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)