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 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,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
