I have some input data, with timestamps in the input file in the form of hours from the date time specified in the filename.
This is a bit useless, so I need to convert it to python datetime.datetime objects, and then put it in a numpy array. I could write a for loop, but I'd like to do something like:
numpy.arange(datetime.datetime(2000, 1,1), datetime.datetime(2000, 1,2), datetime.timedelta(hours=1))
which throws a TypeError.
Can this be done? I'm stuck with python 2.6 and numpy 1.6.1.
See NumPy Datetimes and Timedeltas. Since NumPy 1.7, you can represent datetimes in NumPy using the numpy.datetime64 type, which permits you to do ranges of values:
>>> np.arange(np.datetime64("2000-01-01"), np.datetime64("2000-01-02"), np.timedelta64(1, "h"))
array(['2000-01-01T00', '2000-01-01T01', '2000-01-01T02', '2000-01-01T03',
'2000-01-01T04', '2000-01-01T05', '2000-01-01T06', '2000-01-01T07',
'2000-01-01T08', '2000-01-01T09', '2000-01-01T10', '2000-01-01T11',
'2000-01-01T12', '2000-01-01T13', '2000-01-01T14', '2000-01-01T15',
'2000-01-01T16', '2000-01-01T17', '2000-01-01T18', '2000-01-01T19',
'2000-01-01T20', '2000-01-01T21', '2000-01-01T22', '2000-01-01T23'],
dtype='datetime64[h]')
For NumPy 1.6, which has a much less useful datetime64 type, you can use a suitable list comprehension to build the datetimes (see also Creating a range of dates in Python):
base = datetime.datetime(2000, 1, 1)
arr = numpy.array([base + datetime.timedelta(hours=i) for i in xrange(24)])
This produces
array([2000-01-01 00:00:00, 2000-01-01 01:00:00, 2000-01-01 02:00:00,
2000-01-01 03:00:00, 2000-01-01 04:00:00, 2000-01-01 05:00:00,
2000-01-01 06:00:00, 2000-01-01 07:00:00, 2000-01-01 08:00:00,
2000-01-01 09:00:00, 2000-01-01 10:00:00, 2000-01-01 11:00:00,
2000-01-01 12:00:00, 2000-01-01 13:00:00, 2000-01-01 14:00:00,
2000-01-01 15:00:00, 2000-01-01 16:00:00, 2000-01-01 17:00:00,
2000-01-01 18:00:00, 2000-01-01 19:00:00, 2000-01-01 20:00:00,
2000-01-01 21:00:00, 2000-01-01 22:00:00, 2000-01-01 23:00:00], dtype=object)