def basis_convergence(root):
# Settings
species = ['zr', 'o']
l_max_pairs = [{
'zr': 4,
'o': 3
}, {
'zr': 5,
'o': 4
}, {
'zr': 6,
'o': 5
}, {
'zr': 7,
'o': 6
}]
gw_settings = GWInput(taskname="g0w0",
nempty=2000,
ngridq=[2, 2, 2],
skipgnd=False,
n_omega=32,
freqmax=1.0)
data = process_gw_calculations(root, species, l_max_pairs, gw_settings)
# TODO API for generating basis labels needs to change
l_max_values = [
OrderedDict([('zr', 4), ('o', 3)]),
OrderedDict([('zr', 5), ('o', 4)]),
OrderedDict([('zr', 6), ('o', 5)]),
OrderedDict([('zr', 7), ('o', 6)])
]
energy_indices = ['i0', 'i1', 'i2', 'i3', 'i4', 'i5', 'i6']
energy_cutoff = [80, 100, 120, 150, 180, 200, 250]
settings = {
'rgkmax': 8,
'l_max_values': l_max_values,
'n_img_freq': 32,
'q_grid': [2, 2, 2],
'n_empty_ext': [2000] * len(l_max_values),
# Extensions, not energies
'max_energy_cutoffs': energy_indices
}
# Post-process
basis_labels = get_basis_labels(root, settings)
plot_data(l_max_pairs, data, basis_labels)
print_results(data, energy_cutoff, '(4,3)')
print_results(data, energy_cutoff, '(5,4)')
print_results(data, energy_cutoff, '(6,5)')
print_results(data, energy_cutoff, '(7,6)')
plot_65_data(data, basis_labels)
return
def basis_convergence(root):
"""
Main routine for plotting basis convergence
:param root:
:return:
"""
# Settings
species = ['zr', 'o']
l_max_pairs = [{'zr': 6, 'o': 5}, {'zr': 7, 'o': 6}]
gw_settings = GWInput(taskname="g0w0",
nempty=3000,
ngridq=[2, 2, 2],
skipgnd=False,
n_omega=32,
freqmax=1.0)
data = process_gw_calculations(root, species, l_max_pairs, gw_settings)
# TODO API for generating basis labels needs to change
l_max_values = [
OrderedDict([('zr', 6), ('o', 5)]),
OrderedDict([('zr', 7), ('o', 6)])
]
energy_indices = ['i0', 'i1', 'i2', 'i3', 'i4', 'i5']
energy_cutoffs = [80, 100, 120, 150, 180, 200]
settings = {
'rgkmax': 8,
'l_max_values': l_max_values,
'n_img_freq': 32,
'q_grid': [2, 2, 2],
'n_empty_ext': [gw_settings.nempty] * len(l_max_values),
# Extensions, not energies
'max_energy_cutoffs': energy_indices
}
# Post-process
basis_labels = get_basis_labels(root, settings)
print_results(data, energy_cutoffs, '(6,5)')
print_results(data, energy_cutoffs, '(7,6)')
# Plot just for set 9
# plot_data(l_max_pairs, data, basis_labels)
plot_65_data(data, basis_labels)
quit()
# Plot for set 8 and set 9
set8_data = get_set8_data()
plot_data_for_set8_and_set9(set8_data, data)
return
def gw_basis_convergence(root: str):
"""
Plot the quasi particle - KS gap w.r.t bs LO basis, for l-max = (4, 3)
"""
D = OrderedDict
info = """ Converge QP gap w.r.t the s-channel LOs of Zr
"""
print(info)
lmax_32 = 0
plot_delta_E_qp = True
plot_sigma = False
save_plots = False
max_energy_exts_set4_spd = ['i0', 'i1', 'i2', 'i3'] # 'i4'
settings_set4_spd = {'rgkmax': 8,
'l_max_values': [D([('zr', 3), ('o', 2)])],
'n_img_freq': 32,
'q_grid': [2, 2, 2],
'n_empty_ext': [2000],
'max_energy_cutoffs': max_energy_exts_set4_spd
}
# S out of core
data_set4 = parse_gw_results(root, settings_set4_spd)
delta_E_qp_set4 = data_set4['delta_E_qp'][:, lmax_32] * ha_to_mev
E_qp_set4 = data_set4['E_qp'][:, lmax_32] * ha_to_mev
E_ks_set4 = data_set4['E_ks'][:, lmax_32] * ha_to_mev
basis_labels = get_basis_labels(root, settings_set4_spd, verbose=True)
basis_labels_set4 = combine_species_basis_labels(basis_labels, species_per_line=True)['(3,2)']
n_los_set4 = n_local_orbitals(basis_labels)['(3,2)']
# Give some changes in QP gap w.r.t. calculations
print('s out of core')
print('KS:', E_ks_set4)
print('QP:', E_qp_set4)
print('QP-KS:', delta_E_qp_set4)
process_basis_numbers(delta_E_qp_set4, basis_labels_set4)
