def _rmsd(res):
#mol is an md universe
#res,name_file_dcd,mol=args
mol = MD.Universe(pdb_file, dcd_file)
print "["+str(res)+"] Aligning Trajectory"
ref_sel = ref.selectAtoms('backbone and resid ' + str(res),'backbone and around ' + cutoff + ' (backbone and resid ' + str(res) + ')')
print ref_sel.atoms
ref_sel_str = ''
for i in list(ref_sel):
useful =str(i).split(":")[1]
ref_sel_str += '(' + useful.replace('>',' ').replace("'",'').replace(',',' and').replace('of','and') + ') or '
#ref_sel_str += "(" + useful.replace('>',' ').replace("'",'') + ') or '
ref_sel_str = ref_sel_str[:-4]
#print ref_sel_str
filenamedcd = "LA" + cutoff + "rmsd.noh.R" + str(res) + '.dcd'
MDA.rms_fit_trj(mol, ref, select=ref_sel_str, filename = filenamedcd ) #'name N and type N and resname MET resid 1 and segid P1')
output = ''
print "["+str(res)+"] Calculating RMSD for residue " + str(res) + "..." + str( 1 + range(res_beg,res_end).index(res) ) + " of " + str(res_num)
mol = MD.Universe(pdb_file, filenamedcd)
cf_timer = time.time()
atoms_mol = mol.selectAtoms("resid " + str(res) + " and not name H")
mol.trajectory[ref_frame]
atoms_ref = copy.deepcopy(mol.selectAtoms("resid " + str(res) + " and not name H").coordinates())
rmsd = []
for ts1 in mol.trajectory[0:-1:skip]:
rmsd.append(MDA.rmsd(atoms_mol.coordinates(), atoms_ref))
rmsd = numpy.array(rmsd)
numpy.savetxt(name_file_dcd + "rmsd.noh.LA" + cutoff +".%04d.txt" % res , rmsd)
os.remove(filenamedcd)
def _bfactor(res):
global ave_flag
#mol is an md universe
#res,name_file_dcd,mol=args
ref = MD.Universe(pdb_file)
mol = MD.Universe(pdb_file, dcd_file)
print "["+str(res)+"] Aligning Trajectory"
ref_sel = ref.selectAtoms('backbone and resid ' + str(res),'backbone and around ' + cutoff + ' (backbone and resid ' + str(res) + ')')
print ref_sel.atoms
#for i in list(ref_sel):
# print i
# print ref_sel.indices
ref_sel_str = ''
for i in list(ref_sel):
useful =str(i).split(":")[1]
ref_sel_str += '(' + useful.replace('>',' ').replace("'",'').replace(',',' and').replace('of','and') + ') or '
#ref_sel_str += "(" + useful.replace('>',' ').replace("'",'') + ') or '
ref_sel_str = ref_sel_str[:-4]
#print ref_sel_str
filenamedcd = "LA" + cutoff + "b-factor.R" + str(res) + '.' + chain + '.dcd'
MDA.rms_fit_trj(mol, ref, select=ref_sel_str, filename = filenamedcd ) #'name N and type N and resname MET resid 1 and segid P1')
output = ''
print "["+str(res)+"] Calculating RMSF for residue " + str(res) + " of " + str(res_num)
mol = MD.Universe(pdb_file, filenamedcd)
cf_timer = time.time()
atoms_mol = mol.selectAtoms("resid " + str(res) + " and name CA and segid " +str(chain))
atoms_ref = ref.selectAtoms("resid " + str(res) + " and name CA and segid " +str(chain))
var = []
if ave_flag == 0:
for ts1 in mol.trajectory[0:-1:skip]:
var.append((numpy.linalg.norm(numpy.subtract(atoms_mol.coordinates(), atoms_ref.coordinates())))**2)
elif ave_flag:
ref_coord = []
for ts1 in mol.trajectory[0:-1:skip]:
ref_coord.append(atoms_mol.coordinates()[0])
ref_coord = numpy.array(ref_coord)
#print ref_coord.shape
#ref_coord = ref_coord[1:5]
#print numpy.average( ref_coord[:,0])
#print numpy.average( ref_coord[:,1])
#print numpy.average( ref_coord[:,2])
ave = [numpy.average( ref_coord[:,0]) , numpy.average( ref_coord[:,1]), numpy.average( ref_coord[:,2])]
for ts1 in mol.trajectory[0:-1:skip]:
var.append((numpy.linalg.norm(numpy.subtract(atoms_mol.coordinates(), ave)))**2)
var = numpy.array(var)
rmsf = numpy.average(var)
