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'
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 prada1_shell(self,window=None,granularity=1.2,bins=20,ybins=10,segment=None,delta=None,extra_min=False,extra_max=False,\
ram=4,increment=1,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(False,bins,segment,delta)
rv_min=0
rv_max=0
if extra_min:
rv_min=1
bins+=1
if extra_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
if not self.__tr_mode_in_file__:
'# Error: the trajectory must be in a file'
return
self.network=network(kinetic=True,verbose=False)
self.file_traj.open()
mmram=ram*1024*1024*1024
iterations=int(mmram/(self.particles*bins*8))
b_frame=window*increment
e_frame=self.file_traj.frames-1-window*increment
first_period=1
salida=1
if (b_frame+iterations*increment)>e_frame:
iterations=((e_frame-b_frame)/increment)
while (iterations>0):
print b_frame+iterations*increment
traj_aux=f_kin_anal.prada1_infile(self.file_traj.unit,ybins,bins,segment[0],delta,rv_min,rv_max,\
b_frame,iterations,increment,window,self.particles,self.dimensions)
ranges=pyn_math.build_ranges(traj_aux)
network_per=kinetic_network(traj_aux,ranges=ranges,traj_out=False,verbose=False)
self.network.merge_net(network_per,verbose=True)
del(traj_aux)
del(network_per)
b_frame+=iterations*increment
if (b_frame+iterations*increment)>e_frame:
iterations=((e_frame-b_frame)/increment)
# cerrar unidad
self.file_traj.close()
self.__offset__=window
if clusters:
print '# Entra a 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'
