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'