def pyNCcomputeTestSet(cnstfile1,
saefile1,
nnfdir1,
dtdir,
dtdftpref,
dtpm6dir,
dtpm6pref,
N,
P=1.0):
# Construct pyNeuroChem classes
nc = pync.pyNeuroChem(cnstfile1, saefile1, nnfdir1, 0)
Eact = []
Ecmp = []
Eotr = []
Ndat = 0
Nmol = 0
t = 0.0
for i in range(0, N):
print('|----- File ' + str(i) + ' -----|')
print('Name: ' + dtdir + dtdftpref + str(i) + '_test.dat')
rv = bool(np.random.binomial(1, P))
if (os.path.isfile(dtdir + dtdftpref + str(i) + '_test.dat') and rv):
xyz, typ, Eact_t = gt.readncdat(dtdir + dtdftpref + str(i) +
'_test.dat')
xyz1, typ1, Eotr_t = gt.readncdat(dtpm6dir + dtpm6pref + str(i) +
'_test.dat')
if len(Eact_t) == len(Eotr_t):
Eact += shiftlsttomin(Eact_t)
Eotr += shiftlsttomin(Eotr_t)
#Eact += Eact_t
#Eotr += Eotr_t
Nmol += 1
Ndat += len(Eact_t)
# Set the conformers in NeuroChem
nc.setConformers(confs=xyz, types=typ)
# Compute Forces of Conformations
print('Computing energies 1...')
_t1b = tm.time()
Ecmp_t = nc.computeEnergies()
_t2b = (tm.time() - _t1b) * 1000.0
t += _t2b
print('Computation complete 1. Time: ' +
"{:.4f}".format(_t2b) + 'ms')
Ecmp += shiftlsttomin(Ecmp_t)
#Ecmp += Ecmp_t
Eact = np.array(Eact, dtype=float)
Eotr = np.array(Eotr, dtype=float)
Ecmp = np.array(Ecmp, dtype=float)
return Eact, Ecmp, Eotr, Ndat, Nmol, t
def pyNCcomputeTestSet(cnstfile1,saefile1,nnfdir1,dtdir,dtdftpref,dtpm6dir,dtpm6pref,N,P=1.0):
# Construct pyNeuroChem classes
nc = pync.pyNeuroChem(cnstfile1,saefile1,nnfdir1,0)
Eact = []
Ecmp = []
Eotr = []
Ndat = 0
Nmol = 0
t = 0.0
for i in range(0,N):
rv = bool(np.random.binomial(1,P))
if (os.path.isfile(dtdir + dtdftpref + str(i) + '_test.dat') and rv):
xyz,typ,Eact_t,chk = gt.readncdat(dtdir + dtdftpref + str(i) + '_test.dat')
xyz1,typ1,Eotr_t,chk = gt.readncdat(dtpm6dir + dtpm6pref + str(i) + '_test.dat')
if len(Eact_t) == len(Eotr_t):
#print ('|----- File ' + str(i) + ' -----|')
#print ('Name: ' + dtdir + dtdftpref + str(i) + '_test.dat')
Eact += shiftlsttomin( Eact_t )
Eotr += shiftlsttomin( Eotr_t )
#Eact += Eact_t
#Eotr += Eotr_t
Nmol += 1
Ndat += len( Eact_t )
# Set the conformers in NeuroChem
nc.setConformers(confs=xyz,types=typ)
# Compute Forces of Conformations
print(' ' + str(Nmol) + ') Computing ' + str(len( Eact_t )) + ' energies...')
