Skip to main content

Extracting object properties from an IFC file with IfcOpenShell

Besides the object geometry information, IFC files may contain properties for the IFC objects. The properties can be, for example, some predefined dimension information such as an object volume or a choice of material. Some of the properties are predefined in the IFC standards, but custom ones can be added.

IFC files can be massive and resource-intensive to process, so in some cases, it helps to separate the object properties from the geometry data.

IfcOpenShell is a toolset for processing IFC files. It is written mostly in C++ but also provides a Python interface.

To read an IFC file

>>> ifc_file = ifcopenshell.open("model.ifc")

Fetch all objects of type IfcSlab

>>> slab = ifc_file.by_type("IfcSlab")[1]

Get the list of properties

>>> slab.IsDefinedBy
(#145075=IfcRelDefinesByType('2_fok0__fAcBZmMlQcYwie',#1,$,$,(#27,#59),#145074), #145140=IfcRelDefinesByProperties('3U2LyORgXC2f_hWf6I16C1',#1,$,$,(#27,#59),#145141), #145142=IfcRelDefinesByProperties('2M__N9P55FIx5SxaSe9VVF',#1,$,$,(#27,#59),#145143), #145144=IfcRelDefinesByProperties('0wZfYbFufCg9P3Ppg0Tss4',#1,$,$,(#27,#59),#145145), #145146=IfcRelDefinesByProperties('1jVU$$d$T6jB2JQ0igcqpd',#1,$,$,(#59),#145147))

Pull one property set

>>> one_property_set = slab.IsDefinedBy[1].RelatingPropertyDefinition
>>> one_property_set
#145141=IfcPropertySet('0uC5dGWlrFSx9IjsHVaS8x',#1,'A property set name',$,(#145133,#145134))

List its properties

>>> one_property_set.HasProperties
(#145133=IfcPropertySingleValue('Floor',$,IfcText('Floor 0'),$), #145134=IfcPropertySingleValue('Some custom value',$,IfcText('Value'),$))

Properties can also be numeric values with units

>>> another_property_set.HasProperties
(#145135=IfcPropertySingleValue('Thickness',$,IfcLengthMeasure(2.E-1),$), #145138=IfcPropertySingleValue('Volume',$,IfcVolumeMeasure(7.19999999999365E-2),$), #145136=IfcPropertySingleValue('Net Area',$,IfcAreaMeasure(3.60000015625E-1),$), #145137=IfcPropertySingleValue('Perimeter',$,IfcLengthMeasure(2.39999984374577),$))

The units are defined in the IFC project metadata. In this example, the length units (IfcLengthMeasure) are in meters

>>> project = ifc_file.by_type("IfcProject")[0]
>>> units = project.UnitsInContext.Units
>>> units[2]
#152046=IfcSIUnit(*,.LENGTHUNIT.,$,.METRE.)
>>> units[2].UnitType
'LENGTHUNIT'
>>> units[2].Name
'METRE'

Comments

Popular posts from this blog

I'm not a passionate developer

A family friend of mine is an airlane pilot. A dream job for most, right? As a child, I certainly thought so. Now that I can have grown-up talks with him, I have discovered a more accurate description of his profession. He says that the truth about the job is that it is boring. To me, that is not that surprising. Airplanes are cool and all, but when you are in the middle of the Atlantic sitting next to the colleague you have been talking to past five years, how stimulating can that be? When he says the job is boring, it is not a bad kind of boring. It is a very specific boring. The "boring" you would want as a passenger. Uneventful.  Yet, he loves his job. According to him, an experienced pilot is most pleased when each and every tiny thing in the flight plan - goes according to plan. Passengers in the cabin of an expert pilot sit in the comfort of not even noticing who is flying. As someone employed in a field where being boring is not exactly in high demand, this sounds pro...

PydanticAI + evals + LiteLLM pipeline

I gave a tech talk at a Python meetup titled "Overengineering an LLM pipeline". It's based on my experiences of building production-grade stuff with LLMs I'm not sure how overengineered it actually turned out. Experimental would be a better term as it is using PydanticAI graphs library, which is in its very early stages as of writing this, although arguably already better than some of the pipeline libraries. Anyway, here is a link to it. It is a CLI poker app where you play one hand against an LLM. The LLM (theoretically) gets better with a self-correcting mechanism based on the evaluation score from another LLM. It uses the annotated past games as an additional context to potentially improve its decision-making. https://github.com/juho-y/archipylago-poker

"You are a friendly breadwinner"

A recent blog post by Pete Koomen about how we still lack truly "AI-native" software got me thinking about the kinds of applications I’d like to see. As the blog post says, AI should handle the boring stuff and leave the interesting parts for me. I listed down a few tasks I've dealt with recently and wrote some system prompts for potential agentic AIs: Check that the GDPR subprocessor list is up to date. Also, ensure we have a signed data processing agreement in place with the necessary vendors. Write a summary of what you did and highlight any oddities or potentially outdated vendors. Review our product’s public-facing API. Ensure the domain objects are named consistently. Here's a link to our documentation describing the domain. Conduct a SOC 2 audit of our system and write a report with your findings. Send the report to Slack. Once you get approval, start implementing the necessary changes. These could include HR-related updates, changes to cloud infras...