err_rmsf = numpy.std(var)
bf = (8*numpy.pi*numpy.pi/3)*rmsf*rmsf
err_bf = (8*numpy.pi**2/3)*err_rmsf*2*rmsf # error propagation of quadractic function
filename = name_file_dcd + "b-factor.LA" + cutoff +".%03d." % res + chain + ".txt"
dataout = open(filename, 'w')
dataout.write(str(res) + "\t%.5f" % rmsf + "\t%.5f" % err_rmsf + '\n')
dataout.close()
os.remove(filenamedcd)
def test_rms_fit_trj(self):
"""Testing align.rms_fit_trj() for all atoms (Issue 58)"""
# align to *last frame* in target... just for the heck of it
self.reference.trajectory[-1]
align.rms_fit_trj(self.universe, self.reference, select="all",
filename=self.outfile, verbose=False)
fitted = mda.Universe(PSF, self.outfile)
# RMSD against the reference frame
# calculated on Mac OS X x86 with MDA 0.7.2 r689
# VMD: 6.9378711
self._assert_rmsd(fitted, 0, 6.929083044751061)
self._assert_rmsd(fitted, -1, 0.0)
def test_rms_fit_trj_defaultfilename(self):
filename = 'rmsfit_' + os.path.basename(self.universe.trajectory.filename)
with tempdir.in_tempdir():
# Need to pretend to have the universe trajectory INSIDE the tempdir because
# filename=None uses the full path
self.universe.trajectory.filename = os.path.abspath(
os.path.join(
os.curdir,
os.path.basename(self.universe.trajectory.filename)))
#test filename=none and different selection
align.rms_fit_trj(self.universe, self.reference, select="name CA",
filename=None, verbose=False)
assert_(os.path.exists(filename),
"rms_fit_trj did not write to {}".format(filename))
def test_rms_fit_trj(self):
"""Testing align.rms_fit_trj() for all atoms (Issue 58)"""
# align to *last frame* in target... just for the heck of it
self.reference.trajectory[-1]
align.rms_fit_trj(self.universe,
self.reference,
select="all",
filename=self.outfile,
verbose=False)
fitted = mda.Universe(PSF, self.outfile)
# RMSD against the reference frame
# calculated on Mac OS X x86 with MDA 0.7.2 r689
# VMD: 6.9378711
self._assert_rmsd(fitted, 0, 6.929083044751061)
self._assert_rmsd(fitted, -1, 0.0)
def test_rms_fit_trj_defaultfilename(self):
filename = 'rmsfit_' + os.path.basename(
self.universe.trajectory.filename)
with tempdir.in_tempdir():
# Need to pretend to have the universe trajectory INSIDE the tempdir because
# filename=None uses the full path
self.universe.trajectory.filename = os.path.abspath(
os.path.join(
os.curdir,
os.path.basename(self.universe.trajectory.filename)))
#test filename=none and different selection
align.rms_fit_trj(self.universe,
self.reference,
select="name CA",
filename=None,
verbose=False)
assert_(os.path.exists(filename),
"rms_fit_trj did not write to {}".format(filename))
def _rmsd(res):
#mol is an md universe
#res,name_file_dcd,mol=args
mol = MD.Universe(pdb_file, dcd_file)
print "[" + str(res) + "] Aligning Trajectory"
ref_sel = ref.selectAtoms(
'backbone and resid ' + str(res), 'backbone and around ' + cutoff +
' (backbone and resid ' + str(res) + ')')
print ref_sel.atoms
ref_sel_str = ''
for i in list(ref_sel):
useful = str(i).split(":")[1]
ref_sel_str += '(' + useful.replace('>', ' ').replace("'", '').replace(
',', ' and').replace('of', 'and') + ') or '
#ref_sel_str += "(" + useful.replace('>',' ').replace("'",'') + ') or '
ref_sel_str = ref_sel_str[:-4]
#print ref_sel_str
filenamedcd = "LA" + cutoff + "rmsd.noh.R" + str(res) + '.dcd'
MDA.rms_fit_trj(
mol, ref, select=ref_sel_str, filename=filenamedcd
) #'name N and type N and resname MET resid 1 and segid P1')
output = ''
