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?
-รก.
[1]
http://pytables.github.com/usersguide/libref.html?highlight=vlstring#tables.Table.where
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