## Calories as a function of alcohol in popular beers

In the USA, a standard drink is defined as including 0.6 fluid ounce (18 mL or 14 g) of ethanol (see Alcohol equivalence), meaning that a “standard” 12 oz beer has about 5% ABV. However, beers vary quite a bit in their alcohol content as well as their caloric content, so it seems reasonable to ask: If I have a beer with a given ABV, approximately how many calories does it have?

While browsing the web, I found a table listing the calories in a number of beers and thought it would be interesting to visualize using Python and plot.ly. It is a simple visualization, but one I find neat. So, without further adieu:

Each blue point on the plot is a beer from the beer100.com domestic and international tables — feel free to explore the plot with your mouse. As you can see, unsurprisingly, as a beer’s alcohol content increases, so do the number of calories. Fitting a linear regression to the data, we see that a linear trend fits quite well: $f(x) = (28.2)*x + (8.25)$, where $x$ is the beer’s ABV (in percent). This means that if a beer has an alcoholic content of 5%, we can expect it to have approximately 150 calories (149.25 as predicted by the fit). However, there is quite a bit of variability between different beers of the same ABV. For example, Bud Ice Light and Kronenbourg Imported Dark Beer (whose label is a bit ambiguous, but I am assuming may be Kronenbourg 1664 Brune) are both 5% ABV, but have 115 and 163 calories per 12 oz, respectively.

## Full Moon – December 6, 2014

Last night there was a great full moon, so I tried to capture it with my DSLR, a Canon T3i (which is discontinued, replaced as of today with the T5i), and a Tamron 18-250mm F/3.5-6.3 Macro lens.

To capture it, I setup a tripod inside, facing out an (admittedly) dirty window, disabled the len’s optical stabilization, turned on the T3i’s 3x digital crop zoom, and composed the shot. Only post-processing was to speed up the captured clip because the original was ~10 minutes long, shifting the movie to vertically center the moon’s precession, and adding an ambient background track. I’m impressed with what I was able to capture with such an inexpensive lens.

## HTML to Markdown Converter

I am moving my blog post formatting code over to Markdown and found an incredibly useful HTML to Markdown converter by Dom Christie. It allows me to take HTML, for example:

```<code><h2>A great list of websites:</h2>
<ul>
<li><a href = "http://www.semifluid.com">Semifluid.com</a></li>
<li><a href = "https://www.researchgate.net/profile/Steven_Cholewiak">Steven Cholewiak's ResearchGate.com profile</a></li>
</ul></code>
```

And easily convert it to markdown:

```## A great list of websites:

*   [Semifluid.com](http://www.semifluid.com)
*   [Steven Cholewiak's ResearchGate.com profile](https://www.researchgate.net/profile/Steven_Cholewiak)
```

The code can be found on GitHub at to-markdown and Dom has a live demo available too.

## FoodieTV adds Johannisbeeren Pastries to this week’s episode

Since my Johannisbeeren (red currant) Pastries video on Vimeo is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike license (CC BY-NC-SA 3.0), anyone is free to share, modify, and adapt it, as long as they make sure to note that I created it (and as long as it is not used for commercial purposes).

The FoodieTV app, from Glam Media, took advantage of the open nature of the licensing and just included my video in their latest episode (S2E12):

Cool! FoodieTV is a free app on the Apple iTunes Store (unsolicited shout-out and thank you for the free exposure).

June 19, 2014 1 comment

Another post, another neat force-directed graph. This one illustrates the interconnections between professors and students who have been co-authors on some of my papers and presentations, as scrapped from Google Scholar citations.  It could be described as the first version of a rough illustration of my 2 degrees of separation in academia.

The dark orange circle in the center is myself, light blue circles are papers/presentations, light orange circles are co-authors, and dark-blue circles are co-authors of my co-authors (i.e., have not necessarily directly worked with me on a project).

Unfortunately, as of today, not all of my co-authors have Google Scholar pages, so there are a number of co-authors whose connections and branches are under-represented.  In addition, Google Scholar does not necessarily accumulate all of a given author’s papers/presentations and often makes mistakes misattributing papers to profiles.  So, the veracity of the information represented here should be taken with a grain of salt unless I find a better service for generating these networks.

