def plot_output(g0_wk, gamma):
from triqs.plot.mpl_interface import oplot, plt
initial_delta = semi_random_initial_delta(g0_wk)
next_delta_summation = eliashberg_product(gamma_big, g0_wk, initial_delta)
gamma_dyn_tr, gamma_const_r = preprocess_gamma_for_fft(gamma)
next_delta_fft = eliashberg_product_fft(gamma_dyn_tr, gamma_const_r, g0_wk,
initial_delta)
deltas = [initial_delta, next_delta_summation, next_delta_fft]
warnings.filterwarnings("ignore") #ignore some matplotlib warnings
subp = [4, 3, 1]
fig = plt.figure(figsize=(18, 15))
titles = ['Input', 'Summation', 'FFT']
for k_point in [Idx(0, 0, 0), Idx(1, 0, 0)]:
ax = plt.subplot(*subp)
subp[-1] += 1
oplot(g0_wk[:, k_point])
plt.title('GF')
ax = plt.subplot(*subp)
subp[-1] += 1
oplot(gamma[:, k_point])
plt.title('Gamma')
ax = plt.subplot(*subp)
subp[-1] += 1
oplot(gamma_dyn_tr[:, k_point])
plt.title('Gamma dyn tr')
for delta, title in zip(deltas, titles):
ax = plt.subplot(*subp)
subp[-1] += 1
oplot(delta[:, k_point])
plt.title(title)
ax.legend_ = None
plt.show()
def plot_field(out):
plt.figure(figsize=(3.25 * 2, 8))
for p in out.data:
subp = [2, 1, 1]
ax = plt.subplot(*subp)
subp[-1] += 1
oplotr(p.G_tau['up'], 'b', alpha=0.25)
oplotr(p.G_tau['dn'], 'g', alpha=0.25)
ax.legend().set_visible(False)
subp = [2, 1, 2]
plt.subplot(*subp)
subp[-1] += 1
plt.title(r'$\chi \approx %2.2f$' % out.chi)
plt.plot(out.h_vec, out.m_vec, '-og', alpha=0.5)
plt.plot(out.h_vec, out.m_ref_vec, 'xb', alpha=0.5)
plt.plot(out.h_vec, -out.chi * out.h_vec, '-r', alpha=0.5)
plt.tight_layout()
plt.savefig('figure_static_field.pdf')
def plot_g2(G2, cplx=None, idx_labels=None, w=Idx(0), opt={}, title=None):
data = G2[w, :, :].data
if cplx == 're':
data = data.real
elif cplx == 'im':
data = data.imag
n = data.shape[-1]
N = n**2
subp = [N, N, 1]
colorbar_flag = True
import itertools
for idxs in itertools.product(range(n), repeat=4):
i1, i2, i3, i4 = idxs
d = data[:, :, i1, i2, i3, i4]
ax = plt.subplot(*subp)
subp[-1] += 1
if idx_labels is not None:
labels = [idx_labels[idx] for idx in idxs]
sub_title = r'$c^\dagger_{%s} c_{%s} c^\dagger_{%s} c_{%s}$' % tuple(
labels)
else:
sub_title = str(idxs)
plt.title(sub_title, fontsize=8)
#plt.pcolormesh(d, **opt)
if np.max(np.abs(d)) > 1e-10:
plt.imshow(d, **opt)
if colorbar_flag:
if title is not None:
plt.title(title)
plt.colorbar()
colorbar_flag = False
ax.set_xticks([])
ax.set_yticks([])
plt.axis('equal')
plt.tight_layout()
plt.gca().set_xticks(K_plot)
plt.gca().set_xticklabels(K_labels)
plt.grid(True)
plt.ylabel(r'$\chi(\mathbf{Q})$')
plt.legend(loc='best', fontsize=8)
return line[0].get_color()
plt.figure(figsize=(3.25 * 2, 3))
ps, color = [], None
for filename in np.sort(glob.glob('data_bse_nwf*.h5')):
with HDFArchive(filename, 'r') as a:
p = a['p']
subp = [1, 2, 1]
plt.subplot(*subp)
subp[-1] += 1
color = plot_chi_k(p)
plt.subplot(*subp)
subp[-1] += 1
plt.plot(1. / p.nwf, p.chi_G, 'o', color=color)
ps.append(p)
# -- Extrapolation to nwf -> oo
ps = ParameterCollections(objects=ps)
x, y = 1. / ps.nwf, ps.chi_G
sidx = np.argsort(x)
x, y = x[sidx], y[sidx]
p = np.polyfit(x, y, 1)
y0 = np.polyval(p, 0)
X = np.linspace(0, x.max())
from triqs.plot.mpl_interface import oplot, oplotr, oploti, plt
plt.figure(figsize=(6 * 2, 8))
#subp = [3, 6, 1]
subp = [4, 1, 1]
#idx = 20
