class TestFitExponential(object):
def setUp(self):
self.x = np.linspace(0, 250, 251)
self.a_ref = 20.0
self.y = np.exp(-self.x/self.a_ref)
def tearDown(self):
del self.x
del self.a_ref
del self.y
@dec.skipif(module_not_found('scipy'),
"Test skipped because scipy is not available.")
def test_fit_simple(self):
a = polymer.fit_exponential_decay(self.x, self.y)
assert_(a == self.a_ref)
@dec.skipif(module_not_found('scipy'),
"Test skipped because scipy is not available.")
def test_fit_noisy(self):
noise = np.sin(self.x) * 0.01
y2 = noise + self.y
a = polymer.fit_exponential_decay(self.x, y2)
assert_almost_equal(a, self.a_ref, decimal=3)
class TestPersistenceLength(object):
def setUp(self):
self.u = MDAnalysis.Universe(Plength)
def tearDown(self):
del self.u
def test_ag_VE(self):
ags = [self.u.atoms[:10], self.u.atoms[10:110]]
assert_raises(ValueError, polymer.PersistenceLength, ags)
def _make_p(self):
ags = [r.atoms.select_atoms('name C* N*')
for r in self.u.residues]
p = polymer.PersistenceLength(ags)
return p
def test_run(self):
p = self._make_p()
p.run()
assert_(len(p.results) == 280)
assert_almost_equal(p.lb, 1.485, 3)
@dec.skipif(module_not_found('scipy'),
"Test skipped because scipy is not available.")
def test_fit(self):
p = self._make_p()
p.run()
p.perform_fit()
assert_almost_equal(p.lp, 6.504, 3)
assert_(len(p.fit) == len(p.results))
@dec.skipif(module_not_found('matplotlib'),
"Test skipped because matplotlib is not available.")
@dec.skipif(module_not_found('scipy'),
"Test skipped because scipy is not available.")
def test_plot_ax_return(self):
'''Ensure that a matplotlib axis object is
returned when plot() is called.'''
import matplotlib
p = self._make_p()
p.run()
p.perform_fit()
actual = p.plot()
expected = matplotlib.axes.Axes
assert_(isinstance(actual, expected))
def test_raise_NoDataError(self):
'''Ensure that a NoDataError is raised if
perform_fit() is called before the run()
method of AnalysisBase.'''
p = self._make_p()
assert_raises(NoDataError, p.perform_fit)
class TestPersistenceLength(object):
def setUp(self):
self.u = MDAnalysis.Universe(Plength)
def tearDown(self):
del self.u
def test_ag_VE(self):
ags = [self.u.atoms[:10], self.u.atoms[10:110]]
assert_raises(ValueError, polymer.PersistenceLength, ags)
def _make_p(self):
ags = [r.select_atoms('type C or type N') for r in self.u.residues]
p = polymer.PersistenceLength(ags)
return p
def test_run(self):
p = self._make_p()
p.run()
assert_(len(p.results) == 280)
assert_almost_equal(p.lb, 1.485, 3)
@dec.skipif(module_not_found('scipy'),
"Test skipped because scipy is not available.")
def test_fit(self):
p = self._make_p()
p.run()
p.perform_fit()
assert_almost_equal(p.lp, 6.504, 3)
assert_(len(p.fit) == len(p.results))
class TestNCDFReader2(TestCase):
"""NCDF Trajectory with positions and forces.
Contributed by Albert Solernou
"""
@dec.skipif(module_not_found("netCDF4"),
"Test skipped because netCDF is not available.")
def setUp(self):
self.u = mda.Universe(PFncdf_Top, PFncdf_Trj)
self.prec = 3
def tearDown(self):
self.u.trajectory.close()
del self.u
def test_positions_1(self):
"""Check positions on first frame"""
self.u.trajectory[0]
ref_1 = np.array([[-0.11980818, 18.70524979, 11.6477766],
[-0.44717646, 18.61727142, 12.59919548],
[-0.60952115, 19.47885513, 11.22137547]],
dtype=np.float32)
assert_array_almost_equal(ref_1, self.u.atoms.positions[:3], self.prec)
def test_positions_2(self):
"""Check positions on second frame"""
self.u.trajectory[1]
ref_2 = np.array([[-0.13042036, 18.6671524, 11.69647026],
[-0.46643803, 18.60186768, 12.646698],
[-0.46567637, 19.49173927, 11.21922874]],
dtype=np.float32)
assert_array_almost_equal(ref_2, self.u.atoms.positions[:3], self.prec)
def test_forces_1(self):
"""Check forces on first frame"""
self.u.trajectory[0]
ref_1 = np.array([[49.23017883, -97.05565643, -86.09863281],
[2.97547197, 29.84169388, 11.12069607],
[-15.93093777, 14.43616867, 30.25889015]],
dtype=np.float32)
assert_array_almost_equal(ref_1, self.u.atoms.forces[:3], self.prec)
def test_forces_2(self):
"""Check forces on second frame"""
self.u.trajectory[1]
ref_2 = np.array([[116.39096832, -145.44448853, -151.3155365],
[-18.90058327, 27.20145798, 1.95245135],
[-31.08556366, 14.95863628, 41.10367966]],
dtype=np.float32)
assert_array_almost_equal(ref_2, self.u.atoms.forces[:3], self.prec)
def test_time_1(self):
"""Check time on first frame"""
ref = 35.02
assert_almost_equal(ref, self.u.trajectory[0].time, self.prec)
def test_time_2(self):
"""Check time on second frame"""
ref = 35.04
assert_almost_equal(ref, self.u.trajectory[1].time, self.prec)
class Test_density_from_Universe(TestCase):
topology = TPR
trajectory = XTC
delta = 2.0
selections = {'static': "name OW",
'dynamic': "name OW and around 4 (protein and resnum 1-10)",
}
references = {'static':
{'meandensity': 0.016764271713091212, },
'static_sliced':
{'meandensity': 0.0067057088794023143, },
'dynamic':
{'meandensity': 0.00062423404854011104, },
}
precision = 5
@dec.skipif(module_not_found('scipy'),
"Test skipped because scipy is not available.")
def setUp(self):
self.outfile = 'density.dx'
self.universe = mda.Universe(self.topology, self.trajectory)
def tearDown(self):
del self.universe
def check_density_from_Universe(self, atomselection,
ref_meandensity, **kwargs):
import MDAnalysis.analysis.density
with tempdir.in_tempdir():
D = MDAnalysis.analysis.density.density_from_Universe(
self.universe, atomselection=atomselection,
delta=self.delta, **kwargs)
assert_almost_equal(D.grid.mean(), ref_meandensity,
err_msg="mean density does not match")
D.export(self.outfile)
D2 = MDAnalysis.analysis.density.Density(self.outfile)
assert_almost_equal(D.grid, D2.grid, decimal=self.precision,
err_msg="DX export failed: different grid sizes")
def test_density_from_Universe(self):
self.check_density_from_Universe(
self.selections['static'],
self.references['static']['meandensity'])
def test_density_from_Universe_sliced(self):
self.check_density_from_Universe(
self.selections['static'],
self.references['static_sliced']['meandensity'],
start=1, stop=-1, step=2,
)
def test_density_from_Universe_update_selection(self):
self.check_density_from_Universe(
self.selections['dynamic'],
self.references['dynamic']['meandensity'],
update_selections=True)
class TestLeafletFinder(TestCase):
@dec.skipif(module_not_found('scipy'),
"Test skipped because scipy is not available.")
def setUp(self):
self.universe = MDAnalysis.Universe(Martini_membrane_gro,
Martini_membrane_gro)
self.lipid_heads = self.universe.select_atoms("name PO4")
def tearDown(self):
del self.universe
def test_leaflet_finder(self):
from MDAnalysis.analysis.leaflet import LeafletFinder
lfls = LeafletFinder(self.universe, self.lipid_heads, pbc=True)
top_heads, bottom_heads = lfls.groups()
# Make top be... on top.
if top_heads.center_of_geometry()[2] < bottom_heads.center_of_geometry(
)[2]:
top_heads, bottom_heads = (bottom_heads, top_heads)
assert_equal(top_heads.indices,
np.arange(1, 2150, 12),
err_msg="Found wrong leaflet lipids")
assert_equal(bottom_heads.indices,
np.arange(2521, 4670, 12),
err_msg="Found wrong leaflet lipids")
class TestNCDFWriterErrors(object):
@dec.skipif(module_not_found("netCDF4"), "Test skipped because netCDF is not available.")
def setUp(self):
self.tmpdir = tempdir.TempDir()
self.outfile = os.path.join(self.tmpdir.name, 'out.ncdf')
def tearDown(self):
try:
os.unlink(self.outfile)
except OSError:
pass
del self.tmpdir
del self.outfile
def test_zero_atoms_VE(self):
from MDAnalysis.coordinates.TRJ import NCDFWriter
assert_raises(ValueError, NCDFWriter, self.outfile, 0)
def test_wrong_n_atoms(self):
from MDAnalysis.coordinates.TRJ import NCDFWriter
with NCDFWriter(self.outfile, 100) as w:
u = make_Universe(trajectory=True)
assert_raises(IOError, w.write, u.trajectory.ts)
def test_no_ts(self):
# no ts supplied at any point
from MDAnalysis.coordinates.TRJ import NCDFWriter
with NCDFWriter(self.outfile, 100) as w:
assert_raises(IOError, w.write_next_timestep)
class TestDist(TestCase):
'''Tests for MDAnalysis.analysis.distances.dist().
Imports do not happen at the top level of the module
because of the scipy dependency.'''
