:orphan:

.. The content of this file was generated
.. automatically by the popy.conf.fio.rstdoc_writer module


.. _fit_spec:



.. _fit-method_options_spec:

METHOD_OPTIONS
**************

Type: :term:`dict_record <dict_record>`

method options for fit_script

Example:-

.. code-block:: pyml

    METHOD_OPTIONS:
        py_module: fit
        rand_seed: 12345
        float_format: default


.. _fit-method_options-py_module_spec:

py_module
=========

Type: :term:`one_of(fit) <one_of>`

Python module required to process this script file

Example:-

.. code-block:: pyml

    py_module: fit


.. _fit-method_options-rand_seed_spec:

rand_seed
=========

Type: :term:`int <int>` / :term:`auto <auto>`

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

Example:-

.. code-block:: pyml

    rand_seed: 12345


.. _fit-method_options-float_format_spec:

float_format
============

Type: :term:`str <str>`

Format string for numerical output

Example:-

.. code-block:: pyml

    float_format: default


.. _fit-parallel_spec:

PARALLEL
********

Type: :term:`one_of_record <one_of_record>`

one of many possible servers

Example:-

.. code-block:: pyml

    PARALLEL:
        SINGLE: {}


.. _fit-parallel-single_spec:

SINGLE
======

Type: :term:`dict_record <dict_record>`

single process server spec.

Example:-

.. code-block:: pyml

    SINGLE: {}


.. _fit-parallel-mpi_workers_spec:

MPI_WORKERS
===========

Type: :term:`dict_record <dict_record>`

MPI local server spec.

Example:-

.. code-block:: pyml

    MPI_WORKERS:
        n_workers: auto


.. _fit-parallel-mpi_workers-n_workers_spec:

n_workers
---------

Type: :term:`int <int>` / :term:`auto <auto>`

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

Example:-

.. code-block:: pyml

    n_workers: auto


.. _fit-description_spec:

DESCRIPTION
***********

Type: :term:`dict_record <dict_record>`

Description fields for script.

Example:-

.. code-block:: pyml

    DESCRIPTION:
        name: example
        title: A PKPD model
        author: A.N. Other
        abstract: |
        keywords: []


.. _fit-description-name_spec:

name
====

Type: :term:`str <str>`

Unique name used to distinguish script

Example:-

.. code-block:: pyml

    name: example


.. _fit-description-title_spec:

title
=====

Type: :term:`str <str>`

A longer text string that could serve as a title

Example:-

.. code-block:: pyml

    title: A PKPD model


.. _fit-description-author_spec:

author
======

Type: :term:`str <str>`

Author of the model

Example:-

.. code-block:: pyml

    author: A.N. Other


.. _fit-description-abstract_spec:

abstract
========

Type: :term:`verbatim <verbatim>`

Abstract paragraph describing model

Example:-

.. code-block:: pyml

    abstract: |


.. _fit-description-keywords_spec:

keywords
========

Type: :term:`list <list>`

Keywords list used to categorise models.

Example:-

.. code-block:: pyml

    keywords: []


.. _fit-file_paths_spec:

FILE_PATHS
**********

Type: :term:`dict_record <dict_record>`

file paths for fit_script

Example:-

.. code-block:: pyml

    FILE_PATHS:
        input_data_file: input.csv
        output_folder: auto
        temp_folder: auto
        log_folder: auto
        output_file_ext: ['svg']
        input_solution_file: none
        output_iteration_results: True


