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# So this is just a test to see the fastests way to go from a dataframe with
# two coordinates per row (split into 4 columns - 2 for x and  2 for y) to a 
# linestring for each set of coordinates. 


# minimal example.
library(sfheaders)
library(wk)
library(sf)
library(tidyverse)

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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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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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# 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(raster)
#> Loading required package: sp
library(stars)
#> Loading required package: abind
#> Loading required package: sf
#> Linking to GEOS 3.10.1, GDAL 3.4.0, PROJ 8.2.0; sf_use_s2() is TRUE
library(terra)
#> terra 1.5.23

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#' Customise style of a targets-derived visNetwork object
#'
#' @param v object class "visNetwork" from either `tar_visnetwork` or `tar_glimpse` 
#' @param colors character vector of length 1 or 3. Hex colour codes to define: 
#' Up to Date, Errored and Outdated targets (in that order). If an object from 
#' `tar_glimpse` is provided, only the first colour is used to fill the nodes.
#' @param shapes a character vector of length 3. any of the following: diamond, 
#' dot, star, triangle, triangleDown, hexagon, square.
#' @param background character - Hex colour codes to define the background
#' @param leg_shape_col character - Hex colour code. for tar_visnetwork an 

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library(sf)
library(dplyr)
library(mapview)
mapviewOptions(fgb = FALSE)
plts <- read_sf('data/PlotData/KuamutLocations.shp') 
pnts <- read_sf("data/Kuamut_2022_NewGPS_Points_Shapefiles/PlotCentre.shp")

pnts_clean <- pnts %>% 
  filter(is.na(NUMBER)|NUMBER!=100) %>%
  mutate(nearest = st_nearest_feature(., plts),

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 library(raster)
#> Loading required package: sp
#> Warning: no function found corresponding to methods exports from 'raster' for:
#> 'area'
 
 circ_matrix <- function(n){
   if (!n %% 2){
     stop("n must be an odd number.")
   } 

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library(vapour)
library(sf)
#> Linking to GEOS 3.10.2, GDAL 3.4.3, PROJ 8.2.0; sf_use_s2() is TRUE
#> WARNING: different compile-time and runtime versions for GEOS found:
#> Linked against: 3.10.2-CAPI-1.16.0 compiled against: 3.10.1-CAPI-1.16.0
#> It is probably a good idea to reinstall sf, and maybe rgeos and rgdal too
library(rstac)

mpc_dem <- function(aoi, src = c("cop-dem-glo-30", "alos-dem")) {

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library(tibble)
library(purrr)

t1 <- tibble(`C1 Template` = sample(c(166:180), 10),
             `C2 Template` = sample(c(120:130), 10))

t1
#> # A tibble: 10 × 2
#>    `C1 Template` `C2 Template`