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.
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