.. _fit-file_paths-input_data_file_spec:

input_data_file
===============

Type: :term:`input_file <input_file>`

path to input comma separated value file in popy data format

Example:-

.. code-block:: pyml

    input_data_file: input.csv


.. _fit-file_paths-output_folder_spec:

output_folder
=============

Type: :term:`output_folder <output_folder>` / :term:`auto <auto>`

Output folder - results of computation stored here

Example:-

.. code-block:: pyml

    output_folder: auto


.. _fit-file_paths-temp_folder_spec:

temp_folder
===========

Type: :term:`output_folder <output_folder>` / :term:`auto <auto>`

Temp folder - temporary files stored here

Example:-

.. code-block:: pyml

    temp_folder: auto


.. _fit-file_paths-log_folder_spec:

log_folder
==========

Type: :term:`output_folder <output_folder>` / :term:`auto <auto>`

Log folder - log files stored here

Example:-

.. code-block:: pyml

    log_folder: auto


.. _fit-file_paths-output_file_ext_spec:

output_file_ext
===============

Type: :term:`list_of(pdf,png,svg) <list_of>`

Output file extension - determines graphical output file format.

Example:-

.. code-block:: pyml

    output_file_ext: ['svg']


.. _fit-file_paths-input_solution_file_spec:

input_solution_file
===================

Type: :term:`input_file <input_file>` / :term:`none <none>`

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

Example:-

.. code-block:: pyml

    input_solution_file: none


.. _fit-file_paths-output_iteration_results_spec:

output_iteration_results
========================

Type: :term:`bool <bool>`

Option to output iteration-by-iteration values.

Example:-

.. code-block:: pyml

    output_iteration_results: True


.. _fit-data_fields_spec:

DATA_FIELDS
***********

Type: :term:`dict_record <dict_record>`

data fields for popy.dat.fields object

Example:-

.. code-block:: pyml

    DATA_FIELDS:
        type_field: TYPE
        id_field: ID
        time_field: TIME


.. _fit-data_fields-type_field_spec:

type_field
==========

Type: :term:`str <str>`

Field name in data file that contains row type info, e.g. obs/dose etc

Example:-

.. code-block:: pyml

    type_field: TYPE


.. _fit-data_fields-id_field_spec:

id_field
========

Type: :term:`str <str>`

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

Example:-

.. code-block:: pyml

    id_field: ID


.. _fit-data_fields-time_field_spec:

time_field
==========

Type: :term:`str <str>`

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

Example:-

.. code-block:: pyml

    time_field: TIME


.. _fit-preprocess_spec:

PREPROCESS
**********

Type: :term:`verbatim <verbatim>`

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

Example:-

.. code-block:: pyml

    PREPROCESS: |


.. _fit-effects_spec:

EFFECTS
*******

Type: :term:`repeat_verb_record <repeat_verb_record>`

EFFECT params to define hierarchical population model

Example:-

.. code-block:: pyml

    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])


.. _fit-model_params_spec:

MODEL_PARAMS
************

Type: :term:`verbatim <verbatim>`

Defines the mapping from c[X], f[X] and r[X] variables to individual model m[X] parameters.

Example:-

.. code-block:: pyml

    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]


.. _fit-states_spec:

STATES
******

Type: :term:`verbatim <verbatim>`

Optional section for setting initial values of s[X] variables
can also set slabel[X] text labels.

Example:-

.. code-block:: pyml

    STATES: |


.. _fit-derivatives_spec:

DERIVATIVES
***********

Type: :term:`verbatim <verbatim>`

Define how the covariates and effects determine flows
between compartments.

Example:-

.. code-block:: pyml

    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]


.. _fit-predictions_spec:

PREDICTIONS
***********

Type: :term:`verbatim <verbatim>`

Define the final predicted m[X] variables to be output
by the compartment model system.

Example:-

.. code-block:: pyml

    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)


.. _fit-ode_solver_spec:

ODE_SOLVER
**********

Type: :term:`one_of_record <one_of_record>`

one of many possible solvers

Example:-

.. code-block:: pyml

    ODE_SOLVER:
        SCIPY_ODEINT: {atol: 1e-06, rtol: 1e-06, max_nsteps: 10000000, use_sens: False}


.. _fit-ode_solver-no_solver_spec:

NO_SOLVER
=========

Type: :term:`dict_record <dict_record>`

Null method for blank derivatives.

