merging 496 back to master (#689)

* merging 496 back to master

* turn into proper code blocks

* Update beam.conf

reset beam config

* get failing tests back up

* touch to get new build

* flaky test

* touch to rebuild

* make await short

* ignore single mode spec as on master

* analysis scripts

* cleaning up sf-light data

* change await to 60 sec

* updating PhyssimCalcLinkStats

* restart maybe flakey test

Author: colinsheppard

build.gradle

@@ -259,7 +259,7 @@ scalafmt {
     // configFilePath = ".scalafmt.conf" // .scalafmt.conf in the project root is default value, provide only if other location is needed
 }
 
-compileScala.dependsOn(scalafmtAll)
+//compileScala.dependsOn(scalafmtAll)
 
 // Task to run scala tests, as Scala tests not picked up by Gradle by default.
 task spec(dependsOn: ['testClasses'], type: JavaExec) {
@@ -570,3 +570,4 @@ task execute(type:JavaExec) {
         }
     }
 }
+

docs/developers.rst

@@ -112,37 +112,37 @@ Production versus test data. Any branch beginning with "production" or "applicat
 
 However, sometimes troubleshooting / debugging / development happens on a production branch. The cleanest way to get changes to source code or other non-production files back into master is the following.
 
-Checkout your production branch:
+Checkout your production branch::
 
   git checkout production-branch
 
-Bring branch even with master
+Bring branch even with master::
 
   git merge master
 
 Resolve conflicts if needed
 
-Capture the files that are different now between production and master:
+Capture the files that are different now between production and master::
 
   git diff --name-only HEAD master > diff-with-master.txt
 
 You have created a file "diff-with-master.txt" containing a listing of every file that is different.
 
 IMPORTANT!!!! -- Edit the file diff-with-master.txt and remove all production-related data (this typically will be all files underneath "production" sub-directory.
 
-Checkout master
+Checkout master::
 
   git checkout master
 
-Create a new branch off of master, this is where you will stage the files to then merge back into master:
+Create a new branch off of master, this is where you will stage the files to then merge back into master::
 
   git checkout -b new-branch-with-changes-4ci
 
-Do a file by file checkout of all differing files from production branch onto master:
+Do a file by file checkout of all differing files from production branch onto master::
 
   cat diff-with-master.txt | xargs git checkout production-branch --
 
-Note, if any of our diffs include the deletion of a file on your production branch, then you will need to remove (i.e. with "git remove" these before you do the above "checkout" step and you should also remove them from the diff-with-master.txt". If you don't do this, you will see an error message ("did not match any file(s) known to git.") and the checkout command will not be completed.
+Note, if any of our diffs include the deletion of a file on your production branch, then you will need to remove (i.e. with "git remove" these before you do the above "checkout" step and you should also remove them from the diff-with-master.txt"). If you don't do this, you will see an error message ("did not match any file(s) known to git.") and the checkout command will not be completed.
 
 Finally, commit the files that were checked out of the production branch, push, and go create your pull request!
 

src/main/R/beam-utilities.R

@@ -3,14 +3,16 @@ load.libraries(c('GEOquery','XML'))
 
