djnavarro

# author: Danielle Navarro
# date: 2020-06-23

# packages
library(ggplot2)
library(dplyr)
library(scico)
library(here)

rectgenerate <- function(seed) {

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library(flametree)  # github.com/djnavarro/flametree
library(voronoise)  # github.com/djnavarro/voronoise
library(dplyr)

# set seed
seed <- 1
set.seed(seed)

# the "flametree" itself
ftree <- flametree_grow(

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library(dplyr)
library(ambient)

intrusion <- function(filename, shade = "#b3b3ff", seed = 1, 
                      frak_one = 3, frak_two = 2, 
                      pixels_wide = 1000, pixels_high = 1000,
                      colors_used = 1000) {
  
  # transformation functions ------------------------------------------------
  

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library(flametree) # https://github.com/djnavarro/flametree
library(scopes)    # https://github.com/djnavarro/scopes
library(ggforce)
library(ggplot2)
library(dplyr)
library(ambient)

# settings
pixels <- 1000
fname <- "~/Desktop/scopetree.png"

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library(tidyverse)
img <- bomrang::get_satellite_imagery(
  product_id = "IDE00427.201912280400.tif", 
  cache = TRUE
) %>% stars::st_as_stars() 

ggplot() + 
  stars::geom_stars(data = img)

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# illustrate that the temporally-accrued evidence in an Ornstein-
# Uhlenbeck model of perceptual decision-making is bounded

t <- 500     # length of time to run the process
drift <- .2  # drift rate measures the perceptual signal strength
leak <- .9   # evidence leakage rate 

# momentary samples are presumed to generate normally distributed 
# evidence with mean corresponding to drift rate, and (for reasons
# known but to Roger) standard deviation .1

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library(flametree)
library(ggplot2)
library(gganimate)

dat <- flametree_grow(time = 13)

pic <- ggplot(
  data = dat,
  mapping = aes(
    x = coord_x,

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# https://github.com/djnavarro/jasmines

library(jasmines)
library(dplyr)

simple_plot <- function(octaves) {
  
  dat <- use_seed(259) %>%
    scene_rows(
      n = 5, 

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library(jasmines) 
library(dplyr)

# data structure to plot
dat <- use_seed(1) %>%
  scene_bubbles(n = 3, grain = 10000) %>% 
  mutate(x = x * 2, y = y * 2) %>% 
  unfold_tempest(
    iterations = 4, 
    scale = .05, 

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library(ambient) # may need dev version: https://github.com/thomasp85/ambient
library(viridis)

# specify the seed and grain
seed <- 272
grain <- 1000

# specify the circle
origin_x <- .3
origin_y <- .5