The first step of my plan, building a Raspberry Pi based photovoltaic monitoring solution, is finished. I created a python package that works with the Kostal Piko 5.5 inverter (and theoretically should work with other Kostal inverters as well) and offers a clean interface for accessing the data:
#create a new piko instance
p = Piko('host', 'username', 'password')
#get current power
#get voltage from string 1
The next steps are collecting the data and processing it.
The library can be downloaded from https://pypi.python.org/pypi/pikopy
The code is on Github: https://github.com/Tafkas/KostalPikoPy
I have been carrying my Fitbit for a little over two years with me and it keeps tracking my daily steps. It also tracks my distance covered by multiplying those steps using the stride length which you can either provide explicitly or implicitly setting your heights. In the winter of 2012 I bought my first Garmin Forerunner 410 GPS watch to help me track my running (and other outdoor) activities. Since then I have worn it at every activity. Additionally before every running session I started a Fitbit activity to measure my step count. Over the last 18 month I have recorded 69 runs using both, the Fitbit and the watch. I put the data in a csv file so I could load it into R. Then I performed a linear regression to determine my stride length.
In an ideal world, where the GPS watch would perform with a 100% accuracy and the Fitbit would recognize every single step taken, the regression fit would meet the origin. Unfortunately neither of them is that accurate. Our model gives us an intercept of 398.46m with a slope of 1.00421m and an adjusted R^2 of 0.97505 Based on that I have covered a whole lap of a competitive running track before I made a single step.
I have noticed that the placement of the Fitbit has a huge impact on the amount of steps recorded. Attached to the pocket of my running shorts it seems to miss certain steps. I get much better results attaching it directly at my waist. Overall I am quite satisfied with the accuracy provided by the Fitbit. I you do a lot of interval training or vary your stride from session to session your result will be far more off.
The code for the regression is on Github:
A friend of mine had a photovoltaic system (consisting of 14 solar panels) installed on his rooftop last year. After a little talk I offered to setup a monitoring solution which provides him the data in real-time. The current setup comes with an inverter by the company Kostal.
The Kostal Piko 5.5 runs an internal web server showing statistics like current power, daily energy, total energy plus specific information for each string. Unfortunately the web page and the bundled software look like they were designed in the late 90s and development has been abandoned:
The power inverter itself logs data at either a 15 minutes interval for about 100 days or at an hour interval for about 400 days. At the current setting the 15 minutes interval has been selected. Furthermore my friend took notes irregularly from the built-in liquid-crystal display.
The plan was set. A Raspberry Pi would fetch data from the inverter, store it in a database and a web application would render the results to a browser:
The project is live at http://solarpi.tafkas.net
I have been tracking my sleep for almost two years now using my Fitbit. I started with the Fitbit Ultra and then moved on the the Fitbit One after it came out. In October 2013 I found out about the Sleep Cycle app for the iPhone. For weeks, Sleep Cycle was listed as the best-selling health app in Germany, where currently (as of January 2014) it is in second place. The program promises, to wake you up in the morning without being tired. This is indeed possible if the alarm goes off in light sleep and not in deep sleep. After reading all the positive reviews on the AppStore I decided to give it a try.
The app promises to wake you up in a time frame up to 30 minutes prior to the alarm you set if it detects your movement in the morning. Even more important to me than the actual smart alarm feature was the possibility to collect some data while sleeping. In the morning you are presented with a chart of your sleep pattern of last night:
The app also allows to export the database as a comma seprarated file containing: time you went to bed, time you woke up, sleep quality in %, wake up mood and user defined sleep notes. This gives you the opportunity to do some more analysis. I decided to fire up R and create my own charts.
So far I have used the app to track 100 nights of sleep and decided to peak into the data. Let’s take a look how long I slept each night:
It looks like the longer I slept the higher the sleep quality is. A scatter plot of the data gives:
The chart takes also the sleep notes into consideration. You can see clearly that sleeping away from home results in lower sleep quality. The same applies for exercising (note: I tagged a sleep with exercising when I worked out late in the evening). On the contrary taking a 3mg melatonin increased the sleep quality.
Averaging the sleep quality by month shows, that the January worse than the previous month. One explanation is a vacation I took, where I did not sleep so well at all.
