enstat allows you to compute the average (and variance) of chunked data, without the need to load all data at once. This done by keeping in memory the sum of the first (and second) statistical moment, as well as the normalisation. A common practical application is computing the average of an ensemble of realisations.
A simple example: Suppose that we have 100 realisations each with 1000 blocks, and we want to know the ensemble average of each block:
import enstat ensemble = enstat.static() for realisation in range(100): sample = np.random.random(1000) ensemble.add_sample(sample) mean = ensemble.mean() print(mean.shape)
Note that enstat is very much aimed as user friendliness, as it keeps track of shapes by itself, without the need to pre-specify.