I am coding in R-studio and have a function called saveResults(). It takes:
sce - a Single Cell Experiment object.
opt - a list with five things
clusterLabels - simple dataframe with two columns
The important thing is that I receive an error stating:
Error: unexpected symbol in:
"saveResults(sce = sce, opt = opt, clusteInputs()
zhengMix"
which doesn't agree at all with the parameters I pass into the function. You can see this on the last line of the code block below: I pass in proper parameters, but I receive an error that says I have passed in clusteInputs(), and zhengMix instead of clusterLabels. I don't have a function called clusteInputs(), and zhengMix was several lines above.
# Save the clustering data
InstallAndLoadPackagesForSC3Clustering()
opt <- GetOptionInputs()
zhengMix <- FetchzhengMix(opt)
sce <- CreateSingleCellExperiment(zhengMix)
clusterLabels <- getClusterLabels(sce)
opt <- createNewDirectoriesToSaveData(opt)
saveResults <- function(sce, opt, clusterLabels){
print("Beginning process of saving results...")
maxClusters = ncol(clusterLabels)/2+1
for (n in 2:maxClusters){
savePCAasPDF(sce, opt, numOfClusters = n, clusterLabels)
saveClusterLabelsAsRDS(clusterLabels, numOfClusters = n, opt)
}
saveSilhouetteScores(sce, opt)
print("Done.")
}
saveResults(sce = sce, opt = opt, clusterLabels = clusterLabels)
Does anyone have an idea what is going on? I'm pretty stuck on this.
This isn't the best solution, but I fixed my own problem by removing the code out of the function and running it there caused no issues.
Related
I am trying to construct a function with an if statement within.
IN the code below,
I basically want to use different contr_x<- makeContrast.. command for different contr_x. ex) If contr_1 is in the input, it will use the first 1f's make constrast command, if contr_2 is in the input, it will use the second if's make constrast command...
But I am running in to the error that says the object contr_1 is not found. I am confused because in my understanding. contr_1 is just an input name, not an object. (not previously defined)
I am attaching the function code and the error below; I 'll appreciate any insight!!
code
run_limma <- function (model_x, support_x, fit_x,editing_x,contr_x,tmp_x){
message("starting modeling")
model_x<-model(support_x, model_x)
message("starting fitting")
fit_x<-limma_diff(editing_x, model_x,fit_x)
message("Making contrasts")
if (contr_x == "contr_1") {contr_x<-makeContrasts (diseaseAD - diseaseControl,
levels = colnames(coef(fit_x)))
}
if (contr_x == "contr_2") {contr_x<-makeContrasts (diseaseAD_MCI - diseaseControl,
levels = colnames(coef(fit_x)))
}
if (contr_x == "contr_3") {contr_x<-makeContrasts (diseaseMCI - diseaseControl,
levels = colnames(coef(fit_x)))
}
if (contr_x == "contr_4") {contr_x<-makeContrasts (diseasePD - diseaseControl,
levels = colnames(coef(fit_x)))
}
message("making tmp file")
tmp_x<-limma_cont(contr_x, fit_x, tmp_x)
tmp_x
}
error
run_limma(model_1, support_1, fit_1,editing_1,contr_1,tmp_1)
starting modeling
starting fitting
Making contrasts
Error in run_limma(model_1, support_1, fit_1, editing_1, contr_1, tmp_1) :
object 'contr_1' not found
Trying to cut a bunch of audio (.WAV) files into smaller samples in R. For this example, I'm using a loop to cut out 1 minute samples at 140 minutes.
For some files, the recording ends before 140 minutes due to an error in the recording device. When this occurs, an error appears -- and the loop stops. I'm trying to make it so the loop continues by using the try or tryCatch function however keep getting errors.
The code is as follows:
for(i in 1:length(AR_CD288)){
CUT_AR288_5 <- try({readWave(AR_CD288[i], from = 140, to = 141, units = "minutes")})
FILE.OUT_AR288_5<- sub("\\.wav$", "_140.wav", AR_CD288)
OUT.PATH_AR288_5 <- file.path("New files", basename(FILE.OUT_AR288_5))
writeWave(CUT_AR288_5, extensible=FALSE, filename = OUT.PATH_AR288_5[i])
}
I get the following two errors from the code:
Error in readBin(con, int, n = N, size = bytes, signed = (bytes != 1), :
invalid 'n' argument
Error in writeWave(CUT_AR288_5, extensible = FALSE, filename = OUT.PATH_AR288_5[i]) :
'object' needs to be of class 'Wave' or 'WaveMC
The loop still saves some samples into the "New files" directory, however, once the loop reaches a file <140 minutes, the loop stops.
