def plot_p(p):
xlim = [-50, 50]
subp = [3, 1, 1]
plt.subplot(*subp)
subp[-1] += 1
oploti(p.g0_w[0, 0], label=r'$G_0(i\omega_n)$')
oploti(p.g_w[0, 0], label=r'$G(i\omega_n)$')
oploti(p.sigma_w[0, 0], label=r'$\Sigma(i\omega_n)$')
plt.legend(loc='best')
plt.xlim(xlim)
plt.subplot(*subp)
subp[-1] += 1
oplotr(p.G_tau_raw['up'], alpha=0.75)
oplotr(p.G_tau['up'])
plt.gca().legend().set_visible(False)
plt.subplot(*subp)
subp[-1] += 1
G_l = p.G_l.copy()
for b, g in G_l:
G_l[b].data[:] = np.abs(g.data)
oplotr(G_l['up'], 'o-')
plt.semilogy([], [])
plt.gca().legend().set_visible(False)
plt.tight_layout()
plt.savefig('figure_field_sc_gf.svg')
def plot_chi_k(p, color=None):
chi_k = p.chi_kw[:, Idx(0)]
k_vecs, k_plot, K_plot, K_labels = get_path()
kx, ky, kz = k_vecs.T
interp = np.vectorize(lambda kx, ky, kz : np.squeeze(chi_k([kx, ky, kz]).real))
interp = interp(kx, ky, kz)
p.chi_G = np.squeeze(chi_k[Idx(0, 0, 0)].real)
line = plt.plot(k_plot, interp, '-', label=r'$N_{\nu} = $'+'${:d}$'.format(p.nwf))
plt.gca().set_xticks(K_plot); plt.gca().set_xticklabels(K_labels)
plt.grid(True); plt.ylabel(r'$\chi(\mathbf{Q})$')
plt.legend(loc='best', fontsize=8)
return line[0].get_color()
def plot_histos(title,histos):
a = plt.gca()
a.set_xlim((0,beta))
a.set_xlabel('$\\Delta\\tau$')
a.set_ylabel(title)
for name, histo in histos.items():
w = histo.data
dtau = [histo.mesh_point(n) for n in range(len(histo))]
plt.plot(dtau,w,label=name,linewidth=0.7)
a.legend(loc='upper center',prop={'size':10})
def setup_fig():
axes = plt.gca()
axes.set_ylabel('$G(\\tau)$')
axes.legend(loc='lower center', prop={'size': 10})
def setup_fig():
axes = plt.gca()
axes.set_ylabel('$G(\\tau)$')
axes.legend(loc='lower center',prop={'size':10})
a['Delta_iw_fit'] = Delta_iw_fit
# -- Plot
from pytriqs.plot.mpl_interface import oplot, oplotr, oploti, plt
plt.figure(figsize=(10, 10))
ylim = [-4, 4]
plt.plot([w_min.imag]*2, ylim, 'sr-', lw=0.5)
plt.plot([w_max.imag]*2, ylim, 'sr-', lw=0.5)
for i1, i2 in itertools.product(range(2), repeat=2):
oplotr(Delta_iw[i1, i2], alpha=0.1)
oploti(Delta_iw[i1, i2], alpha=0.1)
oplotr(Delta_iw_ref[i1, i2], lw=4, alpha=0.25)
oploti(Delta_iw_ref[i1, i2], lw=4, alpha=0.25)
oplotr(Delta_iw_fit[i1, i2])
oploti(Delta_iw_fit[i1, i2])
ax = plt.gca()
ax.legend_ = None
plt.ylim([-1, 1])
plt.tight_layout()
plt.savefig(figure_filename)
plt.show(); exit()
ed_arch = HDFArchive(chi_ed_filename,'r')
pp = PdfPages('chi.pdf')
spin_names = ('up','dn')
spin_labels = {'up':'\uparrow\uparrow', 'dn':'\downarrow\downarrow'}
# G_tau
delta_max_text = ""
for sn in spin_names:
g = arch['G_tau'][sn]
g_ed = ed_arch['G_tau'][sn]
oplot(g, mode='R', lw=0.5, label="QMC, $%s$" % spin_labels[sn])
oplot(g_ed, mode='R', lw=0.5, label="ED, $%s$" % spin_labels[sn])
delta_max = np.max(np.abs(g.data[:,0,0] - g_ed.data[:,0,0]))
delta_max_text += "$\\delta^{max}_{%s} = %f$\n" % (spin_labels[sn],delta_max)
ax = plt.gca()
ax.set_title('$G(\\tau)$')
