brickschema 0.1.11

A library for working with the Brick ontology for buildings (brickschema.org)

Brick Ontology Python package

Documentation available at readthedocs

Installation

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 platforms. (Note: no fundamental limitations here, just some packaging complexity due to cross-compiling the .so). To install support for the reasonable Reasoner, use

pip install brickschema[reasonable]

Features

OWLRL Inference

brickschema makes it easier to employ OWLRL reasoning on your graphs. 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 OWL inference, import the OWLRLInferenceSession class (this automatically chooses the fastest reasoner; check out the inference module documentation for how to use a specific reasoner). Create a brickschema.Graph with your ontology rules and instances loaded in and apply the reasoner's session to it:

from brickschema.graph import Graph
from brickschema.namespaces import BRICK
from brickschema.inference import OWLRLInferenceSession

g = Graph(load_brick=True)
g.load_file("test.ttl")

sess = OWLRLInferenceSession()
inferred_graph = sess.expand(g)
print(f"Inferred graph has {len(inferred_graph)} triples")

Haystack Inference

Requires a JSON export of a Haystack model. First, export your Haystack model as JSON; we are using the public reference model carytown.json. Then you can use this package as follows:

import json
from brickschema.inference import HaystackInferenceSession
haysess = HaystackInferenceSession("http://project-haystack.org/carytown#")
model = json.load(open('carytown.json'))
model = haysess.infer_model(model)
print(len(model))

points = model.query("""SELECT ?point ?type WHERE {
    ?point rdf:type/rdfs:subClassOf* brick:Point .
    ?point rdf:type ?type
}""")
print(points)

SQL ORM

from brickschema.graph import Graph
from brickschema.namespaces import BRICK
from brickschema.orm import SQLORM, Location, Equipment, Point

# loads in default Brick ontology
g = Graph(load_brick=True)
# load in our model
g.load_file("test.ttl")
# put the ORM in a SQLite database file called "brick_test.db"
orm = SQLORM(g, connection_string="sqlite:///brick_test.db")

# get the points for each equipment
for equip in orm.session.query(Equipment):
    print(f"Equpiment {equip.name} is a {equip.type} with {len(equip.points)} points")
    for point in equip.points:
        print(f"    Point {point.name} has type {point.type}")
# filter for a given name or type
hvac_zones = orm.session.query(Location)\
                        .filter(Location.type==BRICK.HVAC_Zone)\
                        .all()
print(f"Model has {len(hvac_zones)} HVAC Zones")

Validate with Shape Constraint Language

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.validate import Validator
from rdflib import Graph

dataG = Graph()
dataG.parse('myBuilding.ttl', format='turtle')
shapeG = Graph()
shapeG.parse('extraShapes.ttl', format='turtle')
v = Validator()
result = v.validate(dataG, shacl_graphs=[shapeG])
print(result.textOutput)

• result.conforms: If True, result.textOutput is Validation Report\nConforms: True.
• result.textOutput: Text output of pyshacl.validate(), appended with extra info that provides offender hint for each violation to help the user locate the particular violation in the data graph. See readthedocs for sample output.
• result.violationGraphs: List of violations, each presented as a graph.

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 extraShapes.ttl

Development

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

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