- Language: en
METHOD_OPTIONS¶
Type: dict_record
method options for gen_script
Example:-
METHOD_OPTIONS:
py_module: gen
rand_seed: 12345
float_format: default
py_module¶
Type: one_of(gen)
Python module required to process this script file
Example:-
py_module: gen
rand_seed¶
Option to set seed to make run result reproducible -e.g. when debugging.
Example:-
rand_seed: 12345
PARALLEL¶
Type: one_of_record
one of many possible servers
Example:-
PARALLEL:
SINGLE: {}
DESCRIPTION¶
Type: dict_record
Description fields for script.
Example:-
DESCRIPTION:
name: example
title: A PKPD model
author: A.N. Other
abstract: |
keywords: []
FILE_PATHS¶
Type: dict_record
file paths for script
Example:-
FILE_PATHS:
output_folder: auto
temp_folder: auto
log_folder: auto
output_file_ext: ['svg']
delete_old_files_flag: False
output_folder¶
Type: output_folder / auto
Output folder - results of computation stored here
Example:-
output_folder: auto
temp_folder¶
Type: output_folder / auto
Temp folder - temporary files stored here
Example:-
temp_folder: auto
log_folder¶
Type: output_folder / auto
Log folder - log files stored here
Example:-
log_folder: auto
output_file_ext¶
Type: list_of(pdf,png,svg)
Output file extension - determines graphical output file format.
Example:-
output_file_ext: ['svg']
DATA_FIELDS¶
Type: dict_record
data fields for popy.dat.fields object
Example:-
DATA_FIELDS:
type_field: TYPE
id_field: ID
time_field: TIME
type_field¶
Type: str
Field name in data file that contains row type info, e.g. obs/dose etc
Example:-
type_field: TYPE
EFFECTS¶
Type: repeat_verb_record
EFFECT params to define hierarchical population model
Example:-
EFFECTS:
POP: |
c[AMT] = 100.0
f[KA] = 0.2
f[CL] = 2.0
f[V1] = 50
f[Q] = 1.0
f[V2] = 80
f[KA_isv,CL_isv,V1_isv,Q_isv,V2_isv] = [
[0.1],
[0.01, 0.03],
[0.01, -0.01, 0.09],
[0.01, 0.02, 0.01, 0.07],
[0.01, 0.02, 0.01, 0.01, 0.05],
]
f[PNOISE] = 0.15
ID: |
c[ID] = sequential(50)
t[DOSE] = 2.0
t[OBS] ~ unif(1.0, 50.0; 5)
# t[OBS] = range(1.0, 50.0; 5)
r[KA, CL, V1, Q, V2] ~ mnorm([0,0,0,0,0], f[KA_isv,CL_isv,V1_isv,Q_isv,V2_isv])
PREPROCESS¶
Type: verbatim
Code that preprocesses the input data. Use this to filter rows and create derived covariates.
Example:-
PREPROCESS: |
MODEL_PARAMS¶
Type: verbatim
Defines the mapping from c[X], f[X] and r[X] variables to individual model m[X] parameters.
Example:-
MODEL_PARAMS: |
m[KA] = f[KA] * exp(r[KA])
m[CL] = f[CL] * exp(r[CL])
m[V1] = f[V1] * exp(r[V1])
m[Q] = f[Q] * exp(r[Q])
m[V2] = f[V2] * exp(r[V2])
m[ANOISE] = 0.001
m[PNOISE] = f[PNOISE]
STATES¶
Type: verbatim
Optional section for setting initial values of s[X] variables can also set slabel[X] text labels.
Example:-
STATES: |
DERIVATIVES¶
Type: verbatim
Define how the covariates and effects determine flows between compartments.
Example:-
DERIVATIVES: |
# s[DEPOT,CENTRAL,PERI] = @dep_two_cmp_cl{dose:@bolus{amt:c[AMT]}}
d[DEPOT] = @bolus{amt:c[AMT]} - m[KA]*s[DEPOT]
d[CENTRAL] = m[KA]*s[DEPOT] - s[CENTRAL]*m[CL]/m[V1] - s[CENTRAL]*m[Q]/m[V1] + s[PERI]*m[Q]/m[V2]
d[PERI] = s[CENTRAL]*m[Q]/m[V1] - s[PERI]*m[Q]/m[V2]
PREDICTIONS¶
Type: verbatim
Define the final predicted m[X] variables to be output by the compartment model system.
