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# Warp and plot the EGM2008 1' geoid model
# geoid COGS hosted here: https://www.agisoft.com/downloads/geoids/

library(vapour)
library(gdalio)
library(terra)
source(system.file("raster_format/raster_format.codeR", package = "gdalio", mustWork = TRUE))

geoid <- '/vsicurl/https://s3-eu-west-1.amazonaws.com/download.agisoft.com/gtg/us_nga_egm2008_1.tif'

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library(gdalio)
library(terra)
library(viridisLite)
source(system.file("raster_format/raster_format.codeR", package = "gdalio", mustWork = TRUE))

Sys.setenv(GOOGLE_APPLICATION_CREDENTIALS = "MYKEY.json")

gee_src <- "EEDAI:CGIAR/SRTM90_V4"

src_info <- vapour::vapour_raster_info(gee_src)

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library(rstac)
library(sf)
library(raytrix)
library(rayshader)

# set region and search stac!
region <- st_bbox(st_buffer(st_point(c(-151.739,-16.501)), 0.063)) %>%
  st_as_sfc() %>%
  st_set_crs(4326)

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#rstac example/test
library(vapour)
library(gdalio)
library(rstac)
library(sf)
library(dplyr)
library(scico)
source(system.file("raster_format/raster_format.codeR", package = "gdalio", mustWork = TRUE))

# get random swiss region

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#anglr questions
# example...
library(gdalio)
library(rayshader)
library(topography)
library(gdalwebsrv)
library(anglr)
source(system.file("raster_format/raster_format.codeR", package = "gdalio", mustWork = TRUE))

#functions...

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# example...
library(gdalio)
library(rayshader)
library(topography)
library(gdalwebsrv)

rotate <- function(x) t(apply(x, 2, rev))


gdalio_rayshader_matrix <- function(dsn, ...) {

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if (!"here" %in% installed.packages()[,"Package"]) install.packages('here')

library(here)

dir_home <- here()

# Function to un-nest all files in sub directories.
unnest_dir <- function(.root, sep="_DIR_SEP_"){
  #list all the files in the directory and sub dirs
  files <- list.files(.root, full.names = F, recursive = T)

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# easy height data for rayshader
# remotes::install_github("hypertidy/topography")
library(gdalio)
library(topography)
library(sf)
library(rayshader)
library(scico)
library(dplyr)
# ------ get data -------------
# Get county data for England and Wales

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# Raster Mask Benchmark
library(sf)
library(terra)
library(dplyr)
library(elevatr)
library(microbenchmark)
# library(tictoc)
library(bench) 

# ------ get data -------------

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# Find area difference of two lines and group by whether one line is higher or
# lower than the other. Possiblt a long way round but it works!

#--------- Load packages -------------
library(wk)
library(sf)
library(tidyverse)

#------------- Create some fake data in long form ---------------
fake_df <- tibble(scenario = c(rep('high',6),rep('low',6)),