def get_eint_c(filename):
# Reads conformers from a file
xyz, typ, Eact = hdt.readncdat(filename, np.float32)
energies = ani_energy_conf(xyz, typ)
return abs((energies - Eact).sum() / float(len(energies))) * 627.5095
def plot_irc_data(axes, file, rcf, title, ntwl, cnstfile, saefile, dir, idx):
xyz, typ, Eact = hdt.readncdat(file, np.float32)
Rc = np.load(rcf)
# Shift reference to reactant
#Eact = Eact[::-1]
Eact = hdt.hatokcal * (Eact - Eact[0])
# Plot reference results
axes.plot(Rc['x'][:, 1], Eact, color='black', linewidth=3)
# Plot ANI results
color = cm.rainbow(np.linspace(0, 1, len(ntwl)))
terr = np.zeros(len(ntwl))
derr = np.zeros(len(ntwl))
berr = np.zeros(len(ntwl))
for i, (nt, c) in enumerate(zip(ntwl, color)):
ncr = pync.conformers(dir + cnstfile, dir + saefile,
rcdir + nt[0] + 'networks/', 0)
# Set the conformers in NeuroChem
ncr.setConformers(confs=xyz, types=list(typ))
# Compute Energies of Conformations
E1 = ncr.energy()
# Shift ANI E to reactant
E1 = hdt.hatokcal * (E1 - E1[0])
# Calculate error
errn = hdt.calculaterootmeansqrerror(E1, Eact)
terr[i] = errn
derr[i] = np.abs(
np.abs((E1[0] - E1[-1])) - np.abs((Eact[0] - Eact[-1])))
berr[i] = np.abs(E1.max() - Eact.max())
# Plot
axes.plot(Rc['x'][:, 1],
E1,
'r--',
color=c,
label="[" + nt[1] + "]: " + "{:.2f}".format(errn),
linewidth=2)
#axes.plot([Rc['x'][:,1].min(),Rc['x'][:,1].max()],[E1[-1],E1[-1]], 'r--', color=c)
#axes.plot([Rc['x'][:,1].min(),Rc['x'][:,1].max()],[E1[0],E1[0]], 'r--', color=c)
axes.set_xlim([Rc['x'][:, 1].min(), Rc['x'][:, 1].max()])
axes.legend(loc="upper left", fontsize=12)
if idx < 6:
axes.set_title(title, color='green', fontdict={'weight': 'bold'})
else:
axes.set_title(title, color='red', fontdict={'weight': 'bold'})
return terr, derr, berr
def pyNCcomputeTestSet(cnstfile1, saefile1, nnfdir1, dtdir, P=1.0):
# Construct pyNeuroChem classes
nc = pync.conformers(cnstfile1, saefile1, nnfdir1, 0)
files = os.listdir(dtdir)
Eact = []
Ecmp = []
Ndat = 0
Nmol = 0
t = 0.0
for i in files:
print('|----- File ' + str(i) + ' -----|')
print('Name: ' + dtdir + i)
rv = bool(np.random.binomial(1, P))
if rv:
xyz, species, energy = gt.readncdat(dtdir + i, np.float32)
shiftlsttomin(energy)
Eact.extend(energy)
Nmol += 1
Ndat += energy.shape[0]
# Set the conformers in NeuroChem
nc.setConformers(confs=xyz, types=list(species))
# Compute Forces of Conformations
print('Computing energies...')
_t1b = tm.time()
energy_comp = nc.energy()
_t2b = (tm.time() - _t1b) * 1000.0
t += _t2b
print('Computation complete. Time: ' + "{:.4f}".format(_t2b) +
'ms')
shiftlsttomin(energy_comp)
Ecmp.extend(energy_comp)
# for j,k in zip(energy,energy_comp):
# print(' ',j, ':', k)
Eact = np.concatenate([Eact])
Ecmp = np.concatenate([Ecmp])
return Eact, Ecmp, Ndat, Nmol, t
plt.rc('font', **font)
# Set required files for pyNeuroChem
wkdir = '/home/jujuman/Dropbox/ChemSciencePaper.AER/ANI-1-ntwk/'
# Network Files
cnstfile = wkdir + 'rHCNO-4.6A_32-3.1A_a8-8.params'
saefile = wkdir + 'sae_6-31gd.dat'
nnfdir = wkdir + 'networks/'
# Construct pyNeuroChem classes
nc = pync.conformers(cnstfile, saefile, nnfdir, 0)
# Read nc DATA
xyz, typ, Eact = gt.readncdat(
'/home/jujuman/Dropbox/ChemSciencePaper.AER/TestCases/Retinol/data/retinolconformer_DFT.dat',
np.float32)
xyz1, typ1, Eact1 = gt.readncdat(
'/home/jujuman/Dropbox/ChemSciencePaper.AER/TestCases/Retinol/data/retinolconformer_DFTB.dat'
)
# Set the conformers in NeuroChem
nc.setConformers(confs=xyz, types=list(typ))
# Print some data from the NeuroChem
print('Number of Atoms Loaded: ' + str(nc.getNumAtoms()))
print('Number of Confs Loaded: ' + str(nc.getNumConfs()))
# Compute Forces of Conformations
print('Computing energies...')