print "[" + str(res) + "] Calculating RMSD for residue " + str(
res) + "..." + str(
1 + range(res_beg, res_end).index(res)) + " of " + str(res_num)
mol = MD.Universe(pdb_file, filenamedcd)
cf_timer = time.time()
atoms_mol = mol.selectAtoms("resid " + str(res) + " and not name H")
mol.trajectory[ref_frame]
atoms_ref = copy.deepcopy(
mol.selectAtoms("resid " + str(res) + " and not name H").coordinates())
rmsd = []
for ts1 in mol.trajectory[0:-1:skip]:
rmsd.append(MDA.rmsd(atoms_mol.coordinates(), atoms_ref))
rmsd = numpy.array(rmsd)
numpy.savetxt(name_file_dcd + "rmsd.noh.LA" + cutoff + ".%04d.txt" % res,
rmsd)
os.remove(filenamedcd)
def rms_fit(self):
"""Superimpose whole trajectory on first frame"""
if self.trajectoryAligned and not self.rerun:
self.vout('Trajectory of {0} already fitted\n'.format(self.name))
else:
self.vout('Fitting trajectory of {0}\n'.format(self.name))
self.universe.trajectory.rewind()
if not self.verbose:
# redirect stdout
nirvana = open('/dev/null', 'w')
sys.stdout = nirvana
sys.stderr = nirvana
align.rms_fit_trj(self.universe,
self.universe,
select='backbone',
filename=self.alignedTrj)
if not self.verbose:
# restore stdout
sys.stdout = sys.__stdout__
sys.stderr = sys.__stderr__
nirvana.close()
# pickle trajectory meta data
dumpFile = open(self.trjmetafile, 'wb')
pickle.dump(self.trjMetaData,
dumpFile,
protocol=pickle.HIGHEST_PROTOCOL)
dumpFile.close()
# load fittet trajectory
self.vout("Reading fittet trajectory of {0}\n".format(self.name))
self.universe = mda.Universe(self.topology, self.alignedTrj)
self.trajectoryAligned = True
def rms_fit(self):
"""Superimpose whole trajectory on first frame"""
if self.trajectoryAligned and not self.rerun:
self.vout('Trajectory of {0} already fitted\n'.format(self.name))
else:
self.vout('Fitting trajectory of {0}\n'.format(self.name))
self.universe.trajectory.rewind()
if not self.verbose:
# redirect stdout
nirvana = open('/dev/null', 'w')
sys.stdout = nirvana
sys.stderr = nirvana
align.rms_fit_trj(self.universe,
self.universe,
select='backbone',
filename=self.alignedTrj)
if not self.verbose:
# restore stdout
sys.stdout = sys.__stdout__
sys.stderr = sys.__stderr__
nirvana.close()
# pickle trajectory meta data
dumpFile = open(self.trjmetafile, 'wb')
pickle.dump(self.trjMetaData, dumpFile, protocol=pickle.HIGHEST_PROTOCOL)
dumpFile.close()
# load fittet trajectory
self.vout("Reading fittet trajectory of {0}\n".format(self.name))
self.universe = mda.Universe(self.topology, self.alignedTrj)
self.trajectoryAligned = True
def _ct_part(res):
#mol is an md universe
# res,name_file_dcd,mol=args
ref = MD.Universe(pdb_file)
mol = MD.Universe(pdb_file, dcd_file)
print "[" + str(res) + "] Aligning Trajectory"
ref_sel = ref.selectAtoms(
'backbone and resid ' + str(res), 'backbone and around ' + cutoff +
' (backbone and resid ' + str(res) + ')')
# print ref_sel.atoms
# for i in list(ref_sel):
# print i
# print ref_sel.indices
ref_sel_str = ''
for i in list(ref_sel):
useful = str(i).split(":")[1]
ref_sel_str += '(' + useful.replace('>', ' ').replace("'", '').replace(
',', ' and').replace('of', 'and') + ') or '
#ref_sel_str += "(" + useful.replace('>',' ').replace("'",'') + ') or '
ref_sel_str = ref_sel_str[:-4]
# print ref_sel_str
filenamedcd = "LA" + cutoff + ".R" + str(res) + '.dcd'
MDA.rms_fit_trj(
mol, ref, select=ref_sel_str, filename=filenamedcd
) #'name N and type N and resname MET resid 1 and segid P1')
output = ''