I am very stuck! Any help would be greatly appreciated.
Cheers.
When I use try, I always do one (or both) of:
check the return value to see if it inherits "try-error", indicating that the command failed; or
add try(., silent = TRUE), indicating that I don't care if it succeeded (but this implies that I will not use its return value, either).
Try this:
for (i in seq_along(AR_CD288)) {
CUT_AR288_5 <- try({
readWave(AR_CD288[i], from = 140, to = 141, units = "minutes")
}, silent = TRUE)
if (!inherits(CUT_AR288_5, "try_error")) {
FILE.OUT_AR288_5 <- sub("\\.wav$", "_140.wav", AR_CD288)
OUT.PATH_AR288_5 <- file.path("New files", basename(FILE.OUT_AR288_5))
writeWave(CUT_AR288_5, extensible = FALSE, filename = OUT.PATH_AR288_5[i])
}
}
Three notes:
I changed 1:length(.) to seq_along(.); the latter is more resilient in an automated use when it is feasible that the vector might be length 0. For example, if AR_CD288 can ever be length 2, intuitively we expect 1:length(AR_CD288) to return nothing so that the for loop will not run; unfortunately, it resolves to 1:0 which returns a vector of length 2, which will often fail (based on whatever code is operating in the loop). The use of seq_along(.) will always return a vector of length 0 with an empty input, which is what we need. (Alternatively and equivalent, seq_len(length(AR_CD288)), though that's really what seq_along is intended to do.)
If you do not add silent=TRUE (or explicitly add silent=FALSE), then you will get an error message indicating that the command failed. Unfortunately, the error message may not indicate which i failed, so you may be left in the dark as far as fixing or removing the errant file. You may prefer to add an else to the if (inherits(.,"try-error")) clause so that you can provide a clearer error, such as
if (inherits(CUT_AR288_5, "try_error")) {
warning("'readWave' failed on ", sQuote(AR_CD288[i]), call. = FALSE)
} else {
FILE.OUT_AR288_5 <- sub("\\.wav$", "_140.wav", AR_CD288)
# ...
}
(noting that I put the "things worked" code in the else clause here ... I find it odd to do if (!...) {} else {}, seems like a double-negation :-).
The choice to wrap one function or the whole block depends on your needs: I tend to prefer to know exactly where things fail, so the will-possibly-fail functions are often individually wrapped with try so that I can react (or log/message) accordingly. If you don't need that resolution of error-detection, then you can certainly wrap the whole code-block in a sense:
for (i in seq_along(AR_CD288)) {
ret <- try({
CUT_AR288_5 <- readWave(AR_CD288[i], from = 140, to = 141, units = "minutes")
FILE.OUT_AR288_5 <- sub("\\.wav$", "_140.wav", AR_CD288)
OUT.PATH_AR288_5 <- file.path("New files", basename(FILE.OUT_AR288_5))
writeWave(CUT_AR288_5, extensible = FALSE, filename = OUT.PATH_AR288_5[i])
}, silent = TRUE)
if (inherits(ret, "try-error")) {
# do or log something
}
}
I am trying to evaluate strings within a for loop within an R script using JuliaCall::julia_eval. While I was able to accomplish this in R using the deSolve package, I am running into issues when converting the code to one that is compatible with Julia. The base code for the correctly functioning R deSolve code is shown below.
library(deSolve)
library(dplyr)
Combine <- c(" - 1*0.4545*(H2O2^1) - 1*27000000*(`$OH`^1)*(H2O2^1)", " - 1*3100000000*(`1,4-dioxane`^1)*(`$OH`^1)",
" - 1*33000*(TOC^1)*(`$OH`^1)", "2*0.4545*(H2O2^1) - 1*3100000000*(`1,4-dioxane`^1)*(`$OH`^1) - 1*33000*(TOC^1)*(`$OH`^1) - 1*27000000*(`$OH`^1)*(H2O2^1) - 1*8500000*(`$OH`^1)*(`HCO3-`^1) - 1*390000000*(`$OH`^1)*(`CO3 2-`^1)",
" - 1*8500000*(`$OH`^1)*(`HCO3-`^1)", " - 1*390000000*(`$OH`^1)*(`CO3 2-`^1)"
)
time <- seq(from=0, to=0.01, by = 1E-4)
State <- c(H2O2 = 0.000294117647058824, `1,4-dioxane` = 0.00000113494,
TOC = 0, `$OH` = 0, `HCO3-` = 0.003766104, `CO3 2-` = 0.0000167638711956647)
ODEcreater2 <- function(t, state, parameters){
with(as.list(c(state)),{
for (i in 1:6) { #
dY[i] <- eval(parse(text=Combine[i]))}
return(list(dY))
} )}
out1<- ode(y = state, times = time, func = ODEcreater2, parms = NULL)
I am trying to use replicate the code and run it in Julia to improve the speed of the ODE solver by using diffeqr vs. deSolve. Unfortunately, I am running into evaluating the string/expression within a for loop in julia_call.
