Set font size, figsize, and bounding box of plots

Started taking Brano's comments into account (section 3)

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)