print "[" + str(res) + "] Calculating C(t) for residue " + str(
res) + " of " + str(res_num)
mol = MD.Universe(pdb_file, filenamedcd)
cf_timer = time.time()
N_atoms = mol.selectAtoms("resid " + str(res) +
" and (name N or name HN) and segid " +
str(chain))
if len(N_atoms.coordinates()) == 2:
bond_vec = []
ct_numpy = []
ct_std_numpy = []
ct_python = []
for ts1 in mol.trajectory[0:-1:skip]:
coords = N_atoms.coordinates()
coords = numpy.subtract(coords[1], coords[0])
bond_vec.append(coords / numpy.linalg.norm(coords))
print "[" + str(
res) + "] Calculating Correlation function as Chen 2004"
for t in range(len(bond_vec) / 2):
T = t * step
# x_chen.append(T)
if t == 0:
a = numpy.array(bond_vec)
b = numpy.array(bond_vec)
else:
a = numpy.array(bond_vec[:-t])
b = numpy.array(bond_vec[t:])
np_dot = inner1d(a, b)
np_dot = (3 * np_dot**2 - 1) / 2
ct = numpy.mean(np_dot)
ct_std = numpy.std(np_dot)
# ct = (3*(X)/a.shape[0]- 1)/2
ct_numpy.append(ct)
ct_std_numpy.append(ct_std)
# NH_dot = 0
# for tal in range(len(bond_vec)-t):
# NH_dot += (3*(numpy.dot(bond_vec[tal], bond_vec[tal+t])**2)-1)/2
# NH_dot = NH_dot/(len(bond_vec)-t)
# ct_python.append(NH_dot)
# output += str(res) + "\t" + str(T) + "\t" + str(NH_dot) + "\n"
output += str(res) + "\t" + str(
T) + "\t%.5f" % ct + "\t%.5f" % ct_std + "\n"
# output += str(res) + "\t" + str(T) + "\t" + str(NH_dot) + "\t" + str(ct) + "\n"
# print str(res) + "\t" + str(T) + "\t" + str(NH_dot) + "\t" + str(ct) + "\n"
# time.sleep(5)
ct_numpy = numpy.around(ct_numpy, 5)
print "[" + str(res) + "] Done in " + str(time.time() -
cf_timer) + " seconds.\n"
filename = name_file_dcd + "ct_numpy.LA" + cutoff + ".%03d." % res + chain + ".txt"
outfile = open(filename, 'w')
outfile.write(output)
outfile.close()
print "[" + str(res) + "] Collecting Trash"
os.remove(filenamedcd)
print "[" + str(res) + "] Done for real"
topology = sys.argv[1]
init_pdb = sys.argv[2]
traj_file = sys.argv[3]
lig_name = sys.argv[4]
filter_lig = 'resname ' + lig_name
filter_structure = 'protein or ' + filter_lig
init = mda.Universe(topology, init_pdb)
traj = mda.Universe(topology, traj_file)
frames = traj.trajectory
nframes = len(frames)
align.rms_fit_trj(traj, init, select="protein and backbone")
ref = init
avg_select = ref.selectAtoms(filter_structure)
align_struct = traj.selectAtoms(filter_structure)
p_avg = numpy.zeros_like(avg_select.positions)
# do a quick average of the protein
for ts in frames:
p_avg += align_struct.positions
p_avg /= nframes
# temporarily replace positions with the average
avg_select.set_positions(p_avg)
from MDAnalysis.tests.datafiles import PSF, DCD, PDB_small
try:
import matplotlib
matplotlib.use('agg')
from pylab import plot, xlabel, ylabel, savefig
have_matplotlib = True
except:
have_matplotlib = False
ref = Universe(PSF, PDB_small) # reference structure 4AKE
trj = Universe(PSF, DCD) # trajectory of change 1AKE->4AKE
rms_fit_trj(trj, ref, filename="output/rmsfit.dcd", select="not name H*",
mass_weighted=True, rmsdfile="output/rmsfit_rmsd.dat")
print "Fitted trajectory: output/rmsfit.dcd"
rmsd = numpy.loadtxt("output/rmsfit_rmsd.dat")
print "RMSD: output/rmsfit_rmsd.dat"
if have_matplotlib:
plot(rmsd, 'k-', linewidth=2)
xlabel("frame number")
ylabel(r"mass weighted heavy atom RMSD ($\AA$)")
savefig("figures/rmsdfit_rmsd.pdf")
savefig("figures/rmsdfit_rmsd.png")
print "RMSD graph: figures/rmsdfit_rmsd.{pdf,png}"
def _rmsf(res):
global ave_flag
global fop_flag
#mol is an md universe
#res,name_file_dcd,mol=args
