Beispiel #1
0
def h1_moToIAO(parms, printvals=False):
    #LOAD IN IAOS
    act_iao = [5, 9, 6, 8, 11, 12, 7, 13, 1]
    iao = np.load('../../../pyscf/ub3lyp_full/b3lyp_iao_b.pickle')
    iao = iao[:, act_iao]

    #LOAD IN MOS
    act_mo = [5, 6, 7, 8, 9, 10, 11, 12, 13]
    chkfile = '../../../pyscf/chk/Cuvtz_r1.725_s1_B3LYP_1.chk'
    mol = lib.chkfile.load_mol(chkfile)
    m = ROKS(mol)
    m.__dict__.update(lib.chkfile.load(chkfile, 'scf'))
    mo = m.mo_coeff[:, act_mo]
    s = m.get_ovlp()

    #IAO ordering: ['del','del','yz','xz','x','y','z2','z','s']
    #MO ordering:  dxz, dyz, dz2, delta, delta, px, py, pz, 4s
    #es,edpi,edz2,edd,epi,epz,tpi,tdz,tsz,tds=parms
    #e=np.diag([edpi,edpi,edz2,edd,edd,epi,epi,epz,es])

    es, epi, epz, tpi, tdz, tsz, tds = parms
    ed = 0
    e = np.diag([ed, ed, ed, ed, ed, epi, epi, epz, es])
    e[[0, 1, 5, 6], [5, 6, 0, 1]] = tpi
    e[[2, 7], [7, 2]] = tdz
    e[[8, 7], [7, 8]] = tsz
    e[[8, 2], [2, 8]] = tds

    mo_to_iao = reduce(np.dot, (mo.T, s, iao))
    e = reduce(np.dot, (mo_to_iao.T, e, mo_to_iao))
    e[np.abs(e) < 1e-10] = 0
    e = (e + e.T) / 2

    return e
Beispiel #2
0
def h1_moToIAO(printvals=False):
    #LOAD IN IAOS
    act_iao = [5, 9, 6, 8, 11, 12, 7, 13, 1]
    iao = np.load('../../../pyscf/ub3lyp_full/b3lyp_iao_b.pickle')
    iao = iao[:, act_iao]

    #LOAD IN MOS
    act_mo = [5, 6, 7, 8, 9, 10, 11, 12, 13]
    chkfile = '../../../pyscf/chk/Cuvtz_r1.725_s1_B3LYP_0.chk'
    mol = lib.chkfile.load_mol(chkfile)
    m = ROKS(mol)
    m.__dict__.update(lib.chkfile.load(chkfile, 'scf'))
    mo = m.mo_coeff[:, act_mo]
    s = m.get_ovlp()

    #IAO ordering: ['del','del','yz','xz','x','y','z2','z','s']
    #MO ordering:  dxz, dyz, dz2, delta, delta, px, py, pz, 4s
    e = np.diag(m.mo_energy[act_mo]) * 27.2114

    if (printvals):
        w, vr = np.linalg.eigh(e)
        print('MO eigenvalues, Jsd=0 ------------------------------------')
        print(w)

    mo_to_iao = reduce(np.dot, (mo.T, s, iao))
    e = reduce(np.dot, (mo_to_iao.T, e, mo_to_iao))
    e[np.abs(e) < 1e-10] = 0
    e = (e + e.T) / 2

    if (printvals):
        w, vr = np.linalg.eigh(e)
        print('IAO eigenvalues, Jsd=0 ------------------------------------')
        print(w)

    return e
Beispiel #3
0
def format_df_iao(df):
    df['beta'] = -1000
    #LOAD IN IAOS
    act_iao = [5, 9, 6, 8, 11, 12, 7, 13, 1]
    iao = np.load('../../../pyscf/ub3lyp_full/b3lyp_iao_b.pickle')
    iao = iao[:, act_iao]

