Flood exposure monitoring app

About

App showing estimated flood exposure based on WorldPop and Floodscan.

Usage

Running the app locally:

1. Install the dependencies with pip install -r requirements.txt
2. Install the local package with pip install -e .
3. Create a local .env file with the variables:

PROD_BLOB_SAS=<provided-on-request>
DEV_BLOB_SAS=<provided-on-request>
AZURE_DB_PW_DEV=<provided-on-request>
AZURE_DB_UID=<provided-on-request>
STAGE=<"dev" or "prod">

token for the blob storage account. 4. Run the app with python app.py for debugging, or gunicorn -w 4 -b 127.0.0.1:8000 app:server for production.

To add a new ISO3 code

Changes need to be made in this repo so that flood exposure data from a new ISO3 can be displayed in the app. Once there is new data in the Postgres database (as output from here):

1. Pull this code locally and create a new branch
2. Add the new ISO3 code to the list of ISO3S here
3. Run python download_geodata.py to update the boundary files in assets/geo/ and the adm database table. Note that both the dev and prod databases will need to be updated. This can be configured via the STAGE environment variable.
4. Commit the changes and open a PR on GitHub for review

Development

All code is formatted according to black and flake8 guidelines. The repo is set-up to use pre-commit. Before you start developing in this repository, you will need to run

pre-commit install

The markdownlint hook will require Ruby to be installed on your computer.

You can run all hooks against all your files using

pre-commit run --all-files

It is also strongly recommended to use jupytext to convert all Jupyter notebooks (.ipynb) to Markdown files (.md) before committing them into version control. This will make for cleaner diffs (and thus easier code reviews) and will ensure that cell outputs aren't committed to the repo (which might be problematic if working with sensitive data).