def si_anderson_imp_ext_ni_h(t,u,nocc,nimp,nsites):
ncore=2*nocc-2*nimp
cocc=range(4*nimp,4*nimp+ncore)
#cb_edge = 1.
#gap = 2.0*cb_edge/(nsites-2)
hlat=utils.zeros([nsites,nsites])
hlat[0,1:] = t
hlat[1:,0] = t
cbstates = [0, 1./3, -1./3, 2./3, -2./3, 1., -1.]
for i in xrange(nsites-1):
hlat[i+1,i+1] = cbstates[i]
'''
for i in xrange(nsites-1):
hlat[i+1,i+1] = -cb_edge + i*gap
hlat[0,i+1] = t
hlat[i+1,0] = t
#hlat[0,0] += u/2
#hlat[0,0] = -0.5*u
hu=utils.zeros([nsites,nsites])
#hu[0,0] = 0.5*u
'''
hlat_sp=_spinify(hlat)
pemb,pcore,pvirt=embed.embed(hlat,nimp,nocc)
pall=N.hstack([pemb,pcore,pvirt])
hall=N.dot(pall.T,N.dot(hlat,pall))
hall_sp=_spinify(hall)
hall_sp_diag=N.diag(hall_sp)
e0=sum(hall_sp_diag[cocc])
return hlat, hall, hlat_sp, hall_sp, e0
def embed_run(dset='data1',
delay=150,
vdim=7,
xdim=0,
ydim=1,
file_prefix=None):
'''
Renders a parameterized delay coordinate embedding.
'''
data = numpy.loadtxt('data/ps8/{0}'.format(dset), dtype=numpy.float64)
vs = embed.embed(data[:, 0], delay, vdim)
plot.embedded(vs,
xdim,
ydim,
'k.',
markersize=0.6,
ybound=(0, 2 * numpy.pi),
yticks=(0, numpy.pi / 2, numpy.pi, 3 * numpy.pi / 2,
2 * numpy.pi),
yticklabels=('0', r'$\frac{\pi}{2}$', r'$\pi$',
r'$\frac{3\pi}{2}$', r'$2\pi$'),
title=r'Delay Coordinate Embedding: {0}, $\tau={1}$'.format(
dset, _get_actual_delay(dset, 1, delay)),
xlabel=r'$\theta(t + {0})$'.format(
_get_actual_delay(dset, xdim, delay)),
ylabel=r'$\theta(t + {0})$'.format(
_get_actual_delay(dset, ydim, delay)),
aspect='equal',
file_prefix=file_prefix)
def si_anderson_imp_ext_ni_h(t, u, nocc, nimp, nsites):
ncore = 2 * nocc - 2 * nimp
cocc = range(4 * nimp, 4 * nimp + ncore)
#cb_edge = 1.
#gap = 2.0*cb_edge/(nsites-2)
hlat = utils.zeros([nsites, nsites])
'''
hlat[0,1:] = t
hlat[1:,0] = t
cbstates = [0.]
for i in xrange(1,nsites/2):
cbstates.append(i/(nsites/2-1.))
cbstates.append(-i/(nsites/2-1.))
for i in xrange(nsites-1):
hlat[i+1,i+1] = cbstates[i]
'''
cb_edge = 1.
lamda = 1.05
def FindScaledt(n, z):
return cb_edge * (1 + lamda**(-1)) * (1 - lamda**(-(n - z) - 1)) * (
lamda**(-0.5 * (n - z))) / (2 * N.sqrt(
(1 - lamda**(-2 * (n - z) - 1)) * (1 - lamda**(-2 *
(n - z) - 3))))
hlat[0, 1] = hlat[1, 0] = t
#for n in xrange(1,nsites-1):
for n in xrange(2, nsites - 1):
#hlat[n,n+1]=hlat[n+1,n]=FindScaledt(n,0.)
hlat[n, n + 1] = hlat[n + 1, n] = FindScaledt(nsites - n, 0.)
hlat[1, nsites - 1] = hlat[nsites - 1, 1] = FindScaledt(1, 0.)
print t
print hlat
#hlat[0,0] = 0.5*u
hlat_sp = _spinify(hlat)
pemb, pcore, pvirt = embed.embed(hlat, nimp, nocc)
pall = N.hstack([pemb, pcore, pvirt])
hall = N.dot(pall.T, N.dot(hlat, pall))
hall_sp = _spinify(hall)
hall_sp_diag = N.diag(hall_sp)
e0 = sum(hall_sp_diag[cocc])
return hlat, hall, hlat_sp, hall_sp, e0
def si_anderson_imp_ext_ni_h(t,nocc,nimp,nsites):
ncore=2*nocc-2*nimp
cocc=range(4*nimp,4*nimp+ncore)
hlat=utils.zeros([nsites,nsites])
for i in xrange(nsites):
for j in xrange(nsites):
if abs(i-j)==1:
hlat[i,j]=t
hlat_sp=_spinify(hlat)
pemb,pcore,pvirt=embed.embed(hlat,nimp,nocc)
pall=N.hstack([pemb,pcore,pvirt])
hall=N.dot(pall.T,N.dot(hlat,pall))
hall_sp=_spinify(hall)
hall_sp_diag=N.diag(hall_sp)
e0=sum(hall_sp_diag[cocc])
return hlat, hall, hlat_sp, hall_sp, e0
def hubbard_imp_ext_ni_h(t, u, nocc, nimp, nsites):
ncore = 2 * nocc - 2 * nimp
cocc = range(4 * nimp, 4 * nimp + ncore)
hlat = utils.zeros([nsites, nsites])
for i in xrange(nsites):
hlat[i, i] = +u / 2
for j in xrange(nsites):
if abs(i - j) == 1:
hlat[i, j] = t
hlat_sp = _spinify(hlat)
pemb, pcore, pvirt = embed.embed(hlat, nimp, nocc)
pall = N.hstack([pemb, pcore, pvirt])
hall = N.dot(pall.T, N.dot(hlat, pall))
hall_sp = _spinify(hall)
hall_sp_diag = N.diag(hall_sp)
e0 = sum(hall_sp_diag[cocc])
return hlat, hall, hlat_sp, hall_sp, e0
def embedfilt(x):
# D = len(x)
D = 20
L = len(x)
d = int((D-1)/2) # finds the middle of the embedding dimension
w = D - 1 - d
x = list(x)
xx = x[:d]
[xx.append(x[i]) for i in range(len(x))]
[xx.append(x[L - w + 1:L][i]) for i in range(len(x[L - w + 1:L]))]
print len(xx)
embedded_matrix = embed.embed(xx, D, 1)
u, s, v = np.linalg.svd(embedded_matrix)
U = u[0]
print U, len(U)
print x, len(x)
c = U*x
return s
def embed_run(dset='data1', delay=150, vdim=7, xdim=0, ydim=1, file_prefix=None):
'''
Renders a parameterized delay coordinate embedding.