    #LOAD IN MOS
    act_mo = [5, 6, 7, 8, 9, 10, 11, 12, 13]
    chkfile = '../../../pyscf/chk/Cuvtz_r1.725_s1_B3LYP_1.chk'
    mol = lib.chkfile.load_mol(chkfile)
    m = ROKS(mol)
    m.__dict__.update(lib.chkfile.load(chkfile, 'scf'))
    mo = m.mo_coeff[:, act_mo]
    s = m.get_ovlp()

    #IAO ordering: ['del','del','yz','xz','x','y','z2','z','s']
    #MO ordering:  dxz, dyz, dz2, delta, delta, px, py, pz, 4s

    df['iao_n_3dd'] = 0
    df['iao_n_3dpi'] = 0
    df['iao_n_3dz2'] = 0
    df['iao_n_3d'] = 0
    df['iao_n_2pz'] = 0
    df['iao_n_2ppi'] = 0
    df['iao_n_4s'] = 0
    df['iao_t_pi'] = 0
    df['iao_t_dz'] = 0
    df['iao_t_ds'] = 0
    df['iao_t_sz'] = 0

    for z in range(df.shape[0]):
        print(z)
        e = np.zeros((9, 9))
        orb1 = [1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 8, 3]
        orb2 = [1, 2, 3, 4, 5, 6, 7, 8, 9, 6, 7, 8, 9, 9]
        for i in range(len(orb1)):
            e[orb1[i] - 1, orb2[i] - 1] = df['mo_' + str(orb1[i]) + '_' +
                                             str(orb2[i])].values[z]

        mo_to_iao = reduce(np.dot, (mo.T, s, iao))
        e = reduce(np.dot, (mo_to_iao.T, e, mo_to_iao))
        e[np.abs(e) < 1e-10] = 0
        e = (e + e.T) / 2

        df['iao_n_3dd'].iloc[z] = np.sum(np.diag(e)[[0, 1]])
        df['iao_n_3dpi'].iloc[z] = np.sum(np.diag(e)[[2, 3]])
        df['iao_n_3dz2'].iloc[z] = np.diag(e)[6]
        df['iao_n_3d'].iloc[z] = np.sum(np.diag(e)[[0, 1, 2, 3, 6]])
        df['iao_n_2pz'].iloc[z] = np.sum(np.diag(e)[7])
        df['iao_n_2ppi'].iloc[z] = np.sum(np.diag(e)[[4, 5]])
        df['iao_n_4s'].iloc[z] = np.sum(np.diag(e)[8])
        df['iao_t_pi'].iloc[z] = 2 * (e[3, 4] + e[2, 5])
        df['iao_t_ds'].iloc[z] = 2 * e[6, 8]
        df['iao_t_dz'].iloc[z] = 2 * e[6, 7]
        df['iao_t_sz'].iloc[z] = 2 * e[7, 8]

    return df
Beispiel #4
0
def new_gs(parms):
    print(parms)
    e4s, e3d, epi, ez, tpi, tds, tdz, tsz = parms

    #dx, dy, dz2, dd, px, py, pz, 4s
    H = np.diag([e3d, e3d, e3d, e3d, e3d, epi, epi, ez, e4s])
    H[[0, 1, 5, 6], [5, 6, 0, 1]] = tpi
    H[[2, 7], [7, 2]] = tdz
    H[[7, 8], [8, 7]] = tsz
    H[[2, 8], [8, 2]] = tds

    w, vr = np.linalg.eigh(H)

    from pyscf import lib, gto, scf, mcscf, fci, lo, ci, cc
    from pyscf.scf import ROHF, UHF, ROKS
    from pyscf2qwalk import print_qwalk_mol
    chkfile = "../../pyscf/chk/Cuvtz_r1.725_s1_B3LYP_1.chk"
    mol = lib.chkfile.load_mol(chkfile)
    m = ROKS(mol)
    m.__dict__.update(lib.chkfile.load(chkfile, 'scf'))

