Language: en

METHOD_OPTIONS¶

method options for tut_script

Example:-

METHOD_OPTIONS:
    py_module: tut
    rand_seed: 12345
    float_format: default

py_module¶

Type: one_of(tut)

Python module required to process this script file

Example:-

py_module: tut

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: pk_first_order
    title: First order absorption model with peripheral compartment
    author: J.R. Hartley
    abstract: |
        A two compartment PK model with bolus dose and
        first order absorption, similar to a nonmem advan4trans4 model.
    keywords: ['tutorial', 'pk', 'advan4', 'dep_two_cmp', 'first order']

name¶

Type: str

Unique name used to distinguish script

Example:-

name: pk_first_order

title¶

Type: str

A longer text string that could serve as a title

Example:-

title: First order absorption model with peripheral compartment

author¶

Type: str

Author of the model

Example:-

author: J.R. Hartley

abstract¶

Type: verbatim

Abstract paragraph describing model

Example:-

abstract: |
    A two compartment PK model with bolus dose and
    first order absorption, similar to a nonmem advan4trans4 model.

keywords¶

Type: list

Keywords list used to categorise models.

Example:-

keywords: ['tutorial', 'pk', 'advan4', 'dep_two_cmp', 'first order']

FILE_PATHS¶

Type: dict_record

file paths

Example:-

FILE_PATHS:
    output_folder: auto
    log_folder: auto
    output_file_ext: ['svg']
    output_iteration_results: True

output_folder¶

Type: output_folder / auto

Output folder - results of computation stored here

Example:-

output_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']

output_iteration_results¶

Type: bool

Option to output iteration-by-iteration values.

Example:-

output_iteration_results: True

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:
        split_field: None

        split_dict: {}

        gen_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

        fit_params: |
            f[KA] ~ P1.0
            f[CL] ~ P1.0
            f[V1] ~ P20
            f[Q] ~ P0.5
            f[V2] ~ P100
            f[KA_isv,CL_isv,V1_isv,Q_isv,V2_isv] ~ spd_matrix() [
                [0.05],
                [0.01, 0.05],
                [0.01, 0.01, 0.05],
                [0.01, 0.01, 0.01, 0.05],
                [0.01, 0.01, 0.01, 0.01, 0.05],
            ]
            f[PNOISE] ~ P0.1

    INDIV:
        split_field: ID

        split_dict: "*"

        gen_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])

        fit_params: |
            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¶

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: {}

gen_params¶

Type: verbatim

Defines f[X] and r[X] variables in split field ranges for gen_scripts

Example:-

gen_params: |

fit_params¶

Type: verbatim

Defines f[X] and r[X] variables in split field ranges for fit_scripts

Example:-

fit_params: |

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

FIT_METHODS¶

Type: list_record

one of many possible fitters

Example:-

FIT_METHODS:
    - JOE:
        ODE_SOLVER: {INHERIT: {}}
        max_n_main_iterations: 30
        CONVERGER: {OBJ_INC: {}}
        recompute_jacobians_flag: False
        use_chain: False
        reset_re_flag: True

JOE¶

Type: dict_record

Joint Optimisation and Estimation method

Example:-

JOE:
    ODE_SOLVER: {INHERIT: {}}
    max_n_main_iterations: 50
    CONVERGER: {OBJ_INC: {}}
    recompute_jacobians_flag: False
    use_chain: False
    reset_re_flag: True

max_n_main_iterations¶

Type: int

Maximum number of iterations

Example:-

max_n_main_iterations: 50

CONVERGER¶

Type: one_of_record

Convergence methods.

Example:-

CONVERGER:
    OBJ_INC: {}

NONE¶

Type: dict_record

No convergence testing.

Example:-

NONE: {}

OBJ_INC¶

Type: dict_record

Objective function increase converger.

Example:-

OBJ_INC: {}

OBJ_SIM¶

Type: dict_record

Objective function similar converger.

Example:-

OBJ_SIM: {}

recompute_jacobians_flag¶

Type: bool

Option to recompute jacobians more often, e.g in noise optimiser.

Example:-

recompute_jacobians_flag: False

use_chain¶

Type: bool

Option to use chain rule when computing derivatives.

Example:-

use_chain: False

reset_re_flag¶

Type: bool

Reset r[X]=0.0 before each r[X] optimisation

Example:-

reset_re_flag: True

COVARIANCE¶

Type: one_of_record

Covariance methods used to estimate standard errors.

Example:-

COVARIANCE:
    NO_COVARIANCE: {}

NO_COVARIANCE¶

Type: dict_record

Method of computing covariance matrix.

Example:-

NO_COVARIANCE: {}

FINITE_DIFF¶

Type: dict_record

Method of computing covariance matrix.

Example:-

FINITE_DIFF:
    var_covariance_flag: False
    inv_hessian_flag: False

var_covariance_flag¶

Type: bool

Optionally compute standard errors for f[X] covariance elements this could take a long time for large covariance matrices. Note the results of the standard error computation for the main f[X] parameters combined with the variances will be different.

Example:-

var_covariance_flag: False

inv_hessian_flag¶

Type: bool

Optionally invert the hessian and output results to disk. this is mainly to generate PoPy documentation output.

Example:-

inv_hessian_flag: False

OUTPUT_SCRIPTS¶

Type: dict_record

scripts to output for further processing

Example:-

OUTPUT_SCRIPTS:
    GEN: {output_mode: gen_script_and_run, sim_time_step: 1.0, share_axes: True}
    FIT: {output_mode: gen_script_and_run, sim_time_step: 1.0, share_axes: True}
    COMP: {output_mode: gen_script_and_run}
    TUTSUM: {output_mode: gen_script_and_run}

GEN¶

Type: dict_record

options to pass to gen script.

Example:-

GEN:
    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

FIT¶

Type: dict_record

options to pass to fit script.

Example:-

FIT:
    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

COMP¶

Type: dict_record

options to pass to comp script.

Example:-

COMP:
    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

TUTSUM¶

Type: dict_record

options to pass to tutsum script.

Example:-

TUTSUM:
    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

Tut Script

Comp Script

Documentation

METHOD_OPTIONS¶

py_module¶

rand_seed¶

float_format¶

DESCRIPTION¶

name¶

title¶

author¶

abstract¶

keywords¶

FILE_PATHS¶

output_folder¶

log_folder¶

output_file_ext¶

output_iteration_results¶

DATA_FIELDS¶

type_field¶

id_field¶

time_field¶

LEVEL_PARAMS¶

split_field¶

split_dict¶

gen_params¶

fit_params¶

MODEL_PARAMS¶

STATES¶

DERIVATIVES¶

PREDICTIONS¶

ODE_SOLVER¶

NO_SOLVER¶

ANALYTIC¶

SCIPY_ODEINT¶

atol¶

rtol¶

max_nsteps¶

use_sens¶

FIT_METHODS¶

JOE¶

max_n_main_iterations¶

CONVERGER¶

NONE¶

OBJ_INC¶

OBJ_SIM¶

recompute_jacobians_flag¶

use_chain¶

reset_re_flag¶

COVARIANCE¶

NO_COVARIANCE¶

FINITE_DIFF¶

var_covariance_flag¶

inv_hessian_flag¶

OUTPUT_SCRIPTS¶

GEN¶

output_mode¶

sim_time_step¶

share_axes¶

FIT¶

output_mode¶

sim_time_step¶

share_axes¶

COMP¶

output_mode¶

TUTSUM¶

output_mode¶

Additional Information

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