Would it be an option to have
* raw data on one table
* all imaginable columns used for query conditions in another table
(but how to grow it in columns without deleting & recreating?)
and fetch indexes for the first based on .whereList(condition) of the second?
Are there alternatives?
-á.
On Mon, Mar 26, 2012 at 18:29, Alvaro Tejero Cantero <alv...@minin.es> wrote:
> Hi there,
>
> I am following advice by Anthony and giving a go at representing
> different sensors in my dataset as columns in a Table, or in several
> Tables. This is about in-kernel queries.
>
> The documentation of condvars in Table.where [1] says "condvars should
> consist of identifier-like strings pointing to Column (see The Column
> class) instances of this table, or to other values (which will be
> converted to arrays)".
>
> Conversion to arrays will likely exhaust the memory and be slow.
> Furthermore, when I tried with a toy example (naively extrapolating
> the behaviour of indexing in numpy), I obtained
>
> In [109]: valuesext = [x['V01'] for x in tet1.where("""(b>18) &
> (a<4)""", condvars={'a':tet1.cols.V01,'b':tet2.cols.V02})]
>
> (... elided output)
> ValueError: variable ``b`` refers to a column which is not part of
> table ``/tetrode1
>
> I am interested in the scenario where an in-kernel query is applied to
> a table based in columns *from other tables* that still are aligned
> with the current table (same number of elements). These conditions may
> be sophisticated and mix columns from the local table as well.
>
> One obvious solution would be to put all aligned columns on the same
> table. But adding columns to a table is cumbersome, and I cannot think
> beforehand of the many precomputed columns that I would like to use as
> query conditions.
>
> What do you recommend in this scenario?
>
> -á.
>
> [1]
> http://pytables.github.com/usersguide/libref.html?highlight=vlstring#tables.Table.where
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