filter stat files before plot

[django_unres.git] / files / matplotlib_fit_hist.py

diff --git a/files/matplotlib_fit_hist.py b/files/matplotlib_fit_hist.py
index a932a04..bd4dbea 100755 (executable)
--- a/files/matplotlib_fit_hist.py
+++ b/files/matplotlib_fit_hist.py
@@ -20,17 +20,17 @@ def prob_T(x,a):
 #x,y= np.loadtxt('1L2Y_NH_GB000.stat',usecols=(0,11),skiprows=10000,unpack=True)
 #x,y= np.loadtxt('1L2Y_B_GB000.stat',usecols=(0,10),skiprows=30,unpack=True)
 g=int(sys.argv[1])
-with open('file_GB000.stat','r') as f:
+with open('md.stat','r') as f:
   for line in f:
     pass
   ncolumns=len(line.split())
 
 if ncolumns==14:  
- x,y= np.loadtxt('file_GB000.stat',usecols=(0,10),skiprows=10,unpack=True)
- x1,e,r= np.loadtxt('file_GB000.stat',usecols=(0,3,5),skiprows=0,unpack=True)
+ x,y= np.loadtxt('md.stat',usecols=(0,10),skiprows=10,unpack=True)
+ x1,e,r,gy= np.loadtxt('md.stat',usecols=(0,3,5,12),skiprows=0,unpack=True)
 else:
- x,y= np.loadtxt('file_GB000.stat',usecols=(0,6),skiprows=10,unpack=True)
- x1,e= np.loadtxt('file_GB000.stat',usecols=(0,3),skiprows=0,unpack=True)
+ x,y= np.loadtxt('md.stat',usecols=(0,6),skiprows=10,unpack=True)
+ x1,e,gy= np.loadtxt('md.stat',usecols=(0,3,8),skiprows=0,unpack=True)
  
 h,bin=np.histogram(y,bins=50,density=True)