In order to create plots for CEFER and existing algorithms;

Run as following:
python plotgen.py -t 0 -m 0 -d 0 -f mytraces -g contact.gml -s 1 -sparam 1.0 -samp 0 -n 0.0 -e 0 -a 1 -p 0 -sprob 0.15

PARAMETERS:
-t: 0: continous, 1: discrete (default: 0)
-m: 0:si, 1:sir, 2:seir, 3:sis (default 0)
-d: 0: static 1: dynamic (default: 0)
-f: trace foldername (default: tracedir) (foldername where trace data is stored in pkl format)
-g: graphpath (default: graph.gml) (requires graph to be gml type)
-s: sparsetype(0: no sparsity, 1: l1, 2: l2, 3: l1+l2) (default:1)
-sparam: sparsityparameter lambda(default: 0.5)
-samp: sampling frequency(default: 0)
-n: noiserate(default: 0.0)
-e: errortype(0: abse, 1: lse ) (default:0)
-a: 0:original_cefer, 1:cover_cefer (default:1)
-p: 0: serial, 1: parallel(default: 0)
-sprob: spreading probability(default:0.15)
-s2id: susceptible to infected distribution (0: expo, 1:
powerlaw, 2:weibull, 3:lognormal, 4:rayleigh )
-s2idparam: susceptible to infected distribution parameter
-i2rd: infected to recovered distribution (0: expo, 1:
powerlaw, 2:weibull, 3:lognormal, 4:rayleigh )
-i2rdparam: infected to recovered distribution parameter
-s2ed: susceptible to exposed distribution(0: expo, 1:
powerlaw, 2:weibull, 3:lognormal, 4:rayleigh )
-s2edparam: susceptible to exposed distribution parameter
-e2id: exposed to infected distribution(0: expo, 1:
powerlaw, 2:weibull, 3:lognormal, 4:rayleigh )
-e2idparam: exposed to infected distribution parameter
-i2sd: infected to susceptible distribution(0: expo, 1:
powerlaw, 2:weibull, 3:lognormal, 4:rayleigh )
-i2sdparam: infected to susceptible distribution parameter

If you use this code, please cite the following paper.

