9364808b3d
- mark task 04 as implemented in the project overview - add convert_json.py as the entry script for task 04 - clarify output paths for JSON-LD conversion artifacts
362 lines
13 KiB
Python
362 lines
13 KiB
Python
"""Task 04 - Convert the CSV corpus to JSON-LD.
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Reads ./out/csv/ (validated file-by-file against MANIFEST.csv) and writes
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two ontology-annotated variants:
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- FULL: one compact JSON-LD file per coupon under ./out/json/full/, bulk
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points embedded. Like spec 07's RDF modeling, bulk points (cycles, loop
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points, map points) carry no @type - the containing predicate types them;
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this keeps FULL lean, so its size lands below the 0.7-1.2 GB the spec
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sketched for a fully typed serialization.
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- HYBRID: single ./out/json/hybrid/dataset.jsonld holding every metadata
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entity; every per-point array (cycles, loops, xrf, profilometry, indents)
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is replaced by a {sourceFile, rows, sha256} reference into the CSV tree.
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Appends both sizes to ./out/bench/storage_sizes.csv and writes
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./out/.done/04.ok. Spec: docs/specs/04_convert_json.md.
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"""
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from __future__ import annotations
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import argparse
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import hashlib
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import json
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import logging
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import re
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import shutil
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import sys
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from pathlib import Path
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import yaml
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from rdflib import Graph
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from common.storage_sizes import update_storage_sizes
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logger = logging.getLogger(__name__)
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REPO_ROOT = Path(__file__).resolve().parent
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TASK_ID = "04"
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DEPENDS_ON = ["01", "03"]
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VOCAB = "https://sandia.gov/ontology/tribology#"
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ID_BASE = "https://sandia.gov/ontology/tribology/id/"
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CONTEXT = {
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"@vocab": VOCAB,
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"tribo": VOCAB,
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"qudt": "http://qudt.org/schema/qudt/",
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"unit": "http://qudt.org/vocab/unit/",
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"prov": "http://www.w3.org/ns/prov#",
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"batch": ID_BASE + "batch/",
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"wafer": ID_BASE + "wafer/",
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"coupon": ID_BASE + "coupon/",
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"track": ID_BASE + "track/",
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"run": ID_BASE + "run/",
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"instrument": ID_BASE + "instrument/",
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"partOf": {"@id": "tribo:partOf", "@type": "@id"},
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"cutFrom": {"@id": "tribo:cutFrom", "@type": "@id"},
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"performedOn": {"@id": "tribo:performedOn", "@type": "@id"},
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"performedBy": {"@id": "tribo:performedBy", "@type": "@id"},
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"duringRun": {"@id": "tribo:duringRun", "@type": "@id"},
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"started_at": {"@id": "prov:startedAtTime"},
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}
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INT_RE = re.compile(r"^-?[0-9]+$")
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FLOAT_RE = re.compile(r"^-?([0-9]+\.[0-9]*|\.[0-9]+|[0-9]+)([eE][+-]?[0-9]+)?$")
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MEASUREMENTS = [
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# (csv file, json key, @type, instrument id, points key)
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("xrf_map.csv", "xrf", "CompositionMeasurement", "m4_tornado", "points"),
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("profilometry.csv", "profilometry", "ProfilometryMeasurement", "profilometer", "points"),
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("nanoindentation.csv", "nanoindentation", "NanoindentationMeasurement", "ti980", "indents"),
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]
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class ValidationError(RuntimeError):
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pass
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def coerce(s: str) -> int | float | str:
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if INT_RE.match(s):
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return int(s)
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if FLOAT_RE.match(s):
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return float(s)
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return s
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class JsonConverter:
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def __init__(self, cfg: dict, csv_root: Path, json_root: Path) -> None:
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self.cfg = cfg
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self.csv_root = csv_root
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self.json_root = json_root
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self.manifest = self._load_manifest()
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self.graph: list[dict] = [] # hybrid metadata entities
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self.full_bytes = 0
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self.full_files = 0
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self.counts = {"coupons": 0, "tracks": 0, "cycle_rows": 0, "loop_rows": 0, "xrf_rows": 0}
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def _load_manifest(self) -> dict[str, tuple[int, int, str]]:
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manifest: dict[str, tuple[int, int, str]] = {}
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with open(self.csv_root / "MANIFEST.csv", encoding="ascii") as fh:
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header = fh.readline().strip()
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if header != "path,rows,bytes,sha256":
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raise ValidationError(f"MANIFEST.csv: unexpected header {header!r}")
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for line in fh:
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path, rows, nbytes, sha = line.strip().split(",")
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manifest[path] = (int(rows), int(nbytes), sha)
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logger.info("manifest loaded: %d entries", len(manifest))
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return manifest
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def read_csv(self, relpath: str) -> tuple[list[str], list[list[str]]]:
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"""Read one corpus file, verifying bytes and row count against MANIFEST."""
