def update_propability_mass(self):
"""Create a total flux matrix, and propogate self.pv one time-step."""
J = self.get_total_flux_matrix()
if self.update_method == 'exact':
self.pv = util.exact_update_method(J,
self.pv,
dt=self.simulation.dt)
elif self.update_method == 'approx':
if self.approx_order == None:
self.pv = util.approx_update_method_tol(J,
self.pv,
tol=self.tol,
dt=self.simulation.dt,
norm=self.norm)
else:
self.pv = util.approx_update_method_order(
J,
self.pv,
approx_order=self.approx_order,
dt=self.simulation.dt)
else:
raise Exception('Unrecognized population update method: "%s"' %
self.update_method) # pragma: no cover
def update_propability_mass(self):
"""Create a total flux matrix, and propogate self.pv one time-step."""
# import scipy.linalg as spla
# import matplotlib.pyplot as plt
#
# if self.simulation.t > .09:
#
# for key, val in self.total_input_dict.items():
# print key, val
#
# evs = spla.eigvals(J*self.simulation.dt)
# evs_re = np.real(evs)
# evs_im = np.imag(evs)
# plt.plot(evs_re, evs_im, '.')
# plt.show()
if self.update_method == 'exact':
J = self.get_total_flux_matrix()
self.pv = util.exact_update_method(J,
self.pv,
dt=self.simulation.dt)
elif self.update_method == 'approx':
J = self.get_total_flux_matrix()
if self.approx_order == None:
self.pv = util.approx_update_method_tol(J,
self.pv,
tol=self.tol,
dt=self.simulation.dt,
norm=self.norm)
else:
self.pv = util.approx_update_method_order(
J,
self.pv,
approx_order=self.approx_order,
dt=self.simulation.dt)
elif self.update_method == 'gmres':
self.update_propability_mass_backward_euler(
lambda J, x0: spsla.gmres(J, x0, x0=x0, tol=self.tol)[0])
else:
raise Exception('Unrecognized population update method: "%s"' %
self.update_method) # pragma: no cover
# Checking stability of
if len(np.where(self.pv < 0)[0]) != 0 or np.abs(
np.abs(self.pv).sum() - 1) > 1e-15:
self.pv[np.where(self.pv < 0)] = 0
self.pv /= self.pv.sum()
logger.critical('Normalizing Probability Mass')
def update_propability_mass(self):
"""Create a total flux matrix, and propogate self.pv one time-step."""
J = self.get_total_flux_matrix()
if self.update_method == 'exact':
self.pv = util.exact_update_method(J, self.pv, dt=self.simulation.dt)
elif self.update_method == 'approx':
if self.approx_order == None:
self.pv = util.approx_update_method_tol(J, self.pv, tol=self.tol, dt=self.simulation.dt, norm=self.norm)
else:
self.pv = util.approx_update_method_order(J, self.pv, approx_order=self.approx_order, dt=self.simulation.dt)
else:
raise Exception('Unrecognized population update method: "%s"' % self.update_method) # pragma: no cover
def update_propability_mass(self):
"""Create a total flux matrix, and propogate self.pv one time-step."""
# import scipy.linalg as spla
# import matplotlib.pyplot as plt
#
# if self.simulation.t > .09:
#
# for key, val in self.total_input_dict.items():
# print key, val
#
# evs = spla.eigvals(J*self.simulation.dt)
# evs_re = np.real(evs)
# evs_im = np.imag(evs)
# plt.plot(evs_re, evs_im, '.')
# plt.show()
if self.update_method == 'exact':
J = self.get_total_flux_matrix()
self.pv = util.exact_update_method(J, self.pv, dt=self.simulation.dt)
elif self.update_method == 'approx':
J = self.get_total_flux_matrix()
if self.approx_order == None:
self.pv = util.approx_update_method_tol(J, self.pv, tol=self.tol, dt=self.simulation.dt, norm=self.norm)
else:
self.pv = util.approx_update_method_order(J, self.pv, approx_order=self.approx_order, dt=self.simulation.dt)
elif self.update_method == 'gmres':
self.update_propability_mass_backward_euler(lambda J, x0: spsla.gmres(J, x0, x0=x0, tol=self.tol)[0])
else:
raise Exception('Unrecognized population update method: "%s"' % self.update_method) # pragma: no cover
# Checking stability of
if len(np.where(self.pv<0)[0]) != 0 or np.abs(np.abs(self.pv).sum() - 1) > 1e-15:
self.pv[np.where(self.pv<0)] = 0
self.pv /= self.pv.sum()
logger.critical('Normalizing Probability Mass')
def update_propability_mass(self):
"""Create a total flux matrix, and propogate self.pv one time-step."""
J = self.get_total_flux_matrix()
if self.update_method == 'exact':
self.pv = util.exact_update_method(J, self.pv, dt=self.simulation.dt)
elif self.update_method == 'approx':
if self.approx_order == None:
self.pv = util.approx_update_method_tol(J, self.pv, tol=self.tol, dt=self.simulation.dt, norm=self.norm)
else:
self.pv = util.approx_update_method_order(J, self.pv, approx_order=self.approx_order, dt=self.simulation.dt)
else:
raise Exception('Unrecognized population update method: "%s"' % self.update_method) # pragma: no cover
# Checking stability of
if len(np.where(self.pv<0)[0]) != 0 or np.abs(np.abs(self.pv).sum() - 1) > 1e-15:
self.pv[np.where(self.pv<0)] = 0
self.pv /= self.pv.sum()
logger.critical('Normalizing Probability Mass')