def berezovska2012(self,window=None,ksi=0.5,granularity=1.2,bins=20,segment=None,delta=None,clusters=True,verbose=False):
ref_max=self.traj.max()
ref_min=self.traj.min()
rv_min=0
rv_max=0
if segment==None:
opt_range=0
mmx=ref_max
mmn=ref_min
else:
opt_range=1
mmn=segment[0]
mmx=segment[1]
if mmn>ref_min:
rv_min=1
if mmx<ref_max:
rv_max=1
if delta!=None:
opt=1
else:
delta=1.0 # Its given by gannas function
opt=2
if self.dimensions!=1:
print '# Method not implemented yet for more than 1D.'
return
if verbose:
if rv_min:
print '# Extra node for x <', mmn
if rv_max:
print '# Extra node for x >', mmx
self.traj_nodes=f_kin_anal.ganna(opt_range,opt,bins,mmn,mmx,delta,rv_min,rv_max,self.traj,ksi,window,self.particles,self.frames)
self.__offset__=window
self.network=kinetic_network(self.traj_nodes,ranges=[self.traj_nodes.min(),self.traj_nodes.max()],verbose=False)
if clusters:
self.network.symmetrize(new=False,verbose=verbose)
self.network.mcl(granularity=granularity,pruning=True,verbose=verbose)
num_nodes=self.network.num_nodes
aux_list=numpy.empty(num_nodes,dtype=int,order='F')
for ii in range(num_nodes):
aux_list[ii]=self.network.node[ii].cluster
new_num_frames=self.traj_nodes.shape[0]
self.traj_clusters=f_kin_anal.trajnodes2trajclusters(aux_list,self.traj_nodes,num_nodes,new_num_frames,self.particles)
del(num_nodes,new_num_frames,aux_list)
self.__type_clusters__='berezovska2012'
def build_traj_clusters(self):
num_nodes=self.network.num_nodes
aux_list=numpy.empty(num_nodes,dtype=int,order='F')
for ii in range(num_nodes):
aux_list[ii]=self.network.node[ii].cluster
new_num_frames=self.traj_nodes.shape[0]
self.traj_clusters=f_kin_anal.trajnodes2trajclusters(aux_list,self.traj_nodes,num_nodes,new_num_frames,self.particles)
del(num_nodes,new_num_frames,aux_list)
def prada1(self,window=None,granularity=1.2,bins=20,ybins=10,segment=None,delta=None,clusters=True,verbose=False):
if self.dimensions!=1:
print '# Method not implemented yet for more than 1D.'
return
bins,mmx,mmn,delta=pyn_math.parameters_bins(self.traj,bins,segment,delta)
rv_min=0
rv_max=0
if mmn>self.traj.min():
rv_min=1
bins+=1
if mmx<self.traj.max():
rv_max=1
bins+=1
if verbose:
if rv_min:
print '# Extra node for x <', mmn
if rv_max:
print '# Extra node for x >', mmx
traj_aux=f_kin_anal.prada1(ybins,bins,mmn,mmx,delta,rv_min,rv_max,self.traj,window,self.particles,self.frames)
ranges=pyn_math.build_ranges(traj_aux)
self.network,self.traj_nodes=kinetic_network(traj_aux,ranges=ranges,traj_out=True,verbose=verbose)
del(traj_aux)
self.__offset__=window
if clusters:
self.network.symmetrize(new=False,verbose=verbose)
self.network.mcl(granularity=granularity,pruning=True,verbose=verbose)
num_nodes=self.network.num_nodes
aux_list=numpy.empty(num_nodes,dtype=int,order='F')
for ii in range(num_nodes):
aux_list[ii]=self.network.node[ii].cluster
new_num_frames=self.traj_nodes.shape[0]
self.traj_clusters=f_kin_anal.trajnodes2trajclusters(aux_list,self.traj_nodes,num_nodes,new_num_frames,self.particles)
del(num_nodes,new_num_frames,aux_list)
self.__type_clusters__='prada1'
def prada2(self,window=None,granularity=1.2,bins=10,ybins=10,sbins=10,segment=None,delta=None,clusters=True,verbose=False):
ref_max=self.traj.max()
ref_min=self.traj.min()
if segment==None:
opt_range=0
mmx=ref_max
mmn=ref_min
else:
opt_range=1
mmn=segment[0]
mmx=segment[1]
if delta!=None:
opt=1
else:
delta=1.0 # Its given by gannas function
opt=2
bins,mmx,mmn,delta=pyn_math.parameters_bins(opt_range,opt,bins,mmn,mmx,delta)
traj_aux=f_kin_anal.prada2(ybins,sbins,bins,mmn,mmx,delta,self.traj,window,self.particles,self.frames)
ranges=pyn_math.build_ranges(traj_aux)
self.network,self.traj_nodes=kinetic_network(traj_aux,ranges=ranges,traj_out=True,verbose=False)
del(traj_aux)
self.__offset__=window
if clusters:
self.network.symmetrize(new=False,verbose=verbose)
self.network.mcl(granularity=granularity,pruning=True,verbose=verbose)
num_nodes=self.network.num_nodes
aux_list=numpy.empty(num_nodes,dtype=int,order='F')
for ii in range(num_nodes):
aux_list[ii]=self.network.node[ii].cluster
new_num_frames=self.traj_nodes.shape[0]
self.traj_clusters=f_kin_anal.trajnodes2trajclusters(aux_list,self.traj_nodes,num_nodes,new_num_frames,self.particles,self.dimensions)
del(num_nodes,new_num_frames,aux_list)
self.__type_clusters__='prada2'