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data = (self.csv_root / relpath).read_bytes()
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exp_rows, exp_bytes, exp_sha = self.manifest[relpath]
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if len(data) != exp_bytes or hashlib.sha256(data).hexdigest() != exp_sha:
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raise ValidationError(f"{relpath}: bytes/sha256 mismatch vs MANIFEST")
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lines = data.decode("ascii").split("\n")
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header = lines[0].split(",")
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rows = [ln.split(",") for ln in lines[1:] if ln]
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if len(rows) != exp_rows:
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raise ValidationError(f"{relpath}: {len(rows)} rows != manifest {exp_rows}")
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return header, rows
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def rows_as_dicts(self, relpath: str) -> list[dict]:
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header, rows = self.read_csv(relpath)
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keys = [h.lower() for h in header]
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return [
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{k: coerce(v) for k, v in zip(keys, row) if v != ""}
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for row in rows
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]
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def source_ref(self, relpath: str) -> dict:
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rows, _, sha = self.manifest[relpath]
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return {"sourceFile": relpath, "rows": rows, "sha256": sha}
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# --- flat entities (hybrid graph) ---
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def convert_flat(self) -> None:
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for inst in self.cfg["instruments"]:
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self.graph.append({
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"@id": f"instrument:{inst['instrument_id']}",
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"@type": "Instrument",
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"name": inst["name"],
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"role": inst["role"],
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})
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for row in self.rows_as_dicts("batches.csv"):
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self.graph.append({"@id": f"batch:{row['batch_code']}", "@type": "Batch", **row})
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self.graph.append({
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"@id": f"batch:{row['batch_code']}-simtra",
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"@type": "SimtraProfile",
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"performedOn": f"batch:{row['batch_code']}",
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"performedBy": "instrument:simtra",
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**self.source_ref(f"simtra/simtra_profile_{row['batch_code']}.csv"),
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})
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for row in self.rows_as_dicts("runs.csv"):
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self.graph.append({"@id": f"run:{row['run_code']}", "@type": "Run", **row})
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# --- per-coupon conversion ---
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def convert_coupons(self) -> None:
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h = self.cfg["hierarchy"]
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full_dir = self.json_root / "full"
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full_dir.mkdir(parents=True, exist_ok=True)
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for batch in self.cfg["deposition_matrix"]:
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b_code = batch["batch_code"]
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for w in range(1, h["wafers_per_batch"] + 1):
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wafer_code = f"{b_code}-W{w}"
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wafer_dir = f"batch_{b_code}/wafer_W{w}"
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info = self.rows_as_dicts(f"{wafer_dir}/wafer_info.csv")[0]
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self.graph.append({
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"@id": f"wafer:{wafer_code}", "@type": "Wafer",
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"partOf": f"batch:{b_code}", **info,
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})
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for c in range(1, h["coupons_per_wafer"] + 1):
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self.convert_coupon(f"{wafer_dir}/coupon_C{c:02d}", full_dir)
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logger.debug("wafer %s converted", wafer_code)
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logger.info("batch %s converted: %.1f MiB full so far", b_code, self.full_bytes / (1024 * 1024))
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def convert_coupon(self, rel_dir: str, full_dir: Path) -> None:
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info = self.rows_as_dicts(f"{rel_dir}/coupon_info.csv")[0]
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code = info["coupon_code"]
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coupon_id = f"coupon:{code}"
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is_friction = info["run_code"] != "RESERVE"
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full_node: dict = {
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"@context": CONTEXT, "@id": coupon_id, "@type": "Coupon",
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"cutFrom": f"wafer:{info['wafer_code']}", **info,
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}
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hybrid_node: dict = {
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"@id": coupon_id, "@type": "Coupon",
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"cutFrom": f"wafer:{info['wafer_code']}", **info,
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}
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for csv_name, key, cls, inst, points_key in MEASUREMENTS:
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relpath = f"{rel_dir}/{csv_name}"
