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library(terra)
library(stringr)

#---- static code ----

# read in the original data
r <- rast("LPX-Bern_SH1_gpppft.nc")

# first subset to only the required years
# 1978 - 2020

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find . -name '*.zip' -exec sh -c 'unzip {};'

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gdal_translate -a_ullr -180 90 180 -90 -a_srs EPSG:4326 world.topo.bathy.200408.3x5400x2700.jpg tmp.tif

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# Interpolation using a loess smoother
# and optimal span determination

library(MODISTools)
library(phenocamr)
library(tidyverse)

VI <- mt_subset(
  product = "MOD13Q1",
  lon = -72.1755,

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library(rvest)

# base html url
url <- "http://dapds00.nci.org.au/thredds/fileServer/ks32/CLEX_Data/PLUMBER2/v1-0/Flux/"

# catalogue
html_page = read_html("https://dap.nci.org.au/thredds/remoteCatalogService?catalog=http://dapds00.nci.org.au/thredds/catalog/ks32/CLEX_Data/PLUMBER2/v1-0/Flux/catalog.xml")

# grab files from the catalogue
files = html_page %>%

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# convert pngs to mp4
ffmpeg -r 2 -i tmp/%03d.png -y  out.mp4

# convert mp4 to twitter compatible format
ffmpeg -i out.mp4  -vf scale=1280:720 -b:v 5000K -minrate 5000K -maxrate 5000K -b:a 128K -r 30 -f mp4 -vcodec libx264 -profile:v high -pix_fmt yuv420p -strict -2  -ac 2 -acodec aac twitter.mp4

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# data wrangling
library(tidyverse)

# waffle plot
library(waffle)

# combining graphs
library(patchwork)

# text magic

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#!/bin/bash
# convert equirectangular 2 fisheye

files=`ls *.jpg`

for i in $files;
do
noext=`echo $i | cut -d'.' -f1`

echo $noext

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# required libraries
library(phenor)

# where to store the data
out_dir <- "data-raw/"

# sites to process, stored in a csv file
# with columns site, veg_type, roi (check for extra spaces
# which can mess with name formatting)
sites <- read.table("data/sites.csv", sep = ",", header = TRUE)