def test():
from pyscf import lib, gto, scf
import pyqmc.testwf as testwf
mol = gto.M(atom="Li 0. 0. 0.; H 0. 0. 1.5", basis="cc-pvtz", unit="bohr", spin=0)
for mf in [scf.RHF(mol).run(), scf.ROHF(mol).run(), scf.UHF(mol).run()]:
print("")
nconf = 10
nelec = np.sum(mol.nelec)
slater = PySCFSlaterUHF(mol, mf)
configs = np.random.randn(nconf, nelec, 3)
print("testing internals:", testwf.test_updateinternals(slater, configs))
for delta in [1e-3, 1e-4, 1e-5, 1e-6, 1e-7]:
print(
"delta",
delta,
"Testing gradient",
testwf.test_wf_gradient(slater, configs, delta=delta),
)
print(
"delta",
delta,
"Testing laplacian",
testwf.test_wf_laplacian(slater, configs, delta=delta),
)
print(
"delta",
delta,
"Testing pgradient",
testwf.test_wf_pgradient(slater, configs, delta=delta),
)
def test_wfs():
"""
Ensure that the wave function objects are consistent in several situations.
"""
from pyscf import lib, gto, scf
from pyqmc.slateruhf import PySCFSlaterUHF
from pyqmc.jastrowspin import JastrowSpin
from pyqmc.multiplywf import MultiplyWF
mol = gto.M(atom='Li 0. 0. 0.; H 0. 0. 1.5', basis='cc-pvtz', unit='bohr')
mf = scf.RHF(mol).run()
mf_rohf = scf.ROHF(mol).run()
mf_uhf = scf.UHF(mol).run()
epsilon = 1e-5
nconf = 10
epos = np.random.randn(nconf, 4, 3)
for wf in [
JastrowSpin(mol),
MultiplyWF(PySCFSlaterUHF(mol, mf), JastrowSpin(mol)),
PySCFSlaterUHF(mol, mf_uhf),
PySCFSlaterUHF(mol, mf),
PySCFSlaterUHF(mol, mf_rohf)
]:
for k in wf.parameters:
wf.parameters[k] = np.random.rand(*wf.parameters[k].shape)
assert testwf.test_wf_gradient(wf, epos, delta=1e-5)[0] < epsilon
assert testwf.test_wf_laplacian(wf, epos, delta=1e-5)[0] < epsilon
assert testwf.test_wf_pgradient(wf, epos, delta=1e-5)[0] < epsilon
for k, item in testwf.test_updateinternals(wf, epos).items():
assert item < epsilon
def test():
from pyscf import lib, gto, scf
np.random.seed(10)
mol = gto.M(atom='Li 0. 0. 0.; H 0. 0. 1.5', basis='cc-pvtz',unit='bohr')
l = dir(mol)
nconf=20
configs=np.random.randn(nconf,np.sum(mol.nelec),3)
abasis=[GaussianFunction(0.2),GaussianFunction(0.4)]
bbasis=[GaussianFunction(0.2),GaussianFunction(0.4)]
jastrow=JastrowSpin(mol,a_basis=abasis,b_basis=bbasis)
jastrow.parameters['bcoeff']=np.random.random(jastrow.parameters['bcoeff'].shape)
jastrow.parameters['acoeff']=np.random.random(jastrow.parameters['acoeff'].shape)
import pyqmc.testwf as testwf
for key, val in testwf.test_updateinternals(jastrow, configs).items():
print(key, val)
print()
for delta in [1e-3,1e-4,1e-5,1e-6,1e-7]:
print('delta', delta, "Testing gradient",
testwf.test_wf_gradient(jastrow,configs,delta=delta))
print('delta', delta, "Testing laplacian",
testwf.test_wf_laplacian(jastrow,configs,delta=delta))
print('delta', delta, "Testing pgradient",
testwf.test_wf_pgradient(jastrow,configs,delta=delta))
print()
def test():
from pyscf import lib,gto,scf
from pyqmc.jastrow import Jastrow2B
from pyqmc.slater import PySCFSlaterRHF
nconf=10
mol = gto.M(atom='Li 0. 0. 0.; H 0. 0. 1.5', basis='cc-pvtz',unit='bohr')
mf = scf.RHF(mol).run()
slater=PySCFSlaterRHF(nconf,mol,mf)
jastrow=Jastrow2B(nconf,mol)
jastrow.parameters['coeff']=np.random.random(jastrow.parameters['coeff'].shape)
configs=np.random.randn(nconf,4,3)
wf=MultiplyWF(nconf,slater,jastrow)
wf.parameters['wf2coeff']=np.ones(len(wf.parameters['wf2coeff']))