    m.mo_coeff[:, 5:14] = np.dot(m.mo_coeff[:, 5:14], vr)
    print_qwalk_mol(mol, m, basename="new_gs/new_gs")
Beispiel #5
0
import json
from pyscf import lib, gto, scf, mcscf, fci, lo, ci, cc
from pyscf.scf import ROHF, UHF, ROKS
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from pyscf2qwalk import print_qwalk_mol

charge = 0
S = [1, 1, 3, 3, 1, 3]
r = 1.725
method = 'B3LYP'
basis = 'vtz'
el = 'Cu'

occ = np.arange(14)
mo_coeff = None
for run in range(len(S)):
    chkfile = "../chk/" + el + basis + "_r" + str(r) + "_s" + str(
        S[run]) + "_" + method + "_" + str(run) + ".chk"
    mol = lib.chkfile.load_mol(chkfile)
    m = ROKS(mol)
    m.__dict__.update(lib.chkfile.load(chkfile, 'scf'))

    if (mo_coeff is None): mo_coeff = m.mo_coeff[:, occ]
    else: mo_coeff = np.concatenate((mo_coeff, m.mo_coeff[:, occ]), axis=1)

#Write to file
m.mo_coeff = mo_coeff
print_qwalk_mol(mol, m, basename="../orbs/all")
Beispiel #6
0
def analyze(df):
    df['gsw'] = np.round(df['gsw'], 2)

    df['n_3dd'] = df['t_8_8'] + df['t_9_9']
    df['n_3dpi'] = df['t_5_5'] + df['t_6_6']
    df['n_3dz2'] = df['t_7_7']
    df['n_3d'] = df['n_3dd'] + df['n_3dpi'] + df['n_3dz2']
    df['n_2ppi'] = df['t_10_10'] + df['t_11_11']
    df['n_2pz'] = df['t_12_12']
    df['n_2p'] = df['n_2ppi'] + df['n_2pz']
    df['n_4s'] = df['t_13_13']
    df['t_pi'] = 2 * (df['t_5_10'] + df['t_6_11'])
    df['t_dz'] = 2 * df['t_7_12']
    df['t_ds'] = 2 * df['t_7_13']
    df['t_sz'] = 2 * df['t_12_13']

    #PAIRPLOTS --------------------------------------------------------------------------
    #sns.pairplot(df,vars=['energy','n_3dd','n_3dpi','n_3dz2','n_3d'],hue='basestate',markers=['o']+['.']*8)
    #sns.pairplot(df,vars=['energy','n_2ppi','n_2pz','n_2p','n_4s'],hue='basestate',markers=['o']+['.']*8)
    #sns.pairplot(df,vars=['energy','t_pi','t_dz','t_ds','t_sz'],hue='basestate',markers=['o']+['.']*8)
    #plt.show()
    #exit(0)

    #FITS --------------------------------------------------------------------------
    y = df['energy']
    yerr = df['energy_err']
    #BIGGEST
    #X=df[['n_3dd','n_3d','n_2ppi','n_2pz','n_2p','n_4s','t_pi','t_sz','t_dz','t_ds']]
    #SMALLEST
    #X=df[['n_3d','n_2ppi','n_2pz']]
    #SEQUENTIALLY BETTER
    #X=df[['n_3d','n_2ppi','n_2pz','t_pi']]
    #X=df[['n_3d','n_2ppi','n_2pz','t_ds']]
    #X=df[['n_3d','n_2ppi','n_2pz','t_pi','t_ds']]
    #X=df[['n_3d','n_2ppi','n_2pz','t_pi','t_ds','t_dz']]
    X = df[['n_3d', 'n_2ppi', 'n_2pz', 't_pi', 't_ds', 't_sz']]
    #X=df[['n_3d','n_2ppi','n_2pz','t_pi','t_ds','t_dz','t_sz']]

    X = sm.add_constant(X)
    ols = sm.OLS(y, X).fit()
    print(ols.params)
    print(ols.summary())

    df['pred'] = ols.predict(X)
    g = sns.FacetGrid(df,
                      hue='basestate',
                      hue_kws=dict(marker=['o'] + ['.'] * 8))
    g.map(plt.errorbar, "pred", "energy", "energy_err", fmt='o').add_legend()
    plt.plot(df['energy'], df['energy'], 'k--')
    plt.show()
    exit(0)