The R code for the data wrangling and the charts:
After the 2013 Berlin Marathon sold out in less than four hours, the organizers decided to alter the registration process for 2014. First there was a pre-registration phase followed by a random selection from the pool of registrants to receive a spot. Those who were selected had to register until November 11th, 2013. Any spots that were not confirmed till the 11th would be offered to pre-registered candidates according to the order in which they were randomly selected.
At is a list of all registered participants of the event. Being curious how many runners are on the list I had two options: 1. going through the entire list and counting or 2. download the entire list and let the computer do the math. I chose the latter. If you checked the website already you saw that they present only parts of the list at a time a reload it asynchronously while you scroll down. I wrote a little python script that queries the data and saves the JSON response to a csv file.
Checking the file we see there are only 16,707 participants so far. Sure, there are spots sold to agencies or given to sponsors, but how many will be handed out in the second wave? Until they announce the results let’s look the data:
The distribution of the year of birth shows a bimodal pattern. Most runners are born in the late 60s or early 70s and then there is a second spike a around 1980.
Looking at the top 10 participating nations it is no surprise that Germany stands out by far. Followed by Great Britain and Denmark. The only non-European country in the top 10 are the United States of America:
The code for generating the images:
Update: The official registration period is over and 23,286 runners have signed up.
Two days ago the official hard-float Oracle Java 7 JDK has been announced on the official Raspberry Pi blog. Prior to this there was only the OpenJDK implementation which was lacking performance.
Furterhmore the Raspberry Pi Foundation announced that future Raspbian images would ship with. Oracle Java by default.
If you want to give it a spin you can install the JDK with:
sudo apt-get update && sudo apt-get install oracle-java7-jdk
If you work a lot on the command line you are probably familiar with the top utility to see what process is taking the most CPU or memory. There’s a similar utility called htop that is much more powerful. On top of the information that top provides, htop additionally shows your usage per CPU, as well as a meaningful text graph of your memory and swap usage right at the top.
As if that wasn’t enough, you can just use your up/down arrow keys to select a process, and then you can kill it with the F9 key if you like, or you can change the priority by using the F7 and F8 keys.
To install htop simply use
sudo apt-get install htop
Once installed you can start it using
After I moved back from New Jersey in June 2008 I started to track my body weight more seriously. My routine usually consists of getting up and after finishing the morning bathroom I would step on my scale. That way I try to ensure that the condition for each weighing are as similar as possible. I recorded my weight on paper and eventually would put everything into a spreadsheet for further analysis.
In January 2011 I upgraded my bathroom scale to the Withings WiFi Body Scale. That way I could automate the process of tracking my weight by just stepping on it. No more writing on paper and eventually transferring everything into spreadsheets.
Using the exported data we can fire up R to look at the data:
The generated chart shows my weight loss in 2009, where I started cycling again after years of absence. From my lowest in the Fall of 2009 I gradually gained weight throughout 2010, 2011 until 2012, where I hit the 100 kg mark around Christmas time:
That was enough and I decided it was time to start working out again. So far, I am at a good downwards trend. I have to keep up that momentum. My next goal is to get between 80 kg and 85 kg and then maintain my weight. The color of the dots reflect my body fat percentage and there seems to be a strong correlation between body fat and my actual weight:
Doing a simple linear regression gives us an adjusted R-squared of 0.8419
One of the most important features in quantified self is the ability to export your data in an open format. Fitbit lets you download your personal data if you subscribe to a premium membership. Alternatively they provide an API at dev.fitbit.com/ that allows developers to interact with Fitbit data in their own applications, products and services.
In a blog post at quantifiedself.com Mark Levitt shows a way how to export your Fitbit data into Google Spreadsheets. I explored to API myself adding and removing some of the fields to get more insights to the data.
In a future post I will delve into the data in order to understand some of my own physical activity patterns.
Update 14 November 2014: I removed the Active Score since it has been dropped by the Fitbit API
If you are overclocking your Raspberry Pi or you just curious how hot this little guy gets, there are two ways to get the internal temperature. Assuming you are running Raspbian as your operating system.
$ /opt/vc/bin/vcgencmd measure_temp
This gives you the temperate in in degrees Celsius:
If you need the temperature to be more precise (e.g. storing it in an database or for further processing) use the following command.
$ cat /sys/class/thermal/thermal_zone0/temp
This will give you the temperature in Millidegrees Celsius:
From my personal experience the temperature ranges from about 50°C to 55°C and I have never seen my Raspberry Pi running over 58°C.