_t1b = tm.time()
Ecmp_t = nc.computeEnergies()
_t2b = (tm.time() - _t1b) * 1000.0
t += _t2b
#print('Computation complete. Time: ' + "{:.4f}".format(_t2b) + 'ms')
Ecmp += shiftlsttomin( Ecmp_t )
#Ecmp += Ecmp_t99
else:
print (str(len(Eact_t)) + '!=' + str(len(Eotr_t)) + ' File: ' + dtdir + dtdftpref + str(i) + '_test.dat')
else:
print('File not found: ' + dtdir + dtdftpref + str(i) + '_test.dat')
Eact = np.array(Eact,dtype=float)
Eotr = np.array(Eotr,dtype=float)
Ecmp = np.array(Ecmp,dtype=float)
return Eact,Ecmp,Eotr,Ndat,Nmol,t
y += 1.0
z += 1.0
return xyz, typ, Na
# Set required files for pyNeuroChem
wkdir1 = '/home/jujuman/Research/GDB-11-wB97X-6-31gd/train_08_1/'
#Network 1 Files
cnstfile1 = wkdir1 + 'rHCNO-4.5A_32-3.1A_a8-8.params'
saefile1 = wkdir1 + 'sae_6-31gd.dat'
nnfdir1 = wkdir1 + 'networks/'
# Construct pyNeuroChem classes
nc1 = pync.pyNeuroChem(cnstfile1, saefile1, nnfdir1, 0)
idx = []
tme = []
for i in range(1, 20):
xyz, typ, Na = generatemolgrid(['H', 'C', 'N', 'O'], 10, 10)
# Set the conformers in NeuroChem
nc1.setConformers(confs=xyz, types=typ)
# Print some data from the NeuroChem
print('1) Number of Atoms Loaded: ' + str(nc1.getNumAtoms()))
print('1) Number of Confs Loaded: ' + str(nc1.getNumConfs()))
# Compute Forces of Conformations
font = {'family': 'Bitstream Vera Sans', 'weight': 'normal', 'size': 14}
plt.rc('font', **font)
# Set required files for pyNeuroChem
wkdir = '/home/jujuman/Research/GDB-11-wB97X-6-31gd/train_08_5/'
# Network Files
cnstfile = wkdir + 'rHCNO-4.7A_32-3.2A_a8-8.params'
saefile = wkdir + 'sae_6-31gd.dat'
nnfdir = wkdir + 'networks/'
# Construct pyNeuroChem classes
nc = pync.pyNeuroChem(cnstfile, saefile, nnfdir, 0)
# Read nc DATA
xyz, typ, Eact = gt.readncdat(
'/home/jujuman/Dropbox/ChemSciencePaper.AER/TestCases/Retinol/data/retinolconformer_DFT.dat'
)
xyz1, typ1, Eact1 = gt.readncdat(
'/home/jujuman/Dropbox/ChemSciencePaper.AER/TestCases/Retinol/data/retinolconformer_DFTB.dat'
)
Eact = np.array(Eact)
Eact1 = np.array(Eact1)
# Set the conformers in NeuroChem
nc.setConformers(confs=xyz, types=typ)
cnstfile = wkdir + 'rHCNO-4.6A_32-3.1A_a8-8.params'
saefile = wkdir + 'sae_6-31gd.dat'
nnfdir = wkdir + 'networks/'
xyz = [[0.00000,0.00000,0.37124,0.00000, 0.00000,-0.37124]
,[0.00000,0.372635,0.00000,0.00000,-0.372635, 0.00000]]
#,[0.00000,0.00000,0.41000,0.00000, 0.00000,-0.41000]]
xyz = [[0.0, 0.0, 0.118604, 0.0, 0.67007291, -0.40437055, 0.0, -0.75981897, -0.474415], [0.0, 0.0, 0.118604, 0.0, 0.67007291, -0.4043705, 0.0, -0.75981897, -0.474415]]
typ = ['O','H','H']
