def propagate_with_sink(time, F, D, edges, sink_index):
n = len(F)
rate = init_rate_matrix(n, F, D, pbc=True)
# undo PBC
rate[0, -1] = 0.
rate[-1, 0] = 0.
rate[0, 0] = -rate[1, 0]
rate[-1, -1] = -rate[-2, -1]
# sink
rate[-1, -1] = -1000000
rate[-2, -1] = 0.
#rate[-1,-2] = 0.
# fixed
#rate[:,0] = 0.
rate[0, :] = 0.
#rate[0,0] = 1.
# test
#print "rate",rate
#print "test",np.sum(rate,axis=0)
prop = scipy.linalg.expm2(time * rate)
#print prop
init = np.ones(len(prop))
init[0] = 1.
profile = np.dot(prop, init)
return profile
def check_propagator(self, lagtime):
import scipy
rate = init_rate_matrix(self.model.dim_v, self.model.v, self.model.w,
self.model.pbc)
vals, vecs = np.linalg.eig(rate)
line = ""
for v in vals:
if v.imag < 1e-10: VAL = v.real
else: VAL = v
line += str(VAL) + " "
propagator = scipy.linalg.expm2(lagtime * rate)
vals, vecs = np.linalg.eig(propagator)
line2 = ""
for v in vals:
if v.imag < 1e-10: VAL = v.real
else: VAL = v
line2 += str(VAL) + " "
tiny = 1e-10
count = np.sum(propagator < tiny)
#log_like = np.float64(0.0) # use high precision
#b = transition[ilag,:,:]*np.log(propagator.clip(tiny))
#log_like += np.sum(b)
print "count", count
print "ratematrix", line
print "propagatormatrix", line2
def set_model(self, model, data, ncosF, ncosD, ncosDrad):
self.data = data # transitions etc
ncosP = 0
# derive model
if self.do_radial > 0:
if model == "RadCosinusModel":
self.model = RadCosinusModel(self.data, self.D0, ncosF, ncosD, ncosP, ncosDrad)
elif model == "RadModel":
self.model = RadModel(self.data, self.D0, ncosF, ncosD, ncosP)
else:
raise ValueError("model %s not found" % model)
bessel0_zeros, bessels = setup_bessel_functions(self.lmax, self.model.dim_rad)
self.model.bessels = bessels
self.model.bessel0_zeros = bessel0_zeros
self.model.rate = init_rate_matrix(self.model.dim_v, self.model.v, self.model.w, self.pbc)
else:
if model == "CosinusModel":
self.model = CosinusModel(self.data, self.D0, ncosF, ncosD, ncosP)
# this will default to Model(self,data) if ncosF and ncosD are both 0
elif model == "SinusCosinusModel":
self.model = SinusCosinusModel(self.data, self.D0, ncosF, ncosD, ncosP)
elif model == "StepModel":
self.model = StepModel(self.data, self.D0, ncosF, ncosD, ncosP)
elif model == "OneStepModel":
self.model = OneStepModel(self.data, self.D0, ncosF, ncosD, ncosP)
elif model == "Model":
self.model = Model(self.data, self.D0)
else:
raise ValueError("model %s not found" % model)
assert self.pbc == self.model.pbc # make sure PBC for model and transition matrix are identical
def check_propagator(self, lagtime):
import scipy
rate = init_rate_matrix(self.model.dim_v, self.model.v, self.model.w, self.model.pbc)
vals, vecs = np.linalg.eig(rate)
line = ""
for v in vals:
if v.imag < 1e-10:
VAL = v.real
else:
VAL = v
line += str(VAL) + " "
propagator = scipy.linalg.expm2(lagtime * rate)
vals, vecs = np.linalg.eig(propagator)
line2 = ""
for v in vals:
if v.imag < 1e-10:
VAL = v.real
else:
VAL = v
line2 += str(VAL) + " "
tiny = 1e-10
count = np.sum(propagator < tiny)
# log_like = np.float64(0.0) # use high precision
# b = transition[ilag,:,:]*np.log(propagator.clip(tiny))
# log_like += np.sum(b)
print "count", count
print "ratematrix", line
print "propagatormatrix", line2
def propagate_with_sink(time,F,D,edges,sink_index):
n = len(F)
rate = init_rate_matrix(n,F,D,pbc=True)
# undo PBC
rate[0,-1] = 0.
rate[-1,0] = 0.
rate[0,0] = - rate[1,0]
rate[-1,-1] = -rate[-2,-1]
# sink
rate[-1,-1] = -1000000
rate[-2,-1] = 0.
#rate[-1,-2] = 0.
# fixed
#rate[:,0] = 0.
rate[0,:] = 0.
#rate[0,0] = 1.
