Generate plots at the right size. The size shouldn't be set in the latex code

2 files changed, 49 insertions, 31 deletions

diff --git a/data/combined/graphs/plots.py b/data/combined/graphs/plots.py
index 6f37b27..a4b01f3 100755
--- a/data/combined/graphs/plots.py
+++ b/data/combined/graphs/plots.py
@@ -8,15 +8,19 @@ import os
 import scipy
 import matplotlib as mpl
 
-mpl.rcParams['font.size'] = 8
+mpl.rcParams['font.size'] = 5
 mpl.rcParams['lines.linewidth'] = 0.5
-mpl.rcParams['figure.figsize'] = 6,5
-mpl.rcParams['legend.fontsize'] = 8
-mpl.rcParams['axes.linewidth'] = 0.8
+mpl.rcParams['figure.figsize'] = 2.2,2.2
+mpl.rcParams['legend.fontsize'] = 5
+mpl.rcParams['axes.linewidth'] = 0.5
+mpl.rcParams['figure.subplot.hspace'] = 0.4
+mpl.rcParams['figure.subplot.wspace'] = 0.4
+legend_width = 0.2
+#mpl.rcParams.update(params)
 out_dir = sys.argv[1]
 
 #limbs distribution
-plt.figure()
+plt.figure(figsize=(5.6,1.7))
 data = np.loadtxt("../limbs-avg-zdiff/data.csv",delimiter=",")
 data = data[#(data[:,1] == 25) 
              ((data != -1).all(1))
@@ -27,22 +31,25 @@ mean = data.mean(0)
 var = data.std(0)
 for i in range(len(mean)):
     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]))
+    n,b,p = plt.hist((data[:,i]-mean[i]),bins=100,normed=1,linewidth=0,color="lightgreen")
+    plt.plot(b,mlab.normpdf(b,0,var[i]),"red")
+    plt.xticks([])
+    plt.yticks([])
+    plt.xlabel("$x_"+str(i+1)+"-E[x_"+str(i+1)+"|y]$")
+    plt.ylabel("$P(x_"+str(i+1)+"-E[x_"+str(i+1)+"|y])$")
 plt.savefig(os.path.join(out_dir,"limbs.pdf"),bbox_inches="tight",pad_inches=0.05)
 
 #frames distribution
-fig = plt.figure(figsize=(6,4))
+plt.figure(figsize=(4.5,2))
 x = np.loadtxt("frames.txt",usecols=(0,))
 y = range(1,len(x)+1)
 width=0.8
-plt.bar(y,x/x.sum()*100,width=width)
+plt.bar(y,x/x.sum()*100,width=width,linewidth=0,color="lightgreen")
 plt.xlim(0.8,26)
 plt.xticks([i+width/2. for i in range(1,len(x),5)], range(1,len(x),5))
 plt.xlabel("Individual")
 plt.ylabel("Frame ratio [%]")
 plt.ylim(0,17)
-ax = plt.gca()
 plt.savefig(os.path.join(out_dir,"frames.pdf"),bbox_inches="tight",pad_inches=0.05)
 
 l = ["3","5","10","all"]
@@ -52,9 +59,10 @@ 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)
-    plt.xlabel("Recall [%]")
-    plt.ylabel("Precision [%]")
-    plt.legend(loc="best")
+plt.xlabel("Recall [%]")
+plt.ylabel("Precision [%]")
+leg =plt.legend(loc="best")
+leg.get_frame().set_linewidth(legend_width)
 plt.axis([0,100,50,100])
 plt.savefig(os.path.join(out_dir,"offline-nb.pdf"),bbox_inches="tight",pad_inches=0.05)
 
@@ -63,9 +71,10 @@ 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)
-    plt.xlabel("Recall [%]")
-    plt.ylabel("Precision [%]")
-    plt.legend(loc="lower left")
+plt.xlabel("Recall [%]")
+plt.ylabel("Precision [%]")
+leg = plt.legend(loc="lower left")
+leg.get_frame().set_linewidth(legend_width)
 plt.axis([0,100,50,100])
 plt.savefig(os.path.join(out_dir,"offline-sht.pdf"),bbox_inches="tight",pad_inches=0.05)
 
