How to use the pyqmc.slateruhf.PySCFSlaterUHF function in pyqmc

def test_accumulator():
    """ Tests that the accumulator gets inserted into the data output correctly.
    """
    import pandas as pd
    from pyqmc.mc import vmc, initial_guess
    from pyscf import gto, scf
    from pyqmc.energy import energy
    from pyqmc.slateruhf import PySCFSlaterUHF
    from pyqmc.accumulators import EnergyAccumulator

    mol = gto.M(
        atom="Li 0. 0. 0.; Li 0. 0. 1.5", basis="cc-pvtz", unit="bohr", verbose=5
    )
    mf = scf.RHF(mol).run()
    nconf = 5000
    wf = PySCFSlaterUHF(mol, mf)
    coords = initial_guess(mol, nconf)

    df, coords = vmc(
        wf, coords, nsteps=30, accumulators={"energy": EnergyAccumulator(mol)}
    )
    df = pd.DataFrame(df)
    eaccum = EnergyAccumulator(mol)
    eaccum_energy = eaccum(coords, wf)
    df = pd.DataFrame(df)
    print(df["energytotal"][29] == np.average(eaccum_energy["total"]))

    assert df["energytotal"][29] == np.average(eaccum_energy["total"])

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-4
    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])

from pyscf import lib, gto, scf
    from pyqmc.slateruhf import PySCFSlaterUHF
    from pyqmc.jastrowspin import JastrowSpin
    from pyqmc.dmc import limdrift, rundmc
    from pyqmc.mc import vmc
    from pyqmc.accumulators import EnergyAccumulator
    from pyqmc.func3d import CutoffCuspFunction
    from pyqmc.multiplywf import MultiplyWF
    from pyqmc.coord import OpenConfigs
    import pandas as pd

    mol = gto.M(atom="H 0. 0. 0.", basis="sto-3g", unit="bohr", spin=1)
    mf = scf.UHF(mol).run()
    nconf = 1000
    configs = OpenConfigs(np.random.randn(nconf, 1, 3))
    wf1 = PySCFSlaterUHF(mol, mf)
    wf = wf1
    wf2 = JastrowSpin(mol, a_basis=[CutoffCuspFunction(5, 0.2)], b_basis=[])
    wf2.parameters["acoeff"] = np.asarray([[[1.0, 0]]])
    wf = MultiplyWF(wf1, wf2)

    dfvmc, configs_ = vmc(
        wf, configs, nsteps=50, accumulators={"energy": EnergyAccumulator(mol)}
    )
    dfvmc = pd.DataFrame(dfvmc)
    print(
        "vmc energy",
        np.mean(dfvmc["energytotal"]),
        np.std(dfvmc["energytotal"]) / np.sqrt(len(dfvmc)),
    )

    warmup = 200

def test_vmc():
    """
    Test that a VMC calculation of a Slater determinant matches Hartree-Fock within error bars.
    """
    nconf = 1000
    mol = gto.M(
        atom="Li 0. 0. 0.; Li 0. 0. 1.5", basis="cc-pvtz", unit="bohr", verbose=1
    )

    mf_rhf = scf.RHF(mol).run()
    mf_uhf = scf.UHF(mol).run()
    nsteps = 300
    warmup = 30

    for wf, mf in [
        (PySCFSlaterUHF(mol, mf_rhf), mf_rhf),
        (PySCFSlaterUHF(mol, mf_uhf), mf_uhf),
    ]:

        #Without blocks
        coords = initial_guess(mol, nconf)
        df, coords = vmc(
            wf, coords, nsteps=nsteps, accumulators={"energy": EnergyAccumulator(mol)}, verbose=True
        )

        df = pd.DataFrame(df)
        df = reblock(df["energytotal"][warmup:], 20)
        en = df.mean()
        err = df.sem()
        assert en - mf.energy_tot() < 5 * err, "pyscf {0}, vmc {1}, err {2}".format(
            mf.energy_tot(), en, err
        )

"""
    Test that a VMC calculation of a Slater determinant matches Hartree-Fock within error bars.
    """
    nconf = 1000
    mol = gto.M(
        atom="Li 0. 0. 0.; Li 0. 0. 1.5", basis="cc-pvtz", unit="bohr", verbose=1
    )

    mf_rhf = scf.RHF(mol).run()
    mf_uhf = scf.UHF(mol).run()
    nsteps = 300
    warmup = 30

    for wf, mf in [
        (PySCFSlaterUHF(mol, mf_rhf), mf_rhf),
        (PySCFSlaterUHF(mol, mf_uhf), mf_uhf),
    ]:

        #Without blocks
        coords = initial_guess(mol, nconf)
        df, coords = vmc(
            wf, coords, nsteps=nsteps, accumulators={"energy": EnergyAccumulator(mol)}, verbose=True
        )

        df = pd.DataFrame(df)
        df = reblock(df["energytotal"][warmup:], 20)
        en = df.mean()
        err = df.sem()
        assert en - mf.energy_tot() < 5 * err, "pyscf {0}, vmc {1}, err {2}".format(
            mf.energy_tot(), en, err
        )

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-4
    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))