# METHOD_OPTIONS

Type: dict_record

method options for plt_vpc

Example:-

    METHOD_OPTIONS:
        py_module: vpc

## py_module

Type: one_of(vpc)

Python module required to process this script file

Example:-

    py_module: vpc

# PARALLEL

Type: one_of_record

one of many possible servers

Example:-

    PARALLEL:
        SINGLE: {progress_bar_length: 0}

## SINGLE

Type: dict_record

single process server spec.

Example:-

    SINGLE:
        progress_bar_length: 0

### progress_bar_length

Type: int

Length of progress bar on screen (default=0, off)

Example:-

    progress_bar_length: 0

## MPI_WORKERS

Type: dict_record

MPI local server spec.

Example:-

    MPI_WORKERS:
        n_workers: auto
        cpu_load: 1.0
        progress_bar_length: 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

### progress_bar_length

Type: int

Length of progress bar on screen (default=0, off)

Example:-

    progress_bar_length: 0

# FILE_PATHS

Type: dict_record

file paths

Example:-

    FILE_PATHS:
        output_folder: auto
        temp_folder: auto
        log_folder: auto
        output_file_ext: ['svg']
        solutions:
            fit_obs: ../input_observations_soln.pyml
            msim_obs: msim_obs/msim_observations_soln.pyml
            pred_pop: predicted_mean_population_solution.pyml

## 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']

## solutions

Type: dict

Solutions to compare

Example:-

    solutions:
        fit_obs: ../input_observations_soln.pyml
        msim_obs: msim_obs/msim_observations_soln.pyml
        pred_pop: predicted_mean_population_solution.pyml

# PREPROCESS

Type: verbatim

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

Example:-

    PREPROCESS: |

# 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

# OUTPUT_GRAPHS

Type: list_record

List of graphs to output.

Example:-

    OUTPUT_GRAPHS:
        - COMB_QUANT_SIM_VPC:
            output_subdir: none
            graph_title: My rather excellent graph
            x_axis_label: TIME
            x_var: TIME
            x_scale: linear
            y_axis_label_list: ['Drug concentration in plasma', 'Bio-marker concentration in plasma']
            y_var_src_list: ['observed_data', 'final_fit_ppred', 'final_fit_ipred']
            y_var_list: ['DV_CENTRAL', 'DV_CENTRAL', 'DV_CENTRAL']
            y_var_label_list: ['Drug conc. (units)', 'Drug conc. (units)', 'Drug conc. (units)']
            y_var_colour_list: ['b', 'g']
            y_scale_list: ['linear', 'log']
            bin_indexer: {EQUAL_SPACE: {n_bins: auto}}
            quantile_list: [0.05, 0.5, 0.95]
            min_bin_count: 3
            options_list: ['legend_above', 'grid', 'plot_orig_points']
            conf_interval_prop: 0.9
            y_var_norm_list: ['DV_CENTRAL_pred', 'DV_CENTRAL_pred', 'DV_CENTRAL_pred']
            norm_method: none
            split_field: none
            export_stats: False

## COMB_QUANT_SIM_VPC

Type: dict_record

Plot vpc data using histograms of each simulation one graph.

Example:-

    COMB_QUANT_SIM_VPC:
        output_subdir: none
        graph_title: My rather excellent graph
        x_axis_label: TIME
        x_var: TIME
        x_scale: linear
        y_axis_label_list: ['Drug concentration in plasma', 'Bio-marker concentration in plasma']
        y_var_src_list: ['observed_data', 'final_fit_ppred', 'final_fit_ipred']
        y_var_list: ['DV_CENTRAL', 'DV_CENTRAL', 'DV_CENTRAL']
        y_var_label_list: ['Drug conc. (units)', 'Drug conc. (units)', 'Drug conc. (units)']
        y_var_colour_list: ['b', 'g']
        y_scale_list: ['linear', 'log']
        bin_indexer: {EQUAL_SPACE: {n_bins: auto}}
        quantile_list: [0.05, 0.5, 0.95]
        min_bin_count: 3
        options_list: ['legend_above', 'grid', 'plot_orig_points']
        conf_interval_prop: 0.9
        y_var_norm_list: ['DV_CENTRAL_pred', 'DV_CENTRAL_pred', 'DV_CENTRAL_pred']
        norm_method: none
        split_field: none
        export_stats: False

### output_subdir

Type: output_folder

Destination subdirectory for these plots.

