- Language: en
METHOD_OPTIONS¶
Type: dict_record
method options for sim_script
Example:-
METHOD_OPTIONS:
py_module: sim
rand_seed: 12345
float_format: default
py_module¶
Type: one_of(sim)
Python module required to process this script file
Example:-
py_module: sim
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
Example:-
FILE_PATHS:
input_data_file: input.csv
extra_data_file: none
output_folder: auto
temp_folder: auto
log_folder: auto
output_file_ext: ['svg']
delete_old_files_flag: False
solutions:
pop: ./path_to_final_pop_solution.pyml
indiv: ./path_to_final_indiv_solution.pyml
input_data_file¶
Type: input_file
path to input comma separated value file in popy data format
Example:-
input_data_file: input.csv
extra_data_file¶
Type: input_file / none
path to extra comma separated value file in popy data format
Example:-
extra_data_file: none
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
PREPROCESS¶
Type: verbatim
Code that preprocesses the input data. Use this to filter rows and create derived covariates.
Example:-
PREPROCESS: |
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_OPTIONS¶
Type: dict_record
Output options for sim_script
Example:-
OUTPUT_OPTIONS:
sim_time_step: -1.0
OUTPUT_SCRIPTS¶
Type: dict_record
scripts to output for further processing
Example:-
OUTPUT_SCRIPTS:
GRPH:
output_mode: none
grph_list: ['SPAG_GRPH']
x_var: TIME
x_axis_label: TIME
y_var_list: ['DV_CENTRAL_sim', 'DV_CENTRAL']
y_var_src_list: ['sim', 'orig']
y_var_label_list: ['Drug conc. (units)', 'Drug conc. (units)']
split_field: none
share_axes: False
y_scale: linear
GRPH¶
Type: dict_record
Options to pass to plt_grph_script.
Example:-
GRPH:
output_mode: none
grph_list: ['SPAG_GRPH']
x_var: TIME
x_axis_label: TIME
y_var_list: ['DV_CENTRAL_sim', 'DV_CENTRAL']
y_var_src_list: ['sim', 'orig']
y_var_label_list: ['Drug conc. (units)', 'Drug conc. (units)']
split_field: none
share_axes: False
y_scale: linear
grph_list¶
Type: list(str)
List of graph types to generate in popy_grph script.
Example:-
grph_list: ['SPAG_GRPH']
y_var_list¶
Type: list(str)
List of y variable names to be plotted on graph.
Example:-
y_var_list: ['DV_CENTRAL_sim', 'DV_CENTRAL']
y_var_src_list¶
Type: list(str)
Source of data has to be either sim or orig
Example:-
y_var_src_list: ['sim', 'orig']
y_var_label_list¶
Type: list(str)
List of y variable labels to be plotted on graph.
Example:-
y_var_label_list: ['Drug conc. (units)', 'Drug conc. (units)']
y_scale¶
Type: one_of(linear,log)
y axis scale - can be either ‘linear’ or ‘log’.
Example:-
y_scale: linear