@dec.skipif(module_not_found('scipy'),
"Test skipped because scipy is not available.")
def setUp(self):
import MDAnalysis.analysis.distances
import scipy
import scipy.spatial
self.u = MDAnalysis.Universe(GRO)
self.ag = self.u.atoms[:20]
self.u2 = MDAnalysis.Universe(GRO)
self.ag2 = self.u2.atoms[:20]
self.ag2.positions = np.random.shuffle(self.ag2.positions)
self.expected = np.diag(
scipy.spatial.distance.cdist(self.ag.positions,
self.ag2.positions))
def tearDown(self):
del self.u
del self.ag
del self.u2
del self.ag2
del self.expected
def test_pairwise_dist(self):
'''Ensure that pairwise distances between atoms are
correctly calculated.'''
actual = MDAnalysis.analysis.distances.dist(self.ag, self.ag2)[2]
assert_equal(actual, self.expected)
def test_pairwise_dist_offset_effect(self):
'''Test that feeding in offsets to dist() doesn't alter
pairwise distance matrix.'''
actual = MDAnalysis.analysis.distances.dist(self.ag,
self.ag2,
offset=229)[2]
assert_equal(actual, self.expected)
def test_offset_calculation(self):
'''Test that offsets fed to dist() are correctly calculated.'''
actual = MDAnalysis.analysis.distances.dist(self.ag,
self.ag2,
offset=33)[:2]
assert_equal(
actual,
np.array([self.ag.atoms.resids + 33, self.ag2.atoms.resids + 33]))
def test_mismatch_exception(self):
'''A ValueError should be raised if the two atomgroups
don't have the same number of atoms.'''
with self.assertRaises(ValueError):
MDAnalysis.analysis.distances.dist(self.ag[:19], self.ag2)
class TestContactMatrix(TestCase):
@dec.skipif(module_not_found('scipy'),
"Test skipped because scipy is not available.")
def setUp(self):
import MDAnalysis.analysis.distances
self.coord = np.array(
[
[1, 1, 1],
[5, 5, 5],
[1.1, 1.1, 1.1],
[11, 11, 11], # neighboring image with pbc
[21, 21, 21]
], # non neighboring image with pbc
dtype=np.float32)
self.box = np.array([10, 10, 10], dtype=np.float32)
self.shape = (5, 5)
self.res_no_pbc = np.array(
[[1, 0, 1, 0, 0], [0, 1, 0, 0, 0], [1, 0, 1, 0, 0],
[0, 0, 0, 1, 0], [0, 0, 0, 0, 1]],
dtype=np.bool)
self.res_pbc = np.array(
[[1, 0, 1, 1, 1], [0, 1, 0, 0, 0], [1, 0, 1, 1, 1],
[1, 0, 1, 1, 1], [1, 0, 1, 1, 1]],
dtype=np.bool)
def test_np(self):
contacts = MDAnalysis.analysis.distances.contact_matrix(
self.coord, cutoff=1, returntype="numpy")
assert_equal(contacts.shape, self.shape,
"wrong shape (should be {0})".format(self.shape))
assert_equal(contacts, self.res_no_pbc)
def test_sparse(self):
contacts = MDAnalysis.analysis.distances.contact_matrix(
self.coord, cutoff=1.5, returntype="sparse")
assert_equal(contacts.shape, self.shape,
"wrong shape (should be {0})".format(self.shape))
assert_equal(contacts.toarray(), self.res_no_pbc)
def test_box_numpy(self):
contacts = MDAnalysis.analysis.distances.contact_matrix(self.coord,
box=self.box,
cutoff=1)
assert_equal(contacts.shape, self.shape,
"wrong shape (should be {0})".format(self.shape))
assert_equal(contacts, self.res_pbc)
def test_box_sparse(self):
contacts = MDAnalysis.analysis.distances.contact_matrix(
self.coord, box=self.box, cutoff=1, returntype='sparse')
assert_equal(contacts.shape, self.shape,
"wrong shape (should be {0})".format(self.shape))
assert_equal(contacts.toarray(), self.res_pbc)
class TestFitExponential(object):
def setUp(self):
self.x = np.linspace(0, 250, 251)
self.a_ref = 20.0
self.y = np.exp(-self.x / self.a_ref)
def tearDown(self):
del self.x
del self.a_ref
del self.y
@dec.skipif(module_not_found('scipy'),
"Test skipped because scipy is not available.")
def test_fit_simple(self):
a = polymer.fit_exponential_decay(self.x, self.y)
assert_(a == self.a_ref)
@dec.skipif(module_not_found('scipy'),
"Test skipped because scipy is not available.")
def test_fit_noisy(self):
y2 = self.y + (np.random.random(len(self.y)) - 0.5) * 0.05
a = polymer.fit_exponential_decay(self.x, y2)
assert_(np.rint(a) == self.a_ref)
class TestBetween(TestCase):
'''Tests for MDAnalysis.analysis.distances.between().
Imports do not happen at the top level of the module
because of the scipy dependency.'''
@dec.skipif(module_not_found('scipy'),
"Test skipped because scipy is not available.")
def setUp(self):
import MDAnalysis.analysis.distances
import scipy
import scipy.spatial
self.u = MDAnalysis.Universe(GRO)
self.ag = self.u.atoms[:10]
self.ag2 = self.u.atoms[12:33]
self.group = self.u.atoms[40:]
self.distance = 5.9
self.distance_matrix_1 = scipy.spatial.distance.cdist(
self.group.positions, self.ag.positions)
self.mask_1 = np.unique(
np.where(self.distance_matrix_1 <= self.distance)[0])
self.group_filtered = self.group[self.mask_1]
self.distance_matrix_2 = scipy.spatial.distance.cdist(
self.group_filtered.positions, self.ag2.positions)
self.mask_2 = np.unique(
np.where(self.distance_matrix_2 <= self.distance)[0])
self.expected = self.group_filtered[self.mask_2].indices
def tearDown(self):
del self.u
del self.ag
del self.ag2
del self.group
del self.distance
del self.distance_matrix_1
del self.distance_matrix_2
del self.mask_1
del self.mask_2
del self.group_filtered
del self.expected
def test_between_simple_case_indices_only(self):
'''Test MDAnalysis.analysis.distances.between() for
a simple input case. Checks the sorted atom indices
of returned AtomGroup against sorted expected index
values.'''
actual = sorted(
MDAnalysis.analysis.distances.between(self.group, self.ag,
self.ag2,
self.distance).indices)
assert_equal(actual, self.expected)
class TestDensity(TestCase):
nbins = 3, 4, 5
counts = 100
Lmax = 10.
@dec.skipif(module_not_found('scipy'),
"Test skipped because scipy is not available.")
def setUp(self):
import MDAnalysis.analysis.density
self.bins = [np.linspace(0, self.Lmax, n + 1) for n in self.nbins]
h, edges = np.histogramdd(self.Lmax * np.random.random(
(self.counts, 3)),
bins=self.bins)
self.D = MDAnalysis.analysis.density.Density(
h, edges, parameters={'isDensity': False}, units={'length': 'A'})
self.D.make_density()
def test_shape(self):
assert_equal(self.D.grid.shape, self.nbins)
def test_edges(self):
for dim, (edges, fixture) in enumerate(zip(self.D.edges, self.bins)):
assert_almost_equal(edges,
fixture,
err_msg="edges[{0}] mismatch".format(dim))
def test_midpoints(self):
midpoints = [0.5 * (b[:-1] + b[1:]) for b in self.bins]
for dim, (mp, fixture) in enumerate(zip(self.D.midpoints, midpoints)):
assert_almost_equal(mp,
fixture,
err_msg="midpoints[{0}] mismatch".format(dim))
def test_delta(self):
deltas = np.array([self.Lmax]) / np.array(self.nbins)
assert_almost_equal(self.D.delta, deltas)
def test_grid(self):
dV = self.D.delta.prod() # orthorhombic grids only!
# counts = (rho[0] * dV[0] + rho[1] * dV[1] ...) = sum_i rho[i] * dV
assert_almost_equal(self.D.grid.sum() * dV, self.counts)
def test_origin(self):
midpoints = [0.5 * (b[:-1] + b[1:]) for b in self.bins]
origin = [m[0] for m in midpoints]
assert_almost_equal(self.D.origin, origin)
class _BaseHausdorffDistance(TestCase):
'''Base Class setup and unit tests
for various Hausdorff distance
calculation properties.'''
@dec.skipif(module_not_found('scipy'), 'scipy not available')
def setUp(self):
self.random_angles = np.random.random((100, )) * np.pi * 2
self.random_columns = np.column_stack(
(self.random_angles, self.random_angles, np.zeros((100, ))))
self.random_columns[..., 0] = np.cos(self.random_columns[..., 0])
self.random_columns[..., 1] = np.sin(self.random_columns[..., 1])
self.random_columns_2 = np.column_stack(
(self.random_angles, self.random_angles, np.zeros((100, ))))
self.random_columns_2[1:,
0] = np.cos(self.random_columns_2[1:, 0]) * 2.0
self.random_columns_2[1:,
1] = np.sin(self.random_columns_2[1:, 1]) * 2.0
# move one point farther out so we don't have two perfect circles
self.random_columns_2[0, 0] = np.cos(self.random_columns_2[0, 0]) * 3.3
self.random_columns_2[0, 1] = np.sin(self.random_columns_2[0, 1]) * 3.3
self.path_1 = self.random_columns
self.path_2 = self.random_columns_2
def tearDown(self):
del self.random_angles
del self.random_columns
del self.random_columns_2
del self.path_1
del self.path_2
def test_symmetry(self):
'''Ensure that the undirected (symmetric)
Hausdorff distance is actually symmetric
for a given Hausdorff metric, h.'''
forward = self.h(self.path_1, self.path_2)
reverse = self.h(self.path_2, self.path_1)
self.assertEqual(forward, reverse)
def test_hausdorff_value(self):
'''Test that the undirected Hausdorff
distance matches expected value for
the simple case here.'''
actual = self.h(self.path_1, self.path_2)
# unless I pin down the random generator
# seems unstable to use decimal > 2
assert_almost_equal(actual, self.expected, decimal=2)
class _NCDFReaderTest(_TRJReaderTest):
@dec.skipif(module_not_found("netCDF4"),
"Test skipped because netCDF is not available.")
