def test_rmsf_weights(self):
CA = self.universe.atoms.CA
rmsfs_mass = rms.RMSF(CA, weights='mass')
rmsfs_mass.run()
rmsfs_custom = rms.RMSF(CA, weights=CA.masses)
rmsfs_custom.run()
assert_almost_equal(rmsfs_mass.rmsf,
rmsfs_custom.rmsf,
5,
err_msg="error: rmsf profile should match test "
"values")
def test_rmsf_single_frame(self, universe):
rmsfs = rms.RMSF(universe.select_atoms('name CA')).run(start=5, stop=6)
assert_almost_equal(rmsfs.rmsf,
0,
5,
err_msg="error: rmsfs should all be zero")
def rmsf_plot(trajectories, title, structure=False):
fig, ax = plt.subplots(len(trajectories),
1,
sharex=True,
figsize=(15, 4 * len(trajectories)),
squeeze=False)
for i in range(len(trajectories)):
protein = trajectories[i].select_atoms("protein")
calphas = protein.select_atoms("name CA")
rmsfer = rms.RMSF(calphas).run()
ax[i, 0].plot(calphas.resnums, rmsfer.rmsf, color='green')
if structure:
for j, k, in enumerate(structure['helix']):
ax[i, 0].axvline(k, color='#3b3b3b', alpha=0.1)
# for j, k, in enumerate(structure['pi_helix']):
# ax[i,0].axvline(k, color='grey', alpha=0.2)
ax[i, 0].set_ylabel('RMSF ($\AA$)')
ax[i, 0].set_title(title.format(i + 1))
ax[len(trajectories) - 1, 0].set_xlabel('residue ID')
plt.tight_layout()
plt.show()
return fig, calphas.resnums, rmsfer.rmsf
def test_rmsf(self, universe):
rmsfs = rms.RMSF(universe.select_atoms('name CA'))
rmsfs.run()
test_rmsfs = np.load(rmsfArray)
assert_almost_equal(rmsfs.rmsf, test_rmsfs, 5,
err_msg="error: rmsf profile should match test "
"values")
def test_rmsf_old_run(self, universe):
# start/stop arguments should be given at class initialization
with pytest.deprecated_call():
rmsfs = rms.RMSF(universe.select_atoms('name CA')).run(start=5,
stop=6)
assert_almost_equal(rmsfs.rmsf, 0, 5,
err_msg="error: rmsfs should all be zero")
def setup(self, n_atoms, step, weights):
self.u = MDAnalysis.Universe(PSF, DCD)
self.ag = self.u.atoms[:n_atoms]
self.RMSF_inst = rms.RMSF(atomgroup=self.ag,
start=None,
stop=None,
step=step,
weights=weights)
def time_series_rmsf(u, sel=STANDARD_SELECTION) -> List[float]:
"""Extract RMSF timeseries from an Universe."""
align_universe(u, AlignType.MEAN_FRAME)
rmsf = rms.RMSF(u.select_atoms(sel=sel)).run().rmsf
return cast(List[float], rmsf)
def test_rmsf_old_run(self):
rmsfs = rms.RMSF(self.universe.select_atoms('name CA'))
rmsfs.run(start=5, stop=6)
assert_almost_equal(rmsfs.rmsf,
0,
5,
err_msg="error: rmsfs should all be zero")
def test_ensemble_superimposition_to_reference_non_weighted(self):
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 sum(rmsfs1.rmsf) > sum(rmsfs2.rmsf), "Ensemble aligned on all " \
"atoms should have lower full-atom RMSF than ensemble aligned on only CAs."
