A couple of quick scripts to plot up COVID-19 data in a handful of counties across the US.

These plots are designed to emulate some of the data being used by Johnson Space Center to determine when more employees can return to work onsite. Two plots are generated: Daily confirmed cases and Cumulative confirmed cases. In both plots, bars represent daily values and lines represent a 5-day moving average. In the Daily confirmed cases plot, a red or green shaded box covering the previous 14 days of data indicates whether the trend of daily cases is positive (red) or negative (green).

These plots autogenerate daily and can be accessed at http://covid-harris.herokuapp.com/

All data used here are from the Johns Hopkins Whiting School of Engineering Center for Systems Science and Engineering. https://github.com/CSSEGISandData/COVID-19/tree/master/csse_covid_19_data/csse_covid_19_daily_reports

This app was created on top of python-getting-started. The readme for the original infrastructure is below.

Create a virtual environment to test the app on your local machine.

$ conda create virtualenv -n covid

Start up that new env.

$ conda env export -n covid
$ source activate covid
$ pip install -r requirements.txt

Then run the thing...

$ python manage.py collectstatic

(answer yes)

$ heroku local web

Now access the local deployment at http://localhost:5000

If you are having issues getting this to run, your 5000 port may be busy. Kill all processes on that port with:

$ kill `lsof -i :5000`

NOTE: This app currently has webhooks setup so that you do not need to push to heroku. If pushed to github, Heroku app will automatically rebuild and redeploy. In other words, skip this step...

$ git add .
$ git commit -m "commit message"
$ git push heroku master
$ heroku open

Done forget to also push to github (here; origin master) where you should definitely also be saving these files:

$ git add .
$ git commit -m "commit message"
$ git push origin master

The file /uploads/core/view.py is where your python code (the actual code being deployed, in this case DensityX.py) lives.

The file /uploads/templates/base.html is the base html file that is essentially your index.html file. All other html files extend base.

The file /uploads/core/templates/core/home.html is where the stuff on your index page is defined.

Use python-getting-started from heroku for an example of a working setup with all required files (readme for original is below)

requirements.txt file - all dependencies and versions go here. To get version: run python, import module, type:

$ print module.__version__

urls.py - list of all the "pages" on the web interface

view.py - where the actual python code gets called and executed. The script to run must be wrapped as a function like:

def my_python_script(some_thing)

A barebones Python app, which can easily be deployed to Heroku.

This application supports the Getting Started with Python on Heroku article - check it out.

Make sure you have Python installed properly. Also, install the Heroku Toolbelt and Postgres.

$ git clone [email protected]:heroku/python-getting-started.git
$ cd python-getting-started

$ pip install -r requirements.txt

$ createdb python_getting_started

$ python manage.py migrate
$ python manage.py collectstatic

$ heroku local

Your app should now be running on localhost:5000.

$ heroku create
$ git push heroku master

$ heroku run python manage.py migrate
$ heroku open

or

For more information about using Python on Heroku, see these Dev Center articles:

• Python on Heroku

git clone https://github.com/kaylai/DensityX-heroku.git
cd DensityX-heroku
brew install postgresql
conda create virtualenv -n venv
source activate venv
[Switch to Venv Machine]
heroku local web

pip install -r requirements.txt
python manage.py collectstatic
yes
[Switch to local machine]

1. Set a webook in heroku so that the heroku server builds and deploys everytime this github is updated
2. Spin up a DigitalOcean droplet running Ubuntu
3. Install the necessary items, including anaconda. wget "url" to download the installer .sh file, then follow the instructions here.
4. git clone your github repo into your root folder on the DO server
5. You'll need to store your github credentials so that the DO terminal doesn't ask for a username/pass every time you try to git push. See this stackexchange discussion.
6. Create a cron job to automatically git pull, run fetch.py, git add, git commit, and git push. Also add the output of these commands to log files so that you can debug

Cron runs in a lightweight environment, so you need to tell it to run in bash or some shell, and you need to provide absolute paths to everything. To edit your crontab, type:

crontab -e

Then, add this to the first line of your crontab

01 00 * * * su -s /bin/sh root -c 'cd /root/COVID_JSC && /usr/bin/git fetch --all && /usr/bin/git reset --hard origin/master && /usr/bin/git pull origin master && /root/anaconda3/bin/python fetch.py | /usr/bin/tee /root/fetchlog.log && /usr/bin/git add * | /usr/bin/tee /root/addstar.log && /usr/bin/git commit -m "Cron autoupdate" | /usr/bin/tee /root/commitlog.log && /usr/bin/git push -u origin master >> /root/gitpushlog.log 2>&1'

This tells cron to run this command on the first minute of the 0th hour (midnight) every day of month, every day, every day of week. The su -s executes as a super user. The logs get saved into the root folder of the DO server, so you'll have to log into the DO terminal to see them.

• Kayla Iacovino - github

This project is licensed under the MIT License - see the LICENSE.md file for details