def setUp(self):
self.universe = mda.Universe(self.topology, self.filename)
self.prec = 3
def test_slice_iteration(self):
frames = [ts.frame for ts in self.universe.trajectory[4:-2:4]]
assert_equal(frames,
np.arange(self.universe.trajectory.n_frames)[4:-2:4],
err_msg="slicing did not produce the expected frames")
def test_metadata(self):
data = self.universe.trajectory.trjfile
assert_equal(data.Conventions, 'AMBER')
assert_equal(data.ConventionVersion, '1.0')
class _NCDFReaderTest(_TRJReaderTest):
@dec.skipif(module_not_found("netCDF4"),
"Test skipped because netCDF is not available.")
def setUp(self):
self.universe = mda.Universe(self.topology, self.filename)
self.prec = 3
def test_slice_iteration(self):
frames = [ts.frame for ts in self.universe.trajectory[4:-2:4]]
assert_equal(frames,
np.arange(self.universe.trajectory.n_frames)[4:-2:4],
err_msg="slicing did not produce the expected frames")
def test_metadata(self):
data = self.universe.trajectory.trjfile
assert_equal(data.Conventions, 'AMBER')
assert_equal(data.ConventionVersion, '1.0')
def test_dt(self):
ref = 0.0
assert_almost_equal(ref, self.universe.trajectory.dt, self.prec)
assert_almost_equal(ref, self.universe.trajectory.ts.dt, self.prec)
def test_get_writer(self):
with self.universe.trajectory.Writer('out.ncdf') as w:
assert_(w.n_atoms == len(self.universe.atoms))
assert_(w.remarks.startswith('AMBER NetCDF format'))
def test_get_writer_custom_n_atoms(self):
with self.universe.trajectory.Writer('out.ncdf',
n_atoms=42,
remarks='Hi!') as w:
assert_(w.n_atoms == 42)
assert_(w.remarks == 'Hi!')
def test_wrong_natoms(self):
assert_raises(ValueError,
mda.coordinates.TRJ.NCDFReader,
self.filename,
n_atoms=2)
def test_read_on_closed(self):
self.universe.trajectory.close()
assert_raises(IOError, self.universe.trajectory.__getitem__, 2)
class TestNCDF2DCD(TestCase):
@dec.skipif(module_not_found("netCDF4"),
"Test skipped because netCDF is not available.")
def setUp(self):
self.u = mda.Universe(PRMncdf, NCDF)
# create the DCD
self.tmpdir = tempdir.TempDir()
self.dcd = self.tmpdir.name + '/ncdf-2-dcd.dcd'
DCD = mda.Writer(self.dcd, n_atoms=self.u.atoms.n_atoms)
for ts in self.u.trajectory:
DCD.write(ts)
DCD.close()
self.w = mda.Universe(PRMncdf, self.dcd)
def tearDown(self):
try:
os.unlink(self.dcd)
except (AttributeError, OSError):
pass
del self.u
del self.w
del self.tmpdir
@attr('issue')
def test_unitcell(self):
"""NCDFReader: Test that DCDWriter correctly writes the CHARMM
unit cell"""
for ts_orig, ts_copy in zip(self.u.trajectory,
self.w.trajectory):
assert_almost_equal(
ts_orig.dimensions,
ts_copy.dimensions,
3,
err_msg="NCDF->DCD: unit cell dimensions wrong at frame {0:d}".format(
ts_orig.frame))
def test_coordinates(self):
for ts_orig, ts_copy in zip(self.u.trajectory,
self.w.trajectory):
assert_almost_equal(
self.u.atoms.positions,
self.w.atoms.positions,
3,
err_msg="NCDF->DCD: coordinates wrong at frame {0:d}".format(
ts_orig.frame))
class _GromacsWriterIssue117(TestCase):
"""Issue 117: Cannot write XTC or TRR from AMBER NCDF"""
ext = None
prec = 5
@dec.skipif(module_not_found("netCDF4"),
"Test skipped because netCDF is not available.")
def setUp(self):
self.universe = mda.Universe(PRMncdf, NCDF)
self.tmpdir = tempdir.TempDir()
self.outfile = self.tmpdir.name + '/xdr-writer-issue117' + self.ext
self.Writer = mda.Writer(self.outfile,
n_atoms=self.universe.atoms.n_atoms)
def tearDown(self):
try:
os.unlink(self.outfile)
except:
pass
del self.universe
del self.Writer
@attr('issue')
def test_write_trajectory(self):
"""Test writing Gromacs trajectories from AMBER NCDF (Issue 117)"""
self.universe.trajectory
for ts in self.universe.trajectory:
self.Writer.write_next_timestep(ts)
self.Writer.close()
uw = mda.Universe(PRMncdf, self.outfile)
# check that the coordinates are identical for each time step
for orig_ts, written_ts in zip(self.universe.trajectory,
uw.trajectory):
assert_array_almost_equal(written_ts._pos,
orig_ts._pos,
self.prec,
err_msg="coordinate mismatch "
"between original and written "
"trajectory at frame %d (orig) vs %d "
"(written)" %
(orig_ts.frame, written_ts.frame))
class TestLeafletFinder(TestCase):
@dec.skipif(module_not_found('scipy'),
"Test skipped because scipy is not available.")
def setUp(self):
self.universe = MDAnalysis.Universe(Martini_membrane_gro, Martini_membrane_gro)
self.lipid_heads = self.universe.select_atoms("name PO4")
self.lipid_head_string = "name PO4"
def tearDown(self):
del self.universe
del self.lipid_heads
del self.lipid_head_string
def test_leaflet_finder(self):
from MDAnalysis.analysis.leaflet import LeafletFinder
lfls = LeafletFinder(self.universe, self.lipid_heads, pbc=True)
top_heads, bottom_heads = lfls.groups()
# Make top be... on top.
if top_heads.center_of_geometry()[2] < bottom_heads.center_of_geometry()[2]:
top_heads,bottom_heads = (bottom_heads,top_heads)
assert_equal(top_heads.indices, np.arange(1,2150,12), err_msg="Found wrong leaflet lipids")
assert_equal(bottom_heads.indices, np.arange(2521,4670,12), err_msg="Found wrong leaflet lipids")
def test_string_vs_atomgroup_proper(self):
from MDAnalysis.analysis.leaflet import LeafletFinder
lfls_ag = LeafletFinder(self.universe, self.lipid_heads, pbc=True)
lfls_string = LeafletFinder(self.universe, self.lipid_head_string, pbc=True)
groups_ag = lfls_ag.groups()
groups_string = lfls_string.groups()
assert_equal(groups_string[0].indices, groups_ag[0].indices)
assert_equal(groups_string[1].indices, groups_ag[1].indices)
def test_optimize_cutoff(self):
from MDAnalysis.analysis.leaflet import optimize_cutoff
cutoff, N = optimize_cutoff(self.universe, self.lipid_heads, pbc=True)
assert_equal(N, 2)
assert_almost_equal(cutoff, 10.5, decimal=4)
class _NCDFWriterTest(TestCase):
@dec.skipif(module_not_found("netCDF4"),
"Test skipped because netCDF is not available.")