    #ROTATE TO IAOS --------------------------------------------------------------------------
    #Gather IAOs
    f = '../../pyscf/analyze/b3lyp_iao_b.pickle'  #IAOs which span the MOs
    a = np.load(f)

    chkfile = '../../pyscf/chk/Cuvtz_r1.725_s1_B3LYP_1.chk'  #MOs we used to calculate RDM elements
    mol = lib.chkfile.load_mol(chkfile)
    m = ROKS(mol)
    m.__dict__.update(lib.chkfile.load(chkfile, 'scf'))

    s = m.get_ovlp()
    H1 = np.diag([
        -3.1957, -3.1957, -3.1957, -3.1957, -3.1957, -1.6972, -1.6972, -2.5474,
        0
    ])
    H1[0, 5] = 0.7858
    H1[5, 0] = 0.7858
    H1[1, 6] = 0.7858
    H1[6, 1] = 0.7858
    H1[2, 7] = -0.8329
    H1[7, 2] = -0.8329
    H1[7, 8] = 1.6742
    H1[8, 7] = 1.6742

    mo_coeff = m.mo_coeff[:, 5:14]
    e1 = reduce(np.dot, (a.T, s, mo_coeff, H1, mo_coeff.T, s.T, a))
    e1 = (e1 + e1.T) / 2.

    plt.matshow(e1, vmax=5, vmin=-5, cmap=plt.cm.bwr)
    plt.colorbar()
    labels = [
        '3s', '4s', '3px', '3py', '3pz', '3dxy', '3dyz', '3dz2', '3dxz',
        '3dx2-y2', '2s', '2px', '2py', '2pz'
    ]
    plt.xticks(np.arange(14), labels, rotation=90)
    plt.yticks(np.arange(14), labels)
    plt.show()
Beispiel #7
0
    def init_density(self,
                     in_dmat=None,
                     scf_obj=None,
                     env_method=None,
                     kpts=None):
        """
        Initializes the periodic subsystem density

        Parameters
        ----------
            in_dmat : ndarray, optional
                New subsystem density matrix (default is None).
            scf_obj : pyscf:pbc:scf object, optional
                The PySCF subsystem object (default is None).
            env_method : str, optional
                Subsystem environment energy method (default is None).
            kpts : ndarray, optional
                Kpoints to use in calculation (default is None).

        Returns
        -------
            dmat : ndarray
                The new / guessed density matrix
        """

        import numpy as np
        import pyscf
        from pyscf.scf import RKS, RHF, ROKS, ROHF, UKS, UHF

        if in_dmat is not None:
            return in_dmat

        if scf_obj is None: scf_obj = self.env_scf
        if env_method is None: env_method = self.env_method
        if kpts is None: kpts = self.kpts

        nkpts = len(kpts)
        nao = scf_obj.cell.nao_nr()

        dmat = np.zeros((2, nkpts, nao, nao))

        mol = scf_obj.cell.to_mol()
        mol.verbose = 0

        if self.unrestricted:
            if env_method in ('hf', 'hartree-fock'):
                mf = UHF(mol)
            else:
                mf = UKS(mol)
                mf.xc = env_method
            mf.kernel()
            dtemp = mf.make_rdm1()

        elif mol.spin != 0:
            if env_method in ('hf', 'hartree-fock'):
                mf = ROHF(mol)
            else:
                mf = ROKS(mol)
                mf.xc = env_method
            mf.kernel()
            dtemp = mf.make_rdm1()

        else:
            if env_method in ('hf', 'hartree-fock'):
                mf = RHF(mol)
            else:
                mf = RKS(mol)
                mf.xc = env_method
            mf.kernel()
            dtemp = mf.make_rdm1() / 2.0
            dtemp = [dtemp, dtemp]

        for k in range(nkpts):
            dmat[0, k] = dtemp[0]
            dmat[1, k] = dtemp[0]