#xyz,typ,Na = gt.readxyz('/home/jujuman/Dropbox/ChemSciencePaper.AER/TestCases/Ranolazine/optimization/test_AO2.xyz')
# Construct pyNeuroChem class
nc = pync.pyNeuroChem(cnstfile, saefile, nnfdir, 0)
# Set the conformers in NeuroChem
nc.setConformers(confs=xyz,types=typ[0])
# Print some data from the NeuroChem
print( 'Number of Atoms Loaded: ' + str(nc.getNumAtoms()) )
print( 'Number of Confs Loaded: ' + str(nc.getNumConfs()) )
# Compute Energies of Conformations
E = nc.computeEnergies()
F = nc.computeAnalyticalForces()
print ('-----------------DATA---------------')
#E = gt.hatokcal*E
print (E)
print (F)
def produce_scan(ax,title,xlabel,cnstfile,saefile,nnfdir,dtdir,dt1,dt2,dt3,smin,smax,iscale,ishift):
xyz, typ, Eact, chk = gt.readncdat(dtdir + dt1,np.float32)
xyz2, typ2, Eact2, chk = gt.readncdat(dtdir + dt2)
xyz3, typ3, Eact3, chk = gt.readncdat(dtdir + dt3)
#gt.writexyzfile("/home/jujuman/Dropbox/ChemSciencePaper.AER/TestCases/Dihedrals/4-Cyclohexyl-1-butanol/optimization/dihedral_"+dt1+".xyz",xyz,typ)
#Eact = np.array(Eact)
#Eact2 = np.array(Eact2)
#Eact3 = np.array(Eact3)
# Construct pyNeuroChem classes
nc1 = pync.pyNeuroChem(cnstfile, saefile, nnfdir, 1)
# Set the conformers in NeuroChem
nc1.setConformers(confs=xyz, types=typ)
# Print some data from the NeuroChem
print('1) Number of Atoms Loaded: ' + str(nc1.getNumAtoms()))
print('1) Number of Confs Loaded: ' + str(nc1.getNumConfs()))
# Compute Forces of Conformations
print('Computing energies 1...')
_t1b = tm.time()
Ecmp1 = nc1.energy()
print('Computation complete 1. Time: ' + "{:.4f}".format((tm.time() - _t1b) * 1000.0) + 'ms')
n = smin
m = smax
Ecmp1 = gt.hatokcal * Ecmp1
Eact = gt.hatokcal * Eact
Eact2 = gt.hatokcal * Eact2
Eact3 = gt.hatokcal * Eact3
IDX = np.arange(0, Eact.shape[0], 1, dtype=float) * iscale + ishift
IDX = IDX[n:m]
Eact = Eact[n:m]
Eact2 = Eact2[n:m]
Eact3 = Eact3[n:m]
Ecmp1 = Ecmp1[n:m]
Ecmp1 = Ecmp1 - Ecmp1.min()
Eact = Eact - Eact.min()
Eact2 = Eact2 - Eact2.min()
Eact3 = Eact3 - Eact3.min()
rmse1 = gt.calculaterootmeansqrerror(Eact, Ecmp1)
rmse3 = gt.calculaterootmeansqrerror(Eact, Eact2)
rmse4 = gt.calculaterootmeansqrerror(Eact, Eact3)
print("Spearman corr. 1: " + "{:.3f}".format(st.spearmanr(Ecmp1, Eact)[0]))
print("Spearman corr. 2: " + "{:.3f}".format(st.spearmanr(Eact2, Eact)[0]))
print("Spearman corr. 3: " + "{:.3f}".format(st.spearmanr(Eact3, Eact)[0]))
ax.plot(IDX, Eact, '-', marker=r'o', color='black', label='DFT',
linewidth=2, markersize=7)
ax.plot(IDX, Ecmp1, ':', marker=r'D', color='red', label='ANI-1 RMSE: ' + '%s' % float('%.3g' % rmse1) + ' kcal/mol',