def setUp(self):
self.universe = mda.Universe(self.topology, self.filename)
self.prec = 5
ext = ".ncdf"
self.tmpdir = tempdir.TempDir()
self.outfile = os.path.join(self.tmpdir.name, 'ncdf-writer-1' + ext)
self.outtop = os.path.join(self.tmpdir.name, 'ncdf-writer-top.pdb')
self.Writer = mda.coordinates.TRJ.NCDFWriter
def tearDown(self):
for f in self.outfile, self.outtop:
try:
os.unlink(f)
except OSError:
pass
del self.universe
del self.Writer
del self.tmpdir
def test_write_trajectory(self):
t = self.universe.trajectory
with self.Writer(self.outfile, t.n_atoms, dt=t.dt) as W:
self._copy_traj(W)
self._check_new_traj()
import netCDF4
#for issue #518 -- preserve float32 data in ncdf output
dataset = netCDF4.Dataset(self.outfile, 'r', format='NETCDF3')
coords = dataset.variables['coordinates']
time = dataset.variables['time']
assert_equal(coords.dtype,
np.float32,
err_msg='ncdf coord output not float32')
assert_equal(time.dtype,
np.float32,
err_msg='ncdf time output not float32')
def test_OtherWriter(self):
t = self.universe.trajectory
with t.OtherWriter(self.outfile) as W:
self._copy_traj(W)
self._check_new_traj()
def _copy_traj(self, writer):
for ts in self.universe.trajectory:
writer.write_next_timestep(ts)
def _check_new_traj(self):
uw = mda.Universe(self.topology, self.outfile)
# check that the trajectories are identical for each time step
for orig_ts, written_ts in zip(self.universe.trajectory,
uw.trajectory):
assert_array_almost_equal(written_ts._pos,
orig_ts._pos,
self.prec,
err_msg="coordinate mismatch between "
"original and written trajectory at "
"frame %d (orig) vs %d (written)" %
(orig_ts.frame, written_ts.frame))
# not a good test because in the example trajectory all times are 0
assert_almost_equal(orig_ts.time,
written_ts.time,
self.prec,
err_msg="Time for step {0} are not the "
"same.".format(orig_ts.frame))
assert_array_almost_equal(written_ts.dimensions,
orig_ts.dimensions,
self.prec,
err_msg="unitcells are not identical")
# check that the NCDF data structures are the same
nc_orig = self.universe.trajectory.trjfile
nc_copy = uw.trajectory.trjfile
for k, dim in nc_orig.dimensions.items():
try:
dim_new = nc_copy.dimensions[k]
except KeyError:
raise AssertionError("NCDFWriter did not write "
"dimension '{}'".format(k))
else:
assert_equal(len(dim),
len(dim_new),
err_msg="Dimension '{0}' size mismatch".format(k))
for k, v in nc_orig.variables.items():
try:
v_new = nc_copy.variables[k]
except KeyError:
raise AssertionError("NCDFWriter did not write "
"variable '{}'".format(k))
else:
try:
assert_array_almost_equal(v[:],
v_new[:],
self.prec,
err_msg="Variable '{}' not "
"written correctly".format(k))
except TypeError:
assert_array_equal(v[:],
v_new[:],
err_msg="Variable {} not written "
"correctly".format(k))
@attr('slow')
def test_TRR2NCDF(self):
trr = mda.Universe(GRO, TRR)
with self.Writer(self.outfile, trr.trajectory.n_atoms,
velocities=True) as W:
for ts in trr.trajectory:
W.write_next_timestep(ts)
uw = mda.Universe(GRO, self.outfile)
for orig_ts, written_ts in zip(trr.trajectory, uw.trajectory):
assert_array_almost_equal(written_ts._pos,
orig_ts._pos,
self.prec,
err_msg="coordinate mismatch between "
"original and written trajectory at "
"frame %d (orig) vs %d (written)" %
(orig_ts.frame, written_ts.frame))
assert_array_almost_equal(written_ts._velocities,
orig_ts._velocities,
self.prec,
err_msg="velocity mismatch between "
"original and written trajectory at "
"frame %d (orig) vs %d (written)" %
(orig_ts.frame, written_ts.frame))
assert_almost_equal(orig_ts.time,
written_ts.time,
self.prec,
err_msg="Time for step {0} are not the "
"same.".format(orig_ts.frame))
assert_array_almost_equal(written_ts.dimensions,
orig_ts.dimensions,
self.prec,
err_msg="unitcells are not identical")
del trr
@attr('issue')
def test_write_AtomGroup(self):
"""test to write NCDF from AtomGroup (Issue 116)"""
p = self.universe.select_atoms("not resname WAT")
p.write(self.outtop)
with self.Writer(self.outfile, n_atoms=p.n_atoms) as W:
for ts in self.universe.trajectory:
W.write(p)
uw = mda.Universe(self.outtop, self.outfile)
pw = uw.atoms
for orig_ts, written_ts in zip(self.universe.trajectory,
uw.trajectory):
assert_array_almost_equal(p.positions,
pw.positions,
self.prec,
err_msg="coordinate mismatch between "
"original and written trajectory at "
"frame %d (orig) vs %d (written)" %
(orig_ts.frame, written_ts.frame))
assert_almost_equal(orig_ts.time,
written_ts.time,
self.prec,
err_msg="Time for step {0} are not the "
"same.".format(orig_ts.frame))
assert_array_almost_equal(written_ts.dimensions,
orig_ts.dimensions,
self.prec,
err_msg="unitcells are not identical")
class TestPCA(object):
""" Test the PCA class """
def setUp(self):
self.u = MDAnalysis.Universe(PSF, DCD)
self.u.transfer_to_memory()
self.pca = pca.PCA(self.u, select='backbone and name CA', align=False)
self.pca.run()
self.n_atoms = self.u.select_atoms('backbone and name CA').n_atoms
def test_cov(self):
atoms = self.u.select_atoms('backbone and name CA')
xyz = np.zeros((self.pca.n_frames, self.pca._n_atoms * 3))
for i, ts in enumerate(self.u.trajectory):
xyz[i] = atoms.positions.ravel()
cov = np.cov(xyz, rowvar=0)
assert_array_almost_equal(self.pca.cov, cov, 4)
def test_cum_var(self):
assert_almost_equal(self.pca.cumulated_variance[-1], 1)
l = self.pca.cumulated_variance
l = np.sort(l)
assert_almost_equal(self.pca.cumulated_variance, l, 5)
def test_pcs(self):
assert_equal(self.pca.p_components.shape,
(self.n_atoms * 3, self.n_atoms * 3))
def test_different_steps(self):
dot = self.pca.transform(self.u.select_atoms('backbone and name CA'),
start=5,
stop=7,
step=1)
assert_equal(dot.shape, (2, self.n_atoms * 3))
def test_transform(self):
ag = self.u.select_atoms('backbone and name CA')
pca_space = self.pca.transform(ag, n_components=1)
assert_equal(pca_space.shape, (self.u.trajectory.n_frames, 1))
# Accepts universe as input, but shapes are not aligned due to n_atoms
@raises(ValueError)
def test_transform_mismatch(self):
pca_space = self.pca.transform(self.u, n_components=1)
assert_equal(pca_space.shape, (self.u.trajectory.n_frames, 1))
@staticmethod
def test_transform_universe():
u1 = MDAnalysis.Universe(waterPSF, waterDCD)
u2 = MDAnalysis.Universe(waterPSF, waterDCD)
pca_test = pca.PCA(u1).run()
pca_test.transform(u2)
@staticmethod
@dec.skipif(module_not_found('scipy'),
"Test skipped because scipy is not available.")
def test_cosine_content():
rand = MDAnalysis.Universe(RANDOM_WALK_TOPO, RANDOM_WALK)
pca_random = pca.PCA(rand).run()
dot = pca_random.transform(rand.atoms)
content = pca.cosine_content(dot, 0)
assert_almost_equal(content, .99, 1)
class TestEncoreDimensionalityReduction(TestCase):
@dec.skipif(parser_not_found('DCD'),
'DCD parser not available. Are you using python 3?')
def setUp(self):
# Create universe from templates defined in setUpClass
self.ens1 = mda.Universe(
self.ens1_template.filename,
self.ens1_template.trajectory.timeseries(format='fac'),
format=mda.coordinates.memory.MemoryReader)
self.ens2 = mda.Universe(
self.ens2_template.filename,
self.ens2_template.trajectory.timeseries(format='fac'),
format=mda.coordinates.memory.MemoryReader)
def tearDownClass(self):
del self.ens1
del self.ens2
@classmethod
@dec.skipif(parser_not_found('DCD'),
'DCD parser not available. Are you using python 3?')
def setUpClass(cls):
# To speed up tests, we read in trajectories from file only once,
# and then recreate them from their coordinate array for each test
super(TestEncoreDimensionalityReduction, cls).setUpClass()
cls.ens1_template = mda.Universe(PSF, DCD)
cls.ens2_template = mda.Universe(PSF, DCD2)
cls.ens1_template.transfer_to_memory()
cls.ens2_template.transfer_to_memory()
# Filter ensembles to only include every 5th frame
cls.ens1_template = mda.Universe(
cls.ens1_template.filename,
np.copy(
cls.ens1_template.trajectory.timeseries(
format='fac')[::5, :, :]),
format=mda.coordinates.memory.MemoryReader)
cls.ens2_template = mda.Universe(
cls.ens2_template.filename,
np.copy(
cls.ens2_template.trajectory.timeseries(
format='fac')[::5, :, :]),
format=mda.coordinates.memory.MemoryReader)
@classmethod
def tearDownClass(cls):
del cls.ens1_template
del cls.ens2_template
@dec.slow
def test_dimensionality_reduction_one_ensemble(self):
dimension = 2
coordinates, details = encore.reduce_dimensionality(self.ens1)
print(coordinates)
assert_equal(
coordinates.shape[0],
dimension,
err_msg="Unexpected result in dimensionality reduction: {0}".
format(coordinates))
@dec.slow
def test_dimensionality_reduction_two_ensembles(self):
dimension = 2
coordinates, details = \
encore.reduce_dimensionality([self.ens1, self.ens2])
assert_equal(
coordinates.shape[0],
dimension,
err_msg="Unexpected result in dimensionality reduction: {0}".
format(coordinates))
@dec.slow
def test_dimensionality_reduction_three_ensembles_two_identical(self):
coordinates, details = \
encore.reduce_dimensionality([self.ens1, self.ens2, self.ens1])
coordinates_ens1 = coordinates[:,
np.where(
details["ensemble_membership"] ==
1)]
coordinates_ens3 = coordinates[:,
np.where(
details["ensemble_membership"] ==
3)]
assert_almost_equal(
coordinates_ens1,
coordinates_ens3,
decimal=0,
err_msg="Unexpected result in dimensionality reduction: {0}".
format(coordinates))
@dec.slow
def test_dimensionality_reduction_specified_dimension(self):
dimension = 3
coordinates, details = encore.reduce_dimensionality(
[self.ens1, self.ens2],
method=encore.StochasticProximityEmbeddingNative(
dimension=dimension))
assert_equal(
coordinates.shape[0],
dimension,
err_msg="Unexpected result in dimensionality reduction: {0}".
format(coordinates))
@dec.slow
def test_dimensionality_reduction_SPENative_direct(self):
dimension = 2
method = encore.StochasticProximityEmbeddingNative(dimension=dimension)
distance_matrix = encore.get_distance_matrix(self.ens1)
coordinates, details = method(distance_matrix)
assert_equal(
coordinates.shape[0],
dimension,
err_msg="Unexpected result in dimensionality reduction: {0}".
format(coordinates))
@dec.slow
@dec.skipif(module_not_found('sklearn'),
"Test skipped because sklearn is not available.")
def test_dimensionality_reduction_PCA_direct(self):
dimension = 2
method = encore.PrincipalComponentAnalysis(dimension=dimension)
coordinates = self.ens1.trajectory.timeseries(format='fac')
coordinates = np.reshape(coordinates, (coordinates.shape[0], -1))
coordinates, details = method(coordinates)
assert_equal(
coordinates.shape[0],
dimension,
err_msg="Unexpected result in dimensionality reduction: {0}".
format(coordinates))
@dec.slow
@dec.skipif(module_not_found('sklearn'),
"Test skipped because sklearn is not available.")
def test_dimensionality_reduction_different_method(self):
dimension = 3
coordinates, details = \
encore.reduce_dimensionality(
[self.ens1, self.ens2],
method=encore.PrincipalComponentAnalysis(dimension=dimension))
assert_equal(
coordinates.shape[0],
dimension,
err_msg="Unexpected result in dimensionality reduction: {0}".
