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##############################################################################
# MDTraj: A Python Library for Loading, Saving, and Manipulating
# Molecular Dynamics Trajectories.
# Copyright 2012-2017 Stanford University and the Authors
#
# Authors: Robert McGibbon
# Contributors:
#
# MDTraj is free software: you can redistribute it and/or modify
# it under the terms of the GNU Lesser General Public License as
# published by the Free Software Foundation, either version 2.1
# of the License, or (at your option) any later version.
#
# This library is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Lesser General Public License for more details.
#
# You should have received a copy of the GNU Lesser General Public
# License along with MDTraj. If not, see <http://www.gnu.org/licenses/>.
##############################################################################
import itertools
import numpy as np
import pytest
import mdtraj as md
from mdtraj.geometry.distance import (
_displacement,
_displacement_mic,
compute_displacements,
compute_distances,
compute_distances_core,
compute_distances_t,
find_closest_contact,
)
from mdtraj.testing import assert_allclose, eq
class Test_Distance:
def test_generator(self, gen_random_ptraj):
self.pairs2 = itertools.combinations(range(self.N_ATOMS), 2)
a = compute_distances(self.ptraj, self.pairs)
b = compute_distances(self.ptraj, self.pairs2)
eq(a, b)
def test_0(self, gen_random_ptraj):
a = compute_distances(self.ptraj, self.pairs, periodic=False, opt=True)
b = compute_distances(self.ptraj, self.pairs, periodic=False, opt=False)
eq(a, b)
def test_compute_distances_core_nonperiodic(self, gen_random_ptraj):
a = compute_distances(self.ptraj, self.pairs, periodic=False, opt=True)
b = compute_distances_core(
self.ptraj.xyz,
self.pairs,
unitcell_vectors=self.ptraj.unitcell_vectors,
periodic=False,
opt=True,
)
eq(a, b)
a = compute_distances(self.ptraj, self.pairs, periodic=False, opt=False)
b = compute_distances_core(
self.ptraj.xyz,
self.pairs,
unitcell_vectors=self.ptraj.unitcell_vectors,
periodic=False,
opt=False,
)
eq(a, b)
def test_compute_distances_core_periodic(self, gen_random_ptraj):
# opt
a = compute_distances(self.ptraj, self.pairs, periodic=True, opt=True)
b = compute_distances_core(
self.ptraj.xyz,
self.pairs,
unitcell_vectors=self.ptraj.unitcell_vectors,
periodic=True,
opt=True,
)
eq(a, b, decimal=3)
# no-opt
a = compute_distances(self.ptraj, self.pairs, periodic=True, opt=False)
b = compute_distances_core(
self.ptraj.xyz,
self.pairs,
unitcell_vectors=self.ptraj.unitcell_vectors,
periodic=True,
opt=False,
)
eq(a, b, decimal=3)
def test_1(self, gen_random_ptraj):
a = compute_displacements(self.ptraj, self.pairs, periodic=False, opt=True)
b = compute_displacements(self.ptraj, self.pairs, periodic=False, opt=False)
eq(a, b)
def test_2(self, gen_random_ptraj):
a = compute_distances(self.ptraj, self.pairs, periodic=False, opt=False)
b = compute_displacements(self.ptraj, self.pairs, periodic=False, opt=False)
eq(a, np.sqrt(np.sum(np.square(b), axis=2)))
def test_3(self, gen_random_ptraj):
a = compute_distances(self.ptraj, self.pairs, periodic=False, opt=True)
b = compute_displacements(self.ptraj, self.pairs, periodic=False, opt=True)
eq(a, np.sqrt(np.sum(np.square(b), axis=2)))