Example:-

.. code-block:: pyml

    NO_SOLVER: {}


.. _fit-ode_solver-analytic_spec:

ANALYTIC
========

Type: :term:`dict_record <dict_record>`

Analytic method for solving ODE

Example:-

.. code-block:: pyml

    ANALYTIC: {}


.. _fit-ode_solver-scipy_odeint_spec:

SCIPY_ODEINT
============

Type: :term:`dict_record <dict_record>`

odeint solver record

Example:-

.. code-block:: pyml

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


.. _fit-ode_solver-scipy_odeint-atol_spec:

atol
----

Type: :term:`float <float>`

Absolute tolerance of ode solver.

Example:-

.. code-block:: pyml

    atol: 1e-06


.. _fit-ode_solver-scipy_odeint-rtol_spec:

rtol
----

Type: :term:`float <float>`

Relative tolerance of ode solver.

Example:-

.. code-block:: pyml

    rtol: 1e-06


.. _fit-ode_solver-scipy_odeint-max_nsteps_spec:

max_nsteps
----------

Type: :term:`int <int>`

Maximum number of steps allowed in ode solver.

Example:-

.. code-block:: pyml

    max_nsteps: 10000000


.. _fit-ode_solver-scipy_odeint-use_sens_spec:

use_sens
--------

Type: :term:`bool <bool>`

Option to use sensitivity equations in ode solver.

Example:-

.. code-block:: pyml

    use_sens: False


.. _fit-fit_methods_spec:

FIT_METHODS
***********

Type: :term:`list_record <list_record>`

one of many possible fitters

Example:-

.. code-block:: pyml

    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


.. _fit-fit_methods-joe_spec:

JOE
===

Type: :term:`dict_record <dict_record>`

Joint Optimisation and Estimation method

Example:-

.. code-block:: pyml

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


.. _fit-fit_methods-joe-max_n_main_iterations_spec:

max_n_main_iterations
---------------------

Type: :term:`int <int>`

Maximum number of iterations

Example:-

.. code-block:: pyml

    max_n_main_iterations: 50


.. _fit-fit_methods-joe-converger_spec:

CONVERGER
---------

Type: :term:`one_of_record <one_of_record>`

Convergence methods.

Example:-

.. code-block:: pyml

    CONVERGER:
        OBJ_INC: {}


.. _fit-fit_methods-joe-converger-none_spec:

NONE
^^^^

Type: :term:`dict_record <dict_record>`

No convergence testing.

Example:-

.. code-block:: pyml

    NONE: {}


.. _fit-fit_methods-joe-converger-obj_inc_spec:

OBJ_INC
^^^^^^^

Type: :term:`dict_record <dict_record>`

Objective function increase converger.

Example:-

.. code-block:: pyml

    OBJ_INC: {}


.. _fit-fit_methods-joe-converger-obj_sim_spec:

OBJ_SIM
^^^^^^^

Type: :term:`dict_record <dict_record>`

Objective function similar converger.

Example:-

.. code-block:: pyml

    OBJ_SIM: {}


.. _fit-fit_methods-joe-converger-obj_lin_spec:

OBJ_LIN
^^^^^^^

Type: :term:`dict_record <dict_record>`

Objective function similar converger.

Example:-

.. code-block:: pyml

    OBJ_LIN: {}


.. _fit-fit_methods-joe-recompute_jacobians_flag_spec:

recompute_jacobians_flag
------------------------

Type: :term:`bool <bool>`

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

Example:-

.. code-block:: pyml

    recompute_jacobians_flag: False


.. _fit-fit_methods-joe-use_chain_spec:

use_chain
---------

Type: :term:`bool <bool>`

Option to use chain rule when computing derivatives.

Example:-

.. code-block:: pyml

    use_chain: False


.. _fit-fit_methods-joe-reset_re_flag_spec:

reset_re_flag
-------------

Type: :term:`bool <bool>`

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

Example:-

.. code-block:: pyml

    reset_re_flag: True


.. _fit-covariance_spec:

COVARIANCE
**********

Type: :term:`one_of_record <one_of_record>`

Covariance methods used to estimate standard errors.

