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import numpy as np
import pytest
from dynasor.sample import DynamicSample
from dynasor.post_processing import get_sample_averaged_over_independent_runs
dynamic_correlation_functions = ['Fqt', 'Fqt_coh', 'Fqt_coh_A_A', 'Fqt_coh_A_B', 'Fqt_coh_B_B',
'Fqt_incoh', 'Fqt_incoh_A', 'Fqt_incoh_B',
'Sqw', 'Sqw_coh', 'Sqw_coh_A_A', 'Sqw_coh_A_B', 'Sqw_coh_B_B',
'Sqw_incoh', 'Sqw_incoh_A', 'Sqw_incoh_B']
def get_random_dynamic_data_dict(n_q, n_t):
""" generate a random data_dict """
data_dict = dict()
data_dict['q_points'] = np.linspace([0, 0, 0], [1.0, 1.0, 1.5], n_q)
data_dict['time'] = np.linspace(0, 10, n_t)
data_dict['omega'] = np.linspace(0, 3, n_t)
for name in dynamic_correlation_functions:
data_dict[name] = np.random.normal(0, 10, (n_q, n_t))
return data_dict
def get_random_dynamic_sample():
n_q = 50
n_t = 200
data_dict = get_random_dynamic_data_dict(n_q, n_t)
sample = DynamicSample(data_dict, **get_meta_data())
return sample
def get_meta_data():
meta_data = dict()
meta_data['cell'] = np.diag([11.5, 18.2, 10.1])
meta_data['atom_types'] = ['A', 'B']
meta_data['particle_counts'] = dict(A=100, B=250)
meta_data['pairs'] = [('A', 'A'), ('A', 'B'), ('B', 'B')]
return meta_data
def test_average_dynamic_samples():
# setup samples to average over
n_samples = 25
meta_data_ref = get_meta_data()
samples = [get_random_dynamic_sample() for _ in range(n_samples)]
# average
sample_ave = get_sample_averaged_over_independent_runs(samples)
assert isinstance(sample_ave, DynamicSample)
# check dimensions are correct
assert sorted(sample_ave.dimensions) == ['omega', 'q_points', 'time']
assert np.allclose(sample_ave.omega, samples[0].omega)
assert np.allclose(sample_ave.q_points, samples[0].q_points)
assert np.allclose(sample_ave.time, samples[0].time)
# check meta data is correct
assert len(sample_ave.meta_data) == 4
assert np.allclose(sample_ave.meta_data['cell'], meta_data_ref['cell'])
assert sample_ave.meta_data['particle_counts'] == meta_data_ref['particle_counts']
assert sample_ave.meta_data['atom_types'], meta_data_ref['atom_types']
assert sample_ave.meta_data['pairs'], meta_data_ref['pairs']
# check value of correlation functions is correct
for name in dynamic_correlation_functions:
average = np.mean([sample[name] for sample in samples], axis=0)
assert np.allclose(average, sample_ave[name])
def test_raises_error_with_inconsistent_samples():
n_samples = 25
# inconsistent meta_data
samples = [get_random_dynamic_sample() for _ in range(n_samples)]
samples[10].meta_data['cell'] = 4.19 * np.eye(3)
with pytest.raises(AssertionError):
get_sample_averaged_over_independent_runs(samples)
# inconsistent dimensions
samples = [get_random_dynamic_sample() for _ in range(n_samples)]
samples[4].q_points[0, 0] = 0.212354
with pytest.raises(AssertionError):
get_sample_averaged_over_independent_runs(samples)
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