def test_gaussian_date_generator(self):
""" Confirm that the generated dates are distributed according
to a Gaussian. We doe this assertion 10 times.
"""
expected_mean = datetime(2014, 6, 1)
expected_sigma_in_days = 100
gg = gaussian_date_generator(expected_mean, expected_sigma_in_days)
for _ in range(10):
distribution = [next(gg) for _ in range(100000)]
seconds = [convert_datetime_to_seconds(d) for d in distribution]
actual_mean = self.mean(seconds)
diff = actual_mean - convert_datetime_to_seconds(expected_mean)
diff_in_days = seconds_to_days(diff)
one_day = 1
# assert stuff
self.assertLessEqual(diff_in_days, one_day)
actual_sigma = self.standard_deviation(seconds)
diff = abs(actual_sigma - days_to_seconds(expected_sigma_in_days))
self.assertLessEqual(diff, days_to_seconds(1))
def gaussian_date_generator(mean, sigma_in_days):
"""
Yields a generator for datetime objects where the dates are distributed
according to a Gaussian, with standard deviation sigma. The distribution of
dates is generated by first generating a distribution of numbers (in seconds)
using the gaussian_generator defined above and then transforming those numbers
back to dates.
"""
mean_in_seconds = convert_datetime_to_seconds(mean)
sigma_in_seconds = days_to_seconds(sigma_in_days)
gg = gaussian_generator(mean_in_seconds, sigma_in_seconds)
while True:
seconds = next(gg)
yield datetime.fromtimestamp(seconds)
