Tidepool Data Science Simulator

-- Project Status: Active

-- Project Disclaimer: This work is Pre-Production

Project Objective

The purpose of this project is to enable simulation of patient metabolism and the interactions of controllers such as Tidepool Loop. The current phase is for supporting FDA risk analysis. The longer term goal is to support many activities such as Tidepool Loop performance analysis and evaluation of algorithms for settings optimization.

Definition of Done

The current phase of this project will be done when the code base has been tested thoroughly and the current FDA risk analysis and iCGM risk sensitivity analysis have been implemented in this environment.

Project Description

During refactoring of the FDA risk analysis it became clear that increased generality/abstraction in an object-oriented approach to that code would support multiple projects of interest to Tidepool and Tidepool Data Science. The current state of this project is a generic simulator that facilitates answering questions around Tidepool Loop risk, Tidepool Loop performance, data-driven user modeling, and many others.

The refactored code on which this is based is in /notebooks/TEMPLATE_Run_Risk_Scenario_in_pyloopkit_in_colab_v0_5.ipynb

Technologies

Python
Anaconda for our virtual environments
Pandas for working with data (99% of the time)
Plotly for visualization
Pytest for testing
Travis for continuous integration testing
Black for code style
Flake8 for linting
Sphinx for documentation
Numpy docstring format

Getting Started with the Conda Virtual Environment

Install Miniconda. CAUTION for python virtual env users: Anaconda will automatically update your .bash_profile so that conda is launched automatically when you open a terminal. You can deactivate with the command conda deactivate or you can edit your bash_profile.
If you are new to Anaconda check out their getting started docs.
Clone this repo (for help see this tutorial).
In a terminal, navigate to the directory where you cloned this repo.
Run conda update -n base -c defaults conda to update to the latest version of conda
Run conda env create -f conda-environment.yml. This will download all of the package dependencies and install them in a conda (python) virtual environment.
Run conda env list to get a list of conda environments and select the environment that was created from the environmental.yml file (hint: environment name is at the top of the file)
Run conda activate <conda-env-name> or source activate <conda-env-name> to start the environment.
Run deactivate to stop the environment.

Getting Started with this project

Current FDA scenarios are being kept /data/raw/fda_risk_scenarios within this repo.
Current demo use cases are the /src.

Contributing Guide

All are welcome to contribute to this project.
Naming convention for notebooks is [short_description]-[initials]-[date_created]-[version], e.g. initial_data_exploration-jqp-2020-04-25-v-0-1-0.ipynb. A short _ delimited description, the creator's initials, date of creation, and a version number,
Naming convention for data files, figures, and tables is [PHI (if applicable)]-[short_description]-[date created or downloaded]-[code_version], e.g. raw_project_data_from_mnist-2020-04-25-v-0-1-0.csv, or project_data_figure-2020-04-25-v-0-1-0.png.

NOTE: PHI data is never stored in github and the .gitignore file includes this requirement as well.

Featured Notebooks/Analysis/Deliverables

Important TODOs

Integrate iCGM sensor and analysis scenarios (size: medium)
Testing and cleanup (size: medium to large)
Decouple input scenario format from Loop (size: medium)
Integrate visuals and metrics repos (size: small)
Surface all parameters for configuration (size: small)
Control randomness via config seed(s) (size: small)
Speed up Pyloopkit, possible in this code base (size: medium to large)

Tidepool Data Science Team

Name (with github link)	Tidepool Slack
Ed Nykaza	@ed
Jason Meno	@jason
Cameron Summers	@Cameron Summers

ps2 / data-science-simulator