add demos to README.md
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Abe Pazos
@@ -189,7 +189,9 @@ val v8 = billow(seed, x, y, z, ::perlinLinear, octaves, lacunarity, gain)
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## Demos
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### DemoCubicNoise2D01
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Demonstrates how to render dynamic grayscale patterns using 3D cubic Hermite interpolation.
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The program draws one point per pixel on the screen, calculating the color intensity of each point
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based on a 3D cubic Hermite noise function.
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@@ -197,7 +199,12 @@ val v8 = billow(seed, x, y, z, ::perlinLinear, octaves, lacunarity, gain)
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### DemoFunctionalComposition01
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Demonstrates how to chain methods behind noise functions like `simplex3D` to
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alter its output. By default `simplex3D` produces one double value, but
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by calling `.withVector2Output()` it produces `Vector2` instances instead.
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The `.gradient()` method alters the output to return the direction of fastest
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increase. Read more in [WikiPedia](https://en.wikipedia.org/wiki/Gradient).
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@@ -205,7 +212,15 @@ val v8 = billow(seed, x, y, z, ::perlinLinear, octaves, lacunarity, gain)
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### DemoGradientPerturb2D
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Demonstrates how to generate a dynamic fractal-based visual effect
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using 2D gradient perturbation and simplex noise.
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This method initializes a color buffer to create an image and applies fractal gradient noise to set
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each pixel's brightness, producing a dynamic visual texture. The fractal effect is achieved by layering multiple
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levels of noise, and each pixel's color intensity is based on the noise function results.
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The output is continuously updated to produce animated patterns.
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CPU-based.
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@@ -213,7 +228,15 @@ val v8 = billow(seed, x, y, z, ::perlinLinear, octaves, lacunarity, gain)
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### DemoGradientPerturb3D
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Demonstrates how to generate a dynamically evolving visual
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representation of fractal noise. The program uses 3D gradient perturbation and simplex noise
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to produce a grayscale gradient on a color buffer.
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The visual output is created by iteratively computing the fractal gradient perturbation and simplex
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noise for each pixel in the color buffer, applying a perturbation based on time, and rendering the
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result as an image.
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CPU-based.
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@@ -221,7 +244,15 @@ val v8 = billow(seed, x, y, z, ::perlinLinear, octaves, lacunarity, gain)
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### DemoScatter01
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Demonstrates how to create an animated visualization of scattered points.
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The program creates an animated ellipse with increasing and decreasing height.
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Then, scatters points inside it with a placementRadius of 20.0.
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The animation reveals that the scattering positions are somewhat stable between
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animation frames.
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The ellipse's contour is revealed and hidden every other second.
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@@ -229,7 +260,12 @@ val v8 = billow(seed, x, y, z, ::perlinLinear, octaves, lacunarity, gain)
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### DemoSimplex01
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Demonstrates how to use the `simplex` method to obtain noise values based on a seed and an x value.
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The program creates 20 horizontal contours with 40 steps each in which each 2D step and each 2D control point
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is affected by noise.
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Time is used as a noise argument to produce an animated effect.
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@@ -246,7 +282,11 @@ Demonstrate the generation of uniformly distributed points inside a list of tria
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### DemoValueNoise2D01
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Demonstrates how to render grayscale noise patterns dynamically using 3D quintic noise.
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The program draws one point per pixel on the screen, calculating the color intensity of
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each point based on a 3D quintic noise function. The noise value is influenced by the
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pixel's 2D coordinates and animated over time.
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@@ -275,7 +315,10 @@ The noise color can be set using a `color` or a `gain` property.
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### glsl/DemoSimplexGLSL
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A sine oscillator with randomized parameters
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Render an animated Simplex3D texture using shaders.
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The uniforms in the shader are controlled by
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randomized sine oscillators.
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@@ -285,8 +328,7 @@ A sine oscillator with randomized parameters
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Demo that visualizes a 2D Hammersley point set.
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The application is configured to run at 720x720 resolution. The program computes
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400 2D Hammersley points mapped within the bounds of the application's resolution.
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The program computes 400 2D Hammersley points mapped within the window bounds.
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These points are visualized by rendering circles at their respective positions.
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@@ -297,14 +339,12 @@ These points are visualized by rendering circles at their respective positions.
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Demo program rendering a 3D visualization of points distributed using the Hammersley sequence in 3D space.
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The application is set up at a resolution of 720x720 pixels. Within the visual
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program, a sphere mesh is created and a set of 1400 points is generated using
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the Hammersley sequence. Each point is translated and rendered as a small sphere
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in 3D space. This is achieved by mapping the generated points into a scaled domain.
