[Swift-commit] r3381 - text/parco10submission

[noreply at svn.ci.uchicago.edu]

Author: wozniak
Date: 2010-06-16 16:50:53 -0500 (Wed, 16 Jun 2010)
New Revision: 3381

Modified:
   text/parco10submission/paper.tex
Log:
Center figures


Modified: text/parco10submission/paper.tex
===================================================================
--- text/parco10submission/paper.tex	2010-06-16 20:43:15 UTC (rev 3380)
+++ text/parco10submission/paper.tex	2010-06-16 21:50:53 UTC (rev 3381)
@@ -4,13 +4,13 @@
 \usepackage{graphicx}
 
 \journal{Parallel Computing}
-\makeatletter 
-\g at addto@macro\@verbatim\small 
-\makeatother 
+\makeatletter
+\g at addto@macro\@verbatim\small
+\makeatother
 
-\makeatletter 
-\g at addto@macro\@verbatim\small 
-\makeatother 
+\makeatletter
+\g at addto@macro\@verbatim\small
+\makeatother
 
 \begin{document}
 % \bibliographystyle{unsrt} % initial temp bib style for editing
@@ -908,7 +908,7 @@
 
 % database pir <simple_mapper;prefix="/ci/pir/UNIPROT.14.0.seq">;
 
-% query  i   <"test.in">; 
+% query  i   <"test.in">;
 % output out <"test.out">;
 % error  err <"test.err">;
 
@@ -941,8 +941,10 @@
 \subsection{fMRI Application Example}
 
 \begin{figure}[htbp]
-\includegraphics[scale=0.5]{img/IMG_fmridataset}
-\caption{FMRI application}
+  \begin{center}
+    \includegraphics[scale=0.5]{img/IMG_fmridataset}
+    \caption{FMRI application}
+  \end{center}
 \end{figure}
 
 \begin{verbatim}
@@ -1063,12 +1065,13 @@
 calling OpenMx to generate and process models in parallel.
 
 \begin{figure}[htbp]
-\includegraphics{img/omxFigure}
-
-\caption{Schematic of a single OpenMx model containing 4 regions of
-interest (I through L) with 5 regression starting values (asymmetric
-connections) of weight 0.75 and 4 residual variances (symmetric connections)
-of weight 1.0}
+  \begin{center}
+    \includegraphics{img/omxFigure}
+    \caption{Schematic of a single OpenMx model containing 4
+      regions of interest (I through L) with 5 regression starting
+      values (asymmetric connections) of weight 0.75 and 4
+      residual variances (symmetric connections) of weight 1.0}
+  \end{center}
 \end{figure}
 
 Using OpenMx's model generator -- a set of functions which creates
@@ -1119,18 +1122,18 @@
 5.	file covMatrix<single_file_mapper;file="speech.cov">;
 6.	Rscript mxScript<single_file_mapper;file="singlemodels.R">;
 7.	int totalperms[] = [1:65536];
-8.	float initweight = .5; 
-9.	foreach perm in totalperms{ 
-10.	   mxModel modmin<single_file_mapper; file=@strcat(perm,".rdata")>; 
-11.	   modmin = mxModelProcessor(covMatrix, mxScript, perm, initweight, “speech”); 
-12.	} 
+8.	float initweight = .5;
+9.	foreach perm in totalperms{
+10.	   mxModel modmin<single_file_mapper; file=@strcat(perm,".rdata")>;
+11.	   modmin = mxModelProcessor(covMatrix, mxScript, perm, initweight, “speech”);
+12.	}
 \end{verbatim}
 
 First, a covariance matrix containing activation data for 4 brain regions, over 8 time points, averaged over a group of subjects in the speech condition was drawn from the experiment database and its location (in this example, on the local file system, though the file could be located anywhere) is mapped in line 5. Line 6 maps the R processing script and lines 1 through 4 define the atomic procedure for invoking R. Each iteration of the foreach loop maps its optimized model output file and calls mxModelProcessor() with the necessary parameters to generate and run a model. Each of these invocations of mxModelProcessor() is independent and is submitted for processing in parallel. Swift passes 5 variables for each invocation: (1) the covariance matrix; (2) the R script containing the call to OpenMx; (3) the permutation number, i.e., the index of the model; (4) the initialization weight for the free parameters of the given model; and (5) the experimental condition. Clearly, in t
 his workflow all free parameters of the given model will have the same initialization weight as Swift is passing only one weight variable. When the job reaches a worker node on Ranger an R process is initialized, the generator creates the desired model by calculating where in the array that permutation of the model matrix falls. OpenMx then estimates the model parameters using a non-linear optimization algorithm called NPSOL (Gill, 1986) and the optimized model is returned and written out by Swift to the location specified in its mapping on line 10.
 
 The above script completed in approximately 40 minutes. The script can
 then be altered to run over multiple experimental conditions by adding
-another outer loop: 
+another outer loop:
 
 Script 2: 4-region exhaustive SEM for 2 experimental conditions