format(coordinates))
@dec.slow
def test_dimensionality_reduction_two_methods(self):
dims = [2, 3]
coordinates, details = \
encore.reduce_dimensionality(
[self.ens1, self.ens2],
method=[encore.StochasticProximityEmbeddingNative(dims[0]),
encore.StochasticProximityEmbeddingNative(dims[1])])
assert_equal(coordinates[1].shape[0], dims[1])
@dec.slow
@dec.skipif(module_not_found('sklearn'),
"Test skipped because sklearn is not available.")
def test_dimensionality_reduction_two_different_methods(self):
dims = [2, 3]
coordinates, details = \
encore.reduce_dimensionality(
[self.ens1, self.ens2],
method=[encore.StochasticProximityEmbeddingNative(dims[0]),
encore.PrincipalComponentAnalysis(dims[1])])
assert_equal(coordinates[1].shape[0], dims[1])
class TestEncoreClustering(TestCase):
@dec.skipif(parser_not_found('DCD'),
'DCD parser not available. Are you using python 3?')
def setUp(self):
# Create universe from templates defined in setUpClass
self.ens1 = mda.Universe(
self.ens1_template.filename,
self.ens1_template.trajectory.timeseries(format='fac'),
format=mda.coordinates.memory.MemoryReader)
self.ens2 = mda.Universe(
self.ens2_template.filename,
self.ens2_template.trajectory.timeseries(format='fac'),
format=mda.coordinates.memory.MemoryReader)
def tearDownClass(self):
del self.ens1
del self.ens2
@classmethod
@dec.skipif(parser_not_found('DCD'),
'DCD parser not available. Are you using python 3?')
def setUpClass(cls):
# To speed up tests, we read in trajectories from file only once,
# and then recreate them from their coordinate array for each test
super(TestEncoreClustering, cls).setUpClass()
cls.ens1_template = mda.Universe(PSF, DCD)
cls.ens2_template = mda.Universe(PSF, DCD2)
cls.ens1_template.transfer_to_memory()
cls.ens2_template.transfer_to_memory()
# Filter ensembles to only include every 5th frame
cls.ens1_template = mda.Universe(
cls.ens1_template.filename,
np.copy(
cls.ens1_template.trajectory.timeseries(
format='fac')[::5, :, :]),
format=mda.coordinates.memory.MemoryReader)
cls.ens2_template = mda.Universe(
cls.ens2_template.filename,
np.copy(
cls.ens2_template.trajectory.timeseries(
format='fac')[::5, :, :]),
format=mda.coordinates.memory.MemoryReader)
@classmethod
def tearDownClass(cls):
del cls.ens1_template
del cls.ens2_template
@dec.slow
def test_clustering_one_ensemble(self):
cluster_collection = encore.cluster(self.ens1)
expected_value = 7
assert_equal(
len(cluster_collection),
expected_value,
err_msg="Unexpected results: {0}".format(cluster_collection))
@dec.slow
def test_clustering_two_ensembles(self):
cluster_collection = encore.cluster([self.ens1, self.ens2])
expected_value = 14
assert_equal(
len(cluster_collection),
expected_value,
err_msg="Unexpected results: {0}".format(cluster_collection))
@dec.slow
def test_clustering_three_ensembles_two_identical(self):
cluster_collection = encore.cluster([self.ens1, self.ens2, self.ens1])
expected_value = 40
assert_equal(
len(cluster_collection),
expected_value,
err_msg="Unexpected result: {0}".format(cluster_collection))
@dec.slow
def test_clustering_two_methods(self):
cluster_collection = encore.cluster(
[self.ens1],
method=[
encore.AffinityPropagationNative(),
encore.AffinityPropagationNative()
])
assert_equal(
len(cluster_collection[0]),
len(cluster_collection[1]),
err_msg="Unexpected result: {0}".format(cluster_collection))
@dec.slow
def test_clustering_AffinityPropagationNative_direct(self):
method = encore.AffinityPropagationNative()
distance_matrix = encore.get_distance_matrix(self.ens1)
cluster_assignment, details = method(distance_matrix)
expected_value = 7
assert_equal(
len(set(cluster_assignment)),
expected_value,
err_msg="Unexpected result: {0}".format(cluster_assignment))
@dec.slow
@dec.skipif(module_not_found('sklearn'),
"Test skipped because sklearn is not available.")
def test_clustering_AffinityPropagation_direct(self):
method = encore.AffinityPropagation()
distance_matrix = encore.get_distance_matrix(self.ens1)
cluster_assignment, details = method(distance_matrix)
expected_value = 7
assert_equal(
len(set(cluster_assignment)),
expected_value,
err_msg="Unexpected result: {0}".format(cluster_assignment))
@dec.slow
@dec.skipif(module_not_found('sklearn'),
"Test skipped because sklearn is not available.")
def test_clustering_KMeans_direct(self):
clusters = 10
method = encore.KMeans(clusters)
coordinates = self.ens1.trajectory.timeseries(format='fac')
coordinates = np.reshape(coordinates, (coordinates.shape[0], -1))
cluster_assignment, details = method(coordinates)
assert_equal(
len(set(cluster_assignment)),
clusters,
err_msg="Unexpected result: {0}".format(cluster_assignment))
@dec.slow
@dec.skipif(module_not_found('sklearn'),
"Test skipped because sklearn is not available.")
def test_clustering_DBSCAN_direct(self):
method = encore.DBSCAN(eps=0.5, min_samples=2)
distance_matrix = encore.get_distance_matrix(self.ens1)
cluster_assignment, details = method(distance_matrix)
expected_value = 2
assert_equal(
len(set(cluster_assignment)),
expected_value,
err_msg="Unexpected result: {0}".format(cluster_assignment))
@dec.slow
@dec.skipif(module_not_found('sklearn'),
"Test skipped because sklearn is not available.")
def test_clustering_two_different_methods(self):
cluster_collection = encore.cluster(
[self.ens1],
method=[
encore.AffinityPropagation(preference=-7.5),
encore.DBSCAN(min_samples=2)
])
print(cluster_collection)
print(cluster_collection)
assert_equal(
len(cluster_collection[0]),
len(cluster_collection[1]),
err_msg="Unexpected result: {0}".format(cluster_collection))
@dec.slow
@dec.skipif(module_not_found('sklearn'),
"Test skipped because sklearn is not available.")
def test_clustering_method_w_no_distance_matrix(self):
cluster_collection = encore.cluster([self.ens1],
method=encore.KMeans(10))
print(cluster_collection)
assert_equal(
len(cluster_collection),
10,
err_msg="Unexpected result: {0}".format(cluster_collection))
@dec.slow
@dec.skipif(module_not_found('sklearn'),
"Test skipped because sklearn is not available.")
def test_clustering_two_methods_one_w_no_distance_matrix(self):
cluster_collection = encore.cluster(
[self.ens1],
method=[encore.KMeans(17),
encore.AffinityPropagationNative()])
print(cluster_collection)
assert_equal(
len(cluster_collection[0]),
len(cluster_collection[0]),
err_msg="Unexpected result: {0}".format(cluster_collection))
@dec.slow
@dec.skipif(module_not_found('sklearn'),
"Test skipped because sklearn is not available.")
