Quick Start Guide

Instructions to get started with using gempyor and flepiMoP, directly following the steps described in "Before Any Run", by activating your install and running some sample commands.

🧱 Set Up

Before completing this Quick Start Guide, make sure you have followed all the steps in the Before any run section to ensure you have access to the correct files needed to run your model with flepiMoP.

Activating The Conda Environment

First step in using flepiMoP is activating the conda environment that it has been installed in:

conda activate flepimop-env

Assuming that you installed flepiMoP to the default conda environment name, but if you choose to install elsewhere please adjust the above command accordingly.

Define Environment Variables (Optional)

If you choose not to define environment variables, remember to use the full or relative path names for navigating to the right directories and provide appropriate flepi/project path arguments in future steps.

flepiMoP frequently uses two environment variables to refer to specific directories both as a default for CLI arguments and throughout the documentation:

1. FLEPI_PATH: Refers to the directory where flepiMoP is installed to, and

2. PROJECT_PATH: Refers to the directory where flepiMoP is being ran from.

Furthermore, you'll likely be navigating between these directories frequently in production usage so having these environment variables set can save some typing.

For example, if you're on a Mac or Linux/Unix based operating system and storing the flepiMoP code in a directory called Github, you define the FLEPI_PATH and PROJECT_PATH environmental variables to be your directory locations as follows:

On Linux/MacOS or in linux shells on windows setting an environment variable can be done by:

export FLEPI_PATH=/your/path/to/flepiMoP
export PROJECT_PATH=/your/path/to/flepiMoP/examples/tutorials

Where /your/path/to is the directory containing flepiMoP. If you have already navigated to your flepiMoP directory you can just do:

export FLEPI_PATH=$(pwd)
export PROJECT_PATH=$(pwd)/examples/tutorials

You can check that the variables have been set by either typing env to see all defined environmental variables, or typing echo $FLEPI_PATH to see the value of FLEPI_PATH.

However, if you're on Windows:

set FLEPI_PATH=C:\your\path\to\flepiMoP
set PROJECT_PATH=C:\your\path\to\flepiMoP\examples\tutorials

Where /your/path/to is the directory containing flepiMoP. If you have already navigated to your flepiMoP directory you can just do:

set FLEPI_PATH=%CD%
set PROJECT_PATH=%CD%\examples\tutorials

You can check that the variables have been set by either typing set to see all defined environmental variables, or typing echo $FLEPI_PATH$ to see the value of FLEPI_PATH.

🚀 Run the code

Everything is now ready 🎉 .

The next step depends on what sort of simulation you want to run: One that includes inference (fitting model to data) or only a forward simulation (non-inference). Inference is run from R, while forward-only simulations are run directly from the Python package gempyor.

First, navigate to the PROJECT_PATH folder and make sure to delete any old model output files that are there:

cd $PROJECT_PATH        # goes to your project repository
rm -r model_output/     # delete the outputs of past run if there are

For the following examples we use an example config from flepimop_sample, but you can provide the name of any configuration file you want.

To get started, let's start with just running a forward simulation (non-inference).

Non-inference run

Stay in the PROJECT_PATH folder, and run a simulation directly from forward-simulation Python package gempyor. Call flepimop simulate providing the name of the configuration file you want to run. For example here, we use config_sample_2pop.yml.

flepimop simulate config_sample_2pop.yml

This will produce a model_output folder, which you can look at using provided post-processing functions and scripts.

We recommend using model_output_notebook.Rmd as a starting point to interact with your model outputs. First, modify the YAML preamble in the notebook (make sure the configuration file listed matches the one used in simulation), then knit this markdown. This will produce plots of the prevalence of infection states over time. You can edit this markdown to produce any figures you'd like to explore your model output.

For your first flepiMoP run, it's better to run each command individually as described above to be sure each exits successfully. However, eventually you can put all these steps together in a script, seen below:

export FLEPI_PATH=/Users/YourName/Github/flepiMoP
export PROJECT_PATH=/Users/YourName/Github/flepiMoP/examples/tutorials
cd $PROJECT_PATH
rm -rf model_output
flepimop simulate config.yml

Note that you only have to re-run the installation steps once each time you update any of the files in the flepimop repository (either by pulling changes made by the developers and stored on Github, or by changing them yourself). If you're just running the same or different configuration file, just repeat the final steps:

rm -rf model_output
flepimop simulate new_config.yml

Inference run

An inference run requires a configuration file that has the inference section. Stay in the $PROJECT_PATH folder, and run the inference script, providing the name of the configuration file you want to run. For example here, we use config_sample_2pop_inference.yml.

flepimop-inference-main -c config_sample_2pop_inference.yml

This will run the model and create a lot of output files in $PROJECT_PATH/model_output/.

The last few lines visible on the command prompt should be:

[[1]]

[[1]][[1]]

[[1]][[1]][[1]]

NULL

If you want to quickly do runs with options different from those encoded in the configuration file, you can do that from the command line, for example

flepimop-inference-main -j 1 -n 1 -k 1 -c config_inference.yml

where:

• n is the number of parallel inference slots,

• j is the number of CPU cores to use on your machine (if j > n, only n cores will actually be used. If j <n, some cores will run multiple slots in sequence)

• k is the number of iterations per slots.

Again, it is helpful to run the model output notebook (model_output_notebook.Rmd to explore your model outputs. Knitting this file for an inference run will also provide an analysis of your fits: the acceptance probabilities, likelihoods overtime, and the fits against the provided ground truth.

For your first flepiMoP inference run, it's better to run each command individually as described above to be sure each exits successfully. However, eventually you can put all these steps together in a script, seen below:

export FLEPI_PATH=/Users/YourName/Github/flepiMoP
export PROJECT_PATH=/Users/YourName/Github/flepiMoP/examples/tutorials
cd $FLEPI_PATH
pip install --no-deps -e flepimop/gempyor_pkg/
Rscript build/local_install.R
cd $PROJECT_PATH
rm -rf model_output
flepimop-inference-main -c config_inference.yml

Note that you only have to re-run the installation steps once each time you update any of the files in the flepimop repository (either by pulling changes made by the developers and stored on Github, or by changing them yourself). If you're just running the same or different configuration file, just repeat the final steps

rm -rf model_output
flepimop-inference-main -c config_inference_new.yml

📈 Examining model output

If your run is successful, you should see your output files in the model_output folder. The structure of the files in this folder is described in the Model Output section. By default, all the output files are .parquet format (a compressed format which can be imported as dataframes using R's arrow package arrow::read_parquet or using the free desktop application Tad for quick viewing). However, you can add the option --write-csv to the end of the commands to run the code (e.g., flepimop simulate --write-csv config.yml) to have everything saved as .csv files instead ;

🪜 Next steps

These configs and notebooks should be a good starting point for getting started with flepiMoP. To explore other running options, see How to run: Advanced.