Language: en

METHOD_OPTIONS¶

method options for msim_script

Example:-

METHOD_OPTIONS:
    py_module: msim
    rand_seed: 12345
    float_format: default

py_module¶

Type: one_of(msim)

Python module required to process this script file

Example:-

py_module: msim

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

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

DESCRIPTION¶

Type: dict_record

Description fields for script.

Example:-

DESCRIPTION:
    name: example
    title: A PKPD model
    author: A.N. Other
    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: A.N. Other

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 pop_msim

Example:-

FILE_PATHS:
    input_data_file: input.csv
    output_folder: auto
    temp_folder: auto
    log_folder: auto
    output_file_ext: ['svg']
    input_solution_file: 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

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']

input_solution_file¶

Type: input_file / none

Solution containing f[X] values from a previous run.

Example:-

input_solution_file: solution.pyml

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

PREPROCESS¶

Type: verbatim

Code that preprocesses the input data. Use this to filter rows and create derived covariates.

Example:-

PREPROCESS: |

EFFECTS¶

Type: repeat_verb_record

EFFECT params to define hierarchical population model

Example:-

EFFECTS:
    POP: |
        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

    ID: |
        r[KA, CL, V1, Q, V2] ~ mnorm([0,0,0,0,0], f[KA_isv,CL_isv,V1_isv,Q_isv,V2_isv])

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 likelihoods computed by comparing p[X] vs c[X].

Example:-

PREDICTIONS: |
    p[DV_CENTRAL_pred] = s[CENTRAL]/m[V1]
    var = m[ANOISE]**2 + m[PNOISE]**2 * p[DV_CENTRAL_pred]**2
    c[DV_CENTRAL_sim] ~ norm(p[DV_CENTRAL_pred], 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_supersections: True
        use_jacobian: False
        use_sens: False
        use_tcrit: 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:
    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_OPTIONS¶

Type: dict_record

Output options for msim_script

Example:-

OUTPUT_OPTIONS:
    sim_time_step: -1.0
    n_pop_samples: 100

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

n_pop_samples¶

Type: int

Number of new populations to sample.

Example:-

n_pop_samples: 100

OUTPUT_SCRIPTS¶

Type: dict_record

scripts to output for further processing

Example:-

OUTPUT_SCRIPTS:
    VPC:
        output_mode: none
        vpc_list: ['COMB_QUANT_SIM_VPC']
        y_var_src_list: ['sim', 'orig']
        y_var_list: ['DV_CENTRAL_sim', 'DV_CENTRAL']
        y_var_norm_list: ['DV_CENTRAL_pred', 'DV_CENTRAL_pred']
        y_var_label_list: ['Drug conc. (units)', 'Drug conc. (units)']
        x_var: TIME
        split_field: none
        share_axes: False
        y_scale: linear

VPC¶

Type: dict_record

Options to pass to vpc_script.

Example:-

VPC:
    output_mode: none
    vpc_list: ['COMB_QUANT_SIM_VPC']
    y_var_src_list: ['sim', 'orig']
    y_var_list: ['DV_CENTRAL_sim', 'DV_CENTRAL']
    y_var_norm_list: ['DV_CENTRAL_pred', 'DV_CENTRAL_pred']
    y_var_label_list: ['Drug conc. (units)', 'Drug conc. (units)']
    x_var: TIME
    split_field: none
    share_axes: False
    y_scale: linear

output_mode¶

Type: one_of(none,create,run)

Output options.

Example:-

output_mode: none

vpc_list¶

Type: list(str)

List of vpc types to generate in popy_vpc script.

Example:-

vpc_list: ['COMB_QUANT_SIM_VPC']

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_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_norm_list¶

Type: list(str)

List of y variable names to normalise y_var_list.

Example:-

y_var_norm_list: ['DV_CENTRAL_pred', 'DV_CENTRAL_pred']

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)']

x_var¶

Type: str

x axis variable name.

Example:-

x_var: TIME

split_field¶

Type: str

field in c[X] data use each value to split data.

Example:-

split_field: none

y_scale¶

Type: one_of(linear,log)

y axis scale - can be either ‘linear’ or ‘log’.

Example:-

y_scale: linear

MSim Script

MTut Script

Documentation

METHOD_OPTIONS¶

py_module¶

rand_seed¶

float_format¶

PARALLEL¶

SINGLE¶

MPI_WORKERS¶

n_workers¶

DESCRIPTION¶

name¶

title¶

author¶

abstract¶

keywords¶

FILE_PATHS¶

input_data_file¶

output_folder¶

temp_folder¶

log_folder¶

output_file_ext¶

input_solution_file¶

DATA_FIELDS¶

type_field¶

id_field¶

time_field¶

PREPROCESS¶

EFFECTS¶

MODEL_PARAMS¶

STATES¶

DERIVATIVES¶

PREDICTIONS¶

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_OPTIONS¶

sim_time_step¶

n_pop_samples¶

OUTPUT_SCRIPTS¶

VPC¶

output_mode¶

vpc_list¶

y_var_src_list¶

y_var_list¶

y_var_norm_list¶

y_var_label_list¶

x_var¶

split_field¶

share_axes¶

y_scale¶

Additional Information

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