def prob_T(x,a):
gg=np.float128(g)
- aa=np.float128(10**(-gg-2)*a)
- Tr=np.float128(300.)
- return np.exp( np.log(aa) + (gg-2)/2*np.log(x) - gg*x/(2*Tr) )
+# aa=np.float128(10**(-gg-2)*a)
+ aa=np.float128(a)
+ Tr=np.float128(t_bath)
+ 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)
#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])
+t_bath=float(sys.argv[2])
with open('md.stat','r') as f:
for line in f:
pass
if ncolumns==14:
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)
+ x1,e,r,gy,nc= np.loadtxt('md.stat',usecols=(0,3,5,12,6),skiprows=0,unpack=True)
else:
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)
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
plt.clf()
plt.xlabel('step')
plt.ylabel('radius of gyration')
-plt.plot(x1,g,'.')
+plt.plot(x1,gy,'.')
plt.savefig('md_gyr.png')
if ncolumns==14:
plt.ylabel('RMSD')
plt.plot(x1,r,'.')
plt.savefig('md_rms.png')
-
\ No newline at end of file
+
+ plt.clf()
+ plt.xlabel('step')
+ plt.ylabel('fraction of native side-chain concacts')
+ plt.plot(x1,nc,'.')
+ plt.savefig('md_fracn.png')
+