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In [1]:



    


import numpy as np
import matplotlib.pyplot as plt
from scipy.interpolate import interp1d
from scipy.interpolate import griddata

Plotting contour plot of biased FES





In [3]:



    


%matplotlib inline 

#STRIDE 500!!
#read the data in from a text file 
fesdata = np.genfromtxt('fes.dat',comments='#');

dim1=985
dim2=986
fesdata = fesdata[:,0:3]
enertokcal=4.184
#some post-processing to be compatible with contourf 
X=np.reshape(fesdata[:,0],[dim1,dim2],order="F")  #order F was 20% faster than A/C
Y=np.reshape(fesdata[:,1],[dim1,dim2],order="F") 
Z=np.reshape((fesdata[:,2]-np.min(fesdata[:,2]))/enertokcal,[dim1,dim2],order="F")  #convert to kcal/mol

#what spacing do you want?  
spacer=1.0
lines=10 #this goes to 80 kcal/mol (2*40)
levels=np.linspace(0,lines*spacer,num=(lines+1),endpoint=True)

fig=plt.figure(figsize=(10,8)) 
axes = fig.add_subplot(111)

plt.contourf(X, Y, Z, levels, cmap=plt.cm.bone,)
plt.colorbar()
plt.xlabel('$d1$')
plt.ylabel('$d2$')
axes.set_ylim([.1,.5])
axes.set_xlim([0.1,0.5])
plt.rcParams.update({'font.size': 18})
plt.show()



    


















    
























In [3]:



    


%matplotlib inline 

#STRIDE 1K!!
#read the data in from a text file 
fesdata = np.genfromtxt('fes_1K.dat',comments='#');
dim1=985
dim2=985
fesdata = fesdata[:,0:3]
enertokcal=4.184
#some post-processing to be compatible with contourf 
X=np.reshape(fesdata[:,0],[dim1,dim2],order="F")  #order F was 20% faster than A/C
Y=np.reshape(fesdata[:,1],[dim1,dim2],order="F") 
Z=np.reshape((fesdata[:,2]-np.min(fesdata[:,2]))/enertokcal,[dim1,dim2],order="F")  #convert to kcal/mol

#what spacing do you want?  
spacer=1.0
lines=10 #this goes to 80 kcal/mol (2*40)
levels=np.linspace(0,lines*spacer,num=(lines+1),endpoint=True)

fig=plt.figure(figsize=(10,8)) 
axes = fig.add_subplot(111)

plt.contourf(X, Y, Z, levels, cmap=plt.cm.bone,)
plt.colorbar()
plt.xlabel('$d1$')
plt.ylabel('$d2$')
axes.set_ylim([.1,.5])
axes.set_xlim([0.1,0.5])
plt.rcParams.update({'font.size': 12})
plt.show()



    


















    
























In [6]:



    


%matplotlib inline 

#STRIDE 1K!!
#read the data in from a text file 
fesdata = np.genfromtxt('fes_20K.dat',comments='#');
dim1=972
dim2=974
fesdata = fesdata[:,0:3]
enertokcal=4.184
#some post-processing to be compatible with contourf 
X=np.reshape(fesdata[:,0],[dim1,dim2],order="F")  #order F was 20% faster than A/C
Y=np.reshape(fesdata[:,1],[dim1,dim2],order="F") 
Z=np.reshape((fesdata[:,2]-np.min(fesdata[:,2]))/enertokcal,[dim1,dim2],order="F")  #convert to kcal/mol

#what spacing do you want?  
spacer=1.0
lines=10 #this goes to 80 kcal/mol (2*40)
levels=np.linspace(0,lines*spacer,num=(lines+1),endpoint=True)

fig=plt.figure(figsize=(10,8)) 
axes = fig.add_subplot(111)

plt.contourf(X, Y, Z, levels, cmap=plt.cm.bone,)
plt.colorbar()
plt.xlabel('$d1$')
plt.ylabel('$d2$')
axes.set_ylim([.1,.5])
axes.set_xlim([0.1,0.5])
plt.rcParams.update({'font.size': 12})
plt.show()