# test
#print "rate",rate
#print "test",np.sum(rate,axis=0)
prop = scipy.linalg.expm2(time*rate)
#print prop
init = np.ones(len(prop))
init[0] = 1.
profile = np.dot(prop,init)
return profile
def init_log_like(self):
# initialize log_like
if self.do_radial:
self.model.rate = init_rate_matrix(self.model.dim_v, self.model.v, self.model.w, self.pbc)
log_like = rad_log_like_lag(
self.model.dim_v,
self.model.dim_rad,
self.data.dim_lt,
self.model.rate,
self.model.wrad,
self.data.list_lt,
self.data.list_trans,
self.model.redges,
self.lmax,
self.model.bessel0_zeros,
self.model.bessels,
0.0,
)
else:
log_like = log_like_lag(
self.model.dim_v,
self.data.dim_lt,
self.model.v,
self.model.w,
self.model.list_lt,
self.data.list_trans,
self.pbc,
)
if log_like is None:
raise ValueError("Initial propagator has non-positive elements")
elif np.isnan(log_like):
raise ValueError("Initial likelihood diverges")
self.log_like = log_like
# add smoothing to diffusion profile
if self.k > 0.0:
E_w = string_energy(self.model.w, self.k, self.pbc)
self.string_vecs = string_vecs(len(self.model.w), self.pbc)
self.log_like = log_like - E_w # minus sign because surface=log_like
print "initial log-likelihood:", self.log_like
self.all_log_like = np.zeros(self.nmc, float)
# TODO make nicer
if self.model.ncosF > 0:
self.naccv_coeff = np.zeros(self.model.ncosF, int)
if self.model.ncosD > 0:
self.naccw_coeff = np.zeros(self.model.ncosD, int)
if self.do_radial:
if self.model.ncosDrad > 0:
self.naccwrad_coeff = np.zeros(self.model.ncosDrad, int)
else:
self.model.ncosDrad = -1
def init_log_like(self):
# initialize log_like
if self.do_radial:
self.model.rate = init_rate_matrix(self.model.dim_v, self.model.v,
self.model.w, self.pbc)
log_like = rad_log_like_lag(
self.model.dim_v, self.model.dim_rad, self.data.dim_lt,
self.model.rate, self.model.wrad, self.data.list_lt,
self.data.list_trans, self.model.redges, self.lmax,
self.model.bessel0_zeros, self.model.bessels, 0.)
else:
log_like = log_like_lag(self.model.dim_v, self.data.dim_lt,
self.model.v, self.model.w,
self.model.list_lt, self.data.list_trans,
self.pbc)
if log_like is None:
raise ValueError("Initial propagator has non-positive elements")
elif np.isnan(log_like):
raise ValueError("Initial likelihood diverges")
self.log_like = log_like
# add smoothing to diffusion profile
if self.k > 0.:
E_w = string_energy(self.model.w, self.k, self.pbc)
self.string_vecs = string_vecs(len(self.model.w), self.pbc)
self.log_like = log_like - E_w # minus sign because surface=log_like
print "initial log-likelihood:", self.log_like
self.all_log_like = np.zeros(self.nmc, float)
# TODO make nicer
if self.model.ncosF > 0:
self.naccv_coeff = np.zeros(self.model.ncosF, int)
if self.model.ncosD > 0:
self.naccw_coeff = np.zeros(self.model.ncosD, int)
if self.do_radial:
if self.model.ncosDrad > 0:
self.naccwrad_coeff = np.zeros(self.model.ncosDrad, int)
else:
self.model.ncosDrad = -1
def set_model(self, model, data, ncosF, ncosD, ncosDrad, F_profile=None):
self.data = data # transitions etc
ncosP = 0
# derive model
if self.do_radial > 0:
if model == "RadCosinusModel":
self.model = RadCosinusModel(self.data, self.D0, ncosF, ncosD,
ncosP, ncosDrad)
elif model == "RadModel":
self.model = RadModel(self.data, self.D0, ncosF, ncosD, ncosP)
else:
raise ValueError("model %s not found" % model)
bessel0_zeros, bessels = setup_bessel_functions(
self.lmax,
self.model.dim_rad,
)
self.model.bessels = bessels
self.model.bessel0_zeros = bessel0_zeros
self.model.rate = init_rate_matrix(self.model.dim_v, self.model.v,
self.model.w, self.pbc)
else:
if model == "CosinusModel":
self.model = CosinusModel(self.data, self.D0, ncosF, ncosD,
ncosP)
# this will default to Model(self,data) if ncosF and ncosD are both 0
elif model == "SinusCosinusModel":
self.model = SinusCosinusModel(self.data, self.D0, ncosF,
ncosD, ncosP)
elif model == "StepModel":
self.model = StepModel(self.data, self.D0, ncosF, ncosD, ncosP)
elif model == "OneStepModel":
self.model = OneStepModel(self.data, self.D0, ncosF, ncosD,
ncosP)
elif model == "Model":
self.model = Model(self.data, self.D0)
else:
raise ValueError("model %s not found" % model)
assert self.pbc == self.model.pbc # make sure PBC for model and transition matrix are identical