import sys
def prob_T(x,a):
- g=np.float128(105.)
+ gg=np.float128(g)
+ aa=np.float128(10**(-gg-2)*a)
Tr=np.float128(300.)
- return a * ( x**((g-2)/2) * np.exp( -g*x/(2*Tr) ) )
+ return np.exp( np.log(aa) + (gg-2)/2*np.log(x) - gg*x/(2*Tr) )
+# return aa * ( x**((gg-2)/2) * np.exp( -gg*x/(2*Tr) ) )
#x,y= np.loadtxt('1L2Y_L_GB000.stat',usecols=(0,10),skiprows=30,unpack=True)
#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)
-if (sys.argv[1] == '1L2Y_NH_GB000.stat'):
+g=int(sys.argv[2])
+if (sys.argv[1] == '1L2Y_NH_GB000.stat' or sys.argv[1] == '1L2Y_NH_GB.stat'):
x,y= np.loadtxt(sys.argv[1],usecols=(0,11),skiprows=10000,unpack=True)
else:
x,y= np.loadtxt(sys.argv[1],usecols=(0,10),skiprows=10000,unpack=True)
center = (bin[:-1] + bin[1:]) / 2
#print bin
#print center
-popt, pcov = curve_fit(prob_T, center, h, p0=10E-107)
+popt, pcov = curve_fit(prob_T, center, h)
xfine = np.linspace(min(bin), max(bin), 100)
#print popt