def test_basic_Etot(self):
calc = calcs()['HSE_dielectric']
Etot = VASPBasicAnalysis(calc).Etot
answer = -.71601536E+02 / 20
self.assertEqual(Etot, answer)
def test_basic_Efermi(self):
calc = calcs()['SCAN_geometry']
Efermi = VASPBasicAnalysis(calc).Efermi
answer = 0.3567
self.assertEqual(Efermi, answer)
def test_basic_formula(self):
calc = calcs()['COHP']
formula = VASPBasicAnalysis(calc).formula(True)
answer = 'Mo1N4Zn3'
self.assertEqual(formula, answer)
def test_basic_els_from_outcar(self):
calc = calcs()['PBE_sp']
els = VASPBasicAnalysis(calc).ordered_els_from_outcar
answer = ['O', 'Fe', 'As']
self.assertEqual(els, answer)
def test_basic_is_converged(self):
for calc in calcs():
check = VASPBasicAnalysis(calcs()[calc]).is_converged
answer = True
self.assertEqual(check, answer)
def dos(calc_dir,
what_to_plot={'total' : {'spins' : ['summed'],
'orbitals' : ['all']}},
colors_and_labels = {'total-summed-all' : {'color' : 'black',
'label' : 'total'}},
xlim=(0, 0.1), ylim=(-10, 4),
xticks=(False, [0, 0.1]), yticks=(False, [-10, 4]),
xlabel=r'$DOS/e^-$', ylabel=r'$E-E_F\/(eV)$',
legend=True,
smearing=0.2,
shift='Fermi', normalization='electron',
cb_shift=False,
vb_shift=False,
show=False,
doscar='DOSCAR.lobster',
):
"""
Args:
calc_dir (str) - path to calculation with DOSCAR
what_to_plot (dict) - {element or 'total' (str) : {'spins' : list of spins to include ('summed', 'up', and or 'down'),
'orbitals' : list of orbitals to include (str)}}
colors_and_labels (dict) - {element-spin-orbital (str) : {'color' : color (str),
'label' : label (str)}}
xlim (tuple) - (xmin (float), xmax (float))
ylim (tuple) - (ymin (float), ymax (float))
xticks (tuple) - (bool to show label or not, (xtick0, xtick1, ...))
yticks (tuple) - (bool to show label or not, (ytick0, ytick1, ...))
xlabel (str) - x-axis label
ylabel (str) - y-axis label
legend (bool) - include legend or not
smearing (float or False) - std. dev. for Gaussian smearing of DOS or False for no smearing
shift (float or 'Fermi') - if 'Fermi', make Fermi level 0; else shift energies by shift
cb_shift (tuple or False) - shift all energies >= cb_shift[0] (float) by cb_shift[1] (float)
vb_shift (tuple or False) - shift all energies <= vb_shift[0] (float) by vb_shift[1] (float)
normalization ('electron', 'atom', or False) - divide populations by number of electrons, number of atoms, or not at all
show (bool) - if True, show figure; else just return ax
Returns:
matplotlib axes object
"""
set_rc_params()
if show == True:
fig = plt.figure(figsize=(2.5,4))
ax = plt.subplot(111)
if 'lobster' in doscar:
Efermi = 0.
else:
Efermi = VASPBasicAnalysis(calc_dir).Efermi
if shift == 'Fermi':
shift = -Efermi
if normalization == 'electron':
normalization = VASPBasicAnalysis(calc_dir).params_from_outcar(num_params=['NELECT'], str_params=[])['NELECT']
elif normalization == 'atom':
normalization = VASPBasicAnalysis(calc_dir).nsites
occupied_up_to = Efermi + shift
print(occupied_up_to)
dos_lw = 1
for element in what_to_plot:
for spin in what_to_plot[element]['spins']:
for orbital in what_to_plot[element]['orbitals']:
tag = '-'.join([element, spin, orbital])
color = colors_and_labels[tag]['color']
label = colors_and_labels[tag]['label']
d = VASPDOSAnalysis(calc_dir, doscar=doscar).energies_to_populations(element=element,
orbital=orbital,
spin=spin)
if spin == 'down':
flip_sign = True
else:
flip_sign = False
d = ProcessDOS(d, shift=shift,
flip_sign=flip_sign,
normalization=normalization,
cb_shift=cb_shift,
vb_shift=vb_shift).energies_to_populations
energies = sorted(list(d.keys()))
populations = [d[E] for E in energies]
occ_energies = [E for E in energies if E <= occupied_up_to]
occ_populations = [d[E] for E in occ_energies]
unocc_energies = [E for E in energies if E > occupied_up_to]
unocc_populations = [d[E] for E in unocc_energies]
if smearing:
occ_populations = gaussian_filter1d(occ_populations, smearing)
unocc_populations = gaussian_filter1d(unocc_populations, smearing)
ax = plt.plot(occ_populations, occ_energies, color=color, label=label, alpha=0.9, lw=dos_lw)
ax = plt.plot(unocc_populations, unocc_energies, color=color, label='__nolegend__', alpha=0.9, lw=dos_lw)
ax = plt.fill_betweenx(occ_energies, occ_populations, color=color, alpha=0.2, lw=0)
ax = plt.xticks(xticks[1])
ax = plt.yticks(yticks[1])
if not xticks[0]:
ax = plt.gca().xaxis.set_ticklabels([])
if not yticks[0]:
ax = plt.gca().yaxis.set_ticklabels([])
ax = plt.xlabel(xlabel)
ax = plt.ylabel(ylabel)
ax = plt.xlim(xlim)
ax = plt.ylim(ylim)
if legend:
ax = plt.legend(loc='upper right')
if show:
plt.show()
return ax
def cohp(calc_dir,
pairs_to_plot=['total'],
colors_and_labels = {'total' : {'color' : 'black',
'label' : 'total'}},
tdos=False,
xlim=(-0.5, 0.5), ylim=(-10, 4),
xticks=(False, [-0.5, 0.5]), yticks=(False, [-10, 4]),
xlabel=r'$-COHP/e^-$', ylabel=r'$E-E_F\/(eV)$',
legend=True,
smearing=1,
shift=0, normalization='electron',
show=False,
zero_line='horizontal'):
"""
Args:
calc_dir (str) - path to calculation with DOSCAR
pairs_to_plot (list) - list of 'el1_el2' to plot and/or 'total'
colors_and_labels (dict) - {pair (str) : {'color' : color (str),
'label' : label (str)}}
tdos (str or bool) - if not False, 'DOSCAR' or 'DOSCAR.losbter' to retrieve tDOS from
xlim (tuple) - (xmin (float), xmax (float))
ylim (tuple) - (ymin (float), ymax (float))
xticks (tuple) - (bool to show label or not, (xtick0, xtick1, ...))
