Note: I think datetime64 is doing the right thing. So I'll just leave the post up in case it's useful.

As of numpy 1.7.0, seconds passed in to a np.datetime64 are interpreted as being in a local timezone. Is there a clean and fast way to import a unix utc seconds to np.datetime64? I've got arrays with 50M of these and it seems that there should be a way to tell np.datetime64 that my seconds value is UTC, no?

```
datetime.datetime.utcfromtimestamp(1338624706)
datetime.datetime(2012, 6, 2, 8, 11, 46)  # this is the time I'm looking for

np.datetime64(1338624706, 's')
numpy.datetime64('2012-06-02T01:11:46-0700')  # Darn you ISO!  Off by 7 hours

dt64 = np.datetime64(1338624706, 's')
dt64.astype(datetime.datetime)
datetime.datetime(2012, 6, 2, 8, 11, 46)  # Wait, did it do the right thing?

# This seems like the best option at the moment,
# but requires building datetime.datetime objects:
dt64 = np.datetime64(datetime.datetime.utcfromtimestamp(1338624706))
numpy.datetime64('2012-06-02T01:11:46.000000-0700') # Show this
dt64.astype(datetime.datetime)
datetime.datetime(2012, 6, 2, 8, 11, 46)  # Looks like it worked

```

I really do not want to resort to string operations. I would be nice to be able to convert an array of unix utc ints or floats straight to the correct dt64.

<https://stackoverflow.com/a/13704307/417578> implies that numpy 1.8.0 might do what I want, but is there something that will work in 1.7.0?