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import numpy as np
from dynasor.sample import Sample
from typing import List
from copy import deepcopy
def get_sample_averaged_over_independent_runs(
samples: List[Sample], live_dangerously=False) -> Sample:
"""
Compute an averaged sample from multiple samples obtained from identical independent runs.
Note, all the meta_data and dimensions in all samples must be the same,
else ValueError is raised (unless ``live_dangerously`` is set to True).
Parameters
----------
samples
list of all sample objects to be averaged over
live_dangerously
setting True allows for averaging over samples which meta-data information is not identical.
"""
# get meta data and dimensions from first sample
sample_ref = samples[0]
data_dict = dict()
meta_data = deepcopy(sample_ref.meta_data)
# test that all samples have identical dimensions
for sample in samples:
assert sorted(sample.dimensions) == sorted(sample_ref.dimensions)
for dim in sample_ref.dimensions:
assert np.allclose(sample[dim], sample_ref[dim])
for dim in sample_ref.dimensions:
data_dict[dim] = sample_ref[dim]
# test that all samples have identical meta_data
if not live_dangerously:
for sample in samples:
assert len(sample.meta_data) == len(meta_data)
for key, val in meta_data.items():
if isinstance(val, dict):
for k, v in val.items():
assert sample_ref.meta_data[key][k] == sample.meta_data[key][k]
elif isinstance(val, np.ndarray):
assert np.allclose(sample.meta_data[key], val)
elif isinstance(val, float):
assert np.isclose(sample.meta_data[key], val)
else:
assert sample.meta_data[key] == val
# average all correlation functions
for key in sample.available_correlation_functions:
data = []
for sample in samples:
data.append(sample[key])
data_average = np.nanmean(data, axis=0)
data_dict[key] = data_average
return sample.__class__(data_dict, **meta_data)
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