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Start on the transforms post
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export default function randomBiUnit(): number {
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export default function randomBiUnit() {
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return Math.random() * 2 - 1;
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}
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@ -51,6 +51,8 @@ First, $S$. We're generating images, so everything is in two dimensions: $S \in
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all points that are "in the system." To generate our final image, we just plot every point in the system
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like a coordinate chart.
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TODO: What is a stationary point? How does it relate to the chaos game?
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For example, if we say $S = \{(0,0), (1, 1), (2, 2)\}$, there are three points to plot:
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import {VictoryChart, VictoryTheme, VictoryScatter, VictoryLegend} from "victory";
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export default function randomInteger(min: number, max: number): number {
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export default function randomInteger(min: number, max: number) {
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return Math.floor(Math.random() * (max - min)) + min;
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}
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---
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slug: 2024/11/playing-with-fire-transforms
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title: "Playing with fire: Transforms and variations"
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date: 2024-11-15 13:00:00
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authors: [bspeice]
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tags: []
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---
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4
blog/2024-11-15-playing-with-fire/2-transforms/coefs.ts
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blog/2024-11-15-playing-with-fire/2-transforms/coefs.ts
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export interface Coefs {
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a: number, b: number, c: number,
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d: number, e: number, f: number
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}
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blog/2024-11-15-playing-with-fire/2-transforms/index.mdx
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blog/2024-11-15-playing-with-fire/2-transforms/index.mdx
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---
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slug: 2024/11/playing-with-fire-transforms
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title: "Playing with fire: Transforms and variations"
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date: 2024-11-15 13:00:00
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authors: [bspeice]
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tags: []
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---
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Now that we have a basic chaos game in place, it's time to spice things up. Transforms and variations create the
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interesting patterns that fractal flames are known for.
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<!-- truncate -->
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This blog post uses a set of reference parameters ([available here](../params.flame)) to demonstrate a practical
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implementation of the fractal flame algorithm. If you're interested in tweaking the parameters, or generating
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your own art, [Apophysis](https://sourceforge.net/projects/apophysis/) is a good introductory tool.
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## Transforms and variations
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import CodeBlock from '@theme/CodeBlock'
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We previously introduced "transforms" as the "functions" of an "iterated function system." Their general format is:
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$$
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F_i(x,y) = (a_i \cdot x + b_i \cdot y + c_i, \hspace{0.2cm} d_i \cdot x + e_i \cdot y + f_i)
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$$
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Let's also start defining some types we can use in our code. The transform coefficients are a good place to start:
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```typescript
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interface Coefs {
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a: number, b: number, c: number,
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d: number, e: number, f: number
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}
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```
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We also introduced the Sierpinski Gasket functions ($F_0$, $F_1$, and $F_2$), demonstrating how they are related to
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the general format. For example:
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$$
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\begin{align*}
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F_0(x,y) &= \left({x \over 2}, {y \over 2}\right) \\
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&= (a_0 \cdot x + b_0 \cdot y + c_o, d_0 \cdot x + e_0 \cdot y + f_0) \\
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& a_0 = 0.5 \hspace{0.2cm} b_0 = 0 \hspace{0.2cm} c_0 = 0 \\
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& d_0 = 0 \hspace{0.2cm} e_0 = 0.5 \hspace{0.2cm} f_0 = 0
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\end{align*}
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$$
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However, these transforms are pretty boring. We can build more exciting images by using some additional functions
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within the transform. These "sub-functions" are called "variations":
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$$
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F_i(x, y) = V_j(a_i \cdot x + b_i \cdot y + c_i, \hspace{0.2cm} d_i \cdot x + e_i \cdot y + f_i)
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$$
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The fractal flame paper lists 49 variation functions ($V_j$ above), but the sky's the limit here.
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For example, the official `flam3` implementation supports
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[98 variations](https://github.com/scottdraves/flam3/blob/7fb50c82e90e051f00efcc3123d0e06de26594b2/variations.c).
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In code, variations are pretty easy:
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```typescript
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type Variation = (x: number, y: number) => [number, number];
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````
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Our reference image will focus on just four variations:
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### Linear (variation 0)
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This variation returns the $x$ and $y$ coordinates as-is. In a way, the Sierpinski Gasket is
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a fractal flame using only the linear variation.
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$$
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V_0(x,y) = (x,y)
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$$
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```typescript
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function linear(x: number, y: number) {
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return [x, y];
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}
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```
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### Julia (variation 13)
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This variation still uses just the $x$ and $y$ coordinates, but does some crazy things with them:
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<small>TODO: Is this related to the Julia set?</small>
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$$
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\begin{align*}
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r &= \sqrt{x^2 + y^2} \\
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\theta &= \text{arctan}(x / y) \\
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\Omega &= \left\{
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\begin{array}{lr}
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0 \hspace{0.4cm} \text{w.p. } 0.5 \\
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\pi \hspace{0.4cm} \text{w.p. } 0.5 \\
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\end{array}
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\right\} \\
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V_{13}(x, y) &= \sqrt{r} \cdot (\text{cos} ( \theta / 2 + \Omega ), \text{sin} ( \theta / 2 + \Omega ))
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\end{align*}
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$$
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```typescript
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function julia(x: number, y: number) {
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const r = Math.sqrt(Math.pow(x, 2) + Math.pow(y, 2));
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const theta = Math.atan2(x, y);
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const omega = Math.random() > 0.5 ? 0 : Math.PI;
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return [
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r * Math.cos(theta / 2 + omega),
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r * Math.sin(theta / 2 + omega)
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]
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}
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```
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### Popcorn (variation 17)
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This is known as a "dependent variation" because it depends on knowing the transform coefficients:
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$$
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V_{17}(x,y) = (x + c \cdot \text{sin}(\text{tan }3y), y + f \cdot \text{sin}(\text{tan }3x))
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$$
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```typescript
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function popcorn({c, f}: Coefs) {
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return (x: number, y: number) => [
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x + c * Math.sin(Math.tan(3 * y)),
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y + f * Math.sin(Math.tan(3 * x))
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]
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}
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```
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### PDJ (variation 24)
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This is known as a "parametric" variation because it has additional parameters given to it:
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$$
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p_1 = \text{pdj.a} \hspace{0.2cm} p_2 = \text{pdj.b} \hspace{0.2cm} p_3 = \text{pdj.c} \hspace{0.2cm} p_4 = \text{pdj.d} \\
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V_{24} = (\text{sin}(p_1 \cdot y) - \text{cos}(p_2 \cdot x), \text{sin}(p_3 \cdot x) - \text{cos}(p_4 \cdot y))
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$$
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```typescript
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function pdj(a: number, b: number, c: number, d: number) {
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return (x: number, y: number) => [
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Math.sin(a * y) - Math.cos(b * x),
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Math.sin(c * x) - Math.cos(d * y)
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]
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}
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```
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