Smaller variation between times of different days - algorithm

Question

I have working on a algorithm that select a set of date/time objects with a certain characteristic, but with no success.
The data to be used are in a list of lists of date/time objects,
e.g.:
lstDays[i][j], i <= day chooser, j <= time chooser
What is the problem? I need a set of nearest date/time objects. Each time of this set must come from different days.
For example: [2012-09-09 12:00,2012-09-10 12:00, 2012-09-11 12:00]
This example of a set of date/time objects is the best example because it minimize to zero.
Important
Trying to contextualize this: I want to observe if a phenomenon occurs at the same time in differents days. If not, I want to evaluate if distance between the hours is reasonable for my study.
I would like a generic algorithm to any number of days and time. This algorithm should return all set of datetime objects and its time distance:
[2012-09-09 12:00,2012-09-10 12:00, 2012-09-11 12:00], 0
[2012-09-09 13:00,2012-09-10 13:00, 2012-09-11 13:05], 5
and so on.
:: "0", because the diff between all times on the first line from datetime objects is zero seconds.
:: "5", because the diff between all times on the second line from datetime objects is five seconds.
Edit: Code here
for i in range(len(lstDays)):
for j in range(len(lstDays[i])):
print lstDays[i][j]
Output:
2013-07-18 11:16:00
2013-07-18 12:02:00
2013-07-18 12:39:00
2013-07-18 13:14:00
2013-07-18 13:50:00
2013-07-19 11:30:00
2013-07-19 12:00:00
2013-07-19 12:46:00
2013-07-19 13:19:00
2013-07-22 11:36:00
2013-07-22 12:21:00
2013-07-22 12:48:00
2013-07-22 13:26:00
2013-07-23 11:18:00
2013-07-23 11:48:00
2013-07-23 12:30:00
2013-07-23 13:12:00
2013-07-24 11:18:00
2013-07-24 11:42:00
2013-07-24 12:20:00
2013-07-24 12:52:00
2013-07-24 13:29:00
Note: lstDays[i][j] is a datetime object.
lstDays = [ [/*datetime objects from a day i*/], [/*datetime objects from a day i+1*/], [/*datetime objects from a day i+2/*], ... ]
And I am not worried with perfomance, a priori.
Hope that you can help me! (:

Answer 1

Generate a histogram:
hours = [0] * 24
for object in objects: # whatever your objects are
# assuming object.date_time looks like '2013-07-18 10:55:00'
hour = object.date_time[11:13] # assuming the hour is in positions 11-12
hours[int(hour)] += 1
for hour in xrange(24):
print '%02d: %d' % (hour, hours[hour])

Answer 2

You can always resort to calculating the times into a list, then estimate the differences, and group those objects that are below that limit. All packed into a dictionary with the difference as the value and the the timestamps as keys. If this is not exactly what you need, I'm pretty sure it should be easy to select whatever result you need from it.
import numpy
import datetime
times_list = [object1.time(), object2(), ..., objectN()]
limit = 5 # limit of five seconds
groups = {}
for time in times_list:
delta_times = numpy.asarray([(tt-time).total_seconds() for tt in times_list])
whr = numpy.where(abs(delta_times) < limit)[0]
similar = [str(times_list[ii]) for ii in whr]
if len(similar) > 1:
similar.sort()
max_time = numpy.max(delta_times[whr]) # max? median? mean?
groups[tuple(similar)] = max_time