Example:-

.. code-block:: pyml

    COVARIANCE:
        NO_COVARIANCE: {}


.. _fit-covariance-no_covariance_spec:

NO_COVARIANCE
=============

Type: :term:`dict_record <dict_record>`

Do NOT compute covariance matrix and standard errors.

Example:-

.. code-block:: pyml

    NO_COVARIANCE: {}


.. _fit-covariance-sandwich_spec:

SANDWICH
========

Type: :term:`dict_record <dict_record>`

Use sandwich operator to estimate covariance matrix.

Example:-

.. code-block:: pyml

    SANDWICH:
        var_covariance_flag: False
        inv_hessian_flag: False


.. _fit-covariance-sandwich-var_covariance_flag_spec:

var_covariance_flag
-------------------

Type: :term:`bool <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:-

.. code-block:: pyml

    var_covariance_flag: False


.. _fit-covariance-sandwich-inv_hessian_flag_spec:

inv_hessian_flag
----------------

Type: :term:`bool <bool>`

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

Example:-

.. code-block:: pyml

    inv_hessian_flag: False


.. _fit-output_scripts_spec:

OUTPUT_SCRIPTS
**************

Type: :term:`dict_record <dict_record>`

List of scripts to output for further processing

Example:-

.. code-block:: pyml

    OUTPUT_SCRIPTS:
        GRPH: {output_mode: none, share_axes: False}
        SIM: {output_mode: run, sim_time_step: 1.0, share_axes: False}
        MSIM: {output_mode: create}
        FITSUM: {output_mode: run}


.. _fit-output_scripts-grph_spec:

GRPH
====

Type: :term:`dict_record <dict_record>`

Options to pass to grph script.

Example:-

.. code-block:: pyml

    GRPH:
        output_mode: none
        share_axes: False


.. _fit-output_scripts-grph-output_mode_spec:

output_mode
-----------

Type: :term:`one_of(none,create,run) <one_of>`

Output options.

Example:-

.. code-block:: pyml

    output_mode: none


.. _fit-output_scripts-grph-share_axes_spec:

share_axes
----------

Type: :term:`bool <bool>`

Option to share axes between individuals when plotting
graphical data

Example:-

.. code-block:: pyml

    share_axes: False


.. _fit-output_scripts-sim_spec:

SIM
===

Type: :term:`dict_record <dict_record>`

Options to pass to sim script.

Example:-

.. code-block:: pyml

    SIM:
        output_mode: none
        sim_time_step: -1.0
        share_axes: False


.. _fit-output_scripts-sim-output_mode_spec:

output_mode
-----------

Type: :term:`one_of(none,create,run) <one_of>`

Output options.

Example:-

.. code-block:: pyml

    output_mode: none


.. _fit-output_scripts-sim-sim_time_step_spec:

sim_time_step
-------------

Type: :term:`float <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:-

.. code-block:: pyml

    sim_time_step: -1.0


.. _fit-output_scripts-sim-share_axes_spec:

share_axes
----------

Type: :term:`bool <bool>`

Option to share axes between individuals when plotting
graphical data

Example:-

.. code-block:: pyml

    share_axes: False


.. _fit-output_scripts-msim_spec:

MSIM
====

Type: :term:`dict_record <dict_record>`

options to pass to msim script.

Example:-

.. code-block:: pyml

    MSIM:
        output_mode: none


.. _fit-output_scripts-msim-output_mode_spec:

output_mode
-----------

Type: :term:`one_of(none,create,run) <one_of>`

Output options.

Example:-

.. code-block:: pyml

    output_mode: none


.. _fit-output_scripts-fitsum_spec:

FITSUM
======

Type: :term:`dict_record <dict_record>`

options to pass to fitsum_script.

Example:-

.. code-block:: pyml

    FITSUM:
        output_mode: none


.. _fit-output_scripts-fitsum-output_mode_spec:

output_mode
-----------

Type: :term:`one_of(none,create,run) <one_of>`

Output options.

Example:-

.. code-block:: pyml

    output_mode: none



