def flux_cut(self,traj=None,cut=None,verbose=False):
if cut==None:
print "# Cut value needed."
return
if self.dimensions>1:
print "# Not implemented yet"
return
if traj == None:
traj_inp=pyn_math.standard_traj(self.traj,particles=self.particles,dimensions=self.dimensions)
return f_kin_anal.flux_cut(traj_inp,cut,traj_inp.shape[0],traj_inp.shape[1],traj_inp.shape[2])
def kinetic_network(self,traj=None,ranges=None,bins=None,labels=True,verbose=False):
if traj in ['CLUSTERS','Clusters','clusters']:
if type(self.traj_clusters) not in [numpy.ndarray]:
self.traj_clusters=numpy.array(self.traj_clusters,order="Fortran")
self.network_clusters=kinetic_network(self.traj_clusters,ranges=[self.traj_clusters.min(),self.traj_clusters.max()],verbose=verbose)
return
elif traj in ['NODES','Nodes','nodes']:
if type(self.traj_nodes) not in [numpy.ndarray]:
self.traj_nodes=numpy.array(self.traj_nodes,order="Fortran")
self.network=kinetic_network(self.traj_nodes,ranges=[self.traj_nodes.min(),self.traj_nodes.max()],verbose=verbose)
return
else:
self.traj=pyn_math.standard_traj(self.traj,particles=self.particles,dimensions=self.dimensions)
if ranges==None:
ranges=pyn_math.build_ranges(self.traj)
else:
ranges=pyn_math.standard_ranges(ranges)
self.network,self.traj_nodes=kinetic_network(self.traj,ranges=ranges,bins=bins,traj_out=True,labels=labels,verbose=verbose)
return
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 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 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
def __init__ (self,traject=None,columns=None,frames=None,particles=None,dimensions=None,\
traject_type='Traj',in_ram=True,in_file=False,by_frame=False,verbose=True):
self.dimensions=dimensions
self.particles=particles
self.frames=frames
self.file_traj=None
self.file_nodes=None
self.file_clusters=None
self.traj=None
self.traj_nodes=None
self.traj_clusters=None
self.network=None
self.network_clusters=None
self.__tr_mode_in_ram__ = False
self.__tr_mode_in_file__ = False
self.__tr_mode_by_frame__ = False
self.__no_mode_in_ram__ = False
self.__no_mode_in_file__ = False
self.__no_mode_by_frame__ = False
self.__cl_mode_in_ram__ = False
self.__cl_mode_in_file__ = False
self.__cl_mode_by_frame__ = False
self.__type_nodes__=None
self.__type_clusters__=None
self.__offset__=0
if in_file: in_ram=False
if traject_type in ['TRAJ','Traj','traj']:
self.__tr_mode_in_ram__ = in_ram
self.__tr_mode_in_file__ = in_file
self.__tr_mode_by_frame__ = by_frame
elif traject_type in ['NODES','Nodes','nodes']:
self.__no_mode_in_ram__ = in_ram
self.__no_mode_in_file__ = in_file
self.__no_mode_by_frame__ = by_frame
elif traject_type in ['CLUSTERS','Clusters','clusters']:
self.__cl_mode_in_ram__ = in_ram
self.__cl_mode_in_file__ = in_file
self.__cl_mode_by_frame__ = by_frame
if traject==None:
if self.frames!=None:
if self.dimensions==None:
self.dimensions=1
if self.particles==None:
self.particles=1
if traject_type in ['TRAJ','Traj','traj']:
self.traj=numpy.zeros((self.frames,self.particles,self.dimensions),dtype=float,order='F')
elif traject_type in ['NODES','Nodes','nodes']:
self.traj_nodes=numpy.zeros((self.frames,self.particles,self.dimensions),dtype=float,order='F')
elif traject_type in ['CLUSTERS','Clusters','clusters']:
self.traj_clusters=numpy.zeros((self.frames,self.particles,self.dimensions),dtype=float,order='F')
if type(traject) in [str]:
if type(columns) in [int]:
self.particles=1
self.dimensions=1
columns=[columns]
elif type(columns) in [tuple,list]:
if (len(columns))==1:
self.particles=1
self.dimensions=1
else:
if particles==None and dimensions==None:
print '# Please, make use of the variables "particles" or/and "dimensions":'
print '# traj:=[100 frames, 3 dimensions] --> "particles=1" or/and "dimensions=3"'
print '# traj:=[100 frames, 8 particles] --> "particles=8" or/and "dimensions=1"'
return
elif particles==1 or dimensions>=1:
self.particles=1
self.dimensions=len(columns)
elif particles>1 or dimensions==1:
self.dimensions=1
self.particles=len(columns)
elif columns in ['ALL','All','all']:
fff=open(traject,'r')
line=fff.readline().split()
nn=len(line)
fff.close()
columns=[ii for ii in range(nn)]
if nn>1:
if particles==None and dimensions==None:
print '# Please, make use of the variables "particles" or/and "dimensions":'
print '# traj:=[100 frames, 3 dimensions] --> "particles=1" or/and "dimensions=3"'
print '# traj:=[100 frames, 8 particles] --> "particles=8" or/and "dimensions=1"'
return
elif particles==1 or dimensions>=1:
self.particles=1
self.dimensions=nn
elif particles>1 or dimensions==1:
self.dimensions=1
self.particles=nn
else:
self.dimensions=1
self.particles=1
if self.__tr_mode_in_ram__ and not self.__tr_mode_by_frame__:
if traject.endswith('.bin'):
self.file_traj=pyn_traj_file(name=traject,frames=frames,particles=particles,dimensions=dimensions,opt='r')
self.file_traj.open()
self.traj=self.file_traj.extract()
else:
self.file_traj=pyn_traj_file(name=traject,opt='r')
self.frames = 0
self.file_traj.open()
for line in self.file_traj.file: self.frames += 1
self.file_traj.close()
self.traj=numpy.empty((self.frames,self.particles,self.dimensions),dtype=float,order='F')
self.file_traj.open()
gg=-1
for line in self.file_traj.file:
line=line.split()
gg+=1
for kk in range(len(columns)):
self.traj[gg,0,kk]=float(line[columns[kk]])
self.file_traj.close()
self.traj=pyn_math.standard_traj(self.traj,self.particles,self.dimensions)
if self.__tr_mode_in_file__:
self.file_traj=pyn_traj_file(name=traject,frames=frames,particles=particles,dimensions=dimensions)
if self.dimensions==None or self.particles==None:
print '# Error: Input variables "dimensions" and "particles" needed.'
return
if type(traject) in [list,tuple,numpy.ndarray]:
self.traj=pyn_math.standard_traj(traject,particles=self.particles,dimensions=self.dimensions)
self.frames=self.traj.shape[0]
self.particles=self.traj.shape[1]
self.dimensions=self.traj.shape[2]
if verbose:
self.info()