 clean.and.relabel <- function(ev,factor.to.scale.personal.back,factor.to.scale.transit.back,val.of.time=16.9){
   # Clean and relabel
-  ev[vehicle_type=="bus",vehicle_type:="Bus"]
-  ev[vehicle_type=="CAR" | substr(vehicle,1,5)=="rideH",vehicle_type:="TNC"]
-  ev[vehicle_type=="subway",vehicle_type:="BART"]
+  ev[vehicle_type=="bus",vehicle_type:="BUS-DEFAULT"]
+  ev[vehicle_type=="CAR",vehicle_type:="Car"]
+  ev[substr(vehicle,1,5)=="rideH",mode:="ride_hail"]
+  ev[vehicle_type=="subway",vehicle_type:="SUBWAY-DEFAULT"]
   ev[vehicle_type=="SUV",vehicle_type:="Car"]
-  ev[vehicle_type=="cable_car",vehicle_type:="Cable_Car"]
-  ev[vehicle_type=="tram",vehicle_type:="Muni"]
-  ev[vehicle_type=="rail",vehicle_type:="Rail"]
-  ev[vehicle_type=="ferry",vehicle_type:="Ferry"]
+  ev[vehicle_type=="cable_car",vehicle_type:="CABLE_CAR-DEFAULT"]
+  ev[vehicle_type=="tram",vehicle_type:="TRAM-DEFAULT"]
+  ev[vehicle_type=="rail",vehicle_type:="RAIL-DEFAULT"]
+  ev[vehicle_type=="ferry",vehicle_type:="FERRY-DEFAULT"]
+  transit.types <- c('BUS-DEFAULT','FERRY-DEFAULT','TRAM-DEFAULT','RAIL-DEFAULT','CABLE_CAR-DEFAULT','SUBWAY-DEFAULT')
   ev[,tripmode:=ifelse(mode%in%c('subway','bus','rail','tram','walk_transit','drive_transit','cable_car','ferry'),'transit',as.character(mode))]
   ev[,hour:=time/3600]
   ev[,hr:=round(hour)]
@@ -25,9 +27,9 @@ clean.and.relabel <- function(ev,factor.to.scale.personal.back,factor.to.scale.t
   if(is.factor(ev$fuel[1]))ev[,fuel:=as.numeric(as.character(fuel))]
   ev[start.y<=0.003 | end.y <=0.003,':='(start.x=NA,start.y=NA,end.x=NA,end.y=NA)]
   ev[length==Inf,length:=NA]
-  ev[vehicle_type%in%c('BART','Ferry','Muni','Rail','Cable_Car') & !is.na(start.x)  & !is.na(start.y)  & !is.na(end.y)  & !is.na(end.y),length:=dist.from.latlon(start.y,start.x,end.y,end.x)]
-  ev[vehicle_type%in%c('BART','Bus','Cable_Car','Muni','Rail'),num_passengers:=round(num_passengers*factor.to.scale.personal.back)]
-  ev[vehicle_type%in%c('BART','Bus','Cable_Car','Muni','Rail'),capacity:=round(capacity*factor.to.scale.transit.back)]
+  ev[vehicle_type%in%transit.types & !is.na(start.x)  & !is.na(start.y)  & !is.na(end.y)  & !is.na(end.y),length:=dist.from.latlon(start.y,start.x,end.y,end.x)]
+  ev[vehicle_type%in%transit.types,num_passengers:=round(num_passengers*factor.to.scale.personal.back)]
+  ev[vehicle_type%in%transit.types,capacity:=round(capacity*factor.to.scale.transit.back)]
   ev[num_passengers > capacity,num_passengers:=capacity]
   ev[,pmt:=num_passengers*length/1609]
   ev[is.na(pmt),pmt:=0]
@@ -41,8 +43,8 @@ clean.and.relabel <- function(ev,factor.to.scale.personal.back,factor.to.scale.t
   ev
 }
 
-pretty.titles <- c('TNC Number'='ridehail_num',
-                   'TNC Price'='ridehail_price',
+pretty.titles <- c('Ride Hail Number'='ridehail_num',
+                   'Ride Hail Price'='ridehail_price',
                    'Transit Capacity'='transit_capacity',
                    'Transit Price'='transit_price',
                    'Toll Price'='toll_price',
@@ -57,8 +59,8 @@ to.title <- function(abbrev){
   }
 }
 pretty.modes <- function(ugly){
-  pretty.list <- c('TNC'='ride_hail',
-                   'TNC'='ride_hail',
+  pretty.list <- c('Ride Hail'='ride_hail',
+                   'Ride Hail - Transit'='ride_hail_transit',
                    'Cable Car'='cable_car',
                    'Car'='car',
                    'Walk'='walk',
@@ -89,7 +91,7 @@ parse.link.stats <- function(link.stats.file,net.file=NA){
 }
 
 my.colors <- c(blue='#377eb8',green='#227222',orange='#C66200',purple='#470467',red='#B30C0C',yellow='#C6A600',light.green='#C0E0C0',magenta='#D0339D',dark.blue='#23128F',brown='#542D06',grey='#8A8A8A',dark.grey='#2D2D2D',light.yellow='#FFE664',light.purple='#9C50C0',light.orange='#FFB164',black='#000000')
-mode.colors <- c(TNC='red',Car='grey',Walk='green',Transit='blue')
+mode.colors <- c('Ride Hail'='red',Car='grey',Walk='green',Transit='blue','Ride Hail - Transit'='purple')
 mode.colors <- data.frame(key=names(mode.colors),color=mode.colors,color.hex=my.colors[mode.colors])
 