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A set of 1400 points is generated using the Hammersley sequence.
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Each point is translated and rendered as a small sphere
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in 3D space.
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The rendering utilizes the Orbital extension, enabling an interactive 3D camera
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to navigate the scene. The visualization relies on the draw loop for continuous
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rendering of the points.
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The rendering uses the Orbital extension, enabling an interactive 3D camera
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to navigate the scene.
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@@ -312,17 +352,15 @@ rendering of the points.
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### hammersley/DemoHammersley4D01
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Demo that visualizes a 4D Hammersley point set in a 3D space, with colors determined by the 4th dimension.
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Demo visualizing a 4D Hammersley point set in a 3D space, with colors determined by the 4th dimension.
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The application is configured at a resolution of 720x720 pixels. A sphere mesh is created
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using the `sphereMesh` utility, and a total of 10,000 4D points are generated with the
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`hammersley4D` sequence. These points are scaled, translated, and rendered as small spheres.
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A total of 10,000 4D points are generated with the `hammersley4D` sequence.
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These points are mapped to a cubical volume and rendered as small spheres.
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The color of each sphere is modified based on the 4th dimension of its corresponding point by
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shifting the hue in HSV color space.
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This program employs the `Orbital` extension, enabling camera interaction for 3D navigation
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of the scene. Rendering occurs within the draw loop, providing continuous visualization
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of the point distribution.
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of the scene.
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@@ -330,7 +368,15 @@ of the point distribution.
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### hash/DemoCircleHash01
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Demonstrates how to draw circles distributed within two subregions of a rectangular area
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using uniform random distribution and a hash-based method for randomness.
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The application divides the window area into two subregions, offsets the edges inwards,
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and then calculates two circles representing these subregions. Points are then generated and drawn
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within these circles using two different methods:
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- A uniform random distribution within the first circle.
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- A hash-based deterministic random point generation within the second circle.
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@@ -338,7 +384,12 @@ of the point distribution.
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### hash/DemoRectangleHash01
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Demonstrates how to generate and draw random points within two subregions of a rectangular area
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using two different randomization methods.
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The first subregion generates points using a _uniform_ random distribution, while the second subregion
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generates points deterministically with a _hash-based_ randomization approach. The points are visualized
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as small circles.
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@@ -346,7 +397,12 @@ of the point distribution.
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### hash/DemoUHash01
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Demonstrates how to render a dynamic grid of points where the color of each point
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is determined using a hash-based noise generation method.
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The application dynamically updates the visual output by calculating a 3D hash
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value for each point in the grid, based on the current time and the point's coordinates.
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The hash value is then used to determine the grayscale color intensity of each point.
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@@ -354,15 +410,36 @@ of the point distribution.
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### linearrange/DemoLinearRange01
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Demonstrates how to create a linear range with two [org.openrndr.shape.Rectangle]s.
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This range is then sampled at 100 random locations using the `uniform` method to get and render interpolated
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rectangles. The random seed changes once per second.
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[source code](src/jvmDemo/kotlin/linearrange/DemoLinearRange01.kt)
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### linearrange/DemoLinearRange02
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Demonstrates how to create a linear range with two [org.openrndr.shape.Circle]s.
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This range is then sampled at 100 random locations using the `hash` method to get and render interpolated
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circles. The random seed changes once per second.
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Colors are calculated based on the index of each circle.
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[source code](src/jvmDemo/kotlin/linearrange/DemoLinearRange02.kt)
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### phrases/DemoUHashPhrase01
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Demonstrate uniform hashing function phrase in a shadestyle
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Demonstrate the use of a uniform hashing function phrase in a ShadeStyle.
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The hashing function uses the screen coordinates and the current time to
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calculate the brightness of each pixel.
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Multiple GLSL hashing functions are defined in orx-shader-phrases.
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@@ -370,8 +447,7 @@ Demonstrate uniform hashing function phrase in a shadestyle
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### rseq/DemoRseq2D01
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This demo sets up a window with dimensions 720x720 and renders frames
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demonstrating 2D quasirandomly distributed points. The points are generated
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Demonstrates quasirandomly distributed 2D points. The points are generated
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using the R2 sequence and drawn as circles with a radius of 5.0.
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@@ -380,7 +456,7 @@ using the R2 sequence and drawn as circles with a radius of 5.0.
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### rseq/DemoRseq3D01
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This demo renders a 3D visualizationof points distributed using the R3 quasirandom sequence. Each point is
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This demo renders a 3D visualization of points distributed using the R3 quasirandom sequence. Each point is
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represented as a sphere and positioned in 3D space based on the quasirandom sequence values.
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The visualization setup includes:
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