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A library for working with the Brick ontology for buildings (

Project description

Brick Ontology Python package

Build Documentation Status

Documentation available at readthedocs


The brickschema package requires Python >= 3.6. It can be installed with pip:

pip install brickschema

The brickschema package offers several installation configuration options for reasoning. The default bundled OWLRL reasoner delivers correct results, but exhibits poor performance on large or complex ontologies (we have observed minutes to hours) due to its bruteforce implementation.

The Allegro reasoner has better performance and implements enough of the OWLRL profile to be useful. We execute Allegrograph in a Docker container, which requires the docker package. To install support for the Allegrograph reasoner, use

pip install brickschema[allegro]

The reasonable Reasoner offers even better performance than the Allegro reasoner, but is currently only packaged for Linux and MacOS platforms. To install support for the reasonable Reasoner, use

pip install brickschema[reasonable]


The main Graph object is just a subclass of the excellent RDFlib Graph library, so all features on rdflib.Graph will also work here.

Brief overview of the main features of the brickschema package:

import brickschema

# creates a new rdflib.Graph with a recent version of the Brick ontology
# preloaded.
g = brickschema.Graph(load_brick=True)
# OR use the absolute latest Brick:
# g = brickschema.Graph(load_brick_nightly=True)
# OR create from an existing model
# g = brickschema.Graph(load_brick=True).from_haystack(...)

# load in data files from your file system
# ...or by URL (using rdflib)
g.parse("", format="ttl")

# perform reasoning on the graph (edits in-place)
g.expand(profile="tag") # infers Brick classes from Brick tags

# validate your Brick graph against built-in shapes (or add your own)
valid, _, resultsText = g.validate()
if not valid:
    print("Graph is not valid!")

# perform SPARQL queries on the graph
res = g.query("""SELECT ?afs ?afsp ?vav WHERE  {
    ?afs    a       brick:Air_Flow_Sensor .
    ?afsp   a       brick:Air_Flow_Setpoint .
    ?afs    brick:isPointOf ?vav .
    ?afsp   brick:isPointOf ?vav .
    ?vav    a   brick:VAV
for row in res:

# start a blocking web server with an interface for performing
# reasoning + querying functions
# now visit in http://localhost:8080



brickschema makes it easier to employ reasoning on your graphs. Simply call the expand method on the Graph object with one of the following profiles:

  • "rdfs": RDFS reasoning
  • "owlrl": OWL-RL reasoning (using 1 of 3 implementations below)
  • "vbis": add VBIS tags to Brick entities
  • "tag": infer Brick classes from Brick tags
from brickschema import Graph

g = Graph(load_brick=True)
print(f"Inferred graph has {len(g)} triples")

The package will automatically use the fastest available reasoning implementation for your system:

  • reasonable (fastest, Linux-only for now): pip install brickschema[reasonable]
  • Allegro (next-fastest, requires Docker): pip install brickschema[allegro]
  • OWLRL (default, native Python implementation): pip install brickschema

To use a specific reasoner, specify "reasonable", "allegrograph" or "owlrl" as the value for the backend argument to graph.expand.

Haystack Translation

brickschema can produce a Brick model from a JSON export of a Haystack model. Then you can use this package as follows:

import json
from brickschema import Graph
model = json.load(open("haystack-export.json"))
g = Graph(load_brick=True).from_haystack("", model)
points = g.query("""SELECT ?point ?type WHERE {
    ?point rdf:type/rdfs:subClassOf* brick:Point .
    ?point rdf:type ?type

VBIS Translation

brickschema can add VBIS tags to a Brick model easily

from brickschema import Graph
g = Graph(load_brick=True)

vbis_tags = g.query("""SELECT ?equip ?vbistag WHERE {
    ?equip  <> ?vbistag

Web-based Interaction

brickschema now supports interacting with a Graph object in a web browser. Executing g.serve(<http address>) on a graph object from your Python script or interpreter will start a webserver listening (by default) at http://localhost:8080 . This uses Yasgui to provide a simple web interface supporting SPARQL queries and inference.

Brick model validation

The module utilizes the pySHACL package to validate a building ontology against the Brick Schema, its default constraints (shapes) and user provided shapes.

from brickschema import Graph

g = Graph(load_brick=True)
valid, _, _ = g.validate()
print(f"Graph is valid? {valid}")

# validating using externally-defined shapes
external = Graph()
valid, _, _ = g.validate(shape_graphs=[external])
print(f"Graph is valid? {valid}")

The module provides a command brick_validate similar to the pyshacl command. The following command is functionally equivalent to the code above.

brick_validate myBuilding.ttl -s other_shapes.ttl


Brick requires Python >= 3.6. We use pre-commit hooks to automatically run code formatters and style checkers when you commit.

Use Poetry to manage packaging and dependencies. After installing poetry, install dependencies with:

# -D flag installs development dependencies
poetry install -D

Enter the development environment with the following command (this is analogous to activating a virtual environment.

poetry shell

On first setup, make sure to install the pre-commit hooks for running the formatting and linting tools:

# from within the environment; e.g. after running 'poetry shell'
pre-commit install

Run tests to make sure build is not broken

# from within the environment; e.g. after running 'poetry shell'
make test


Docs are written in reStructured Text. Make sure that you add your package requirements to docs/requirements.txt

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