linewidth=2, markersize=5)
ax.plot(IDX, Eact2, ':', marker=r'v', color='blue', label='DFTB RMSE: ' + '%s' % float('%.3g' % rmse3) + ' kcal/mol',
linewidth=2, markersize=5)
ax.plot(IDX, Eact3, ':', marker=r'*', color='orange', label='PM6 RMSE: ' + '%s' % float('%.3g' % rmse4) + ' kcal/mol',
linewidth=2, markersize=7)
#ax.plot(IDX, Eact, color='black', label='DFT', linewidth=3)
#ax.scatter(IDX, Eact, marker='o', color='black', linewidth=4)
th = ax.set_title(title,fontsize=16)
th.set_position([0.5,1.005])
# Set Limits
ax.set_xlim([ IDX.min(),IDX.max()])
ax.set_ylim([Eact.min()-1.0,Eact.max()+1.0])
ax.set_ylabel('$\Delta$E calculated (kcal/mol)')
ax.set_xlabel(xlabel)
ax.legend(bbox_to_anchor=(0.2, 0.98), loc=2, borderaxespad=0., fontsize=14)
x += 1.0
y += 1.0
z += 1.0
return xyz,typ,Na
# Set required files for pyNeuroChem
wkdir1 = '/home/jujuman/Research/GDB-11-wB97X-6-31gd/train_08_1/'
#Network 1 Files
cnstfile1 = wkdir1 + 'rHCNO-4.5A_32-3.1A_a8-8.params'
saefile1 = wkdir1 + 'sae_6-31gd.dat'
nnfdir1 = wkdir1 + 'networks/'
# Construct pyNeuroChem classes
nc1 = pync.pyNeuroChem(cnstfile1,saefile1,nnfdir1,0)
idx = []
tme = []
for i in range(1,20):
xyz,typ,Na = generatemolgrid(['H','C','N','O'],10,10)
# Set the conformers in NeuroChem
nc1.setConformers(confs=xyz,types=typ)
# Print some data from the NeuroChem
print( '1) Number of Atoms Loaded: ' + str(nc1.getNumAtoms()) )
print( '1) Number of Confs Loaded: ' + str(nc1.getNumConfs()) )
# Compute Forces of Conformations
def produce_scan(ax, title, xlabel, cnstfile, saefile, nnfdir, dtdir, dt1, dt2,
dt3, smin, smax, iscale, ishift):
xyz, typ, Eact, chk = gt.readncdat(dtdir + dt1)
xyz2, typ2, Eact2, chk = gt.readncdat(dtdir + dt2)
xyz3, typ3, Eact3, chk = gt.readncdat(dtdir + dt3)
Eact = np.array(Eact)
Eact2 = np.array(Eact2)
Eact3 = np.array(Eact3)
# Construct pyNeuroChem classes
nc1 = pync.pyNeuroChem(cnstfile, saefile, nnfdir, 1)
# Set the conformers in NeuroChem
nc1.setConformers(confs=xyz, types=typ)
# Print some data from the NeuroChem
print('1) Number of Atoms Loaded: ' + str(nc1.getNumAtoms()))
print('1) Number of Confs Loaded: ' + str(nc1.getNumConfs()))
# Compute Forces of Conformations
print('Computing energies 1...')
_t1b = tm.time()
Ecmp1 = np.array(nc1.computeEnergies())
print('Computation complete 1. Time: ' +
"{:.4f}".format((tm.time() - _t1b) * 1000.0) + 'ms')
n = smin
m = smax
Ecmp1 = gt.hatokcal * Ecmp1
Eact = gt.hatokcal * Eact
Eact2 = gt.hatokcal * Eact2
Eact3 = gt.hatokcal * Eact3