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meta = {"@type": cls, "performedOn": coupon_id, "performedBy": f"instrument:{inst}"}
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full_node[key] = {**meta, points_key: self.rows_as_dicts(relpath)}
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hybrid_node[key] = {**meta, **self.source_ref(relpath)}
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self.counts["xrf_rows"] += self.manifest[f"{rel_dir}/xrf_map.csv"][0]
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afm = self.rows_as_dicts(f"{rel_dir}/afm.csv")[0]
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afm_node = {"@type": "AFMMeasurement", "performedOn": coupon_id, "performedBy": "instrument:afm", **afm}
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full_node["afm"] = afm_node
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hybrid_node["afm"] = afm_node
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if is_friction:
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tracks_full = []
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for t in range(1, self.cfg["friction_assignment"]["tracks_per_friction_coupon"] + 1):
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full_track, hybrid_track = self.convert_track(f"{rel_dir}/track_T{t}", coupon_id, info["run_code"])
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tracks_full.append(full_track)
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self.graph.append(hybrid_track)
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full_node["tracks"] = tracks_full
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out_path = full_dir / f"{code}.jsonld"
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text = json.dumps(full_node, separators=(",", ":"))
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data = text.encode("ascii")
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out_path.write_bytes(data)
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self.full_bytes += len(data)
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self.full_files += 1
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self.graph.append(hybrid_node)
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self.counts["coupons"] += 1
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def convert_track(self, rel_dir: str, coupon_id: str, run_code: str) -> tuple[dict, dict]:
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info = self.rows_as_dicts(f"{rel_dir}/track_info.csv")[0]
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track_id = f"track:{info['track_code']}"
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base = {
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"@id": track_id, "@type": "FrictionTest",
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"partOf": coupon_id, "duringRun": f"run:{run_code}",
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"performedBy": "instrument:rapid", **info,
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}
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wear = self.rows_as_dicts(f"{rel_dir}/wear.csv")[0]
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wear_node = {"@type": "WearMeasurement", **wear}
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cycles_path = f"{rel_dir}/cof_vs_cycle.csv"
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loops_path = f"{rel_dir}/friction_loops.csv"
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full_track = {
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**base,
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"cycles": self.rows_as_dicts(cycles_path),
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"loop_points": self.rows_as_dicts(loops_path),
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"wear": wear_node,
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}
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hybrid_track = {
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**base,
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"cycles": self.source_ref(cycles_path),
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"loop_points": self.source_ref(loops_path),
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"wear": wear_node,
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}
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self.counts["cycle_rows"] += len(full_track["cycles"])
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self.counts["loop_rows"] += len(full_track["loop_points"])
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self.counts["tracks"] += 1
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return full_track, hybrid_track
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# --- outputs ---
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def write_hybrid(self) -> int:
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hybrid_dir = self.json_root / "hybrid"
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hybrid_dir.mkdir(parents=True, exist_ok=True)
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path = hybrid_dir / "dataset.jsonld"
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with open(path, "w", encoding="ascii", newline="\n") as fh:
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json.dump({"@context": CONTEXT, "@graph": self.graph}, fh, separators=(",", ":"))
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return path.stat().st_size
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def validate_counts(self) -> None:
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v = self.cfg["volumes"]
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expected = {
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"coupons": v["coupons_total"],
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"tracks": v["tracks_total"],
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"cycle_rows": v["cycle_rows_total"],
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"loop_rows": v["loop_points_total"],
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"xrf_rows": v["xrf_points_total"],
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}
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for key, exp in expected.items():
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if self.counts[key] != exp:
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raise ValidationError(f"count mismatch: {key}: converted {self.counts[key]} != expected {exp}")
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logger.info("all converted counts match lab_config volumes: %s", self.counts)
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def validate_sample(paths: list[Path]) -> None:
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"""Smoke test (spec 04 + test-pipeline-validation): json.load + rdflib parse."""