_t1b = tm.time()
# Import pyNeuroChem import pyNeuroChem as pync import numpy as np import hdnntools as hdt import matplotlib.pyplot as plt file = '/home/jujuman/Research/GDB-11-wB97X-6-31gd/dnnts_rxns/benz_dbm_rxns/scans_dbm_benz_1/irc.dat' xyz,typ,Eact = hdt.readncdat(file,np.float32) # Set required files for pyNeuroChem wkdir = '/home/jujuman/Dropbox/ChemSciencePaper.AER/networks/ANI-c08f-ntwk-cv/' cnstfile = 'rHCNO-4.6A_16-3.1A_a4-8.params' saefile = 'sae_6-31gd.dat' nc = [pync.conformers(wkdir + cnstfile, wkdir + saefile, wkdir + 'cv_c08e_ntw_' + str(l) + '/networks/', 0) for l in range(5)] rcdir = '/home/jujuman/Research/ANI-DATASET/RXN1_TNET/ani_benz_rxn1_ntwk/' ncr1 = pync.conformers(rcdir + '../' + cnstfile, rcdir + '../' + saefile, rcdir + '/networks/', 0) # Set the conformers in NeuroChem ncr1.setConformers(confs=xyz, types=list(typ)) # Compute Energies of Conformations E1 = ncr1.energy() # Plot plt.plot(hdt.hatokcal * (E1), color='red', label='ANI', linewidth=2) plt.plot (hdt.hatokcal*(Eact), color='black', label='DFT', linewidth=2) for net in nc:
ircf = '/home/jujuman/Research/GDB-11-wB97X-6-31gd/dnnts_rxns/benz_dbm_rxns/scans_dbm_benz_4/irc.dat'
# Set required files for pyNeuroChem
rcdir = '/home/jujuman/Research/ANI-DATASET/RXN1_TNET/training/rxn1to6/ani_benz_rxn_ntwk/'
cnstfile = 'rHCNO-4.6A_16-3.1A_a4-8.params'
saefile = 'sae_6-31gd.dat'
files = os.listdir(ddir)
nc = pync.conformers(rcdir + '../../' + cnstfile, rcdir + '../../' + saefile,
rcdir + '/networks/', 0)
files = list({("_".join(f.split("_")[:-1])) for f in files})
files = sorted(files, key=lambda x: int(x.split('-')[1].split('.')[0]))
files = [i + '_train.dat' for i in files]
datairc = hdt.readncdat(ircf, type=np.float32)
atm = 9
xr = datairc[0][0][atm]
print(xr)
E1 = []
EA = []
dx = []
for f in files:
print(ddir + f)
data = hdt.readncdat(ddir + f, type=np.float32)
# Set the conformers in NeuroChem
nc.setConformers(confs=data[0], types=list(data[1]))
import hdnntools as gt from mpl_toolkits.mplot3d import Axes3D import matplotlib.pyplot as plt import os import matplotlib as mpl # Set required files for pyNeuroChem anipath = '/home/jujuman/Dropbox/ChemSciencePaper.AER/ANI-c08e-ntwk' cnstfile = anipath + '/rHCNO-4.6A_16-3.1A_a4-8.params' saefile = anipath + '/sae_6-31gd.dat' nnfdir = anipath + '/networks/' #dtdir = '/home/jujuman/Research/GDB-11-wB97X-6-31gd/dnntsgdb11_01/data/' #xyz,typ,Eact,tmp = gt.readncdat(dtdir + 'gdb11_s01-1_test.dat',np.float32) file = '/home/jujuman/Research/GDB-11-wB97X-6-31gd/dnntsgdb11_02/testdata/gdb11_s02-0_test.dat' xyz,typ,Eact = gt.readncdat(file,np.float32) file2 = '/home/jujuman/Research/GDB-11-wB97X-6-31gd/dnntsgdb11_02/testdata/gdb11_s02-3_test.dat' xyz2,typ2,Eact2 = gt.readncdat(file2,np.float32) file3 = '/home/jujuman/Research/GDB-11-wB97X-6-31gd/dnntsgdb11_02/testdata/gdb11_s02-7_test.dat' xyz3,typ3,Eact3 = gt.readncdat(file3,np.float32) # Construct pyNeuroChem class nc = pync.conformers(cnstfile, saefile, nnfdir, 0) # Set the conformers in NeuroChem nc.setConformers(confs=xyz,types=list(typ)) # Compute Energies of Conformations E1 = nc.energy()
def produce_scan(ax,title,xlabel,cnstfile,saefile,nnfdir,dtdir,dt1,dt2,dt3,smin,smax,iscale,ishift):
xyz, typ, Eact = gt.readncdat(dtdir + dt1,np.float32)
xyz2, typ2, Eact2 = gt.readncdat(dtdir + dt2)
xyz3, typ3, Eact3 = 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.conformers(cnstfile, saefile, nnfdir, 0)
# Set the conformers in NeuroChem
nc1.setConformers(confs=xyz, types=list(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(Ecmp1)
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: ' + gt.to_precision(rmse1,2) + ' kcal/mol',
linewidth=2, markersize=5)
ax.plot(IDX, Eact2, ':', marker=r'v', color='blue', label='DFTB RMSE: ' + gt.to_precision(rmse3,2) + ' kcal/mol',
linewidth=2, markersize=5)
ax.plot(IDX, Eact3, ':', marker=r'*', color='orange', label='PM6 RMSE: ' + gt.to_precision(rmse4,2) + ' 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 plot_irc_data(axes, file, rcf, title):
xyz, typ, Eact = hdt.readncdat(file, np.float32)
Rc = np.load(rcf)
# Set required files for pyNeuroChem
wkdir = '/home/jujuman/Dropbox/ChemSciencePaper.AER/networks/ANI-c08f-ntwk-cv/'
cnstfile = 'rHCNO-4.6A_16-3.1A_a4-8.params'
saefile = 'sae_6-31gd.dat'
nc = [
pync.conformers(wkdir + cnstfile, wkdir + saefile,
wkdir + 'cv_c08e_ntw_' + str(l) + '/networks/', 0)
for l in range(5)
]
rcdir = '/home/jujuman/Research/ANI-DATASET/RXN1_TNET/training/rxn1to6/ani_benz_rxn_ntwk/'
ncr1 = pync.conformers(rcdir + '../../' + cnstfile,
rcdir + '../../' + saefile, rcdir + '/networks/', 0)
ncr2 = pync.molecule(rcdir + '../../' + cnstfile,
rcdir + '../../' + saefile, rcdir + '/networks/', 0)
ncr3 = pync.molecule(rcdir + '../../' + cnstfile,
rcdir + '../../' + saefile, rcdir + '/networks/', 0)
# Compute reactant E
ncr2.setMolecule(coords=xyz[0], types=list(typ))
Er = ncr2.energy()
# Compute product E
ncr3.setMolecule(coords=xyz[-1], types=list(typ))
Ep = ncr3.energy()
#Eact = Eact[::-1]
dE_ani = hdt.hatokcal * (Er - Ep)
dE_dft = hdt.hatokcal * (Eact[0] - Eact[-1])
print('Delta E R/P ANI:', dE_ani, 'Delta E R/P ANI:', dE_dft, 'Diff:',
abs(dE_ani - dE_dft))
# Set the conformers in NeuroChem
ncr1.setConformers(confs=xyz, types=list(typ))
# Compute Energies of Conformations
E1 = ncr1.energy()
# Shift
E1 = E1 - E1[0]
Eact = Eact - Eact[0]
# Plot
errn = hdt.calculaterootmeansqrerror(hdt.hatokcal * E1,
