Colorado High Park Fire Time-lapse Videos

On the first day of the Colorado High Park Fire (June 9) I was in the Red Feather Lakes area, so I took a couple short time-lapse videos of the smoke plumes. As of this writing the fire has burned more than 82,000 acres and destroyed over 190 homes, and is only partially contained.

Both time-lapse sequences consist of around 600 photos taken at 1 second intervals on my Nikon D5100 and rendered at a rate of 24fps. In other words, each video depicts roughly 10 minutes. I was on the Northeast side of the fire which was still several miles away on the South side of Hwy 14. I hope to go back to this same spot in a couple weeks for a before and after shot.

I spent more time tweaking the first sequence which is why it contains significantly less flicker than the second sequence. I was quite happy with the way the time-lapse videos came out considering that these are the first that I have ever created.

Sequence 1: ISO 100, 18mm, f/18, 1/50sec.

Sequence 2: ISO 100, 34mm, f/18, 1/60sec.

The Long Road to IPv6

IPV6 launch day is just around the corner. Less than four days to go at the time of this posting. I couldn’t think of a reason why I should be left out of all the fun, so I took the plunge and configured my server (Bamboo) to serve up this site from both IPv4 and IPv6 addresses. My server is now IPv6 ready, but it will probably be several more years until I see such an address from my home ISP [unless I move].

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Text Mining Wikipedia for Misspelled Words

Have you ever read an article on Wikipedia and wondered if the article was more accurate or syntactically correct than if the article had been written by an individual or small team? I have, and this curiosity led me to ask a question [many questions in reality]. This paper is a demonstration of both the scientific method and of applying text mining to answer the question and test the hypothesis. The layout of this paper is rather untraditional in that it will mirror the steps of the scientific method nearly one-for-one. The question asked is how does the percentage of misspellings on Wikipedia, relative to total content, change through time? Continue reading →

Mac OS X Filenames with Apostrophe’s and Ruby 1.9 Unicode Support

Ruby 1.9’s m17n (multilingualization) engine has eased some of the pains of supporting different encodings. Unfortunately, it makes things a little more difficult in the simplest of cases. I was introduced to this problem for the first time with the error message invalid multibyte char (US-ASCII). This is a mini crash course for understanding the new behavior of Ruby strings and different encodings when used on an HFS+ file system. Continue reading →