Advanced Python: IFC + computational geometry

Transcript

1. Advanced python IFC + computational geometry
2. Guest lecturer • Thomas Krijnen • past: Post-doctoral researcher @ TU Delft, NL • past: PhD @ TU Eindhoven, NL • Founder AECgeeks • Maintainer IfcOpenShell.org • ✉ mail@thomaskrijnen.com • 🐤 @aothms
3. Why python - Batteries included: - web: flask, django, e.g view.ifcopenshell.org - machine learning: pytorch, tensorflow, keras, scikit, … - geometry: pythonOCC, shapely, … - graphs: networkx, rdflib, … - Readable syntax, reasonable semantics wrt typing, expressive types (e.g sets, containment operators, …) - Interpreted (no lengthy compilation times) and high performance possible (e.g numpy, numba, pypy, …)
4. Work (1) - geometric analysis for code compliance checking https://github.com/opensourceBIM/voxelization_toolkit
5. Work (2) - model validation https://github.com/buildingSMART/validate
6. Setup https://docs.conda.io/en/latest/miniconda.html conda create -n course2022 conda activate course2022 conda install -c conda-forge pythonocc-core ifcopenshell jupyter
7. Anatomy of a simple Python script
8. Data extraction using IfcOpenShell Python
9. Entities and attributes
10. Inverse attributes
11. Comparison with a SPARQL query <http://standards.buildingsmart.org/IFC/DEV/IFC2x3/TC1/OWL#IfcWallStandardCase > basic graph pattern aggregate function / makes this a property path, a sequence of two predicates in this used because in IfcOWL the specific type of the property nominal is emitted. IfcAreaMeasure in this case.
12. Example with geometry
13. The complexity of IFC
14. Continue on http://web.archive.org/web/20200711032105/http://www. pythonocc.org/quick-examples/machine-learning-and- building-models/ https://academy.ifcopenshell.org/posts/using-ifcopenshell- and-pythonocc-to-construct-new-geometry/ https://github.com/AECgeeks/tue-python-workshop-2022