SAVSNet Python functions

This folder contains the savsnet Python module, containing Python functions to analyse SAVSNet data. The functions are generic, and other users may find them useful.

GP Smoothing of case timeseries

The savsnet/prev_ts_GP module contains functions that build a Binomial regression model with a Gaussian process linear predictor to model a timeseries of Binomial random variables (e.g. prevalence). See docstrings for each function for further details. For convenience, the module may be run as a script, for example:

$ python savsnet/prev_ts_GP.py -c trauma -s dog cat -i 1000 -o pred myData.csv

where myData.csv is a CSV file containing (minimally) a 'Date' column (ISO format), 'Consult_reason', and 'Species' columns.

See

$ python savsnet/prev_ts_GP.py --help

for further details.

Plotting of GP smooths

The savsnet/plot_ts_GP module contains functions for plotting posterior predictive distributions from savsnet/prev_ts_GP output. Documentation for individuals functions are contained in the docstrings. For convenience, the module may be run as a script, for example:

$ python savsnet/plot_ts_GP.py -d myData.csv -s dog cat -c trauma -p pred_dog_trauma.pkl pred_cat_trauma.pkl -o gpFigure.pdf

See

$ python savsnet/plot_ts_GP.py --help 

for further details.

License

This software is release under the MIT license. Please refer to the LICENSE file contained in the same directory as this file for further details.