'''
data = numpy.loadtxt('data/ps8/{0}'.format(dset), dtype=numpy.float64)
vs = embed.embed(data[:,0], delay, vdim)
plot.embedded(
vs,
xdim,
ydim,
'k.',
markersize=0.6,
ybound=(0, 2*numpy.pi),
yticks=(
0,
numpy.pi/2,
numpy.pi,
3*numpy.pi/2,
2*numpy.pi
),
yticklabels=(
'0',
r'$\frac{\pi}{2}$',
r'$\pi$',
r'$\frac{3\pi}{2}$',
r'$2\pi$'
),
title=r'Delay Coordinate Embedding: {0}, $\tau={1}$'.format(
dset,
_get_actual_delay(dset, 1, delay)),
xlabel=r'$\theta(t + {0})$'.format(_get_actual_delay(dset, xdim, delay)),
ylabel=r'$\theta(t + {0})$'.format(_get_actual_delay(dset, ydim, delay)),
aspect='equal',
file_prefix=file_prefix
)
def sp_greens():
# main loop for single-particle GF
utils.dtype=N.complex128
nimp=1
nimp_sp=2*nimp
nocc=20
nsites=40
nsites_sp=nsites*2
ndim=2
sz=0
# sites numbered in order as imp+bath ... ext
sites_imp=range(0,2*nimp_sp)
sites_ext=range(2*nimp_sp,nsites_sp)
ncore=2*nocc-2*nimp
cocc=range(2*nimp_sp,2*nimp_sp+ncore)
vocc=range(2*nimp_sp+ncore,nsites_sp)
N.set_printoptions(precision=3)
t=-1. # hopping
delta=0.2 # broadening
# for u in [0.0,4.0,10.0]:
for u in [1.0]:
# Single impurity Anderson model
mu=0.
hlat,hall,hlat_sp,hall_sp,e0=models_embed.si_anderson_imp_ext_ni_h(t,nocc,nimp,nsites)
hop,himp,hcs_imp,hds_imp,hcs_ext,hds_ext,hext=models_embed.si_anderson_imp_ext_h(hall_sp,u,nocc,nimp,nsites)
# single site Hubbard in DMET basis
# mu=u/2
# hlat,hall,hlat_sp,hall_sp,e0=models_embed.hubbard_imp_ext_ni_h(t,u,nocc,nimp,nsites)
# hop,himp,hcs_imp,hds_imp,hcs_ext,hds_ext,hext=models_embed.hubbard_imp_ext_h(hall_sp,u,nocc,nimp,nsites)
# g.s embedding basis
pemb,pcore,pvirt=embed.embed(hlat,nimp,nocc)
# perturbation operator
perturb=utils.zeros([nsites])
perturb[0]=1.
p_coeffs=N.array([1])
perturb_dop=models.ContractedD(N.array([0]),p_coeffs)
perturb_cop=models.ContractedC(N.array([0]),p_coeffs)
fd=file("ph_siam.out."+str(u),"w")
for omega in N.arange(-2,2,0.1):
ops_dict=response_embed.mb_sp_ops(hlat,perturb,omega+1j*delta,nimp,nocc,pemb,pcore,pvirt)
configs_dict=response_embed.mb_configs(nsites,nimp,nimp_sp,2*nocc-nimp_sp,0)
neutral_ops_configs,plus_ops_configs,minus_ops_configs=response_embed.mb_sp_ops_configs(ops_dict,configs_dict)
# basis is setup, now build matrix representations
perturb_dop_mat=opbasis_ni.oimp_matrix_bra_ket_form(perturb_dop,minus_ops_configs,neutral_ops_configs,cocc,vocc)
perturb_cop_mat=opbasis_ni.oimp_matrix_bra_ket_form(perturb_cop,plus_ops_configs,neutral_ops_configs,cocc,vocc)
# h, N-1 configs
himp_mat_minus=opbasis_ni.oimp_matrix_form(himp,minus_ops_configs,cocc,vocc)
himp_ext_mat_minus=opbasis_ni.himp_ext_matrix_form(hcs_imp,hds_ext,hds_imp,hcs_ext,minus_ops_configs,cocc,vocc)
hext_mat_minus=opbasis_ni.hext_matrix_form(hext,minus_ops_configs,cocc,vocc,e0)
hmat_minus=himp_mat_minus+himp_ext_mat_minus+hext_mat_minus
unit_mat_minus=opbasis_ni.oimp_matrix_form(models.Unit(),minus_ops_configs,cocc,vocc)
# h, N+1 configs
himp_mat_plus=opbasis_ni.oimp_matrix_form(himp,plus_ops_configs,cocc,vocc)
himp_ext_mat_plus=opbasis_ni.himp_ext_matrix_form(hcs_imp,hds_ext,hds_imp,hcs_ext,plus_ops_configs,cocc,vocc)
hext_mat_plus=opbasis_ni.hext_matrix_form(hext,plus_ops_configs,cocc,vocc,e0)
hmat_plus=himp_mat_plus+himp_ext_mat_plus+hext_mat_plus
unit_mat_plus=opbasis_ni.oimp_matrix_form(models.Unit(),plus_ops_configs,cocc,vocc)
# h, neutral configs
himp_mat=opbasis_ni.oimp_matrix_form(himp,neutral_ops_configs,cocc,vocc)
himp_ext_mat=opbasis_ni.himp_ext_matrix_form(hcs_imp,hds_ext,hds_imp,hcs_ext,neutral_ops_configs,cocc,vocc)
hext_mat=opbasis_ni.hext_matrix_form(hext,neutral_ops_configs,cocc,vocc,e0)
hmat=himp_mat+himp_ext_mat+hext_mat
unit_mat=opbasis_ni.oimp_matrix_form(models.Unit(),neutral_ops_configs,cocc,vocc)
# get neutral ground-state energy
es,cs=la.peigh(hmat,unit_mat)
e0=es[0]
psi0=cs[:,0]
print e0
print psi0
# all matrices setup, solve linear response (complex)
psi1_minus=la.solve_perturb(omega+1j*delta,unit_mat_minus,hmat_minus,e0,psi0,perturb_dop_mat,
project=False,sign_ham=-1.)
