Tag: Quantified Self

Hackintosh Benchmark 2.0

Since I am obsessed with quantifying things, I absolutely had to benchmark my new Hackintosh rig to see what my new purchases had afforded cost me. I ran a handful of different benchmarks under both OS X and Windows boots to see what the Intel Core i5-4690K, EVGA GeForce GTX 970 SC, and Samsung 850 Pro could do. So without further ado, here are the results of my benchmarks!

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Half a Year with Dash

I’ve been using Dash in my car for almost six months now to track my driving habits and monitor my car’s status. The app interfaces over Bluetooth with an OBD device to read engine codes and access metrics such as speed, fuel efficiency, and engine status. The app tracks every trip and assigns a “Driving Score” based on driving behavior to encourage “better” driving habits. Currently the only way to access the data is via an IFTTT channel that logs each trip to a line of a Google Spreadsheet. The developer is working on an API (aptly named Chassis) that will hopefully make access to the data even easier in the coming future. In the meantime, I just used the IFTTT spreadsheet output to assemble some stats and charts.

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Foursquare Heatmap

I’m a regular user of the location-based social network Foursquare mainly as a source of recommendations for new places to try. I typically check in everywhere I go with the exception of private residences (can’t let people stalk me that easily), so I have a pretty extensive log covering my location history. While it’s not quite as extensive as the Google Maps Location History, it does a good job representing the places I visit.

In the past I’ve messed around with making heatmaps of latitude/longitude coordinate pairs without much success. It always required tedious manipulations to properly overlay on top of a Google Maps image and wasn’t really worth the effort. I recently stumbled across a Python-based heatmap tool created by Seth Golub that takes a list of coordinates and turns them into a beautiful heatmap that can be overlaid on a OpenStreetMap. Once I figured out how to get Python Image Library installed properly, I used my private Foursquare feed to grab every checkin over the past year. Extracting and exporting the GPS coordinates included with each XML element only required a few lines of code. The resulting maps, which have been limited to only show downtown and the general Austin area, are displayed below.

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200k LastFM Scrobbles

I recently surpassed the 200,000 song scrobble mark on LastFM since joining way back in March 2005. I threw together a little infographic summarizing my overall top artists, albums, and songs over the past 8+ years. I’m going to make a yearly summary so that all my charts aren’t overwhelmed by the stuff I listened to back in high school. Click on the image for the full-sized infographic. Everything was created in Adobe Illustrator using information and images from the LastFM website.

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3 Months of Fitbit

I bought myself a Fitbit One right before the New Year in hopes of motivating more physical activity to offset my stationary lab lifestyle. It’s been just over three months since I started tracking my daily steps and most of my sleep. While I haven’t started running yet (I promise I’ll start eventually), it’s been fascinating to see my walking activity tracked throughout the day. One of my biggest complaints about Fitbit’s services is that they make you pay to access in-depth information about your activity. I think that it’s absolutely ludicrous to charge $49.99 a year to see the full extent of your data. With that said, they do offer an API for third-party development with access to the raw data (steps, distance, etc.) so I used this to automatically pull my daily activity into a Google Drive spreadsheet for easy access and analysis. Using this data, I generate my “health tracking” page featuring several charts of my ongoing Fitbit activity. The remainder of this post presents a summary of my Fitbit activity over the past 90 days since I got the One.

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