add negative seconds using numpy date time

Question

I'm trying to add an array of time offsets (in seconds, which can be both positive and negative) to a constant timestamp using numpy.

numpy version is 1.19.1, python = 3.7.4

If "offsets" is all positive numbers, things work just fine:

time0 = numpy.datetime64("2007-04-03T15:06:48.032208Z")
offsets = numpy.arange(0, 10)
time = offsets.astype("datetime64[s]")
time2 = time0 + time

But, if offsets includes some negative numbers:

offsets = numpy.arange(-5, 5)
time = offsets.astype("datetime64[s]")
time2 = time0 + time

Traceback (most recent call last): File "", line 1, in numpy.core._exceptions.UFuncTypeError: ufunc 'add' cannot use operands with types dtype('<M8[ms]') and dtype('<M8[s]')

How do I deal with an offsets array that can contain both positive and negative numbers?

Any insight appreciated, I'm stumped here.

Catherine

Answer 1

The error tells you that you cannot perform addition of two dates (two datetime64[ns] objects). As you can imagine, adding say May 12 and May 19 together does not make logical sense. Running your first example produces the same error in my environment, even with only positive values in the offsets array.

Instead, you can convert your offsets values into timedelta values:

import numpy

time0 = numpy.datetime64("2007-04-03T15:06:48.032208Z")
offsets = numpy.arange(0, 10)
time = offsets.astype(numpy.timedelta64(1, "s"))
time2 = time0 + time
print(time2)

# ['2007-04-03T15:06:48.032208' '2007-04-03T15:06:49.032208'
# '2007-04-03T15:06:50.032208' '2007-04-03T15:06:51.032208'
# '2007-04-03T15:06:52.032208' '2007-04-03T15:06:53.032208'
# '2007-04-03T15:06:54.032208' '2007-04-03T15:06:55.032208'
# '2007-04-03T15:06:56.032208' '2007-04-03T15:06:57.032208']

offsets = numpy.arange(-5, 5)
time = offsets.astype(numpy.timedelta64(1, "s"))
time2 = time0 + time
print(time2)

# ['2007-04-03T15:06:43.032208' '2007-04-03T15:06:44.032208'
# '2007-04-03T15:06:45.032208' '2007-04-03T15:06:46.032208'
# '2007-04-03T15:06:47.032208' '2007-04-03T15:06:48.032208'
# '2007-04-03T15:06:49.032208' '2007-04-03T15:06:50.032208'
# '2007-04-03T15:06:51.032208' '2007-04-03T15:06:52.032208']