Quick Start Guide
Quick instructions on how to install Prerequisites, install flepiMoP itself, and then run through a quick example of how to use flepiMoP.
flepiMoP
is flexible pipeline for modeling epidemics. It has functionality for simulating epidemics as well as doing inference for simulation parameters and post-processing of simulation/inference outputs. It is written in a combination of python and R and uses anaconda to manage installations which allows flepiMoP
to enforce version constraints across both languages.
Prerequisites
flepiMoP
requires the following:
git
, and
If you do not have git
installed you can go to the downloads page to find the appropriate installation for your system. It's also recommended, but not required, to have a GitHub account. If you're totally new to git
and GitHub, GitHub has a very nice introduction to the basics of git that is worth reading before continuing.
If you do not have conda
installed you can go to the downloads page to find the appropriate installation for your system. We would recommend selecting the Anaconda Distribution
installer of conda
.
Installing flepiMoP
flepiMoP
Navigate to the parent location of where you would like to install flepiMoP
, a subdirectory called flepiMoP
will be created there. For example, if you navigate to ~/Desktop
then flepiMoP
will be installed to ~/Desktop/flepiMoP
.
This installation script is currently only designed for Linux/MacOS operating systems or linux shells for windows. If you need windows native installation please reach out for assistance.
$ curl -LsSf -o flepimop-install "https://raw.githubusercontent.com/HopkinsIDD/flepiMoP/refs/heads/main/bin/lint"
$ chmod +x flepimop-install
This installations script will guide you through a series of prompts to determine how and where to install flepiMoP
. Loosely this script:
Determines what directory
flepiMoP
is being installed into,Optionally gets a clone of
flepiMoP
if it is not present at the install location,Creates a conda environment to house the installation,
Installs
flepiMoP
's dependencies and custom packages to this conda environment, andFinally prints out a summary of the installation with helpful debugging information.
For more help on how to use the installation script you can do ./flepimop-install -h
to get help information. For first time users accepting the default prompt will be the best choice (as shown below):
$ ./flepimop-install
An explicit $USERDIR was not provided, please set one (or press enter to use '/Users/example/Desktop'):
Using '/Users/example/Desktop' for $USERDIR.
An explicit $FLEPI_PATH was not provided, please set one (or press enter to use '/Users/example/Desktop/flepiMoP'):
Using '/Users/example/Desktop/flepiMoP' for $FLEPI_PATH.
Did not find flepiMoP at '/Users/example/Desktop/flepiMoP', do you want to clone the repo? (y/n) y
Cloning on your behalf.
Cloning into '/Users/example/Desktop/flepiMoP'...
remote: Enumerating objects: 28513, done.
remote: Counting objects: 100% (3424/3424), done.
remote: Compressing objects: 100% (845/845), done.
remote: Total 28513 (delta 2899), reused 2786 (delta 2576), pack-reused 25089 (from 2)
Receiving objects: 100% (28513/28513), 145.99 MiB | 26.32 MiB/s, done.
Resolving deltas: 100% (14831/14831), done.
An explicit $FLEPI_CONDA was not provided, please set one (or press enter to use 'flepimop-env'):
Using 'flepimop-env' name for $FLEPI_CONDA.
...
Once the prompts are done the installer will output information about the installations that it is doing. After the installation has completed you should see an installation summary similar to:
flepiMoP installation summary:
> flepiMoP version: ec707d36cd9f8675466c05cbaba295cc4f4a7112
> flepiMoP path: /Users/example/Desktop/flepiMoP
> flepiMoP conda env: flepimop-env
> conda: 24.9.2
> R 4.3.3: /opt/anaconda3/envs/flepimop-env/bin/R
> Python 3.11.12: /opt/anaconda3/envs/flepimop-env/bin/python
> gempyor version: 2.1
> R flepicommon version: 0.0.1
> R flepiconfig version: 3.0.0
> R inference version: 0.0.1
To activate the flepimop conda environment, run:
conda activate flepimop-env
This summary gives a brief overview of the R/python/package versions installed. If you encounter any issues with your installation please include this information with your issue report.
Activating A flepiMoP
Installation
flepiMoP
InstallationTo activate flepiMoP
you need to activate the conda environment that it is installed to with:
$ conda activate flepimop-env
Or replacing flepimop-env
with the appropriate conda environment if you decided on a non-default conda environment. Once you do this you should have the flepimop
CLI available to you with:
$ flepimop --help
Usage: flepimop [OPTIONS] COMMAND [ARGS]...
Flexible Epidemic Modeling Platform (FlepiMoP) Command Line Interface
Options:
--help Show this message and exit.
Commands:
batch-calibrate Submit a calibration job to a batch system.
compartments Add commands for working with FlepiMoP compartments.
modifiers
patch Merge configuration files.
simulate Forward simulate a model using gempyor.
sync Sync flepimop files between local and remote locations.
Defining Environment Variables (Optional)
flepiMoP
frequently uses two environment variables to refer to specific directories both as a default for CLI arguments and throughout the documentation:
FLEPI_PATH
: Refers to the directory whereflepiMoP
is installed to, andPROJECT_PATH
: Refers to the directory whereflepiMoP
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.
Continuing with the same paths from the installation example flepiMoP
was installed to /Users/example/Desktop/flepiMoP
. On Linux/MacOS or in linux shells on windows setting an environment variable can be done by:
export FLEPI_PATH=/Users/example/Desktop/flepiMoP
export PROJECT_PATH=/Users/example/Desktop/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 environment variables, or typing echo $FLEPI_PATH
and echo $PROJECT_PATH
to see the values of FLEPI_PATH
and PROJECT_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 environment variables, or typing echo $FLEPI_PATH$
and echo $PROJECT_PATH$
to see the values of FLEPI_PATH
and PROJECT_PATH
.
For more information on the usage of environment variables with flepiMoP
please refer to the Environment Variables documentation.
Run flepiMoP
flepiMoP
Now that flepiMoP
has been successfully installed on your system you will be able to use the tool to model epidemics.
First, navigate to the PROJECT_PATH
folder and make sure to delete any old model output files that are there:
$ cd $PROJECT_PATH
$ rm -r model_output/
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 (ifj
>n
, onlyn
cores will actually be used. Ifj
<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 ;
Updating flepiMoP
flepiMoP
You can use the flepimop-install
script provided by the flepiMoP
repository to update your install of flepiMoP
with:
$ cd $FLEPI_PATH
$ ./bin/flepimop-install -u
Or to reinstall flepiMoP
from scratch (say if your conda environment is very out of date or in a bad state) you can do so with:
$ ./bin/flepimop-install -r -u
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.
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