Example:-

    output_subdir: none

### graph_title

Type: str

Title of graph.

Example:-

    graph_title: My rather excellent graph

### x_axis_label

Type: str

x axis label text

Example:-

    x_axis_label: TIME

### x_var

Type: str

x axis variable name.

Example:-

    x_var: TIME

### x_scale

Type: one_of(linear,log)

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

Example:-

    x_scale: linear

### y_axis_label_list

Type: list(str)

List of labels for y variables that appear on axes.

Example:-

    y_axis_label_list: ['Drug concentration in plasma', 'Bio-marker concentration in plasma']

### y_var_src_list

Type: list(str)

Names of the solution folders that contain the predictions to be plotted

Example:-

    y_var_src_list: ['observed_data', 'final_fit_ppred', 'final_fit_ipred']

### y_var_list

Type: list(str)

List of y variable names (i.e. columns in the predictions table) to be plotted on graph.

Example:-

    y_var_list: ['DV_CENTRAL', 'DV_CENTRAL', 'DV_CENTRAL']

### y_var_label_list

Type: list(str)

List of y variable labels which will appear in the legend as <y_var_label> (<y_var_src>).

Example:-

    y_var_label_list: ['Drug conc. (units)', 'Drug conc. (units)', 'Drug conc. (units)']

### y_var_colour_list

Type: list(str)

List of colours for y variable plot lines.

Example:-

    y_var_colour_list: ['b', 'g']

### y_scale_list

Type: list(str)

List of scales (linear or log) for multiple y axes.

Example:-

    y_scale_list: ['linear', 'log']

### bin_indexer

Type: one_of_record

Possible bin indexing methods.

Example:-

    bin_indexer:
        EQUAL_SPACE: {n_bins: auto}

#### EQUAL_SPACE

Type: dict_record

Equally spaced index builder.

Example:-

    EQUAL_SPACE:
        n_bins: auto

##### n_bins

Type: int / auto

Number of equally spaced bins in index.

Example:-

    n_bins: auto

#### UNEQUAL_SPACE

Type: dict_record

fixed, irregularly spaced user defined bin edges.

Example:-

    UNEQUAL_SPACE:
        bin_edge_list: [2.0, 4.0, 8.0]

##### bin_edge_list

Type: list(float)

List of user-defined bin edges.

Example:-

    bin_edge_list: [2.0, 4.0, 8.0]

#### K_MEANS

Type: dict_record

Deduce bin centres automatically using k-means algorithm for a pre-determined number of bins.

Example:-

    K_MEANS:
        n_bins: 10
        init_method: random

##### n_bins

Type: int

Number of bins.

Example:-

    n_bins: 10

##### init_method

Type: one_of(random)

Initialisation method for k-means algorithm.

Example:-

    init_method: random

#### POP_INDEX

Type: dict_record

Take unique values from first individual as bin centres.

Example:-

    POP_INDEX: {}

### quantile_list

Type: list(float)

List of quantiles to plot for each bin of the histogram.

Example:-

    quantile_list: [0.05, 0.5, 0.95]

### min_bin_count

Type: int

Number of data points required to plot an individual bin.

Example:-

    min_bin_count: 3

### options_list

Type: list_of(legend,legend_above,grid,contour_conf_intervals,plot_orig_points)

List of visual options to apply to individual plots.

Example:-

    options_list: ['legend_above', 'grid', 'plot_orig_points']

### conf_interval_prop

Type: float

Confidence interval proportion to display for each quantile.

Example:-

    conf_interval_prop: 0.9

### y_var_norm_list

Type: list(str)

List of y variable names (i.e. columns in the predictions table) to normalise y_var_list.

Example:-

    y_var_norm_list: ['DV_CENTRAL_pred', 'DV_CENTRAL_pred', 'DV_CENTRAL_pred']

### norm_method

Type: one_of(none,pred_corr,predvar_corr)

Normalisation method applied to predictions in each bin.

Example:-

    norm_method: none

### split_field

Type: str

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

Example:-

    split_field: none

### export_stats

Type: bool

Export any computed statistics to a .csv file

Example:-

    export_stats: False