I spoke too soon, this code fails with the example you gave:
def test_concatenate(self): unit_ary_1 = UnitArray(numpy.array((1,2,3)), units=meters) unit_ary_2 = UnitArray(numpy.array((1,2,3)), units=meters)
# create another instance with different units. This instance # is never used, but calls __new__ with different units unit_ary_3 = UnitArray(numpy.array((1,2,3)), units=feet)
new_unit_ary = numpy.concatenate([unit_ary_1, unit_ary_2]) self.assertEqual(new_unit_ary.units, meters)
Any other ideas?
Bryce
Pierre GM wrote:
On Wednesday 28 March 2007 12:42:52 Bryce Hendrix wrote:
Thanks Pierre, works like a charm. One question though, how is defining a class attribute in __new__ any more thread-safe?
It's not, of course, and that's why it shouldn't be used. However, it's quite convenient and easier to use, and do you really need thread-safe objects ? In your example, if "yourdefaultunit" takes some simple value, you could use that value directly instead of the class attributes, which could be slightly messier to read but definitely thread-safe.
The second aspect about initialization is that when a ndarray is viewed as one of its subclasses, the actual memory space has already been allocated, so there's no call to __new__. Instead, you have to rely on __array_finalize__ to initialize the attributes specific to your subclass. _______________________________________________ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion