diff --git a/README.md b/README.md index fa8187b5bdb1da01331e8559ebc40524979966d2..bd73a568be18fa7afcd2b9132ce525e016ec8c18 100644 --- a/README.md +++ b/README.md @@ -41,6 +41,7 @@ Each folder (e.g run_1,run_2…run_“i” etc) corresponds to a different geome * windsor_i.stp : geometry step (~1mb) * geo_parameters_1.csv : parameters that define the geometry (explained in the associated paper) * boundary_i.vtu : Boundary VTU (~500mb) +* `boundary_dual_area_i.npy`: Native-point surface quadrature weights (`m^2`) for `boundary_i.vtu`, in exact point order. * volume_i.vtu : Volume field VTU (~20GB) * force_mom_i.csv : forces/moments time-averaged (Cd,Cs,Cl,Cmy) * force_mom_varref_i.csv: forces/moments time-averaged (Cd,Cs,Cl,Cmy) using unique reference area per geometry @@ -151,6 +152,12 @@ License ---- This dataset is provided under the CC BY SA 4.0 license, please see LICENSE.txt for full license text. +## Native-point surface areas + +Each `run_N/boundary_dual_area_N.npy` is a one-dimensional little-endian float32 array containing one barycentric dual-area weight in `m^2` per native point of `run_N/boundary_N.vtu`, in its original point order. The array is valid only for that exact raw VTU. The definition, generator, hashes, and complete 350-run manifest are in [`surface_dual_areas/`](surface_dual_areas/). + version history: --------------- + +* 17/08/2026 - Added native-point surface-area arrays for all 350 available runs, with reproducibility metadata. * 17/08/2026 - Added deterministic benchmark train/validation/test splits, including nested data-efficiency and out-of-distribution evaluation protocols; documented current per-run asset coverage. diff --git a/run_0/boundary_dual_area_0.npy b/run_0/boundary_dual_area_0.npy new file mode 100644 index 0000000000000000000000000000000000000000..6d12644b3c193e00f4ccbcdffc7db8ccefe4888f --- /dev/null +++ b/run_0/boundary_dual_area_0.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:66412c5b810b57f6625a403a455871129867880bc78e7614993da3eaa16761af +size 8752600 diff --git a/run_1/boundary_dual_area_1.npy b/run_1/boundary_dual_area_1.npy new file mode 100644 index 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b/surface_dual_areas/README.md @@ -0,0 +1,16 @@ +# WindsorML native-point dual surface areas + +This directory describes the case-local `boundary_dual_area_.npy` +payloads for the native WindsorML boundary meshes. + +For `run_N`, the payload is `run_N/boundary_dual_area_N.npy`. It is a +one-dimensional little-endian float32 NPY with one value per point of the +corresponding native `run_N/boundary_N.vtu`, in exact native point order. +Values are in square metres (`m^2`). + +For each native triangle with area `A`, `A/3` is accumulated at each of its +three vertices. Coordinates and accumulation are promoted to float64; only +the final payload is cast to float32. A degenerate native triangle contributes +zero area; its count is recorded in the corresponding case record. The array +is defined only for the exact raw VTU identified by each case record and must +not be applied to a reordered, resampled, or retriangulated mesh. diff --git a/surface_dual_areas/algorithm.json b/surface_dual_areas/algorithm.json new file mode 100644 index 0000000000000000000000000000000000000000..cdd390cb259790efc3b338c643cde3f4e084b3c5 --- /dev/null +++ b/surface_dual_areas/algorithm.json @@ -0,0 +1,37 @@ +{ + "algorithm_id": "native-point-fan-barycentric-dual-area-v1", + "association": "point", + "calculation_dtype": "float64", + "coordinate_units": "m", + "definition": "each native triangle of area A contributes A/3 to each vertex", + "geometry_hash": { + "algorithm": "sha256", + "array_encoding": "name NUL, NumPy dtype.str NUL, compact JSON shape NUL, bytes", + "domain_utf8_nul": "windsorml-native-boundary-triangle-geometry-v1", + "ordered_arrays": [ + "points", + "connectivity", + "offsets", + "cell_types" + ] + }, + "index_contract": "payload index equals native VTU point ID and PointData tuple ID", + "required_cell_data_array_names": [ + "faceid" + ], + "required_point_data_array_names": [ + "Normals", + "cfxavg", + "cfyavg", + "cfzavg", + "cpavg", + "cpvar", + "yplusavg" + ], + "schema": "windsorml-native-surface-dual-area-algorithm-v1", + "source_cell_type": "VTK_TRIANGLE (5)", + "source_mesh": "native WindsorML boundary VTU", + "storage_dtype": ".npy` +payloads for the native WindsorML boundary meshes. + +For `run_N`, the payload is `run_N/boundary_dual_area_N.npy`. It is a +one-dimensional little-endian float32 NPY with one value per point of the +corresponding native `run_N/boundary_N.vtu`, in exact native point order. +Values are in square metres (`m^2`). + +For each native triangle with area `A`, `A/3` is accumulated at each of its +three vertices. Coordinates and accumulation are promoted to float64; only +the final payload is cast to float32. A degenerate native triangle contributes +zero area; its count is recorded in the corresponding case record. The array +is defined only for the exact raw VTU identified by each case record and must +not be applied to a reordered, resampled, or retriangulated mesh. +""" + + +ALGORITHM_RECORD: dict[str, Any] = { + "schema": "windsorml-native-surface-dual-area-algorithm-v1", + "algorithm_id": ALGORITHM_ID, + "association": "point", + "source_mesh": "native WindsorML boundary VTU", + "source_cell_type": "VTK_TRIANGLE (5)", + "index_contract": "payload index equals native VTU point ID and PointData tuple ID", + "definition": "each native triangle of area A contributes A/3 to each vertex", + "coordinate_units": COORDINATE_UNITS, + "units": AREA_UNITS, + "calculation_dtype": "float64", + "storage_format": "npy", + "storage_dtype": " str: + return datetime.now(timezone.utc).isoformat().replace("+00:00", "Z") + + +def parse_case_id(case_id: str) -> int: + match = RUN_PATTERN.fullmatch(case_id) + if match is None: + raise ValueError(f"Invalid WindsorML case ID {case_id!r}; expected run_N") + number = int(match.group(1)) + if case_id != f"run_{number}": + raise ValueError(f"Non-canonical WindsorML case ID: {case_id!r}") + return number + + +def source_path(source_root: Path, case_id: str) -> Path: + number = parse_case_id(case_id) + return source_root / case_id / f"boundary_{number}.vtu" + + +def output_path(output_root: Path, case_id: str) -> Path: + number = parse_case_id(case_id) + return output_root / case_id / f"{OUTPUT_PREFIX}{number}.npy" + + +def validate_separate_roots(source_root: Path, output_root: Path) -> None: + if source_root == output_root: + raise ValueError("--source-root and --output-root must be different") + if output_root.is_relative_to(source_root): + raise ValueError(f"Output root {output_root} is inside source root {source_root}") + if source_root.is_relative_to(output_root): + raise ValueError(f"Source root {source_root} is inside output root {output_root}") + + +def require_unchanged_source(path: Path, expected: os.stat_result) -> None: + actual = path.stat() + if ( + actual.st_size != expected.st_size + or actual.st_mtime_ns != expected.st_mtime_ns + or actual.st_ino != expected.st_ino + or actual.st_dev != expected.st_dev + ): + raise RuntimeError(f"Source changed during calculation: {path}") + + +def file_sha256(path: Path) -> str: + digest = hashlib.sha256() + with path.open("rb") as stream: + while block := stream.read(HASH_CHUNK_BYTES): + digest.update(block) + return digest.hexdigest() + + +def iter_slices(length: int, chunk: int) -> Iterable[slice]: + for start in range(0, length, chunk): + yield slice(start, min(start + chunk, length)) + + +def canonical_geometry_sha256( + points: np.ndarray, + connectivity: np.ndarray, + offsets: np.ndarray, + cell_types: np.ndarray, +) -> str: + digest = hashlib.sha256() + digest.update(GEOMETRY_HASH_DOMAIN) + for name, value in ( + ("points", points), + ("connectivity", connectivity), + ("offsets", offsets), + ("cell_types", cell_types), + ): + array = np.ascontiguousarray(value) + digest.update(name.encode("utf-8") + b"\0") + digest.update(array.dtype.str.encode("ascii") + b"\0") + digest.update( + json.dumps(list(array.shape), separators=(",", ":")).encode("ascii") + b"\0" + ) + raw = array.view(np.uint8).reshape(-1) + for block in iter_slices(raw.size, HASH_CHUNK_BYTES): + digest.update(memoryview(raw[block])) + return digest.hexdigest() + + +def atomic_json(path: Path, payload: dict[str, Any]) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + temporary = path.with_name(f".