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  • 🦠gempyor: modeling infectious disease dynamics
    • Modeling infectious disease dynamics
    • Model Implementation
      • flepiMoP's configuration file
      • Specifying population structure
      • Specifying compartmental model
      • Specifying initial conditions
      • Specifying seeding
      • Specifying observational model
      • Distributions
      • Specifying time-varying parameter modifications
      • Other configuration options
      • Code structure
    • Model Output
  • 📈Model Inference
    • Inference Description
    • Inference Implementation
      • Specifying data source and fitted variables
      • (OLD) Configuration options
      • (OLD) Configuration setup
      • Code structure
    • Inference Model Output
    • Inference with EMCEE
  • 🖥️More
    • Setting up the model and post-processing
      • Config writer
      • Diagnostic plotting scripts
      • Create a post-processing script
      • Reporting
    • Advanced
      • File descriptions
      • Numerical methods
      • Additional parameter options
      • Swapping model modules
      • Using plug-ins 🧩[experimental]
  • 🛠️How To Run
    • Before any run
    • Quick Start Guide
    • Multiple Configuration Files
    • Synchronizing Files
    • Advanced run guides
      • Running with Docker locally 🛳
      • Running locally in a conda environment 🐍
      • Running on AWS 🌳
      • Running On A HPC With Slurm
    • Common errors
    • Useful commands
    • Tips, tricks, FAQ
  • 🗜️Development
    • Git and GitHub Usage
    • Guidelines for contributors
  • Deprecated pages
    • Module specification
  • JHU Internal
    • US specific How to Run
      • Running with Docker locally (outdated/US specific) 🛳
      • Running on Rockfish/MARCC - JHU 🪨🐠
      • Running with docker on AWS - OLD probably outdated
        • Provisioning AWS EC2 instance
        • AWS Submission Instructions: Influenza
        • AWS Submission Instructions: COVID-19
      • Running with RStudio Server on AWS EC2
    • Inference scratch
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On this page
  • Before any run
  • ⇅ Get set up to use Github
  • 🔐 Access the flepiMoP model code
  • Locally install flepiMoP
  • 🐍 Install conda
  • ⬇️ Install flepiMoP packages and dependencies
  • 🤔 Deciding how to run
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  1. How To Run

Before any run

Instructions on how to begin engaging with flepiMoP locally by covering GitHub setup, conda installation, and finally installation of flepiMoP itself.

PreviousUsing plug-ins 🧩[experimental]NextQuick Start Guide

Last updated 2 months ago

Before any run

⇅ Get set up to use Github

You need to interact with Github to run and edit flepiMoP code. is a web platform for people to share and manage software, and it is based on a 'version control' software called git that helps programmers keep track of changes to code. Flepimop core code as well as example projects using flepimop code are all stored on Github, and frequently being updated. The first step to using flepimop for your own project is making sure you're set up to interact with code shared on Github.

If you are totally new to Github, navigate to and Sign Up for a new account. Read about the .

To work with flepimop code, you can do some tasks from the Github website, but you'll also need a way to 'clone' the code to your own local computer and keep it up to date with versions hosted online. You can do this either using a user interface like , or, using commands from the command line. Make sure you have one or both installed.

If you are a veteran user, make sure you're signed in on Github.com and through whatever method you use locally on your computer to interact with Github.

🔐 Access the flepiMoP model code

In order to run a model with flepiMoP, you will need to clone the flepiMoP code to your machine.

To clone the flepiMoP code repository:

  • If you're using the command line in a terminal, first navigate to the local directory you'll use as the directory for the files that make up flepiMoP. Then, use the command: git clone https://github.com/HopkinsIDD/flepiMoP

  • If you're using Github Desktop, go File -> Clone Repository, switch to the "URL" tab and copy the URL https://github.com/HopkinsIDD/flepiMoP there. For the "Local Path" option, make sure you choose your desired directory.

You can routinely ensure that your local clone of the flepiMoP code is up to date with upstream flepiMoP by navigating in terminal to your flepiMoP directory and using the command: git pull

Locally install flepiMoP

🐍 Install conda

Installation of conda may take a few minutes.

⬇️ Install flepiMoP packages and dependencies

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.

To install flepiMoP locally navigate to the flepiMoP directory and run the following command:

./build/local_install_or_update

This script will do the following:

  1. Determine $FLEPI_PATH and $FLEPI_CONDA environment variables,

  2. Create and activate a conda environment to install flepiMoP into,

  3. Install gempyor and related Python dependencies to the conda environment from (2), and

  4. Install necessary R packages and dependencies to the conda environment from (2).

🤔 Deciding how to run

In order to complete flepiMoP installation, you must have installed on your machine. conda is a tool that will assist you in managing software environments and code packages on your device, and it will be very helpful in ensuring consistent, reproducible environments across different projects. To install conda follow according to your operating system. We would recommend selecting the Anaconda Distribution installer of conda.

Please inspect the output to ensure that the installation has gone smoothly. If you encounter any issues please report them in a . After this step you should be clear to move on to the to activate your installation and do some test runs.

The code is written in a combination of and . The Python part of the model is a package called , and includes all the code to simulate the epidemic model and the observational model and apply time-dependent interventions. The R component conducts the (optional) parameter inference, and all the (optional) provided pre and post processing scripts are also written in R. Most uses of the code require interacting with components written in both languages, and thus making sure that both are installed along with a set of required packages. However, Python alone can be used to do forward simulations of the model using gempyor.

Because of the need for multiple software packages and dependencies, we describe different ways you can run the model, depending on the requirements of your model setup. See for a quick introduction to using gempyor and flepiMoP. We also provide some more ways to run our model, particularly for doing more complex model inference tasks.

🛠️
Github
Github.com
basics of git
Github Desktop
git
conda
the directions
GitHub issue
Quick Start Guide
R
Python
gempyor
Quick Start Guide
advanced