gfminus=N.dot(psi1_minus,N.dot(perturb_dop_mat,psi0))
psi1_plus=la.solve_perturb(omega+1j*delta,unit_mat_plus,hmat_plus,e0,psi0,perturb_cop_mat,project=False)
gfplus=N.dot(psi1_plus,N.dot(perturb_cop_mat,psi0))
d_gf,c_gf=_sp_gf(hlat,omega+1j*delta,perturb,nocc)
print omega, gfplus.imag,c_gf.imag, gfminus.imag,d_gf.imag
print >>fd, omega-mu, gfplus.imag+gfminus.imag,c_gf.imag+d_gf.imag
def ph_greens():
# main loop for general density-density (ph) response functions
utils.dtype = N.complex128
nimp = 1
nimp_sp = 2 * nimp
nocc = 10
nsites = 20
nsites_sp = nsites * 2
ndim = 2
sz = 0
# sites numbered in order as imp+bath ... ext
sites_imp = range(0, 2 * nimp_sp)
sites_ext = range(2 * nimp_sp, nsites_sp)
ncore = 2 * nocc - 2 * nimp
cocc = range(2 * nimp_sp, 2 * nimp_sp + ncore)
vocc = range(2 * nimp_sp + ncore, nsites_sp)
N.set_printoptions(precision=3)
t = -1. # hopping
delta = 0.0 #25 # broadening
# for u in [0.0,4.0,10.0]:
for u in [0.0, 0.5, 1.0, 2.0, 4.0, 8.0]: #,1.0,4.0,8.0]:
# Single impurity Anderson model
mu = 0.
hlat, hall, hlat_sp, hall_sp, e0 = models_embed.si_anderson_imp_ext_ni_h(
t, nocc, nimp, nsites)
hop, himp, hcs_imp, hds_imp, hcs_ext, hds_ext, hext = models_embed.si_anderson_imp_ext_h(
hall_sp, u, nocc, nimp, nsites)
# single site Hubbard in DMET basis
# mu=u/2
# hlat,hall,hlat_sp,hall_sp,e0=models_embed.hubbard_imp_ext_ni_h(t,u,nocc,nimp,nsites)
# hop,himp,hcs_imp,hds_imp,hcs_ext,hds_ext,hext=models_embed.hubbard_imp_ext_h(hall_sp,u,nocc,nimp,nsites)
# g.s embedding basis
pemb, pcore, pvirt = embed.embed(hlat, nimp, nocc)
# perturbation operator
perturb = utils.zeros([nsites, nsites])
perturb[0, 0] = 1.
p_coeffs = {}
p_coeffs[0, 0] = 1.
p_coeffs[1, 1] = 1.
perturbop = models.ContractedCD(p_coeffs)
fd = file("ph_siam.out." + str(u), "w")
for omega in N.arange(0, 4, 0.01):
if abs(omega - 2.0) < 0.00001:
continue
ops_dict = response_embed.mb_ph_ops(hlat, perturb,
omega + 1j * delta, nimp, nocc,
pemb, pcore, pvirt)
configs_dict = response_embed.mb_configs(nsites, nimp, nimp_sp,
2 * nocc - nimp_sp, 0)
neutral_ops_configs = response_embed.mb_ph_ops_configs(
ops_dict, configs_dict)
# basis is setup, now build matrix representations
perturb_mat = opbasis_ni.oimp_matrix_form(perturbop,
neutral_ops_configs,
cocc, vocc)
# h, neutral configs
himp_mat = opbasis_ni.oimp_matrix_form(himp, neutral_ops_configs,
cocc, vocc)
himp_ext_mat = opbasis_ni.himp_ext_matrix_form(
hcs_imp, hds_ext, hds_imp, hcs_ext, neutral_ops_configs, cocc,
vocc)
hext_mat = opbasis_ni.hext_matrix_form(hext, neutral_ops_configs,
cocc, vocc, e0)
#print 'himp_mat size: ',himp_mat.shape
#print 'himp_mat',himp_mat[:,0]
#print 'hext_mat size: ',hext_mat.shape
#print 'himp_ext_mat size: ',himp_ext_mat.shape
hmat = himp_mat + himp_ext_mat + hext_mat
#print 'total size hmat: ',hmat.shape
unit_mat = opbasis_ni.oimp_matrix_form(models.Unit(),
neutral_ops_configs, cocc,
vocc)
# get neutral ground-state energy
es, cs = la.peigh(hmat, unit_mat)
e0 = es[0]
psi0 = cs[:, 0]
# all matrices setup, solve linear response (complex)
psi1 = la.solve_perturb(omega - 1j * delta,
unit_mat,
hmat,
e0,
psi0,
perturb_mat,
project=True)
ph_gf = N.dot(N.conj(psi1), N.dot(perturb_mat, psi0))
ph_gf0 = _second_order_energy(hlat, nocc, perturb,
omega + 1j * delta)
#print 'hlat size: ',hlat.shape
#print 'hlat: '
#print hlat
#print omega, ph_gf.imag,ph_gf0.imag
print omega, ph_gf, 2 * ph_gf0
print >> fd, omega - mu, ph_gf.imag, ph_gf0.imag, ph_gf.real, 2 * ph_gf0.real
def sp_greens():
# main loop for single-particle GF
utils.dtype = N.complex128
nimp = 1
nimp_sp = 2 * nimp
nocc = 20
nsites = 40
nsites_sp = nsites * 2
ndim = 2
sz = 0
# sites numbered in order as imp+bath ... ext
sites_imp = range(0, 2 * nimp_sp)
sites_ext = range(2 * nimp_sp, nsites_sp)
ncore = 2 * nocc - 2 * nimp
cocc = range(2 * nimp_sp, 2 * nimp_sp + ncore)
vocc = range(2 * nimp_sp + ncore, nsites_sp)
N.set_printoptions(precision=3)
t = -1. # hopping
delta = 0.2 # broadening
# for u in [0.0,4.0,10.0]:
for u in [1.0]:
# Single impurity Anderson model
mu = 0.