def test_0p(self, gen_random_ptraj):
a = compute_distances(self.ptraj, self.pairs, periodic=True, opt=True)
b = compute_distances(self.ptraj, self.pairs, periodic=True, opt=False)
eq(a, b, decimal=3)
def test_1p(self, gen_random_ptraj):
a = compute_displacements(self.ptraj, self.pairs, periodic=True, opt=True)
b = compute_displacements(self.ptraj, self.pairs, periodic=True, opt=False)
eq(a, b, decimal=3)
def test_2p(self, gen_random_ptraj):
a = compute_distances(self.ptraj, self.pairs, periodic=True, opt=False)
b = compute_displacements(self.ptraj, self.pairs, periodic=True, opt=False)
assert a.shape == (len(self.ptraj), len(self.pairs))
assert b.shape == (len(self.ptraj), len(self.pairs), 3), str(b.shape)
b = np.sqrt(np.sum(np.square(b), axis=2))
eq(a, b, decimal=5)
def test_3p(self, gen_random_ptraj):
a = compute_distances(self.ptraj, self.pairs, periodic=True, opt=True)
b = compute_displacements(self.ptraj, self.pairs, periodic=True, opt=True)
eq(a, np.sqrt(np.sum(np.square(b), axis=2)))
def test_4(self, gen_random_ptraj):
# using a really big box, we should get the same results with and without
# pbcs
box = np.array([[100, 0, 0], [0, 200, 0], [0, 0, 300]])
box = np.zeros((self.N_FRAMES, 3, 3)) + box # broadcast it out
a = _displacement_mic(self.xyz, self.pairs, box, False)
b = _displacement(self.xyz, self.pairs)
eq(a, b, decimal=3)
def test_5(self, gen_random_ptraj):
# simple wrap around along the z axis.
xyz = np.array([[[0.0, 0.0, 0.0], [0.0, 0.0, 2.2]]])
box = np.eye(3, 3).reshape(1, 3, 3)
result = _displacement_mic(xyz, np.array([[0, 1]]), box, True)
eq(result, np.array([[[0, 0, 0.2]]]))
def test_6(self, get_fn):
ext_ref = (
np.array(
[
17.4835,
22.2418,
24.2910,
22.5505,
12.8686,
22.1090,
7.4472,
22.4253,
19.8283,
20.6935,
],
)
/ 10
)
traj = md.load(get_fn("test_good.nc"), top=get_fn("test.parm7"))
self._run_amber_traj(traj, ext_ref)
def test_7(self, get_fn):
ext_ref = (
np.array(
[
30.9184,
23.9040,
25.3869,
28.0060,
25.9704,
24.6836,
23.0508,
27.1983,
24.4954,
26.7448,
],
)
/ 10
)
traj = md.load(get_fn("test_bad.nc"), top=get_fn("test.parm7"))
self._run_amber_traj(traj, ext_ref)
def _run_amber_traj(self, traj, ext_ref):
# Test triclinic case where simple approach in Tuckerman text does not
# always work
distopt = md.compute_distances(traj, [[0, 9999]], opt=True)
distslw = md.compute_distances(traj, [[0, 9999]], opt=False)
dispopt = md.compute_displacements(traj, [[0, 9999]], opt=True)
dispslw = md.compute_displacements(traj, [[0, 9999]], opt=False)
eq(distopt, distslw, decimal=5)
eq(dispopt, dispslw, decimal=5)
assert_allclose(distopt.flatten(), ext_ref, atol=2e-5)
# Make sure distances from displacements are the same
eq(np.sqrt((dispopt.squeeze() ** 2).sum(axis=1)), distopt.squeeze())
eq(np.sqrt((dispslw.squeeze() ** 2).sum(axis=1)), distslw.squeeze())
eq(dispopt, dispslw, decimal=5)
def test_closest_contact(self, gen_random_ptraj):
box_size = np.array([3.0, 4.0, 5.0])
traj = md.Trajectory(xyz=self.xyz * box_size, topology=None)
self._verify_closest_contact(gen_random_ptraj, traj)
traj.unitcell_lengths = np.array([box_size for i in range(self.N_FRAMES)])
traj.unitcell_angles = np.array([[90.0, 90.0, 90.0] for i in range(self.N_FRAMES)])
self._verify_closest_contact(gen_random_ptraj, traj=traj)
traj.unitcell_angles = np.array([[80.0, 90.0, 100.0] for i in range(self.N_FRAMES)])
