On Jan 15, 3:53 pm, George Sakkis <[EMAIL PROTECTED]> wrote:
> On Jan 14, 7:56 pm, Luke <[EMAIL PROTECTED]> wrote:
>
>
>
> > I am writing an order management console. I need to create an import
> > system that is easy to extend. For now, I want to accept an dictionary
> > of values and map them to my data model. The thing is, I need to do
> > things to certain columns:
>
> > - I need to filter some of the values (data comes in as YYYY-MM-
> > DDTHH:MM:SS-(TIMEZONE-OFFSET) and it needs to map to Order.date as a
> > YYYY-MM-DD field)
> > - I need to map parts of an input column to more than one model param
> > (for instance if I get a full name for input--like "John Smith"--I
> > need a function to break it apart and map it to
> > Order.shipping_first_name and Order.shipping_last_name)
> > - Sometimes I need to do it the other way too... I need to map
> > multiple input columns to one model param (If I get a shipping fee, a
> > shipping tax, and a shipping discount, I need them added together and
> > mapped to Order.shipping_fee)
>
> > I have begun this process, but I'm finding it difficult to come up
> > with a good system that is extensible and easy to understand. I won't
> > always be the one writing the importers, so I'd like it to be pretty
> > straight-forward. Any ideas?
>
> > Oh, I should also mention that many times the data will map to several
> > different models. For instance, the importer I'm writing first would
> > map to 3 different models (Order, OrderItem, and OrderCharge)
>
> > I am not looking for anybody to write any code for me. I'm simply
> > asking for inspiration. What design patterns would you use here? Why?
>
> The specific transformations you describe are simple to be coded
> directly but unless you constrain the set of possible transformations
> that can take place, I don't see how can this be generalized in any
> useful way. It just seems too open-ended.
>
> The only pattern I can see here is breaking down the overall
> transformation to independent steps, just like the three you
> described. Given some way to specify each separate transformation,
> their combination can be factored out. To illustrate, here's a trivial
> example (with dicts for both input and output):
>
> class MultiTransformer(object):
> def __init__(self, *tranformers):
> self._tranformers = tranformers
>
> def __call__(self, input):
> output = {}
> for t in self._tranformers:
> output.update(t(input))
> return output
>
> date_tranformer = lambda input: {'date' : input['date'][:10]}
> name_tranformer = lambda input: dict(
> zip(('first_name', 'last_name'),
> input['name']))
> fee_tranformer = lambda input: {'fee' : sum([input['fee'],
> input['tax'],
> input['discount']])}
> tranformer = MultiTransformer(date_tranformer,
> name_tranformer,
> fee_tranformer)
> print tranformer(dict(date='2007-12-22 03:18:99-EST',
> name='John Smith',
> fee=30450.99,
> tax=459.15,
> discount=985))
> # output
> #{'date': '2007-12-22', 'fee': 31895.140000000003,
> 'first_name': #'J', 'last_name': 'o'}
>
> You can see that the MultiTransformer doesn't buy you much by itself;
> it just allows dividing the overall task to smaller bits that can be
> documented, tested and reused separately. For anything more
> sophisticated, you have to constrain what are the possible
> transformations that can happen. I did something similar for
> transforming CSV input rows (http://pypi.python.org/pypi/csvutils/) so
> that it's easy to specify 1-to-{0,1} transformations but not 1-to-many
> or many-to-1.
>
> HTH,
> George
thank you that is very helpful. I will ponder that for a while :)
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