hlat, hall, hlat_sp, hall_sp, e0 = models_embed.si_anderson_imp_ext_ni_h(
t, nocc, nimp, nsites)
hop, himp, hcs_imp, hds_imp, hcs_ext, hds_ext, hext = models_embed.si_anderson_imp_ext_h(
hall_sp, u, nocc, nimp, nsites)
# single site Hubbard in DMET basis
# mu=u/2
# hlat,hall,hlat_sp,hall_sp,e0=models_embed.hubbard_imp_ext_ni_h(t,u,nocc,nimp,nsites)
# hop,himp,hcs_imp,hds_imp,hcs_ext,hds_ext,hext=models_embed.hubbard_imp_ext_h(hall_sp,u,nocc,nimp,nsites)
# g.s embedding basis
pemb, pcore, pvirt = embed.embed(hlat, nimp, nocc)
# perturbation operator
perturb = utils.zeros([nsites])
perturb[0] = 1.
p_coeffs = N.array([1])
perturb_dop = models.ContractedD(N.array([0]), p_coeffs)
perturb_cop = models.ContractedC(N.array([0]), p_coeffs)
fd = file("ph_siam.out." + str(u), "w")
for omega in N.arange(-2, 2, 0.1):
ops_dict = response_embed.mb_sp_ops(hlat, perturb,
omega + 1j * delta, nimp, nocc,
pemb, pcore, pvirt)
configs_dict = response_embed.mb_configs(nsites, nimp, nimp_sp,
2 * nocc - nimp_sp, 0)
neutral_ops_configs, plus_ops_configs, minus_ops_configs = response_embed.mb_sp_ops_configs(
ops_dict, configs_dict)
# basis is setup, now build matrix representations
perturb_dop_mat = opbasis_ni.oimp_matrix_bra_ket_form(
perturb_dop, minus_ops_configs, neutral_ops_configs, cocc,
vocc)
perturb_cop_mat = opbasis_ni.oimp_matrix_bra_ket_form(
perturb_cop, plus_ops_configs, neutral_ops_configs, cocc, vocc)
# h, N-1 configs
himp_mat_minus = opbasis_ni.oimp_matrix_form(
himp, minus_ops_configs, cocc, vocc)
himp_ext_mat_minus = opbasis_ni.himp_ext_matrix_form(
hcs_imp, hds_ext, hds_imp, hcs_ext, minus_ops_configs, cocc,
vocc)
hext_mat_minus = opbasis_ni.hext_matrix_form(
hext, minus_ops_configs, cocc, vocc, e0)
hmat_minus = himp_mat_minus + himp_ext_mat_minus + hext_mat_minus
unit_mat_minus = opbasis_ni.oimp_matrix_form(
models.Unit(), minus_ops_configs, cocc, vocc)
# h, N+1 configs
himp_mat_plus = opbasis_ni.oimp_matrix_form(
himp, plus_ops_configs, cocc, vocc)
himp_ext_mat_plus = opbasis_ni.himp_ext_matrix_form(
hcs_imp, hds_ext, hds_imp, hcs_ext, plus_ops_configs, cocc,
vocc)
hext_mat_plus = opbasis_ni.hext_matrix_form(
hext, plus_ops_configs, cocc, vocc, e0)
hmat_plus = himp_mat_plus + himp_ext_mat_plus + hext_mat_plus
unit_mat_plus = opbasis_ni.oimp_matrix_form(
models.Unit(), plus_ops_configs, cocc, vocc)
# h, neutral configs
himp_mat = opbasis_ni.oimp_matrix_form(himp, neutral_ops_configs,
cocc, vocc)
himp_ext_mat = opbasis_ni.himp_ext_matrix_form(
hcs_imp, hds_ext, hds_imp, hcs_ext, neutral_ops_configs, cocc,
vocc)
hext_mat = opbasis_ni.hext_matrix_form(hext, neutral_ops_configs,
cocc, vocc, e0)
hmat = himp_mat + himp_ext_mat + hext_mat
unit_mat = opbasis_ni.oimp_matrix_form(models.Unit(),
neutral_ops_configs, cocc,
vocc)
# get neutral ground-state energy
es, cs = la.peigh(hmat, unit_mat)
e0 = es[0]
psi0 = cs[:, 0]
print e0
print psi0
# all matrices setup, solve linear response (complex)
psi1_minus = la.solve_perturb(omega + 1j * delta,
unit_mat_minus,
hmat_minus,
e0,
psi0,
perturb_dop_mat,
project=False,
sign_ham=-1.)