# Total number of LOs per calculation (i.e. sum Zr and O basis sizes together)
n_los_set4 = sum_los_per_species(n_los_set4)
# S in core
data_set4_in_core = parse_gw_results(root, settings_set4_spd, dir_prefix='s_in_core_')
delta_E_qp_s_core = data_set4_in_core['delta_E_qp'][:, lmax_32] * ha_to_mev
E_qp_set4_s_core = data_set4_in_core['E_qp'][:, lmax_32] * ha_to_mev
E_ks_set4_s_core = data_set4_in_core['E_ks'][:, lmax_32] * ha_to_mev
print('s in core')
print('KS:', E_ks_set4_s_core)
print('QP:', E_qp_set4_s_core)
print('QP-KS:', delta_E_qp_s_core)
quit()
# --------------------------------------
# QP vs LO basis size, for l-max = (4,3)
# --------------------------------------
if plot_delta_E_qp:
fig, ax = plt.subplots()
ax.set_xlabel('N LOs')
ax.set_ylabel('Quasiparticle Gap - KS Gap at Gamma (meV)')
ax.plot(n_los_set4, delta_E_qp_set4, color='blue', marker='o', markersize=8)
for i, txt in enumerate(basis_labels_set4):
ax.annotate(txt, (n_los_set4[i], delta_E_qp_set4[i]))
if save_plots:
plt.savefig('qp_convergence_lmax43.ps', dpi=300, facecolor='w', edgecolor='w',
orientation='portrait', papertype=None, transparent=True, bbox_inches=None, pad_inches=0.1)
plt.show()
# ----------------------------------------------------------------------
# Plot VBT and CBB real self-energies w.r.t. LO energy cut-off
# for set 3, only
# ----------------------------------------------------------------------
if plot_sigma:
print("Requires rewriting")
pass
# # What we expect: VBT converges with the oxygen LOs, and is well-converged
# re_self_energy_VBM = data_set4['re_self_energy_VBM'][:, lmax_32] * ha_to_mev
# fig, ax = plt.subplots()
#
# fig.set_size_inches(12, 10)
# plt.rcParams.update({'font.size': 11})
# ax.tick_params(axis='both', which='major', labelsize=14)
#
# ax.set_xlim(82, 120)
# ax.set_xlabel('N LOs', fontsize=16)
# ax.set_ylabel('Re{Self Energy} at VBM (meV)', fontsize=16)
# ax.plot(n_los_set4, re_self_energy_VBM, 'ro', markersize=10)
# for i, txt in enumerate(basis_labels_set4):
# ax.annotate(txt, (n_los_set4[i], re_self_energy_VBM[i]))
#
# if save_plots:
# plt.savefig('VBM_convergence_set3_lmax43.jpeg', dpi=300, facecolor='w', edgecolor='w',
# orientation='portrait', transparent=True, bbox_inches=None, pad_inches=0.1)
#
# plt.show()
#
# # CBm is strongly dependent on the number of Zr LOs, and is still not converged.
# re_self_energy_CBm = data_set4['re_self_energy_CBm'][:, lmax_32] * ha_to_mev
# fig, ax = plt.subplots()
#
# fig.set_size_inches(12, 10)
# plt.rcParams.update({'font.size': 11})
# ax.tick_params(axis='both', which='major', labelsize=14)
#
# ax.set_xlim(82, 120)
# ax.set_xlabel('N LOs', fontsize=16)
# ax.set_ylabel('Re{Self Energy} at CBm (meV)', fontsize=16)
# ax.plot(n_los_set4, re_self_energy_CBm, 'ro', markersize=10)
# for i, txt in enumerate(basis_labels_set4):
# ax.annotate(txt, (n_los_set4[i], re_self_energy_CBm[i]))
#
# if save_plots:
# plt.savefig('CBm_convergence_set3_lmax43.jpeg', dpi=300, facecolor='w', edgecolor='w',
# orientation='portrait', transparent=True, bbox_inches=None, pad_inches=0.1)
#
# plt.show()
return
def gw_basis_convergence(root: str):
"""
Plot the quasi particle - KS gap w.r.t bs LO basis, for l-max = (4, 3)
"""
D = OrderedDict
info = """ Highest d-states are removed from the core and put into the valence.
i10 is ~ converged for all O and all but the p-channel of Zr.
As such, i12 - 18 attempt to converge the p-channel.
Quasi-particle gaps shows a linear decrease as N LOs in the p-channel are increased.
This was witnessed in the d-channel prior to removing semi-core d-states, hence set_pd will remove
semi-core p-states.
i 18 falls off a cliff, hence excluded.
