Merge 5263d8c into 65582b2

.gitignore

@@ -10,6 +10,12 @@ test/media/
 # media files generated by scripts
 scripts/media/
 
+# ipynb checkpoints in pythonscripts
+scripts/pythonscripts/.ipynb_checkpoints
+
+# idm acc trace files stored in lmfit
+scripts/lmfit/
+
 # Files generated by invoking Julia with --code-coverage
 *.jl.cov
 *.jl.*.cov

scripts/experiments.jl

@@ -51,6 +51,25 @@ for i in 1:length(s)
         JLD.save("media/upper_$i.jld","m",m,"p",p,"md",md,"veh_id_list",veh_id_list,"ts",ts,"te",te)
 end
 
+#*****************Generate idm params for lmfit************
+s = scenarios_upper
+f = FilteringEnvironment()
+name = "upper"
+
+for i in 1:length(s)
+        veh_id_list = s[i][1]
+        ts = s[i][2][1]
+        te = s[i][2][2]
+        filename = "media/upper_$i.jld";
+        id_list,ts,te = JLD.load(filename,"veh_id_list","ts","te");
+        scene_list_true = replay_scenelist(f,id_list=id_list,ts=ts,te=te);
+
+        dirname = "$(name)_$(i)"
+        mkdir("lmfit/$(dirname)")
+        feat_dict = scenelist2idmfeatures(f,scene_list_true,id_list=id_list,
+                        scenario_name=name,scenario_number=i);
+end
+
 
 #************Docstring example code from multiscenarios_pf in helpers.jl*****
 # USEFUL to keep around for making tikz plots later on by rerunning this script
@@ -87,30 +106,31 @@ coll_mat_list = [coll_mat_idm,coll_mat_cidm,coll_mat_lmidm,coll_mat_pf];
 coll_barchart(coll_mat_list,filename = "media/coll_barchart_upper.svg");
 
 
-#********************Train upper test lower******************
-# We need to show a variability in the generated scenarios
-# So we need to combine the particle sets of different vehicles together
-# And then for the same set of vehicles in the lower merge i.e. test domain
-# We show significantly different driving behavior by sampling from the particle set
+#***********Histogram plot****************
+# Full example in docstring of Filtering/metrics.jl vel_distribution
+a = PGFPlots.Axis([veh_hist_true,v_hist_idm,v_hist_cidm,v_hist_lmidm,v_hist_pf,
+Plots.Command(raw"\legend{true,idm,cidm,lmidm,pf}")
+]);
+
+aidm = PGFPlots.Axis([veh_hist_true,v_hist_idm,Plots.Command(raw"\legend{true,idm}")],
+xlabel="velocity",ylabel="density",title="True vs IDM");
 
+acidm = PGFPlots.Axis([veh_hist_true,v_hist_cidm,Plots.Command(raw"\legend{true,c-idm}")],
+xlabel="velocity",ylabel="density",title="True vs C-IDM");
 
-#************LsqFit failed experimentation***************
-# model(t, p) = p[1] * exp.(-p[2] * t)
-# tdata = range(0,stop=10,length=20)
-# ydata = model(tdata, [1.0 2.0]) + 0.01*randn(length(tdata))
-# p0 = [0.5,0.5]
-# fit = curve_fit(model, tdata, ydata, p0)
+almidm = PGFPlots.Axis([veh_hist_true,v_hist_lmidm,Plots.Command(raw"\legend{true,lm-idm}")],
+xlabel="velocity",ylabel="density",title="True vs LM-IDM");
 
-# function test_lmfit(d,p)   
-#         print("test_lmfit called\n")
-#         print("says:d=$d\n") 
-#         t = d["t"]
-#         return p[1]*exp.(-p[2]*t)
-# end
+apf = PGFPlots.Axis([veh_hist_true,v_hist_pf,Plots.Command(raw"\legend{true,pf}")],
+xlabel="velocity",ylabel="density",title="True vs pf");
 
-# test_tdata = []
-# for t in tdata
-#         push!(test_tdata,Dict("t"=>t))
-# end
+PGFPlots.save("media/vhist_idm.svg",aidm)
+PGFPlots.save("media/vhist_cidm.svg",acidm)
+PGFPlots.save("media/vhist_lmidm.svg",almidm)
+PGFPlots.save("media/vhist_pf.svg",apf)
 
-# curve_fit(test_lmfit,test_tdata,ydata,[0.5,0.5])
+#********************Train upper test lower******************
+# We need to show a variability in the generated scenarios
+# So we need to combine the particle sets of different vehicles together
+# And then for the same set of vehicles in the lower merge i.e. test domain
+# We show significantly different driving behavior by sampling from the particle set