def test_rmsf_identical_frames(self):
# write a dummy trajectory of all the same frame
with mda.Writer(self.outfile, self.universe.atoms.n_atoms) as W:
for _ in range(self.universe.trajectory.n_frames):
W.write(self.universe)
self.universe = mda.Universe(GRO, self.outfile)
rmsfs = rms.RMSF(self.universe.select_atoms('name CA'))
rmsfs.run()
assert_almost_equal(rmsfs.rmsf,
0,
5,
err_msg="error: rmsfs should all be 0")
def test_rmsf_identical_frames(self, universe, tmpdir):
outfile = os.path.join(str(tmpdir), 'rmsf.xtc')
# write a dummy trajectory of all the same frame
with mda.Writer(outfile, universe.atoms.n_atoms) as W:
for _ in range(universe.trajectory.n_frames):
W.write(universe)
universe = mda.Universe(GRO, outfile)
rmsfs = rms.RMSF(universe.select_atoms('name CA'))
rmsfs.run()
assert_almost_equal(rmsfs.rmsf, 0, 5,
err_msg="error: rmsfs should all be 0")
def run_rmsf(universe):
prot = universe.select_atoms("protein")
R = rms.RMSF(prot)
R.run()
CA = prot.select_atoms("name CA")
CA_rmsf = R.rmsf[np.in1d(prot.ids, CA.ids)]
print(CA.resids)
print(CA_rmsf)
return CA, CA_rmsf
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.")
def test_rmsf_xtc(run):
"""Align multiple times + RMSF"""
u = mda.Universe(TPR, XTC)
u2 = u.copy()
average = align.AverageStructure(u2, u2, select='protein and name CA',
ref_frame=0).run()
ref = average.universe
aligner = align.AlignTraj(u2, ref,
select='protein and name CA',
in_memory=True).run()
c_alphas = u2.select_atoms('protein and name CA')
R = rms.RMSF(c_alphas).run()
assert 1 == 1
def rmsf_selected_residues(trajectories, residues, title):
fig, ax = plt.subplots(len(trajectories),
1,
sharex=True,
figsize=(15, 4 * len(trajectories)),
squeeze=False)
for i in range(len(trajectories)):
protein = trajectories[i].select_atoms("protein")
# calphas = protein.select_atoms("name CA and ( resid 3:32 or resid 39:67 or resid 73:105 or resid 116:142 or resid 165:205 or resid 213:245 or resid 255:279 )")
calphas = protein.select_atoms(
"name CA and ( resid 4:29 or resid 39:67 or resid 73:115 or resid 118:142 or resid 168:201 or resid 210:245 or resid 252:279 )"
)
rmsfer = rms.RMSF(calphas).run()
ax[i, 0].plot(list(range(len(calphas))), rmsfer.rmsf)
ax[i, 0].set_ylabel('RMSF ($\AA$)')
ax[i, 0].set_title(title.format(i + 1))
ax[len(trajectories) - 1, 0].set_xlabel('residue')
plt.tight_layout()
plt.show()
return fig
def RMSF(u, PU_directory_output):
# calcule le RMSF de la PU
print("Processing RMSF...")
average = align.AverageStructure(u,
u,
select='protein and name CA',
ref_frame=0).run()
ref = average.universe
aligner = align.AlignTraj(u,
ref,
select='protein and name CA',
in_memory=True).run()
c_alphas = u.select_atoms('protein and name CA')
R = rms.RMSF(c_alphas).run()
plt.figure()
ax3 = plt.subplot(111)
ax3.plot(c_alphas.resids, R.rmsf, 'b-', label="RMSF")
ax3.legend(loc="best")
ax3.set_title("RMSF")
ax3.set_xlabel("Residue")
ax3.set_ylabel(r"RMSF ($\AA$)")
ax3.figure.savefig(PU_directory_output + "RMSF.png")
print("Done!")
local_rmsf = np.zeros((local_n, N_atoms), dtype=np.float)
#folder = 'data/charmm36m/2us/'
folder = 'data/amber/T3/'
for i, j in enumerate(range(ind_start, ind_end)):
struct = folder + 'chain.pdb'
traj = folder + 'chain_{}.xtc'.format(j + 1)
print(j, traj)
sys.stdout.flush()
system = md.Universe(struct, traj)
reference = md.Universe(struct)
aligner = align.AlignTraj(system, reference, select="all",
in_memory=True).run()
R = rms.RMSF(system.select_atoms('not name H*'))
R.run(start=start, verbose=True)
local_rmsf[i, :] = R.rmsf
comm.barrier()
## Gather data
comm.Gather(local_rmsf, rmsf, root=0)
## save or print
if rank == 0:
print(rmsf.shape)
#npy_file = '6_rmsf/charmm36m/rmsf_2us'
npy_file = '6_rmsf/amber/T3_rmsf_800ns'
np.save(npy_file, rmsf)
print('RMSF are saved to ' + npy_file)
