• 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

Type: int / auto

Option to set seed to make run result reproducible -e.g. when debugging.

Example:-

rand_seed: 12345

float_format

Type: str

Format string for numerical output

Example:-

float_format: default

DESCRIPTION

Type: dict_record

Description fields for script.

Example:-

DESCRIPTION:
    name: example
    title: A PKPD model
    author: J.R. Hartley
    abstract: |
    keywords: []

name

Type: str

Unique name used to distinguish script

Example:-

name: example

title

Type: str

A longer text string that could serve as a title

Example:-

title: A PKPD model

author

Type: str

Author of the model

Example:-

author: J.R. Hartley

abstract

Type: verbatim

Abstract paragraph describing model

Example:-

abstract: |

keywords

Type: list

Keywords list used to categorise models.

Example:-

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']

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

id_field

Type: str

Field name in data file that contains identity string for each data row e.g. obs/dose etc

Example:-

id_field: ID

time_field

Type: str

Field name in data file that contains time or event for each data row

Example:-

time_field: TIME

LEVEL_PARAMS

Type: repeat_record

Level params to define hierarchical population model

Example:-

LEVEL_PARAMS:
    GLOBAL:
        params: |
            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

        split_field: None

        split_dict: {}

    INDIV:
        params: |
            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])

        split_field: ID

        split_dict: "*"

params

Type: verbatim

Defines names subsets, defined by ranges of split field values

Example:-

params: |

split_field

Type: str

Field to split level into sub levels

Example:-

split_field: none

split_dict

Type: dict / star

Defines named subsets, defined by ranges of split field values

Example:-

split_dict: {}

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)

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_sens: False}

NO_SOLVER

Type: dict_record

Null method for blank derivatives.

Example:-

NO_SOLVER: {}

ANALYTIC

Type: dict_record

Analytic method for solving ODE

Example:-

ANALYTIC: {}

SCIPY_ODEINT

Type: dict_record

odeint solver record

Example:-

SCIPY_ODEINT:
    atol: 1e-06
    rtol: 1e-06
    max_nsteps: 10000000
    use_sens: False

atol

Type: float

Absolute tolerance of ode solver.

Example:-

atol: 1e-06

rtol

Type: float

Relative tolerance of ode solver.

Example:-

rtol: 1e-06

max_nsteps

Type: int

Maximum number of steps allowed in ode solver.

Example:-

max_nsteps: 10000000

use_sens

Type: bool

Option to use sensitivity equations in ode solver.

Example:-

use_sens: False

OUTPUT_SCRIPTS

Type: dict_record

scripts to output for further processing

Example:-

OUTPUT_SCRIPTS:
    GRPH: {output_mode: no_output, share_axes: True}
    SIM: {output_mode: gen_script_and_run, sim_time_step: 1.0, share_axes: True}
    GENSUM: {output_mode: gen_script_and_run}

GRPH

Type: dict_record

options to pass to plt_grph script.

Example:-

GRPH:
    output_mode: no_output
    share_axes: False

output_mode

Type: one_of(no_output,gen_script_only,gen_script_and_run)

Output options.

Example:-

output_mode: no_output

share_axes

Type: bool

Option to share axes between individuals when plotting graphical data

Example:-

share_axes: False

SIM

Type: dict_record

options to pass to sim script.

Example:-

SIM:
    output_mode: no_output
    sim_time_step: -1.0
    share_axes: False

output_mode

Type: one_of(no_output,gen_script_only,gen_script_and_run)

Output options.

Example:-

output_mode: no_output

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

share_axes

Type: bool

Option to share axes between individuals when plotting graphical data

Example:-

share_axes: False

GENSUM

Type: dict_record

options to pass to gensum script.

Example:-

GENSUM:
    output_mode: no_output

output_mode

Type: one_of(no_output,gen_script_only,gen_script_and_run)

Output options.

Example:-

output_mode: no_output
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