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: {}
MPI_WORKERS¶
Type: dict_record
MPI local server spec.
Example:-
MPI_WORKERS:
n_workers: auto
cpu_load: 1.0
n_workers¶
Number of workers to use on this machine, defaults to number of processors, but could be more or fewer.
Example:-
n_workers: auto
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']
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
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
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}
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
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