"""
print(info)
lmax_32 = 0
plot_delta_E_qp = True
plot_sigma = False
save_plots = False
# The drop in energy for i18 is enormous
max_energy_exts_set4 = [
'i0', 'i1', 'i8', 'i12', 'i13', 'i14', 'i15', 'i16', 'i17'
]
settings_set4 = {
'rgkmax': 8,
'l_max_values': [D([('zr', 3), ('o', 2)])],
'n_img_freq': 32,
'q_grid': [2, 2, 2],
'n_empty_ext': [2000],
'max_energy_cutoffs': max_energy_exts_set4
}
data_set4 = parse_gw_results(root, settings_set4)
delta_E_qp_set4 = data_set4['delta_E_qp'][:, lmax_32] * ha_to_mev
basis_labels = get_basis_labels(root, settings_set4)
basis_labels_set4 = combine_species_basis_labels(
basis_labels, species_per_line=True)['(3,2)']
n_los_set4 = n_local_orbitals(basis_labels)['(3,2)']
# Give some changes in QP gap w.r.t. calculations
print(delta_E_qp_set4)
process_basis_numbers(delta_E_qp_set4, basis_labels_set4)
# Total number of LOs per calculation (i.e. sum Zr and O basis sizes together)
n_los_set4 = sum_los_per_species(n_los_set4)
# --------------------------------------
# QP vs LO basis size, for l-max = (4,3)
# --------------------------------------
if plot_delta_E_qp:
fig, ax = plt.subplots()
ax.set_xlabel('N LOs')
ax.set_ylabel('Quasiparticle Gap - KS Gap at Gamma (meV)')
ax.plot(n_los_set4,
delta_E_qp_set4,
color='blue',
marker='o',
linestyle='solid',
linewidth=3,
markersize=8)
for i, txt in enumerate(basis_labels_set4):
ax.annotate(txt, (n_los_set4[i], delta_E_qp_set4[i]))
if save_plots:
plt.savefig('qp_convergence_lmax43.ps',
dpi=300,
facecolor='w',
edgecolor='w',
orientation='portrait',
papertype=None,
transparent=True,
bbox_inches=None,
pad_inches=0.1)
plt.show()
# Set 3 by itself, where one is primarily looking at the l=2 channel of Zr.
# for i in range(0, delta_E_qp_set3.size):
# print(max_energy_exts_set3[i], delta_E_qp_set3[i])
#
# fig, ax = plt.subplots()
# fig.set_size_inches(12, 10)
# plt.rcParams.update({'font.size': 11}) # Affects fonts of the labels only
# ax.tick_params(axis='both', which='major', labelsize=14)
#
# ax.set_xlim(80, 125)
# ax.set_ylim(1580, 1660)
# ax.set_xlabel('N LOs', fontsize=16)
# ax.set_ylabel('Quasiparticle Gap - KS Gap at Gamma (meV)', fontsize=16)
# ax.plot(n_los_set3, delta_E_qp_set3, 'ro', markersize=10)
# for i, txt in enumerate(basis_labels_set3):
# ax.annotate(txt, (n_los_set3[i], delta_E_qp_set3[i]))
# Note, can use plt. or fig. to save and show
if save_plots:
# ALso note that ps cuts off the edges, hence using jpeg
plt.savefig('qp_convergence_set3_lmax43.jpeg',
dpi=300,
facecolor='w',
edgecolor='w',
orientation='portrait',
transparent=True,
bbox_inches=None,
pad_inches=0.1)
plt.show()
# ----------------------------------------------------------------------
# Plot VBT and CBB real self-eneries w.r.t. LO energy cut-off
# for set 3, only
# ----------------------------------------------------------------------
if plot_sigma:
# What we expect: VBT converges with the oxygen LOs, and is well-converged
re_self_energy_VBM = data_set4[
're_self_energy_VBM'][:, lmax_32] * ha_to_mev
fig, ax = plt.subplots()
fig.set_size_inches(12, 10)
plt.rcParams.update({'font.size': 11})
ax.tick_params(axis='both', which='major', labelsize=14)
ax.set_xlim(82, 120)
ax.set_xlabel('N LOs', fontsize=16)
ax.set_ylabel('Re{Self Energy} at VBM (meV)', fontsize=16)
ax.plot(n_los_set4, re_self_energy_VBM, 'ro', markersize=10)
for i, txt in enumerate(basis_labels_set4):
ax.annotate(txt, (n_los_set4[i], re_self_energy_VBM[i]))
if save_plots:
plt.savefig('VBM_convergence_set3_lmax43.jpeg',
dpi=300,
facecolor='w',
edgecolor='w',
orientation='portrait',
transparent=True,
bbox_inches=None,
pad_inches=0.1)
plt.show()