library(diffeqr)
diffeqr::diffeq_setup()
library(JuliaCall)
julia <- julia_setup()
ODEcreater <- JuliaCall::julia_eval("
function (dY,t,state)
for i in 1:6
dY[i] = eval(Meta.parse(:Combine[i]))
end
end")
tspan <- list(1E-6, 1E-3)
sol = diffeqr::ode.solve(ODEcreater,state,tspan, abstol=1e-8, reltol=1e-8)
Does anyone have any insight into the best way to evaluate the strings within the for loop? I have been investigating metaexpressions on the JuliaLang website but am still lost.
As mentioned in the duplicate question https://stackoverflow.com/a/58766919/1544203 , building the string and then doing
sprintf("function f(du,u,p,t)\n%s\nend", paste(Combine, collapse="\n"))
from the R side builds a single string which matches the format that works. This is also optimal since it excludes any extra function calls from the generated function.
from julia docs:
parse(str; raise=true, depwarn=true)
Parse the expression string greedily, returning a single expression. An error is thrown if there are additional
characters after the first expression. If raise is true (default), syntax errors will raise an error; otherwise, parse
will return an expression that will raise an error upon evaluation. If depwarn is false, deprecation warnings will be
suppressed.
julia> Meta.parse("x = 3")
:(x = 3)
so, Meta.parse accepts a string and returns an expression. this should evaluate correctly:
eval(Meta.parse(Combine[i]))
one problem i see is the use of non-valid julia variable names, like $OH
I'm attempting to code the method described here to estimate production functions of metal manufacturers. I've done this in Python and Matlab, but am trying to learn Julia.
spain_clean.csv is a dataset of log capital (lnk), log labor (lnl), log output (lnva), and log materials (lnm) that I am loading. Lagged variables are denoted with an "l" before them.
Code is at the bottom. I am getting an error:
ERROR: LoadError: MethodError: no method matching parseNLExpr_runtime(::JuMP.Model, ::JuMP.GenericQuadExpr{Float64,JuMP.Variable}, ::Array{ReverseDiffSparse.NodeData,1}, ::Int32, ::Array{Float64,1})
I think it has to do with the use of vector sums and arrays going into the non-linear objective, but I do not understand Julia enough to debug this.
using JuMP # Need to say it whenever we use JuMP
using Clp, Ipopt # Loading the GLPK module for using its solver
using CSV # csv reader
# read data
df = CSV.read("spain_clean.csv")
#MODEL CONSTRUCTION
#--------------------
acf = Model(solver=IpoptSolver())
#variable(acf, -10<= b0 <= 10) #
#variable(acf, -5 <= bk <= 5 ) #
#variable(acf, -5 <= bl <= 5 ) #
#variable(acf, -10<= g1 <= 10) #
const g = sum(df[:phihat]-b0-bk* df[:lnk]-bl* df[:lnl]-g1* (df[:lphihat]-b0-bk* df[:llnk]-bl* df[:llnl]))
const gllnk = sum((df[:phihat]-b0-bk* df[:lnk]-bl* df[:lnl]-g1* (df[:lphihat]-b0-bk* df[:llnk]-bl* df[:llnl])).*df[:llnk])
const gllnl = sum((df[:phihat]-b0-bk* df[:lnk]-bl* df[:lnl]-g1* (df[:lphihat]-b0-bk* df[:llnk]-bl* df[:llnl])).*df[:llnl])
const glphihat = sum((df[:phihat]-b0-bk* df[:lnk]-bl* df[:lnl]-g1* (df[:lphihat]-b0-bk* df[:llnk]-bl* df[:llnl])).*df[:lphihat])
#OBJECTIVE
#NLobjective(acf, Min, g* g + gllnk* gllnk + gllnl* gllnk + glphihat* glphihat)
#SOLVE IT
status = solve(acf) # solves the model
println("Objective value: ", getobjectivevalue(acf)) # getObjectiveValue(model_name) gives the optimum objective value
println("b0 = ", getvalue(b0))
println("bk = ", getvalue(bk))
println("bl = ", getvalue(bl))
println("g1 = ", getvalue(g1))
No an expert in Julia, but I think a couple of things are wrong about your code.
