• Language: en

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: {}

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

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

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
Vpc Script
FitSum Script
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