Language: en

METHOD_OPTIONS¶

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

PARALLEL¶

Type: one_of_record

one of many possible servers

Example:-

PARALLEL:
    SINGLE: {}

SINGLE¶

Type: dict_record

single process server spec.

Example:-

SINGLE: {}

MPI_WORKERS¶

Type: dict_record

MPI local server spec.

Example:-

MPI_WORKERS:
    n_workers: auto
    cpu_load: 1.0

n_workers¶

Type: int / auto

Number of workers to use on this machine, defaults to number of processors, but could be more or fewer.

Example:-

n_workers: auto

cpu_load¶

Type: float

Desired load on CPU as a decimal greater than 0.0 (0%) and less than or equal to 1.0 (100%)

Example:-

cpu_load: 1.0

DESCRIPTION¶

Type: dict_record

Description fields for script.

Example:-

DESCRIPTION:
    name: my_pkpd_model
    title: My PKPD model
    author: My Name
    abstract: |
        Abstract describing the model
    keywords: ['pkpd']

name¶

Type: str

Unique name used to distinguish script

Example:-

name: my_pkpd_model

title¶

Type: str

A longer text string that could serve as a title

Example:-

title: My PKPD model

author¶

Type: str

Author of the model

Example:-

author: My Name

abstract¶

Type: verbatim

Abstract paragraph describing model

Example:-

abstract: |
    Abstract describing the model

keywords¶

Type: list

Keywords list used to categorise models.

Example:-

keywords: ['pkpd']

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']

delete_old_files_flag¶

Type: bool

Option to delete any existing files before running.

Example:-

delete_old_files_flag: False

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

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]
    plabel[DV_CENTRAL] = 'Drug conc. (units)'
    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:
    CPPLSODA:
        atol: 1e-06
        rtol: 1e-06
        max_nsteps: 10000000
        use_supersections: True
        use_sens: False
        hmin: 0.0

NO_SOLVER¶

Type: dict_record

Null method for blank derivatives.

Example:-

NO_SOLVER: {}

ANALYTIC¶

Type: dict_record

Analytic method for solving ODE

Example:-

ANALYTIC:
    use_supersections: auto
    use_sens: True

use_supersections¶

Type: bool / auto

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

use_sens¶

Type: bool

Option to use sensitivity equations in ode solver.

Example:-

use_sens: True

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

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_supersections¶

Type: bool / auto

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

use_jacobian¶

Type: bool

Option to use jacobian in ode solver.

Example:-

use_jacobian: False

use_sens¶

Type: bool

Option to use sensitivity equations in ode solver.

Example:-

use_sens: True

use_tcrit¶

Type: bool

Option to set lsoda tcrit to start and end of subsection. Note this is an experimental option.

Example:-

use_tcrit: False

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

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_supersections¶

Type: bool / auto

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

use_sens¶

Type: bool

Option to use sensitivity equations in ode solver.

Example:-

use_sens: True

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

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_supersections¶

Type: bool / auto

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

use_sens¶

Type: bool

Option to use sensitivity equations in ode solver.

Example:-

use_sens: True

hmin¶

Type: float

Minimum step size of ode solver.

Example:-

hmin: 1e-12

OUTPUT_SCRIPTS¶

Type: dict_record

scripts to output for further processing

Example:-

OUTPUT_SCRIPTS:
    GRPH: {output_mode: none, grph_list: ['OBS_vs_TIME']}
    TABLE: {output_mode: none, tables_list: []}
    SIM:
        output_mode: run
        sim_time_step: 1.0
        grph_list: ['OBS_vs_TIME']
        tables_list: []
    GENSUM: {output_mode: run}

GRPH¶

Type: dict_record

options to pass to grph script.

Example:-

GRPH:
    output_mode: none
    grph_list: ['OBS_vs_TIME']

output_mode¶

Type: one_of(none,create,run)

Output options.

Example:-

output_mode: none

grph_list¶

Type: list(str)

Graph types to plt

Example:-

grph_list: ['OBS_vs_TIME']

TABLE¶

Type: dict_record

Options to pass to TAB script.

Example:-

TABLE:
    output_mode: none
    tables_list: []

output_mode¶

Type: one_of(none,create,run)

Output options.

Example:-

output_mode: none

tables_list¶

Type: list_record

List of tables to create.

Example:-

tables_list:
    - TABLE:
        input_solution: final_fit_ipred
        output_file: my_table.csv
        cx_columns: "*"
        mx_columns: "*"
        sx_columns: "*"
        px_columns: "*"

TABLE¶

Type: dict_record

Table properties.

