def trip_time(
self,
traj=None,
from_state=None,
from_segment=None,
to_state=None,
to_segment=None,
no_return=False,
mean=False,
norm=False,
verbose=False,
):
opt_mean = 0
opt_norm = 0
opt_no_return = 0
opt_to_segment = 0
opt_from_segment = 0
opt_to_state = 0
opt_from_state = 0
if mean:
opt_mean = 1
if norm:
opt_norm = 1
if no_return:
opt_no_return = 1
if from_state != None:
opt_from_state = 1
else:
from_state = 0
if from_segment != None:
opt_from_segment = 1
else:
from_segment = [0.0, 0.0]
if to_state != None:
opt_to_state = 1
else:
to_state = 0
if to_segment != None:
opt_to_segment = 1
else:
to_segment = [0.0, 0.0]
if opt_to_segment == 0 and opt_to_state == 0:
print "# the input variable to_state or to_segment is needed"
return
if traj == None:
traj_inp = self.traj
aux_dims = self.dimensions
elif traj in ["CLUSTERS", "Clusters", "clusters"]:
traj_inp = self.traj_clusters
aux_dims = 1
elif traj in ["NODES", "Nodes", "nodes"]:
traj_inp = self.traj_nodes
aux_dims = 1
else:
print "# A readable traj is needed"
return
traj_inp = pyn_math.standard_traj(traj_inp, particles=self.particles, dimensions=aux_dims)
if type(from_state) in [int, float]:
from_num_states = 1
from_state = [from_state]
elif type(state) in [list, tuple]:
from_num_states = len(from_state)
if type(to_state) in [int, float]:
to_num_states = 1
to_state = [to_state]
elif type(to_state) in [list, tuple]:
to_num_states = len(to_state)
tt_mean = f_kin_anal.tt_dist(
opt_norm,
opt_no_return,
opt_from_state,
opt_from_segment,
opt_to_state,
opt_to_segment,
from_state,
from_segment,
to_state,
to_segment,
traj_inp,
traj_inp.shape[0],
traj_inp.shape[1],
traj_inp.shape[2],
from_num_states,
to_num_states,
)
tt_dist = copy.deepcopy(f_kin_anal.distrib)
tt_x = copy.deepcopy(f_kin_anal.distrib_x)
f_kin_anal.free_distrib()
if verbose:
print "# Mean first passage time:", tt_mean, "frames."
if mean:
return tt_mean
else:
return tt_x, tt_dist
def committed_trip_time(
self,
traj=None,
states=None,
segments=None,
commitment=None,
no_return=False,
mean=False,
norm=False,
verbose=False,
):
opt_mean = 0
opt_norm = 0
opt_segments = 0
opt_states = 0
opt_noreturn = 0
if mean:
opt_mean = 1
if norm:
opt_norm = 1
if no_return:
opt_noreturn = 1
if states != None:
opt_states = 1
segments = [[0, 0]]
num_segments = 1
else:
opt_segments = 0
states = [0]
num_states = 1
if opt_segments == 0 and opt_states == 0:
print "# the input variable states or segments is needed"
return
if traj == None:
traj_inp = self.traj
aux_dims = self.dimensions
elif traj in ["CLUSTERS", "Clusters", "clusters"]:
traj_inp = self.traj_clusters
aux_dims = 1
elif traj in ["NODES", "Nodes", "nodes"]:
traj_inp = self.traj_nodes
aux_dims = 1
else:
print "# A readable traj is needed"
return
traj_inp = pyn_math.standard_traj(traj_inp, particles=self.particles, dimensions=aux_dims)
if type(states) in [int, float]:
num_states = 1
states = [states]
elif type(states) in [list, tuple]:
num_states = len(states)
if opt_segments:
num_segments = len(segments)
if commitment == None:
if opt_segments:
num_commits = num_segments
else:
num_commits = num_states
commitment = [True for ii in range(num_commits)]
else:
num_commits = len(commitment)
commitment_in = [int(ii) for ii in commitment]
ctt_mean = f_kin_anal.ctt_dist(
opt_norm,
opt_noreturn,
opt_states,
opt_segments,
states,
segments,
commitment_in,
traj_inp,
traj_inp.shape[0],
traj_inp.shape[1],
traj_inp.shape[2],
num_states,
num_segments,
num_commits,
)
ctt_dist = copy.deepcopy(f_kin_anal.distrib)
ctt_x = copy.deepcopy(f_kin_anal.distrib_x)
f_kin_anal.free_distrib()
if verbose:
print "# Mean first passage time:", ctt_mean, "frames."
if mean:
return ctt_mean
else:
return ctt_x, ctt_dist
def life_time(self, traj=None, state=None, segment=None, sel_dim=0, mean=False, norm=False, verbose=False):
opt_mean = 0
opt_norm = 0
opt_segm = 0
if mean:
opt_mean = 1
if norm:
opt_norm = 1
if segment:
opt_segm = 1
if type(sel_dim) in [int, float]:
sel_dim = [sel_dim]
num_sel_dim = 1
elif type(sel_dim) in [list, tuple]:
num_sel_dim = len(sel_dim)
segments = numpy.zeros((self.dimensions, 2), dtype=float, order="F")
if opt_segm:
segment = numpy.array(segment)
if len(segment.shape) == 1:
segment.resize(1, segment.shape[0])
for ii in range(num_sel_dim):
segments[sel_dim[ii], 0] = segment[ii, 0]
segments[sel_dim[ii], 1] = segment[ii, 1]
num_states = 1
state = [0]
else:
if type(state) in [int, float]:
num_states = 1
state = [state]
elif type(state) in [list, tuple]:
num_states = len(state)
if traj == None:
if self.__tr_mode_in_file__:
infile = self.file_traj
infile.unit = len(pyn_math.pyn_f90units) + 1
pyn_math.pyn_f90units.append(infile.unit)
lt_mean = f_kin_anal.life_time_dist_infile(
infile.name,
infile.binary,
infile.unit,
opt_norm,
opt_segm,
state,
segments,
sel_dim,
self.particles,
self.dimensions,
num_states,
num_sel_dim,
)
pyn_math.pyn_f90units.remove(infile.unit)
infile.unit = None
else:
traj_inp = pyn_math.standard_traj(self.traj, particles=self.particles, dimensions=self.dimensions)
lt_mean = f_kin_anal.life_time_dist(
opt_norm,
opt_segm,
traj_inp,
state,
segments,
traj_inp.shape[0],
traj_inp.shape[1],
traj_inp.shape[2],
num_states,
)
elif traj in ["CLUSTERS", "Clusters", "clusters"]:
traj_inp = pyn_math.standard_traj(self.traj_clusters, particles=self.particles, dimensions=1)
lt_mean = f_kin_anal.life_time_dist(
opt_norm, traj_inp, state, traj_inp.shape[0], traj_inp.shape[1], traj_inp.shape[2], num_states
)
elif traj in ["NODES", "Nodes", "nodes"]:
traj_inp = pyn_math.standard_traj(self.traj_nodes, particles=self.particles, dimensions=1)
lt_mean = f_kin_anal.life_time_dist(
opt_norm, traj_inp, state, traj_inp.shape[0], traj_inp.shape[1], traj_inp.shape[2], num_states
)
else:
print "# A readable traj is needed"
return
lt_dist = copy.deepcopy(f_kin_anal.distrib)
lt_x = copy.deepcopy(f_kin_anal.distrib_x)
f_kin_anal.free_distrib()
if verbose:
print "# Mean life time:", lt_mean, "frames."
if mean:
return lt_mean
else:
return lt_x, lt_dist
