marcusvolz

# Load packages
library(generative) # devtools::install_github("marcusvolz/generative")
library(ggplot2)

for (i in c(3, 86, 111)) {
  # Set parameters
  seed <- i
  set.seed(seed)
  n <- 400
  n_circles <- 4 # 4

marcusvolz

# Load packages
library(generative) # install_github("marcusvolz/generative")
library(tidyverse)

# Create lots of plots
for (i in 1:200) {
  # Set paramter values
  set.seed(i)
  r_0 <- runif(1, 0, 1000) # 15000
  eps <- runif(1, 0, 500) # 550

marcusvolz

# The shell math model is from the paper by M.B. Cortie (1989)
# https://www.researchgate.net/publication/238757952_Models_for_mollusc_shell_shape

# Load packages
library(mathart) # devtools::install_github("marcusvolz/mathart)
library(tidyverse)

# Set parameters (see mathart::mollusc() documentation for details)
n_s <- 650L
n_t <- 2000L

marcusvolz

# The curve is a generalisation of the butterfly curve (Fay, 1989)
# See the Wiki for details: https://en.wikipedia.org/wiki/Butterfly_curve_(transcendental)

# Load packages
library(mathart) # devtools::install_github("marcusvolz/mathart")
library(tidyverse)

# Set parameters
n <- 1000
a <- 2

marcusvolz

# Load packages
library(generative) # devtools::install_github("marcusvolz/generative")
library(ggplot2)

# Generate data (see generative::interpolate_squares documentation for details)
df <- interpolate_squares(seed = 1, n = 25, ease_function = "")

# Create plot
p <- ggplot() +
  geom_segment(aes(x, y, xend = xend, yend = yend), df, alpha = 0.03,

marcusvolz

# Load packages
library(generative) # devtools::install_github("marcusvolz/generative")
library(ggplot2)

# Set parameters (see generative::procedural_ring documentation for details)
seed <- 3
n <- 400
n_circles <- 4
r_0 <- 500
eps <- 500

marcusvolz

# Load packages
library(mathart) # devtools::install_github("marcusvolz/mathart")
library(tidyverse)

# Set parameters
# Rose curve wiki: https://en.m.wikipedia.org/wiki/Rose_(mathematics)
# Nearest neighbour graph wiki: https://en.m.wikipedia.org/wiki/Nearest_neighbor_graph
n <- 9
d <- 8 # Note: k = n/d is a curve parameter
N <- 11 # Controls the total curve length