def gather_all():
'''
Gathers all your data and stores into
'''
df = None
for f in [
'Cuvtz_r1.725_s1_UB3LYP_11.chk.vmc.gosling.json',
'Cuvtz_r1.725_s1_UB3LYP_12.chk.vmc.gosling.json',
'Cuvtz_r1.725_s3_UB3LYP_13.chk.vmc.gosling.json'
]:
statinfo = os.stat(f)
if (statinfo.st_size > 0):
print(f)
data = json.load(open(f, 'r'))
obdm, __ = get_qwalk_dm(data['properties']['tbdm_basis1'])
obdm2, __, tbdm, __ = get_qwalk_dm(
data['properties']['tbdm_basis2'])
energy = data['properties']['total_energy']['value'][0] * 27.2114
energy_err = data['properties']['total_energy']['error'][
0] * 27.2114
print(obdm.shape, obdm2.shape, tbdm.shape)
#MO ordering
#1-body
orb1 = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 8, 3]
orb2 = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 6, 7, 8, 9, 9]
mo = sum_onebody(obdm, orb1, orb2)
mo_labels = [
'mo_' + str(orb1[i]) + '_' + str(orb2[i])
for i in range(len(orb1))
]
#2-body
orb1 = [0, 1, 2, 3, 4, 5]
u = sum_U(tbdm, orb1)
u_labels = ['u' + str(orb1[i]) for i in range(len(orb1))]
orb1 = [0, 0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 3, 3, 4]
orb2 = [1, 2, 3, 4, 5, 2, 3, 4, 5, 3, 4, 5, 4, 5, 5]
j = sum_J(tbdm, orb1, orb2)
j_labels = [
'j_' + str(orb1[i]) + '_' + str(orb2[i])
for i in range(len(orb1))
]
dat = np.array([energy, energy_err] + list(mo) + list(u) + list(j))
d = pd.DataFrame(dat[:, np.newaxis].T,
columns=['energy', 'energy_err'] + mo_labels +
u_labels + j_labels)
d = d.astype('double')
if (df is None): df = d
else: df = pd.concat((df, d), axis=0)
else: print(f + ' does not exist')
fout = 'vmc_gosling.pickle'
df.to_pickle(fout)
return df
def gather_all(N,gsw,basename):
'''
Gathers all your data and stores into
'''
df=None
for j in range(1,N+1):
f=basename+'/gsw'+str(np.round(gsw,2))+'_'+str(j)+'.vmc.gosling.json'
statinfo=os.stat(f)
if(statinfo.st_size>0):
print(f)
data=json.load(open(f,'r'))
obdm,__=get_qwalk_dm(data['properties']['tbdm_basis1'])
obdm2,__,tbdm,__=get_qwalk_dm(data['properties']['tbdm_basis2'])
energy=data['properties']['total_energy']['value'][0]*27.2114
energy_err=data['properties']['total_energy']['error'][0]*27.2114
print(obdm.shape,obdm2.shape,tbdm.shape)
#MO ordering
#1-body
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]
mo=sum_onebody(obdm,orb1,orb2)
mo_labels=['mo_'+str(orb1[i])+'_'+str(orb2[i]) for i in range(len(orb1))]
#IAO ordering (4s, dxy, dyz, dz2, dxz, dx2-y2, px, py, pz)
#1-body
orb1=[0,1,2,3,4,5,6,7,8,0,0,3,2,4]
orb2=[0,1,2,3,4,5,6,7,8,3,8,8,7,6]
iao=sum_onebody(obdm2,orb1,orb2)
iao_labels=['iao_'+str(orb1[i])+'_'+str(orb2[i]) for i in range(len(orb1))]
#2-body
orb1=[0,1,2,3,4,5,6,7,8]
u=sum_U(tbdm,orb1)
u_labels=['u'+str(orb1[i]) for i in range(len(orb1))]
orb1=[0,0,0,0,0,1,1,1,1,2,2,2,3,3,4,0,0,0]
orb2=[1,2,3,4,5,2,3,4,5,3,4,5,4,5,5,6,7,8]
j=sum_J(tbdm,orb1,orb2)
j_labels=['j_'+str(orb1[i])+'_'+str(orb2[i]) for i in range(len(orb1))]
dat=np.array([energy,energy_err]+list(mo)+list(iao)+list(u)+list(j))
d=pd.DataFrame(dat[:,np.newaxis].T,columns=['energy','energy_err']+mo_labels+iao_labels+u_labels+j_labels)
d=d.astype('double')
if(df is None): df=d
else: df=pd.concat((df,d),axis=0)
else: print(f+' does not exist')
fout=basename+'/gosling.pickle'
df.to_pickle(fout)
return df
def gather_all():
'''
Gathers all your data and stores into
'''
df=None
for i in [0,1,2,3,4,5,6,7,14]:
