Doug Rohde’s lens (light, efficient, neural simulator) is an awesome tool. However, given that it has not been actively maintained since 2000, its shelf-life is probably limited. I still have some legacy projects that were developed in lens (mainly using SRNs) and like to be able to re-run and tweak them. At some point, I’ll move them to tensorflow, but in the meantime, if I can get them running on linux, that would be great.

My primary linux box is a virtual machine under VirtualBox on a Mac running Ubuntu 18.04.1 LTS. I got lens running here by consulting this page. My notes are a bit more compact than the details at that page, and actually add crucial details now that it is hard to find legacy packages for tcl/tk.

1. Get tcl and tk packages:
   • tcl: http://old-releases.ubuntu.com/ubuntu/pool/universe/t/tcl8.3/
     • Get both tcl8.3_8.3.5-14_amd64.deb and the corresponding tcl8.3-dev package.
   • tk: http://old-releases.ubuntu.com/ubuntu/pool/universe/t/tk8.3/
     • Get matching tk8.3 and tk8.3-dev packages
2. Install those guys, following instructions like these, to wit:
   • sudo apt install ./name.deb
   • replace ‘name.deb’ with a package name; I assume you should tcl8.3 first, followed by tcl8.3-dev, tk8.3, and tk8.3-dev
3. Choose where you will install lens. Personally, I like easy access to it right off my home directory in a folder called LENS.
4. Download the code to that directory and unpack it:
   • sudo wget http://tedlab.mit.edu/~dr/Lens/Dist/lens.tar.gz
   • sudo tar zxf lens.tar.gz
   • sudo rm lens.tar.gz
5. Replace every instance of CLK_TCK with CLOCKS_PER_SEC in the files in Src; a one-line way of doing this from this page:
   • sed -i 's%CLK_TCK%CLOCKS_PER_SEC%g' ./Src/command.c ./TclTk/tcl8.3.4/unix/tclUnixPort.h
6.  In Src/system.h, comment out the “include <bits/nan.h>” line; those functions have been integrated into math.h, which is also included. Not doing this leads to errors at compile.
7. Edit the Makefile. Minimally, replace the line “CFLAGS = -Wall -O4 -march=i486” with “CFLAGS = -Wall -O4“. I also had some weird problems where it was generating a HOSTTYPE directory for i586 that would not work that went away when I simply commented out every other HOSTTYPE section except the default one. Inelegant, but it worked.
8. Then build it: sudo make all 
   • If it didn’t work, I’m sorry. That’s all I’ve got…
9.  Then deviate slightly from the installation directions. In your ~/.bashrc, add these lines (and save it and then start a new terminal or ‘source ~/.bashrc‘):
   • export LENSDIR=${HOME}/LENS # or whatever your location is
   • export LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:${LENSDIR}/Bin
   • export PATH=${PATH}:${LD_LIBRARY_PATH}:${LENSDIR}/Bin
10. You should now be able to execute lens anywhere by typing ‘lens‘

Do X in Y: Use a function to save many plots to a list in R

You know that miserable feeling when you realize you are copying and pasting snippets of code and modifying them with hard-coded variables? I usually ignore it and press on, but last night, I took the time to convert my code to a function. It was surprisingly easy (it took maybe 5 minutes), and had unexpected benefits. For example, the task I was doing required creating many (e.g., 8-30) ggplot objects, pushing them into a list, and then using multiplot to create PDFs. Every time I ran a chunk of code for one set of graphs, I found myself fidgeting while R Studio created each of the graphs in the plot window. My efforts to suppress that unwanted plotting were for naught, but when I converted to a function, all that extra plotting went away. Creating a 16-panel plot is probably 10x faster using the function! Here’s the code for the function (apologies to any programmers whose sensibilities I offend; I’m a hack, not a hacker).


#####################################################################################
# 2018.04.30, Jim Magnuson
library(foreach)
library(ggplot2)
library(scales)

plot.to.list <- function(dat, x.vars, x.names, y.vars, y.names, textsize=12,


jitteramount=0.1, ...) {
someplots <- list()
at = 0
foreach(xvar=x.vars, xname=x.names) %do% {

foreach(yvar=y.vars,yname=y.names) %do% {


# First, get correlation between the current pair
# NB: the 'get' command is crucial for evaluating the
# strings as variable names, but this doesn't work in
# the ggplot code below
acor = sprintf("%.3f",with(dat, cor(get(xvar),get(yvar)) ))
at = at + 1 # increment list position
# now add a ggplot object to the list
# NB: instead of 'get', we use 'aes_string' in place of 'aes'
someplots[[at]] <- ggplot(dat,aes_string(x=xvar,y=yvar)) +


geom_jitter(position=position_jitter(jitteramount)) +
geom_smooth(method='lm', se=FALSE, linetype="dashed") +
scale_x_continuous(breaks= pretty_breaks()) + # from scales, try it, you'll like it
scale_y_continuous(breaks= pretty_breaks()) +
theme(plot.title = element_text(hjust = 1)) + # right justify title
theme(panel.background = element_rect(colour="black", fill="white"),
axis.text.x = element_text(size=textsize, face="plain", colour="black"),
axis.text.y = element_text(size=textsize, face="plain", colour="black"),
axis.title.x = element_text(size=textsize, face="bold", colour="black", vjust=-.4),
plot.title = element_text(size=11),
axis.title.y = element_text(size=textsize, face="bold", colour="black", vjust=1)) +
xlab(xname) + ylab(yname) +
ggtitle(paste("r =",acor)) # plot r as title

}


}
return(someplots)

}


####################################################################################

yvars=c("RT","RT_lenC") # variables w/in trace.sub I want to be on y axes
ynames=c("RT", "Adjusted RT") # Better labels than the variable names
xvars=c("NB", "DEL", "ADD", "SUB") # more trace.sub vars that I want on x axes
xnames=c("Neighbors", "Deletions", "Additions", "Substitutions") # Better labels
# now call the function:
nb.plots = plot.to.list(dat=trace.sub, x.vars=xvars, y.vars=yvars,
x.names=xnames, y.names=ynames)

# now create a PDF with all the plots
pdf("trace_neighbor_types.pdf",height=7,width=13.7)
# get multiplot function here:
#      http://www.cookbook-r.com/Graphs/Multiple_graphs_on_one_page_(ggplot2)/
multiplot(plotlist = nb.plots, cols=4)
dev.off()