makeinputfiles: Makes input files for condor runs of goldstein

Description

Wrapper to create arbitrary numbers of condor-compatible goldstein runnable input files. Function sample.from.exp.est() samples from the appropriate distribution.

This function is not designed for the general user: it is tailored for use in the environment of the National Oceanographic Centre, with a particular version of the specialist model “goldstein”.

Usage

makeinputfiles(number.of.runs = 100, gaussian = TRUE,
   directoryname="~/goldstein/genie-cgoldstein/", filename="QWERTYgoin",
   expert.estimates, area.outside=0.05)
sample.from.exp.est(number.of.runs, expert.estimates,
   gaussian=TRUE, area.outside=0.05)

Arguments

number.of.runs

Number of condor runs to generate

gaussian

Boolean variable with default TRUE meaning use a lognormal distribution, and FALSE meaning a uniform distribution. In the case of a Gaussian distribution, only the upper and lower columns are used: here these values are interpreted as the \(2.5\%\rm{ile}\) and \(97.5\%\rm{ile}\) respectively and a lognormal distribution with the appropriate parameters is used.

Note that this approach discards the “best” value, but OTOH it seemed to me that my expert chose his “best” value as an arithmetic (sic) mean of his high and low values, and thus has limited information content.

directoryname

Name of directory to which input files are written

filename

Basename of input files

expert.estimates

Dataframe holding expert estimates (supplied by a climate scientist). Use data(expert.estimates) to load a sample dataset that was supplied by Bob Marsh

area.outside

Area of tails of the lognormal distribution (on a log scale) that fall outside the expert ranges. Default value of 0.05 means interpret a and b as the \(2.5\%\rm{ile}\) and \(97.5\%\rm{ile}\) respectively.

Value

Returns zero on successful completion. The function is used for its side-effect of creating a bunch of Goldstein input files.

Details

This function creates condor-compatible goldstein runnable input files that are placed in directory /working/jrd/sat/rksh/goldstein. The database results.table is made using the shell scripts currently in /users/sat/rksh/goldstein/emulator.

Note that makeinputfiles(number.of.runs=n) creates files numbered from \(0\) to \(n-1\): so be careful of off-by-one errors. It's probably best to avoid reference to the “first”, “second” file etc. Instead, refer to files using their suffix number. Note that the suffix number is not padded with zeros due to the requirements of Condor.

The suffix number of a file matches the name of its tmp file (so, for example, file with suffix number 15 writes output to files tmp/tmp.15 and tmp/tmp.avg.15).

Examples

Run this code

# NOT RUN {
  data(expert.estimates) system("mkdir /users/sat/rksh/tmp",ignore=TRUE)
  makeinputfiles(number.of.runs = 100, gaussian = TRUE,
  directoryname="~/tmp/", expert.estimate=expert.estimates)
# }
# NOT RUN {
data(results.table)
data(expert.estimates)


output.col <- 25
wanted.row <- 1:27
wanted.cols <- c(2:9,12:19)

val <- results.table[wanted.row , wanted.cols]

mins  <- expert.estimates$low 
maxes <- expert.estimates$high

normalize <- function(x){(x-mins)/(maxes-mins)}
unnormalize <- function(x){mins + (maxes-mins)*x}

jj <- t(apply(val,1,normalize))

jj <- as.data.frame(jj)
names(jj) <- names(val)
val <- as.matrix(jj)

scales.optim <- exp(c( -2.63, -3.03, -2.24, 2.61,
-1.65, -3.13, -3.52, 3.16, -3.32, -2.53, -0.25,  -2.55, -4.98, -1.59,
-4.40, -0.81))

d  <- results.table[wanted.row ,  output.col]
A <- corr.matrix(val, scales=scales.optim)
Ainv <- solve(A)

x <- sample.from.exp.est(1000,exp=expert.estimates)
x <- t(apply(x,1,normalize))
ensemble <- interpolant.quick(x , d , val , Ainv, scales=scales.optim) 
hist(ensemble)
# }