# CBm is strongly dependent on the number of Zr LOs, and is still not converged.
re_self_energy_CBm = data_set4[
're_self_energy_CBm'][:, lmax_32] * ha_to_mev
fig, ax = plt.subplots()
fig.set_size_inches(12, 10)
plt.rcParams.update({'font.size': 11})
ax.tick_params(axis='both', which='major', labelsize=14)
ax.set_xlim(82, 120)
ax.set_xlabel('N LOs', fontsize=16)
ax.set_ylabel('Re{Self Energy} at CBm (meV)', fontsize=16)
ax.plot(n_los_set4, re_self_energy_CBm, 'ro', markersize=10)
for i, txt in enumerate(basis_labels_set4):
ax.annotate(txt, (n_los_set4[i], re_self_energy_CBm[i]))
if save_plots:
plt.savefig('CBm_convergence_set3_lmax43.jpeg',
dpi=300,
facecolor='w',
edgecolor='w',
orientation='portrait',
transparent=True,
bbox_inches=None,
pad_inches=0.1)
plt.show()
return
def gw_basis_convergence(root:str):
"""
Plot the quasipartilce-KS gap w.r.t bs LO basis, for l-max = (4, 3(
"""
D = OrderedDict
lmax_43 = 0
plot_delta_E_qp = True
plot_sigma = False
save_plots = False
# Set 2
max_energy_exts_set2 = [90, 160, 230, 300]
settings_set2 = {'rgkmax': 7,
'l_max_values': [D([('zr', 4), ('o', 3)])],
'n_img_freq': 32,
'q_grid': [2, 2, 2],
'n_empty_ext': [1000],
'max_energy_cutoffs': max_energy_exts_set2}
data_set2 = parse_gw_results(root + "/A1_var_cutoff", settings_set2)
delta_E_qp_set2 = data_set2['delta_E_qp'][:, lmax_43] * ha_to_mev
basis_labels = get_basis_labels(root + "/A1_var_cutoff", settings_set2)
basis_labels_set2 = combine_species_basis_labels(basis_labels, species_per_line=True)['(4,3)']
n_los_set2 = n_local_orbitals(basis_labels)['(4,3)']
# Set 3
# i3 wouldn't work and i7. GW VBT and CBB get f****d up, hence omitted
# i0 i1 i2 i4 i5 i6
# energy_cutoffs = {'zr': {0: [150, 200, 250, 200, 120, 120],
# 1: [150, 200, 250, 200, 120, 120],
# 2: [300, 300, 300, 350, 350, 400],
# 3: [150, 200, 250, 200, 120, 120],
# 4: [150, 200, 250, 200, 120, 120]},
#
# 'o': {0: [150, 200, 250, 200, 120, 120],
# 1: [150, 200, 250, 200, 120, 120],
# 2: [150, 200, 250, 200, 120, 120],
# 3: [150, 200, 250, 200, 120, 120]}
# }
#max_energy_exts_set3 = ['i0', 'i1', 'i2', 'i4', 'i5', 'i6', 'i7']
max_energy_exts_set3 = ['i5', 'i6', 'i7']
settings_set3 = {'rgkmax': 7,
'l_max_values': [D([('zr', 4), ('o', 3)])],
'n_img_freq': 32,
'q_grid': [2, 2, 2],
'n_empty_ext': [1000], # Note in some cases, I actually used 2000 in input
'max_energy_cutoffs':max_energy_exts_set3 }
data_set3 = parse_gw_results(root + "/A1_set3", settings_set3)
delta_E_qp_set3 = data_set3['delta_E_qp'][:, lmax_43] * ha_to_mev
basis_labels = get_basis_labels(root + "/A1_set3", settings_set3)
n_los_set3 = n_local_orbitals(basis_labels)['(4,3)']
basis_labels_set3 = combine_species_basis_labels(basis_labels, species_per_line=True)['(4,3)']
# Give some changes in QP gap w.r.t. calculations
process_basis_numbers(delta_E_qp_set2, max_energy_exts_set2)
process_basis_numbers(delta_E_qp_set3, max_energy_exts_set3)
# Total number of LOs per calculation (i.e. sum Zr and O basis sizes together)
n_los_set2 = sum_los_per_species(n_los_set2)
n_los_set3 = sum_los_per_species(n_los_set3)
# --------------------------------------
# QP vs LO basis size, for l-max = (4,3)
# --------------------------------------
if plot_delta_E_qp:
# Set 2 and 3
fig, ax = plt.subplots()
ax.set_xlabel('N LOs')
ax.set_ylabel('Quasiparticle Gap - KS Gap at Gamma (meV)')
ax.plot(n_los_set2, delta_E_qp_set2, color='blue', marker='o', linestyle='solid', linewidth=3, markersize=8)
for i, txt in enumerate(basis_labels_set2):
ax.annotate(txt, (n_los_set2[i], delta_E_qp_set2[i]))
ax.plot(n_los_set3, delta_E_qp_set3, 'ro', markersize=8)
if save_plots:
plt.savefig('qp_convergence_lmax43.ps', dpi=300, facecolor='w', edgecolor='w',
orientation='portrait', papertype=None, transparent=True, bbox_inches=None, pad_inches=0.1)
plt.show()
