In [1]:

    

f2 = open('rdfRefLow.dat', 'r');
lines = f2.readlines();
f2.close();
g3 = []
for line in lines:
    p = line.split()
    if len(p) > 1:
        g3.append([float(p[0]),float(p[1])]) 
f2 = open('rdfCGLow.dat', 'r');
lines = f2.readlines();
f2.close();
g2 = []
for line in lines:
    p = line.split()
    if len(p) > 1:
        g2.append([float(p[0]),float(p[1])]) 
f2 = open('rdfAALow.dat', 'r');
lines = f2.readlines();
f2.close();
g1 = []
for line in lines:
    p = line.split()
    if len(p) > 1:
        g1.append([float(p[0]),float(p[1])])


    

In [2]:

    

%pylab inline


    

    




Populating the interactive namespace from numpy and matplotlib

In [3]:

    

gn1 = np.array(g1)
gn2 = np.array(g2)
gn3 = np.array(g3)


    

In [4]:

    

plt.figure(figsize=(8,6), dpi=80)
plt.axis([0, 17, 0,8])
plt.xlabel(r'$r,\  (\AA)$',fontsize=20)
plt.ylabel(r'$\rho(r),\  RDF$',fontsize=20)
plt.plot(10*gn1[:,0],gn1[:,1],'g-.', label="All-Atom")
plt.plot(10*gn2[:,0],gn2[:,1],'r-', label="Reproduction Test")
plt.plot(10*gn3[:,0],gn3[:,1],'b--', label="Reference CG")
plt.legend(loc='upper right')
savefig("rdfL.pdf",bbox_inches='tight', dpi=300)


    

In [46]:

    




    

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