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
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
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
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")
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")
#There should be 5 3d TM orbitals. Let's check this!
assert len(TM_3d_orbitals)==5
##############################################################################################
if("U" in method):
if("HF" in method):
m=UHF(mol)
else:
m=UKS(mol)
m.xc=method[1:]
else:
if(method=="ROHF"):
m=ROHF(mol)
else:
m=ROKS(mol)
m.xc=method
##############################################################################################
dm=np.zeros(m.init_guess_by_minao().shape)
#The 3s is always doubly-occupied for the TM atom
for s in TM_3s_orbitals:
for spin in [0,1]:
dm[spin,s,s]=1
#The 4s is always at least singly-occupied for the TM atom
for s in TM_4s_orbitals:
dm[0,s,s]=1
#Control the 4s double-occupancy
charge = 0
S = 1
r = 1.725
method = 'UB3LYP'
basis = 'vtz'
el = 'Cu'
nppi = []
npz = []
#for run in range(19):
for run in [0, 1, 2, 3, 4, 5, 6, 7, 14]:
chkfile = el + basis + "_r" + str(r) + "_s" + str(
S) + "_" + method + "_" + str(run) + ".chk"
print(chkfile)
mol = lib.chkfile.load_mol(chkfile)
m = ROKS(mol)
m.__dict__.update(lib.chkfile.load(chkfile, 'scf'))
#Build RDM on IAO basis
s = m.get_ovlp()
mo_occ = m.mo_occ
M = m.mo_coeff[0][:, mo_occ[0] > 0]
M = reduce(np.dot, (a.T, s, M))
dm_u = np.dot(M, M.T)
M = m.mo_coeff[1][:, mo_occ[1] > 0]
M = reduce(np.dot, (a.T, s, M))
dm_d = np.dot(M, M.T)
#plt.title("S="+str(mol_spin))
#plt.matshow(dm_u+dm_d - np.diag(np.diag(dm_u+dm_d)),vmin=-1,vmax=1,cmap=plt.cm.bwr)
#plt.show()
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()
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
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
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()