# Set 3 by itself, where one is primarily looking at the l=2 channel of Zr.
for i in range(0, delta_E_qp_set3.size):
print(max_energy_exts_set3[i], delta_E_qp_set3[i])
fig, ax = plt.subplots()
fig.set_size_inches(12, 10)
plt.rcParams.update({'font.size': 11}) # Affects fonts of the labels only
ax.tick_params(axis='both', which='major', labelsize=14)
ax.set_xlim(80, 125)
ax.set_ylim(1580, 1660)
ax.set_xlabel('N LOs', fontsize=16)
ax.set_ylabel('Quasiparticle Gap - KS Gap at Gamma (meV)', fontsize=16)
ax.plot(n_los_set3, delta_E_qp_set3, 'ro', markersize=10)
for i, txt in enumerate(basis_labels_set3):
ax.annotate(txt, (n_los_set3[i], delta_E_qp_set3[i]))
# Note, can use plt. or fig. to save and show
if save_plots:
# ALso note that ps cuts off the edges, hence using jpeg
plt.savefig('qp_convergence_set3_lmax43.jpeg', dpi=300, facecolor='w', edgecolor='w',
orientation='portrait', transparent=True, bbox_inches=None, pad_inches=0.1)
plt.show()
# ----------------------------------------------------------------------
# Plot VBT and CBB real self-eneries w.r.t. LO energy cut-off
# for set 3, only
# ----------------------------------------------------------------------
if plot_sigma:
# What we expect: VBT converges with the oxygen LOs, and is well-converged
re_self_energy_VBM = data_set3['re_self_energy_VBM'][:, lmax_43] * ha_to_mev
fig, ax = plt.subplots()
fig.set_size_inches(12, 10)
plt.rcParams.update({'font.size': 11})
ax.tick_params(axis='both', which='major', labelsize=14)
ax.set_xlim(82, 120)
ax.set_xlabel('N LOs', fontsize=16)
ax.set_ylabel('Re{Self Energy} at VBM (meV)', fontsize=16)
ax.plot(n_los_set3, re_self_energy_VBM, 'ro', markersize=10)
for i, txt in enumerate(basis_labels_set3):
ax.annotate(txt, (n_los_set3[i], re_self_energy_VBM[i]))
if save_plots:
plt.savefig('VBM_convergence_set3_lmax43.jpeg', dpi=300, facecolor='w', edgecolor='w',
orientation='portrait', transparent=True, bbox_inches=None, pad_inches=0.1)
plt.show()
# CBm is strongly dependent on the number of Zr LOs, and is still not converged.
re_self_energy_CBm = data_set3['re_self_energy_CBm'][:, lmax_43] * ha_to_mev
fig, ax = plt.subplots()
fig.set_size_inches(12, 10)
plt.rcParams.update({'font.size': 11})
ax.tick_params(axis='both', which='major', labelsize=14)
ax.set_xlim(82, 120)
ax.set_xlabel('N LOs', fontsize=16)
ax.set_ylabel('Re{Self Energy} at CBm (meV)', fontsize=16)
ax.plot(n_los_set3, re_self_energy_CBm, 'ro', markersize=10)
for i, txt in enumerate(basis_labels_set3):
ax.annotate(txt, (n_los_set3[i], re_self_energy_CBm[i]))
if save_plots:
plt.savefig('CBm_convergence_set3_lmax43.jpeg', dpi=300, facecolor='w', edgecolor='w',
orientation='portrait', transparent=True, bbox_inches=None, pad_inches=0.1)
plt.show()
return
def gw_basis_convergence(root: str):
"""
Plot the quasi particle - KS gap w.r.t bs LO basis, for l-max = (4, 3)
"""
D = OrderedDict
info = """ This run does two things:
i0 - i5 converge the p-channel of Zr
i6 - i9 check the sensitivity of large values in check l-channel
One sees that Zr d is already converged at 11 LOs, and Zr-f is converged at 10 LOs
Some sensitivity w.r.t the Zr-p, within ~ 4 meV spread. p at 11 LOs looks converged to ~ 2 meV
For increasing Zr-s from 9 to 12 LOs, we get the outlier at the bottom of the plot: i.e. ~ 7 meV change
As such, the calculation following this one should take the highest s out of the core and converge the Zr-s channel
"""
print(info)
lmax_32 = 0
plot_delta_E_qp = True
plot_sigma = False
save_plots = False
max_energy_exts_set4_pd = [
'i0', 'i1', 'i2', 'i3', 'i4', 'i5', 'i6', 'i7', 'i8', 'i9'
]
settings_set4_pd = {
'rgkmax': 8,
'l_max_values': [D([('zr', 3), ('o', 2)])],
'n_img_freq': 32,
'q_grid': [2, 2, 2],
'n_empty_ext': [2000],
'max_energy_cutoffs': max_energy_exts_set4_pd
}
data_set4 = parse_gw_results(root, settings_set4_pd)
delta_E_qp_set4 = data_set4['delta_E_qp'][:, lmax_32] * ha_to_mev
basis_labels = get_basis_labels(root, settings_set4_pd, verbose=True)
basis_labels_set4 = combine_species_basis_labels(
basis_labels, species_per_line=True)['(3,2)']
n_los_set4 = n_local_orbitals(basis_labels)['(3,2)']
# Give some changes in QP gap w.r.t. calculations
print(delta_E_qp_set4)