gfminus = N.dot(psi1_minus, N.dot(perturb_dop_mat, psi0))
psi1_plus = la.solve_perturb(omega + 1j * delta,
unit_mat_plus,
hmat_plus,
e0,
psi0,
perturb_cop_mat,
project=False)
gfplus = N.dot(psi1_plus, N.dot(perturb_cop_mat, psi0))
d_gf, c_gf = _sp_gf(hlat, omega + 1j * delta, perturb, nocc)
print omega, gfplus.imag, c_gf.imag, gfminus.imag, d_gf.imag
print >> fd, omega - mu, gfplus.imag + gfminus.imag, c_gf.imag + d_gf.imag
# methods = ['None', 'MNN', 'SeuratV3', 'ScAlign', 'ICP', 'ICP2', 'ICP2_xentropy']
#arguments = '--methods SeuratV3 --datasets panc8 --input_space GENE --epochs=5 --no_standardize'
arguments = '--methods SeuratV3 --datasets panc8 --input_space GENE --epochs=10 --seurat_env_path C:\\Users\\Amir\\Anaconda3\\envs\\seuratV3'
#arguments = '--methods SeuratV3 --datasets panc8-all --input_space GENE --epochs=10 --seurat_env_path C:\\Users\\Amir\\Anaconda3\\envs\\seuratV3'
args = parser.parse_args(arguments.split())
#%%
datasets = {}
alignment_tasks = []
#%%
if 'Kowalcyzk' in args.datasets:
datasets['Kowalcyzk'] = data.get_data('Kowalcyzk')
embed.embed(datasets,
'Kowalcyzk',
args.n_PC,
do_standardize=not args.no_standardize)
embed.visualize(datasets,
'Kowalcyzk',
cell_type_key='cell_type',
batch_key='cell_age')
alignment_tasks.append(
alignment_task.AlignmentTask('Kowalcyzk', 'cell_age', 'cell_type',
'young', 'old'))
alignment_tasks.append(
alignment_task.AlignmentTask('Kowalcyzk', 'cell_age', 'cell_type',
'young', 'old', 'LT'))
alignment_tasks.append(
alignment_task.AlignmentTask('Kowalcyzk', 'cell_age', 'cell_type',
'young', 'old', 'MPP'))
alignment_tasks.append(
def run(graph_type, nnodes, k, f):
base_dir = '/dfs/scratch0/manans/rewire-exp-' + graph_type
# clean directories
os.system('rm ' + base_dir + '/train/*')
os.system('rm ' + base_dir + '/test/*')
os.system('rm ' + base_dir + '-emb/train/*')
os.system('rm ' + base_dir + '-emb/test/*')
e = GraphEvaluator(graph_type = graph_type,
nnodes = nnodes, k = k,
graph_directory = base_dir)
e.initialize()
header = graph_type + \
'_k=' + str(k) + \
'_n=' + str(nnodes) + \
'_e=' + str(e.graph().GetEdges()) + \
'_t=' + datetime.datetime.now().isoformat().split('.')[0] + ','
means = " ,"
stds = " ,"
'''
# method 1: random
header += "random,"
v = stats([accuracy(e.evaluate_random()) for i in xrange(100)])
means += v[0] + ","
stds += v[1] + ","
# method 2-11: node2vec
for i in tqdm([10, 20, 30, 40, 50, 60, 70, 80, 90, -1]):
header += ("n2v-0." + str(i) + "," if i > 0 else "n2v-1.0,")
embeddings = embed(train_input_directory = None,
train_label_mapping = None,
test_input_directory = base_dir + '/test',
test_output_directory = base_dir + '-emb/test',
method = 'n2v',
sample = int((float(i) / 100) * nnodes) if i > 0 else -1,
verbose = False)
v = stats([accuracy(e.evaluate(embeddings)) for i in xrange(100)])
means += v[0] + ","
stds += v[1] + ","
os.system('rm ' + base_dir + '-emb/train/*')
os.system('rm ' + base_dir + '-emb/test/*')
'''
# method 12: nf-original
header += "nf-original,"
train_labels = {}
for fl in os.listdir(base_dir + '/train'):
train_labels[fl] = float(fl.split('.')[0])/k
embeddings = embed(train_input_directory = base_dir + '/train',
train_label_mapping = train_labels,
test_input_directory = base_dir + '/test',
test_output_directory = base_dir + '-emb/test/',
method = 'nf-original',
train = True,
verbose = False)
v = stats([accuracy(e.evaluate(embeddings)) for i in xrange(100)])
means += v[0] + ","
stds += v[1] + ","
# remove ending comma
header = header[:-1]
means = means[:-1]
stds = stds[:-1]
f.write(header + "\n")
f.write(means + "\n")
f.write(stds + "\n")
print(header)
print(means)
print(stds)
print(">>>------")
def main():
# main loop for single-particle GF
utils.dtype = N.complex128
nimp = 4
nimp_sp = 2 * nimp
nsites = 8
nocc = nsites / 2
nsites_sp = nsites * 2
ndim = 2
sz = 0
# sites numbered in order as imp+bath ... ext
sites_imp = range(0, 2 * nimp_sp)
sites_ext = range(2 * nimp_sp, nsites_sp)
ncore = 2 * nocc - 2 * nimp
cocc = range(2 * nimp_sp, 2 * nimp_sp + ncore)
vocc = range(2 * nimp_sp + ncore, nsites_sp)
N.set_printoptions(precision=3)
#t=1. # hopping
gamma = 0.005
rho0 = (nsites - 2) / 2.
t = 0.1 #N.sqrt(gamma/(rho0*N.pi))
#gamma = (t**2)*pi*0.1
delta = 0.005 # broadening
#a=12.5
# for u in [0.0,4.0,10.0]:
for u in [0., 2.5, 5., 7.5]:
u *= t
# Single impurity Anderson model
mu = 0.
hlat, hall, hlat_sp, hall_sp, e0 = models_embed.si_anderson_imp_ext_ni_h(
t, u, nocc, nimp, nsites)
hop, himp, hcs_imp, hds_imp, hcs_ext, hds_ext, hext = models_embed.si_anderson_imp_ext_h(
hall_sp, u, nocc, nimp, nsites)
#print hext
# single site Hubbard in DMET basis
#mu=u/2
#hlat,hall,hlat_sp,hall_sp,e0=models_embed.hubbard_imp_ext_ni_h(t,u,nocc,nimp,nsites)
#hop,himp,hcs_imp,hds_imp,hcs_ext,hds_ext,hext=models_embed.hubbard_imp_ext_h(hall_sp,u,nocc,nimp,nsites)
# g.s embedding basis
pemb, pcore, pvirt = embed.embed(hlat, nimp, nocc)
# perturbation operator
perturb = utils.zeros([nsites])
perturb[0] = 1.