 download.from.nersc <- function(experiment.dir,include.pattern='*'){

src/main/R/bin/csv2Rdata.R

@@ -15,8 +15,7 @@ option_list <- list(
 )
 if(interactive()){
   #setwd('~/downs/')
-  args<-'/Users/critter/Downloads/2014-Clipper.csv'
-  args<-'/Users/critter/Downloads/output 3/application-sfbay/base__2018-06-18_16-21-35/ITERS/it.1/1.events.csv'
+  args<-'/Users/critter/Downloads/output 2/application-sfbay/RH2Transit_Plus10__2018-09-26_04-55-53/ITERS/it.10/10.events.csv'
   args <- parse_args(OptionParser(option_list = option_list,usage = "csv2Rdata.R [file-to-convert]"),positional_arguments=T,args=args)
 }else{
   args <- parse_args(OptionParser(option_list = option_list,usage = "csv2Rdata.R [file-to-convert]"),positional_arguments=T)

src/main/R/bin/exp2plots.R

@@ -17,17 +17,20 @@ option_list <- list(
 )
 if(interactive()){
   #setwd('~/downs/')
-  args<-'/Users/critter/Documents/beam/beam-output/experiments/2018-04/surge-pricing/'
-  args<-'/Users/critter/Documents/beam/beam-output/experiments/2018-04/ridehail-price/'
+  args<-'/Users/critter/Downloads/RH2Transit-2Iter/RH2Transit'
+  args<-'/Users/critter/Downloads/cost-sensitivities/cost-sensitivity/'
+  #args<-'/Users/critter/Downloads/diffusion-5iter/diffusion/'
   args <- parse_args(OptionParser(option_list = option_list,usage = "exp2plots.R [experiment-directory]"),positional_arguments=T,args=args)
 }else{
   args <- parse_args(OptionParser(option_list = option_list,usage = "exp2plots.R [experiment-directory]"),positional_arguments=T)
 }
 ######################################################################################################
 
-factor.to.scale.personal.back <- 35
-factor.to.scale.transit.back <- 2
+# TODO make these come from conf file
+factor.to.scale.personal.back <- 20
+factor.to.scale.transit.back <- 1/.22 # inverse of param: beam.agentsim.tuning.transitCapacity
 plot.congestion <- F
+plot.modality.styles <- F
 
 ######################################################################################################
 # Load the exp config
@@ -72,21 +75,25 @@ for(run.i in 1:nrow(exp)){
     }
     links[[length(links)+1]] <- link
   }
-  last.iter.minus.5 <- ifelse(last.iter>4,last.iter - 4,1)
-  for(the.iter in last.iter.minus.5:last.iter){
-    pop.csv <- pp(run.dir,output.dir,'/ITERS/it.',the.iter,'/',the.iter,'.population.csv.gz')
-    if(file.exists(pop.csv)){
-      pop <- csv2rdata(pop.csv)
-      pop[,iter:=the.iter]
-      streval(pp('pop[,',fact,':="',the.level,'"]'))
-      pops[[length(pops)+1]] <- pop
+  if(plot.modality.styles){
+    last.iter.minus.5 <- ifelse(last.iter>4,last.iter - 4,1)
+    for(the.iter in last.iter.minus.5:last.iter){
+      pop.csv <- pp(run.dir,output.dir,'/ITERS/it.',the.iter,'/',the.iter,'.population.csv.gz')
+      if(file.exists(pop.csv)){
+        pop <- csv2rdata(pop.csv)
+        pop[,iter:=the.iter]
+        streval(pp('pop[,',fact,':="',the.level,'"]'))
+        pops[[length(pops)+1]] <- pop
+      }
     }
   }
 }
 ev <- rbindlist(evs,use.names=T,fill=T)
 rm('evs')
-pop <- rbindlist(pops,use.names=T,fill=T)
-rm('pops')
+if(plot.modality.styles){
+  pop <- rbindlist(pops,use.names=T,fill=T)
+  rm('pops')
+}
 if(plot.congestion){
   link <- rbindlist(links,use.names=T,fill=T)
   rm('links')
@@ -97,12 +104,12 @@ ev <- clean.and.relabel(ev,factor.to.scale.personal.back,factor.to.scale.transit
 setkey(ev,type)
 
