djnavarro

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 ------------------------------------------------
  

djnavarro

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"

djnavarro

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

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

djnavarro

# 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

djnavarro

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

dat <- flametree_grow(time = 13)

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

djnavarro

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, 

djnavarro

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

djnavarro

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

library(jasmines)
library(dplyr)

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

djnavarro

# name myself on git
system2(
  'git config --global user.email "d.navarro@unsw.edu.au";
  git config --global user.name "Danielle Navarro"'
)

djnavarro

library(ggplot2)
library(scales)

# transformation
log_sign_trans <- trans_new(
  name = "log_sign",
  transform = function(x) {sign(x) * log(abs(x))}, 
  inverse = function(x){sign(x)} * exp(abs(x))
)