def prob_T(x,a):
gg=np.float128(g)
- aa=np.float128(10**(-gg-2)*a)
+# aa=np.float128(10**(-gg-2)*a)
+ aa=np.float128(a)
Tr=np.float128(t_bath)
- return np.exp( np.log(aa) + (gg-2)/2*np.log(x) - gg*x/(2*Tr) )
+ return np.exp( aa + (gg-2)/2*np.log(x) - gg*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)
center = (bin[:-1] + bin[1:]) / 2
#print bin
#print center
-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)
xfine = np.linspace(min(bin), max(bin), 100)
#print popt