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An Introduction

An Introduction. Dmitri Kavetski, Michael Leonard, Mark Thyer, June 2009. Introduction. Fortran 90/95 (F95) is a well established language many legacy programs written in F95 users may be attracted to F95 advantages (speed, memory, object handling, etc.)

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An Introduction

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  1. An Introduction Dmitri Kavetski, Michael Leonard, Mark Thyer, June 2009

  2. Introduction • Fortran 90/95 (F95) is a well established language • many legacy programs written in F95 • users may be attracted to F95 advantages (speed, memory, object handling, etc.) • R is open-source graphical/statistical package • Many statistical algorithms • Good support for generating plots • Fast growing list of packages • RFortran is an open source software library that links to R from Fortran

  3. Motivation 1 – R Graphics • Fortran has very limited graphics functionality • This is especially frustrating when: • interactively analysing/simulating data • generating numerous diagnostic plots • Typically Fortran users dump outputs to file and plot later • RFortran provides Fortran users with ability to produce high quality R graphics directly from their code

  4. Motivation 2 – R Functions • Many researchers are releasing packages of their latest statistical/numerical algorithms work via the R platform • Number of R packages is constantly increasing • RFortran provides Fortran users with ability to utilise these algorithms in their own code • Enables researchers to trial and incorporate the latest algorithms • Expedites research results

  5. Why use RFortran? • Why not just use Fortran? • you want graphics or functions in R • Why not just use R then? • R is slower (scripted) • Large amount of F95 Legacy Code • Why not just plug a F95 DLL into R? • User convenience, DLLs can be tricky at times • Some programs are hard to package as DLLs • Aimed at users who have significant F95 code and wish to use some of R’s graphics and packages to make life a little easier!

  6. RFortran Overview • RFortran is a suite of modules that are compiled with a Fortran program • Uses the Component Object Model (COM) interface developed by Microsoft • RFortran is Windows only. • Currently can only be used with Compaq (CVF6.6) or Intel Visual Fortran (IVF) compiler • Planned to investigate if a compiler independent version can be developed

  7. Core Functionality • RFortran has 3 essential functions: • Rinit: initialise R, COM interface, etc. • Rput: put data/commands from F95 to R • Rget: get data from R into F95 • Numerous other functions for string handling, error logging, missing values, etc.

  8. Example: Simple Graph • Scenario: you have some data in F95 to plot • Initialise R (line 11) • Transfer some data to R (line 12) • Run a R commands (line 13,14)

  9. Example: Simple Graph • A basic plot

  10. Example: Graph using R Scripts • Scenario: Produce a higher quality graph using with R scripts • Load R script(s) (line 19) • Transfer data (line20 - 22) • Call the function in the script (line 23) 01 program AustraliaRainfall_ScriptedGraph ! Invoke an R script to make a plot. 02use RFortran ! Access RFortan 03implicit none 04integer :: i,j,ok 05real(8) :: x(178,139),lon(178),lat(139) ! Data,Longitude, Latitudes 06real(8) :: coast(61656,2) ! lat/lon coordinates of Aust. coastline ! read data here - full code provided at www.rfortran.org 17 lon = 112.0+(/(i,i=0,177)/)*0.25 ! 178 x 0.25 degree increments 18 lat= -44.5+(/(j,j=0,138)/)*0.25 ! 139 x 0.25 degree increments 19 ok = Rinit(Rfortran_ScriptPathIn='../../../Rscripts/tutorial_examples/') 20 ok = Rput("lat",lat); ok = Rput("lon",lon) !Rainfall grid lat/lon values 21 ok = Rput("coast",coast,mv=(coast<=-9999)) !Missing vals due to islands 22 ok = Rput("x",x,mv=(x<0)) !Missing vals due to ocean pixels 23 ok = Rput("AustraliaRainfall_ScriptedGraph(lat,lon,x,coast)")

  11. Example: Graph using R Scripts 01AustraliaRainfall_ScriptedGraph<-function(lat,lon,x,coast){ 02 image(lon,lat,x, xlab="Longitude",ylab="Latitude",cex.lab=1.5,cex.axis=1.5, 03 breaks=c(100,200,500,1000,2000,5000),col=topo.colors(5)[5:1]) 04contour(lon,lat,x,levels=c(100,200,500,1000,2000,5000),add=TRUE,labcex=1) 05lines(coast) 06 site.x<-c(115.9,138.6,147.3,145.0,151.0,153.1,130.9) 07 site.y<-c(-31.95,-34.92,-42.87,-37.78,-34.0,-27.48,-12.47) 08names<-c("Perth","Adelaide","Hobart","Melb.","Sydney","Brisbane","Darwin") 09 text(site.x,site.y,names,cex=1.2) 10 } • Scripting allows detailed command sequences Simple Graph HQ Graph using R Scripts

  12. Example: Using R Functions • Scenario: you want to exploit an R function • Initialise R (line 11) • Transfer some data to R (line 12) • Run some R command (line 13) • Get results back into F95 (line 14,15) • Use the results in F95 computations (line 18)

  13. Example: Using R Functions • The main point is getting data back to F95 • Intercept = 456.5 mm, Slope = 5.76 mm

  14. Feature List Core Functionality • Initialisation (Rinit) • Auto-opens R, COM interface and other set-up tasks • Transfer R commands to R From Fortran • Transfer variables between R and Fortran • Types supported: integer(2), integer(4), real(4), real(8), logical, character • Scalar, 1D, 2D and 3D arrays • For Rget allocatable arrays can be sized to match R variable • Errors – all error messages output to a log file • Notifies for syntax errors • When transfer variables from R to Fortran notifies for • Type, dimension and size mismatches - • Invalid values (NA, NaN, Inf) Enhanced Functionality - specialized Fortran functions that perform several tasks (e.g. plot data, add axis labels, legend etc) - full list available on www.rfortran.org

  15. Case Study 1 – BATEA • Used to automatically generate large numbers of diagnostic plots stored in a .pdf file

  16. Case Study 1 – BENEFITS • Removes tedium of writing/reading output files • Single pdf file with minimal user intervention simplifies archiving, enables quick comparison • Open source software means diagnostics can be shared between all users.

  17. Case Study 2 – Joint Probability • Used to exploit functionality in R to speed up research output • A catchment model is written in F90 with boundary values from a statistical model in R

  18. Case Study 2 – BENEFITS • Exploited strength of each platform, numerical model in F90, new statistical aspects in R (copula package) • Saved the user having to write a F90 copula package or port the F90 model to R. • Were able to quickly calibrate and evaluate different copula models without investing time. Important if you are not sure of usefulness.

  19. Summary • RFortran provides an easy-to-use useful means for linking to R • RFortran can have numerous benefits, primarily: • access to graphics facilities available • access to numerical/statistical algorithms • Current Status • Beta Release of Version 1.0 is currently available • Download installer from www.rfortran.org, test it and provide feedback. • Visit www.rfortran.org for further information • Information for Users • Feature List • Requirements • Download and Installation • Describes how to use RFortran • Tutorial examples (with source code) • FAQ • Other Information for Developers • How does RFortran Work, What is COM? etc

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