 ## Prep data needed to do quick version of energy calc
-en <- data.table(read.csv('~/Dropbox/ucb/vto/beam-all/beam/test/input/sf-light/energy/energy-consumption.csv'))
-setkey(en,vehicleType)
+en <- data.table(read.csv('~/Dropbox/ucb/vto/beam-all/beam/production/application-sfbay/samples/vehicleTypes.csv'))
+setkey(en,vehicleTypeId)
 en <- u(en)
 ## Energy Density in MJ/liter or MJ/kWh
 # https://en.wikipedia.org/wiki/Gasoline_gallon_equivalent
-en.density <- data.table(fuelType=c('gasoline','diesel','electricity'),density=c(31.81905,36.14286,3.6))
+#en.density <- data.table(fuelType=c('gasoline','diesel','electricity','biodiesel'),density=c(31.81905,36.14286,3.6,33.2))
 ev[tripmode%in%c('car') & vehicle_type=='Car',':='(num_passengers=1)]
 ev[,pmt:=num_passengers*length/1609]
 ev[is.na(pmt),pmt:=0]
@@ -132,8 +139,9 @@ for(fact in factors){
   toplot[,frac:=num/tot]
   toplot[,tripmode:=pretty.modes(tripmode)]
   setkey(toplot,the.factor,tripmode)
-  p <- ggplot(toplot,aes(x=the.factor,y=frac*100,fill=tripmode))+geom_bar(stat='identity',position='stack')+labs(x="Scenario",y="% of Trips",title=pp('Factor: ',fact),fill="Trip Mode")+
-      scale_fill_manual(values=as.character(mode.colors$color.hex[match(sort(u(toplot$tripmode)),mode.colors$key)]))
+  p <- ggplot(toplot,aes(x=the.factor,y=frac*100,fill=tripmode))+geom_bar(stat='identity',position='stack')+
+    labs(x="Scenario",y="% of Trips",title=pp('Factor: ',fact),fill="Trip Mode")+
+    scale_fill_manual(values=as.character(mode.colors$color.hex[match(sort(u(toplot$tripmode)),mode.colors$key)]))
   pdf.scale <- .6
   ggsave(pp(plots.dir,'mode-split-by-',fact,'.pdf'),p,width=10*pdf.scale,height=6*pdf.scale,units='in')
   write.csv(toplot,file=pp(plots.dir,'mode-split-by-',fact,'.csv'))
@@ -150,7 +158,7 @@ for(fact in factors){
   ggsave(pp(plots.dir,'mode-split-by-hour-by-',fact,'.pdf'),p,width=10*pdf.scale,height=6*pdf.scale,units='in')
   write.csv(toplot,file=pp(plots.dir,'mode-split-by-hour-',fact,'.csv'))
 