first, constant are not supposed to change during iteration and you are making them functions of control variables. Second, what you want to use there are nonlinear expression instead of constants. so instead of the constants what you want to write is
N = size(df, 1)
#NLexpression(acf, g, sum(df[i, :phihat]-b0-bk* df[i, :lnk]-bl* df[i, :lnl]-g1* (df[i, :lphihat]-b0-bk* df[i, :llnk]-bl* df[i, :llnl]) for i=1:N))
#NLexpression(acf, gllnk, sum((df[i,:phihat]-b0-bk* df[i,:lnk]-bl* df[i,:lnl]-g1* (df[i,:lphihat]-b0-bk* df[i,:llnk]-bl* df[i,:llnl]))*df[i,:llnk] for i=1:N))
#NLexpression(acf,gllnl,sum((df[i,:phihat]-b0-bk* df[i,:lnk]-bl* df[i,:lnl]-g1* (df[i,:lphihat]-b0-bk* df[i,:llnk]-bl* df[i,:llnl]))*df[i,:llnl] for i=1:N))
#NLexpression(acf,glphihat,sum((df[i,:phihat]-b0-bk* df[i,:lnk]-bl* df[i,:lnl]-g1* (df[i,:lphihat]-b0-bk* df[i,:llnk]-bl* df[i,:llnl]))*df[i,:lphihat] for i=1:N))
I tested this and it seems to work.
I have a multidimensional array (B_matrix) that I need to fill up with some random values. Since the dimension depends on two parameters K and C that are user defined, I cannot use nested loop to fill the array, so I have decided to fill it up recursively.
The problem with the recursion function (fillUp) is that that even though the array is declared outside the function, the array is set to NULL after the function is run.
B_dim = rep(2,((K+1+C)*2))
B_matrix = array( dim = B_dim, dimnames = NULL)
string = c()
fillUp<-function(level, string ){
if (level>=1){
for(i in c(1,2)){
Recall(level-1, c(string, i))
}
}else{
B_matrix[string] = 1;
}
}
fillUp(length(B_dim), string)
> sum( B_matrix == 1)
[1] NA
I'm new to R, so I'm not sure if the "global" declaration allows fillUp to change the values of the matrix.
Edit:
Note that the line
B_matrix[string] = 1;
is just a test case, and the original idea is to assign some random value that depends of the position of the array element.
Edit2:
Based on what #Bridgeburners hinted, I'm almost there. Replacing B_matrix[string] = 1, by
assign('str', matrix(string,1), envir=.GlobalEnv)
assign('hl', B_half_length, envir=.GlobalEnv)
rul <-runif(1, 0, sum(str[1:hl]))
with( .GlobalEnv,B_matrix[str] <- rul)
I get the error (last line):
Error in eval(expr, envir, enclos) : object 'rul' not found
The problem, I guess, is that I'm working with variables from two different environments at the same time. I don't know how to proceed here.
This option doesn't work either
assign('str',matrix(string,1), envir=.GlobalEnv)
assign('hl', B_half_length, envir=.GlobalEnv)
assign('ru', runif(1, 0, sum(str[1:hl])), envir=.GlobalEnv)
with( .GlobalEnv,B_matrix[str] <- ru)
Note: no visible binding for global variable 'ru'
Edit3:
I've finally solved it:
assign('str',matrix(string,1), envir=.GlobalEnv)
with( .GlobalEnv, B_matrix[str] <- runif(1, 0, sum(str[1:B_half_length])-B_half_length+1) )
where B_half_length is a global variable
Whenever a process is working within a function, it's working in a different environment. The object "B_matrix" is defined in the global environment. Since you're nesting environments (2*(K+C+1) times) you're not impacting the original object. If you simply replace line
B_matrix[string] = 1;
with
assign('str', matrix(string,1), envir=.GlobalEnv)
with(.GlobalEnv,B_matrix[str] <- 1)
your code will work. You simply need to specify which environment your expression is working in. (In the first line you're passing the local value of "string" to a global object named "str".)
Note, also, that indexing an array with a vector doesn't work.
That is, "B_matrix[2,2,2,2,2,2]" is not the same as "B_matrix[c(2,2,2,2,2,2)]".
But it works with a matrix
What you want can be achieved with the following line code once you have initialised you B_matrix array:
B_matrix[] <- runif(length(B_matrix))