Results after first run

initialize.jl

@@ -14,7 +14,7 @@ data_type_functions = Dict(
     :driven_distance => RP.driven_distance,
     :requested_distance => RP.requested_distance,
     :mean_relative_delay => RP.mean_relative_delay,
-    :mean_occupancy => RP.mean_occupancy,
+   # :mean_occupancy => RP.mean_occupancy, #BUG In Reading TUples from a CSV File
 )
 
 
@@ -40,7 +40,6 @@ end
 function simulate_rp(paths::Dict, N::Int64, x, y, t0::Float64, specs; served = 10*N, requested=10*N)
 
     #Make Model
-    #TODO Make Random gens
     model=RP.get_model(;N_bus=N,ν=x/t0,specs...);
     RP.run!(model;requested=requested, served=served)
     data = Dict()
@@ -53,9 +52,9 @@ function simulate_rp(paths::Dict, N::Int64, x, y, t0::Float64, specs; served = 1
     # Save the calculated Data
     data = DataFrame(data)
 
-    #CSV.write(paths[:data]*"$(x)_$(y).csv", data)
+    #CSV.write(paths[:data]*"$index.csv", data)
     open(paths[:data]*"$index.csv", "w") do io
-        writedlm(io, Iterators.flatten(([names(data)], eachrow(data))), ',')
+        writedlm(io, Iterators.flatten(([names(data)], eachrow(data))), ';')
     end
     #Save the model for possible later reference
     RP.savemodel(paths[:model]*"$index.model",model;route_matrix=false)

put_together.jl

 using Plots
 using CSV
+using DataFrames
+using DelimitedFiles
 
 path = ARGS[1]
 len = eval(Meta.parse(ARGS[2]))
@@ -7,26 +9,30 @@ archive_path = ARGS[3]
 xname = "frequency"
 yname = "dt_earliest_pickup"
 
-df = Dataframe(path*"1.csv")
+lm = readdlm(path*"1.csv", ',', header=true, Any)
+file=CSV.File(path*"1.csv")
+println(file)
+df = DataFrame(file)
 
 
 for i in 2:len
     #TODO put all the generated CSV Files together into one big file which is then saved to the correct location
-    temp = DataFrame(path*"$i.csv")
+    temp = DataFrame(CSV.File(path*"$i.csv"))
     append!(df, temp)
 end
 println("Saving Combined Dataframe to $archive_path")
-CSV.write(archive_path*"$name.csv", df)
+CSV.write(archive_path*"results.csv", df)
 
-x, y = df[xname], df[yname]
+x, y = df[!,xname], df[!,yname]
 
-ks = names[df]
-deleteat!(ks, xname)
-deleteat!(ks, yname)
+ks = names(df)
+print(ks)
+filter!(e->e!= xname, ks)
+filter!(e->e!= yname, ks)
 
 
 for name in ks
-    data = df[name]
+    data = df[!,name]
     map = heatmap(1:size(data,1),
     1:size(data,2), data,
     #c=cgrad([:blue, :white,:red, :yellow]),

sim.jl

 using Pkg
-#Pkg.activate(".")
+Pkg.activate(".")
 
 #Pkg.add(url="https://gitlab.gwdg.de/smuehle1/RidePooling/", rev="planned_pickup")
 #Pkg.instantiate()   #there may be an error here concerning PyCall. The dependencies assume Python to be installed on your system, with package 'matplotlib' installed.