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 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 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"
