-
Notifications
You must be signed in to change notification settings - Fork 32
Closed
Labels
newNewly createdNewly created
Description
Hi all,
when running the attached PETab Problem I see failing forward integration.
I tried to simulate the model with
petab_problem = petab.Problem.from_yaml(yaml_file)
model = import_petab_problem(petab_problem)
simulate_petab(petab_problem, model)and get the following errors
[Warning] AMICI:NaN: AMICI encountered a NaN value for xdot[0] (Cre_plasma_icg)
[Warning] AMICI:NaN: AMICI encountered a NaN value for w[27]!
[Warning] AMICI:CVODES:CVode:OTHER: AMICI ERROR: in module CVODES in function CVode : The right-hand side routine failed at the first call.
[Warning] AMICI:simulation: AMICI forward simulation failed at t = 0.000000:
AMICI failed to integrate the forward problem
[Warning] AMICI:CVODES:CVode:WARNING: AMICI ERROR: in module CVODES in function CVode : At the end of the first step, there are still some root functions identically 0. This warning will not be issued again.
[Warning] AMICI:CVODES:CVode:WARNING: AMICI ERROR: in module CVODES in function CVode : At the end of the first step, there are still some root functions identically 0. This warning will not be issued again.
[Warning] AMICI:NaN: AMICI encountered a NaN value for xdot[0] (Cre_plasma_icg)
[Warning] AMICI:NaN: AMICI encountered a NaN value for w[26]!
[Warning] AMICI:CVODES:CVode:OTHER: AMICI ERROR: in module CVODES in function CVode : The right-hand side routine failed at the first call.
[Warning] AMICI:simulation: AMICI forward simulation failed at t = 0.000000:
AMICI failed to integrate the forward problemDirectly loading the model with AMICI and simulating it works without problems, i.e. the following code is successful for the model file
sbml_importer = amici.SbmlImporter(model_file)
model_dir = output_dir / "amici" / mid
sbml_importer.sbml2amici(mid, model_dir)
# load the model module
model_module = amici.import_model_module(mid, model_dir)
# instantiate model
model = model_module.getModel()
# model properties
print("Model parameters:", list(model.getParameterIds()), "\n")
print("Model const parameters:", list(model.getFixedParameterIds()), "\n")
print("Model outputs:", list(model.getObservableIds()), "\n")
print("Model states:", list(model.getStateIds()), "\n")
# instantiate solver
solver = model.getSolver()
solver.setAbsoluteTolerance(1e-10)
# run forward simulation
t = np.linspace(0, 20, num=5)
model.setTimepoints(t)
rdata = amici.runAmiciSimulation(model, solver)
print(t)
print(rdata.x)resulting in
--- amici ---
Model parameters: ['conversion_ml_per_l', 'conversion_l_per_ml', 'BW', 'HEIGHT', 'COBW', 'Fblood', 'HCT', 'FVgi', 'FVbi', 'FVli', 'FVlu', 'FVve', 'FVar', 'FVpo', 'FVhv', 'FQgi', 'FQh', 'FQlu', 'f_cirrhosis', 'f_shunts', 'f_tissue_loss', 'f_bloodflow', 'f_cardiac_output', 'f_exercise', 'resection_rate', 'Mr_icg', 'ti_icg', 'Ri_icg', 'LI__ICGIM_Vmax', 'LI__ICGIM_Km', 'LI__ICGIM_ki_bil', 'LI__f_oatp1b3', 'LI__ICGLI2CA_Vmax', 'LI__ICGLI2CA_Km', 'LI__ICGLI2BI_Vmax']
Model const parameters: []
Model outputs: ['yCre_plasma_icg', 'yCgi_plasma_icg', 'yCli_plasma_icg', 'yClu_plasma_icg', 'yCve_icg', 'yCar_icg', 'yCpo_icg', 'yChv_icg', 'yAfeces_icg', 'yLI__bil_ext', 'yLI__icg', 'yLI__icg_bi', 'yIVDOSE_icg', 'ycum_dose_icg', 'yFVre', 'yFQre', 'yBSA', 'yCO', 'yQC', 'yQlu', 'yQre', 'yQh', 'yQgi', 'yQpo', 'yQha', 'yKi_icg', 'yAre_plasma_icg', 'yXre_plasma_icg', 'yMre_plasma_icg', 'yAgi_plasma_icg', 'yXgi_plasma_icg', 'yMgi_plasma_icg', 'yAli_plasma_icg', 'yXli_plasma_icg', 'yMli_plasma_icg', 'yAlu_plasma_icg', 'yXlu_plasma_icg', 'yMlu_plasma_icg', 'yAve_icg', 'yXve_icg', 'yMve_icg', 'yAar_icg', 'yXar_icg', 'yMar_icg', 'yApo_icg', 'yXpo_icg', 'yMpo_icg', 'yAhv_icg', 'yXhv_icg', 'yMhv_icg', 'yER_icg', 'yCLinfusion_icg', 'yVbi', 'yVgi', 'yVli', 'yVlu', 'yVre', 'yVve', 'yVar', 'yVpo', 'yVhv', 'yVre_plasma', 'yVre_tissue', 'yVgi_plasma', 'yVgi_tissue', 'yVli_plasma', 'yVli_tissue', 'yVlu_plasma', 'yVlu_tissue', 'yVfeces']
Model states: ['Cre_plasma_icg', 'Cgi_plasma_icg', 'Cli_plasma_icg', 'Clu_plasma_icg', 'Cve_icg', 'Car_icg', 'Cpo_icg', 'Chv_icg', 'Afeces_icg', 'LI__bil_ext', 'LI__icg', 'LI__icg_bi', 'IVDOSE_icg', 'cum_dose_icg']
[ 0. 5. 10. 15. 20.]
[[0. 0. 0. 0. 0. 0. 0. 0. 0. 0.01 0. 0. 0. 0. ]
[0. 0. 0. 0. 0. 0. 0. 0. 0. 0.01 0. 0. 0. 0. ]
[0. 0. 0. 0. 0. 0. 0. 0. 0. 0.01 0. 0. 0. 0. ]
[0. 0. 0. 0. 0. 0. 0. 0. 0. 0.01 0. 0. 0. 0. ]
[0. 0. 0. 0. 0. 0. 0. 0. 0. 0.01 0. 0. 0. 0. ]]Best Matthias
Metadata
Metadata
Assignees
Labels
newNewly createdNewly created