ref = MD.Universe(pdb_file)
mol = MD.Universe(pdb_file, dcd_file)
print "[" + str(res) + "] Aligning Trajectory"
ref_sel = ref.selectAtoms(
'backbone and resid ' + str(res), 'backbone and around ' + cutoff +
' (backbone and resid ' + str(res) + ')')
print ref_sel.atoms
#for i in list(ref_sel):
# print i
# print ref_sel.indices
ref_sel_str = ''
for i in list(ref_sel):
useful = str(i).split(":")[1]
ref_sel_str += '(' + useful.replace('>', ' ').replace("'", '').replace(
',', ' and').replace('of', 'and') + ') or '
#ref_sel_str += "(" + useful.replace('>',' ').replace("'",'') + ') or '
ref_sel_str = ref_sel_str[:-4]
#print ref_sel_str
filenamedcd = "LA" + cutoff + "rmsf.R" + str(res) + '.' + chain + '.dcd'
MDA.rms_fit_trj(
mol, ref, select=ref_sel_str, filename=filenamedcd
) #'name N and type N and resname MET resid 1 and segid P1')
output = ''
print "[" + str(res) + "] Calculating RMSF for residue " + str(
res) + "..." + str(
1 + range(res_beg, res_end).index(res)) + " of " + str(res_num)
mol = MD.Universe(pdb_file, filenamedcd)
cf_timer = time.time()
atoms_mol = mol.selectAtoms("resid " + str(res) +
" and name CA and segid " + str(chain))
atoms_ref = ref.selectAtoms("resid " + str(res) +
" and name CA and segid " + str(chain))
var = []
if ave_flag == 0:
for ts1 in mol.trajectory[0:-1:skip]:
var.append((numpy.linalg.norm(
numpy.subtract(atoms_mol.coordinates(),
atoms_ref.coordinates())))**2)
elif ave_flag:
ref_coord = []
for ts1 in mol.trajectory[0:-1:skip]:
ref_coord.append(atoms_mol.coordinates()[0])
ref_coord = numpy.array(ref_coord)
#print ref_coord.shape
#ref_coord = ref_coord[1:5]
#print numpy.average( ref_coord[:,0])
#print numpy.average( ref_coord[:,1])
#print numpy.average( ref_coord[:,2])
ave = [
numpy.average(ref_coord[:, 0]),
numpy.average(ref_coord[:, 1]),
numpy.average(ref_coord[:, 2])
]
for ts1 in mol.trajectory[0:-1:skip]:
var.append(
(numpy.linalg.norm(numpy.subtract(atoms_mol.coordinates(),
ave)))**2)
var = numpy.array(var)
rmsf = numpy.average(var)
err_rmsf = numpy.std(var)
filename = name_file_dcd + "rmsf.LA" + cutoff + ".%04d." % res + chain + ".txt"
dataout = open(filename, 'w')
dataout.write(str(res) + "\t%.5f" % rmsf + "\t%.5f" % err_rmsf + '\n')
dataout.close()
os.remove(filenamedcd)
if fop_flag == 1:
output = str(res) + '\n'
for i in range(len(var)):
output += '%.5f' % var[i] + '\n'
fopfile = open(
name_file_dcd + "rmsf.LA" + cutoff + ".%04d." % res + chain +
".fulldata", 'w')
fopfile.write(output)
fopfile.close()
def _ct_part(res):
#mol is an md universe
# res,name_file_dcd,mol=args
ref = MD.Universe(pdb_file)
mol = MD.Universe(pdb_file, dcd_file)
print "["+str(res)+"] Aligning Trajectory"
ref_sel = ref.selectAtoms('backbone and resid ' + str(res),'backbone and around ' + cutoff + ' (backbone and resid ' + str(res) + ')')
# print ref_sel.atoms
# for i in list(ref_sel):
# print i
# print ref_sel.indices
ref_sel_str = ''
for i in list(ref_sel):
useful =str(i).split(":")[1]
ref_sel_str += '(' + useful.replace('>',' ').replace("'",'').replace(',',' and').replace('of','and') + ') or '
#ref_sel_str += "(" + useful.replace('>',' ').replace("'",'') + ') or '
ref_sel_str = ref_sel_str[:-4]
# print ref_sel_str
filenamedcd = "LA" + cutoff + ".R" + str(res) + '.dcd'
MDA.rms_fit_trj(mol, ref, select=ref_sel_str, filename = filenamedcd ) #'name N and type N and resname MET resid 1 and segid P1')
output = ''
print "["+str(res)+"] Calculating C(t) for residue " + str(res) + " of " + str(res_num)
