# 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)
# 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('md.stat',usecols=(0,10),skiprows=10,unpack=True)
- x1,e,r,gy,nc= np.loadtxt('md.stat',usecols=(0,3,5,12,6),skiprows=0,unpack=True)
+ x,y= np.loadtxt('md.stat',usecols=(1,10),skiprows=10,unpack=True)
+ x1,e,r,gy,nc= np.loadtxt('md.stat',usecols=(1,3,5,12,6),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)
+ x,y= np.loadtxt('md.stat',usecols=(1,6),skiprows=10,unpack=True)
+ x1,e,gy= np.loadtxt('md.stat',usecols=(1,3,8),skiprows=0,unpack=True)
+
+x=x*0.0489
+x1=x1*0.0489
h,bin=np.histogram(y,bins=50,density=True)
h,bin=np.histogram(y,bins=50,density=True)
-popt, pcov = curve_fit(prob_T, center, h)
+start = (g-2)/2*np.log(t_bath) - g*t_bath/(2*t_bath)
+popt, pcov = curve_fit(prob_T, center, h, p0=-start)