IDX = np.arange(0, Eact.shape[0], 1, dtype=float) * iscale + ishift
IDX = IDX[n:m]
Eact = Eact[n:m]
Eact2 = Eact2[n:m]
Eact3 = Eact3[n:m]
Ecmp1 = Ecmp1[n:m]
Ecmp1 = Ecmp1 - Ecmp1.min()
Eact = Eact - Eact.min()
Eact2 = Eact2 - Eact2.min()
Eact3 = Eact3 - Eact3.min()
rmse1 = gt.calculaterootmeansqrerror(Eact, Ecmp1)
rmse3 = gt.calculaterootmeansqrerror(Eact, Eact2)
rmse4 = gt.calculaterootmeansqrerror(Eact, Eact3)
print("Spearman corr. 1: " + "{:.3f}".format(st.spearmanr(Ecmp1, Eact)[0]))
print("Spearman corr. 2: " + "{:.3f}".format(st.spearmanr(Eact2, Eact)[0]))
print("Spearman corr. 3: " + "{:.3f}".format(st.spearmanr(Eact3, Eact)[0]))
ax.plot(IDX,
Eact,
'-',
marker=r'o',
color='black',
label='DFT',
linewidth=2,
markersize=7)
ax.plot(IDX,
Ecmp1,
':',
marker=r'D',
color='red',
label='ANI-1 RMSE: ' + '%s' % float('%.3g' % rmse1) + ' kcal/mol',
linewidth=2,
markersize=5)
ax.plot(IDX,
Eact2,
':',
marker=r'v',
color='blue',
label='DFTB RMSE: ' + '%s' % float('%.3g' % rmse3) + ' kcal/mol',
linewidth=2,
markersize=5)
ax.plot(IDX,
Eact3,
':',
marker=r'*',
color='orange',
label='PM6 RMSE: ' + '%s' % float('%.3g' % rmse4) + ' kcal/mol',
linewidth=2,
markersize=7)
#ax.plot(IDX, Eact, color='black', label='DFT', linewidth=3)
#ax.scatter(IDX, Eact, marker='o', color='black', linewidth=4)
th = ax.set_title(title, fontsize=16)
th.set_position([0.5, 1.005])
# Set Limits
ax.set_xlim([IDX.min(), IDX.max()])
ax.set_ylim([Eact.min() - 1.0, Eact.max() + 1.0])
ax.set_ylabel('$\Delta$E calculated (kcal/mol)')
ax.set_xlabel(xlabel)
ax.legend(bbox_to_anchor=(0.2, 0.98), loc=2, borderaxespad=0., fontsize=14)
def produce_scan(title, xlabel, cnstfile, saefile, nnfdir, dtdir, dt1, smin, smax, iscale, ishift, atm):
xyz, frc, typ, Eact, chk = gt.readncdatwforce(dtdir + dt1)
xyz = np.asarray(xyz, dtype=np.float32)
xyz = xyz.reshape((xyz.shape[0], len(typ), 3))
print(xyz)
frc = np.asarray(frc, dtype=np.float32)
frc = frc.reshape((frc.shape[0], len(typ), 3))
Eact = np.array(Eact)
# Construct pyNeuroChem classes
nc1 = pync.pyNeuroChem(cnstfile, saefile, nnfdir, 0)
# Set the conformers in NeuroChem
nc1.setConformers(confs=xyz, types=typ)
# Print some data from the NeuroChem
print("1) Number of Atoms Loaded: " + str(nc1.getNumAtoms()))
print("1) Number of Confs Loaded: " + str(nc1.getNumConfs()))
# Compute Energies of Conformations
print("Computing energies...")
_t1b = tm.time()
Ecmp1 = nc1.energy()
print("Energy computation complete. Time: " + "{:.4f}".format((tm.time() - _t1b) * 1000.0) + "ms")
# Compute Forces of Conformations
print("Compute forces...")
