amici.amici.ReturnDataPtr

class amici.amici.ReturnDataPtr(*args)[source]

Swig-Generated class that implements smart pointers to ReturnData as objects.

Attributes

FIM

nplist x nplist, row-major)

J

nx x nx, row-major)

chi2

chi2 value

cpu_time

computation time of forward solve [ms]

cpu_timeB

computation time of backward solve [ms]

llh

loglikelihood value

nJ

dimension of the augmented objective function for 2nd order ASA

ne

number of events

newton_maxsteps

maximal number of newton iterations for steady state calculation

nk

number of fixed parameters

nmaxevent

maximal number of occurring events (for every event type)

np

total number of model parameters

nplist

number of parameter for which sensitivities were requested

nt

number of considered timepoints

numerrtestfails

nt)

numerrtestfailsB

nt)

numnonlinsolvconvfails

number of linear solver convergence failures forward problem (dimension: nt)

numnonlinsolvconvfailsB

number of linear solver convergence failures backward problem (dimension: nt)

numrhsevals

nt)

numrhsevalsB

nt)

numsteps

nt)

numstepsB

nt)

nw

number of columns in w

nx

number of states

nx_solver

number of states with conservation laws applied

nx_solver_reinit

number of solver states to be reinitialized after preequilibration

nxtrue

number of states in the unaugmented system

ny

number of observables

nytrue

number of observables in the unaugmented system

nz

number of event outputs

nztrue

number of event outputs in the unaugmented system

o2mode

flag indicating whether second order sensitivities were requested

order

nt)

posteq_cpu_time

computation time of the steady state solver [ms] (postequilibration)

posteq_cpu_timeB

computation time of the steady state solver of the backward problem [ms] (postequilibration)

posteq_numlinsteps

number of linear steps by Newton step for steady state problem.

posteq_numsteps

number of Newton steps for steady state problem (preequilibration) [newton, simulation, newton] (length = 3) (postequilibration)

posteq_numstepsB

number of simulation steps for adjoint steady state problem (postequilibration) [== 0 if analytical solution worked, > 0 otherwise]

posteq_status

flags indicating success of steady state solver (postequilibration)

posteq_t

time when steadystate was reached via simulation (postequilibration)

posteq_wrms

weighted root-mean-square of the rhs when steadystate was reached (postequilibration)

preeq_cpu_time

computation time of the steady state solver [ms] (preequilibration)

preeq_cpu_timeB

computation time of the steady state solver of the backward problem [ms] (preequilibration)

preeq_numlinsteps

number of linear steps by Newton step for steady state problem.

preeq_numsteps

number of Newton steps for steady state problem (preequilibration) [newton, simulation, newton] (length = 3)

preeq_numstepsB

number of simulation steps for adjoint steady state problem (preequilibration) [== 0 if analytical solution worked, > 0 otherwise]

preeq_status

flags indicating success of steady state solver (preequilibration)

preeq_t

time when steadystate was reached via simulation (preequilibration)

preeq_wrms

weighted root-mean-square of the rhs when steadystate was reached (preequilibration)

pscale

scaling of parameterization (lin,log,log10)

rdata_reporting

reporting mode

res

nt*ny, row-major)

rz

nmaxevent x nz, row-major)

s2llh

(nJ-1) x nplist, row-major)

s2rz

second order parameter derivative of event trigger output (dimension: nmaxevent x nztrue x nplist x nplist, row-major)

sensi

sensitivity order

sensi_meth

sensitivity method

sigmay

nt x ny, row-major)

sigmaz

nmaxevent x nz, row-major)

sllh

nplist)

sres

nt*ny x nplist, row-major)

srz

nmaxevent x nz x nplist, row-major)

ssigmay

nt x nplist x ny, row-major)

ssigmaz

parameter derivative of event output standard deviation (dimension: nmaxevent x nz, row-major)

status

status code

sx

nt x nplist x nx, row-major)

sx0

nplist x nx, row-major)

sx_ss

nplist x nx, row-major)

sy

nt x nplist x ny, row-major)

sz

nmaxevent x nz, row-major)

ts

nt)

w

w data from the model (recurring terms in xdot, for imported SBML models from python, this contains the flux vector) (dimensions: nt x nw, row major)

x

nt x nx, row-major)

x0

nx)

x_ss

nx)

xdot

nx)

y

nt x ny, row-major)

z

nmaxevent x nz, row-major)