from scipy import interpolate
from scipy.spatial import ConvexHull
parser = argparse.ArgumentParser(
description="Prepare the directories for the new ground states to run VASP."
)
parser.add_argument('--genstr',
default='genstr.out',
help="the genstr.out file")
parser.add_argument('-x',
action='store_true',
help="actually create the directories.")
args = parser.parse_args()
strcorrenergy = pd.read_csv('strcorrenergy.csv', index_col='str_id')
gs = atat_module.get_df('gs', 'gs.out')
interp_func = interpolate.interp1d(gs['c'], gs['E_fit'])
strcorrenergy['E_gs'] = interp_func(strcorrenergy['c'])
strcorrenergy['E_diff'] = strcorrenergy['E_per_site'] - strcorrenergy['E_gs']
str_below_hull = strcorrenergy[(strcorrenergy['E_diff'] < -0.0001)]
str_id_list = []
for c in sorted(list(set(str_below_hull['c']))):
str_below_hull_c = str_below_hull[np.isclose(str_below_hull['c'], c)]
str_id_list.append(str_below_hull_c['E_diff'].idxmin())
if len(str_id_list) >= 3:
str_lowest_hanging = strcorrenergy.loc[str_id_list]
str_gs = gs[['c']].copy()
str_gs['E_diff'] = 0
concat_df = pd.concat([str_lowest_hanging, str_gs])
help="the range of y-axis")
parser.add_argument('-s',
metavar='savefig_name',
help="if specified, save to file but not display")
args = parser.parse_args()
if args.s:
mpl.use('Agg')
import matplotlib.pyplot as plt
plt.style.use('research')
colors = plt.rcParams['axes.color_cycle']
n_colors = len(colors)
for idx, i in enumerate(args.file):
df = atat_module.get_df('phb', i)
color = colors[idx % n_colors]
plt.plot(df['x1'], df.index.values, 'x-', ms=3, color=color, label=i)
plt.plot(df['x2'], df.index.values, 'x-', ms=3, color=color)
plt.xlabel(r'Concentration (-1 ~ 1)')
plt.ylabel('Temperature (K)')
if args.xlim:
plt.xlim(*args.xlim)
if args.ylim:
plt.ylim(*args.ylim)
plt.legend(loc=0, fontsize='x-small', handlelength=1, labelspacing=0.1)
plt.tight_layout()
if args.s:
plt.savefig(args.s)
"Check if the energy discrepancy is too large (measured by multiples of std), or lies too much above the hull (measured by quantile)."
)
parser.add_argument(
'-q',
required=True,
type=float,
help=
"the quantile of the energy at a certain concentration, above which to remove."
)
parser.add_argument('-d',
required=True,
type=float,
help="the multiple of std, above which to remove.")
args = parser.parse_args()
quantile = float(args.q)
std_multi = float(args.d)
fit = atat_module.get_df('fit', 'fit.out')
above_limit_list = []
for c in sorted(list(set(fit['c']))):
fit_c = fit[np.isclose(fit['c'], c)]
fit_c_above_limit = fit_c[fit_c['E'] > fit_c['E'].quantile(quantile)]
above_limit_list.append(fit_c_above_limit)
above_limit = pd.concat(above_limit_list)
to_be_gone = above_limit[above_limit['E_diff'] > std_multi *
fit['E_diff'].std()]
print(to_be_gone)
metavar='savefig_name',
help="if specified, save to file but not display")
args = parser.parse_args()
if args.s:
mpl.use('Agg')
import matplotlib.pyplot as plt
plt.style.use('research')
#plt.rcParams['axes.prop_cycle'] = plt.rcParamsDefault['axes.prop_cycle']
colors = plt.rcParams['axes.color_cycle']
if args.gs:
strcorrenergy = pd.read_csv('strcorrenergy.csv', index_col='str_id')
predstr = atat_module.get_df('predstr', 'predstr.out')
fit = atat_module.get_df('fit', 'fit.out')
gs = atat_module.get_df('gs', 'gs.out')
if args.gsline_vasp:
gsline_E = '-'
gsline_E_fit = ''
interp_func = interpolate.interp1d(gs['c'], gs['E'])
else:
gsline_E = ''
gsline_E_fit = '-'
interp_func = interpolate.interp1d(gs['c'], gs['E_fit'])
if args.gslim:
x1 = args.gslim[0]
x2 = args.gslim[1]
'--return_single',
action='store_true',
help="return single peak, rather than however many it detects")
args = parser.parse_args()
if args.s and not args.p:
mpl.use('Agg')
import matplotlib.pyplot as plt
plt.style.use('research')
plot = True if args.p or args.s else None
if args.filetype == 'emc2':
df = pd.concat([
atat_module.get_df('emc2', filename, special=args.special)
for filename in args.filename
])
cm = True if args.special == 'cm' else False
df = df.sort_index()
idxsls = pd.IndexSlice
if not cm:
if args.special == 'innerT':
df = df.loc[idxsls[args.mu[0]:args.mu[1], args.T[0]:args.T[1]], :]
else:
df = df.loc[idxsls[args.T[0]:args.T[1], args.mu[0]:args.mu[1]], :]
else:
df = df.loc[idxsls[:, args.T[0]:args.T[1]], :]
if args.trans:
df_trans = atat_module.get_emc2_transition(
help="if specified, save to file but not display")
args = parser.parse_args()
if args.s:
mpl.use('Agg')
import matplotlib.pyplot as plt
plt.style.use('research')
if args.file:
f = args.file
else:
f = StringIO(
check_output("getclus -e | grep -v '^0' | grep -v '^1'",
shell=True).decode())
clusinfo = atat_module.get_df('clusinfo', f)
clusinfo['eci'] *= 1e3
pair = clusinfo[clusinfo['n_pts'] == 2]
trip = clusinfo[clusinfo['n_pts'] == 3]
quad = clusinfo[clusinfo['n_pts'] == 4]
is_trip = None
is_quad = None
if trip.values.any():
is_trip = True
orig_for_trip = np.ceil(pair['d'].iloc[-1]) + 1
if quad.values.any():
is_quad = True
orig_for_quad = orig_for_trip + np.ceil(trip['d'].iloc[-1]) + 1