Example:-
PREDICTIONS: |
p[DV_CENTRAL] = s[CENTRAL]/m[V1]
var = m[ANOISE]**2 + m[PNOISE]**2 * p[DV_CENTRAL]**2
c[DV_CENTRAL] ~ norm(p[DV_CENTRAL], var)
POSTPROCESS¶
Type: verbatim
Code that postprocesses the output data. Use this to filter rows and create derived covariates, after the main data curves have been generated.
Example:-
POSTPROCESS: |
ODE_SOLVER¶
Type: one_of_record
one of many possible solvers
Example:-
ODE_SOLVER:
SCIPY_ODEINT:
atol: 1e-06
rtol: 1e-06
max_nsteps: 10000000
use_supersections: True
use_jacobian: False
use_sens: False
use_tcrit: False
ANALYTIC¶
Type: dict_record
Analytic method for solving ODE
Example:-
ANALYTIC:
use_supersections: auto
use_sens: True
use_supersections¶
Option to combine sections into supersections, which can make PoPy run faster, however with discontinuous ODE params you may need to turn this off (closer to nonmem approach).
Example:-
use_supersections: auto
SCIPY_ODEINT¶
Type: dict_record
odeint solver record
Example:-
SCIPY_ODEINT:
atol: 1e-06
rtol: 1e-06
max_nsteps: 10000000
use_supersections: auto
use_jacobian: False
use_sens: True
use_tcrit: False
use_supersections¶
Option to combine sections into supersections, which can make PoPy run faster, however with discontinuous ODE params you may need to turn this off (closer to nonmem approach).
Example:-
use_supersections: auto
CPPODE¶
Type: dict_record
C++ version of original cvode c library.
Example:-
CPPODE:
atol: 1e-06
rtol: 1e-06
max_nsteps: 10000000
use_supersections: auto
use_sens: True
use_supersections¶
Option to combine sections into supersections, which can make PoPy run faster, however with discontinuous ODE params you may need to turn this off (closer to nonmem approach).
Example:-
use_supersections: auto
CPPLSODA¶
Type: dict_record
C++ version of original cvode c library.
Example:-
CPPLSODA:
atol: 1e-06
rtol: 1e-06
max_nsteps: 10000000
use_supersections: auto
use_sens: True
hmin: 1e-12
use_supersections¶
Option to combine sections into supersections, which can make PoPy run faster, however with discontinuous ODE params you may need to turn this off (closer to nonmem approach).
Example:-
use_supersections: auto
OUTPUT_SCRIPTS¶
Type: dict_record
scripts to output for further processing
Example:-
OUTPUT_SCRIPTS:
GRPH: {output_mode: none, share_axes: True, y_scale: linear}
SIM:
output_mode: run
sim_time_step: 1.0
share_axes: True
comb_grph_flag: False
y_scale: linear
GENSUM: {output_mode: run}
GRPH¶
Type: dict_record
options to pass to grph script.
Example:-
GRPH:
output_mode: none
share_axes: False
y_scale: linear
y_scale¶
Type: one_of(linear,log)
y axis scale - can be either ‘linear’ or ‘log’.
Example:-
y_scale: linear
SIM¶
Type: dict_record
options to pass to sim script.
Example:-
SIM:
output_mode: none
sim_time_step: -1.0
share_axes: False
comb_grph_flag: False
y_scale: linear
sim_time_step¶
Type: float
Size of time step when creating smooth curve predictions note setting this to a negative value, results in simulated predictions for each individual ONLY at time points in the original data set.
Example:-
sim_time_step: -1.0
comb_grph_flag¶
Type: bool
Option to output combined spag graph plot PoPy documentation sometimes requires this
Example:-
comb_grph_flag: False
y_scale¶
Type: one_of(linear,log)
y axis scale - can be either ‘linear’ or ‘log’.
Example:-
y_scale: linear