idx = 0
vmax = np.max(np.abs(ana.gamma_m.data.real))
opt = dict(vmin=-vmax, vmax=vmax, cmap='PuOr')
#opt = dict(vmin=-10., vmax=10., cmap='PuOr')
#opt = dict(cmap='PuOr')
ax = plt.subplot(*subp)
subp[-1] += 1
oplot(ana.G_iw)
if True:
g2 = ana.gamma_m
label = 'gamma ana'
s = np.squeeze(g2.data[0, :, :])
ax = plt.subplot(*subp)
subp[-1] += 1
plt.title('Re ' + label + ' [i,:,:]')
plt.pcolormesh(s.real, **opt)
plt.colorbar()
def test_cf_G_tau_and_G_iw_nonint(verbose=False):
beta = 3.22
eps = 1.234
niw = 64
ntau = 2 * niw + 1
H = eps * c_dag(0, 0) * c(0, 0)
fundamental_operators = [c(0, 0)]
ed = TriqsExactDiagonalization(H, fundamental_operators, beta)
# ------------------------------------------------------------------
# -- Single-particle Green's functions
G_tau = GfImTime(beta=beta,
statistic='Fermion',
n_points=ntau,
target_shape=(1, 1))
G_iw = GfImFreq(beta=beta,
statistic='Fermion',
n_points=niw,
target_shape=(1, 1))
G_iw << inverse(iOmega_n - eps)
G_tau << Fourier(G_iw)
G_tau_ed = GfImTime(beta=beta,
statistic='Fermion',
n_points=ntau,
target_shape=(1, 1))
G_iw_ed = GfImFreq(beta=beta,
statistic='Fermion',
n_points=niw,
target_shape=(1, 1))
ed.set_g2_tau(G_tau_ed[0, 0], c(0, 0), c_dag(0, 0))
ed.set_g2_iwn(G_iw_ed[0, 0], c(0, 0), c_dag(0, 0))
# ------------------------------------------------------------------
# -- Compare gfs
from triqs.utility.comparison_tests import assert_gfs_are_close
assert_gfs_are_close(G_tau, G_tau_ed)
assert_gfs_are_close(G_iw, G_iw_ed)
# ------------------------------------------------------------------
# -- Plotting
if verbose:
from triqs.plot.mpl_interface import oplot, plt
subp = [3, 1, 1]
plt.subplot(*subp)
subp[-1] += 1
oplot(G_tau.real)
oplot(G_tau_ed.real)
plt.subplot(*subp)
subp[-1] += 1
diff = G_tau - G_tau_ed
oplot(diff.real)
oplot(diff.imag)
plt.subplot(*subp)
subp[-1] += 1
oplot(G_iw)
oplot(G_iw_ed)
plt.show()
parm = ParameterCollection(
U = p.U,
beta = p.beta,
nw = 1,
nwf = p.n_iw / 2,
nwf_gf = p.n_iw,
)
a = analytic_hubbard_atom(**parm.dict())
a.G_iw.name = r'$G_{analytic}$'
plt.figure(figsize=(3.25*2, 3*2))
subp = [2, 2, 1]
plt.subplot(*subp); subp[-1] += 1
oploti(p.G_iw)
oploti(a.G_iw)
plt.subplot(*subp); subp[-1] += 1
diff = a.G_iw.copy()
diff << p.G_iw['up'] - a.G_iw
diff.name = r'$G_{ctint} - G_{analytic}$'
oplot(diff)
plt.subplot(*subp); subp[-1] += 1
vmax = np.max(np.abs(p.chi_m.data.real))
opt = dict(vmax=vmax, vmin=-vmax, cmap='PuOr')
data = np.squeeze(p.chi_m.data.real)
plt.pcolormesh(data, **opt)
G, Sigma = {}, {}
for solver in solver_lst:
dat = HDFArchive('results/' + solver + '.h5', 'r')
G[solver] = dat['G']
Sigma[solver] = G0_iw.copy()
Sigma[solver] << inverse(G0_iw) - inverse(G[solver])
# === For every block and solver, plot Green function and Self energy
block_lst = list(G[solver_lst[0]].indices)
n_blocks = len(block_lst)
for g, name in [[G, 'G'], [Sigma, '$\Sigma$']]:
plt.subplots(n_blocks, 1, figsize=(10, 6 * n_blocks))
for i, block in enumerate(block_lst, 1):
fig = plt.subplot(n_blocks, 1, i)
fig.set_title(name + "[" + block + "]")
for solver in solver_lst:
marker = marker_lst[solver_lst.index(solver)]
oplot(g[solver][block][0, 0],
marker,
name=name + "[0,0]_%s" % solver)
plt.xlabel("$\omega_n$")
plt.ylabel(name + "[" + block + "]$(i\omega_n)$")
plt.tight_layout()
plt.show()