hdt.hatokcal * Eact)
axes.plot(Rc['x'][:, 1],
hdt.hatokcal * (E1),
color='red',
label="{:.2f}".format(errn),
linewidth=2)
axes.plot(Rc['x'][:, 1],
hdt.hatokcal * (Eact),
'r--',
color='black',
linewidth=3)
err = []
for n, net in enumerate(nc):
# Set the conformers in NeuroChem
net.setConformers(confs=xyz, types=list(typ))
# Compute Energies of Conformations
E1 = net.energy()
E1 = E1 - E1[0]
err.append(
hdt.calculaterootmeansqrerror(hdt.hatokcal * E1,
hdt.hatokcal * Eact))
# Plot
if n == len(nc) - 1:
mean = np.mean(np.asarray(err))
axes.plot(Rc['x'][:, 1],
hdt.hatokcal * (E1),
color='blue',
label="{:.2f}".format(mean),
linewidth=1)
else:
axes.plot(Rc['x'][:, 1],
hdt.hatokcal * (E1),
color='blue',
linewidth=1)
axes.plot(Rc['x'][:, 1],
hdt.hatokcal * (E1),
color='blue',
linewidth=1)
axes.set_xlim([Rc['x'][:, 1].min(), Rc['x'][:, 1].max()])
axes.legend(loc="upper right", fontsize=8)
axes.set_title(title)
return np.array([errn, np.mean(err)])
# Import pyNeuroChem
import pyNeuroChem as pync
import numpy as np
import hdnntools as hdt
import matplotlib.pyplot as plt
from matplotlib.pyplot import cm
wkdir = '/home/jujuman/Research/GDB-11-wB97X-6-31gd/dnnts_rxns/benz_dbm_rxns_full/irc_dbm_benz_1/'
file = wkdir + 'irc.dat'
Rc = np.load(wkdir + 'reaction_coordinate.npz')
#xyz,typ,Na = hdt.readxyz2(file1)
xyz, typ, Ea = hdt.readncdat(file, type=np.float32)
# Set required files for pyNeuroChem
rcdir = '/home/jujuman/Research/ANI-DATASET/RXN1_TNET/training/rxn1to6/ani_benz_rxn_ntwk/'
cnstfile = '../../rHCNO-4.6A_16-3.1A_a4-8.params'
saefile = '../../sae_6-31gd.dat'
ncr = pync.conformers(rcdir + cnstfile, rcdir + saefile, rcdir + 'networks/',
1)
# Set the conformers in NeuroChem
ncr.setConformers(confs=xyz, types=list(typ))
# Compute Energies of Conformations
E1 = ncr.energy()
# Shift ANI E to reactant
E1 = E1[0:][::-1]
Ea = Ea[0:][::-1]
def get_eint_m(filename):
xyz, typ, Eact = hdt.readncdat(filename, np.float32)
energies = [ani_energy_bad(m, typ) for m in xyz]
return abs((energies - Eact).sum() / float(len(energies))) * 627.5095
print(data[1])
xyz.append(data[0])
nc.setMolecule(coords=data[0][0], types=list(data[1]))
Eact.append(nc.energy()[0])
xyz = np.vstack(xyz)
#print(xyz)
hdt.writexyzfile(
'/home/jujuman/Dropbox/ChemSciencePaper.AER/JustinsDocuments/ACS_april_2017/DipeptidePhiPsi/data.xyz',
xyz, list(data[1]))
data2 = hdt.readncdat(
'/home/jujuman/Dropbox/ChemSciencePaper.AER/JustinsDocuments/ACS_april_2017/DipeptidePhiPsi/data.dat',
type=np.float32)
Eact = np.array(Eact)
Edft = data2[2]
z = np.array(hdt.hatokcal * (Eact - Eact.min()),
dtype=np.float32).reshape(x.shape[0], x.shape[0])
z2 = np.array(hdt.hatokcal * (Edft - Edft.min()),