@@ -74,9 +83,10 @@ 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)
-    plt.xlabel("Recall [%]")
-    plt.ylabel("Precision [%]")
-    plt.legend(loc="best")
+plt.xlabel("Recall [%]")
+plt.ylabel("Precision [%]")
+leg = plt.legend(loc="best")
+leg.get_frame().set_linewidth(legend_width)
 plt.axis([0,100,50,100])
 plt.savefig(os.path.join(out_dir,"online-nb.pdf"),bbox_inches="tight",pad_inches=0.05)
 
@@ -85,9 +95,10 @@ 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)
-    plt.xlabel("Recall [%]")
-    plt.ylabel("Precision [%]")
-    plt.legend(loc="best")
+plt.xlabel("Recall [%]")
+plt.ylabel("Precision [%]")
+leg = plt.legend(loc="best")
+leg.get_frame().set_linewidth(legend_width)
 plt.axis([0,100,50,100])
 plt.savefig(os.path.join(out_dir,"online-sht.pdf"),bbox_inches="tight",pad_inches=0.05)
 
@@ -99,7 +110,8 @@ 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")
+leg = plt.legend(loc="best")
+leg.get_frame().set_linewidth(legend_width)
 plt.axis([0,100,50,100])
 plt.savefig(os.path.join(out_dir,"face.pdf"),bbox_inches="tight",pad_inches=0.05)
 
@@ -113,7 +125,8 @@ 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")
+leg = plt.legend(loc="best")
+leg.get_frame().set_linewidth(legend_width)
 plt.axis([0,100,50,100])
 plt.savefig(os.path.join(out_dir,"back.pdf"),bbox_inches="tight",pad_inches=0.05)
 
@@ -125,6 +138,7 @@ 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")
+leg = plt.legend(loc="best")
+leg.get_frame().set_linewidth(legend_width)
 plt.axis([0,100,50,100])
 plt.savefig(os.path.join(out_dir,"var.pdf"),bbox_inches="tight",pad_inches=0.05)
diff --git a/data/pair-matching/roc.py b/data/pair-matching/roc.py
index 19a1acf..7396ab4 100755
--- a/data/pair-matching/roc.py
+++ b/data/pair-matching/roc.py
@@ -6,11 +6,14 @@ import matplotlib as mpl
 import math
 from sets import ImmutableSet
 
-mpl.rcParams['font.size'] = 8
+mpl.rcParams['font.size'] = 5
 mpl.rcParams['lines.linewidth'] = 0.5
-mpl.rcParams['figure.figsize'] = 6,5
-mpl.rcParams['legend.fontsize'] = 8
-mpl.rcParams['axes.linewidth'] = 0.8
+mpl.rcParams['figure.figsize'] = 2.2,2.2
+mpl.rcParams['legend.fontsize'] = 5
+mpl.rcParams['axes.linewidth'] = 0.5
+mpl.rcParams['figure.subplot.hspace'] = 0.4
+mpl.rcParams['figure.subplot.wspace'] = 0.4
+legend_width = 0.2
 
 def distance(a,b):
     return math.sqrt(np.square(a-b).sum())
@@ -43,6 +46,7 @@ def gen_pairs(var,sk_data):
     return result
 
 if __name__ == "__main__":
+    plt.figure(figsize=(3,2.2))
     ap = np.loadtxt("associatepredict.txt",delimiter=",")
     indices = [i for i in range(ap.shape[0]) if ap[i,1]<0.1]
     ap_false = ap[:,1][indices]
@@ -74,9 +78,9 @@ if __name__ == "__main__":
         true_pos = np.array(true_pos)
         true_pos = true_pos[indices]
         plt.plot(false_pos,true_pos,label="$\sigma$ = "+str(s))
-    plt.legend(loc="lower right")
+    leg = plt.legend(loc="lower right")
+    leg.get_frame().set_linewidth(legend_width)
     plt.savefig("roc.pdf",bbox_inches="tight",pad_inches=0.05)
-    plt.show()