process_basis_numbers(delta_E_qp_set4, basis_labels_set4)
# Total number of LOs per calculation (i.e. sum Zr and O basis sizes together)
n_los_set4 = sum_los_per_species(n_los_set4)
# --------------------------------------
# QP vs LO basis size, for l-max = (4,3)
# --------------------------------------
if plot_delta_E_qp:
fig, ax = plt.subplots()
ax.set_xlabel('N LOs')
ax.set_ylabel('Quasiparticle Gap - KS Gap at Gamma (meV)')
ax.plot(n_los_set4,
delta_E_qp_set4,
color='blue',
marker='o',
markersize=8)
for i, txt in enumerate(basis_labels_set4):
ax.annotate(txt, (n_los_set4[i], delta_E_qp_set4[i]))
if save_plots:
plt.savefig('qp_convergence_lmax43.ps',
dpi=300,
facecolor='w',
edgecolor='w',
orientation='portrait',
papertype=None,
transparent=True,
bbox_inches=None,
pad_inches=0.1)
plt.show()
# ----------------------------------------------------------------------
# Plot VBT and CBB real self-energies w.r.t. LO energy cut-off
# for set 3, only
# ----------------------------------------------------------------------
if plot_sigma:
print("Requires rewriting")
pass
# # What we expect: VBT converges with the oxygen LOs, and is well-converged
# re_self_energy_VBM = data_set4['re_self_energy_VBM'][:, lmax_32] * ha_to_mev
# fig, ax = plt.subplots()
#
# fig.set_size_inches(12, 10)
# plt.rcParams.update({'font.size': 11})
# ax.tick_params(axis='both', which='major', labelsize=14)
#
# ax.set_xlim(82, 120)
# ax.set_xlabel('N LOs', fontsize=16)
# ax.set_ylabel('Re{Self Energy} at VBM (meV)', fontsize=16)
# ax.plot(n_los_set4, re_self_energy_VBM, 'ro', markersize=10)
# for i, txt in enumerate(basis_labels_set4):
# ax.annotate(txt, (n_los_set4[i], re_self_energy_VBM[i]))
#
# if save_plots:
# plt.savefig('VBM_convergence_set3_lmax43.jpeg', dpi=300, facecolor='w', edgecolor='w',
# orientation='portrait', transparent=True, bbox_inches=None, pad_inches=0.1)
#
# plt.show()
#
# # CBm is strongly dependent on the number of Zr LOs, and is still not converged.
# re_self_energy_CBm = data_set4['re_self_energy_CBm'][:, lmax_32] * ha_to_mev
# fig, ax = plt.subplots()
#
# fig.set_size_inches(12, 10)
# plt.rcParams.update({'font.size': 11})
# ax.tick_params(axis='both', which='major', labelsize=14)
#
# ax.set_xlim(82, 120)
# ax.set_xlabel('N LOs', fontsize=16)
# ax.set_ylabel('Re{Self Energy} at CBm (meV)', fontsize=16)
# ax.plot(n_los_set4, re_self_energy_CBm, 'ro', markersize=10)
# for i, txt in enumerate(basis_labels_set4):
# ax.annotate(txt, (n_los_set4[i], re_self_energy_CBm[i]))
#
# if save_plots:
# plt.savefig('CBm_convergence_set3_lmax43.jpeg', dpi=300, facecolor='w', edgecolor='w',
# orientation='portrait', transparent=True, bbox_inches=None, pad_inches=0.1)
#
# plt.show()
return
def gw_basis_convergence(root: str):
"""
Plot the quasi particle - KS gap w.r.t to the LO basis
Have one data point for converged QP gap with l-max = (3, 2)
Then plot for several LOs in the channels
l-max = (4, 2)
l-max = (3, 3)
l-max = (5, 3)
l-max = (4, 4)
"""
D = OrderedDict
# --------------------------------------------
# Extra Zr l-Channel. l_max(Zr, O) = (4, 2)
# zr_lmax4_o_lmax2_rgkmax8
# --------------------------------------------
settings_zr = {'rgkmax': 8,
'l_max_values': [D([('zr', 4), ('o', 2)])],
'n_img_freq': 32,
'q_grid': [2, 2, 2],
'n_empty_ext': [2000],
'max_energy_cutoffs': ['i0', 'i1', 'i2', 'i3', 'i4']
}
data_set_zr = parse_gw_results(root, settings_zr)
E_qp_zr = data_set_zr['E_qp'][:, 0] * ha_to_mev
E_ks_zr = data_set_zr['E_ks'][:, 0] * ha_to_mev
delta_E_qp_zr = data_set_zr['delta_E_qp'][:, 0] * ha_to_mev
assert np.allclose(E_ks_zr, 3865.10677893), "KS gap shouldn't change"
# Get LO labels
basis_labels = get_basis_labels(root, settings_zr, verbose=True)
basis_labels_zr = combine_species_basis_labels(basis_labels, species_per_line=True)['(4,2)']
n_los_zr = n_local_orbitals(basis_labels)['(4,2)']
print('Zr Calculation:', basis_labels_zr)
# --------------------------------------------
# Extra O l-channel. l_max(Zr, O) = (3, 3)
# zr_lmax3_o_lmax3_rgkmax8
# --------------------------------------------
max_energy_exts_o = ['i0', 'i1', 'i2']
settings_o = {'rgkmax': 8,
'l_max_values': [D([('zr', 3), ('o', 3)])],
'n_img_freq': 32,