-  target <- data.frame(tripmode=rep(c('Car','Walk','Transit','TNC'),length(u(toplot$the.factor))),
+  target <- data.frame(tripmode=rep(c('Car','Walk','Transit','Ride Hail'),length(u(toplot$the.factor))),
                        perc=rep(c(79,4,13,5),length(u(toplot$the.factor))),
                        the.factor=rep(u(toplot$the.factor),each=4))
   p <- ggplot(toplot,aes(x=tripmode,y=frac*100))+geom_bar(stat='identity')+facet_wrap(~the.factor)+geom_point(data=target,aes(y=perc),colour='red')
@@ -188,24 +196,23 @@ for(fact in factors){
   toplot <- pt[,.(fuel=sum(fuel),numVehicles=as.double(length(fuel)),numberOfPassengers=as.double(sum(num_passengers)),pmt=sum(pmt),vmt=sum(length)/1609,mpg=sum(length)/1609/sum(fuel/3.78)),by=c('the.factor','vehicle_type','tripmode')]
   toplot <- toplot[vehicle_type!='Human' & tripmode!="walk"]
   if(nrow(en)>30){
-    toplot <- join.on(toplot,en[vehicle%in%c('SUBWAY-DEFAULT','BUS-DEFAULT','CABLE_CAR-DEFAULT','CAR','FERRY-DEFAULT','TRAM-DEFAULT','RAIL-DEFAULT')|vehicleType=='TNC'],'vehicle_type','vehicleType','fuelType')
+    toplot <- join.on(toplot,en[vehicleTypeId%in%c('SUBWAY-DEFAULT','BUS-DEFAULT','CABLE_CAR-DEFAULT','Car','FERRY-DEFAULT','TRAM-DEFAULT','RAIL-DEFAULT') | substr(vehicleTypeId,0,3)=='BEV'],'vehicle_type','vehicleTypeId','primaryFuelType')
   }else{
-    toplot <- join.on(toplot,en,'vehicle_type','vehicleType','fuelType')
+    toplot <- join.on(toplot,en,'vehicle_type','vehicleTypeId','primaryFuelType')
   }
-  toplot <- join.on(toplot,en.density,'fuelType','fuelType')
-  toplot[,energy:=fuel*density]
-  toplot[vehicle_type=='TNC',tripmode:='TNC']
-  toplot[vehicle_type%in%c('Car','TNC'),energy:=energy*factor.to.scale.personal.back*10]
-  toplot[vehicle_type%in%c('Car','TNC'),numVehicles:=numVehicles*factor.to.scale.personal.back]
-  toplot[vehicle_type%in%c('Car','TNC'),pmt:=pmt*factor.to.scale.personal.back]
-  toplot[vehicle_type%in%c('Car','TNC'),numberOfPassengers:=numVehicles]
+  toplot[,energy:=fuel] # fuel is now in units of J so no need to convert from L to J
+  toplot[tripmode%in%c('car','ride_hail'),energy:=energy*factor.to.scale.personal.back]
+  toplot[tripmode%in%c('car','ride_hail'),numVehicles:=numVehicles*factor.to.scale.personal.back]
+  toplot[tripmode%in%c('car','ride_hail'),pmt:=pmt*factor.to.scale.personal.back]
+  toplot[tripmode%in%c('car','ride_hail'),numberOfPassengers:=numVehicles]
   toplot[,ag.mode:=tripmode]
   toplot[tolower(ag.mode)%in%c('bart','bus','cable_car','muni','rail','tram','transit'),ag.mode:='Transit']
   toplot[ag.mode=='car',ag.mode:='Car']
+  toplot[ag.mode=='ride_hail',ag.mode:='Ride Hail']
   toplot.ag <- toplot[,.(energy=sum(energy),pmt=sum(pmt)),by=c('the.factor','ag.mode')]
   pdf.scale <- .6
   setkey(toplot.ag,the.factor,ag.mode)
-  p <- ggplot(toplot.ag,aes(x=the.factor,y=energy/1e6,fill=ag.mode))+geom_bar(stat='identity',position='stack')+labs(x="Scenario",y="Energy Consumption (TJ)",title=to.title(fact),fill="Trip Mode")+
+  p <- ggplot(toplot.ag,aes(x=the.factor,y=energy/1e12,fill=ag.mode))+geom_bar(stat='identity',position='stack')+labs(x="Scenario",y="Energy Consumption (TJ)",title=to.title(fact),fill="Trip Mode")+
       scale_fill_manual(values=as.character(mode.colors$color.hex[match(sort(u(toplot.ag$ag.mode)),mode.colors$key)]))
   ggsave(pp(plots.dir,'energy-by-mode-',fact,'.pdf'),p,width=10*pdf.scale,height=6*pdf.scale,units='in')
   write.csv(toplot.ag,file=pp(plots.dir,'energy-by-mode-',fact,'.csv'))
@@ -219,11 +226,11 @@ for(fact in factors){
   ggsave(pp(plots.dir,'energy-per-pmt-by-vehicle-type-',fact,'.pdf'),p,width=10*pdf.scale,height=6*pdf.scale,units='in')
 
   # Deadheading 
-  toplot <- pt[vehicle_type=='TNC',.(dead=num_passengers==0,miles=length/1609,hr,the.factor)]
+  toplot <- pt[tripmode=='ride_hail',.(dead=num_passengers==0,miles=length/1609,hr,the.factor)]
   setkey(toplot,hr,dead)
   dead.frac <- toplot[,.(dead.frac=pp(roundC(100*sum(miles[dead==T])/sum(miles),1),"% Empty")),by=c('the.factor')]
   toplot <- toplot[,.(miles=sum(miles)),by=c('dead','hr','the.factor')]
-  p <- ggplot(toplot,aes(x=hr,y=miles,fill=dead))+geom_bar(stat='identity')+labs(x="Hour",y="Vehicle Miles Traveled",fill="Empty",title=pp("TNC Deadheading"))+geom_text(data=dead.frac,hjust=1,aes(x=24,y=max(toplot$miles),label=dead.frac,fill=NA))+facet_wrap(~the.factor)
+  p <- ggplot(toplot,aes(x=hr,y=miles,fill=dead))+geom_bar(stat='identity')+labs(x="Hour",y="Vehicle Miles Traveled",fill="Empty",title=pp("Ride Hail Deadheading"))+geom_text(data=dead.frac,hjust=1,aes(x=24,y=max(toplot$miles),label=dead.frac,fill=NA))+facet_wrap(~the.factor)
   pdf.scale <- .6
   ggsave(pp(plots.dir,'dead-heading.pdf'),p,width=10*pdf.scale,height=6*pdf.scale,units='in')
 }
@@ -244,32 +251,34 @@ if(plot.congestion){
   }
 }
 