p_coeffs = N.array([1])
perturb_dop = models.ContractedD(N.array([0]), p_coeffs)
perturb_cop = models.ContractedC(N.array([0]), p_coeffs)
fd = file(
"siam_emb_dos_nsites" + str(nsites) + '_U' + str(u) + '_nimp' +
str(nimp) + '_eta' + str(delta), "w")
for omega in N.arange(-1., 1., 0.01):
ops_dict = response_embed.mb_sp_ops(hlat, perturb,
omega + 1j * delta, nimp, nocc,
pemb, pcore, pvirt)
configs_dict = response_embed.mb_configs(nsites, nimp, nimp_sp,
2 * nocc - nimp_sp, 0)
neutral_ops_configs, plus_ops_configs, minus_ops_configs = response_embed.mb_sp_ops_configs(
ops_dict, configs_dict)
#print minus_ops_configs
# basis is setup, now build matrix representations
perturb_dop_mat = opbasis_ni.oimp_matrix_bra_ket_form(
perturb_dop, minus_ops_configs, neutral_ops_configs, cocc,
vocc)
perturb_cop_mat = opbasis_ni.oimp_matrix_bra_ket_form(
perturb_cop, plus_ops_configs, neutral_ops_configs, cocc, vocc)
#print plus_ops_configs
# h, N-1 configs
#print minus_ops_configs
himp_mat_minus = opbasis_ni.oimp_matrix_form(
himp, minus_ops_configs, cocc, vocc)
himp_ext_mat_minus = opbasis_ni.himp_ext_matrix_form(
hcs_imp, hds_ext, hds_imp, hcs_ext, minus_ops_configs, cocc,
vocc)
#print himp_ext_mat_minus
hext_mat_minus = opbasis_ni.hext_matrix_form(
hext, minus_ops_configs, cocc, vocc, e0)
hmat_minus = himp_mat_minus + himp_ext_mat_minus + hext_mat_minus
unit_mat_minus = opbasis_ni.oimp_matrix_form(
models.Unit(), minus_ops_configs, cocc, vocc)
#print hext_mat_minus
# h, N+1 configs
himp_mat_plus = opbasis_ni.oimp_matrix_form(
himp, plus_ops_configs, cocc, vocc)
himp_ext_mat_plus = opbasis_ni.himp_ext_matrix_form(
hcs_imp, hds_ext, hds_imp, hcs_ext, plus_ops_configs, cocc,
vocc)
hext_mat_plus = opbasis_ni.hext_matrix_form(
hext, plus_ops_configs, cocc, vocc, e0)
hmat_plus = himp_mat_plus + himp_ext_mat_plus + hext_mat_plus
unit_mat_plus = opbasis_ni.oimp_matrix_form(
models.Unit(), plus_ops_configs, cocc, vocc)
# h, neutral configs
himp_mat = opbasis_ni.oimp_matrix_form(himp, neutral_ops_configs,
cocc, vocc)
himp_ext_mat = opbasis_ni.himp_ext_matrix_form(
hcs_imp, hds_ext, hds_imp, hcs_ext, neutral_ops_configs, cocc,
vocc)
hext_mat = opbasis_ni.hext_matrix_form(hext, neutral_ops_configs,
cocc, vocc, e0)
hmat = himp_mat + himp_ext_mat + hext_mat
unit_mat = opbasis_ni.oimp_matrix_form(models.Unit(),
neutral_ops_configs, cocc,
vocc)
# get neutral ground-state energy
es, cs = la.peigh(hmat, unit_mat)
e_gs = es[0]
psi0 = cs[:, 0]
#print e0
#print psi0
#print N.dot(perturb_dop_mat,psi0)
# all matrices setup, solve linear response (complex)
psi1_minus = la.solve_perturb(omega + 1j * delta,
unit_mat_minus,
hmat_minus,
e_gs,
psi0,
perturb_dop_mat,
project=False,
sign_ham=-1.)
gfminus = N.dot(psi1_minus, N.dot(perturb_dop_mat, psi0))
psi1_plus = la.solve_perturb(omega + 1j * delta,
unit_mat_plus,
hmat_plus,
e_gs,
psi0,
perturb_cop_mat,
project=False)
#print N.dot(perturb_cop_mat,psi0)
#print hmat
gfplus = N.dot(psi1_plus, N.dot(perturb_cop_mat, psi0))
d_gf, c_gf = _sp_gf(hlat, omega + 1j * delta, perturb, nocc)
#print omega, gfplus.imag,c_gf.imag, gfminus.imag,d_gf.imag
#print >>fd, omega-mu, -1.*(gfplus.imag),-1.*(c_gf.imag+d_gf.imag)
print >> fd, omega - mu, -(1 / N.pi) * (gfplus.imag + gfminus.imag)
def ph_greens():
# main loop for general density-density (ph) response functions
utils.dtype=N.complex128
nimp=2
nimp_sp=2*nimp
nocc=12
nsites=24
nsites_sp=nsites*2
ndim=2
sz=0
# sites numbered in order as imp+bath ... ext
sites_imp=range(0,2*nimp_sp)
sites_ext=range(2*nimp_sp,nsites_sp)
ncore=2*nocc-2*nimp
cocc=range(2*nimp_sp,2*nimp_sp+ncore)
vocc=range(2*nimp_sp+ncore,nsites_sp)
N.set_printoptions(precision=3)
t=-1. # hopping
delta=0.01 # broadening
# for u in [0.0,4.0,10.0]:
for u in [4.0]:
# Single impurity Anderson model
mu=0.
hlat,hall,hlat_sp,hall_sp,e0=models_embed.si_anderson_imp_ext_ni_h(t,nocc,nimp,nsites)
hop,himp,hcs_imp,hds_imp,hcs_ext,hds_ext,hext=models_embed.si_anderson_imp_ext_h(hall_sp,u,nocc,nimp,nsites)
# single site Hubbard in DMET basis
# mu=u/2
# hlat,hall,hlat_sp,hall_sp,e0=models_embed.hubbard_imp_ext_ni_h(t,u,nocc,nimp,nsites)
# hop,himp,hcs_imp,hds_imp,hcs_ext,hds_ext,hext=models_embed.hubbard_imp_ext_h(hall_sp,u,nocc,nimp,nsites)
# g.s embedding basis
pemb,pcore,pvirt=embed.embed(hlat,nimp,nocc)
# perturbation operator
perturb=utils.zeros([nsites,nsites])
perturb[0,0]=1.
p_coeffs={}
p_coeffs[0,0]=1.
p_coeffs[1,1]=1.
