On Thu, 05 Jun 2014, Nikolaas N. Oosterhof wrote:
>    In mvpa2/base/dataset.py:

>  >  # shortcut that allows for more finger/screen-friendly specification of
>  >  # attribute extraction
>  >  DAE = DatasetAttributeExtractor

>  > +class DatasetAttributeInjector(object):
>  > +    """Inject attributes into a dataset
>  > +
>  > +    This is useful for passing attributes into a dataset at any level
>  > +    of a processing pipeline -- even if they do not fit into the original
>  > +    dataset (shape).

>    Why is it useful to pass attributes when they do not fit the dataset
>    shape? Could you provide an example of a use case?

my tentative example could be:  original dataset is a full time series,
then you use some mapper to extract trials (thus changing # of samples)
and want to complement them with some additional features which might
e.g. depend on those trials data

I bet Michael have a better usecase ;)

-- 
Yaroslav O. Halchenko, Ph.D.
http://neuro.debian.net http://www.pymvpa.org http://www.fail2ban.org
Research Scientist,            Psychological and Brain Sciences Dept.
Dartmouth College, 419 Moore Hall, Hinman Box 6207, Hanover, NH 03755
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