The most similar example to what I want to do, I think is here: http://groups.google.com/group/sqlalchemy/browse_thread/thread/81cc56d5ed693a48/58a3caa40a7daa39?lnk=gst&q=TypeDecorator#58a3caa40a7daa39 where Frank is storing nutrient weights in a new type.
After (re)reading the doco about custom types at: http://www.sqlalchemy.org/docs/05/reference/sqlalchemy/types.html#sqlalchemy.types.TypeDecorator it seems to me that the reason for using TypeDecorator rather than TypeEngine is something to do with this sentence: "The reason that type behavior is modified using class decoration instead of subclassing is due to the way dialect specific types are used." I suppose there are some complexities with bind_params implementations across dialects. eg. http://www.sqlite.org/c3ref/bind_blob.html In Frank's example he defined two classes: Weight(Object) and WeightType(types.TypeDecorator) but it still seems more natural to me to use the subclassing of TypeEngine to create a new compound type from two(or three) FLOATs. Then I might have to write methods overriding adapt_operator and compare_values and copy(value) but at least I'd understand what I was doing and why. However as TypeDecorator seems to be the recommended way, perhaps I should go with the flow. But what about the adapt_operator, compare_values etc? Will that all somehow be taken care of automagically if I do something like this? Numpy seems to have gained quite a bit of acceptance so I want pull small objects of type "np.array([x,y], np.float32)" straight out of the database and use them directly. Is this possible? Is it sensible? import numpy as np class Vector(object): __slots__ = ['x', 'y'] def __init__(self, x, y): self.x = x self.y = y """ def __repr__(self): return np.array([x,y], np.float32) """ #Both Sage and Numpy seem to prefer a list eg. V([x,y]) #as the instantiation input rather than a python tuple. def __repr__(self): return "[%s, %s]" % (self.x, self.y) class VectorType(types.TypeDecorator): impl = types.Numeric def process_bind_param(self, vector, dialect): if vector is None: return None return [vector.x, vector.y] def process_result_value(self, vector, dialect): if x is None: return None #return Vector(x, y) #or... return np.array([vector.x, vector.y], np.float32) -- You received this message because you are subscribed to the Google Groups "sqlalchemy" group. To post to this group, send email to sqlalchemy@googlegroups.com. To unsubscribe from this group, send email to sqlalchemy+unsubscr...@googlegroups.com. For more options, visit this group at http://groups.google.com/group/sqlalchemy?hl=en.