dtype=np.float32).reshape(x.shape[0], x.shape[0])
rmse = hdt.calculaterootmeansqrerror(z, z2)
print('RMSE: ', rmse)
Spline1 = scipy.interpolate.RectBivariateSpline(x, x, z)
Spline2 = scipy.interpolate.RectBivariateSpline(x, x, z2)
def sortbyother(Y, X):
xy = zip(X, Y)
xy = sorted(xy, key=lambda x: x[0])
X, Y = zip(*xy)
return np.array(Y)
wkdir = '/home/jujuman/Research/wB97X-631gd-train-highgarden/train_08-a3.1A_r4.6_AEV384_1/'
cnstfile1 = wkdir + 'rHCNO-4.6A_16-3.1A_a4-8.params'
saefile1 = wkdir + '../sae_6-31gd.dat'
nnfdir1 = wkdir + 'networks/'
dtdir = '/home/jujuman/Dropbox/ChemSciencePaper.AER/TestCases/C10H20Isomers/'
#xyz,typ,Eact = gt.readncdat('../data_irc.dat')
xyz, typ, Eact = gt.readncdat(dtdir + 'isomer_structures_DFT.dat',
type=np.float32)
#xyz,typ,Eact = gt.readncdat('/home/jujuman/Research/GDB-11-wB97X-6-31gd/dnntsgdb11_01/testdata/gdb11_s01-2_test.dat',type=np.float32)
#xyz2,typ2,Eact2,tmp = gt.readncdat(dtdir + 'isomer_structures_DFTB.dat')
#xyz3,typ3,Eact3,tmp = gt.readncdat(dtdir + 'isomer_structures_PM6.dat')
#xyz = [xyz[0],xyz[1]]
#xyz2 = [xyz2[0],xyz2[1]]
#xyz3 = [xyz3[0],xyz3[1]]
#xyz = np.asarray(xyz,dtype=np.float32)
#xyz = xyz.reshape((xyz.shape[0],len(typ),3))
Eact = np.array(Eact)
#Eact2 = np.array(Eact2)
#Eact3 = np.array(Eact3)
files = sorted(files, key=lambda x: int(x.split('-')[1].split('.')[0]))
print(files)
allarr = []
Natoms = []
Nconfs = []
typarr = []
for i in files:
print(d + i)
nc = 0
for k in namelist:
try:
_timeloop = tm.time()
readarrays = gt.readncdat(d + i + k)
_timeloop2 = (tm.time() - _timeloop)
totaltime += _timeloop2
#print('Computation complete. Time: ' + "{:.4f}".format(_timeloop2) + 'ms')
shapesarr = [x.shape for x in readarrays]
typ = readarrays[1]
except FileNotFoundError:
readarrays = [np.zeros((0, *x[1:])) for x in shapesarr]
ncsub, nat, ndim = readarrays[0].shape
nc += ncsub
readarrays[0] = readarrays[0].reshape(ncsub * nat, ndim)
allarr.append(readarrays)
Natoms.append(nat)
def pyNCcomputeTestSet(cnstfile1,
saefile1,
nnfdir1,
dtdir,
dtdftpref,
dtpm6dir,
dtpm6pref,
N,
P=1.0):
# Construct pyNeuroChem classes
nc = pync.conformers(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 = gt.readncdat(
dtdir + dtdftpref + str(i) + '_test.dat', np.float32)
xyz1, typ1, Eotr_t = gt.readncdat(
dtpm6dir + dtpm6pref + str(i) + '_test.dat', np.float32)
if len(Eact_t) == len(Eotr_t):
#print ('|----- File ' + str(i) + ' -----|')
#print ('Name: ' + dtdir + dtdftpref + str(i) + '_test.dat')
Eact.append(shiftlsttomin(Eact_t))
Eotr.append(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=list(typ))
# Compute Forces of Conformations
print(' ' + str(Nmol) + ') Computing ' + str(len(Eact_t)) +
' energies...')
