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
S = 1
r = 1.725
method = 'UB3LYP'
basis = 'vtz'
el = 'Cu'
df = None
for run in range(16):
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)
obdm = np.array([dm_u, dm_d])
'''
#Gather (MO)
orb1=[0,1,2,3,4,5,6,7,8,9,10,11,12,13,5, 6 ,7 ,7 ,12]
orb2=[0,1,2,3,4,5,6,7,8,9,10,11,12,13,10,11,12,13,13]
one_body=sum_onebody(obdm,orb1,orb2)
one_labels=['t_'+str(orb1[i])+'_'+str(orb2[i]) for i in range(len(orb1))]
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()
