diff --git a/source/_posts/2016-07-19-visualizing-your-iot-data.markdown b/source/_posts/2016-07-19-visualizing-your-iot-data.markdown index b2d1512dd36..311e53d0963 100644 --- a/source/_posts/2016-07-19-visualizing-your-iot-data.markdown +++ b/source/_posts/2016-07-19-visualizing-your-iot-data.markdown @@ -19,9 +19,9 @@ This blog post will show you ways to export data for reporting, visualization, o In this blog post I use the temperature of the [Aare](https://en.wikipedia.org/wiki/Aare) river close to where I live as a show case. The temperatures were recorded with the [Swiss Hydrological Data sensor](/integrations/swiss_hydrological_data) and the name of the sensor is `sensor.aare`. -The database is stored at `/.homeassistant/home-assistant_v2.db` as [SQLite database](https://www.sqlite.org/). In all examples we are going to use the path: `/home/ha/.homeassistant/home-assistant_v2.db` +The database is stored at `/.homeassistant/home-assistant_v2.db` as [SQLite database](https://www.sqlite.org/index.html). In all examples we are going to use the path: `/home/ha/.homeassistant/home-assistant_v2.db` -If you are just curious what's stored in your database then you can use the `sqlite3` command-line tool or a graphical one like [DB Browser for SQLite](http://sqlitebrowser.org/). +If you are just curious what's stored in your database then you can use the `sqlite3` command-line tool or a graphical one like [DB Browser for SQLite](https://sqlitebrowser.org/). The table that is holding the states is called `states`. The `events` tables is responsible for storing the events which occurred. So, we will first check how many entries there are in the `states` table. `sqlite3` needs to know where the databases is located. To work with your database make sure that Home Assistant is not running or create a copy of the existing database. It's recommended to work with a copy. @@ -72,7 +72,7 @@ After the import a graph can be created over the existing data. Graph in LibreOffice

-You can also use [matplotlib](http://matplotlib.org/) to generate graphs as an alternative to a spreadsheet application. This is a powerful Python 2D plotting library. With the built-in support for SQLite in Python it will only take a couple lines of code to visualize your data. +You can also use [matplotlib](https://matplotlib.org/) to generate graphs as an alternative to a spreadsheet application. This is a powerful Python 2D plotting library. With the built-in support for SQLite in Python it will only take a couple lines of code to visualize your data. ```python import sqlite3 diff --git a/source/_posts/2016-07-23-internet-of-things-data-exploration-with-jupyter-notebooks.markdown b/source/_posts/2016-07-23-internet-of-things-data-exploration-with-jupyter-notebooks.markdown index 9475ab7e1b5..0b7f97d1a86 100644 --- a/source/_posts/2016-07-23-internet-of-things-data-exploration-with-jupyter-notebooks.markdown +++ b/source/_posts/2016-07-23-internet-of-things-data-exploration-with-jupyter-notebooks.markdown @@ -26,11 +26,11 @@ One of the graphs created with this tutorial. _TL; DR: Use [this Jupyter Notebook][nb-prev] to visualize of your data_ [blog post by Fabian]: /blog/2016/07/19/visualizing-your-iot-data/ -[DB Browser for SQLite]: http://sqlitebrowser.org/ -[Pandas]: http://pandas.pydata.org/ -[matplotlib]: http://matplotlib.org/ +[DB Browser for SQLite]: https://sqlitebrowser.org/ +[Pandas]: https://pandas.pydata.org/ +[matplotlib]: https://matplotlib.org/ [Jupyter notebook]: https://jupyter.org/ -[nb-prev]: https://nbviewer.jupyter.org/github/home-assistant/home-assistant-notebooks/blob/master/other/DataExploration-1/DataExploration-1.ipynb +[nb-prev]: https://nbviewer.org/github/home-assistant/home-assistant-notebooks/blob/master/other/DataExploration-1/DataExploration-1.ipynb diff --git a/source/_posts/2016-08-19-github-style-calendar-heatmap-of-device-data.markdown b/source/_posts/2016-08-19-github-style-calendar-heatmap-of-device-data.markdown index 90f3d202f0f..69eced8d1f1 100644 --- a/source/_posts/2016-08-19-github-style-calendar-heatmap-of-device-data.markdown +++ b/source/_posts/2016-08-19-github-style-calendar-heatmap-of-device-data.markdown @@ -19,4 +19,4 @@ Heatmap [heatmap]: https://en.wikipedia.org/wiki/Heat_map [Jupyter notebook]: https://jupyter.org/ -[nb-prev]: https://nbviewer.jupyter.org/github/home-assistant/home-assistant-notebooks/blob/master/other/DataExploration-2/DataExploration-2.ipynb +[nb-prev]: https://nbviewer.org/github/home-assistant/home-assistant-notebooks/blob/master/other/DataExploration-2/DataExploration-2.ipynb diff --git a/source/_posts/2017-11-29-hassio-virtual-machine.markdown b/source/_posts/2017-11-29-hassio-virtual-machine.markdown index bf9f1938734..54a43d639f2 100644 --- a/source/_posts/2017-11-29-hassio-virtual-machine.markdown +++ b/source/_posts/2017-11-29-hassio-virtual-machine.markdown @@ -10,7 +10,7 @@ og_image: /images/blog/2017-11-hassio-virtual/social.png --- -The images for the Raspberry Pi family and the Intel NUC are an easy way to get started with [Hass.io](/hassio/). For a test or if you have a system which is already hosting virtual machines then the [**Hass.io installer**](/hassio/installation/#alternative-install-on-generic-linux-server) is an option to use Hass.io in a virtualized environment. In this guide the host is a Fedora 27 system with [libvirt](https://libvirt.org/) support and the guest will be running Debian 9. Hass.io will be installed on the guest. +The images for the Raspberry Pi family and the Intel NUC are an easy way to get started with [Hass.io](/getting-started). For a test or if you have a system which is already hosting virtual machines then the [**Hass.io installer**](/installation/) is an option to use Hass.io in a virtualized environment. In this guide the host is a Fedora 27 system with [libvirt](https://libvirt.org/) support and the guest will be running Debian 9. Hass.io will be installed on the guest.