f='gs'+str(i)+'.vmc_tbdm.gosling.json'
print(f)
data=json.load(open(f,'r'))
obdm,__,tbdm,__=get_qwalk_dm(data['properties']['tbdm_basis'])
energy=data['properties']['total_energy']['value'][0]*27.2114
energy_err=data['properties']['total_energy']['error'][0]*27.2114
one_labels=['n_'+x for x in full_labels]+['4s-3dz2','4s-2pz','3dz2-2pz','3dyz-2py','3dxz-2px']
orb1=[0,1,2,3,4,5,6,7,8,0,0,3,2,4]
orb2=[0,1,2,3,4,5,6,7,8,3,8,8,7,6]
one_body=sum_onebody(obdm,orb1,orb2)
U_labels=['U_'+x for x in full_labels]
orb=np.arange(9)
U=sum_U(tbdm,orb)
orb1=[0,0,0,0,0,1,1,1,1,2,2,2,3,3,4,0,0,0,1,1,1,2,2,2,3,3,3,4,4,4,5,5,5]
orb2=[1,2,3,4,5,2,3,4,5,3,4,5,4,5,5,6,7,8,6,7,8,6,7,8,6,7,8,6,7,8,6,7,8]
V_labels=['V_'+full_labels[orb1[i]]+'_'+full_labels[orb2[i]] for i in range(len(orb1))]
V=sum_V(tbdm,orb1,orb2)
J_labels=['J_'+full_labels[orb1[i]]+'_'+full_labels[orb2[i]] for i in range(len(orb1))]
J=sum_J(tbdm,orb1,orb2)
dat=np.array([energy,energy_err]+list(one_body)+list(U)+list(J))
d=pd.DataFrame(dat[:,np.newaxis].T,columns=['energy','energy_err']+one_labels+U_labels+J_labels)
d=d.astype('double')
d['base_state']=f.split(".")[0]
if(df is None): df=d
else: df=pd.concat((df,d),axis=0)
df['n_3dd']=df['n_3dxy']+df['n_3dx2y2']
df['n_3dpi']=df['n_3dxz']+df['n_3dyz']
df['n_2ppi']=df['n_2px']+df['n_2py']
df['n_3d']=df['n_3dz2']+df['n_3dd']+df['n_3dpi']
df['n_2p']=df['n_2pz']+df['n_2ppi']
df['3dpi-2ppi']=df['3dxz-2px']+df['3dyz-2py']
df=df.drop(columns=['n_3dxy','n_3dx2y2','n_3dxz','n_3dyz','n_2py','n_2px','3dxz-2px','3dyz-2py'])
df['U_3dd']=df['U_3dxy']+df['U_3dx2y2']
df['U_3dpi']=df['U_3dxz']+df['U_3dyz']
df['U_2ppi']=df['U_2px']+df['U_2py']
df['U_3d']=df['U_3dz2']+df['U_3dd']+df['U_3dpi']
df['U_2p']=df['U_2pz']+df['U_2ppi']
df=df.drop(columns=['U_3dxy','U_3dx2y2','U_3dxz','U_3dyz','U_2py','U_2px'])
df['J_3dd']=df['J_3dxy_3dx2y2']
df['J_3dd_3dz2']=df['J_3dxy_3dz2']+df['J_3dz2_3dx2y2']
df['J_3dpi']=df['J_3dyz_3dxz']
df['J_3dpi_3dz2']=df['J_3dyz_3dz2']+df['J_3dz2_3dxz']
df['J_3dpi_3dd']=df['J_3dxy_3dxz']+df['J_3dxy_3dyz']+df['J_3dxz_3dx2y2']+df['J_3dyz_3dx2y2']
df['J_3d']=df['J_3dd']+df['J_3dd_3dz2']+df['J_3dpi']+df['J_3dpi_3dz2']+df['J_3dpi_3dd']
df['J_4s_3dd']=df['J_4s_3dxy']+df['J_4s_3dx2y2']
df['J_4s_3dpi']=df['J_4s_3dxz']+df['J_4s_3dyz']
df['J_4s_3d']=df['J_4s_3dd']+df['J_4s_3dpi']+df['J_4s_3dz2']
df['J_4s_2ppi']=df['J_4s_2px']+df['J_4s_2py']
df['J_4s_2p']=df['J_4s_2ppi']+df['J_4s_2pz']
df['J_3dpi_2pz']=df['J_3dxz_2pz']+df['J_3dyz_2pz']
df['J_3dd_2pz']=df['J_3dxy_2pz']+df['J_3dx2y2_2pz']
df['J_3dpi_2ppi']=df['J_3dxz_2px']+df['J_3dxz_2py']+df['J_3dyz_2px']+df['J_3dyz_2py']
df['J_3dd_2ppi']=df['J_3dxy_2px']+df['J_3dxy_2py']+df['J_3dx2y2_2px']+df['J_3dx2y2_2py']
df['J_3dz2_2ppi']=df['J_3dz2_2px']+df['J_3dz2_2py']
df['J_3d_2p']=df['J_3dpi_2pz']+df['J_3dd_2pz']+df['J_3dz2_2pz']+df['J_3dpi_2ppi']+df['J_3dd_2ppi']+df['J_3dz2_2ppi']
df=df.drop(columns=['J_3dxy_3dx2y2','J_3dxy_3dz2','J_3dz2_3dx2y2','J_3dyz_3dxz',
'J_3dyz_3dz2','J_3dz2_3dxz','J_3dxy_3dxz','J_3dxy_3dyz','J_3dxz_3dx2y2','J_3dyz_3dx2y2',
'J_4s_3dxy','J_4s_3dx2y2','J_4s_3dxz','J_4s_3dyz','J_4s_2px','J_4s_2py',
'J_3dxz_2pz','J_3dyz_2pz','J_3dxy_2pz','J_3dx2y2_2pz','J_3dxz_2px','J_3dxz_2py','J_3dyz_2px','J_3dyz_2pz',
'J_3dxy_2px','J_3dxy_2py','J_3dx2y2_2px','J_3dx2y2_2py','J_3dz2_2px','J_3dz2_2py'])
fout='base_gosling.pickle'
df.to_pickle(fout)
return df