yticks (tuple) - (bool to show label or not, (ytick0, ytick1, ...))
xlabel (str) - x-axis label
ylabel (str) - y-axis label
legend (bool) - include legend or not
smearing (float or False) - std. dev. for Gaussian smearing of DOS or False for no smearing
shift (float or 'Fermi') - if 'Fermi', make Fermi level 0; else shift energies by shift
normalization ('electron', 'atom', or False) - divide populations by number of electrons, number of atoms, or not at all
show (bool) - if True, show figure; else just return ax
zero_line (str) - if 'horizontal', 'vertical', 'both', or False
Returns:
matplotlib axes object
"""
set_rc_params()
if show == True:
fig = plt.figure(figsize=(2.5,4))
ax = plt.subplot(111)
if normalization == 'electron':
normalization = VASPBasicAnalysis(calc_dir).params_from_outcar(num_params=['NELECT'], str_params=[])['NELECT']
elif normalization == 'atom':
normalization = VASPBasicAnalysis(calc_dir).nsites
occupied_up_to = shift
dos_lw = 1
if isinstance(tdos, str):
d = VASPDOSAnalysis(calc_dir, doscar=tdos).energies_to_populations()
if 'lobster' not in tdos:
shift -= VASPBasicAnalysis(calc_dir).Efermi
d = ProcessDOS(d, shift=shift, normalization=normalization).energies_to_populations
energies = sorted(list(d.keys()))
populations = [d[E] for E in energies]
occ_energies = [E for E in energies if E <= occupied_up_to]
occ_populations = [d[E] for E in occ_energies]
unocc_energies = [E for E in energies if E > occupied_up_to]
unocc_populations = [d[E] for E in unocc_energies]
color = 'black'
label = 'tDOS'
if smearing:
occ_populations = gaussian_filter1d(occ_populations, smearing)
unocc_populations = gaussian_filter1d(unocc_populations, smearing)
ax = plt.plot(occ_populations, occ_energies, color=color, label=label, alpha=0.9, lw=dos_lw)
ax = plt.plot(unocc_populations, unocc_energies, color=color, label='__nolegend__', alpha=0.9, lw=dos_lw)
ax = plt.fill_betweenx(occ_energies, occ_populations, color=color, alpha=0.2, lw=0)
for pair in pairs_to_plot:
color = colors_and_labels[pair]['color']
label = colors_and_labels[pair]['label']
d = LOBSTERAnalysis(calc_dir).energies_to_populations(element_pair=pair)
flip_sign = True
d = ProcessDOS(d, shift=shift,
flip_sign=flip_sign,
normalization=normalization).energies_to_populations
energies = sorted(list(d.keys()))
populations = [d[E] for E in energies]
occ_energies = [E for E in energies if E <= occupied_up_to]
occ_populations = [d[E] for E in occ_energies]
unocc_energies = [E for E in energies if E > occupied_up_to]
unocc_populations = [d[E] for E in unocc_energies]
if smearing:
occ_populations = gaussian_filter1d(occ_populations, smearing)
unocc_populations = gaussian_filter1d(unocc_populations, smearing)
ax = plt.plot(occ_populations, occ_energies, color=color, label=label, alpha=0.9, lw=dos_lw)
ax = plt.plot(unocc_populations, unocc_energies, color=color, label='__nolegend__', alpha=0.9, lw=dos_lw)
ax = plt.fill_betweenx(occ_energies, occ_populations, color=color, alpha=0.2, lw=0)
ax = plt.xticks(xticks[1])
ax = plt.yticks(yticks[1])
if not xticks[0]:
ax = plt.gca().xaxis.set_ticklabels([])
if not yticks[0]:
ax = plt.gca().yaxis.set_ticklabels([])
ax = plt.xlabel(xlabel)
ax = plt.ylabel(ylabel)
ax = plt.xlim(xlim)
ax = plt.ylim(ylim)
if zero_line in ['horizontal', 'both']:
ax = plt.plot(xlim, [0, 0], lw=1, ls='--', color='black')
if zero_line in ['vertical', 'both']:
ax = plt.plot([0, 0], ylim, lw=1, ls='--', color='black')
if legend:
ax = plt.legend(loc='upper right')
if show:
plt.show()
return ax