Cosplaying Luffy and Doc Ido

Aug 13-14, 2016

Today I spent a few hours sewing a piece in my Luffy costume. It isn't completely finished but you can see the results below. What needed to be sewn? The shirt was originally a tank top from American Apparel. While Luffy wearing a red tank top is absolutely acceptable in a pinch, it needs to be a 3 button vest to suit say Season 3 Episode 78 where they leave the island of the giants and Nami is sick, so they go to the winter island.

Javantea in Luffy cosplay
Javantea in Luffy cosplay

If you're incredibly lazy, sewing isn't necessary for this cosplay. If you don't intend to wash the shirt or wear it more than a handful of times (shame, shame!), then a cut shirt will actually look reasonably authentic. A really good reason to sew a cosplay is to reduce the likelihood of fraying. Many fabrics when cut will fray. The cotton from this shirt almost certainly will fray in the washer. If you've never sewn a garment, you probably didn't know that because a part of the professional sewing business is ensuring that doesn't happen. Exceptions exist, but Luffy's shirt is most certainly not frayed and you won't want yours to be either.

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Repair, Don't Replace

by Javantea
Dec 27, 2015

Today I sewed two holes in two shirts. Both shirts have survived a long time but both had become unwearable. By coincidence both shirts were a few sizes too small. Both shirts were worn hundreds of times despite not being the perfect shirt for the task and that is certainly the reason why they came apart after so many years of service. The blue work shirt was made in India in the previous decade and sold by Gap with their brand on it. My brother bought me it so that I would have one dress shirt that I could wear it to interviews. The white ringer was made in Los Angeles by good ol' American Apparel in the previous decade. I bought it from Scarecrow Video in Seattle in the early 2000's. Both are probably a decade old at least. Vintage surely.

Neck of stiched ringer

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Small Wide World Video Update

This blog post is a quick video with some backup data for my Small Wide World project.

Welcome to a quick update for Small Wide World. Progress on this project is moving rapidly so I just like to let you know what's going on and how awesome it is. The first improvement you've probably noticed on the website is better looking graphs all over it. That's a good sign that things are improving but that's just showing you the interesting improvements that can be made with a little bit of code. Today I wrote this code I'm going to show you. Here you see a two-node graph with a and b. It's the simplest you can get. You can see that we've added a new node onto a here. So now we have a three node network. And then you see it's the same network c-a-b and we've connected that via a to d. You can see that even though it looks different, we still have c-a-b and d and we've just connected e to a. This is a self-organizing network. It's actually very fast, it uses very few operations so it's going to become the default algorithm for the web, Python, C, and all versions. They will use this algorithm to make the initial pattern. It doesn't work perfectly for all types of graphs, but it works especially well for these types of graphs: graphs which aren't cyclic and are using lots of branches. See that we add g to b. See the angle between g-b-a, g-b-f, and a-b-f are all the same and the length of all the bonds are the same. This is a very easy technique that shows a graph effectively. This makes it so that you can understand the graph more quickly — or at least that is the idea. We've added h, we've added i, we've added j, and k. Then you see we've added l to d, so in order to make a-d-i, i-d-l, and l-d-a equal angles we turned d-i-k sideways. This is just another one rotated because we've added m to a here. You can see n added, we're getting close to where nodes start to overlap with each other. That's one limitation of this algorithm, but I can pretty confidently say that solving that is a job for a more expensive algorithm, for example global optimization. Once this algorithm has finished you can run a global optimization algorithm on it and it will have better success than just a random graph. You can see that k, h, and c are a little crowded, so an optimization algorithm would push these away from each other because k and h are too close to one another. We're almost to the point where it's going to fail. And here we see that it fails with c-h overlapping p-q and c-s overlapping i-k. In order to fix that, an optimization algorithm would only have to move p and q down and i and k up. How much computation does that require? Local minimization algorithms would probably be able to do this in a few million operations (less than a second but far more than this algorithm takes), but if they couldn't properly solve the collision, you'd have to use a global optimization algorithm like basin-hopping or Monte-Carlo Metropolis to finish the job. This could take seconds or even minutes. Small Wide World currently implements basin-hopping using SciPy which I will be testing thoroughly against graphs like these.

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