{path.name}.tmp-{os.getpid()}") + try: + temporary.write_text(json.dumps(payload, indent=2, sort_keys=True) + "\n") + os.replace(temporary, path) + finally: + temporary.unlink(missing_ok=True) + + +def write_once(path: Path, text: str) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + try: + with path.open("x") as stream: + stream.write(text) + except FileExistsError: + return + + +def install_public_generator( + metadata_root: Path, + builder_path: Path, + builder_sha256: str, +) -> None: + """Atomically install an exact builder copy under its public name.""" + target = metadata_root / "generate_dual_areas.py" + if target.is_file(): + if file_sha256(target) != builder_sha256: + raise RuntimeError(f"Published generator differs from campaign builder: {target}") + return + if target.exists(): + raise RuntimeError(f"Generator target is not a regular file: {target}") + temporary = target.with_name(f".{target.name}.tmp-{os.getpid()}") + try: + temporary.write_bytes(builder_path.read_bytes()) + try: + os.link(temporary, target) + except FileExistsError: + pass + finally: + temporary.unlink(missing_ok=True) + if not target.is_file() or file_sha256(target) != builder_sha256: + raise RuntimeError(f"Could not install the exact campaign generator: {target}") + + +def initialize_metadata_root( + output_root: Path, + builder_path: Path, + builder_sha256: str, +) -> Path: + metadata_root = output_root / METADATA_DIRNAME + (metadata_root / "cases").mkdir(parents=True, exist_ok=True) + write_once(metadata_root / "README.md", README_TEXT) + write_once( + metadata_root / "algorithm.json", + json.dumps(ALGORITHM_RECORD, indent=2, sort_keys=True) + "\n", + ) + install_public_generator(metadata_root, builder_path, builder_sha256) + return metadata_root + + +def load_frozen_inventory( + path: Path, + *, + expected_sha256: str | None, + source_root: Path, + builder_sha256: str, + expected_revision: str, + expected_case_count: int, +) -> tuple[dict[str, Any], str]: + inventory_path = path.resolve(strict=True) + inventory_sha256 = file_sha256(inventory_path) + if expected_sha256 is not None and inventory_sha256 != expected_sha256: + raise RuntimeError( + f"Frozen inventory SHA-256 mismatch: {inventory_sha256} != {expected_sha256}" + ) + inventory = json.loads(inventory_path.read_text()) + if not ( + inventory.get("schema") == INVENTORY_SCHEMA + and inventory.get("schema_version") == 1 + and inventory.get("dataset") == "WindsorML" + and inventory.get("source_dataset_repository") == EXPECTED_REPO_ID + and inventory.get("source_dataset_revision") == expected_revision + and inventory.get("expected_case_count") == expected_case_count + and inventory.get("complete_case_count") == expected_case_count + and Path(str(inventory.get("source_root"))).resolve() == source_root + and inventory.get("tools", {}).get("builder", {}).get("sha256") + == builder_sha256 + ): + raise RuntimeError("Frozen inventory contract/tool hash mismatch") + cases = inventory.get("cases") + if not isinstance(cases, list) or len(cases) != expected_case_count: + raise RuntimeError("Frozen inventory has an invalid case list") + if [row.get("case_id") for row in cases] != [ + f"run_{number}" for number in range(expected_case_count) + ]: + raise RuntimeError("Frozen inventory is not exactly run_0..run_349") + return inventory, inventory_sha256 + + +def load_download_manifest( + source_root: Path, + expected_revision: str, + expected_case_count: int, +) -> dict[str, Any]: + path = source_root / "download_manifest.json" + if not path.is_file(): + raise FileNotFoundError(path) + manifest = json.loads(path.read_text()) + required = { + "repo_id": EXPECTED_REPO_ID, + "pinned_revision": expected_revision, + "extension": "vtu", + "file_count": expected_case_count, + "completed_count": expected_case_count, + "failure_count": 0, + "first_case_id": 0, + "last_case_id": expected_case_count - 1, + } + for key, expected in required.items(): + if manifest.get(key) != expected: + raise RuntimeError( + f"Download manifest {key}={manifest.get(key)!r}, expected {expected!r}" + ) + files = manifest.get("files") + if not isinstance(files, list) or len(files) != expected_case_count: + raise RuntimeError("Download manifest has an invalid files inventory") + ids = [item.get("case_id") for item in files] + if ids != list(range(expected_case_count)): + raise RuntimeError("Download manifest case IDs are not exactly contiguous run_0..run_349") + return manifest + + +def resolve_case( + *, + source_root: Path, + case: str | None, + case_index: int | None, + expected_case_count: int, +) -> str: + if case_index is not None: + if case_index < 0 or case_index >= expected_case_count: + raise IndexError( + f"Case index {case_index} is outside [0, {expected_case_count})" + ) + case_id = f"run_{case_index}" + else: + if case is None: + raise ValueError("One of --case or --case-index is required") + case_id = case + number = parse_case_id(case_id) + if number >= expected_case_count: + raise IndexError(f"Case {case_id} is outside the frozen 350-case inventory") + if not source_path(source_root, case_id).is_file(): + raise FileNotFoundError(source_path(source_root, case_id)) + return case_id + + +def geometry_only_vtu( + path: Path, +) -> tuple[Any, np.ndarray, np.ndarray, np.ndarray, np.ndarray, list[str], list[str]]: + reader = vtkXMLUnstructuredGridReader() + reader.SetFileName(str(path)) + reader.UpdateInformation() + + point_selection = reader.GetPointDataArraySelection() + point_names = sorted( + point_selection.GetArrayName(i) + for i in range(point_selection.GetNumberOfArrays()) + ) + cell_selection = reader.GetCellDataArraySelection() + cell_names = sorted( + cell_selection.GetArrayName(i) + for i in range(cell_selection.GetNumberOfArrays()) + ) + missing_point = sorted(REQUIRED_POINT_ARRAY_NAMES - set(point_names)) + missing_cell = sorted(REQUIRED_CELL_ARRAY_NAMES - set(cell_names)) + if missing_point or missing_cell: + raise RuntimeError( + f"Missing source arrays: PointData={missing_point}, CellData={missing_cell}" + ) + for name in point_names: + point_selection.DisableArray(name) + for name in cell_names: + cell_selection.DisableArray(name) + + reader.Update() + if reader.GetErrorCode(): + raise RuntimeError(f"VTK failed with error code {reader.GetErrorCode()}: {path}") + grid = reader.GetOutput() + if grid is None or grid.GetPoints() is None or grid.GetCells() is None: + raise RuntimeError(f"VTK returned incomplete geometry: {path}") + + points = np.asarray(vtk_to_numpy(grid.GetPoints().GetData())) + connectivity = np.asarray(vtk_to_numpy(grid.GetCells().GetConnectivityArray())) + offsets = np.asarray(vtk_to_numpy(grid.GetCells().GetOffsetsArray())) + cell_types = np.asarray(vtk_to_numpy(grid.GetCellTypesArray())) + return grid, points, connectivity, offsets, cell_types, point_names, cell_names + + +def validate_geometry( + points: np.ndarray, + connectivity: np.ndarray, + offsets: np.ndarray, + cell_types: np.ndarray, +) -> dict[str, Any]: + if points.ndim != 2 or points.shape[1] != 3: + raise ValueError(f"Expected point coordinates (N,3), got {points.shape}") + if not np.all(np.isfinite(points)): + raise ValueError("Source contains non-finite coordinates") + n_points = int(points.shape[0]) + n_cells = int(cell_types.size) + if n_points == 0 or n_cells == 0: + raise ValueError("Source has no points or cells") + if connectivity.ndim != 1 or connectivity.size != 3 * n_cells: + raise ValueError("Windsor source is not an all-triangle connectivity array") + if offsets.shape != (n_cells + 1,): + raise ValueError(f"Unexpected cell-offset shape: {offsets.shape}") + if not np.array_equal(offsets, np.arange(n_cells + 1, dtype=offsets.dtype) * 3): + raise ValueError("Cell offsets do not describe exactly three vertices per cell") + type_values, type_counts = np.unique(cell_types, return_counts=True) + if type_values.tolist() != [VTK_TRIANGLE]: + raise ValueError(f"Unsupported Windsor VTK cell types: {type_values.tolist()}") + conn_min = int(connectivity.min()) + conn_max = int(connectivity.max()) + if conn_min < 0 or conn_max >= n_points: + raise ValueError( + f"Connectivity [{conn_min}, {conn_max}] lies outside [0, {n_points})" + ) + ids = connectivity.reshape(n_cells, 3) + repeated_vertex_triangles = int( + np.count_nonzero( + (ids[:, 0] == ids[:, 1]) + | (ids[:, 1] == ids[:, 2]) + | (ids[:, 2] == ids[:, 0]) + ) + ) + if repeated_vertex_triangles: + raise ValueError( + f"Found {repeated_vertex_triangles:,} triangles with repeated point IDs" + ) + bounds = [ + float(points[:, 0].min()), + float(points[:, 0].max()), + float(points[:, 1].min()), + float(points[:, 1].max()), + float(points[:, 2].min()), + float(points[:, 2].max()), + ] + return { + "n_points": n_points, + "n_cells": n_cells, + "n_connectivity_values": int(connectivity.size), + "n_fan_triangles": n_cells, + "connectivity_min": conn_min, + "connectivity_max": conn_max, + "cell_type_histogram": {str(VTK_TRIANGLE): int(type_counts[0])}, + "cell_arity_histogram": {"3": n_cells}, + "repeated_vertex_triangles": repeated_vertex_triangles, + "coordinate_bounds_m": bounds, + } + + +def accumulate_dual_areas( + points: np.ndarray, + connectivity: np.ndarray, + *, + chunk_cells: int, +) -> tuple[np.ndarray, dict[str, Any]]: + n_points = int(points.shape[0]) + n_cells = int(connectivity.size // 3) + ids_all = connectivity.reshape(n_cells, 3) + weights = np.zeros(n_points, dtype=np.float64) + total_area = 0.0 + zero_area = 0 + nonfinite_area = 0 + area_min = math.inf + area_max = -math.inf + + started = time.perf_counter() + for start in range(0, n_cells, chunk_cells): + stop = min(start + chunk_cells, n_cells) + ids = ids_all[start:stop] + p0 = np.asarray(points[ids[:, 0]], dtype=np.float64) + p1 = np.asarray(points[ids[:, 1]], dtype=np.float64) + p2 = np.asarray(points[ids[:, 2]], dtype=np.float64) + ab = p1 - p0 + ac = p2 - p0 + cross = np.cross(ab, ac) + areas = 0.5 * np.sqrt(np.einsum("ij,ij->i", cross, cross)) + finite = np.isfinite(areas) + nonfinite_area += int(np.count_nonzero(~finite)) + if not np.all(finite): + raise RuntimeError("Encountered non-finite native triangle area") + zero_area += int(np.count_nonzero(areas == 0.0)) + if areas.size: + area_min = min(area_min, float(areas.min())) + area_max = max(area_max, float(areas.max())) + total_area += float(areas.sum(dtype=np.float64)) + shares = areas / 3.0 + np.add.at(weights, ids[:, 0], shares) + np.add.at(weights, ids[:, 1], shares) + np.add.at(weights, ids[:, 2], shares) + print( + f"ACCUMULATE {stop:,}/{n_cells:,} triangles; " + f"elapsed={time.perf_counter() - started:.1f}s", + flush=True, + ) + + return weights, { + "surface_area_float64_m2": total_area, + "triangle_area_min_m2": area_min, + "triangle_area_max_m2": area_max, + "zero_area_triangles": zero_area, + "nonfinite_area_triangles": nonfinite_area, + } + + +def write_payload( + output: Path, + weights: np.ndarray, + surface_area: float, + *, + chunk_points: int, +) -> dict[str, Any]: + output.parent.mkdir(parents=True, exist_ok=True) + temporary = output.with_name(f".{output.name}.tmp-{os.getpid()}") + payload: np.memmap | None = None + try: + payload = np.lib.format.open_memmap( + temporary, + mode="w+", + dtype=np.dtype(" 1.0e-11: + raise RuntimeError(f"Float64 area closure failed: {closure64:.3e}") + if closure32 > 2.0e-7: + raise RuntimeError(f"Float32 storage closure failed: {closure32:.3e}") + payload_hash = file_sha256(temporary) + payload_size = temporary.stat().st_size + os.replace(temporary, output) + return { + "payload_sha256": payload_hash, + "payload_size_bytes": payload_size, + "dual_area_sum_float64_m2": sum64, + "stored_dual_area_sum_float64_m2": stored_sum64, + "float64_area_closure_relative": closure64, + "float32_area_closure_relative": closure32, + "zero_weight_points": zero_count, + "negative_weight_points": negative_count, + "nonfinite_weight_points": nonfinite_count, + "dual_area_min_m2": weight_min, + "dual_area_max_m2": weight_max, + } + finally: + if payload is not None: + try: + payload.flush() + finally: + del payload + temporary.unlink(missing_ok=True) + + +def existing_output_is_complete( + *, + source: Path, + output: Path, + record_path: Path, + case_id: str, + expected_revision: str, + builder_sha256: str, + inventory_sha256: str | None, +) -> bool: + if not output.is_file() or not record_path.is_file(): + return False + try: + record = json.loads(record_path.read_text()) + payload = np.load(output, mmap_mode="r", allow_pickle=False) + return bool( + record.get("schema") == CASE_SCHEMA + and record.get("status") == "complete" + and record.get("algorithm_id") == ALGORITHM_ID + and record.get("case_id") == case_id + and record.get("source", {}).get("pinned_revision") == expected_revision + and record.get("source", {}).get("size_bytes") == source.stat().st_size + and record.get("source", {}).get("mtime_ns") == source.stat().st_mtime_ns + and record.get("payload", {}).get("path") + == f"{case_id}/{output.name}" + and record.get("payload", {}).get("size_bytes") == output.stat().st_size + and tuple(record.get("payload", {}).get("shape", [])) == payload.shape + and payload.shape + == (int(record.get("topology", {}).get("n_points", -1)),) + and payload.dtype == np.dtype(" dict[str, Any]: + number = parse_case_id(case_id) + source = source_path(source_root, case_id) + output = output_path(output_root, case_id) + metadata_root = initialize_metadata_root( + output_root, + builder_path, + builder_sha256, + ) + record_path = metadata_root / "cases" / f"{case_id}.json" + source_stat = source.stat() + expected_source_path = f"{case_id}/boundary_{number}.vtu" + if frozen_inventory is not None: + inventory_item = frozen_inventory["cases"][number] + if not ( + inventory_item.get("case_id") == case_id + and inventory_item.get("run_number") == number + and inventory_item.get("source_path") == expected_source_path + and inventory_item.get("source_size_bytes") == source_stat.st_size + and inventory_item.get("source_mtime_ns") == source_stat.st_mtime_ns + ): + raise RuntimeError(f"Frozen source inventory mismatch for {case_id}") + elif source_manifest is not None: + inventory_item = source_manifest["files"][number] + if not ( + inventory_item.get("case_id") == number + and inventory_item.get("path") == expected_source_path + and inventory_item.get("size") == source_stat.st_size + ): + raise RuntimeError(f"Download manifest mismatch for {case_id}") + if not force and existing_output_is_complete( + source=source, + output=output, + record_path=record_path, + case_id=case_id, + expected_revision=expected_revision, + builder_sha256=builder_sha256, + inventory_sha256=inventory_sha256, + ): + print(f"SKIP complete {case_id}: {output}", flush=True) + return json.loads(record_path.read_text()) + + started_at = utc_now() + t0 = time.perf_counter() + print(f"READ geometry only: {source}", flush=True) + ( + grid, + points, + connectivity, + offsets, + cell_types, + point_array_names, + cell_array_names, + ) = geometry_only_vtu(source) + read_seconds = time.perf_counter() - t0 + topology = validate_geometry(points, connectivity, offsets, cell_types) + if frozen_inventory is not None: + piece = frozen_inventory["cases"][number].get("piece", {}) + if not ( + piece.get("points") == topology["n_points"] + and piece.get("cells") == topology["n_cells"] + ): + raise RuntimeError(f"Frozen VTU Piece counts differ for {case_id}") + print( + f"GEOMETRY points={topology['n_points']:,} " + f"triangles={topology['n_cells']:,} read={read_seconds:.1f}s", + flush=True, + ) + + hash_started = time.perf_counter() + geometry_hash = canonical_geometry_sha256( + points, connectivity, offsets, cell_types + ) + source_file_hash = file_sha256(source) + require_unchanged_source(source, source_stat) + hash_seconds = time.perf_counter() - hash_started + print( + f"SOURCE_SHA256 {source_file_hash}; GEOMETRY_SHA256 {geometry_hash}", + flush=True, + ) + + accumulation_started = time.perf_counter() + weights, area_validation = accumulate_dual_areas( + points, + connectivity, + chunk_cells=chunk_cells, + ) + accumulation_seconds = time.perf_counter() - accumulation_started + if area_validation["zero_area_triangles"] > max_zero_area_triangles: + raise RuntimeError( + f"Found {area_validation['zero_area_triangles']:,} zero-area triangles; " + f"maximum permitted is {max_zero_area_triangles:,}" + ) + payload_validation = write_payload( + output, + weights, + area_validation["surface_area_float64_m2"], + chunk_points=output_chunk_points, + ) + require_unchanged_source(source, source_stat) + del weights + + elapsed_seconds = time.perf_counter() - t0 + record: dict[str, Any] = { + "schema": CASE_SCHEMA, + "status": "complete", + "algorithm_id": ALGORITHM_ID, + "case_id": case_id, + "association": "point", + "units": AREA_UNITS, + "source": { + "path": f"{case_id}/boundary_{number}.vtu", + "repo_id": EXPECTED_REPO_ID, + "pinned_revision": expected_revision, + "size_bytes": source_stat.st_size, + "mtime_ns": source_stat.st_mtime_ns, + "sha256": source_file_hash, + "geometry_sha256": geometry_hash, + "coordinate_dtype": points.dtype.str, + "connectivity_dtype": connectivity.dtype.str, + "offset_dtype": offsets.dtype.str, + "cell_type_dtype": cell_types.dtype.str, + "point_data_array_names": point_array_names, + "cell_data_array_names": cell_array_names, + }, + "payload": { + "path": f"{case_id}/{output.name}", + "format": "npy", + "dtype": " None: + parser = argparse.ArgumentParser() + parser.add_argument("--source-root", type=Path, default=DEFAULT_SOURCE_ROOT) + parser.add_argument("--output-root", type=Path, default=DEFAULT_OUTPUT_ROOT) + parser.add_argument("--inventory", type=Path) + parser.add_argument("--expected-inventory-sha256") + group = parser.add_mutually_exclusive_group(required=True) + group.add_argument("--case") + group.add_argument("--case-index", type=int) + parser.add_argument("--expected-case-count", type=int, default=EXPECTED_CASE_COUNT) + parser.add_argument( + "--expected-revision", default=EXPECTED_PINNED_REVISION + ) + parser.add_argument("--chunk-cells", type=int, default=500_000) + parser.add_argument("--output-chunk-points", type=int, default=2_000_000) + parser.add_argument("--max-zero-area-triangles", type=int, default=1) + parser.add_argument("--force", action="store_true") + args = parser.parse_args() + if args.chunk_cells < 1 or args.output_chunk_points < 1: + raise ValueError("Chunk sizes must be positive") + if args.max_zero_area_triangles < 0: + raise ValueError("--max-zero-area-triangles must be nonnegative") + + source_root = args.source_root.resolve() + output_root = args.output_root.resolve() + validate_separate_roots(source_root, output_root) + builder_path = Path(__file__).resolve(strict=True) + builder_sha256 = file_sha256(builder_path) + frozen_inventory: dict[str, Any] | None = None + inventory_sha256: str | None = None + source_manifest: dict[str, Any] | None = None + if args.inventory is not None: + frozen_inventory, inventory_sha256 = load_frozen_inventory( + args.inventory, + expected_sha256=args.expected_inventory_sha256, + source_root=source_root, + builder_sha256=builder_sha256, + expected_revision=args.expected_revision, + expected_case_count=args.expected_case_count, + ) + else: + if args.expected_inventory_sha256 is not None: + raise ValueError("--expected-inventory-sha256 requires --inventory") + if (source_root / "download_manifest.json").is_file(): + source_manifest = load_download_manifest( + source_root, + args.expected_revision, + args.expected_case_count, + ) + case_id = resolve_case( + source_root=source_root, + case=args.case, + case_index=args.case_index, + expected_case_count=args.expected_case_count, + ) + build_case( + source_root=source_root, + output_root=output_root, + case_id=case_id, + source_manifest=source_manifest, + frozen_inventory=frozen_inventory, + inventory_sha256=inventory_sha256, + builder_path=builder_path, + builder_sha256=builder_sha256, + expected_revision=args.expected_revision, + chunk_cells=args.chunk_cells, + output_chunk_points=args.output_chunk_points, + max_zero_area_triangles=args.max_zero_area_triangles, + force=args.force, + ) + + +if __name__ == "__main__": + main() diff --git a/surface_dual_areas/manifest.json b/surface_dual_areas/manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..43d87fb119c2cf50ead5ce0e0868ea4e2d37a41d --- /dev/null +++ b/surface_dual_areas/manifest.json @@ -0,0 +1,4581 @@ +{ + "algorithm_id": "native-point-fan-barycentric-dual-area-v1", + "artifact": "native boundary point dual areas", + "association": "point", + "cases": [ + { + "case_id": "run_0", + "n_points": 2188118, + "payload_path": "run_0/boundary_dual_area_0.npy", + "payload_sha256": "66412c5b810b57f6625a403a455871129867880bc78e7614993da3eaa16761af", + "payload_size_bytes": 8752600, + "record_path": "surface_dual_areas/cases/run_0.json", + "record_sha256": "6bab123c87379a7c66d50261e6118a4bbbd2ce5c6f36284acb74c69d8d3f70ff", + "source_path": "run_0/boundary_0.vtu", + "source_sha256": "d2fa1a3ec75df7c822f5976a2b4d2e159184ef9731d1f9fa89c0d1a772a89f25", + "source_size_bytes": 355873526, + "stored_area_sum_float64_m2": 1.386722754611877 + }, + { + "case_id": "run_1", + "n_points": 2499371, + "payload_path": "run_1/boundary_dual_area_1.npy", + "payload_sha256": "8bca3926f99a343c1b7b699ee2c9e97b08e67cc4784afda73b6c2c06664c3bf5", + "payload_size_bytes": 9997612, + "record_path": "surface_dual_areas/cases/run_1.json", + "record_sha256": "609454d374bba3d67ebbc722fef5379054a524fb87ddae25def27f1b05db79f2", + "source_path": "run_1/boundary_1.vtu", + "source_sha256": "29858382e3e5af17d8934a4308b4d8754fbb5ad8fb32f0c6cf7213121935b989", + "source_size_bytes": 406496462, + "stored_area_sum_float64_m2": 1.5220324630249136 + }, + { + "case_id": "run_2", + "n_points": 2142204, + "payload_path": "run_2/boundary_dual_area_2.npy", + "payload_sha256": "fba911b8e77dc1a5ec0db411f90f37c7e8e2ce2eb924bfde09d908711e28d99b", + "payload_size_bytes": 8568944, + "record_path": "surface_dual_areas/cases/run_2.json", + "record_sha256": "d7aa38b6262dada66816d012124e5a86a9cee5c9183ea0dc3b87f9789afbb224", + "source_path": "run_2/boundary_2.vtu", + "source_sha256": "af8a01750ef8699e1164f156e66b4b6f15939d07a9bc3f73a3116fd67f088f0d", + "source_size_bytes": 348407848, + "stored_area_sum_float64_m2": 1.3768044776118114 + }, + { + "case_id": "run_3", + "n_points": 2421851, + "payload_path": "run_3/boundary_dual_area_3.npy", + "payload_sha256": "a5e6a6a0707719a507ea6e28b3c22d12d65341e1306edde210a5583d4b2ab3c3", + "payload_size_bytes": 9687532, + "record_path": "surface_dual_areas/cases/run_3.json", + "record_sha256": "a2bef87b5a8aa290bc51e9815b9a506fb0f4e6dc53082a983198dd4cde01c6e5", + "source_path": "run_3/boundary_3.vtu", + "source_sha256": "598d6e93b4c9002e1fbc27740631195f129dd06e8bec95b247da7f5d74e0ba50", + "source_size_bytes": 