def test_add_samples(self):
samples = norm.rvs(loc=0,
scale=2,
size=100,
random_state=np.random.RandomState(2))
distro = Distribution(samples)
distro.add_samples(samples)
assert_equal(distro.size, 200)
def _sample_until_normal(array, n_samples, n_resamples, max_resamples, confidence_interval):
"""
Resample from the distribution until a normal distribution is obtained or a maximum is reached.
Args:
array (:py:attr:`array_like`): The array to sample from.
n_samples (:py:attr:`int`): Number of samples.
r_resamples (:py:attr:`int`): Number of resamples to perform initially.
max_resamples (:py:attr:`int`): The maximum number of resamples to perform.
confidence_interval (:py:attr:`array_like`): The percentile points of the distribution that should be stored.
Returns:
:py:class:`uravu.distribution.Distribution`: The resampled distribution.
"""
distro = Distribution(_bootstrap(array.flatten(), n_samples, n_resamples), ci_points=confidence_interval)
while (not distro.normal) and distro.size < max_resamples:
distro.add_samples(_bootstrap(array.flatten(), n_samples, 100))
if distro.size >= max_resamples:
warnings.warn("The maximum number of resamples has been reached, and the distribution is not yet normal.")
return distro