'q_grid': [2, 2, 2],
'n_empty_ext': [2000],
'max_energy_cutoffs': max_energy_exts_o
}
data_set_o = parse_gw_results(root, settings_o)
E_qp_o = data_set_o['E_qp'][:, 0] * ha_to_mev
E_ks_o = data_set_o['E_ks'][:, 0] * ha_to_mev
delta_E_qp_o = data_set_o['delta_E_qp'][:, 0] * ha_to_mev
assert np.allclose(E_ks_o, 3865.10677893), "KS gap shouldn't change"
# Get LO labels
basis_labels = get_basis_labels(root, settings_o, verbose=True)
basis_labels_o = combine_species_basis_labels(basis_labels, species_per_line=True)['(3,3)']
n_los_o = n_local_orbitals(basis_labels)['(3,3)']
print('O Calculation:', basis_labels_o)
# Report Details
print('Quasi-particle gap w.r.t LOs in Zr l=4 channel (O l=2):', E_qp_zr)
print('Quasi-particle minus gap w.r.t LOs in Zr l=4 channel (O l=2):', delta_E_qp_zr)
print('Quasi-particle gap w.r.t LOs in O l=3 channel (Zr l=3):', E_qp_o)
print('Quasi-particle minus gap w.r.t LOs in O l=3 channel (Zr l=3):', delta_E_qp_o)
# ------------------------------------------------
# Using prior Zr and O bases, increase Zr l-channel
# l_max(Zr, O) = (5, 3)
# zr_lmax5_o_lmax3_rgkmax8
# ------------------------------------------------
settings_zr = {'rgkmax': 8,
'l_max_values': [D([('zr', 5), ('o', 3)])],
'n_img_freq': 32,
'q_grid': [2, 2, 2],
'n_empty_ext': [2000],
'max_energy_cutoffs': ['i1', 'i2', 'i3', 'i4'] # i0 failed => 'i0',ignore it for now
}
data_set_zr = parse_gw_results(root, settings_zr)
E_qp_zr = data_set_zr['E_qp'][:, 0] * ha_to_mev
E_ks_zr = data_set_zr['E_ks'][:, 0] * ha_to_mev
print("KS gap has changed by 0.3 meV when moving to the (5, 3) basis\n"
"I assume this is ok, BUT should not")
delta_E_qp_zr = data_set_zr['delta_E_qp'][:, 0] * ha_to_mev
assert np.allclose(E_ks_zr, 3865.3788929), "KS gap shouldn't change"
# Get LO labels
basis_labels = get_basis_labels(root, settings_zr, verbose=True)
basis_labels_zr = combine_species_basis_labels(basis_labels, species_per_line=True)['(5,3)']
n_los_zr = n_local_orbitals(basis_labels)['(5,3)']
print('Zr Calculation (5,3):', basis_labels_zr)
# ------------------------------------------------
# Using prior Zr and O bases, increase O l-channel
# l_max(Zr, O) = (4, 4)
# zr_lmax4_o_lmax4_rgkmax8
# ------------------------------------------------
settings_o = {'rgkmax': 8,
'l_max_values': [D([('zr', 4), ('o', 4)])],
'n_img_freq': 32,
'q_grid': [2, 2, 2],
'n_empty_ext': [2000],
'max_energy_cutoffs': ['i0', 'i1', 'i2']
}
data_set_o = parse_gw_results(root, settings_o)
E_qp_o = data_set_o['E_qp'][:, 0] * ha_to_mev
E_ks_o = data_set_o['E_ks'][:, 0] * ha_to_mev
delta_E_qp_o = data_set_o['delta_E_qp'][:, 0] * ha_to_mev
print(E_ks_o)
assert np.allclose(E_ks_o, 3865.3788929), "KS gap shouldn't change"
# Get LO labels
basis_labels = get_basis_labels(root, settings_o, verbose=True)
basis_labels_o = combine_species_basis_labels(basis_labels, species_per_line=True)['(4,4)']
n_los_o = n_local_orbitals(basis_labels)['(4,4)']
print('O Calculation:', basis_labels_o)
print('Quasi-particle gap w.r.t LOs in Zr l=5 channel (O l=3):', E_qp_zr)
print('Quasi-particle minus gap w.r.t LOs in Zr l=5 channel (O l=3):', delta_E_qp_zr)
print('Quasi-particle gap w.r.t LOs in O l=4 channel (Zr l=4):', E_qp_o)
print('Quasi-particle minus gap w.r.t LOs in O l=4 channel (Zr l=4):', delta_E_qp_o)
# Do plot of Oxygen only, as that effect seems to be large
# TODO Add set 6 converged data point
# This has opened the gap a fair bit - should check tbe basis and see if another channel also affects results
# if plot_delta_E_qp:
# fig, ax = plt.subplots()
# ax.set_xlabel('N LOs')
# ax.set_ylabel('Quasiparticle Gap - KS Gap at Gamma (meV)')
#
# print(n_los_o)
# print(delta_E_qp_o)
#
# ax.plot(n_los_o['o'], delta_E_qp_o, color='blue', marker='o', markersize=8)
#
# for i, txt in enumerate(basis_labels_o):
# ax.annotate(txt, (n_los_o['o'][i], delta_E_qp_o[i]))
#
# if save_plots:
# plt.savefig('qp_convergence_lmax43.ps', dpi=300, facecolor='w', edgecolor='w',
# orientation='portrait', papertype=None, transparent=True, bbox_inches=None, pad_inches=0.1)
# plt.show()
return
def gw_basis_convergence(root: str):
"""
Plot the quasipartilce-KS gap w.r.t basis l-max, for
several max lo energies.