    


















    
























In [3]:



    


%matplotlib inline 

#read the data in from a text file 
fesdata = np.genfromtxt('rew_avg.dat',comments='#');
dim1=200
dim2=200

fesdata = fesdata[:,0:3]
enertokcal=1.0
#some post-processing to be compatible with contourf 
X=np.reshape(fesdata[:,0],[dim1,dim2],order="F")  #order F was 20% faster than A/C
Y=np.reshape(fesdata[:,1],[dim1,dim2],order="F") 
Z=np.reshape((fesdata[:,2]-np.min(fesdata[:,2]))/enertokcal,[dim1,dim2],order="F")  #convert to kcal/mol

#what spacing do you want?  
spacer=1. 
lines=10 #this goes to 80 kcal/mol (2*40)
levels=np.linspace(0,lines*spacer,num=(lines+1),endpoint=True)

fig=plt.figure(figsize=(10,8)) 
axes = fig.add_subplot(111)

plt.contourf(X, Y, Z, levels, cmap=plt.cm.bone,)
plt.colorbar()
plt.xlabel('$d1$')
plt.ylabel('$d2$')
axes.set_ylim([.1,.5])
axes.set_xlim([0.1,0.5])
plt.rcParams.update({'font.size': 12})
plt.show()



    


















    
























In [4]:



    


#read the data in from a text file 
fesdata = np.genfromtxt('G09.PES.dat',comments='#');

dim1=39
dim2=39

fesdata = fesdata[:,0:3]
enertokcal=1.0
#some post-processing to be compatible with contourf 
X=np.reshape(fesdata[:,0]/10.0,[dim1,dim2],order="F")  #order F was 20% faster than A/C
Y=np.reshape(fesdata[:,1]/10.0,[dim1,dim2],order="F") 
Z=np.reshape((fesdata[:,2]-np.min(fesdata[:,2]))/enertokcal,[dim1,dim2],order="F")  #convert to kcal/mol

#what spacing do you want?  
spacer=1. 
lines=10 #this goes to 80 kcal/mol (2*40)
levels=np.linspace(0,lines*spacer,num=(lines+1),endpoint=True)

fig=plt.figure(figsize=(10,8)) 
axes = fig.add_subplot(111)

plt.contourf(X, Y, Z, levels, cmap=plt.cm.bone,)
plt.colorbar()
plt.xlabel('$d1$')
plt.ylabel('$d2$')
axes.set_ylim([.1,.5])
axes.set_xlim([0.1,0.5])
plt.rcParams.update({'font.size': 12})
plt.show()



    


















    
























In [3]:



    


##THIS IS THE PES FROM GAUSSIAN 
fesdata = np.genfromtxt('G09.PES.dat',comments='#');

sig=.05
method='cubic'
newxr=np.arange(fesdata[:,0].min()+.1,fesdata[:,0].max(),sig)
newyr=np.arange(fesdata[:,1].min()+.1,fesdata[:,1].max(),sig)
Xnew,Ynew=np.meshgrid(newxr,newyr)
Znew=griddata(fesdata[:,:2],fesdata[:,2]-np.min(fesdata[:,2]),(Xnew, Ynew),method=method)
%matplotlib inline

#what spacing do you want?  
spacer=1. 
lines=10 #this goes to 80 kcal/mol (2*40)
levels=np.linspace(0,lines*spacer,num=(lines+1),endpoint=True)

fig=plt.figure(figsize=(10,8)) 
axes = fig.add_subplot(111)

plt.contourf(Xnew/10.0, Ynew/10.0, Znew, levels, cmap=plt.cm.bone,)
plt.colorbar()
plt.xlabel('$d1$')
plt.ylabel('$d2$')
axes.set_ylim([.1,.5])
axes.set_xlim([0.1,0.5])
plt.rcParams.update({'font.size': 12})
plt.show()



    


















    
























In [4]:



    