        return dmat
Beispiel #8
0
def desc_ed(df):
    print("DESC_ED")
    #Get descriptors from ci
    mol = gto.Mole()
    cis = fci.direct_uhf.FCISolver(mol)
    sigUs = []
    sigJsd = []
    sigNdz2 = []
    sigNdpi = []
    sigNdd = []
    sigNd = []
    sigN2pz = []
    sigN2ppi = []
    sigN4s = []
    sigTpi = []
    sigTds = []
    sigTdz = []
    sigTsz = []

    sigMONdz2 = []
    sigMONdpi = []
    sigMONdd = []
    sigMONd = []
    sigMON2pz = []
    sigMON2ppi = []
    sigMON4s = []
    sigMOTpi = []
    sigMOTds = []
    sigMOTdz = []
    sigMOTsz = []

    #GET MO TO IAO MATRIX
    act_iao = [5, 9, 6, 8, 11, 12, 7, 13, 1]
    iao = np.load('../../../pyscf/ub3lyp_full/b3lyp_iao_b.pickle')
    iao = iao[:, act_iao]

    #LOAD IN MOS
    act_mo = [5, 6, 7, 8, 9, 10, 11, 12, 13]
    chkfile = '../../../pyscf/chk/Cuvtz_r1.725_s1_B3LYP_1.chk'
    mol = lib.chkfile.load_mol(chkfile)
    m = ROKS(mol)
    m.__dict__.update(lib.chkfile.load(chkfile, 'scf'))
    mo = m.mo_coeff[:, act_mo]
    s = m.get_ovlp()
    mo_to_iao = reduce(np.dot, (mo.T, s, iao))

    for i in range(df.shape[0]):
        ci = df['ci'].iloc[i]
        norb = 9
        nelec = (8, 7)
        if (df['Sz'].iloc[i] == 1.5): nelec = (9, 6)
        ci = ci.reshape((sp.misc.comb(norb, nelec[0], exact=True),
                         sp.misc.comb(norb, nelec[1], exact=True)))
        dm2 = cis.make_rdm12s(ci, norb, nelec)

        sigUs.append(dm2[1][1][8, 8, 8, 8])

        Jsd = 0
        for j in [0, 1, 2, 3, 6]:
            Jsd += 0.25*(dm2[1][0][8,8,j,j] + dm2[1][2][8,8,j,j] - dm2[1][1][8,8,j,j] - dm2[1][1][j,j,8,8])-\
                   0.5*(dm2[1][1][j,8,8,j] + dm2[1][1][8,j,j,8])
        sigJsd.append(Jsd)

        dm = dm2[0][0] + dm2[0][1]

        #IAO ordering: ['del','del','yz','xz','x','y','z2','z','s']
        sigNdz2.append(dm[6, 6])
        sigNdpi.append(dm[2, 2] + dm[3, 3])
        sigNdd.append(dm[0, 0] + dm[1, 1])
        sigNd.append(dm[0, 0] + dm[1, 1] + dm[2, 2] + dm[3, 3] + dm[6, 6])
        sigN2pz.append(dm[7, 7])
        sigN2ppi.append(dm[4, 4] + dm[5, 5])
        sigN4s.append(dm[8, 8])
        sigTpi.append(2 * (dm[3, 4] + dm[2, 5]))
        sigTds.append(2 * dm[6, 8])
        sigTdz.append(2 * dm[6, 7])
        sigTsz.append(2 * dm[7, 8])

