--- 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. 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. TODO: Include the reference image here ## Transforms and variations import CodeBlock from '@theme/CodeBlock' We previously introduced "transforms" as the "functions" of an "iterated function system." Their general format is: $$ F_i(x,y) = (a_i \cdot x + b_i \cdot y + c_i, \hspace{0.2cm} d_i \cdot x + e_i \cdot y + f_i) $$ import coefsSrc from '!!raw-loader!../src/coefs' {coefsSrc} 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: $$ \begin{align*} F_0(x,y) &= \left({x \over 2}, {y \over 2}\right) \\ &= (a_0 \cdot x + b_0 \cdot y + c_0, d_0 \cdot x + e_0 \cdot y + f_0) \\ & a_0 = 0.5 \hspace{0.2cm} b_0 = 0 \hspace{0.2cm} c_0 = 0 \\ & d_0 = 0 \hspace{0.2cm} e_0 = 0.5 \hspace{0.2cm} f_0 = 0 \end{align*} $$ TODO: Explain the applyCoefs function However, these transforms are pretty boring. We can build more exciting images by using 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). Our reference image will focus on just four variations: ### Linear (variation 0) This variation returns the $x$ and $y$ coordinates as-is: $$ V_0(x,y) = (x,y) $$ import linearSrc from '!!raw-loader!../src/linear' {linearSrc} ### Julia (variation 13) This variation still uses just the $x$ and $y$ coordinates, but does crazy things with them: TODO: Is this related to the Julia set? $$ \begin{align*} r &= \sqrt{x^2 + y^2} \\ \theta &= \text{arctan}(x / y) \\ \Omega &= \left\{ \begin{array}{lr} 0 \hspace{0.4cm} \text{w.p. } 0.5 \\ \pi \hspace{0.4cm} \text{w.p. } 0.5 \\ \end{array} \right\} \\ V_{13}(x, y) &= \sqrt{r} \cdot (\text{cos} ( \theta / 2 + \Omega ), \text{sin} ( \theta / 2 + \Omega )) \end{align*} $$ import juliaSrc from '!!raw-loader!../src/julia' {juliaSrc} ### Popcorn (variation 17) This is known as a "dependent variation" because it depends on knowing the transform coefficients (specifically, $c$ and $f$): $$ V_{17}(x,y) = (x + c \cdot \text{sin}(\text{tan }3y), y + f \cdot \text{sin}(\text{tan }3x)) $$ import popcornSrc from '!!raw-loader!../src/popcorn' {popcornSrc} ### PDJ (variation 24) This is known as a "parametric" variation because it has additional parameters given to it: $$ p_1 = \text{pdj.a} \hspace{0.2cm} p_2 = \text{pdj.b} \hspace{0.2cm} p_3 = \text{pdj.c} \hspace{0.2cm} p_4 = \text{pdj.d} \\ V_{24} = (\text{sin}(p_1 \cdot y) - \text{cos}(p_2 \cdot x), \text{sin}(p_3 \cdot x) - \text{cos}(p_4 \cdot y)) $$ import pdjSrc from '!!raw-loader!../src/pdj' {pdjSrc} ## Blending Now, one variation is fun, but we can also combine variations in a single transform by "blending." Each variation receives the same $x$ and $y$ inputs, and we add together each variation's $x$ and $y$ outputs. We'll also give each variation a weight ($v_{ij}$) to control how much it contributes to the transform: $$ F_i(x,y) = \sum_{j} v_{ij} V_j(a_i \cdot x + b_i \cdot y + c_i, \hspace{0.2cm} d_i \cdot x + e_i \cdot y + f_i) $$ The formula looks intimidating, but it's not hard to implement: import blendSource from "!!raw-loader!./blend"; {blendSource} And with that in place, we have enough to render a first full fractal flame. The sliders below change the variation weights for each transform (the $v_{ij}$ parameters); try changing them around to see which parts of the image are controlled by each transform. import Canvas from "../src/Canvas"; import FlameBlend from "./FlameBlend"; ## Post transforms After variation blending, we apply a second set of transform coordinates. The fractal flame below starts with the same initial transforms/variations as the previous fractal flame, but allows modifying the post-transform coefficients. $$ P_i(x, y) = (\alpha_i x + \beta_i y + \gamma_i, \delta_i x + \epsilon_i y + \zeta_i) $$ import FlamePost from "./FlamePost"; ## Final transform import FlameFinal from "./FlameFinal";