diff options
Diffstat (limited to 'data/combined/graphs/plots.py')
| -rwxr-xr-x | data/combined/graphs/plots.py | 87 |
1 files changed, 46 insertions, 41 deletions
diff --git a/data/combined/graphs/plots.py b/data/combined/graphs/plots.py index 98b9bc5..6f37b27 100755 --- a/data/combined/graphs/plots.py +++ b/data/combined/graphs/plots.py @@ -6,12 +6,17 @@ import matplotlib.mlab as mlab import sys import os import scipy +import matplotlib as mpl +mpl.rcParams['font.size'] = 8 +mpl.rcParams['lines.linewidth'] = 0.5 +mpl.rcParams['figure.figsize'] = 6,5 +mpl.rcParams['legend.fontsize'] = 8 +mpl.rcParams['axes.linewidth'] = 0.8 out_dir = sys.argv[1] - #limbs distribution -plt.cla() +plt.figure() data = np.loadtxt("../limbs-avg-zdiff/data.csv",delimiter=",") data = data[#(data[:,1] == 25) ((data != -1).all(1)) @@ -21,13 +26,13 @@ data = data[:,7:]*100 mean = data.mean(0) var = data.std(0) for i in range(len(mean)): - plt.subplot(3,3,i+1) - n,b,p = plt.hist(data[:,i],bins=50,normed=1) + ax = plt.subplot(2,5,i+1) + n,b,p = plt.hist(data[:,i],bins=100,normed=1,linewidth=0) plt.plot(b,mlab.normpdf(b,mean[i],var[i])) -plt.savefig(os.path.join(out_dir,"limbs.pdf")) +plt.savefig(os.path.join(out_dir,"limbs.pdf"),bbox_inches="tight",pad_inches=0.05) #frames distribution -plt.cla() +fig = plt.figure(figsize=(6,4)) x = np.loadtxt("frames.txt",usecols=(0,)) y = range(1,len(x)+1) width=0.8 @@ -38,88 +43,88 @@ plt.xlabel("Individual") plt.ylabel("Frame ratio [%]") plt.ylim(0,17) ax = plt.gca() -plt.savefig(os.path.join(out_dir,"frames.pdf")) +plt.savefig(os.path.join(out_dir,"frames.pdf"),bbox_inches="tight",pad_inches=0.05) l = ["3","5","10","all"] #10-fold, naive -plt.cla() -#ax = plt.subplot(121) -plt.axis([0,100,50,100]) -#ax.set_aspect(2) +plt.figure() for i in l: x,y = np.loadtxt(i+"_nb_off.mat",unpack=True) - plt.plot(100*x,100*y,label="$n_p=$ "+i,linewidth=0.8) + plt.plot(100*x,100*y,label="$n_p=$ "+i) plt.xlabel("Recall [%]") plt.ylabel("Precision [%]") plt.legend(loc="best") -plt.savefig(os.path.join(out_dir,"offline-nb.pdf")) +plt.axis([0,100,50,100]) +plt.savefig(os.path.join(out_dir,"offline-nb.pdf"),bbox_inches="tight",pad_inches=0.05) + #10-fold, SHT -#ax = plt.subplot(122) -#plt.axis([0,100,50,100]) -#ax.set_aspect(2) -plt.cla() +plt.figure() for i in l: x,y = np.loadtxt(i+"_sht_off.mat",unpack=True) - plt.plot(100*x,100*y,label="$n_p=$ "+i,linewidth=0.8) + plt.plot(100*x,100*y,label="$n_p=$ "+i) plt.xlabel("Recall [%]") plt.ylabel("Precision [%]") - plt.legend(loc="best") - + plt.legend(loc="lower left") plt.axis([0,100,50,100]) -plt.savefig(os.path.join(out_dir,"offline-sht.pdf")) +plt.savefig(os.path.join(out_dir,"offline-sht.pdf"),bbox_inches="tight",pad_inches=0.05) #online,NB -plt.cla() +plt.figure() for