        #MO ordering:  dxz, dyz, dz2, delta, delta, px, py, pz, 4s
        mo_dm = reduce(np.dot, (mo_to_iao, dm, mo_to_iao.T))
        sigMONdz2.append(mo_dm[2, 2])
        sigMONdpi.append(mo_dm[0, 0] + mo_dm[1, 1])
        sigMONdd.append(mo_dm[3, 3] + mo_dm[4, 4])
        sigMONd.append(mo_dm[0, 0] + mo_dm[1, 1] + mo_dm[2, 2] + mo_dm[3, 3] +
                       mo_dm[4, 4])
        sigMON2pz.append(mo_dm[7, 7])
        sigMON2ppi.append(mo_dm[6, 6] + mo_dm[5, 5])
        sigMON4s.append(mo_dm[8, 8])
        sigMOTpi.append(2 * (mo_dm[0, 5] + mo_dm[1, 6]))
        sigMOTds.append(2 * mo_dm[2, 8])
        sigMOTdz.append(2 * mo_dm[2, 7])
        sigMOTsz.append(2 * mo_dm[7, 8])

    df['iao_n_3dz2'] = sigNdz2
    df['iao_n_3dpi'] = sigNdpi
    df['iao_n_3dd'] = sigNdd
    df['iao_n_3d'] = sigNd
    df['iao_n_2pz'] = sigN2pz
    df['iao_n_2ppi'] = sigN2ppi
    df['iao_n_4s'] = sigN4s
    df['iao_t_pi'] = sigTpi
    df['iao_t_ds'] = sigTds
    df['iao_t_dz'] = sigTdz
    df['iao_t_sz'] = sigTsz

    df['mo_n_3dz2'] = sigMONdz2
    df['mo_n_3dpi'] = sigMONdpi
    df['mo_n_3dd'] = sigMONdd
    df['mo_n_3d'] = sigMONd
    df['mo_n_2pz'] = sigMON2pz
    df['mo_n_2ppi'] = sigMON2ppi
    df['mo_n_4s'] = sigMON4s
    df['mo_t_pi'] = sigMOTpi
    df['mo_t_ds'] = sigMOTds
    df['mo_t_dz'] = sigMOTdz
    df['mo_t_sz'] = sigMOTsz

    df['Us'] = sigUs
    df['Jsd'] = sigJsd
    return df
Beispiel #9
0
def analyze(df):
    df['gsw'] = np.round(df['gsw'], 2)
    print(list(df))

    df['n_3dd'] = df['t_4_4'] + df['t_5_5']
    df['n_3dpi'] = df['t_1_1'] + df['t_2_2']
    df['n_3dz2'] = df['t_3_3']
    df['n_3d'] = df['n_3dd'] + df['n_3dpi'] + df['n_3dz2']
    df['n_2ppi'] = df['t_6_6'] + df['t_7_7']
    df['n_2pz'] = df['t_8_8']
    df['n_2p'] = df['n_2ppi'] + df['n_2pz']
    df['n_4s'] = df['t_9_9']
    df['t_pi'] = 2 * (df['t_1_6'] + df['t_2_7'])
    df['t_dz'] = 2 * df['t_3_8']
    df['t_sz'] = 2 * df['t_8_9']
    df['t_ds'] = 2 * df['t_3_9']
    df['n'] = df['n_3d'] + df['n_4s'] + df['n_2p']

    #PAIRPLOTS --------------------------------------------------------------------------
    #sns.pairplot(df,vars=['energy','n'],hue='basestate',markers=['o']+['.']*8)
    #sns.pairplot(df,vars=['energy','n_2ppi','n_2pz','n_3d'],hue='basestate',markers=['o']+['.']*8)
    #sns.pairplot(df,vars=['energy','t_pi','t_dz','t_ds','t_sz'],hue='basestate',markers=['o']+['.']*8)
    #plt.show()
    #exit(0)

    #FITS --------------------------------------------------------------------------
    y = df['energy']
    yerr = df['energy_err']
    #BIGGEST
    #X=df[['n_3dd','n_3d','n_2ppi','n_2pz','n_2p','n_4s','t_pi','t_sz','t_dz','t_ds']]
    #SMALLEST
    #X=df[['n_3d','n_2ppi','n_2pz']]
    #SEQUENTIALLY BETTER
    #X=df[['n_3d','n_2ppi','n_2pz','t_pi']]
    #X=df[['n_3d','n_2ppi','n_2pz','t_pi','t_sz']]
    #X=df[['n_3d','n_2ppi','n_2pz','t_pi','t_dz']]
    #X=df[['n_3d','n_2ppi','n_2pz','t_pi','t_dz','t_sz']]