"""
D = OrderedDict
# Details required for generating directory extension
l_max_values = [
D([('zr', 3), ('o', 2)]),
D([('zr', 4), ('o', 3)]),
D([('zr', 5), ('o', 4)]),
D([('zr', 6), ('o', 5)])
]
# Makes more sense to specifiy the extensions rather than use:
# max_energy_exts = max_energy_ext_per_directory(energy_cutoffs)
# Same with n_empty_ext
max_energy_exts = [90, 160, 230, 300]
settings = {
'rgkmax': 7,
'l_max_values': l_max_values,
'n_img_freq': 32,
'q_grid': [2, 2, 2],
'n_empty_ext': [800, 1000, 1300, 2000],
'max_energy_cutoffs': max_energy_exts
}
# Parse data
data = parse_gw_results(root, settings)
delta_E_qp = data['delta_E_qp']
# Print some data
process_basis_numbers(l_max_values,
delta_E_qp,
max_energy_exts,
max_energy=300)
# Generate plots
save_plots = False
plot_delta_E_qp = True
plot_sigma = False
# -----------------
# Plot delta_E_qp
# -----------------
if plot_delta_E_qp:
# --------------------------------------------------------------
# Plot 1
# --------------------------------------------------------------
x = np.arange(0, len(l_max_values))
x_labels = [
"(" + ",".join(str(l) for l in l_pair.values()) + ")"
for l_pair in l_max_values
]
gw_plot = Plot(x, [],
x_label='l_max (Zr, O)',
y_label='Quasiparticle Gap - KS Gap at Gamma (meV)',
xticklabels=x_labels,
legend_title='Max LO Energy (Ha)',
linewidth=3)
for i, energy in enumerate(max_energy_exts):
gw_plot.plot_data(x,
delta_E_qp[i, :] * ha_to_mev,
label=str(energy))
if save_plots:
save_options = {
'file_name': 'qp_convergence_lmax_and_los.jpeg',
'dpi': 300,
'facecolor': 'w',
'edgecolor': 'w',
'orientation': 'landscape',
'bbox_inches': 'tight'
}
gw_plot.save(save_options)
gw_plot.show()
# --------------------------------------------------------------
# Second plot. Same as above but label every point with the basis
# --------------------------------------------------------------
fig, ax = plt.subplots()
fig.set_size_inches(14, 10)
plt.rcParams.update({'font.size': 16})
ax.set_xlabel('l_max (Zr, O)', fontsize=16)
ax.set_ylabel('Quasiparticle Gap - KS Gap at Gamma (meV)', fontsize=16)
ax.set_xticks(x)
ax.set_xticklabels(x_labels)
ax.tick_params(axis='both', which='major', labelsize=16)
# Overlay lines for each LO energy cutoff
for i, energy in enumerate(max_energy_exts):
ax.plot(x, delta_E_qp[i, :] * ha_to_mev, marker='o', markersize=8)
basis_labels = get_basis_labels(root, settings)
# Set basis label for each marker
for j, l_max in enumerate(['(3,2)', '(4,3)', '(5,4)', '(6,5)']):
for i, energy in enumerate(max_energy_exts):
label = ''
for species in ['zr', 'o']:
label += species.capitalize(
) + ': ' + basis_labels[l_max][species][i].rstrip() + '.\n'
ax.annotate(label.rstrip(),
(x[j], delta_E_qp[i, j] * ha_to_mev))
if save_plots:
plt.savefig('qp_convergence_set2_basis_labels.jpeg',
dpi=300,
facecolor='w',
edgecolor='w',
orientation='landscape',
transparent=True,
bbox_inches='tight',
pad_inches=1.)
plt.show()
# ----------------------------------------------------------------------
# Plot VBT and CBB real self-eneries w.r.t. basis and energy parameter
# ----------------------------------------------------------------------
if plot_sigma:
re_self_energy_VBM = data['re_self_energy_VBM']
r_sigma_VBM_plot = Plot(x, [],
x_label='l_max (Zr, O)',
y_label='Re{Self Energy} at VBM (meV) ',
xticklabels=x_labels,
legend_title='Max LO Energy Param (Ha)',
linewidth=3)
for i, energy in enumerate(max_energy_exts):
r_sigma_VBM_plot.plot_data(x,
re_self_energy_VBM[i, :] * ha_to_mev,
label=str(energy))
r_sigma_VBM_plot.show()
re_self_energy_CBm = data['re_self_energy_CBm']
r_sigma_CBm_plot = Plot(x, [],
x_label='l_max (Zr, O)',
y_label='Re{Self Energy} at CBm (meV) ',
xticklabels=x_labels,
legend_title='Max LO Energy Param (Ha)',
linewidth=3)
for i, energy in enumerate(max_energy_exts):
r_sigma_CBm_plot.plot_data(x,
re_self_energy_CBm[i, :] * ha_to_mev,
label=str(energy))
r_sigma_CBm_plot.show()
return