I thought it would look good to blend the images one into the other, so I wrote a quick python script using the Python Image Library. The script blends consecutive images using linear interpolation. An artistic choice to make was how wide the blended regions should be. I tried everything from relatively thin blending regions:

To almost completely blended images:

In the end, however, I decided that what looked the best was actually to have no blending, but rather sharp boundaries between the images. This actually accentuates the effect I was going for, which was to show the changing light over time. Blending the images together actually lessens the effect, rather than enhancing it as had hoped. I plan to get the finished product printed and framed:

Here’s the code for the script I used (apologies for quick-and-dirtiness):

import sys
from PIL import Image

def imageblend(imdir, numimages = 5, blendwidth=0):
    if not blendwidth%2 == 0:
        raise Exception('blendwidth not even')

    im = Image.open(imdir+"im1.jpg")
    (width, height) = im.size

    for i in range(1, numimages):
        imnum = i+1
        centre = i*width/numimages - 1

        im_i = Image.open(imdir+'im%d.jpg'%(imnum))

        for x in range(blendwidth):
            col_ind = centre - (blendwidth/2) + x +1
            col_box = (col_ind, 0, col_ind+1, height-1)
            col_o = im.copy().crop(col_box)
            col_i = im_i.copy().crop(col_box)
            col = Image.blend(col_o, col_i, float(x)/blendwidth)
            im.paste(col, col_box)

        rest_box = (centre+blendwidth/2+1, 0, width-1, height-1)
        rest = im_i.copy().crop(rest_box)
        im.paste(rest, rest_box)

    im.save(imdir+"im_output.jpg")

def main():
    imdir = sys.argv[1]
    imageblend(imdir)

if __name__=='__main__':
    main()
 

I’ll be keeping track at the bottom of my posts.

24

2L bottle provided for size-comparison.

Before counting, I wanted to see if I could reasonably estimate how much money there would be. I considered doing a “random sample” approach, counting the value of a small portion and scaling up to the full weight. However, unfortunately the only means at my disposal to weigh the samples was a bathroom scale inteded for weighing people in 0.1kg increments, so I didn’t think this would be accurate enough. Instead I weighed the entire piggy-bank (which came out to an impressive 4.2kg) and made some simple estimations of what the relative proportions of the coins would be as so:

In considering purchases which would result in me receiving small change, I assume that all values of “cents” are equally likely. I further assume that change is almost always given with the minimum number of coins heuristic. Making these two assumptions, and considering the possible values of change between $0.01 and $0.24 (since all other values will be isomorphic with the addition of some number of dollars and quarters), results in the following relative proportions: 25% dimes, 12.5% nickels and 62.5% pennies.

From this, and from considering the weights of each coin (dime = 1.75g, nickel = 3.95g, penny = 2.35g according to Wikipedia) I can estimate the total number of coins thus:

Using the estimated proportions, I therefore get the following estimations for the number of value of each coin type:

$65.63! That’s a lot of money! Let’s see how my estimations stack up to reality:

Final counts:

So, the final value was quite close (<5% error), but I think we can put that down to fluke. Obviously, my estimated proportions were quite a bit off, although I was correct in predicting that pennies would be significantly more numerous than dimes, which were in turn significantly more numerous than nickels. Most saliently, the proportion of pennies was significantly less than predicted by my simple a priori method. This can likely be attributed to the tendency of pennies to be discarded or ignored which receiving change. I’m not sure why nickels would be tend to be relatively more numerous relative to dimes than I had predicted. Also interesting is that if I use the Wikipedia-listed weights of the coins multiplied by the real counts, the weight should only be around 3.8kg. Perhaps my scale was inaccurate at such a low weight (as it is designed for weighing humans).

Anyways, it was quite interesting to see how much small-change I had accumulated in 5 years. Tomorrow I’ll be off to the bank with 4kg of coins!

tar -cvzf /media/backupdrive/backup.tar.gz /home/myuser

There isn’t anything that simple with all my google stuff.  So, I’ve decided to start working on a python and/or bash tool called gback for automatically making a backup of all your google things:  I’m going to be starting with gmail and docs, and move onto picasa and other services.  I’ve yet to lay down any code, but the project will be hosted on my github account here.  Updates will come as work progresses.

Age at Death for Celebrities

In that distribution, the median age is actually 38, and the most likely age for dying from drugs or alcohol is 34.

But, the myth is about musicians especially, so what about them?  Thankfully, the Wikipedia article also listed the professions of the people cataloged.  The page listed a total of 72 musicians.  What I found when looking at this subset surprised me:  the most likely age for a musician to die from drugs is in fact 27!  The median is also much lower, at 31 years.

Age at Death for Musicians

So perhaps the 27 Club is one of the rare cases of pop culture and urban legends actually stumbling on an accurate statistical occurrence.  Naturally, take what I found with a grain of salt, as it was whipped up in an hour, using a single Wiki article as a source.  Nonetheless, it looks like 27 is indeed a bad year for musicians.

This fallacy is the idea that you’ve already invested enough time/money/whatever into something, so to stop now would be a waste.  It has its root in a number of frailties, from loss aversion to simple pride. It is a fallacy though, because how much you have invested so far in a venture has no bearing on whether that venture is going to be a success or not.  If you wouldn’t have started it, it is wasteful to continue it, rather than the opposite.

Despite knowing full well the fallacious nature of it, whenever I am making a big decision, it always creeps into my head.  How about you, dear readers, what logical fallacy have you had the hardest time keeping out of your mind?