Hi list,
PyTables 2.1.2 with HDF5 1.6.6 and both NumPy 1.3.0 and 1.4.0 on 32-bit
vs
PyTables 2.1.2 with HDF5 1.8.3 and NumPy 1.4.0rc1 on 64-bit
I have lots of clients interpreting detector data and sending pickled
objects over http to a server, which uses PyTables to store it (yes, I'm
very happy now that everything's running; it works very well!). I have a
bug on the client which I didn't notice until I had a crash on my dev
machine. I ultimately found an inconsistency in PyTables.
On the client, I'm interpreting signed 32-bit integers as unsigned ones
(ouch). So, a value of -999 (error flag) is interpreted as 2**32 - 999 =
4294966297L. This value is received by the server, which tries to store
it in a Int32Col.
On 64-bit: no problem! The stored value is actually -999, so some
conversion takes place. This is why I didn't notice.
On 32-bit: in tables.tableExtension.Row.__setitem__(): invalid type
(<type 'long'>) for column ``col``
On 32-bit, I tested with the distributions NumPy (1.3.0) and PyPI's
NumPy (1.4.0), both with the same result.
This shouldn't be, right? I'm not sure if it is NumPy or PyTables...
Thanks,
David
>>> import tables
>>> class MyTable(tables.IsDescription):
... col = tables.Int32Col()
...
>>> data = tables.openFile('/tmp/test.h5', 'w')
>>> data.createTable('/', 'test', MyTable)
/test (Table(0,)) ''
description := {
"col": Int32Col(shape=(), dflt=0, pos=0)}
byteorder := 'little'
chunkshape := (2048,)
>>> row = data.root.test.row
>>> row['col'] = 2**32 - 999
------------------------------------------------------------
Traceback (most recent call last):
File "<ipython console>", line 1, in <module>
File "tableExtension.pyx", line 1309, in
tables.tableExtension.Row.__setitem__
TypeError: invalid type (<type 'long'>) for column ``col``
------------------------------------------------------------------------------
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