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
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_pbc_wfs():
"""
Ensure that the wave function objects are consistent in several situations.
"""
from pyscf.pbc import lib, gto, scf
from pyqmc.supercell import get_supercell
from pyqmc.slater import Slater
from pyqmc.multiplywf import MultiplyWF
from pyqmc import default_jastrow
import pyqmc
mol = gto.M(
atom="H 0. 0. 0.; H 1. 1. 1.",
basis="sto-3g",
unit="bohr",
a=(np.ones((3, 3)) - np.eye(3)) * 4,
)
mf = scf.KRKS(mol, mol.make_kpts((2, 2, 2))).run()
# mf_rohf = scf.KROKS(mol).run()
# mf_uhf = scf.KUKS(mol).run()
epsilon = 1e-5
nconf = 10
supercell = get_supercell(mol, S=(np.ones((3, 3)) - 2 * np.eye(3)))
epos = pyqmc.initial_guess(supercell, nconf)
# For multislaterpbc
# kinds = 0, 3, 5, 6 # G, X, Y, Z
# d1 = {kind: [0] for kind in kinds}
# d2 = d1.copy()
# d2.update({0: [], 3: [0, 1]})
# detwt = [2 ** 0.5, 2 ** 0.5]
# occup = [[d1, d2], [d1]]
# map_dets = [[0, 1], [0, 0]]
for wf in [
MultiplyWF(Slater(supercell, mf), default_jastrow(supercell)[0]),
Slater(supercell, mf),
]:
for k in wf.parameters:
if "mo_coeff" not in k and k != "det_coeff":
wf.parameters[k] = np.random.rand(*wf.parameters[k].shape)
_, epos = pyqmc.vmc(wf, epos, nblocks=1, nsteps=2, tstep=1) # move off node
for fname, func in zip(
["gradient", "laplacian", "pgradient"],
[
testwf.test_wf_gradient,
testwf.test_wf_laplacian,
testwf.test_wf_pgradient,
],
):
err = []
for delta in [1e-4, 1e-5, 1e-6, 1e-7, 1e-8]:
err.append(func(wf, epos, delta))
print(type(wf), fname, min(err))
assert min(err) < epsilon
for k, item in testwf.test_updateinternals(wf, epos).items():
print(k, item)
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 run_tests(wf, epos, epsilon):
_, epos = pyq.vmc(wf, epos, nblocks=1, nsteps=2, tstep=1) # move off node
for k, item in testwf.test_updateinternals(wf, epos).items():
print(k, item)
assert item < epsilon
testwf.test_mask(wf, 0, epos)
for fname, func in zip(
["gradient", "laplacian", "pgradient"],
[
testwf.test_wf_gradient,
testwf.test_wf_laplacian,
testwf.test_wf_pgradient,
],
):
err = [
func(wf, epos, delta) for delta in [1e-4, 1e-5, 1e-6, 1e-7, 1e-8]
]
assert min(err) < epsilon, "epsilon {0}".format(epsilon)
for fname, func in zip(
["gradient_value", "gradient_laplacian"],
[
testwf.test_wf_gradient_value,
testwf.test_wf_gradient_laplacian,
],
):
d = func(wf, epos)
for k, v in d.items():
assert v < 1e-10, (k, v)
def test_wfs():
"""
Ensure that the wave function objects are consistent in several situations.
"""
from pyscf import lib, gto, scf
from pyqmc.slater import PySCFSlater
from pyqmc.jastrowspin import JastrowSpin
from pyqmc.multiplywf import MultiplyWF
from pyqmc.manybody_jastrow import J3
import pyqmc
mol = gto.M(atom="Li 0. 0. 0.; H 0. 0. 1.5", basis="sto-3g", 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 = pyqmc.initial_guess(mol, nconf)
for wf in [
JastrowSpin(mol),
J3(mol),
MultiplyWF(PySCFSlater(mol, mf), JastrowSpin(mol)),
MultiplyWF(PySCFSlater(mol, mf), JastrowSpin(mol), J3(mol)),
PySCFSlater(mol, mf_uhf),
PySCFSlater(mol, mf),
PySCFSlater(mol, mf_rohf),
]:
for k in wf.parameters:
if k != "mo_coeff":
wf.parameters[k] = np.random.rand(*wf.parameters[k].shape)
for k, item in testwf.test_updateinternals(wf, epos).items():
print(k, item)
assert item < epsilon
testwf.test_mask(wf, 0, epos)
_, epos = pyqmc.vmc(wf, epos, nblocks=1, nsteps=2,
tstep=1) # move off node
for fname, func in zip(
["gradient", "laplacian", "pgradient"],
[
testwf.test_wf_gradient,
testwf.test_wf_laplacian,
testwf.test_wf_pgradient,
],
):
err = []
for delta in [1e-4, 1e-5, 1e-6, 1e-7, 1e-8]:
err.append(func(wf, epos, delta)[0])
print(type(wf), fname, min(err))
assert min(err) < epsilon, "epsilon {0}".format(epsilon)
def test_pbc_wfs():
"""
Ensure that the wave function objects are consistent in several situations.
"""
from pyscf.pbc import lib, gto, scf
from pyqmc.slaterpbc import PySCFSlaterPBC, get_supercell
from pyqmc.jastrowspin import JastrowSpin
from pyqmc.multiplywf import MultiplyWF
from pyqmc.coord import OpenConfigs
import pyqmc
mol = gto.M(atom="H 0. 0. 0.; H 1. 1. 1.",
basis="sto-3g",
unit="bohr",
a=np.eye(3) * 4)
mf = scf.KRKS(mol).run()
# mf_rohf = scf.KROKS(mol).run()
# mf_uhf = scf.KUKS(mol).run()
epsilon = 1e-5
nconf = 10
supercell = get_supercell(mol, S=np.eye(3))
epos = pyqmc.initial_guess(supercell, nconf)
for wf in [
MultiplyWF(PySCFSlaterPBC(supercell, mf), JastrowSpin(mol)),
PySCFSlaterPBC(supercell, mf),
# PySCFSlaterPBC(supercell, mf_uhf),
# PySCFSlaterPBC(supercell, mf_rohf),
]:
for k in wf.parameters:
if k != "mo_coeff":
wf.parameters[k] = np.random.rand(*wf.parameters[k].shape)
for fname, func in zip(
["gradient", "laplacian", "pgradient"],
[
testwf.test_wf_gradient,
testwf.test_wf_laplacian,
testwf.test_wf_pgradient,
],
):
err = []
for delta in [1e-4, 1e-5, 1e-6, 1e-7, 1e-8]:
err.append(func(wf, epos, delta)[0])
print(fname, min(err))
assert min(err) < epsilon
for k, item in testwf.test_updateinternals(wf, epos).items():
print(k, item)
assert item < epsilon
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
from pyqmc.coord import OpenConfigs
import pyqmc
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 = pyqmc.initial_guess(mol, nconf)
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:
if k != "mo_coeff":
wf.parameters[k] = np.random.rand(*wf.parameters[k].shape)
for fname, func in zip(
["gradient", "laplacian", "pgradient"],
[
testwf.test_wf_gradient,
testwf.test_wf_laplacian,
testwf.test_wf_pgradient,
],
):
err = []
for delta in [1e-4, 1e-5, 1e-6, 1e-7, 1e-8]:
err.append(func(wf, epos, delta)[0])
print(fname, min(err))
assert min(err) < epsilon
for k, item in testwf.test_updateinternals(wf, epos).items():
print(k, item)
assert item < epsilon
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)
