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Functions81 in github.com/BirdbrainEngineer/lenia_ca

⨍ Functions 81 ◇ Types & classes 12

↓ 24 callersMethodshape

Get the shape of the channels and convolution channels of the `Lenia` instance.

↓ 10 callersMethodchannels

Get the number of channels initialized in the `Lenia` instance.

↓ 6 callersFunctionsample_normal

Samples the normal distribution where the peak (at `x = mu`) is 1. This is not suitable for use as a gaussian probability density function! ### Param

↓ 5 callersFunctioneuclidean_dist

Euclidean distance between points `a` and `b`.

↓ 5 callersMethodtransform

(&mut self, data: &mut [Complex<f64>])

↓ 4 callersMethodconv_channels

src/lenias.rs:204

↓ 4 callersFunctionsigmoid

Sigmoid function.

src/growth_functions.rs:135

↓ 3 callersMethodget_convoluted_as_ref

(&self, conv_channel: usize)

src/lenias.rs:184

↓ 3 callersMethodinverse

↓ 2 callersFunctionpass

Generates a kernel base of a single pixel with n-dimensions.

src/kernels.rs:310

↓ 2 callersMethodshape

↓ 1 callersFunctionc

Refer to Lenia paper or someone more versed in mathematics, I have no clue... I just translated the math into code...

src/kernels.rs:261

↓ 1 callersMethodconvolution_channels

Get the number of convolution channels initialized in the `Lenia` instance.

↓ 1 callersFunctiongaussian_donut_2d

Generates a kernel base of a gaussian donut in 2d. The mean (position of the highest value) is placed at `0.5` in the range `[0.0..1.0]`, where `0.0`

src/kernels.rs:20

↓ 1 callersMethodget_channel_as_mut_ref

Mutable version of `get_channel_as_ref()`.

↓ 1 callersMethodget_grown_as_ref

(&self, conv_channel: usize)

src/lenias.rs:188

↓ 1 callersMethodget_high

↓ 1 callersMethodget_low

↓ 1 callersFunctionnested_png_export

(bit_depth: png::BitDepth, path: String, data: &ndarray::ArrayD<f64>, indexes: &mut Vec<usize>, current_axis:

↓ 1 callersMethodset_conv_channels

(&mut self, num_conv_channels: usize)

src/lenias.rs:143

↓ 1 callersMethodset_growth

(&mut self, f: fn(f64, &[f64]) -> f64, growth_params: Vec<f64>, conv_channel: usize)

src/lenias.rs:159

↓ 1 callersMethodset_high

(&mut self, value: u8)

↓ 1 callersMethodset_low

(&mut self, value: u8)

↓ 1 callersMethodset_source_channel

(&mut self, conv_channel: usize, src_channel: usize)

src/lenias.rs:147

↓ 1 callersMethodshape

src/lenias.rs:168

src/lenias.rs:200

Functionconway_game_of_life

Conway's "Game of life" growth function. `Rulestring: B3/S23`

src/growth_functions.rs:83

Functionconway_game_of_life

Moore neighborhood with radius of 1 in 2D. This is the kernel to use for Conway's game of life.

src/kernels.rs:273

src/lenias.rs:196

Get the current integration step (a.k.a. timestep) parameter `dt` of the `Lenia` instance.

Functionexport_frame_as_png

Export a frame as a png or a bunch of png-s if multidimensional. The function returns a `JoinHandle` because the exporting takes place on a separate

Methodfill_channel

Fills a channel with user data. The shapes of the `data` and the channel(s) in the `Lenia` instance must be the same. ### Parameters `data` - Refer

(&self, f: &mut fmt::Formatter<'_>)

(&self, f: &mut fmt::Formatter<'_>)

Creates a new Kernel struct from an n-dimensional array (`ndarray::ArrayD<f64>`). Creates the normalized version of the kernel. Creates a version of

Functiongaussian_donut_nd

Generates a kernel base of a gaussian donut in n-dimensions. The mean (position of the highest value) is placed at `0.5` in the range `[0.0..1.0]`, w

src/kernels.rs:101

Methodget_activated_as_ref

Retrieve a reference to the specified convolution channel's "activations". Activations are the results from passing the convolution results through t

Methodget_channel_as_mut_ref

(&mut self, channel: usize)

src/lenias.rs:180

Methodget_channel_as_ref

(&self, channel: usize)

src/lenias.rs:172

Methodget_channel_as_ref

Retrieve a referenced to the specified channel's data. ### Parameters `channel` - Index of the channel to get a reference from. ### Panics If the

Methodget_convoluted

Exists for backwards-compatibility with `lenia_ca 0.1.0`; use `get_convolved()` instead. Retrieves a `convolution_channel`'s convoution result and ex

Methodget_convolved

Retrieves a `convolution_channel`'s convoution result and extracts only the real component. Convolution result is also called the "potential distribu

Methodget_convolved_as_ref

Retrieve a reference to the specified convolution channel's convolution result. Convolution result is also called the "potential distribution". Note

Methodget_deltas_as_ref

(&self, channel: usize)

src/lenias.rs:192

Methodget_deltas_as_ref

Retrieve a reference to the specified channel's "deltas". Deltas are the amounts added onto the previous iteration's result to get the current iterat

Functionget_frame

Extract data from n-dimensional array into a 2-dimensional array. Extract a 2d array (`ndarray::Array2`) of `f64` values of a 2d slice of a channel's

Methodget_kernel_as_ref

(&self, conv_channel: usize)

src/lenias.rs:176

Methodget_kernel_as_ref

Retrieve the kernel being used for the specified convolution channels' convolution. ### Parameters `convolution_channel` - Index of the convolution

src/lenias.rs:107

Performs a single iteration of the `Lenia` instance. Channels are updated with the resulting new state of the simulation.

