# lass-Interruptions

Observations:

1. The artwork is square
2. The artwork is made of many short black lines of the same length
3. There are areas of the artwork where the lines are missing
4. The lines are angled randomly
5. The lines are distributed evenly
6. There are clusters of missing lines
7. Although the lines are angled randomly, they seem to tend towards vertical(at least in the first 3 images)
8. The lines can be sorted into a square grid, where the height of each row is shorter than the length of a black line
9. The lines overlap in places
10. Some columns seem to have more vertical-tending lines than others

One of the things I was stumped on with this project was how to create the clusters of missing lines (#6). Golan and Char helped me by telling me to look into perlin noise. At first, using perlin noise seemed no different from using randomness to determine if a line should be missing or not, but by looking at pictures of perlin noise I realized what the issue was. In order to get clusters that are large enough to be noticeable, the perlin noise needs to be scaled up. I achieved this effect by calling dividing my i and j by 80 in the call to noise(). This is also noted in the p5 documentation for noise(), where they state that "As a general rule the smaller the difference between coordinates, the smoother the resulting noise sequence will be."

Something interesting that I saw in this project was that using noise() instead of Math.random() to determine the angle of the lines resulted in the "tending-towards-vertical" property (#7). My guess is that that this is because Math.random() has a completely even distribution from 0 to 1, while noise() has less deviation from 0.5.

Also, I think that my observation #10 that "some columns have more vertical-tending lines than others" was completely wrong. Looking closer, it does not seem like column number has any effect on the number of vertical-tending lines. I think that maybe I tricked myself into believing this was true by seeking out some sort of pattern, but in the end it was replicated much better using just noise.

I think that my re-code was pretty successful, and I am glad that the result was simple to achieve. I toggled with values such as line count and length to try and match the original as best as I could.