perturbop=models.ContractedCD(p_coeffs)
fd=file("ph_siam.out."+str(u),"w")
for omega in N.arange(2.0,8.0,0.1):
ops_dict=response_embed.mb_ph_ops(hlat,perturb,omega+1j*delta,nimp,nocc,pemb,pcore,pvirt)
configs_dict=response_embed.mb_configs(nsites,nimp,nimp_sp,2*nocc-nimp_sp,0)
neutral_ops_configs=response_embed.mb_ph_ops_configs(ops_dict,configs_dict)
# basis is setup, now build matrix representations
perturb_mat=opbasis_ni.oimp_matrix_form(perturbop,neutral_ops_configs,cocc,vocc)
# h, neutral configs
himp_mat=opbasis_ni.oimp_matrix_form(himp,neutral_ops_configs,cocc,vocc)
himp_ext_mat=opbasis_ni.himp_ext_matrix_form(hcs_imp,hds_ext,hds_imp,hcs_ext,neutral_ops_configs,cocc,vocc)
hext_mat=opbasis_ni.hext_matrix_form(hext,neutral_ops_configs,cocc,vocc,e0)
hmat=himp_mat+himp_ext_mat+hext_mat
unit_mat=opbasis_ni.oimp_matrix_form(models.Unit(),neutral_ops_configs,cocc,vocc)
# get neutral ground-state energy
# print 'overlap: '
# for i,w in enumerate(unit_mat):
# for j,v in enumerate(w):
# if abs(unit_mat[i,j]) > 1.e-12:
# print i+1,j+1,unit_mat[i,j]
#hmat[4:,:] = 0.
#hmat[:,4:] = 0.
#print 'hamil: '
#for i,w in enumerate(hmat):
# for j,v in enumerate(w):
# if abs(hmat[i,j]) > 1.e-12:
# print i+1,j+1,hmat[i,j]
#
es,cs=la.peigh(hmat,unit_mat,thresh=1.e-10)
e0=es[0]
psi0=cs[:,0]
#print 'psi0:'
#print psi0
#print 'energy: ',e0
# all matrices setup, solve linear response (complex)
psi1=la.solve_perturb(omega-1j*delta,unit_mat,hmat,e0,psi0,perturb_mat,project=True)
ph_gf=N.dot(N.conj(psi1),N.dot(perturb_mat,psi0))
ph_gf0=_second_order_energy(hlat,nocc,perturb,omega+1j*delta)
#print omega, ph_gf.imag,ph_gf0.imag
print omega, ph_gf,2*ph_gf0,e0
print >>fd, omega-mu, -ph_gf.imag,-2*ph_gf0.imag
with open(log_dir / 'args.txt', 'w') as f:
f.write('\n'.join(sys.argv[1:]))
with open(log_dir / 'args.pickle', 'wb') as f:
pickle.dump(args.__dict__, f)
#%%
datasets = {}
datasets[args.dataset] = data.get_data(args.dataset, args)
crosstab = data.get_data_crosstabulation(datasets[args.dataset], args)
crosstab.to_latex(join(log_dir, 'data_crosstab.tex'))
# if args.input_space == 'PCA' or args.method == 'None':
embed.embed(datasets,
args.dataset,
args.n_PC,
do_standardize=args.standardize,
log_dir=log_dir)
embed.visualize(datasets,
args.dataset,
cell_type_key=celltype_columns[args.dataset],
batch_key=batch_columns[args.dataset],
log_dir=log_dir)
#%%
task = alignment_task.AlignmentTask(args.dataset,
batch_columns[args.dataset],
celltype_columns[args.dataset],
args.source, args.target,
args.leaveOut, args.leaveOutSource)
type=str,
help="labels file to be used in metadata")
# tensorboard
parser.add_argument("--log_dir",
nargs='?',
default=os.path.join(output_dir, "tensorboard"),
type=str,
help="log directory for TensorBoard")
params = parser.parse_args()
if __name__ == "__main__":
if params.mode == "embed":
embed(params)
if params.mode == "sif":
sif(params)
if params.mode == "cluster":
if params.kmeans:
kmeans(params)
if params.opt_k:
opt_k(params)
if params.hierarch_k:
hierarch_k(params)
if params.mode == "project":
if params.pca:
pca(params)
def main():
# main loop for single-particle GF
utils.dtype = N.complex128
nimp = 1
nimp_sp = 2 * nimp
nsites = 300
nocc = nsites / 2
nsites_sp = nsites * 2
ndim = 2
sz = 0
# sites numbered in order as imp+bath ... ext
sites_imp = range(0, 2 * nimp_sp)
sites_ext = range(2 * nimp_sp, nsites_sp)
ncore = 2 * nocc - 2 * nimp
cocc = range(2 * nimp_sp, 2 * nimp_sp + ncore)
vocc = range(2 * nimp_sp + ncore, nsites_sp)
N.set_printoptions(precision=3)
#t=1. # hopping
gamma = 0.05
rho0 = (nsites - 2) / 2.
t = N.sqrt(gamma / (rho0 * N.pi))
#t = 0.1
#gamma = (t**2)*pi*0.1
delta = 1e-12 # broadening
#a=12.5
# for u in [0.0,4.0,10.0]:
for u in [10.]:
u *= gamma
# Single impurity Anderson model
mu = 0.
hlat, hall, hlat_sp, hall_sp, e0 = models_embed.si_anderson_imp_ext_ni_h(
t, u, nocc, nimp, nsites)
hop, himp, hcs_imp, hds_imp, hcs_ext, hds_ext, hext = models_embed.si_anderson_imp_ext_h(
hall_sp, u, nocc, nimp, nsites)
#print hext
# single site Hubbard in DMET basis
#mu=u/2
#hlat,hall,hlat_sp,hall_sp,e0=models_embed.hubbard_imp_ext_ni_h(t,u,nocc,nimp,nsites)
#hop,himp,hcs_imp,hds_imp,hcs_ext,hds_ext,hext=models_embed.hubbard_imp_ext_h(hall_sp,u,nocc,nimp,nsites)
# g.s embedding basis
pemb, pcore, pvirt = embed.embed(hlat, nimp, nocc)
# perturbation operator
perturb = utils.zeros([nsites])
perturb[0] = 1.