##THIS IS THE PES FROM REWEIGHT 
fesdata = np.genfromtxt('rew_avg.dat',comments='#');


sig=.05
method='cubic'
#newxr=np.arange(fesdata[:,0].min(),fesdata[:,0].max(),sig)
#newyr=np.arange(fesdata[:,1].min(),fesdata[:,1].max(),sig)
#Xnew2,Ynew2=np.meshgrid(newxr,newyr)
Znew2=griddata(fesdata[:,:2],fesdata[:,2]-np.min(fesdata[:,2]),(Xnew/10.0, Ynew/10.0),method=method)


#what spacing do you want?  
spacer=1. 
lines=10 #this goes to 80 kcal/mol (2*40)
levels=np.linspace(0,lines*spacer,num=(lines+1),endpoint=True)

fig=plt.figure(figsize=(10,8)) 
axes = fig.add_subplot(111)

plt.contourf(Xnew/10.0, Ynew/10.0, Znew2, levels, cmap=plt.cm.bone,)
plt.colorbar()
plt.xlabel('$d1$')
plt.ylabel('$d2$')
axes.set_ylim([.1,.5])
axes.set_xlim([0.1,0.5])
plt.rcParams.update({'font.size': 12})
plt.show()



    


















    
























In [6]:



    


##THIS IS THE PES FROM REWEIGHT 


#what spacing do you want?  
spacer=.1
lines=10.0 #this goes to 80 kcal/mol (2*40)
levels=np.linspace(0,lines*spacer,num=(lines+1),endpoint=True)

fig=plt.figure(figsize=(10,8)) 
axes = fig.add_subplot(111)

plt.contourf(Xnew/10.0, Ynew/10.0, np.abs(Znew-Znew2), levels, cmap=plt.cm.bone,)
plt.colorbar()
plt.xlabel('$d1$')
plt.ylabel('$d2$')
axes.set_ylim([.1,.5])
axes.set_xlim([0.1,0.5])
plt.rcParams.update({'font.size': 12})
plt.show()



    


















    
























In [22]:



    


def saddle(A):

#returns coordinates of saddles in A maximum in rows, minimum in columns """
   S = []
   for i in range(0,A.shape[0]):
        max = A[i,0]; maxcol = 0;
        for j in range(1,A.shape[1]):
            if(A[i,j] > max):
                max = A[i,j]; maxcol = j
        is_saddle = True
        for k in range(0,A.shape[0]):
            if(k != i):
                if(A[k,maxcol] < max):
                    is_saddle = False; break 
        if is_saddle:
            S.append((i,maxcol)); 
   return S



    











In [23]:



    


S=saddle(Znew-Znew.min())



    











In [24]:



    


print S



    


















    






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In [6]:



    


#read the data in from a text file 
%matplotlib inline 


fesdata = np.genfromtxt('G09BLYP.PES.dat',comments='#');

dim1=39
dim2=39

fesdata = fesdata[:,0:3]
enertokcal=1.0
#some post-processing to be compatible with contourf 
X=np.reshape(fesdata[:,0]/10.0,[dim1,dim2],order="F")  #order F was 20% faster than A/C
Y=np.reshape(fesdata[:,1]/10.0,[dim1,dim2],order="F") 
Z=np.reshape((fesdata[:,2]-np.min(fesdata[:,2]))/enertokcal,[dim1,dim2],order="F")  #convert to kcal/mol

#what spacing do you want?  
spacer=1. 
lines=5 #this goes to 80 kcal/mol (2*40)
levels=np.linspace(0,lines*spacer,num=(lines+1),endpoint=True)

fig=plt.figure(figsize=(10,8)) 
axes = fig.add_subplot(111)

plt.contourf(X, Y, Z, levels, cmap=plt.cm.bone,)
plt.colorbar()
plt.xlabel('$d1$')
plt.ylabel('$d2$')
axes.set_ylim([.1,.6])
axes.set_xlim([0.1,0.6])
plt.rcParams.update({'font.size': 12})
plt.show()



    


















    
























In [ ]:

Content source: UWPRG/Python

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