Thanks to Anton Kireyeu we are able to present another awesome Jupyter notebook. I guess that you all know the graph which Github is using to visualize your commits per day over a time-line. It’s a so-called heatmap. If there are more commits, it’s getting hotter. The latest notebook is capable to do the same thing for your devices. To be more precise, for the hours your devices are home.
Heatmap
I have been working on a new subsystem to complement Home Assistant’s Automation and Scripting components. AppDaemon is a python daemon that consumes events from Home Assistant and feeds them to snippets of python code called “Apps”. An App is a Python class that is instantiated possibly multiple times from AppDaemon and registers callbacks for various system events. It is also able to inspect and set state and call services. The API provides a rich environment suited to home automation tasks that can also leverage all the power of Python.
It’s time for 0.26 and it’s again full of new features and fixes. First I want to highlight that we are now having 500 000 monthly pageviews on the website. A big milestone for us! It’s been an amazing journey. Big thanks to the Home Assistant community for being such a delightful bunch.
This release includes code contributed by 31 different people. The biggest change in this release is a new unit system. Instead of picking Celsius or Fahrenheit you’ll have to pick imperial or metric now. This influences the units for your temperature, distance, and weight. This will simplify any platform or component that needs to know this information. Big thanks to @Teagan42 for her hard work on this!
# Configuration.yaml example
homeassistant:
# 'metric' for the metric system, 'imperial' for the imperial system
unit_system: metric
This blog post will go into detail about the recent performance optimizations that went into the Home Assistant front end. For people not familiar with the app, check out the demo and the source.
TL; DR: Don’t hack the framework, separate responsibilities, ship less, use service workers, use (future) web standards.
This year at Google I/O I saw Monica from the Polymer team talk about web components and performance. In her talk she mentions a mantra that they use in the Polymer team to make things fast: Do less and be lazy.
Do less and be lazy. It sounds so obvious and it took a while before it started to dawn on me. I think most of the code I write is pretty fast, but I don’t often stop to take a harder look at how and when it runs in practice. When do we need the result, can it be postponed?
And thus started my journey to take a critical look at how the Home Assistant app was working and how to make things faster. Below is the list of the different things that I did to make it fast.
I hope this list can be useful to other people, as a guide for optimizing their own apps or for avoiding pitfalls when building a new one.
The first thing to do is to measure. The Home Assistant front end is a mobile web app, so we shouldn’t measure this on a machine with 8 cores and gigabytes of ram but instead measure on devices you expect a mobile web app to run: phones. Below are two timelines recorded with Home Assistant 0.18.2 (pre-optimizations) and Google Chrome 53. On my Mac the app starts in 1400 miliseconds and on my Nexus 5x in ~6500 miliseconds (~4.5 times slower!).
Although the app takes 6500 milliseconds to load on my phone, it would perform well afterwards. Still, that initial load is unacceptable. You expect to open an app on your phone and be able to use it, quickly. After I applied all the changes described below, I managed to reduce startup time to 900 miliseconds (-35%) on my Mac and 2400 miliseconds (-63%) on my Nexus 5x. Check out the demo here.
This is a guest post by Home Assistant user and contributor Nolan Gilley.
Today I’ll show you how I used Home Assistant, a NodeMCU (ESP8266), and a couple of accelerometers to automate our laundry room. This is a rewrite of an old post where I did the same thing using a Moteino & Raspberry Pi. This version only requires a NodeMCU.
We have an older washer and dryer which doesn’t have any form of notification when cycles complete. Home Assistant was the obvious solution, I just needed to create sensors for the washer and dryer. I tried using sound sensors but found them unreliable. I ended up using an accelerometer attached to the back of each appliance. I also added magnetic reed switches on the doors of the washer and dryer to detect whether they’re open or closed. I connected the accelerometers and reed switches to an NodeMCU which will relay the data to my MQTT broker.
Block diagram of schematic
When Home Assistant started the focus has always been on making a great developer experience. Allowing anyone to add support for their favorite devices to Home Assistant easily. This focus has been a great success since we now have 339 components and platforms!
Starting with this release, we are extending our extensability to the frontend. Starting this release, any component can add it’s own page to the frontend. Examples of this today are the map, logbook and history. We are looking forward to all the crazy panels you’ll come up with!
We have also seen an exciting trend of people starting to visualize their Internet of Things data using Jupyter Notebooks, which are a great way to create and share documents that contain code, visualizations, and explanatory text. In case you missed it, the blog post by @kireyeu shows an advanced usecase while our Notebooks in the Home Assistant Notebooks repository cover the basics.
This release also includes a bunch of new integrations, among others three new media player platforms. This means that today Home Assistant can talk to 26 different media players!
The brand-new iFrame panel component allows you to add other websites as pages in the Home Assistant frontend. They will show up in the sidebar and can be used the same way as you open the frontend in your browser but all within one view.
I would like to do a shoutout to @fabianhjr. He has started adding typing data (PEP484) to the Home Assistant core. This will help us identify issues before they are released.
timestamp_local and timestamp_utc) (@fabaff)location extension for Telegram and photo bug fixed (@keatontaylor and @pvizeli)
The first release of Micropython for ESP8266 was delivered a couple of weeks ago. The documentation covers a lot of ground. This post is providing only a little summary which should get you started.
Until a couple of weeks ago, the pre-built MicroPython binary for the ESP8266 was only available to backers of the Kickstarter campaign. This has changed now and it is available to the public for download.
Read on →This is the first blog post by Anton Kireyeu. A new contributor to Home Assistant who will focus on exploring and visualizing Home Assistant data.
As we learned in the recent blog post by Fabian, all operational data of your Home Assistant application is stored locally and is available for exploration. Our first steps were querying data with the DB Browser for SQLite, exporting the data extract as a CSV file and graphing in LibreOffice. But what else can be done with this data and what tools are there available?
This post will help you get set up using a few popular data scientist tools to allow you to locally process your data:
One of the graphs created with this tutorial.
TL; DR: Use this Jupyter Notebook to visualize of your data
Read on →
The history component is tracking everything that is going on within Home Assistant. This means that you have access to all stored information about your home. Our history is not a full-fledged graphical processing and visualization component as you may know from systems and network monitoring tools. The current limitation is that you only can select a day for a visual output of your information and not a period. Also, there is no possibility to drill down on a specific entity.
This blog post will show you ways to export data for reporting, visualization, or further analysis of automation rules.
Read on →It’s time for Home Assistant 0.24 and it’s full of new integration for your Home. It contains some structural changes to our history which requires action from your end, so please keep reading.
MapQuest discontinued their free and direct tile access by Monday, July 11, 2016. With CARTO we found a very cool and suitable solution. They allow us to use their tile for the map. Thank you, CARTO.
Roy Hooper did an amazing job migrating the history support from being tied to SQLite to use the ORM SQLAlchemy instead. This means that you can now use any SQL backend for the history. So besides SQLite you can now databases like MySQL or PostgreSQL. However, this does require that you install SQLAlchemy and run a command to migrate your existing history over. We tried to make the process as seamless as possible by introducing a new command line script:
$ pip3 install SQLAlchemy
$ hass --script db_migrator --config /path/to/config
You can omit the --config option if you use the default configuration directory. Run the script with --help to get more options.
Quick hot fix after we found a bug in the migrator where it wouldn’t work with a database in a non-standard location. Thanks to @n8henrie and @AlucardZero.
configuration.yaml file.