def test_sklearn_affinity_propagation(self):
cc1 = encore.cluster([self.ens1])
cc2 = encore.cluster([self.ens1], method=encore.AffinityPropagation())
assert_equal(len(cc1),
len(cc2),
err_msg="Native and sklearn implementations of affinity "
"propagation don't agree: mismatch in number of "
"clusters: {0} {1}".format(len(cc1), len(cc2)))
class TestHydrogenBondAutocorrel(object):
def setUp(self):
u = self.u = mda.Universe(TRZ_psf, TRZ)
self.H = u.atoms.select_atoms('name Hn')
self.N = u.atoms.select_atoms('name N')
self.O = u.atoms.select_atoms('name O')
self.excl_list = (np.array(range(len(self.H))),
np.array(range(len(self.O))))
def tearDown(self):
del self.H
del self.N
del self.O
del self.u
del self.excl_list
# regression tests for different conditions
def test_continuous(self):
hbond = HBAC(
self.u,
hydrogens=self.H,
acceptors=self.O,
donors=self.N,
bond_type='continuous',
sample_time=0.06,
)
hbond.run()
assert_array_almost_equal(
hbond.solution['results'],
np.array([
1., 0.92668623, 0.83137828, 0.74486804, 0.67741936, 0.60263932
],
dtype=np.float32))
def test_continuous_excl(self):
hbond = HBAC(
self.u,
hydrogens=self.H,
acceptors=self.O,
donors=self.N,
bond_type='continuous',
exclusions=self.excl_list,
sample_time=0.06,
)
hbond.run()
assert_array_almost_equal(
hbond.solution['results'],
np.array([
1., 0.92668623, 0.83137828, 0.74486804, 0.67741936, 0.60263932
],
dtype=np.float32))
def test_intermittent(self):
hbond = HBAC(
self.u,
hydrogens=self.H,
acceptors=self.O,
donors=self.N,
bond_type='intermittent',
sample_time=0.06,
)
hbond.run()
assert_array_almost_equal(
hbond.solution['results'],
np.array([
1., 0.92668623, 0.84310848, 0.79325515, 0.76392961, 0.72287393
],
dtype=np.float32))
def test_intermittent_timecut(self):
hbond = HBAC(
self.u,
hydrogens=self.H,
acceptors=self.O,
donors=self.N,
bond_type='intermittent',
time_cut=0.01, # time cut at traj.dt == continuous
sample_time=0.06,
)
hbond.run()
assert_array_almost_equal(
hbond.solution['results'],
np.array([
1., 0.92668623, 0.83137828, 0.74486804, 0.67741936, 0.60263932
],
dtype=np.float32))
def test_intermittent_excl(self):
hbond = HBAC(
self.u,
hydrogens=self.H,
acceptors=self.O,
donors=self.N,
bond_type='intermittent',
exclusions=self.excl_list,
sample_time=0.06,
)
hbond.run()
assert_array_almost_equal(
hbond.solution['results'],
np.array([
1., 0.92668623, 0.84310848, 0.79325515, 0.76392961, 0.72287393
],
dtype=np.float32))
# For `solve` the test trajectories aren't long enough
# So spoof the results and check that solver finds solution
@dec.skipif(module_not_found('scipy'))
def test_solve_continuous(self):
hbond = HBAC(
self.u,
hydrogens=self.H,
acceptors=self.O,
donors=self.N,
bond_type='continuous',
sample_time=0.06,
)
def actual_function_cont(t):
A1 = 0.75
A2 = 0.25
tau1 = 0.5
tau2 = 0.1
return A1 * np.exp(-t / tau1) + A2 * np.exp(-t / tau2)
hbond.solution['time'] = time = np.arange(0, 0.06, 0.001)
hbond.solution['results'] = actual_function_cont(time)
hbond.solve()
assert_array_almost_equal(
hbond.solution['fit'],
np.array([0.75, 0.5, 0.1]),
)
@dec.skipif(module_not_found('scipy'))
def test_solve_intermittent(self):
hbond = HBAC(
self.u,
hydrogens=self.H,
acceptors=self.O,
donors=self.N,
bond_type='intermittent',
sample_time=0.06,
)
def actual_function_int(t):
A1 = 0.33
A2 = 0.33
A3 = 0.34
tau1 = 5
tau2 = 1
tau3 = 0.1
return A1 * np.exp(-t / tau1) + A2 * np.exp(
-t / tau2) + A3 * np.exp(-t / tau3)
hbond.solution['time'] = time = np.arange(0, 6.0, 0.01)
hbond.solution['results'] = actual_function_int(time)
hbond.solve()
assert_array_almost_equal(
hbond.solution['fit'],
np.array([0.33, 0.33, 5, 1, 0.1]),
)
def test_save(self):
hbond = HBAC(
self.u,
hydrogens=self.H,
acceptors=self.O,
donors=self.N,
bond_type='continuous',
sample_time=0.06,
)
hbond.run()
with tempdir.in_tempdir():
hbond.save_results('hbondout.npz')
loaded = np.load('hbondout.npz')
assert_('time' in loaded)
assert_('results' in loaded)
# setup errors
def test_wronglength_DA(self):
assert_raises(
ValueError,
HBAC,
self.u,
hydrogens=self.H[:-1],
acceptors=self.O,
donors=self.N,
bond_type='intermittent',
exclusions=self.excl_list,
sample_time=0.06,
)
def test_exclusions(self):
excl_list2 = self.excl_list[0], self.excl_list[1][:-1]
assert_raises(
ValueError,
HBAC,
self.u,
hydrogens=self.H,
acceptors=self.O,
donors=self.N,
bond_type='intermittent',
exclusions=excl_list2,
sample_time=0.06,
)
def test_bond_type_VE(self):
assert_raises(
ValueError,
HBAC,
self.u,
hydrogens=self.H,
acceptors=self.O,
donors=self.N,
bond_type='marzipan',
exclusions=self.excl_list,
sample_time=0.06,
)
@dec.skipif(module_not_found('scipy'))
def test_solve_before_run_VE(self):
hbond = HBAC(
self.u,
hydrogens=self.H,
acceptors=self.O,
donors=self.N,
bond_type='continuous',
sample_time=0.06,
)
assert_raises(ValueError, hbond.solve)
@mock.patch('MDAnalysis.coordinates.TRZ.TRZReader._read_frame')
def test_unslicable_traj_VE(self, mock_read):
mock_read.side_effect = TypeError
assert_raises(ValueError,
HBAC,
self.u,
hydrogens=self.H,
acceptors=self.O,
donors=self.N,
bond_type='continuous',
sample_time=0.06)
def test_save_without_run_VE(self):
hbond = HBAC(
self.u,
hydrogens=self.H,
acceptors=self.O,
donors=self.N,
bond_type='continuous',
sample_time=0.06,
)
assert_raises(ValueError, hbond.save_results)
def test_repr(self):
hbond = HBAC(
self.u,
hydrogens=self.H,
acceptors=self.O,
donors=self.N,
bond_type='continuous',
sample_time=0.06,
)
assert_(isinstance(repr(hbond), six.string_types))
class TestPSAnalysis(TestCase):
@dec.skipif(parser_not_found('DCD'),
'DCD parser not available. Are you using python 3?')
@dec.skipif(module_not_found('matplotlib'),
"Test skipped because matplotlib is not available.")
@dec.skipif(module_not_found('scipy'),
"Test skipped because scipy is not available.")
def setUp(self):
self.tmpdir = tempdir.TempDir()
self.iu1 = np.triu_indices(3, k=1)
self.universe1 = mda.Universe(PSF, DCD)
self.universe2 = mda.Universe(PSF, DCD2)
self.universe_rev = mda.Universe(PSF, DCD)
self.universes = [self.universe1, self.universe2, self.universe_rev]
self.psa = PSA.PSAnalysis(self.universes,
path_select='name CA',
targetdir=self.tmpdir.name)
self.psa.generate_paths(align=True)
self.psa.paths[-1] = self.psa.paths[-1][::
-1, :, :] # reverse third path
self._run()
self._plot()
def _run(self):
self.psa.run(metric='hausdorff')
self.hausd_matrix = self.psa.get_pairwise_distances()
self.psa.run(metric='discrete_frechet')
self.frech_matrix = self.psa.get_pairwise_distances()
self.hausd_dists = self.hausd_matrix[self.iu1]
self.frech_dists = self.frech_matrix[self.iu1]
def _plot(self):
self.plot_data = self.psa.plot()
def tearDown(self):
del self.universe1
del self.universe2
del self.universe_rev
del self.psa
del self.tmpdir
def test_hausdorff_bound(self):
err_msg = "Some Frechet distances are smaller than corresponding " \
+ "Hausdorff distances"
assert_array_less(self.hausd_dists, self.frech_dists, err_msg)
def test_reversal_hausdorff(self):
err_msg = "Hausdorff distances changed after path reversal"
assert_array_almost_equal(self.hausd_matrix[1, 2],
self.hausd_matrix[0, 1],
decimal=3,
err_msg=err_msg)
def test_reversal_frechet(self):
err_msg = "Frechet distances did not increase after path reversal"
assert_(self.frech_matrix[1, 2] >= self.frech_matrix[0, 1], err_msg)
def test_dendrogram_produced(self):
err_msg = "Dendrogram dictionary object was not produced"
assert_(type(self.plot_data[1]) is dict, err_msg)
def test_dist_mat_to_vec_i_less_j(self):
"""Test the index of corresponding distance vector is correct if i < j"""
err_msg = "dist_mat_to_vec function returning wrong values"
assert_equal(PSA.dist_mat_to_vec(5, 3, 4), 9, err_msg)
def test_dist_mat_to_vec_i_greater_j(self):
"""Test the index of corresponding distance vector is correct if i > j"""
err_msg = "dist_mat_to_vec function returning wrong values"
assert_equal(PSA.dist_mat_to_vec(5, 4, 3), 9, err_msg)
def test_dist_mat_to_vec_input_numpy_integer_32(self):
"""Test whether inputs are supported as numpy integers rather than normal Integers"""
err_msg = "dist_mat_to_vec function returning wrong values"
assert_equal(
PSA.dist_mat_to_vec(np.int32(5), np.int32(3), np.int32(4)),
np.int32(9), err_msg)
def test_dist_mat_to_vec_input_numpy_integer_16(self):
"""Test whether inputs are supported as numpy integers rather than normal Integers"""
err_msg = "dist_mat_to_vec function returning wrong values"
assert_equal(
PSA.dist_mat_to_vec(np.int16(5), np.int16(3), np.int16(4)),
np.int16(9), err_msg)
class TestNCDFWriterVelsForces(TestCase):
"""Test writing NCDF trajectories with a mixture of options"""
@dec.skipif(module_not_found("netCDF4"),
"Test skipped because netCDF is not available.")
def setUp(self):
self.tmpdir = tempdir.TempDir()
self.outfile = self.tmpdir.name + '/ncdf-write-vels-force.ncdf'
self.prec = 3
self.top = XYZ_mini
self.n_atoms = 3
self.ts1 = mda.coordinates.TRJ.Timestep(self.n_atoms,
velocities=True,
forces=True)
self.ts1._pos[:] = np.arange(self.n_atoms * 3).reshape(self.n_atoms, 3)
self.ts1._velocities[:] = np.arange(self.n_atoms * 3).reshape(
self.n_atoms, 3) + 100
self.ts1._forces[:] = np.arange(self.n_atoms * 3).reshape(
self.n_atoms, 3) + 200
self.ts2 = mda.coordinates.TRJ.Timestep(self.n_atoms,
velocities=True,
forces=True)
self.ts2._pos[:] = np.arange(self.n_atoms * 3).reshape(
self.n_atoms, 3) + 300
self.ts2._velocities[:] = np.arange(self.n_atoms * 3).reshape(
self.n_atoms, 3) + 400
self.ts2._forces[:] = np.arange(self.n_atoms * 3).reshape(
self.n_atoms, 3) + 500
def tearDown(self):
try:
os.unlink(self.outfile)
except:
pass
del self.ts1
del self.ts2
del self.tmpdir
def _write_ts(self, pos, vel, force):
"""Write the two reference timesteps, then open them up and check values
pos vel and force are bools which define whether these properties
should be in TS
"""
with mda.Writer(self.outfile,
n_atoms=self.n_atoms,
velocities=vel,
forces=force) as w:
w.write(self.ts1)
w.write(self.ts2)
u = mda.Universe(self.top, self.outfile)
for ts, ref_ts in zip(u.trajectory, [self.ts1, self.ts2]):
if pos:
assert_almost_equal(ts._pos, ref_ts._pos, self.prec)
else:
assert_raises(mda.NoDataError, getattr, ts, 'positions')
if vel:
assert_almost_equal(ts._velocities, ref_ts._velocities,
self.prec)
else:
assert_raises(mda.NoDataError, getattr, ts, 'velocities')
if force:
assert_almost_equal(ts._forces, ref_ts._forces, self.prec)
else:
assert_raises(mda.NoDataError, getattr, ts, 'forces')
u.trajectory.close()
def test_pos(self):
self._write_ts(True, False, False)
def test_pos_vel(self):
self._write_ts(True, True, False)
def test_pos_force(self):
self._write_ts(True, False, True)
def test_pos_vel_force(self):
self._write_ts(True, True, True)
class TestEncore(TestCase):
@dec.skipif(parser_not_found('DCD'),
'DCD parser not available. Are you using python 3?')