-if('customAttributes' %in% names(pop)){
-  for(fact in factors){
-    streval(pp('pop[,the.factor:=',fact,']'))
-    if(all(c('low','base','high') %in% u(pop$the.factor))){
-      pop[,the.factor:=factor(the.factor,levels=c('low','base','high'))]
-    }else if(all(c('Low','Base','High') %in% u(pop$the.factor))){
-      pop[,the.factor:=factor(the.factor,levels=c('Low','Base','High'))]
-    }else{
-      streval(pp('pop[,the.factor:=factor(the.factor,levels=exp$',fact,')]'))
-    }
-    pop[,style:=customAttributes]
-    pop <- pop[style!='']
-    if(any(u(pop$style)=='class1')){
-      new.names <- c(class1='Multimodals',class2='Empty nesters',class3='Transit takers',class4='Inveterate drivers',class5='Moms in cars',class6='Car commuters')
-      pop[,style:=new.names[as.character(style)]]
-    }
-    toplot <- pop[,.(n=length(type)),by=c('style','the.factor','iter')]
-    toplot <- toplot[,.(n=mean(n)),by=c('style','the.factor')]
-    setkey(toplot,style,the.factor)
+if(plot.modality.styles){
+  if('customAttributes' %in% names(pop)){
+    for(fact in factors){
+      streval(pp('pop[,the.factor:=',fact,']'))
+      if(all(c('low','base','high') %in% u(pop$the.factor))){
+        pop[,the.factor:=factor(the.factor,levels=c('low','base','high'))]
+      }else if(all(c('Low','Base','High') %in% u(pop$the.factor))){
+        pop[,the.factor:=factor(the.factor,levels=c('Low','Base','High'))]
+      }else{
+        streval(pp('pop[,the.factor:=factor(the.factor,levels=exp$',fact,')]'))
+      }
+      pop[,style:=customAttributes]
+      pop <- pop[style!='']
+      if(any(u(pop$style)=='class1')){
+        new.names <- c(class1='Multimodals',class2='Empty nesters',class3='Transit takers',class4='Inveterate drivers',class5='Moms in cars',class6='Car commuters')
+        pop[,style:=new.names[as.character(style)]]
+      }
+      toplot <- pop[,.(n=length(type)),by=c('style','the.factor','iter')]
+      toplot <- toplot[,.(n=mean(n)),by=c('style','the.factor')]
+      setkey(toplot,style,the.factor)
 
-    pdf.scale <- .8
-    p<-ggplot(toplot,aes(x=the.factor,y=n,fill=style))+geom_bar(stat='identity',position='stack')+labs(x='Trip #',y='# Agents',title='Modality Style Distributions')
-    ggsave(pp(plots.dir,'modality-styles.pdf'),p,width=10*pdf.scale,height=8*pdf.scale,units='in')
+      pdf.scale <- .8
+      p<-ggplot(toplot,aes(x=the.factor,y=n,fill=style))+geom_bar(stat='identity',position='stack')+labs(x='Trip #',y='# Agents',title='Modality Style Distributions')
+      ggsave(pp(plots.dir,'modality-styles.pdf'),p,width=10*pdf.scale,height=8*pdf.scale,units='in')
 
-    p<-ggplot(toplot,aes(x=style,y=n,fill=the.factor))+geom_bar(stat='identity',position='dodge')+labs(x='',y='# Agents',fill=fact,title='Modality Style Distributions')
-    ggsave(pp(plots.dir,'modality-styles-dodged.pdf'),p,width=10*pdf.scale,height=6*pdf.scale,units='in')
+      p<-ggplot(toplot,aes(x=style,y=n,fill=the.factor))+geom_bar(stat='identity',position='dodge')+labs(x='',y='# Agents',fill=fact,title='Modality Style Distributions')
+      ggsave(pp(plots.dir,'modality-styles-dodged.pdf'),p,width=10*pdf.scale,height=6*pdf.scale,units='in')
+    }
   }
 }