Modulations, Modulations, Modulations

Here is yet another experiment in generating noisy images. This time the pattern is obvious, regular. The point of all this is to start with noise and modulate the noise until I get something "visually interesting." Below the code I wrote to generate the image is the resultant image itself.

Noise field by A.G. (c) 2017. All Rights Reserved.

Code Reading

I just wanted to say that when I'm in the coding mood/mode, I like to do an insanely large amount of code reading. I go on Github or look at Github Gists, or look at code on-line in tutorials and quickstarts. It's really what helps me the most in learning to code in Python, which I've been doing for a good 6 years now.

There's a whole other side to what I just said that I won't go into too many details about. Suffice to say that I believe in a kind of New Documentation where the purpose of documenting code is precisely for the ambient code-reader who happens to stumble across the code doing searches on the web or on Github.

Think of it this way. If you're writing code that's going to be read by yourself or your team members, you can get away with documenting less or maybe not even at all. But in the open source universe, you have to think of an indeterminate number of people who might read your code 5 or 10 or 50 or 100 years later. So it's important I think to be very "literate" in one's documentation.

You can never take for granted that future code-readers are going to know what you were trying to do, even if they can get your code to work. One thing I learned is that you have to UNDERSTAND the code, how and why it works, along with what it's purpose is. I can write a Python script that does exactly what I needed it to do and yet not fully understand it because I'm just basing my coding on idioms I learned and stuff I saw other coders write in my code-reading experience.

It's tantamount to saying something and not truly understanding it. You can still get the point across to an audience, but what's the point if you don't even fully understand what you are saying? A wise man once told me to only ever speak about what I have knowledge of. I think it should be the same thing with code. Use restraint. Only write code about what you understand and have knowledge of.

I find code-reading a great passtime. I can almost never get the code to run on my machine, but I see how the problem was solved, or at least one possible avenue. Often I find the code very ugly and hard to read, and that's when I go back to the basics and make sure I know what I'm doing when I write code.

I think that a certain responsibility comes from being able to code, just as one has the responsibility to watch what one says when one is speaking. Think before you open your mouth, or before you code something.

Procedural texture synthesis it is called

Procedural texture synthesis by A.G. (c) 2017. All Rights Reserved.

...

Noise Field Breakthrough

Procedural texture synthesis. A.G. (c) 2017. All Rights Reserved.

I had a mild breakthrough. The above image was generated entirely through numerical computation. I essentially created a vector with 65536 values, all either a 0 or 255. I generated various segments of all black or all white, of different lengths, and I created the 65536 vector by randomly choosing between various length "LINES" or line segments.

Then I generated a Gaussian distribution matrix and did a matrix operation equivalent to a difference blend mode. That is, I took my two separate 65536 vectors and turned them into np.array types, did the arithmetic, subtracting one from the other, and then I ended up with the above image.

There must be easier ways to do this, but I chose to work with a vector of 65536 values. I just am learning to construct it in different ways. I will try to do it using list comprehensions and functional programming, to make the job more elegant.

I repeat: The above image was generated entirely through numerical computation in a step by step, therefore algorithmic, method. I just did some of it manually, creating the 65536 vectors.

Noise Field Redux

This time I am "seeding" an image with random black and white pixels.


I used the "L" mode, which is a standard mode for an image that has 8-bit pixels, black and white. I am choosing a black or white pixel at random with the random module:

im.putpixel((x, y), random.choice([0,255]))

The next step is to write modulation functions to modulate the noise field. I am working on a threshold function for when I make noise that is in grayscale. I am also trying to work out a pixelate function, which is proving more difficult than I thought.

The bit at the end with the saving of the image may look weird, but it allows me to save the image with its date and time as filename. For now, this makes it easier for me to work with my images. Here is an image made with the code above.

Noise field generated with Python code. A.G. (c) 2017. All Rights Reserved.