def setUp(self):
# Create universe from templates defined in setUpClass
self.ens1 = mda.Universe(
self.ens1_template.filename,
self.ens1_template.trajectory.timeseries(format='fac'),
format=mda.coordinates.memory.MemoryReader)
self.ens2 = mda.Universe(
self.ens2_template.filename,
self.ens2_template.trajectory.timeseries(format='fac'),
format=mda.coordinates.memory.MemoryReader)
def tearDown(self):
del self.ens1
del self.ens2
@classmethod
@dec.skipif(parser_not_found('DCD'),
'DCD parser not available. Are you using python 3?')
def setUpClass(cls):
# To speed up tests, we read in trajectories from file only once,
# and then recreate them from their coordinate array for each test
super(TestEncore, cls).setUpClass()
cls.ens1_template = mda.Universe(PSF, DCD)
cls.ens2_template = mda.Universe(PSF, DCD2)
cls.ens1_template.transfer_to_memory()
cls.ens2_template.transfer_to_memory()
# Filter ensembles to only include every 5th frame
cls.ens1_template = mda.Universe(
cls.ens1_template.filename,
np.copy(
cls.ens1_template.trajectory.timeseries(
format='fac')[::5, :, :]),
format=mda.coordinates.memory.MemoryReader)
cls.ens2_template = mda.Universe(
cls.ens2_template.filename,
np.copy(
cls.ens2_template.trajectory.timeseries(
format='fac')[::5, :, :]),
format=mda.coordinates.memory.MemoryReader)
@classmethod
def tearDownClass(cls):
del cls.ens1_template
del cls.ens2_template
@staticmethod
def test_triangular_matrix():
scalar = 2
size = 3
expected_value = 1.984
filename = tempfile.mktemp() + ".npz"
triangular_matrix = encore.utils.TriangularMatrix(size=size)
triangular_matrix[0, 1] = expected_value
assert_equal(
triangular_matrix[0, 1],
expected_value,
err_msg=
"Data error in TriangularMatrix: read/write are not consistent")
assert_equal(
triangular_matrix[0, 1],
triangular_matrix[1, 0],
err_msg="Data error in TriangularMatrix: matrix non symmetrical")
triangular_matrix.savez(filename)
triangular_matrix_2 = encore.utils.TriangularMatrix(size=size,
loadfile=filename)
assert_equal(
triangular_matrix_2[0, 1],
expected_value,
err_msg=
"Data error in TriangularMatrix: loaded matrix non symmetrical")
triangular_matrix_3 = encore.utils.TriangularMatrix(size=size)
triangular_matrix_3.loadz(filename)
assert_equal(
triangular_matrix_3[0, 1],
expected_value,
err_msg=
"Data error in TriangularMatrix: loaded matrix non symmetrical")
incremented_triangular_matrix = triangular_matrix + scalar
assert_equal(
incremented_triangular_matrix[0, 1],
expected_value + scalar,
err_msg="Error in TriangularMatrix: addition of scalar gave\
inconsistent results")
triangular_matrix += scalar
assert_equal(
triangular_matrix[0, 1],
expected_value + scalar,
err_msg="Error in TriangularMatrix: addition of scalar gave\
inconsistent results")
multiplied_triangular_matrix_2 = triangular_matrix_2 * scalar
assert_equal(
multiplied_triangular_matrix_2[0, 1],
expected_value * scalar,
err_msg="Error in TriangularMatrix: multiplication by scalar gave\
inconsistent results")
triangular_matrix_2 *= scalar
assert_equal(
triangular_matrix_2[0, 1],
expected_value * scalar,
err_msg="Error in TriangularMatrix: multiplication by scalar gave\
inconsistent results")
@staticmethod
def test_parallel_calculation():
def function(x):
return x**2
arguments = [tuple([i]) for i in np.arange(0, 100)]
parallel_calculation = encore.utils.ParallelCalculation(
function=function, n_jobs=4, args=arguments)
results = parallel_calculation.run()
for i, r in enumerate(results):
assert_equal(r[1],
arguments[i][0]**2,
err_msg="Unexpeted results from ParallelCalculation")
def test_rmsd_matrix_with_superimposition(self):
conf_dist_matrix = encore.confdistmatrix.conformational_distance_matrix(
self.ens1,
encore.confdistmatrix.set_rmsd_matrix_elements,
selection="name CA",
pairwise_align=True,
weights='mass',
n_jobs=1)
reference = rms.RMSD(self.ens1, select="name CA")
reference.run()
for i, rmsd in enumerate(reference.rmsd):
assert_almost_equal(
conf_dist_matrix[0, i],
rmsd[2],
decimal=3,
err_msg=
"calculated RMSD values differ from the reference implementation"
)
def test_rmsd_matrix_with_superimposition_custom_weights(self):
conf_dist_matrix = encore.confdistmatrix.conformational_distance_matrix(
self.ens1,
encore.confdistmatrix.set_rmsd_matrix_elements,
selection="name CA",
pairwise_align=True,
weights='mass',
n_jobs=1)
conf_dist_matrix_custom = encore.confdistmatrix.conformational_distance_matrix(
self.ens1,
encore.confdistmatrix.set_rmsd_matrix_elements,
selection="name CA",
pairwise_align=True,
weights=(self.ens1.atoms.CA.masses, self.ens1.atoms.CA.masses),
n_jobs=1)
for i in range(conf_dist_matrix_custom.size):
assert_almost_equal(conf_dist_matrix_custom[0, i],
conf_dist_matrix[0, i])
def test_rmsd_matrix_without_superimposition(self):
selection_string = "name CA"
selection = self.ens1.select_atoms(selection_string)
reference_rmsd = []
coordinates = self.ens1.trajectory.timeseries(selection, format='fac')
for coord in coordinates:
reference_rmsd.append(
rms.rmsd(coordinates[0], coord, superposition=False))
confdist_matrix = encore.confdistmatrix.conformational_distance_matrix(
self.ens1,
encore.confdistmatrix.set_rmsd_matrix_elements,
selection=selection_string,
pairwise_align=False,
weights='mass',
n_jobs=1)
print(repr(confdist_matrix.as_array()[0, :]))
assert_almost_equal(
confdist_matrix.as_array()[0, :],
reference_rmsd,
decimal=3,
err_msg="calculated RMSD values differ from reference")
@staticmethod
def test_ensemble_superimposition():
aligned_ensemble1 = mda.Universe(PSF, DCD)
align.AlignTraj(aligned_ensemble1,
aligned_ensemble1,
select="name CA",
in_memory=True).run()
aligned_ensemble2 = mda.Universe(PSF, DCD)
align.AlignTraj(aligned_ensemble2,
aligned_ensemble2,
select="name *",
in_memory=True).run()
rmsfs1 = rms.RMSF(aligned_ensemble1.select_atoms('name *'))
rmsfs1.run()
rmsfs2 = rms.RMSF(aligned_ensemble2.select_atoms('name *'))
rmsfs2.run()
assert_equal(
sum(rmsfs1.rmsf) > sum(rmsfs2.rmsf),
True,
err_msg=
"Ensemble aligned on all atoms should have lower full-atom RMSF "
"than ensemble aligned on only CAs.")
@staticmethod
def test_ensemble_superimposition_to_reference_non_weighted():
aligned_ensemble1 = mda.Universe(PSF, DCD)
align.AlignTraj(aligned_ensemble1,
aligned_ensemble1,
select="name CA",
in_memory=True).run()
aligned_ensemble2 = mda.Universe(PSF, DCD)
align.AlignTraj(aligned_ensemble2,
aligned_ensemble2,
select="name *",
in_memory=True).run()
rmsfs1 = rms.RMSF(aligned_ensemble1.select_atoms('name *'))
rmsfs1.run()
rmsfs2 = rms.RMSF(aligned_ensemble2.select_atoms('name *'))
rmsfs2.run()
assert_equal(
sum(rmsfs1.rmsf) > sum(rmsfs2.rmsf),
True,
err_msg=
"Ensemble aligned on all atoms should have lower full-atom RMSF "
"than ensemble aligned on only CAs.")
def test_hes_to_self(self):
results, details = encore.hes([self.ens1, self.ens1])
result_value = results[0, 1]
expected_value = 0.
assert_almost_equal(
result_value,
expected_value,
err_msg="Harmonic Ensemble Similarity to itself not zero: {0:f}".
format(result_value))
def test_hes(self):
results, details = encore.hes([self.ens1, self.ens2], weights='mass')
result_value = results[0, 1]
min_bound = 1E5
self.assertGreater(
result_value,
min_bound,
msg=
"Unexpected value for Harmonic Ensemble Similarity: {0:f}. Expected {1:f}."
.format(result_value, min_bound))
def test_hes_custom_weights(self):
results, details = encore.hes([self.ens1, self.ens2], weights='mass')
results_custom, details_custom = encore.hes(
[self.ens1, self.ens2],
weights=(self.ens1.atoms.CA.masses, self.ens2.atoms.CA.masses))
result_value = results[0, 1]
result_value_custom = results_custom[0, 1]
assert_almost_equal(result_value, result_value_custom)
def test_hes_align(self):
# This test is massively sensitive!
# Get 5260 when masses were float32?
results, details = encore.hes([self.ens1, self.ens2], align=True)
result_value = results[0, 1]
expected_value = 2047.05
assert_almost_equal(
result_value,
expected_value,
decimal=-3,
err_msg=
"Unexpected value for Harmonic Ensemble Similarity: {0:f}. Expected {1:f}."
