ozjimbob

### Plot length summary
  
data=read.csv("plotBAincr.csv",stringsAsFactors=F)

data$Date.start=as.Date(data$Date.start)
data$Date.end=as.Date(data$Date.end)

plotlist=unique(data$Plot)

out_frame=data.frame(Plot="",Date.start="",Date.end="",length=0,stringsAsFactors=F)

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# Run model simulation with given values for: 
# initial cull, maintenance cull, cell target density, allocated budget,
# area to cull, and "control area" (area for which proportional population is calculated)
# (last two are kept separate for the spatial optimisation, where we may choose to cull a subset
# of the target control area for efficiency)
run.sim = function(curr.init.cull, curr.maint.cull, curr.target.density, budget, C.mask, ctrl.mask)
{
  Nx = ncol(K); Ny = nrow(K); # gauge size of map
  IC = K # note - initial population is set to carrying capacity

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Keybase proof

I hereby claim:

• I am ozjimbob on github.
• I am jimbob (https://keybase.io/jimbob) on keybase.
• I have a public key whose fingerprint is 7ABC 13B3 C2B3 2583 C3A2 C03C EC2D AA9F 5376 6533

To claim this, I am signing this object:

ozjimbob

library(rjson)
library(httr)

turl="https://iowadems-caucussitecdn-prod2.azureedge.net/api/statecandidateresults"
a=GET(turl)
arr=c(content(a)$StateResults[[1]]$WinPercentage,content(a)$StateResults[[2]]$WinPercentage,content(a)$StateResults[[3]]$WinPercentage)
nam=c(content(a)$StateResults[[1]]$Candidate$DisplayName,content(a)$StateResults[[2]]$Candidate$DisplayName,content(a)$StateResults[[3]]$Candidate$DisplayName)
colr=c(content(a)$StateResults[[1]]$Candidate$Color,content(a)$StateResults[[2]]$Candidate$Color,content(a)$StateResults[[3]]$Candidate$Color)
rar=round(arr,3)*100
barplot(arr,names.arg=nam,col=colr)

ozjimbob

library(raster)
library(jpeg)

# Spherical environment mapping hillshade function
getv=function(i,a,s){
  ct = dim(i)[1:2]/2
  sx = values(s)/90 * ct[1]
  sy = values(s)/90 * ct[2]
  a = values(a) * 0.01745
  px = floor(ct[1] + sx * -sin(a))

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## Load required packages
# you will need to install these, first do
# eg.   install.packages("tidyverse")
# etc. to install them one by one
library(tidyverse)
library(httr)
library(sf)
library(tmap)

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data = tibble(foo=c("Bah","Fuzz"))
my_column="foo"
data %>% filter(!!my_column := "Bah")

ozjimbob

library("reticulate")
library(tidyverse)
library(purrr)

# We use the python "holidays" package for the basic holidays
# Installed it using pip first
holidays <- import("holidays")
tas_holidays <- holidays$Australia(prov="TAS")

# List of all dates from 2000 to 2018

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# Demonstration - Downloading and animating ACCESS forecast data
# grant.williamson@utas.edu.au

# General functions, including str_replace, map
library(tidyverse) 

# threddscrawler is a useful package for parsing THREDDS data servers
# Install it with:
# devtools::install_github("BigelowLab/threddscrawler")
library(threddscrawler)