    #X=df[['n_3d','n_2ppi','n_2pz','t_dz']]
    #X=df[['n_3d','n_2ppi','n_2pz','t_sz']]

    X = df[['n_3d', 'n_2ppi', 'n_2pz', 't_ds', 't_pi']]
    #X=df[['n_3d','n_2ppi','n_2pz','t_pi','t_ds']]
    #X=df[['n_3d','n_2ppi','n_2pz','t_dz','t_ds']]
    #X=df[['n_3d','n_2ppi','n_2pz','t_sz','t_ds']]
    #X=df[['n_3d','n_2ppi','n_2pz','t_dz','t_sz','t_ds']]

    X = sm.add_constant(X)
    beta = 0.0
    wls = sm.WLS(y, X, weights=np.exp(-beta * (y - min(y)))).fit()
    print(wls.summary())

    df['pred'] = wls.predict(X)
    df['resid'] = df['energy'] - df['pred']
    sns.pairplot(df,
                 vars=['resid', 't_pi', 't_dz', 't_sz'],
                 hue='basestate',
                 markers=['o'] + ['.'] * 8)
    '''
  g = sns.FacetGrid(df,hue='basestate',hue_kws=dict(marker=['o']+['.']*8),palette=sns.color_palette('husl',9))
  g.map(plt.errorbar, "pred", "energy", "energy_err",fmt='o').add_legend()
  plt.plot(df['energy'],df['energy'],'k--')
  '''
    plt.show()
    exit(0)

    #ROTATE TO IAOS --------------------------------------------------------------------------
    #Gather IAOs
    f = '../../pyscf/analyze/b3lyp_iao_b.pickle'  #IAOs which span the MOs
    a = np.load(f)

    chkfile = '../../pyscf/chk/Cuvtz_r1.725_s1_B3LYP_1.chk'  #MOs we used to calculate RDM elements
    mol = lib.chkfile.load_mol(chkfile)
    m = ROKS(mol)
    m.__dict__.update(lib.chkfile.load(chkfile, 'scf'))

    s = m.get_ovlp()

    #dpi,dpi,dz2,dd,dd,ppi,ppi,pz,4s
    #e3d,e2pz,e2ppi,tpi,tdz,tsz=(-3.2487,-2.5759,-1.6910,0.3782,-0.7785,1.1160) #DMC
    e3d, e2pz, e2ppi, tpi, tdz, tsz = (-3.3324, -1.8762, -0.9182, 0.8266, 0, 0
                                       )  #DMC

    H = np.diag([e3d, e3d, e3d, e3d, e3d, e2ppi, e2ppi, e2pz, 0])
    H[0, 5] = tpi
    H[5, 0] = tpi
    H[1, 6] = tpi
    H[6, 1] = tpi
    H[2, 7] = tdz
    H[7, 2] = tdz
    H[7, 8] = tsz
    H[8, 7] = tsz

    mo_coeff = m.mo_coeff[:, 5:14]  #Only include active MOs
    a = a[:, [1, 5, 6, 7, 8, 9, 11, 12, 13]]  #Only include active IAOs
    e1 = reduce(np.dot, (a.T, s, mo_coeff, H, mo_coeff.T, s.T, a))
    e1 = (e1 + e1.T) / 2.

    plt.matshow(e1, vmax=5, vmin=-5, cmap=plt.cm.bwr)
    plt.colorbar()
    #labels=['3s','4s','3px','3py','3pz','3dxy','3dyz','3dz2','3dxz','3dx2-y2','2s','2px','2py','2pz']
    labels = [
        '4s', '3dxy', '3dyz', '3dz2', '3dxz', '3dx2-y2', '2px', '2py', '2pz'
    ]
    plt.xticks(np.arange(len(labels)), labels, rotation=90)
    plt.yticks(np.arange(len(labels)), labels)
    plt.show()