --start 2025-01-01T00:00:00Z --end 2025-01-31T23:59:59Z ``` -## Python API +## What Gets Written -```python -from eopf_geozarr.conversion.geozarr import GeoZarrWriter -from eopf_geozarr.validation.validate import validate_store -from eopf_geozarr.info.summary import summarize +- `_ARRAY_DIMENSIONS` per variable (deterministic axis order) +- Per-variable `grid_mapping` referencing `spatial_ref` +- Multiscales metadata on parent groups; /2 overviews +- Blosc Zstd compression, conservative chunking +- Consolidated metadata index +- Band attribute propagation across levels + +## Consolidated Metadata + +Improves open performance. Spec discussion ongoing; toggle by disabling consolidation if strict minimalism required. + +## Troubleshooting + +| Symptom | Cause | Fix | +|---------|-------|-----| +| Parent group empty | Only leaf groups hold arrays | Use `--groups` or rely on auto-expansion | +| Overlapping chunk error | Misaligned dask vs encoding chunks | Allow auto chunk alignment or reduce spatial_chunk | +| S3 auth failure | Missing env vars or endpoint | Export AWS_* vars / set AWS_ENDPOINT_URL | +| HTML path is a directory | Provided path not file | A default filename is created inside | + +## Development & Contributing +Preferred (reproducible) workflow uses [uv](https://github.com/astral-sh/uv): -src = "https://.../S2B_MSIL2A_... .zarr" -dst = "/tmp/S2B_MSIL2A_..._geozarr.zarr" +```bash +git clone +cd eopf-geozarr -writer = GeoZarrWriter(src, dst, storage_options={}) -writer.write(groups=["/measurements/reflectance"], verbose=True) +# Ensure uv is installed (macOS/Linux quick install) +curl -Ls https://astral.sh/uv/install.sh | sh # or follow official docs -report = validate_store(dst) -print(report.ok) +# Create and sync environment with dev extras +uv sync --extra dev -tree = summarize(dst) -print(tree["summary"]) # or write HTML via CLI +# Run tools through uv (ensures correct virtual env) +uv run pre-commit install +uv run pytest -q ``` -## What it writes +Common tasks: +```bash +uv run ruff check . +uv run mypy src +uv run eopf-geozarr --help +``` -- `_ARRAY_DIMENSIONS` per variable (correct axis order). -- `grid_mapping = "spatial_ref"` per variable; `spatial_ref` holds CRS/georeferencing. -- Multiscales on parent groups; /2 overviews. -- Blosc Zstd compression; conservative chunking; consolidated metadata. -- Overviews keep per-band attributes (grid_mapping reattached across levels). +Fallback (less reproducible) pip editable install: +```bash +pip install -e '.[dev]' +pre-commit install +pytest +``` -## Consolidated metadata +Quality stack: Ruff (lint + format), isort (via Ruff), Mypy (strict), Pytest, Coverage. -Speeds up reads. Some tools note it isn’t in the core Zarr v3 spec yet; data stays valid. You can disable consolidation during writes or remove the index if preferred. +## License & Acknowledgments -## Troubleshooting +Apache 2.0. Built atop xarray, zarr, dask; follows evolving GeoZarr specification. -- Parent group shows no data vars: select leaves (CLI auto-expands). -- S3 errors: check env vars and `AWS_ENDPOINT_URL` for custom endpoints. -- HTML path is a directory: a default filename is created inside. +--- +For questions or issues open a GitHub issue.