_t1b = tm.time()
F = nc1.force()
print("Force computation complete. Time: " + "{:.4f}".format((tm.time() - _t1b) * 1000.0) + "ms")
# Fn = np.array(nc1.computeNumericalForces(dr=0.0001))
n = smin
m = smax
Ecmp1 = gt.hatokcal * Ecmp1
Eact = gt.hatokcal * Eact
IDX = np.arange(0, Eact.shape[0], 1, dtype=float) * iscale + ishift
IDX = IDX
Eact = Eact
Ecmp1 = Ecmp1
Ecmp1 = Ecmp1 - Ecmp1.min()
Eact = Eact - Eact.min()
rmse1 = gt.calculaterootmeansqrerror(Eact, Ecmp1)
print("Spearman corr. 1: " + "{:.3f}".format(st.spearmanr(Ecmp1, Eact)[0]))
fig, axes = plt.subplots(nrows=2, ncols=2)
axes.flat[0].plot(IDX, Eact, "-", color="black", label="DFT", linewidth=6)
axes.flat[0].plot(IDX, Ecmp1, "--", color="red", label="ANI-1", linewidth=6)
# ax.plot(IDX, Eact, color='black', label='DFT', linewidth=3)
# ax.scatter(IDX, Eact, marker='o', color='black', linewidth=4)
th = axes.flat[0].set_title(title, fontsize=13)
th.set_position([0.5, 1.00])
# Set Limits
axes.flat[0].set_xlim([IDX.min(), IDX.max()])
# axes.flat[0].set_ylim([Eact.min()-1.0,Eact.max()+1.0])
axes.flat[0].set_ylabel("$\Delta$E calculated (kcal/mol)")
axes.flat[0].set_xlabel(xlabel)
axes.flat[0].legend(bbox_to_anchor=(0.2, 0.98), loc=2, borderaxespad=0.0, fontsize=12)
# print (Fn)
for i in range(0, 3):
Fq = F[:, :, i][:, atm]
# Fnq = Fn[:,i]
Faq = 1.8897259885789 * np.array(frc)[:, :, i][:, atm]
print(Fq)
print(Faq)
th = axes.flat[i + 1].set_title("Force for atom " + str(atm) + ": coordinate " + str(i), fontsize=13)
th.set_position([0.5, 1.00])
axes.flat[i + 1].set_xlim([IDX.min(), IDX.max()])
axes.flat[i + 1].plot(IDX, Faq, "-", color="black", label="DFT", linewidth=6)
# axes.flat[i+1].plot(IDX, Fnq, '-', color='blue', label='ANI Numerical',
# linewidth=6)
axes.flat[i + 1].plot(IDX, Fq, "--", color="red", label="ANI Analytical", linewidth=6)
axes.flat[i + 1].set_ylabel("Force (Ha/A)")
axes.flat[i + 1].set_xlabel(xlabel)
axes.flat[i + 1].legend(bbox_to_anchor=(0.2, 0.98), loc=2, borderaxespad=0.0, fontsize=12)
font = {"family": "Bitstream Vera Sans", "weight": "normal", "size": 12}
plt.rc("font", **font)
plt.show()
# Set required files for pyNeuroChem
wkdir1 = '/home/jujuman/Research/GDB-11-wB97X-6-31gd/train_08_2/'
wkdir2 = '/home/jujuman/Research/GDB-11-wB97X-6-31gd/train_08_3/'
#Network 1 Files
cnstfile1 = wkdir1 + 'rHCNO-4.6A_32-3.1A_a8-8.params'
saefile1 = wkdir1 + 'sae_6-31gd.dat'
nnfdir1 = wkdir1 + 'networks/'
# Network 2 Files
cnstfile2 = wkdir2 + 'rHCNO-4.6A_32-3.1A_a8-8.params'
saefile2 = wkdir2 + 'sae_6-31gd.dat'
nnfdir2 = wkdir2 + 'networks/'
# Construct pyNeuroChem classes
nc1 = pync.pyNeuroChem(cnstfile1, saefile1, nnfdir1, 0)
nc2 = pync.pyNeuroChem(cnstfile2, saefile2, nnfdir2, 0)
xyz, typ, Eact = gt.readncdat(
'/home/jujuman/Dropbox/Research/ChemSciencePaper/TestCases/C10H20Isomers/isomer_structures_DFT.dat'
)
Eact = np.array(Eact)
# Set the conformers in NeuroChem
nc1.setConformers(confs=xyz, types=typ)
nc2.setConformers(confs=xyz, types=typ)
# Print some data from the NeuroChem
print('Number of Atoms Loaded: ' + str(nc1.getNumAtoms()))
print('Number of Confs Loaded: ' + str(nc1.getNumConfs()))
def produce_scan(title, xlabel, cnstfile, saefile, nnfdir, dtdir, dt1, smin,
smax, iscale, ishift, atm):
xyz, frc, typ, Eact, chk = gt.readncdatwforce(dtdir + dt1)
xyz = np.asarray(xyz, dtype=np.float32)
xyz = xyz.reshape((xyz.shape[0], len(typ), 3))
print(xyz)
frc = np.asarray(frc, dtype=np.float32)
frc = frc.reshape((frc.shape[0], len(typ), 3))
Eact = np.array(Eact)
# Construct pyNeuroChem classes
nc1 = pync.pyNeuroChem(cnstfile, saefile, nnfdir, 0)
# Set the conformers in NeuroChem
nc1.setConformers(confs=xyz, types=typ)
# Print some data from the NeuroChem
print('1) Number of Atoms Loaded: ' + str(nc1.getNumAtoms()))
print('1) Number of Confs Loaded: ' + str(nc1.getNumConfs()))
# Compute Energies of Conformations
print('Computing energies...')