i in l: x,y = np.loadtxt(i+"_nb_on.mat",unpack=True) - plt.plot(100*x,100*y,label="$n_p=$ "+i,linewidth=0.8,markersize=4) + plt.plot(100*x,100*y,label="$n_p=$ "+i) plt.xlabel("Recall [%]") plt.ylabel("Precision [%]") plt.legend(loc="best") -plt.savefig(os.path.join(out_dir,"online-nb.pdf")) +plt.axis([0,100,50,100]) +plt.savefig(os.path.join(out_dir,"online-nb.pdf"),bbox_inches="tight",pad_inches=0.05) + #online,SHT -plt.cla() +plt.figure() for i in l: x,y = np.loadtxt(i+"_sht_on.mat",unpack=True) - plt.plot(100*x,100*y,label="$n_p=$ "+i,linewidth=0.8,markersize=4) + plt.plot(100*x,100*y,label="$n_p=$ "+i) plt.xlabel("Recall [%]") plt.ylabel("Precision [%]") plt.legend(loc="best") -plt.savefig(os.path.join(out_dir,"online-sht.pdf")) - +plt.axis([0,100,50,100]) +plt.savefig(os.path.join(out_dir,"online-sht.pdf"),bbox_inches="tight",pad_inches=0.05) #face -plt.cla() +plt.figure() x,y = np.loadtxt("all_nb_off.mat",unpack=True) a,b = np.loadtxt("face.csv",delimiter=",", unpack=True) -plt.plot(100*x,100*y,linewidth=0.8,label="Skeleton") -plt.plot(100*a,100*b,linewidth=0.8,label="Face") +plt.plot(100*x,100*y,label="Skeleton") +plt.plot(100*a,100*b,label="Face") plt.xlabel("Recall [%]") plt.ylabel("Precision [%]") plt.legend(loc="best") -plt.savefig(os.path.join(out_dir,"face.pdf")) +plt.axis([0,100,50,100]) +plt.savefig(os.path.join(out_dir,"face.pdf"),bbox_inches="tight",pad_inches=0.05) #back -plt.cla() +plt.figure() x,y = np.loadtxt("back_all_sht_on.mat",unpack=True) a,b = np.loadtxt("all_sht_on.mat",unpack=True) c,d = np.loadtxt("front_back_all_sht.mat",unpack=True) -plt.plot(100*a,100*b,linewidth=0.8,label="Train/test toward") -plt.plot(100*x,100*y,linewidth=0.8,label="Train/test away") -plt.plot(100*c,100*d,linewidth=0.8,label="Train toward test away") +plt.plot(100*a,100*b,label="Train/test toward") +plt.plot(100*x,100*y,label="Train/test away") +plt.plot(100*c,100*d,label="Train toward test away") plt.xlabel("Recall [%]") plt.ylabel("Precision [%]") plt.legend(loc="best") -plt.savefig(os.path.join(out_dir,"back.pdf")) +plt.axis([0,100,50,100]) +plt.savefig(os.path.join(out_dir,"back.pdf"),bbox_inches="tight",pad_inches=0.05) #variance-reduction -plt.cla() +plt.figure() x,y = np.loadtxt("half-var-all_sht_on.mat",unpack=True) a,b = np.loadtxt("all_sht_on.mat",unpack=True) -plt.plot(100*x,100*y,linewidth=0.8,label="Reduced noise") -plt.plot(100*a,100*b,linewidth=0.8,label="Original noise") +plt.plot(100*x,100*y,label="Reduced noise") +plt.plot(100*a,100*b,label="Original noise") plt.xlabel("Recall [%]") plt.ylabel("Precision [%]") plt.legend(loc="best") -plt.savefig(os.path.join(out_dir,"var.pdf")) +plt.axis([0,100,50,100]) +plt.savefig(os.path.join(out_dir,"var.pdf"),bbox_inches="tight",pad_inches=0.05) |