I bought a few Amazon Dash Buttons as part of Prime Day. These are the cheaper $4.99 buttons, not the more expensive $19.99 AWS IoT Buttons. In this blog post I’ll walk through how to make these cheaper buttons do what the more expensive button does.
What You Need
- One or more Amazon Dash Buttons. You’ll need to add the button to your Amazon account, but do not pick a product to buy. Just exit the set-up process without picking a product and you’ll be all set.
- Computer with root (sudo) access (or RaspberryPi or other device capable of running Python).
- The code from GitHub.
How exactly does the dash button work? In a nutshell, every time you press it, the button connects to the Wi-Fi network, pings Amazon, and then shuts back down for power savings. We’ll exploit the first step in that process – connecting to the Wi-Fi network. Using Python, we can listen for special “ARP probe” packets the Dash Button sends when it attempts to connect to Wi-Fi. All you need to know is the MAC address of the Dash Button and then listen for these ARP packets. When an ARP packet with your Dash Button’s MAC address is detected, you know the button was pressed, and you can call whatever Python methods you want.
Finding the MAC Address
The first step once you’ve set up your Dash Button (but have no picked a product to actually buy!) is to find the button’s MAC address. Grab your computer and connect it to the same Wi-Fi network as your button, then run the pydashbutton.py script as root and watch for any MAC addresses that are printed when you press the button. One important thing to note is that there seems to be throttling when pressing the button. Pressing the button multiple times per minute might not work.
Running Methods on Button Press
Now that you have the MAC address to listen for, all you need to do is throw some if/else logic into that same listener script to run code when a MAC address is detected. Check out the script and make any modifications you need. I included a simple example for logging button presses to a Google Sheet when pressing a button.
Check out the code on GitHub. Have fun!
Jupyter Notebook extension for Apache Spark integration.
Includes a progress indicator for the current Notebook cell if it invokes a Spark job. Queries the Spark UI service on the backend to get the required Spark job information.
This is really neat. No more checking another tab for job progress when running cells in a notebook!
If you really want to get into the details of Python and learn about how the language was built and how some of its internals are implemented, Fluent Python is the book for you.
It’s a great book to refresh your knowledge of coroutines, asyncio, and other Python goodies.
You’ll definitely want to read this if you’re using AWS Kinesis with Apache Spark to stream data, it’s been extremely valuable:
If you’re just getting started with Flask or you want to learn about the innards of Django (yep, that’s right), “Flask Web Development” is the perfect place to start. This book dives right in with creating a full web application, including Jinja templates, authentication, building a REST API, forms, databases, security, and deployment to Heroku using Git. This book will get you up and running with Flask and then quickly go into detail on how to build a full web application.
However, in my opinion, Flask should be used for small applications, but this book goes into full detail about creating a half-Django for a full web application.
With that in mind, this book is great for learning about Django – how would you implement CSRF token checks? How would you set up database migrations from scratch? How would you handle forms? Django does all of that, but hides it all from developers. This book goes into full detail reimplementing a lot of what Django gives you out-of-the-box, which is great.
Overall I highly recommend “Flask Web Development” if you’re learning either Flask, Django, or just web-backend development in general. Don’t just use what Django gives you out of the box and ignore how it’s implemented. This book will answer questions like “Why does my Django app need a
SECRET_KEY? What is this CSRF error I keep seeing? How do database migrations work? How do I write my own mail handler?”, making you a better Django developer.
Get it here: http://a.co/73ERCK9
I like to start my projects using Flask and Python because it’s fast and quick for most things, yet lightweight.
By default, Flask doesn’t give you much in terms of test frameworks, application settings, deployment, or running the application in production. I always end up making a skeleton that does some of these things, so I decided to put together a GitHub repository with a skeleton Flask project that does it for me.
Have a look here: https://github.com/mikestaszel/flask_startup
This weekend while running a rather large Python job, I ran into a memory error. It turned out that a dictionary I was populating could potentially become too big to fit into RAM. This is where DiskDict saved me some time.
It’s definitely not the best way to solve an issue, but in this case I was working with a limited system where rewriting the surrounding code would have been intrusive. Plus, the job didn’t have time constraints, so DiskDict was a decent workaround.
Wanted to share because it proved useful to me!