For some more information on how this was created, click-through to the post.

## VSS 2014 “DNA” v1

May 3, 2014 1 comment

Here’s an illustration I pulled together using Python, NetworkX, and D3.js to illustrate the interconnections between abstracts that will be presented at the Vision Sciences Society 2014 annual meeting in approximately 2 weeks. Orange dots represent abstracts, Light Blue dots represent authors with at least one first authorship, and Dark Blue dots represent other authors (second through last).

As you can see, there are large numbers of abstracts that have few shared authors.  Those abstracts that share authors often join together to create “chains” of students, advisors, and colleagues.

This is a first version, hastily pulled together, so there are a few problems.  The nodes are assigned to authors by name, which can be a problem for authors sharing the same name (which creates more connections than appropriate for a given node) or who  have inconsistent reporting of their name (for example, omitting the middle initial or alternate spelling, which can create another erroneous node). I am thinking of addressing the duplicate node issue by using a string similarity metric (e.g., Levenshtein distance) to find strings that contain similar names to combine the connections, but this could be an issue if the names are truly different people. Alternatively, I could incorporate the authors’ affiliations, but this carries similar issues (e.g., I report my affiliation as “University of Giessen” while colleagues report it as “Justus-Liebig-Universität Gießen”).

Although there are lingering issues, it is still an interesting illustration of the connections between the different abstracts being presented at VSS 2014.

Here’s the code on GitHub: pyVSSRelationshipPlot

## Equirectangular to Stereographic Projections (Little Planets) in MATLAB

April 20, 2014 1 comment

The camera included in Google’s Android mobile OS has a feature called “Photo Spheres” that allows you to take a series of photos and create a full spherical panorama. The Photo Sphere feature is included on Google Play Edition (GPE) phones –phones that incorporate Google’s version of unadulterated Android– including my Nexus 5. When you take a Photo Sphere, the camera seamlessly stitches the individual photos into an Equirectangular panorama. For example, here is a panorama I took of the rapeseed fields in central Germany:

There is a bit of distortion (see Tissot’s indicatrix), especially at the top and bottom of the image, but this is due to the problem of projecting a sphere onto a plane. On the Nexus 5 (and other GPE phones), the Gallery application includes a feature that allows you to either view the resulting Photo Spheres as spherical panoramas or to create “Little Planets”/”Tiny Planets”, which are actually Stereographic Projections of the spherical panorama. I found the effect really neat, so I wanted to see if I could recreate the projection in MATLAB.

As a teaser, here’s the output for my code:

## Batch Handbrake video file conversion with Python

I needed a quick little piece of code that would go recursively iterate through a folder and its subfolders and convert all of the video files to H.264, so I took advantage of the Handbrake command line interface (CLI) and Python 2.7.x to do the work for me. This code snippet is not long or elaborate, but does the job, so hopefully it will be helpful to others.

Note that the Handbrake CLI options are defined in runstr. As-is, the script will convert videos with AVI, DIVX, FLV, M4V, MKV, MOV, MPG, MPEG, and WMV extensions to H.264 MP4s with the following options:

Here’s the code (available on GitHub):

```import os
import time
import subprocess
import sys

fileList = []
rootdir = raw_input("Root Dir: ")
for root, subFolders, files in os.walk(rootdir):
for file in files:
theFile = os.path.join(root,file)
fileName, fileExtension = os.path.splitext(theFile)
if fileExtension.lower() in ('.avi', '.divx', '.flv', '.m4v', '.mkv', '.mov', '.mpg', '.mpeg', '.wmv'):
fileList.append(theFile)

runstr = '"C:Program FilesHandbrakeHandBrakeCLI.exe" -i "{0}" -o "{1}" --preset="Normal" --two-pass --turbo'

print '=======--------======='

while fileList:
inFile = fileList.pop()
fileName, fileExtension = os.path.splitext(inFile)
outFile = fileName+'.mp4'

print 'Processing',inFile
returncode  = subprocess.call(runstr.format(inFile,outFile))
time.sleep(5)
print 'Removing',inFile
os.remove(inFile)
```