_t1b = tm.time()
Ecmp_t = nc.energy()
_t2b = (tm.time() - _t1b) * 1000.0
t += _t2b
#print('Computation complete. Time: ' + "{:.4f}".format(_t2b) + 'ms')
Ecmp.append(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)
Eact = np.concatenate(Eact)
Eotr = np.concatenate(Eotr)
Ecmp = np.concatenate(Ecmp)
return Eact, Ecmp, Eotr, Ndat, Nmol, t
#for i in range(tmol):
# rmtot[i] = cnt[]
#print(rmtot)
dp = pyt.datapacker(h5dir)
#print('Diff data:',ddif,'tmol:',tmol)
tmol = 0
gcount = 0
bcount = 0
for i, f in enumerate(files):
X = []
E = []
tmol += 1
for e in ends:
data = hdn.readncdat(dtdir + f + e + '.dat')
X.append(np.array(data[0], dtype=np.float32))
E.append(np.array(data[2], dtype=np.float64))
S = data[1]
X = np.concatenate(X)
E = np.concatenate(E)
pr, dl = boltzmann_prob_ratio(E.min(), E, T)
#gid = np.where(E - E.min() <= 300.0/hdn.hatokcal)
#bid = np.where(E - E.min() > 300.0/hdn.hatokcal)
#gid = gid[0][0:E[gid].size-rmtot[i]]
#print(gid.size)
bid = list(np.where(pr > R))
cnstfile = wkdir + 'rHCNO-4.6A_16-3.1A_a4-8.params'
saefile = wkdir + 'sae_6-31gd.dat'
nnfdir = wkdir + 'networks/'
#Network 1 Files
wkdir2 = '/home/jujuman/Dropbox/ChemSciencePaper.AER/ANI-c08e-ccdissotest1-ntwk/'
cnstfile2 = wkdir2 + 'rHCNO-4.6A_16-3.1A_a4-8.params'
saefile2 = wkdir2 + 'sae_6-31gd.dat'
nnfdir2 = wkdir2 + 'networks/'
# Construct pyNeuroChem classes
nc = pync.conformers(cnstfile, saefile, nnfdir, 1)
nc2 = pync.conformers(cnstfile2, saefile2, nnfdir2, 1)
scan = hdt.readncdat(
'/home/jujuman/Research/GDB-11-wB97X-6-31gd/dnnts_testdata/waterdimerscan/ethene_diss.dat',
type=np.float32)
sdat = [hdt.ncdata(hdt.generatedmatsd3(scan[0]), scan[1], scan[0].shape[1])]
sae = hdt.compute_sae(saefile, scan[1])
serg = scan[2] - sae
# Set the conformers in NeuroChem
nc.setConformers(confs=scan[0], types=list(scan[1]))
nc2.setConformers(confs=scan[0], types=list(scan[1]))
x = 0.05 * np.array(range(serg.shape[0]), dtype=np.float64) + 0.6
print(len(x))
popt = np.load('mp_ani_params_test.npz')['param']
#dtdir = '/home/jujuman/Research/GDB-11-wB97X-6-31gd/dnntsgdb11_' + p + '/testdata/'
dtdir = '/home/jujuman/Python/PycharmProjects/HD-AtomNNP/Data-ANI-1-Figures/data/minimized/GDB-' + p + '/'
files = listdir(dtdir)
_timeloop = tm.time()
data = np.empty((0,3),dtype=np.float)
spec = np.empty((0,3),dtype=np.float)
enrg = np.empty((0),dtype=np.float)
for en,i in enumerate(files):
#if 'gdb11' in i and '_test.dat' in i:
if 'gdb11' in i:
# Read NC data
print("Reading file (", en, " of ", len(files) ,"): ", i)
xyz,spc,t_enrg = gt.readncdat(dtdir + i)
sae = compute_sae(spc)
t_enrg = t_enrg - sae
t_data,t_spec = generate_angular_data(xyz,spc,len(spc),1.0)
t_data = t_data.reshape((t_data.shape[0]*t_data.shape[1],3))
t_spec = t_spec.reshape((t_spec.shape[0]*t_spec.shape[1],3))
#print('data shape: ', t_data.shape)
#print('spec shape: ', t_spec.shape)
#for l in t_spec:
# print(l)
import hdnntools as hdt
import numpy as np
import matplotlib.pyplot as plt
#Network 1 Files
wkdir = '/home/jujuman/Dropbox/ChemSciencePaper.AER/networks/ANI-c08f-ntwk/'
cnstfile = wkdir + 'rHCNO-4.6A_16-3.1A_a4-8.params'
saefile = wkdir + 'sae_6-31gd.dat'
nnfdir = wkdir + 'networks/'
wkdir = '/home/jujuman/Research/CrossValidation/GDB-09-Retrain/'
cnstfile = wkdir + 'rHCNO-4.6A_16-3.1A_a4-8.params'
saefile = wkdir + 'sae_6-31gd.dat'
traj = hdt.readncdat(
'/home/jujuman/Dropbox/ChemSciencePaper.AER/JustinsDocuments/ACS_april_2017/ANI-MD-vDFT/benzeneMD/benzene_dft.dat',
type=np.float32)
nc = pync.conformers(cnstfile, saefile, nnfdir, 0)
ncl = [
pync.conformers(cnstfile, saefile,
wkdir + 'cv_c08e_ntw_' + str(l) + '/networks/', 0)
for l in range(5)
]
nc.setConformers(confs=traj[0], types=list(traj[1]))
Ecv = []
for comp in ncl:
comp.setConformers(confs=traj[0], types=list(traj[1]))
E = comp.energy()
