def main(modeldir, genfile, type, write=False):
proj=Project.load_from('%s/ProjectInfo.yaml' % modeldir.split('Data')[0])
pops=numpy.loadtxt('%s/Populations.dat' % modeldir)
map=numpy.loadtxt('%s/Mapping.dat' % modeldir)
frames=numpy.where(map!=-1)[0]
data=dict()
data['rmsd']=numpy.loadtxt('%s.rmsd.dat' % genfile.split('.lh5')[0])
data['rmsd']=data['rmsd'][frames]
com=numpy.loadtxt('%s.vmd_com.dat' % genfile.split('.lh5')[0], usecols=(1,))
refcom=com[0]
data['com']=com[1:]
data['com']=numpy.array(data['com'][frames])
residues=['F36', 'H87', 'I56', 'I90', 'W59', 'Y82', 'hydrophob_dist', 'oxos_dist']
loops=['loop1', 'loop2', 'loop3']
for loop in loops:
data[loop]=numpy.loadtxt('%s.%srmsd.dat' % (genfile.split('.lh5')[0], loop))
data[loop]=data[loop][frames]
for res in residues:
file='%s_%spair.dat' % (genfile.split('.lh5')[0], res)
if os.path.exists(file):
data[res]=numpy.loadtxt(file)
data[res]=data[res][frames]
angles=['phi', 'omega']
for ang in angles:
file='%s_%s.dat' % (genfile.split('.lh5')[0], ang)
if os.path.exists(file):
data[ang]=numpy.loadtxt(file)
data[ang]=data[ang][frames]
ass=io.loadh('%s/Assignments.Fixed.h5' % modeldir)
T=mmread('%s/tProb.mtx' % modeldir)
unbound=numpy.loadtxt('%s/tpt-%s/unbound_%s_states.txt' % (modeldir, type, type), dtype=int)
bound=numpy.loadtxt('%s/tpt-%s/bound_%s_states.txt' % (modeldir, type, type), dtype=int)
Tdense=T.todense()
Tdata=dict()
for i in unbound:
for j in unbound:
if Tdense[i,j]!=0:
if i not in Tdata.keys():
Tdata[i]=[]
Tdata[i].append(j)
#print Tdata
cm=pylab.cm.get_cmap('RdYlBu_r') #blue will be negative components, red positive
Q=tpt.calculate_committors(unbound, bound, T)
ohandle=open('%s/commitor_states.txt' % modeldir, 'w')
for i in range(0,len(Q)):
if Q[i]>0.40 and Q[i]<0.6:
ohandle.write('%s\n' % i)
#t=project.get_random_confs_from_states(ass['arr_0'], [int(i),], 20)
#t[0].save_to_xtc('%s/commottor_state%s.xtc' % (modeldir, i))
if write==True:
for op in sorted(data.keys()):
pylab.figure()
pylab.scatter(data['com'], data[op], c=Q, cmap=cm, alpha=0.7, s=[map_size(i) for i in Q])
pylab.xlabel('L RMSD')
pylab.ylabel(op)
pylab.colorbar()
pylab.show()
def run(TC, Uv, Fv):
# Get committors and flux
logger.info("Getting committors and flux...")
Fc = calculate_committors(Uv, Fv, TC)
logger.info("Calculated forward committors.")
NFlux = calculate_net_fluxes(Uv, Fv, TC)
logger.info("Calculated net flux.")
return Fc, NFlux
def run(TC, Uv, Fv):
# Get committors and flux
logger.info("Getting committors and flux...")
Fc = calculate_committors(Uv, Fv, TC)
logger.info("Calculated forward committors.")
NFlux = calculate_net_fluxes(Uv, Fv, TC)
logger.info("Calculated net flux.")
return Fc, NFlux
def set_coordinate_as_committors(self, lag_time=1, symmetrize='transpose'):
"""
Set the reaction coordinate to be the committors (pfolds).
Employs the reactant, product states provided as the sources, sinks
respectively for the committor calculation.
Parameters
----------
lag_time : int
The MSM lag time to use (in units of frames) in the estimation
of the MSM transition probability matrix from the `counts` matrix.
symmetrize : str {'mle', 'transpose', 'none'}
Which symmetrization method to employ in the estimation of the
MSM transition probability matrix from the `counts` matrix.
"""
t_matrix = MSMLib.build_msm(self.counts, symmetrize)
self.reaction_coordinate_values = tpt.calculate_committors(
[self.reactant], [self.product], t_matrix)
return
def set_coordinate_as_committors(self, lag_time=1, symmetrize='transpose'):
"""
Set the reaction coordinate to be the committors (pfolds).
Employs the reactant, product states provided as the sources, sinks
respectively for the committor calculation.
Parameters
----------
lag_time : int
The MSM lag time to use (in units of frames) in the estimation
of the MSM transition probability matrix from the `counts` matrix.
symmetrize : str {'mle', 'transpose', 'none'}
Which symmetrization method to employ in the estimation of the
MSM transition probability matrix from the `counts` matrix.
"""
t_matrix = MSMLib.build_msm(self.counts, symmetrize)
self.reaction_coordinate_values = tpt.calculate_committors([self.reactant],
[self.product],
t_matrix)
return
def test_committors(self):
Q = tpt.calculate_committors(self.sources, self.sinks, self.tprob)
Q_ref = io.loadh(tpt_get("committors.h5"), 'Data')
npt.assert_array_almost_equal(Q, Q_ref)
def test_committors(self):
Q = tpt.calculate_committors(self.sources, self.sinks, self.tprob)
Q_ref = io.loadh(os.path.join(self.tpt_ref_dir, "committors.h5"), 'Data')
npt.assert_array_almost_equal(Q, Q_ref)