p_coeffs = N.array([1])
perturb_dop = models.ContractedD(N.array([0]), p_coeffs)
perturb_cop = models.ContractedC(N.array([0]), p_coeffs)
#fd=file("siam_emb_dos_nsites"+str(nsites)+'_U'+str(u)+'_nimp'+str(nimp)+'_eta'+str(delta),"w")
fdEnminus = file("En_minus", "w")
fdCnminus = file("Cn_minus", "w")
fdEnplus = file("En_plus", "w")
fdCnplus = file("Cn_plus", "w")
for omega in [1e-08]: #N.arange(-1.,1.,0.1):
#print omega
ops_dict = response_embed.mb_sp_ops(hlat, perturb,
omega + 1j * delta, nimp, nocc,
pemb, pcore, pvirt)
configs_dict = response_embed.mb_configs(nsites, nimp, nimp_sp,
2 * nocc - nimp_sp, 0)
neutral_ops_configs, plus_ops_configs, minus_ops_configs = response_embed.mb_sp_ops_configs(
ops_dict, configs_dict)
#print _ops_configs
# basis is setup, now build matrix representations
perturb_dop_mat = opbasis_ni.oimp_matrix_bra_ket_form(
perturb_dop, minus_ops_configs, neutral_ops_configs, cocc,
vocc)
perturb_cop_mat = opbasis_ni.oimp_matrix_bra_ket_form(
perturb_cop, plus_ops_configs, neutral_ops_configs, cocc, vocc)
# h, N-1 configs
#print ops_dict['d'][0]
himp_mat_minus = opbasis_ni.oimp_matrix_form(
himp, minus_ops_configs, cocc, vocc)
himp_ext_mat_minus = opbasis_ni.himp_ext_matrix_form(
hcs_imp, hds_ext, hds_imp, hcs_ext, minus_ops_configs, cocc,
vocc)
#print himp_ext_mat_minus
hext_mat_minus = opbasis_ni.hext_matrix_form(
hext, minus_ops_configs, cocc, vocc, e0)
hmat_minus = himp_mat_minus + himp_ext_mat_minus + hext_mat_minus
unit_mat_minus = opbasis_ni.oimp_matrix_form(
models.Unit(), minus_ops_configs, cocc, vocc)
#print hext_mat_minus
# h, N+1 configs
himp_mat_plus = opbasis_ni.oimp_matrix_form(
himp, plus_ops_configs, cocc, vocc)
himp_ext_mat_plus = opbasis_ni.himp_ext_matrix_form(
hcs_imp, hds_ext, hds_imp, hcs_ext, plus_ops_configs, cocc,
vocc)
hext_mat_plus = opbasis_ni.hext_matrix_form(
hext, plus_ops_configs, cocc, vocc, e0)
hmat_plus = himp_mat_plus + himp_ext_mat_plus + hext_mat_plus
unit_mat_plus = opbasis_ni.oimp_matrix_form(
models.Unit(), plus_ops_configs, cocc, vocc)
# h, neutral configs
himp_mat = opbasis_ni.oimp_matrix_form(himp, neutral_ops_configs,
cocc, vocc)
himp_ext_mat = opbasis_ni.himp_ext_matrix_form(
hcs_imp, hds_ext, hds_imp, hcs_ext, neutral_ops_configs, cocc,
vocc)
hext_mat = opbasis_ni.hext_matrix_form(hext, neutral_ops_configs,
cocc, vocc, e0)
hmat = himp_mat + himp_ext_mat + hext_mat
unit_mat = opbasis_ni.oimp_matrix_form(models.Unit(),
neutral_ops_configs, cocc,
vocc)
#print hext_mat_minus
# get neutral ground-state energy
es, cs = la.peigh(hmat, unit_mat)
e_gs = es[0]
psi0 = cs[:, 0]
es_minus, cs_minus = la.peigh(hmat_minus, unit_mat_minus)
Enarray = es_minus - e_gs * N.ones(len(es_minus))
print >> fdEnminus, omega, str(list(Enarray))[1:-1].translate(
None, ',')
es_plus, cs_plus = la.peigh(hmat_plus, unit_mat_plus)
Enarray = es_plus - e_gs * N.ones(len(es_minus))
print >> fdEnplus, omega, str(list(Enarray))[1:-1].translate(
None, ',')
gfminus = 0.
mat_el_list_minus = []
for i in xrange(len(es_minus)):
ket = N.dot(perturb_dop_mat, psi0)
bra = N.transpose(cs_minus[:, i].conjugate())
mat_el = N.dot(bra, ket)
mat_el *= mat_el.conjugate()
mat_el_list_minus.append(mat_el.real)
den = omega + 1j * delta - e_gs + es_minus[i]
gfminus += (mat_el / den)
print >> fdCnminus, omega, str(mat_el_list_minus)[1:-1].translate(
None, ',')
gfplus = 0.
mat_el_list_plus = []
for i in xrange(len(es_plus)):
ket = N.dot(perturb_cop_mat, psi0)
bra = N.transpose(cs_plus[:, i].conjugate())
mat_el = N.dot(bra, ket)
mat_el *= mat_el.conjugate()
mat_el_list_plus.append(mat_el.real)
den = omega + 1j * delta + e_gs - es_plus[i]
gfplus += (mat_el / den)
print >> fdCnplus, omega, str(mat_el_list_plus)[1:-1].translate(
None, ',')
import os, sys
sys.path.append("../embed")
from embed import embed
# Directories with stored graph edgelist representations
train_graph_directory = 'train_graphs/'
train_graph_labels = {
x: int(x.split('.')[0]) % 2
for x in os.listdir(train_graph_directory)
}
test_graph_directory = 'test_graphs/'
test_graph_output = 'test_graph_embeddings/'
embeddings = \
embed(train_input_directory = train_graph_directory, # nf is supervised
train_label_mapping = train_graph_labels,
test_input_directory = test_graph_directory,
test_output_directory = test_graph_output,
method = 'nf-o',
tmp_dir = 'bin/tmp.csv',
weights = 'bin/weights.pkl',
train = True,
n_epochs = 1)
