Start on the transforms post

This commit is contained in:
Bradlee Speice 2024-11-18 22:01:31 -05:00
parent b46993bd51
commit e1735404ae
6 changed files with 158 additions and 9 deletions

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export default function randomBiUnit(): number {
export default function randomBiUnit() {
return Math.random() * 2 - 1;
}

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@ -51,6 +51,8 @@ First, $S$. We're generating images, so everything is in two dimensions: $S \in
all points that are "in the system." To generate our final image, we just plot every point in the system
like a coordinate chart.
TODO: What is a stationary point? How does it relate to the chaos game?
For example, if we say $S = \{(0,0), (1, 1), (2, 2)\}$, there are three points to plot:
import {VictoryChart, VictoryTheme, VictoryScatter, VictoryLegend} from "victory";

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export default function randomInteger(min: number, max: number): number {
export default function randomInteger(min: number, max: number) {
return Math.floor(Math.random() * (max - min)) + min;
}

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---
slug: 2024/11/playing-with-fire-transforms
title: "Playing with fire: Transforms and variations"
date: 2024-11-15 13:00:00
authors: [bspeice]
tags: []
---

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export interface Coefs {
a: number, b: number, c: number,
d: number, e: number, f: number
}

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---
slug: 2024/11/playing-with-fire-transforms
title: "Playing with fire: Transforms and variations"
date: 2024-11-15 13:00:00
authors: [bspeice]
tags: []
---
Now that we have a basic chaos game in place, it's time to spice things up. Transforms and variations create the
interesting patterns that fractal flames are known for.
<!-- truncate -->
This blog post uses a set of reference parameters ([available here](../params.flame)) to demonstrate a practical
implementation of the fractal flame algorithm. If you're interested in tweaking the parameters, or generating
your own art, [Apophysis](https://sourceforge.net/projects/apophysis/) is a good introductory tool.
## Transforms and variations
import CodeBlock from '@theme/CodeBlock'
We previously introduced "transforms" as the "functions" of an "iterated function system." Their general format is:
$$
F_i(x,y) = (a_i \cdot x + b_i \cdot y + c_i, \hspace{0.2cm} d_i \cdot x + e_i \cdot y + f_i)
$$
Let's also start defining some types we can use in our code. The transform coefficients are a good place to start:
```typescript
interface Coefs {
a: number, b: number, c: number,
d: number, e: number, f: number
}
```
We also introduced the Sierpinski Gasket functions ($F_0$, $F_1$, and $F_2$), demonstrating how they are related to
the general format. For example:
$$
\begin{align*}
F_0(x,y) &= \left({x \over 2}, {y \over 2}\right) \\
&= (a_0 \cdot x + b_0 \cdot y + c_o, d_0 \cdot x + e_0 \cdot y + f_0) \\
& a_0 = 0.5 \hspace{0.2cm} b_0 = 0 \hspace{0.2cm} c_0 = 0 \\
& d_0 = 0 \hspace{0.2cm} e_0 = 0.5 \hspace{0.2cm} f_0 = 0
\end{align*}
$$
However, these transforms are pretty boring. We can build more exciting images by using some additional functions
within the transform. These "sub-functions" are called "variations":
$$
F_i(x, y) = V_j(a_i \cdot x + b_i \cdot y + c_i, \hspace{0.2cm} d_i \cdot x + e_i \cdot y + f_i)
$$
The fractal flame paper lists 49 variation functions ($V_j$ above), but the sky's the limit here.
For example, the official `flam3` implementation supports
[98 variations](https://github.com/scottdraves/flam3/blob/7fb50c82e90e051f00efcc3123d0e06de26594b2/variations.c).
In code, variations are pretty easy:
```typescript
type Variation = (x: number, y: number) => [number, number];
````
Our reference image will focus on just four variations:
### Linear (variation 0)
This variation returns the $x$ and $y$ coordinates as-is. In a way, the Sierpinski Gasket is
a fractal flame using only the linear variation.
$$
V_0(x,y) = (x,y)
$$
```typescript
function linear(x: number, y: number) {
return [x, y];
}
```
### Julia (variation 13)
This variation still uses just the $x$ and $y$ coordinates, but does some crazy things with them:
<small>TODO: Is this related to the Julia set?</small>
$$
\begin{align*}
r &= \sqrt{x^2 + y^2} \\
\theta &= \text{arctan}(x / y) \\
\Omega &= \left\{
\begin{array}{lr}
0 \hspace{0.4cm} \text{w.p. } 0.5 \\
\pi \hspace{0.4cm} \text{w.p. } 0.5 \\
\end{array}
\right\} \\
V_{13}(x, y) &= \sqrt{r} \cdot (\text{cos} ( \theta / 2 + \Omega ), \text{sin} ( \theta / 2 + \Omega ))
\end{align*}
$$
```typescript
function julia(x: number, y: number) {
const r = Math.sqrt(Math.pow(x, 2) + Math.pow(y, 2));
const theta = Math.atan2(x, y);
const omega = Math.random() > 0.5 ? 0 : Math.PI;
return [
r * Math.cos(theta / 2 + omega),
r * Math.sin(theta / 2 + omega)
]
}
```
### Popcorn (variation 17)
This is known as a "dependent variation" because it depends on knowing the transform coefficients:
$$
V_{17}(x,y) = (x + c \cdot \text{sin}(\text{tan }3y), y + f \cdot \text{sin}(\text{tan }3x))
$$
```typescript
function popcorn({c, f}: Coefs) {
return (x: number, y: number) => [
x + c * Math.sin(Math.tan(3 * y)),
y + f * Math.sin(Math.tan(3 * x))
]
}
```
### PDJ (variation 24)
This is known as a "parametric" variation because it has additional parameters given to it:
$$
p_1 = \text{pdj.a} \hspace{0.2cm} p_2 = \text{pdj.b} \hspace{0.2cm} p_3 = \text{pdj.c} \hspace{0.2cm} p_4 = \text{pdj.d} \\
V_{24} = (\text{sin}(p_1 \cdot y) - \text{cos}(p_2 \cdot x), \text{sin}(p_3 \cdot x) - \text{cos}(p_4 \cdot y))
$$
```typescript
function pdj(a: number, b: number, c: number, d: number) {
return (x: number, y: number) => [
Math.sin(a * y) - Math.cos(b * x),
Math.sin(c * x) - Math.cos(d * y)
]
}
```