Hello Ivan!
Thanks for your quick reply. To be more clear I am
doing some kind of a slice function where a user can
select different fields and one row or vise verse from
arbitrary number of tables. Since each field can have
different types I want to gather the data in a table
form. I thought of doing this with numpy arrays. If I
dump just one table with the flavor "numpy" I do get a
numpy array. That's what you did in pytables if I am
not wrong, so I wanted to do the same thing. I guess I
can use recarray unless you have a better idea for my
problem?
By the way, has anyone tried to implement any kind of
a slice function for arbitrary number of tables?
Best regards, Dragan.
--- Ivan Vilata i Balaguer <[EMAIL PROTECTED]>
wrote:
> dragan savic (el 2007-11-12 a les 08:36:25 -0800) va
> dir::
>
> > I have a question regarding numpy arrays.
> > Lets say I have two arrays:
> >
> > a = array([1,2,3],dtype=float32)
> > b = array([4,5,6],dtype=int16)
> >
> > I want to build a new array like this:
> > new_array = hstack((row_stack(a),row_stack(b)))
> >
> > The result is:
> > array([[ 1., 4.],
> > [ 2., 5.],
> > [ 3., 6.]], dtype=float32)
> >
> > What I was hoping was that the elements from the b
> > array remain int16. Is it possible to have
> homogeneous
> > array in numpy?
>
> Maybe you where looking for an *heterogeneous* array
> (i.e. record array
> or table)? In that case, the ``numpy.rec`` module
> is your friend:
>
> >>> numpy.rec.fromarrays((a, b))
> recarray([(1.0, 4), (2.0, 5), (3.0, 6)],
> dtype=[('f0', '<f4'), ('f1', '<i2')])
>
> (Or maybe you wanted to post to
> [EMAIL PROTECTED])
>
> ::
>
> Ivan Vilata i Balaguer >qo<
> http://www.carabos.com/
> Cárabos Coop. V. V V Enjoy Data
> ""
> >
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