def trip_time (self,traj=None,from_state=None,from_segment=None,to_state=None,to_segment=None,no_return=False,mean=False,norm=False,verbose=False):
opt_mean=0
opt_norm=0
opt_no_return=0
opt_to_segment=0
opt_from_segment=0
opt_to_state=0
opt_from_state=0
if (mean):
opt_mean=1
if (norm):
opt_norm=1
if (no_return):
opt_no_return=1
if from_state!=None:
opt_from_state=1
else:
from_state=0
if from_segment!=None:
opt_from_segment=1
else:
from_segment=[0.0,0.0]
if to_state!=None:
opt_to_state=1
else:
to_state=0
if to_segment!=None:
opt_to_segment=1
else:
to_segment=[0.0,0.0]
if opt_to_segment==0 and opt_to_state==0:
print '# the input variable to_state or to_segment is needed'
return
if traj == None:
traj_inp=self.traj
aux_dims=self.dimensions
elif traj in ['CLUSTERS','Clusters','clusters']:
traj_inp=self.traj_clusters
aux_dims=1
elif traj in ['NODES','Nodes','nodes']:
traj_inp=self.traj_nodes
aux_dims=1
else:
print '# A readable traj is needed'
return
traj_inp=pyn_math.standard_traj(traj_inp,particles=self.particles,dimensions=aux_dims)
if type(from_state) in [int,float]:
from_num_states=1
from_state=[from_state]
elif type(state) in [list,tuple]:
from_num_states=len(from_state)
if type(to_state) in [int,float]:
to_num_states=1
to_state=[to_state]
elif type(to_state) in [list,tuple]:
to_num_states=len(to_state)
tt_mean=f_kin_anal.tt_dist(opt_norm,opt_no_return,opt_from_state,opt_from_segment,opt_to_state,opt_to_segment, \
from_state,from_segment,to_state,to_segment, \
traj_inp,traj_inp.shape[0],traj_inp.shape[1],traj_inp.shape[2],\
from_num_states,to_num_states)
tt_dist=copy.deepcopy(f_kin_anal.distrib)
tt_x=copy.deepcopy(f_kin_anal.distrib_x)
f_kin_anal.free_distrib()
if verbose:
print '# Mean first passage time:', tt_mean,'frames.'
if mean:
return tt_mean
else:
return tt_x, tt_dist
def committed_trip_time (self,traj=None,states=None,segments=None,commitment=None,no_return=False,mean=False,norm=False,verbose=False):
opt_mean=0
opt_norm=0
opt_segments=0
opt_states=0
opt_noreturn=0
if (mean):
opt_mean=1
if (norm):
opt_norm=1
if (no_return):
opt_noreturn=1
if states!=None:
opt_states=1
segments=[[0,0]]
num_segments=1
else:
opt_segments=0
states=[0]
num_states=1
if opt_segments==0 and opt_states==0:
print '# the input variable states or segments is needed'
return
if traj == None:
traj_inp=self.traj
aux_dims=self.dimensions
elif traj in ['CLUSTERS','Clusters','clusters']:
traj_inp=self.traj_clusters
aux_dims=1
elif traj in ['NODES','Nodes','nodes']:
traj_inp=self.traj_nodes
aux_dims=1
else:
print '# A readable traj is needed'
return
traj_inp=pyn_math.standard_traj(traj_inp,particles=self.particles,dimensions=aux_dims)
if type(states) in [int,float]:
num_states=1
states=[states]
elif type(states) in [list,tuple]:
num_states=len(states)
if opt_segments:
num_segments=len(segments)
if commitment==None:
if opt_segments:
num_commits=num_segments
else:
num_commits=num_states
commitment=[True for ii in range(num_commits)]
else:
num_commits=len(commitment)
commitment_in=[int(ii) for ii in commitment]
ctt_mean=f_kin_anal.ctt_dist(opt_norm,opt_noreturn,opt_states,opt_segments, states,segments,commitment_in,\
traj_inp,traj_inp.shape[0],traj_inp.shape[1],traj_inp.shape[2],\
num_states,num_segments,num_commits)
ctt_dist=copy.deepcopy(f_kin_anal.distrib)
ctt_x=copy.deepcopy(f_kin_anal.distrib_x)
f_kin_anal.free_distrib()
if verbose:
print '# Mean first passage time:', ctt_mean,'frames.'
if mean:
return ctt_mean
else:
return ctt_x, ctt_dist
def life_time(self,traj=None,state=None,segment=None,sel_dim=0,mean=False,norm=False,verbose=False):
opt_mean =0
opt_norm =0
opt_segm =0
opt_state=0
num_states=0
if type(sel_dim) in [int,float]:
sel_dim=[sel_dim]
num_sel_dim=1
elif type(sel_dim) in [list,tuple]:
num_sel_dim=len(sel_dim)
if traj == None and not self.__tr_mode_in_file__:
traj_inp=self.traj
aux_dims=self.dimensions
elif traj in ['CLUSTERS','Clusters','clusters']:
traj_inp=self.traj_clusters
aux_dims=1
elif traj in ['NODES','Nodes','nodes']:
traj_inp=self.traj_nodes
aux_dims=1
else:
print '# A readable traj is needed'
return
if (mean):
opt_mean=1
if (norm):
opt_norm=1
if segment!=None:
opt_segm=1
segment=numpy.array(segment)
if len(segment.shape)==1:
segment.resize(1,segment.shape[0])
for ii in range(num_sel_dim):
segment[sel_dim[ii],0]=segment[ii,0]
segment[sel_dim[ii],1]=segment[ii,1]
num_states=1
state=[0]
else:
segment=numpy.zeros((aux_dims,2),dtype=float,order='F')
if state!=None:
opt_state=1
if type(state) in [int,float]:
num_states=1
state=[state]
elif type(state) in [list,tuple]:
num_states=len(state)
else:
num_states=1
state=[0]
if not self.__tr_mode_in_file__:
traj_inp=pyn_math.standard_traj(traj_inp,particles=self.particles,dimensions=aux_dims)
lt_mean=f_kin_anal.life_time_dist(opt_norm,opt_segm,traj_inp,state,segment,traj_inp.shape[0],traj_inp.shape[1],traj_inp.shape[2],num_states)
else:
infile=self.file_traj
infile.unit=len(pyn_math.pyn_f90units)+1
pyn_math.pyn_f90units.append(infile.unit)
lt_mean=f_kin_anal.life_time_dist_infile(infile.name,infile.binary,infile.unit,opt_norm,opt_segm,state,segment,\
sel_dim,self.particles,self.dimensions,num_states,num_sel_dim)
pyn_math.pyn_f90units.remove(infile.unit)
infile.unit=None
lt_dist=copy.deepcopy(f_kin_anal.distrib)
lt_x=copy.deepcopy(f_kin_anal.distrib_x)
f_kin_anal.free_distrib()
if verbose:
print '# Mean life time:', lt_mean,'frames.'
if mean:
return lt_mean
else:
return lt_x, lt_dist
