tspan = numpy.array([0., 150., 300., 450., 600., 900., 1800., 2700., 3600., 7200.]) #10 unevenly spaced time points
obs_names = ['obsAKTPP', 'obsErbB1_ErbB_P_CE', 'obsERKPP']
opts = bayessb.MCMCOpts()
opts.model = hem
opts.tspan = tspan
opts.integrator = 'vode'
opts.nsteps = 50000
scenario = 1
# A few estimation scenarios:
if scenario == 1:
# estimate rates only (not initial conditions)
opts.estimate_params = hem.parameters_rules()
elif scenario == 2:
# use hessian
opts.estimate_params = hem.parameters_rules()
# Warning: hessian-guidance is expensive when fitting many parameters -- the
# time to calculate the hessian increases with the square of the number of
# parameters to fit!
opts.use_hessian = True
opts.hessian_period = opts.nsteps / 6
else:
raise RuntimeError("unknown scenario number")
# values for prior calculation
prior_mean = [numpy.log10(p.value) for p in opts.estimate_params]
# prior_var is set to 6.0 so that (since calc is in log space) parameters can vary within 6 orders of magnitude and not be penalized.
prior_var = 6.0
tspan = numpy.array([0., 150., 300., 450., 600., 900., 1800., 2700., 3600., 7200.]) #10 unevenly spaced time points
obs_names = ['obsAKTPP', 'obsErbB1_ErbB_P_CE', 'obsERKPP']
opts = bayessb.MCMCOpts()
opts.model = model
opts.tspan = tspan
opts.integrator = 'vode'
opts.nsteps = 50000
scenario = 1
# A few estimation scenarios:
if scenario == 1:
# estimate rates only (not initial conditions)
opts.estimate_params = model.parameters_rules()
elif scenario == 2:
# use hessian
opts.estimate_params = model.parameters_rules()
# Warning: hessian-guidance is expensive when fitting many parameters -- the
# time to calculate the hessian increases with the square of the number of
# parameters to fit!
opts.use_hessian = True
opts.hessian_period = opts.nsteps / 6
else:
raise RuntimeError("unknown scenario number")
# values for prior calculation
prior_mean = [numpy.log10(p.value) for p in opts.estimate_params]
# prior_var is set to 6.0 so that (since calc is in log space) parameters can vary within 6 orders of magnitude and not be penalized.
prior_var = 6.0
7200.]) #10 unevenly spaced time points
obs_names = ['obsAKTPP', 'obsErbB1_ErbB_P_CE', 'obsERKPP']
opts = bayessb.MCMCOpts()
opts.model = model
opts.tspan = tspan
opts.integrator = 'vode'
opts.nsteps = 50000
scenario = 1
# A few estimation scenarios:
if scenario == 1:
# estimate rates only (not initial conditions)
opts.estimate_params = model.parameters_rules()
elif scenario == 2:
# use hessian
opts.estimate_params = model.parameters_rules()
# Warning: hessian-guidance is expensive when fitting many parameters -- the
# time to calculate the hessian increases with the square of the number of
# parameters to fit!
opts.use_hessian = True
opts.hessian_period = opts.nsteps / 6
else:
raise RuntimeError("unknown scenario number")
# values for prior calculation
prior_mean = [numpy.log10(p.value) for p in opts.estimate_params]
# prior_var is set to 6.0 so that (since calc is in log space) parameters can vary within 6 orders of magnitude and not be penalized.
prior_var = 6.0
) # 10 unevenly spaced time points
obs_names = ["obsAKTPP", "obsErbB1_ErbB_P_CE", "obsERKPP"]
opts = bayessb.MCMCOpts()
opts.model = hem
opts.tspan = tspan
opts.integrator = "vode"
opts.nsteps = 50000
scenario = 1
# A few estimation scenarios:
if scenario == 1:
# estimate rates only (not initial conditions)
opts.estimate_params = hem.parameters_rules()
elif scenario == 2:
# use hessian
opts.estimate_params = hem.parameters_rules()
# Warning: hessian-guidance is expensive when fitting many parameters -- the
# time to calculate the hessian increases with the square of the number of
# parameters to fit!
opts.use_hessian = True
opts.hessian_period = opts.nsteps / 6
else:
raise RuntimeError("unknown scenario number")
# values for prior calculation
prior_mean = [numpy.log10(p.value) for p in opts.estimate_params]
# prior_var is set to 6.0 so that (since calc is in log space) parameters can vary within 6 orders of magnitude and not be penalized.
prior_var = 6.0
