IFC Extractor

Given a reference IFC model, extracts one small IFC file per "anchor" element (by default every IfcStair), containing that anchor plus any nearby element of interest (walls, railings, slabs, spaces, ...). Useful for producing focused per-element IFC extracts for review — e.g. checking a single staircase and everything physically near it, without opening the full model.

Run from ifc-extractor.ipynb — that notebook is the only way to run this tool.

Features

• Configurable anchor type — extract around IfcStair (default), IfcRamp, or any other IFC entity type present in the model.
• Configurable proximity distance — how far (in meters) to search for nearby elements around each anchor.
• Configurable nearby element types — choose which element types (walls, members, railings, slabs, stair flights, spaces, ...) count as "nearby" and get included in each extract.
• Configurable cleaning options — optionally strip materials, styles, or owner history from the output.
• Live progress log — extraction progress (per file, per anchor, elements found) prints as it happens.
• Resumable runs — a processed.txt file is written to the output folder; re-running the same input/output pair skips anchors already extracted.

Running

Requires Python 3.11 and the dependencies in requirements.txt.

pip install -r requirements.txt

Open ifc-extractor.ipynb and run its cells in order:

1. Imports ExtractionConfig, CleaningOptions, run_pipeline.
2. CONFIGURATION — edit config with your input_folder, output_folder, ifc_queue (the .ifc file names to process), anchor_type, target_types, proximity_distance, and cleaning options.
3. run_pipeline(config) — runs the extraction; progress prints below the cell as it goes.

Example

Given:

C:\Models\Input\
  Building-A.ifc
  Building-B.ifc

Setting input_folder to C:\Models\Input, output_folder to C:\Models\Output, ifc_queue to ["Building-A.ifc", "Building-B.ifc"], anchor_type to "IfcStair", and proximity_distance to 0.5:

from ifc_extractor import ExtractionConfig, CleaningOptions, run_pipeline

config = ExtractionConfig(
    input_folder=r"C:\Models\Input",
    output_folder=r"C:\Models\Output",
    ifc_queue=["Building-A.ifc", "Building-B.ifc"],
    anchor_type="IfcStair",
    target_types=["IfcWall", "IfcRailing", "IfcSlab"],
    proximity_distance=0.5,
    cleaning=CleaningOptions(remove_owner_history=True),
)
run_pipeline(config)

produces one file per staircase found in each model, e.g.:

C:\Models\Output\
  Building-A_stair_1h6dJPWa14QhkSAK3g0f8k.ifc
  Building-A_stair_2p9fLQr823JhmVYT9d1m0z.ifc
  Building-B_stair_3xQqQ6rMj9DEHb0wcqx09c.ifc
  processed.txt

Each output file contains one staircase (its flight and landing) plus every wall, railing, and slab within 0.5 m of it, with owner history stripped, following the source model's schema otherwise unchanged.

Re-running the same input/output folders skips staircases already listed in processed.txt and only processes new or previously-failed anchors — useful for resuming after an interrupted run or after dropping new files into the input folder.

Troubleshooting

• Errors during extraction — the run stops and raises, but any anchors already written before the error stay on disk and are marked done in processed.txt.
• Verifying output visually — open the extracted file next to the original in a viewer such as BIM Vision. Automated tests cover geometry correctness, but a visual side-by-side is the final check for anything subtle.

Tests

pip install -r requirements-dev.txt
python -m pytest tests/

License

This project is licensed under the MIT License.

It depends on IfcOpenShell, licensed under the GNU Lesser General Public License v3.0 or later (LGPL-3.0+), used here as an unmodified library via pip.