_t1b = tm.time()
Ecmp1 = nc1.energy()
print('Energy computation complete. Time: ' +
"{:.4f}".format((tm.time() - _t1b) * 1000.0) + 'ms')
# Compute Forces of Conformations
print('Compute forces...')
_t1b = tm.time()
F = nc1.force()
print('Force computation complete. Time: ' +
"{:.4f}".format((tm.time() - _t1b) * 1000.0) + 'ms')
#Fn = np.array(nc1.computeNumericalForces(dr=0.0001))
n = smin
m = smax
Ecmp1 = gt.hatokcal * Ecmp1
Eact = gt.hatokcal * Eact
IDX = np.arange(0, Eact.shape[0], 1, dtype=float) * iscale + ishift
IDX = IDX
Eact = Eact
Ecmp1 = Ecmp1
Ecmp1 = Ecmp1 - Ecmp1.min()
Eact = Eact - Eact.min()
rmse1 = gt.calculaterootmeansqrerror(Eact, Ecmp1)
print("Spearman corr. 1: " + "{:.3f}".format(st.spearmanr(Ecmp1, Eact)[0]))
fig, axes = plt.subplots(nrows=2, ncols=2)
axes.flat[0].plot(IDX, Eact, '-', color='black', label='DFT', linewidth=6)
axes.flat[0].plot(IDX,
Ecmp1,
'--',
color='red',
label='ANI-1',
linewidth=6)
#ax.plot(IDX, Eact, color='black', label='DFT', linewidth=3)
#ax.scatter(IDX, Eact, marker='o', color='black', linewidth=4)
th = axes.flat[0].set_title(title, fontsize=13)
th.set_position([0.5, 1.00])
# Set Limits
axes.flat[0].set_xlim([IDX.min(), IDX.max()])
#axes.flat[0].set_ylim([Eact.min()-1.0,Eact.max()+1.0])
axes.flat[0].set_ylabel('$\Delta$E calculated (kcal/mol)')
axes.flat[0].set_xlabel(xlabel)
axes.flat[0].legend(bbox_to_anchor=(0.2, 0.98),
loc=2,
borderaxespad=0.,
fontsize=12)
#print (Fn)
for i in range(0, 3):
Fq = F[:, :, i][:, atm]
#Fnq = Fn[:,i]
Faq = (1.8897259885789 * np.array(frc)[:, :, i][:, atm])
print(Fq)
print(Faq)
th = axes.flat[i + 1].set_title("Force for atom " + str(atm) +
": coordinate " + str(i),
fontsize=13)
th.set_position([0.5, 1.00])
axes.flat[i + 1].set_xlim([IDX.min(), IDX.max()])
axes.flat[i + 1].plot(IDX,
Faq,
'-',
color='black',
label='DFT',
linewidth=6)
#axes.flat[i+1].plot(IDX, Fnq, '-', color='blue', label='ANI Numerical',
# linewidth=6)
axes.flat[i + 1].plot(IDX,
Fq,
'--',
color='red',
label='ANI Analytical',
linewidth=6)
axes.flat[i + 1].set_ylabel('Force (Ha/A)')
axes.flat[i + 1].set_xlabel(xlabel)
axes.flat[i + 1].legend(bbox_to_anchor=(0.2, 0.98),
loc=2,
borderaxespad=0.,
fontsize=12)
font = {'family': 'Bitstream Vera Sans', 'weight': 'normal', 'size': 12}
plt.rc('font', **font)
plt.show()