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###
import numpy as np
import pytest
from numpy.testing import assert_array_almost_equal
import MDAnalysis as md
from MDAnalysis.transformations import PositionAverager
from MDAnalysisTests import datafiles
@pytest.fixture()
def posaveraging_universes():
"""
Create the universe objects for the tests.
"""
u = md.Universe(datafiles.XTC_multi_frame, to_guess=())
transformation = PositionAverager(3)
u.trajectory.add_transformations(transformation)
return u
@pytest.fixture()
def posaveraging_universes_noreset():
"""
Create the universe objects for the tests.
Position averaging reset is set to False.
"""
u = md.Universe(datafiles.XTC_multi_frame, to_guess=())
transformation = PositionAverager(3, check_reset=False)
u.trajectory.add_transformations(transformation)
return u
def test_posavging_fwd(posaveraging_universes):
"""
Test if the position averaging function is returning the correct
values when iterating forwards over the trajectory.
"""
ref_matrix_fwd = np.asarray([80.0, 80.0, 80.0])
size = (
posaveraging_universes.trajectory.ts.positions.shape[0],
posaveraging_universes.trajectory.ts.positions.shape[1],
len(posaveraging_universes.trajectory),
)
avgd = np.empty(size)
for ts in posaveraging_universes.trajectory:
np.copyto(avgd[..., ts.frame], ts.positions)
assert_array_almost_equal(ref_matrix_fwd, avgd[1, :, -1], decimal=5)
def test_posavging_bwd(posaveraging_universes):
"""
Test if the position averaging function is returning the correct
values when iterating backwards over the trajectory.
"""
ref_matrix_bwd = np.asarray([10.0, 10.0, 10.0])
size = (
posaveraging_universes.trajectory.ts.positions.shape[0],
posaveraging_universes.trajectory.ts.positions.shape[1],
len(posaveraging_universes.trajectory),
)
back_avgd = np.empty(size)
for ts in posaveraging_universes.trajectory[::-1]:
np.copyto(back_avgd[..., 9 - ts.frame], ts.positions)
assert_array_almost_equal(ref_matrix_bwd, back_avgd[1, :, -1], decimal=5)
def test_posavging_reset(posaveraging_universes):
"""
Test if the automatic reset is working as intended.
"""
size = (
posaveraging_universes.trajectory.ts.positions.shape[0],
posaveraging_universes.trajectory.ts.positions.shape[1],
len(posaveraging_universes.trajectory),
)
avgd = np.empty(size)
for ts in posaveraging_universes.trajectory:
np.copyto(avgd[..., ts.frame], ts.positions)
after_reset = ts.positions.copy()
assert_array_almost_equal(avgd[..., 0], after_reset, decimal=5)
def test_posavging_specific(posaveraging_universes):
"""
Test if the position averaging function is returning the correct values
when iterating over arbitrary non-sequential frames.
check_reset=True
"""
ref_matrix_specr = np.asarray([30.0, 30.0, 30.0])
fr_list = [0, 1, 7, 3]
size = (
posaveraging_universes.trajectory.ts.positions.shape[0],
posaveraging_universes.trajectory.ts.positions.shape[1],
len(fr_list),
)
specr_avgd = np.empty(size)
idx = 0
for ts in posaveraging_universes.trajectory[fr_list]:
np.copyto(specr_avgd[..., idx], ts.positions)
idx += 1
assert_array_almost_equal(
ref_matrix_specr, specr_avgd[1, :, -1], decimal=5
)
def test_posavging_specific_noreset(posaveraging_universes_noreset):
"""
Test if the position averaging function is returning the correct values
when iterating over arbitrary non-sequential frames.
check_reset=False
"""
ref_matrix_specr = np.asarray([36.66667, 36.66667, 36.66667])
fr_list = [0, 1, 7, 3]
size = (
posaveraging_universes_noreset.trajectory.ts.positions.shape[0],
posaveraging_universes_noreset.trajectory.ts.positions.shape[1],
len(fr_list),
)
specr_avgd = np.empty(size)
idx = 0
for ts in posaveraging_universes_noreset.trajectory[fr_list]:
np.copyto(specr_avgd[..., idx], ts.positions)
idx += 1
assert_array_almost_equal(
ref_matrix_specr, specr_avgd[1, :, -1], decimal=5
)
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