ifcbench

Compare IfcOpenShell geometry output across versions, kernels, and iterator flags. Records per-element metrics (bbox, centroid, V/E/F, area, volume, manifold/closed/genus, vertex hash) and timing into a SQLite DB. Diffs and regressions become SQL queries.

Layout

ifcbench/
├── envs.yaml # how to build each IfcOpenShell environment
├── settings.yaml # which (kernel x flags) cells to run
├── envs/ # gitignored; one venv per IfcOpenShell version
├── results/ # gitignored; SQLite DB + raw probe JSONL
├── models.json # manifest of fixtures (sha256, schema, visibility)
├── models/ # gitignored; user-supplied IFC files (nested paths supported)
├── scripts/
│ ├── index_models.py # scan models/, populate models.json
└── src/ifcbench/
├── cli.py
├── runner.py # outer driver — spawns probes, ingests JSONL
├── probe.py # inner — runs in env venv, stdlib only + ifcopenshell
├── metrics.py # geometry metric extractors
├── db.py # SQLite schema + helpers
└── config.py # YAML/JSON loaders

Install

$ pip install -e .

Workflow

To learn about all capabilities, do ifcbench -h

Get test models

Models are mirrored here. A batch of models is stored in a JSON file (example).

$ ifcbench get-models --url https://cloud.thinkmoult.com/models/index.json`

Configure environments

Configure envs.yaml to declare which testing environments you want to setup. Each entry in envs.yaml produces one venv at envs/<label>/:

• install.pip — list of pip install specs (PyPI names, URLs, local wheel paths, source dirs). Pip handles all uniformly.
• install.paths — list of dirs to prepend to sys.path (for source-tree installs that don't have a clean wheel). Written into a .pth file in the venv's site-packages so probes pick them up automatically.
• python — interpreter to base the venv on (must be ABI-compatible with any precompiled wheel).

Then do: ifcbench setup-envs to create the environments.

Configure geometry settings

Edit settings.yaml to declare run rows (kernel x settings).

Each entry in settings.yaml is a (kernel, settings, num_threads) cell. A "setting" is run against every selected env and every selected model (or a filtered subset).

Two timing modes:

• num_threads > 1 — throughput. Per-element times are not meaningful; total wall time per file is.
• num_threads = 1 — per-element timing. Slow but attributable.

Do a test run

Do a run - this will test the geometry of all models using your environment and settings and store the results in the database.

ifcbench run --env ifcopenshell-python-313-0.8.5-linux64 --setting opencascade

Analyse results

$ ifcbench failures 1
$ ifcbench compare 1 2
$ ifcbench compare 1 2 --model foo

Inspect one shape

Export one element's raw mesh, a simple viewable mesh file, and a headless multi-view preview from a chosen benchmark env + setting:

$ ifcbench inspect-shape --env ifcopenshell-python-313-0.8.5-linux64 \
--setting opencascade --model duplex --id 12345

Artifacts are written under results/inspect/.../ by default:

• mesh.json — structured raw geometry + metrics
• mesh.txt — human-readable V/F/E dump
• mesh.obj — simple 3D mesh for external viewers
• preview.svg — headless 4-view contact sheet
• request.json, stdout.log, stderr.log, result.json — execution diagnostics

inspect-shape defaults to num_threads=1 even if the selected benchmark setting uses cpu_count, because single-element debugging is easier to reason about and less brittle that way. Override with --num-threads if needed.

SQLite schema

See src/ifcbench/db.py. Per-element row:

• counts: V, E, F, sub-mesh count
• topology: is_closed, is_manifold, euler_char, genus
• integrals: surface_area, volume
• bbox + centroid (vertex-mean and area-weighted)
• hash_quantised — 16-byte fingerprint of sorted, rounded vertex set
• time_ns — populated when num_threads = 1
• error_kind / error_msg — failures are first-class rows