mol = MD.Universe(pdb_file, filenamedcd)
cf_timer = time.time()
N_atoms = mol.selectAtoms("resid " + str(res) + " and (name N or name HN) and segid " +str(chain))
if len(N_atoms.coordinates()) == 2:
bond_vec = []
ct_numpy = []
ct_std_numpy = []
ct_python = []
for ts1 in mol.trajectory[0:-1:skip]:
coords = N_atoms.coordinates()
coords = numpy.subtract(coords[1], coords[0])
bond_vec.append(coords/numpy.linalg.norm(coords))
print "["+str(res)+"] Calculating Correlation function as Chen 2004"
for t in range(len(bond_vec)/2):
T = t*step
# x_chen.append(T)
if t == 0:
a = numpy.array(bond_vec)
b = numpy.array(bond_vec)
else:
a = numpy.array(bond_vec[:-t])
b = numpy.array(bond_vec[t:])
np_dot = inner1d(a, b)
np_dot = (3*np_dot**2 - 1)/2
ct = numpy.mean(np_dot)
ct_std = numpy.std(np_dot)
# ct = (3*(X)/a.shape[0]- 1)/2
ct_numpy.append(ct)
ct_std_numpy.append(ct_std)
# NH_dot = 0
# for tal in range(len(bond_vec)-t):
# NH_dot += (3*(numpy.dot(bond_vec[tal], bond_vec[tal+t])**2)-1)/2
# NH_dot = NH_dot/(len(bond_vec)-t)
# ct_python.append(NH_dot)
# output += str(res) + "\t" + str(T) + "\t" + str(NH_dot) + "\n"
output += str(res) + "\t" + str(T) + "\t%.5f" % ct + "\t%.5f" % ct_std + "\n"
# output += str(res) + "\t" + str(T) + "\t" + str(NH_dot) + "\t" + str(ct) + "\n"
# print str(res) + "\t" + str(T) + "\t" + str(NH_dot) + "\t" + str(ct) + "\n"
# time.sleep(5)
ct_numpy = numpy.around(ct_numpy, 5)
print "["+str(res)+"] Done in " + str(time.time()-cf_timer) + " seconds.\n"
filename = name_file_dcd + "ct_numpy.LA" + cutoff +".%03d." % res + chain + ".txt"
outfile = open(filename, 'w')
outfile.write(output)
outfile.close()
print "["+str(res)+"] Collecting Trash"
os.remove(filenamedcd)
print "["+str(res)+"] Done for real"
def RMS_fit_trj(*args, **kwargs):
warnings.warn("RMS_fit_trj is deprecated and will be removed. Use rms_fit_trj",
category=DeprecationWarning)
return rms_fit_trj(*args, **kwargs)
try:
import matplotlib
matplotlib.use('agg')
from pylab import plot, xlabel, ylabel, savefig
have_matplotlib = True
except:
have_matplotlib = False
ref = Universe(PSF, PDB_small) # reference structure 4AKE
trj = Universe(PSF, DCD) # trajectory of change 1AKE->4AKE
rms_fit_trj(trj,
ref,
filename="output/rmsfit.dcd",
select="not name H*",
mass_weighted=True,
rmsdfile="output/rmsfit_rmsd.dat")
print "Fitted trajectory: output/rmsfit.dcd"
rmsd = numpy.loadtxt("output/rmsfit_rmsd.dat")
print "RMSD: output/rmsfit_rmsd.dat"
if have_matplotlib:
plot(rmsd, 'k-', linewidth=2)
xlabel("frame number")
ylabel(r"mass weighted heavy atom RMSD ($\AA$)")
savefig("figures/rmsdfit_rmsd.pdf")
savefig("figures/rmsdfit_rmsd.png")
print "RMSD graph: figures/rmsdfit_rmsd.{pdf,png}"
import MDAnalysis.analysis.align as align
topology = sys.argv[1]
init_pdb = sys.argv[2]
traj_file = sys.argv[3]
lig_name = sys.argv[4]
filter_lig = 'resname ' + lig_name
filter_structure = 'protein or ' + filter_lig
init = mda.Universe(topology, init_pdb)
traj = mda.Universe(topology, traj_file)
frames = traj.trajectory
nframes = len(frames)
align.rms_fit_trj(traj, init, select="protein and backbone")
ref = init
avg_select = ref.selectAtoms(filter_structure)
align_struct = traj.selectAtoms(filter_structure)
p_avg = numpy.zeros_like(avg_select.positions)
# do a quick average of the protein
for ts in frames:
p_avg += align_struct.positions
p_avg /= nframes
# temporarily replace positions with the average
avg_select.set_positions(p_avg)