Functionload_from_png

Loads a png into an `ndarray`. ### Parameters `file_path` - Path to the 2d slice of a frame to load. ### Panics If the bit-depth of the png is les

Functionmulti_gaussian_donut_2d

Generates a kernel base of multiple concentric gaussian "donuts" in 2d. Each donut/ring is a single index in the list of parameters. ### Parameters

src/kernels.rs:60

Functionmulti_gaussian_donut_nd

Generates a kernel base of multiple radial gaussian "hyper-donuts" in n-dimensions. Each donut/ring is a single index in the list of parameters. ###

src/kernels.rs:146

Functionmultimodal_normal

Multimodal "gaussian bumps" growth function. While the Lenia paper calls for a unimodal growth function, then strictly speaking, there are no rules!

src/growth_functions.rs:31

Create and initialize a new instance of "Standard Lenia". This version of Lenia can have only a single channel and a single convolution channel and w

src/lenias.rs:65

Initialize a Lenia simulator. Barring wanting to change the type of the `Lenia` instance used by the `Simulator`, this should ever need to be called

(length: usize, inverse: bool)

Pass number on virtually unchanged. Returns `num` multiplied by `params[0]`. Use this growth function if you would like to not use a growth function,

src/growth_functions.rs:143

Functionpolynomial

Standard unimodal "polynomial bump". ### Parameters `params[0]` - mu `params[1]` - sigma `params[2]` - alpha

src/growth_functions.rs:48

Functionpolynomial_nd

Generates a kernel base of "polynomial donuts". The peaks of the individual rings are equally spaced around the center of the kernel. Refer to Lenia

src/kernels.rs:227

Functionprecalculated_linear

Samples from a precalculated distribution. The distribution is made of evenly spaced points from `0.0` to `1.0`. In the likely event of the sample fa

src/growth_functions.rs:71

Functionprecalculated_linear

Generates a kernel base of a radially symmetric sampling of precalculated values. ### Parameters `radius` - Radius of the kernel field to generate.

src/kernels.rs:189

Re-initialize a Lenia simulator. Re-initializes the `Lenia` instance, losing **all** of the previous changes, such as kernel changes, channel additio

Functionsample_exponential

(x: f64, exponent: f64, peak: f64)

Methodset_channels

(&mut self, num_channels: usize)

src/lenias.rs:139

Methodset_channels

Set the number of channels in the `Lenia` instance. In case the number of channels is less than the current number of channels, it is up to the user

Methodset_convolution_channel_source

Set the source channel a given convolution channel should act on. ### Parameters `convolution_channel` - The convolution channel which will have its

Methodset_convolution_channels

Set the number of convolution channels in the `Lenia` instance. If the new number of convolution channels is less than currently, then any convolutio

(&mut self, new_dt: f64)

src/lenias.rs:164

Set the integration step (a.k.a. timestep) parameter `dt` of the `Lenia` instance. ### Parameters `dt` - The new dt value for the `Lenia` instance t

Methodset_growth_function

Set the growth function and its parameters of the specified convolution channel. ### Parameters `f` - Growth function to use. `growth_parameters` -

Methodset_kernel

(&mut self, kernel: ndarray::ArrayD<f64>, conv_channel: usize)

src/lenias.rs:155

Methodset_kernel

Set and make the kernel of the specified convolution channel. ### Parameters `kernel` - n-dimensional array (`ndarray::ArrayD<f64>`), where the numb

Methodset_weights

(&mut self, new_weights: &[f64], conv_channel: usize)

src/lenias.rs:151

Methodset_weights

Set the convolution channel weights for a specific channel. If the length of weights is greater than the number of convolution channels, then the spa

Functionsmooth_life

Basic Smooth Life growth function. Not faithful to proper SmoothLife, and is not capable of simulating every SmoothLife. `params[0]` - Birth range s

src/growth_functions.rs:101

Functionsmooth_life_sigmoid_smoothed

Smooth Life growth function with smoothed stepping. Not faithful to proper SmoothLife and is not capable of simulating every SmoothLife. Step width

src/growth_functions.rs:126

Functionsmoothlife

Generates a kernel base of a "SmoothLife" outer kernel. Not completely faithful to SmoothLife. ### Parameters `radius` - Radius of the kernel to ge

src/kernels.rs:291

Functionstandard_lenia

Standard unimodal "gaussian bump". ### Parameters `params[0]` - **mu**: The position of the mean / highest point of the growth function. `params[1]

src/growth_functions.rs:16

(&self, channel: usize)

src/lenias.rs:208