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for path in paths:
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with open(path, encoding="ascii") as fh:
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json.load(fh)
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g = Graph()
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g.parse(path, format="json-ld")
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if len(g) == 0:
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raise ValidationError(f"{path}: rdflib parsed 0 triples")
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logger.info("sample %s: valid JSON, %d triples via rdflib", path.name, len(g))
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def check_dependencies(out_root: Path) -> None:
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for dep in DEPENDS_ON:
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marker = out_root / ".done" / f"{dep}.ok"
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if not marker.exists():
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raise FileNotFoundError(f"dependency marker missing: {marker} - run task {dep} first")
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def write_marker(marker_path: Path, conv: JsonConverter, hybrid_bytes: int) -> None:
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lines = [
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f"task: '{TASK_ID}'",
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"status: ok",
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f"full_files: {conv.full_files}",
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f"full_bytes: {conv.full_bytes}",
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f"hybrid_bytes: {hybrid_bytes}",
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f"coupons: {conv.counts['coupons']}",
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f"tracks: {conv.counts['tracks']}",
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f"cycle_rows: {conv.counts['cycle_rows']}",
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f"loop_points: {conv.counts['loop_rows']}",
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]
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marker_path.parent.mkdir(parents=True, exist_ok=True)
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with open(marker_path, "w", encoding="utf-8", newline="\n") as fh:
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fh.write("\n".join(lines) + "\n")
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def main() -> int:
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parser = argparse.ArgumentParser(description="Task 04: convert the CSV corpus to JSON-LD")
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parser.add_argument("--log-level", default="INFO", choices=["DEBUG", "INFO", "WARNING", "ERROR"])
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parser.add_argument("--out-root", type=Path, default=REPO_ROOT / "out", help="artifact tree root (default: ./out)")
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args = parser.parse_args()
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logging.basicConfig(
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level=args.log_level,
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stream=sys.stderr,
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format="%(asctime)s %(levelname)-7s %(name)s: %(message)s",
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)
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out_root: Path = args.out_root
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json_root = out_root / "json"
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marker_path = out_root / ".done" / f"{TASK_ID}.ok"
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try:
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check_dependencies(out_root)
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if marker_path.exists():
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logger.info("re-run: removing stale marker %s", marker_path)
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marker_path.unlink()
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if json_root.exists():
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logger.info("re-run: removing previous output %s", json_root)
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shutil.rmtree(json_root)
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with open(out_root / "config" / "lab_config.yaml", encoding="utf-8") as fh:
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cfg = yaml.safe_load(fh)
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conv = JsonConverter(cfg, out_root / "csv", json_root)
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conv.convert_flat()
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conv.convert_coupons()
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conv.validate_counts()
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hybrid_bytes = conv.write_hybrid()
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validate_sample([
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json_root / "full" / "B722-W2-C13.jsonld",
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json_root / "hybrid" / "dataset.jsonld",
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])
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update_storage_sizes(out_root / "bench", "json", [("full", conv.full_bytes), ("hybrid", hybrid_bytes)])
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write_marker(marker_path, conv, hybrid_bytes)
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except Exception:
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logger.critical("task %s failed", TASK_ID, exc_info=True)
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return 1
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print(f"task 04 ok: full {conv.full_files} files, {conv.full_bytes / (1024 * 1024):.1f} MiB; "
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f"hybrid {hybrid_bytes / (1024 * 1024):.1f} MiB")
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print(f"entities: coupons={conv.counts['coupons']} tracks={conv.counts['tracks']} "
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f"cycles={conv.counts['cycle_rows']} loop_points={conv.counts['loop_rows']}")
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print(f"marker: {marker_path}")
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return 0
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if __name__ == "__main__":
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sys.exit(main())
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