def kinetic_network(traj=None,ranges=None,bins=None,traj_out=False,labels=True,verbose=True):
prov_net=network(directed=True,kinetic=True,verbose=False)
ranges=pyn_math.standard_ranges(ranges)
dimensions=ranges.shape[0]
traj=pyn_math.standard_traj(traj,dimensions)
num_frames=traj.shape[0]
num_parts=traj.shape[1]
opt_labels=0
if labels:
opt_labels=1
if bins!=None:
if type(bins) in [int]:
bins=[bins]
if len(bins)!=dimensions:
print '# The length of bins must be equal to the length of ranges'
return
bins=numpy.array(bins,dtype=int,order='F')
traj_net=f_kin_anal.trajbinning2net(opt_labels,traj,ranges,bins,num_frames,num_parts,dimensions)
else:
traj_net=f_kin_anal.traj2net(opt_labels,traj,ranges,num_frames,num_parts,dimensions)
traj_net=pyn_math.standard_traj(traj_net,particles=num_parts,dimensions=1)
prov_net.Ts=True
prov_net.T_ind=copy.deepcopy(f_kin_anal.t_ind)
prov_net.T_wl=copy.deepcopy(f_kin_anal.t_tau)
prov_net.T_start=copy.deepcopy(f_kin_anal.t_start)
prov_net.build_from_Ts()
if opt_labels:
if bins==None:
for ii in range(prov_net.num_nodes):
label=str(f_kin_anal.labels[ii])
prov_net.node[ii].label=label
prov_net.labels[label]=ii
else:
for ii in range(prov_net.num_nodes):
label=str(f_kin_anal.labels_daux[ii])
prov_net.node[ii].label=label
prov_net.labels[label]=ii
f_kin_anal.free_memory_ts()
if verbose:
prov_net.info(update=False,verbose=True)
pass
else:
pass
if traj_out:
return prov_net,traj_net
else:
del(traj_net)
return prov_net
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 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 life_time(self,traj=None,state=None,segment=None,mean=False,norm=False,verbose=False):
opt_mean=0
opt_norm=0
if (mean):
opt_mean=1
if (norm):
opt_norm=1
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(state) in [int,float]:
num_states=1
state=[state]
elif type(state) in [list,tuple]:
num_states=len(state)
lt_mean=f_trajs.life_time_dist(opt_norm,traj_inp,state,traj_inp.shape[0],traj_inp.shape[1],traj_inp.shape[2],num_states)
lt_dist=copy.deepcopy(f_trajs.distrib)
lt_x=copy.deepcopy(f_trajs.distrib_x)
f_trajs.free_distrib()
if verbose:
print '# Mean life time:', lt_mean,'frames.'
if mean:
return lt_mean
else:
return lt_x, lt_dist
def kinetic_network(self,traj=None,ranges=None,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_nodes=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_nodes,self.traj_nodes=kinetic_network(self.traj,ranges=ranges,traj_out=True,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
if mean:
opt_mean = 1
if norm:
opt_norm = 1
if segment:
opt_segm = 1
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)
segments = numpy.zeros((self.dimensions, 2), dtype=float, order="F")
if opt_segm:
segment = numpy.array(segment)
if len(segment.shape) == 1:
segment.resize(1, segment.shape[0])
for ii in range(num_sel_dim):
segments[sel_dim[ii], 0] = segment[ii, 0]
segments[sel_dim[ii], 1] = segment[ii, 1]
num_states = 1
state = [0]
else:
if type(state) in [int, float]:
num_states = 1
state = [state]
elif type(state) in [list, tuple]:
num_states = len(state)
if traj == None:
if self.__tr_mode_in_file__:
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,
segments,
sel_dim,
self.particles,
self.dimensions,
num_states,
num_sel_dim,
)
pyn_math.pyn_f90units.remove(infile.unit)
infile.unit = None
else:
traj_inp = pyn_math.standard_traj(self.traj, particles=self.particles, dimensions=self.dimensions)
lt_mean = f_kin_anal.life_time_dist(
opt_norm,
opt_segm,
traj_inp,
state,
segments,
traj_inp.shape[0],
traj_inp.shape[1],
traj_inp.shape[2],
num_states,
)
elif traj in ["CLUSTERS", "Clusters", "clusters"]:
traj_inp = pyn_math.standard_traj(self.traj_clusters, particles=self.particles, dimensions=1)
lt_mean = f_kin_anal.life_time_dist(
opt_norm, traj_inp, state, traj_inp.shape[0], traj_inp.shape[1], traj_inp.shape[2], num_states
)
elif traj in ["NODES", "Nodes", "nodes"]:
traj_inp = pyn_math.standard_traj(self.traj_nodes, particles=self.particles, dimensions=1)
lt_mean = f_kin_anal.life_time_dist(
opt_norm, traj_inp, state, traj_inp.shape[0], traj_inp.shape[1], traj_inp.shape[2], num_states
)
else:
print "# A readable traj is needed"
return
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_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_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_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()
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_trajs.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_trajs.distrib)
tt_x=copy.deepcopy(f_trajs.distrib_x)
f_trajs.free_distrib()
if verbose:
print '# Mean first passage time:', tt_mean,'frames.'
if mean:
return tt_mean
else:
return tt_x, tt_dist
def first_committed_passage_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]
fcpt_mean=f_trajs.fcpt_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)
fcpt_dist=copy.deepcopy(f_trajs.distrib)
fcpt_x=copy.deepcopy(f_trajs.distrib_x)
f_trajs.free_distrib()
if verbose:
print '# Mean first passage time:', fcpt_mean,'frames.'
if mean:
return fcpt_mean
else:
return fcpt_x, fcpt_dist
def __init__ (self,traject=None,columns=None,particles=None,dimensions=None,verbose=True):
self.file_traj=''
self.file_nodes=''
self.file_clusters=''
self.file_traj_columns=None
self.traj=None
self.dimensions=dimensions
self.particles=particles
self.frames=0
self.traj_nodes=None
self.traj_clusters=None
self.network_nodes=None
self.network_clusters=None
self.__mode_in_ram__=True
self.__mode_in_file__=False
self.__mode_by_frame__=False
self.__type_nodes__=None
self.__type_clusters__=None
self.__offset__=0
if traject==None:
print 'Trajectory needed (name of file or array)'
return
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 type(traject) in [str]:
self.file_traj=traject
self.file_column=columns
self.frames = 0
for line in open(traject): self.frames += 1
self.traj=numpy.empty((self.frames,self.particles,self.dimensions),dtype=float,order='F')
fff=open(traject,'r')
gg=-1
for line in fff:
line=line.split()
gg+=1
for kk in range(len(columns)):
self.traj[gg,0,kk]=float(line[columns[kk]])
fff.close()
self.traj=pyn_math.standard_traj(self.traj,self.particles,self.dimensions)
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:
print '# Loaded:'
self.info()