393897351, + "stored_area_sum_float64_m2": 1.4903519822305893 + }, + { + "case_id": "run_4", + "n_points": 2140228, + "payload_path": "run_4/boundary_dual_area_4.npy", + "payload_sha256": "8f3c3bdaec1d0a50a23a28be127a0644c950fa919db1064f5a73477db14d8a5f", + "payload_size_bytes": 8561040, + "record_path": "surface_dual_areas/cases/run_4.json", + "record_sha256": "3a09b5137c21260b1ae587e3ef6d99ac20df4e955295796071e9f5c7f8a63ca4", + "source_path": "run_4/boundary_4.vtu", + "source_sha256": "57af78eeb9db61a8d7814dae0dc5b0ba8e357080a364ce45ad6a7c0b870307d8", + "source_size_bytes": 348083059, + "stored_area_sum_float64_m2": 1.3698707764338118 + }, + { + "case_id": "run_5", + "n_points": 2320691, + "payload_path": "run_5/boundary_dual_area_5.npy", + "payload_sha256": "37b2331b52c32ece15f36580b4fc0497049e98180fed5c024ff1c78324f8e72b", + "payload_size_bytes": 9282892, + "record_path": "surface_dual_areas/cases/run_5.json", + "record_sha256": "831d9ee72c9b3948f4d1b0fbb4d4a9f7ce30fb88548b682b495b122f1f9d68ee", + "source_path": "run_5/boundary_5.vtu", + "source_sha256": "4df24e0e171fe36a6bc6bc213da6031e462579106269f0f3ed84980e0bdc8a7f", + "source_size_bytes": 377441444, + "stored_area_sum_float64_m2": 1.4861266013087941 + }, + { + "case_id": "run_6", + "n_points": 2252791, + "payload_path": "run_6/boundary_dual_area_6.npy", + "payload_sha256": "4a35552b697816296e2d1ee41779dd60ed675e8a1e370b082809f7803da8e0a0", + "payload_size_bytes": 9011292, + "record_path": "surface_dual_areas/cases/run_6.json", + "record_sha256": "1886a885f0a06f275f7e9f9a37654908add323d83dfda0626bf8f8d2288c05c0", + "source_path": "run_6/boundary_6.vtu", + "source_sha256": "fa434d267173f0ec7a2f3169b519f233f9c6f12ba16c947ae2322210aabb4933", + "source_size_bytes": 366397392, + "stored_area_sum_float64_m2": 1.4223285751345243 + }, + { + "case_id": "run_7", + "n_points": 2175058, + "payload_path": "run_7/boundary_dual_area_7.npy", + "payload_sha256": "89b9969075e9a925fcef2570b2be4b52ef8efc6511091029ede742eaed382e00", + "payload_size_bytes": 8700360, + "record_path": "surface_dual_areas/cases/run_7.json", + "record_sha256": "b6b74e0864633c54aa18dfbcb5fec10805bdc8ced0975828ca35035fa3f3b7e7", + "source_path": "run_7/boundary_7.vtu", + "source_sha256": "4131d1c476fb674443281fe39eab1420879b708dadb5943272a5e3d92f882192", + "source_size_bytes": 353749715, + "stored_area_sum_float64_m2": 1.4197297153620874 + }, + { + "case_id": "run_8", + "n_points": 2428625, + "payload_path": "run_8/boundary_dual_area_8.npy", + "payload_sha256": "35a7b40d7853adab023e55c0215d8be03515129f07364486c43860f6ed510832", + "payload_size_bytes": 9714628, + "record_path": "surface_dual_areas/cases/run_8.json", + "record_sha256": "42fd2f7530e4b4dcd4f930788f7b2b339488084638037baccdc37daee1a8d9e5", + "source_path": "run_8/boundary_8.vtu", + "source_sha256": "fae05f6d1028ed83fd3413537a45a3ab1c3804d5e574ecc2c5b28aa2978d5038", + "source_size_bytes": 394990819, + "stored_area_sum_float64_m2": 1.4838919518153846 + }, + { + "case_id": "run_9", + "n_points": 2237778, + "payload_path": "run_9/boundary_dual_area_9.npy", + "payload_sha256": "067e571778753f38d8d6e72a86a96baf144b43dac44854f558b64ff25f1d578d", + "payload_size_bytes": 8951240, + "record_path": "surface_dual_areas/cases/run_9.json", + "record_sha256": "cbf0a7af393a76ab3a385ea5226c9af7c125cb5bb6a5248e8f1a5e03db1b5ca9", + "source_path": "run_9/boundary_9.vtu", + "source_sha256": "6b671b6b9d61ccbac51427a021bfb59dcfbab4c040d3bd6146d2dd7f4b9d300f", + "source_size_bytes": 363951688, + "stored_area_sum_float64_m2": 1.3701706158490203 + }, + { + "case_id": "run_10", + "n_points": 2123570, + "payload_path": "run_10/boundary_dual_area_10.npy", + "payload_sha256": "2022da3ac9ca4dd1f7a425174ccc6cedbac77b26d1c4fb8cde8564bddc71c0b9", + "payload_size_bytes": 8494408, + "record_path": "surface_dual_areas/cases/run_10.json", + "record_sha256": "38f70253b3a14e84af45b670ba247f4862690c33c34a28028c42103c3d5d182a", + "source_path": "run_10/boundary_10.vtu", + "source_sha256": "d129493bbbec8e582c50a52ea4fd22376ca625ba0abbe889b71cb1ab307b51ad", + "source_size_bytes": 345377165, + "stored_area_sum_float64_m2": 1.3857273405672548 + }, + { + "case_id": "run_11", + "n_points": 2321799, + "payload_path": "run_11/boundary_dual_area_11.npy", + "payload_sha256": 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