print(wf.wf2.parameters['coeff'])
print(wf.parameters)
import pyqmc.testwf as testwf
for delta in [1e-3,1e-4,1e-5,1e-6,1e-7]:
print('delta', delta, "Testing gradient",testwf.test_wf_gradient(wf,configs,delta=delta))
print('delta', delta, "Testing laplacian", testwf.test_wf_laplacian(wf,configs,delta=delta))
print('delta', delta, "Testing pgradient", testwf.test_wf_pgradient(wf,configs,delta=delta))
def test():
"""
Tests that the multi-slater wave function value, gradient and
parameter gradient evaluations are working correctly. Also
checks that VMC energy matches energy calculated in PySCF
"""
mol = gto.M(atom="Li 0. 0. 0.; H 0. 0. 1.5",
basis="cc-pvtz",
unit="bohr",
spin=0)
epsilon = 1e-4
delta = 1e-5
nsteps = 200
warmup = 10
for mf in [scf.RHF(mol).run(), scf.ROHF(mol).run(), scf.UHF(mol).run()]:
# Test same number of elecs
mc = mcscf.CASCI(mf, ncas=4, nelecas=(1, 1))
mc.kernel()
wf = MultiSlater(mol, mf, mc)
nconf = 10
nelec = np.sum(mol.nelec)
epos = initial_guess(mol, nconf)
for k, item in testwf.test_updateinternals(wf, epos).items():
assert item < epsilon
assert testwf.test_wf_gradient(wf, epos, delta=delta)[0] < epsilon
assert testwf.test_wf_laplacian(wf, epos, delta=delta)[0] < epsilon
assert testwf.test_wf_pgradient(wf, epos, delta=delta)[0] < epsilon
# Test same number of elecs
mc = mcscf.CASCI(mf, ncas=4, nelecas=(1, 1))
mc.kernel()
wf = pyqmc.default_msj(mol, mf, mc)[0]
nelec = np.sum(mol.nelec)
epos = initial_guess(mol, nconf)
for k, item in testwf.test_updateinternals(wf, epos).items():
assert item < epsilon
assert testwf.test_wf_gradient(wf, epos, delta=delta)[0] < epsilon
assert testwf.test_wf_laplacian(wf, epos, delta=delta)[0] < epsilon
assert testwf.test_wf_pgradient(wf, epos, delta=delta)[0] < epsilon
# Test different number of elecs
mc = mcscf.CASCI(mf, ncas=4, nelecas=(2, 0))
mc.kernel()
wf = MultiSlater(mol, mf, mc)
nelec = np.sum(mol.nelec)
epos = initial_guess(mol, nconf)
for k, item in testwf.test_updateinternals(wf, epos).items():
assert item < epsilon
assert testwf.test_wf_gradient(wf, epos, delta=delta)[0] < epsilon
assert testwf.test_wf_laplacian(wf, epos, delta=delta)[0] < epsilon
assert testwf.test_wf_pgradient(wf, epos, delta=delta)[0] < epsilon
# Quick VMC test
nconf = 1000
coords = initial_guess(mol, nconf)
df, coords = vmc(wf,
coords,
nsteps=nsteps,
accumulators={"energy": EnergyAccumulator(mol)})
df = pd.DataFrame(df)
df = reblock(df["energytotal"][warmup:], 20)
en = df.mean()
err = df.sem()
assert en - mc.e_tot < 5 * err
print("p1/p2", p1 / p2)
print("log magnitude")
print("m1", m1)
print("m2", m2)
print("m1-m2", m1 - m2)
p_err = np.linalg.norm(p1 / p2 - p1[0] / p2[0])
m_err = np.linalg.norm(m1 - m2 - m1[0] + m2[0])
assert p_err < 1e-10, (p_err, m_err)
assert m_err < 1e-1, (p_err, m_err)
if __name__ == "__main__":
from pyqmc.testwf import (
test_updateinternals,
test_wf_gradient,
test_wf_laplacian,
test_wf_gradient_laplacian,
)
wf1, wf2, configs = generate_test_inputs()
test_recompute(wf1, wf2, configs)
test_updateinternals(wf1, configs)
test_updateinternals(wf2, configs)
test_wf_gradient(wf1, configs)
test_wf_gradient(wf2, configs)
test_wf_laplacian(wf1, configs)
test_wf_laplacian(wf2, configs)
test_wf_gradient_laplacian(wf1, configs)
test_wf_gradient_laplacian(wf2, configs)