self._verify_closest_contact(gen_random_ptraj, traj=traj)
def _verify_closest_contact(self, gen_random_ptraj, traj):
group1 = np.array([i for i in range(self.N_ATOMS // 2)], dtype=int)
group2 = np.array([i for i in range(self.N_ATOMS // 2, self.N_ATOMS)], dtype=int)
contact = find_closest_contact(traj, group1, group2)
pairs = np.array([(i, j) for i in group1 for j in group2], dtype=int)
dists = md.compute_distances(traj, pairs, True)[0]
_ = md.compute_distances(traj, pairs, False)[0]
nearest = np.argmin(dists)
eq(float(dists[nearest]), contact[2], decimal=5)
assert (pairs[nearest, 0] == contact[0] and pairs[nearest, 1] == contact[1]) or (
pairs[nearest, 0] == contact[1] and pairs[nearest, 1] == contact[0]
)
def test_distance_nan(self):
xyz = np.array([[1, 1, 1], [2, 1, 1], [np.nan, np.nan, np.nan]]).reshape(1, 3, 3)
dists = md.compute_distances(md.Trajectory(xyz=xyz, topology=None), [[0, 1]])
assert np.isfinite(dists).all()
def test_closest_contact_nan_pos(self, gen_random_ptraj):
box_size = np.array([3.0, 4.0, 5.0])
xyz = self.rng.standard_normal((2, 20, 3), dtype=np.float32)
xyz *= box_size
# Set the last frame to nan
xyz[-1] = np.nan
# Slice of the last frame, so nans should not cause troubles.
xyz = xyz[:-1]
traj = md.Trajectory(xyz=xyz, topology=None)
self._verify_closest_contact(gen_random_ptraj, traj)
def test_distance_t_inputs(self, gen_random_ptraj):
incorrect_pairs = np.array((0, self.ptraj.n_atoms + 1))
with pytest.raises(ValueError, match="atom_pairs"):
compute_distances_t(self.ptraj, incorrect_pairs, self.times)
incorrect_times = np.array((0, self.ptraj.n_frames + 1))
with pytest.raises(ValueError, match="time_pairs"):
compute_distances_t(self.ptraj, self.pairs, incorrect_times)
def test_distances_t(self, gen_random_ptraj):
a = compute_distances_t(self.ptraj, self.pairs, self.times, periodic=True, opt=True)
b = compute_distances_t(self.ptraj, self.pairs, self.times, periodic=True, opt=False)
# There is a precision difference between cython and python code for ``compute_distances_t``
# on linux-aarch64 for certain seeds (e.g. seed=3220). Pinning for a lower precision check.
eq(a, b, decimal=4)
c = compute_distances_t(self.ptraj, self.pairs, self.times, periodic=False, opt=True)
d = compute_distances_t(self.ptraj, self.pairs, self.times, periodic=False, opt=False)
eq(c, d, decimal=5)
def test_distances_t_at_0(self, gen_random_ptraj):
self.times = np.array([[0, 0]], dtype=np.int32)
a = compute_distances_t(self.ptraj, self.pairs, self.times, periodic=True, opt=True)
b = compute_distances_t(self.ptraj, self.pairs, self.times, periodic=True, opt=False)
c = compute_distances(self.ptraj[:1], self.pairs, periodic=True, opt=True)
d = compute_distances(self.ptraj[:1], self.pairs, periodic=True, opt=False)
eq(a, c)
eq(b, d)
def _run_amber_traj_t(self, traj, ext_ref):
# Test triclinic case where simple approach in Tuckerman text does not
# always work
_ = compute_distances_t(
traj,
atom_pairs=[[0, 9999]],
time_pairs=[[0, 2]],
opt=True,
)
_ = compute_distances_t(
traj,
atom_pairs=[[0, 9999]],
time_pairs=[[0, 2]],
opt=False,
)
def test_amber_t(self, get_fn):
ext_ref = (
np.array(
[
17.4835,
22.2418,
24.2910,
22.5505,
12.8686,
22.1090,
7.4472,
22.4253,
19.8283,
20.6935,
],
)
/ 10
)
traj = md.load(get_fn("test_good.nc"), top=get_fn("test.parm7"))
self._run_amber_traj_t(traj, ext_ref)
|