ax.set_ylabel('$G(\\tau)$')
ax.set_xlim((0,beta))
ax.set_ylim((-1,0.05))
ax.legend(loc='lower center',prop={'size':10})
ax.text(beta/2,-0.5,delta_max_text,horizontalalignment='center')
pp.savefig(plt.gcf())
# G_iw
plt.cla()
delta_max_text = ""
for sn in spin_names:
g = arch['G_iw'][sn]
g_ed = ed_arch['G_iw'][sn]
oplot(g, mode='I', lw=0.5, label="QMC, $%s$" % spin_labels[sn])
plt.subplot(*subp)
subp[-1] += 1
for spin in ("up", "dn"):
plt.plot(arch['G_l'][spin].data.flatten(),
label="cthyb," + {
'up': "$\uparrow\uparrow$",
'dn': "$\downarrow\downarrow$"
}[spin])
plt.plot(ed_arch[spin].data.flatten(),
label="ED," + {
'up': "$\uparrow\uparrow$",
'dn': "$\downarrow\downarrow$"
}[spin])
axes = plt.gca()
axes.set_xlabel('$l$')
axes.set_ylabel('$G_l$')
axes.set_ylim((-2.0, 1.0))
axes.legend(loc='lower center', prop={'size': 10})
plt.subplot(*subp)
subp[-1] += 1
for spin in ("up", "dn"):
plt.plot(np.abs(arch['G_l'][spin].data.flatten()),
label="cthyb," + {
'up': "$\uparrow\uparrow$",
'dn': "$\downarrow\downarrow$"
}[spin])
plt.plot(np.abs(ed_arch[spin].data.flatten()),
def setup_fig():
axes = plt.gca()
axes.set_ylabel('$G(\\tau)$')
axes.legend(loc='best', prop={'size': 8}, ncol=2)
e_group_ed = arch_ed[e_group_name]
beta = e_group['beta']
U = e_group['U']
ed = e_group['ed']
V = e_group['V']
e = e_group['e']
plt.clf()
oplot(rebinning_tau(e_group['G_tau']['up'],300),name="CTHYB,$\uparrow\uparrow$")
oplot(rebinning_tau(e_group['G_tau']['dn'],300),name="CTHYB,$\downarrow\downarrow$")
oplot(rebinning_tau(e_group_ed['G_tau']['up'],300),name="ED,$\uparrow\uparrow$")
oplot(rebinning_tau(e_group_ed['G_tau']['dn'],300),name="ED,$\downarrow\downarrow$")
a = plt.gca()
a.set_ylabel('$G(\\tau)$')
a.set_xlim((0,beta))
a.set_ylim((-1,0))
a.legend(loc='lower right',prop={'size':10})
a.set_title("$U=%.1f$, $\epsilon_d=%.1f$, $V=%.1f$, $\epsilon_k=%.1f$" % (U,ed,V,e))
histo_a = plt.axes([.35, .15, .3, .4], axisbg='y')
opcount_data = []
for w in reversed(e_group['perturbation_order_total'].data):
if w==0: continue
opcount_data.insert(0,w)
histo_a.bar(range(len(opcount_data)), opcount_data)
histo_a.set_title('perturbation_order')
if mpi.rank==0:
arch = HDFArchive('asymm_bath.matrix.h5','w')
pp = PdfPages('G_asymm_bath.matrix.pdf')
# Set hybridization function
for e in epsilon:
delta_w = GfImFreq(indices = [0], beta=beta)
delta_w <<= (V**2) * inverse(iOmega_n - e)
S.G0["up"] <<= inverse(iOmega_n - ed - delta_w)
S.G0["dn"] <<= inverse(iOmega_n - ed - delta_w)
S.solve(**p)
if mpi.rank==0:
arch['epsilon_' + str(e)] = {"up":S.G_tau["up"], "dn":S.G_tau["dn"]}
plt.clf()
oplot(S.G_tau["up"], name="$\uparrow\uparrow$")
oplot(S.G_tau["dn"],name="$\downarrow\downarrow$")
axes = plt.gca()
axes.set_ylabel('$G(\\tau)$')
axes.legend(loc='lower center',prop={'size':10})
axes.set_title("$U=%.1f$, $\epsilon_d=%.1f$, $V=%.1f$, $\epsilon_k=%.1f$" % (U,ed,V,e))
pp.savefig(plt.gcf())
if mpi.rank==0: pp.close()
