RecSQL’s basic idea is to treat numpy record arrays like SQL tables. What it does, in fact, is to represent the arrays as real SQL tables (using SQLite) and provide convenience functions to return recarrays on demand.
This works ok for small tables but less so if you want to access gigabytes of data as recarrays.
>>> from recsql import SQLarray
>>> import numpy
>>> a = numpy.rec.fromrecords(numpy.arange(100).reshape(25,4), names='a,b,c,d')
>>> Q = SQLarray('my_name', a)
>>> print repr(Q.recarray)
rec.array([(0, 1, 2, 3), (4, 5, 6, 7), (8, 9, 10, 11), (12, 13, 14, 15),
(16, 17, 18, 19), (20, 21, 22, 23), (24, 25, 26, 27),
(28, 29, 30, 31), (32, 33, 34, 35), (36, 37, 38, 39),
(40, 41, 42, 43), (44, 45, 46, 47), (48, 49, 50, 51),
(52, 53, 54, 55), (56, 57, 58, 59), (60, 61, 62, 63),
(64, 65, 66, 67), (68, 69, 70, 71), (72, 73, 74, 75),
(76, 77, 78, 79), (80, 81, 82, 83), (84, 85, 86, 87),
(88, 89, 90, 91), (92, 93, 94, 95), (96, 97, 98, 99)],
dtype=[('a', '<i4'), ('b', '<i4'), ('c', '<i4'), ('d', '<i4')])
>>> Q.SELECT('*', 'WHERE a < 10 AND b > 5')
rec.array([(8, 9, 10, 11)],
dtype=[('a', '<i4'), ('b', '<i4'), ('c', '<i4'), ('d', '<i4')])
# creating new SQLarrays:
>>> R = Q.selection('a < 20 AND b > 5')
>>> print R
<recsql.sqlarray.SQLarray object at 0x...>
The latest version of the package is being made available via the internet-thingy at http://sbcb.bioch.ox.ac.uk/users/oliver/software/RecSQL/ or from the direct download URI (for easy_install) http://sbcb.bioch.ox.ac.uk/users/oliver/download/Python/ .
See INSTALL for installation instructions.
A git repository of the package is hosted at http://github.com/orbeckst/RecSQL .
Oliver Beckstein <orbeckst@gmail.com>