Example:-

TABLE:
    input_solution: final_fit_ipred
    output_file: my_table.csv
    cx_columns: "*"
    mx_columns: "*"
    sx_columns: "*"
    px_columns: "*"

input_solution¶

Type: str

Label of the solution to parse

Example:-

input_solution: final_fit_ipred

output_file¶

Type: output_file

Destination file for table

Example:-

output_file: my_table.csv

cx_columns¶

Type: list(str) / star

Columns from covariates/observations table to output

Example:-

cx_columns: "*"

mx_columns¶

Type: list(str) / star

Columns from model_params table to output

Example:-

mx_columns: "*"

sx_columns¶

Type: list(str) / star

Columns from states table to output

Example:-

sx_columns: "*"

px_columns¶

Type: list(str) / star

Columns from predictions table to output

Example:-

px_columns: "*"

SIM¶

Type: dict_record

options to pass to sim script.

Example:-

SIM:
    output_mode: none
    sim_time_step: -1.0
    grph_list: ['OBS_vs_TIME']
    tables_list:
        - TABLE:
            input_solution: final_fit_ipred
            output_file: my_table.csv
            cx_columns: "*"
            mx_columns: "*"
            sx_columns: "*"
            px_columns: "*"

output_mode¶

Type: one_of(none,create,run)

Output options.

Example:-

output_mode: none

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

grph_list¶

Type: list(str)

Graph types to plt

Example:-

grph_list: ['OBS_vs_TIME']

tables_list¶

Type: list_record

List of tables to create.

Example:-

tables_list:
    - TABLE:
        input_solution: final_fit_ipred
        output_file: my_table.csv
        cx_columns: "*"
        mx_columns: "*"
        sx_columns: "*"
        px_columns: "*"

TABLE¶

Type: dict_record

Table properties.

Example:-

TABLE:
    input_solution: final_fit_ipred
    output_file: my_table.csv
    cx_columns: "*"
    mx_columns: "*"
    sx_columns: "*"
    px_columns: "*"

input_solution¶

Type: str

Label of the solution to parse

Example:-

input_solution: final_fit_ipred

output_file¶

Type: output_file

Destination file for table

Example:-

output_file: my_table.csv

cx_columns¶

Type: list(str) / star

Columns from covariates/observations table to output

Example:-

cx_columns: "*"

mx_columns¶

Type: list(str) / star

Columns from model_params table to output

Example:-

mx_columns: "*"

sx_columns¶

Type: list(str) / star

Columns from states table to output

Example:-

sx_columns: "*"

px_columns¶

Type: list(str) / star

Columns from predictions table to output

Example:-

px_columns: "*"

GENSUM¶

Type: dict_record

options to pass to gensum script.

Example:-

GENSUM:
    output_mode: none

output_mode¶

Type: one_of(none,create,run)

Output options.

Example:-

output_mode: none

Gen Script

Sim Script

Documentation

METHOD_OPTIONS¶

py_module¶

rand_seed¶

float_format¶

PARALLEL¶

SINGLE¶

MPI_WORKERS¶

n_workers¶

cpu_load¶

DESCRIPTION¶

name¶

title¶

author¶

abstract¶

keywords¶

FILE_PATHS¶

output_folder¶

temp_folder¶

log_folder¶

output_file_ext¶

delete_old_files_flag¶

DATA_FIELDS¶

type_field¶

id_field¶

time_field¶

EFFECTS¶

PREPROCESS¶

MODEL_PARAMS¶

STATES¶

DERIVATIVES¶

PREDICTIONS¶

POSTPROCESS¶

ODE_SOLVER¶

NO_SOLVER¶

ANALYTIC¶

use_supersections¶

use_sens¶

SCIPY_ODEINT¶

atol¶

rtol¶

max_nsteps¶

use_supersections¶

use_jacobian¶

use_sens¶

use_tcrit¶

CPPODE¶

atol¶

rtol¶

max_nsteps¶

use_supersections¶

use_sens¶

CPPLSODA¶

atol¶

rtol¶

max_nsteps¶

use_supersections¶

use_sens¶

hmin¶

OUTPUT_SCRIPTS¶

GRPH¶

output_mode¶

grph_list¶

TABLE¶

output_mode¶

tables_list¶

TABLE¶

input_solution¶

output_file¶

cx_columns¶

mx_columns¶

sx_columns¶

px_columns¶

SIM¶

output_mode¶

sim_time_step¶

grph_list¶

tables_list¶

TABLE¶

input_solution¶