.format(result_value, expected_value))
def test_ces_to_self(self):
results, details = \
encore.ces([self.ens1, self.ens1],
clustering_method=encore.AffinityPropagationNative(preference = -3.0))
result_value = results[0, 1]
expected_value = 0.
assert_almost_equal(
result_value,
expected_value,
err_msg="ClusteringEnsemble Similarity to itself not zero: {0:f}".
format(result_value))
def test_ces(self):
results, details = encore.ces([self.ens1, self.ens2])
result_value = results[0, 1]
expected_value = 0.51
assert_almost_equal(
result_value,
expected_value,
decimal=2,
err_msg=
"Unexpected value for Cluster Ensemble Similarity: {0:f}. Expected {1:f}."
.format(result_value, expected_value))
@dec.skipif(module_not_found('scipy'),
"Test skipped because scipy is not available.")
def test_dres_to_self(self):
results, details = encore.dres([self.ens1, self.ens1])
result_value = results[0, 1]
expected_value = 0.
assert_almost_equal(
result_value,
expected_value,
decimal=2,
err_msg=
"Dim. Reduction Ensemble Similarity to itself not zero: {0:f}".
format(result_value))
@dec.skipif(module_not_found('scipy'),
"Test skipped because scipy is not available.")
def test_dres(self):
results, details = encore.dres([self.ens1, self.ens2],
selection="name CA and resnum 1-10")
result_value = results[0, 1]
upper_bound = 0.6
self.assertLess(
result_value,
upper_bound,
msg=
"Unexpected value for Dim. reduction Ensemble Similarity: {0:f}. Expected {1:f}."
.format(result_value, upper_bound))
@dec.skipif(module_not_found('scipy'),
"Test skipped because scipy is not available.")
def test_dres_without_superimposition(self):
distance_matrix = encore.get_distance_matrix(encore.merge_universes(
[self.ens1, self.ens2]),
superimpose=False)
results, details = encore.dres([self.ens1, self.ens2],
distance_matrix=distance_matrix)
result_value = results[0, 1]
expected_value = 0.68
assert_almost_equal(
result_value,
expected_value,
decimal=1,
err_msg=
"Unexpected value for Dim. reduction Ensemble Similarity: {0:f}. Expected {1:f}."
.format(result_value, expected_value))
def test_ces_convergence(self):
expected_values = [0.3443593, 0.1941854, 0.06857104, 0.]
results = encore.ces_convergence(self.ens1, 5)
print(results)
for i, ev in enumerate(expected_values):
assert_almost_equal(
ev,
results[i],
decimal=2,
err_msg=
"Unexpected value for Clustering Ensemble similarity in convergence estimation"
)
@dec.skipif(module_not_found('scipy'),
"Test skipped because scipy is not available.")
def test_dres_convergence(self):
expected_values = [0.3, 0.]
results = encore.dres_convergence(self.ens1, 10)
assert_almost_equal(
results[:, 0],
expected_values,
decimal=1,
err_msg=
"Unexpected value for Dim. reduction Ensemble similarity in convergence estimation"
)
@dec.slow
def test_hes_error_estimation(self):
expected_average = 10
expected_stdev = 12
averages, stdevs = encore.hes([self.ens1, self.ens1],
estimate_error=True,
bootstrapping_samples=10,
selection="name CA and resnum 1-10")
average = averages[0, 1]
stdev = stdevs[0, 1]
assert_almost_equal(
average,
expected_average,
decimal=-2,
err_msg=
"Unexpected average value for bootstrapped samples in Harmonic Ensemble imilarity"
)
assert_almost_equal(
stdev,
expected_stdev,
decimal=-2,
err_msg=
"Unexpected standard daviation for bootstrapped samples in Harmonic Ensemble imilarity"
)
@dec.slow
def test_ces_error_estimation(self):
expected_average = 0.03
expected_stdev = 0.31
averages, stdevs = encore.ces(
[self.ens1, self.ens1],
estimate_error=True,
bootstrapping_samples=10,
clustering_method=encore.AffinityPropagationNative(
preference=-2.0),
selection="name CA and resnum 1-10")
average = averages[0, 1]
stdev = stdevs[0, 1]
assert_almost_equal(
average,
expected_average,
decimal=1,
err_msg=
"Unexpected average value for bootstrapped samples in Clustering Ensemble similarity"
)
assert_almost_equal(
stdev,
expected_stdev,
decimal=0,
err_msg=
"Unexpected standard daviation for bootstrapped samples in Clustering Ensemble similarity"
)
@dec.skipif(module_not_found('sklearn'),
"Test skipped because sklearn is not available.")
@dec.slow
def test_ces_error_estimation_ensemble_bootstrap(self):
# Error estimation using a method that does not take a distance
# matrix as input, and therefore relies on bootstrapping the ensembles
# instead
expected_average = 0.03
expected_stdev = 0.02
averages, stdevs = encore.ces(
[self.ens1, self.ens1],
estimate_error=True,
bootstrapping_samples=10,
clustering_method=encore.KMeans(n_clusters=2),
selection="name CA and resnum 1-10")
average = averages[0, 1]
stdev = stdevs[0, 1]
assert_almost_equal(
average,
expected_average,
decimal=1,
err_msg=
"Unexpected average value for bootstrapped samples in Clustering Ensemble similarity"
)
assert_almost_equal(
stdev,
expected_stdev,
decimal=1,
err_msg=
"Unexpected standard daviation for bootstrapped samples in Clustering Ensemble similarity"
)
@dec.slow
@dec.skipif(module_not_found('scipy'),
"Test skipped because scipy is not available.")
def test_dres_error_estimation(self):
average_upper_bound = 0.3
stdev_upper_bound = 0.2
averages, stdevs = encore.dres([self.ens1, self.ens1],
estimate_error=True,
bootstrapping_samples=10,
selection="name CA and resnum 1-10")
average = averages[0, 1]
stdev = stdevs[0, 1]
self.assertLess(
average,
average_upper_bound,
msg=
"Unexpected average value for bootstrapped samples in Dim. reduction Ensemble similarity"
)
self.assertLess(
stdev,
stdev_upper_bound,
msg=
"Unexpected standard deviation for bootstrapped samples in Dim. reduction Ensemble imilarity"
)
class TestNCDF(BaseTimestepInterfaceTest):
@dec.skipif(module_not_found("netCDF4"),
"Test skipped because netCDF is not available.")
def setUp(self):
u = self.u = mda.Universe(PRMncdf, NCDF)
self.ts = u.trajectory.ts
class TestHOLEtraj(TestCase):
filename = MULTIPDB_HOLE
start = 5
stop = 7
# HOLE is so slow so we only run it once and keep it in
# the class; note that you may not change universe.trajectory
# (eg iteration) because this is not safe in parallel
@classmethod
def setUpClass(cls):
cls.universe = MDAnalysis.Universe(cls.filename)
if not executable_not_found("hole"):
with tempdir.in_tempdir():
H = HOLEtraj(cls.universe,
start=cls.start,
stop=cls.stop,
raseed=31415)
H.run()
cls.H = H
else:
cls.H = None
cls.frames = [
ts.frame for ts in cls.universe.trajectory[cls.start:cls.stop]
]
@classmethod
def tearDownClass(cls):
del cls.H
del cls.universe
# This is VERY slow on 11 frames so we just take 2
@attr('slow')
@dec.skipif(executable_not_found("hole"),
msg="Test skipped because HOLE not found")
def test_HOLEtraj(self):
assert_array_equal(
sorted(self.H.profiles.keys()),
self.frames,
err_msg="H.profiles.keys() should contain the frame numbers")
data = np.transpose([(len(p), p.rxncoord.mean(), p.radius.min())
for p in self.H.profiles.values()])
assert_array_equal(data[0], [401, 399],
err_msg="incorrect profile lengths")
assert_array_almost_equal(data[1], [1.98767, 0.0878],
err_msg="wrong mean HOLE rxncoord")
assert_array_almost_equal(data[2], [1.19819, 1.29628],
err_msg="wrong minimum radius")
@attr('slow')
@dec.skipif(executable_not_found("hole"),
msg="Test skipped because HOLE not found")
def test_min_radius(self):
assert_array_almost_equal(self.H.min_radius(),
np.array([[5., 1.19819], [6., 1.29628]]),
err_msg="min_radius() array not correct")
@attr('slow')
@dec.skipif(executable_not_found("hole"),
msg="Test skipped because HOLE not found")
@dec.skipif(module_not_found("matplotlib"))
def test_plot(self):
import matplotlib.axes
ax = self.H.plot(label=True)
assert_(isinstance(ax, matplotlib.axes.Axes),
msg="H.plot() did not produce an Axes instance")
@attr('slow')
@dec.skipif(executable_not_found("hole"),
msg="Test skipped because HOLE not found")
@dec.skipif(module_not_found("matplotlib"))
def test_plot3D(self):
import mpl_toolkits.mplot3d
ax = self.H.plot3D()
assert_(isinstance(ax, mpl_toolkits.mplot3d.Axes3D),
msg="H.plot3D() did not produce an Axes3D instance")
@attr('slow')
@dec.skipif(executable_not_found("hole"),
msg="Test skipped because HOLE not found")
@dec.skipif(module_not_found("matplotlib"))
def test_plot3D_rmax(self):
import mpl_toolkits.mplot3d
ax = self.H.plot3D(rmax=2.5)
assert_(isinstance(ax, mpl_toolkits.mplot3d.Axes3D),
msg="H.plot3D(rmax=float) did not produce an Axes3D instance")
