Hi Jelle,
Interesting topic, this going to be a long thread!
The use of a python ORM is also the choice I would have done. In my mind,
there are two ways to model such a database:
- a "data centric" db: you store the result of the different operations you
ran (for instance a Point and a Cube);
- a "method centric" db: you store the functions that you called and the
parameters passed (for instance "create_point, 10,10,10" and "make_cube,
20,30,40").
For the data centric method, the db size will be more important than the
second solution. For each object, I would only create a key and a "STRING"
field in which I would store the output of the serialization of the python
object (with the pickle module).
The second method require less size but more computing time (since the
different operations has to be computed each time you load the model).
I have the feeling that this discussion is very close to the thread related
to data model of the High Level API. That's something that could be an
additional constraint of the HLA: "All pythonOCC HLA objects must be
serializable to an XML file in order to be exchanged/shared through a
network or stored to a database".
Cheers,
Thomas
2010/12/29 jelle feringa <jelleferi...@gmail.com>
> Hi,
>
> Recently I had enough of storing OCC's cad as files.
> Sometimes all you need is a database.
> Turns out that its really easy to do so. Perhaps its worth sharing how this
> can be done...
> Though this might be pretty trivial technically speaking, it can open up
> interesting ways of collaborating on projects.
>
> -jelle
>
>
> ================================================
> from OCC.Utils.Topology import Topo
>
> __author__ = 'jdf'
>
>
> from sqlalchemy import *
> from sqlalchemy.ext.declarative import declarative_base
> from sqlalchemy.orm import sessionmaker, scoped_session
>
> from OCC.Utils.Construct import make_cube
> #from OCC.TopoDS import TopoDS_Shape
> #
> #TopoDS_Shape.__eq__ = lambda x: TopoDS_Shape.IsEqual(x)
>
> cube = make_cube(1,1,1)
>
> engine = create_engine('sqlite:///jelle.db')
> Base = declarative_base(bind=engine)
> Session = scoped_session(sessionmaker(engine))
>
> def comp_brep(brepA, brepB):
> import ipdb; ipdb.set_trace()
> print 'jajajaj',brepB, brepA
> return brepA.IsEqual(brepB)
>
> class Individual(Base):
> '''used to store data about an individual
> a row forms a generation
> '''
> __tablename__ = 'individual'
>
> id = Column(Integer, primary_key=True)
> #name = Column(String)
> #fullname = Column(String)
> #password = Column(String)
> generation = Column(Integer)
> ancestor_a = Column(PickleType())
> ancestor_b = Column(PickleType())
> brep = Column(PickleType(mutable=False)) #comparator=comp_brep)) #) )
> fitness = Column(Float)
>
> Base.metadata.create_all()
>
>
> indy = Individual()
> indy.ancestor_a = 'parent_a'
> indy.fitness = 12.
> indy.ancestor_b = 'parent_a'
> indy.generation = 1
> #indy.id = 0
> indy.brep = cube
>
> '''
> to INSERT many rows very quickly, use the "executemany" style of
> insertion:
>
>
> connection.execute(table.insert(), [{'foo':'row1'}, {'foo':'row2'},
> {'foo':'row3'}, ...])
> '''
>
> s = Session()
> # s.begin()
> s.add(indy)
> s.commit()
> s.close()
>
> print('closed the previous db session\nlet\'s see if we can read back the
> objects, that be cool...')
>
> engine = create_engine('sqlite:///jelle.db', echo=True)
> Base = declarative_base(bind=engine)
> Session = scoped_session(sessionmaker(engine))
> s = Session()
>
> from OCC.BRep import BRep_Tool
> bt = BRep_Tool().Pnt
>
> for indy in s.query(Individual):
> print indy.id
> for i in Topo(indy.brep).vertices():
> print bt(i).Coord()
> ================================================
>
>
>
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