".pyc" in formula or \
".txt" in formula or \
"tmp" in formula or \
"pickle" in formula:
continue
allcifs = os.listdir('%s/%s' % (jobdir, formula))
for cif in allcifs:
if ".cif" in cif: continue
icsdno = cif[-6:]
if not os.path.exists('%s/%s/icsd_%s' % (jobdir, formula, icsdno)):
continue
subdirs = os.listdir('%s/%s/icsd_%s' % (jobdir, formula, icsdno))
E = []
dirs = []
for subdir in subdirs:
a = vasp.Extract('%s/%s/icsd_%s/%s' %
(jobdir, formula, icsdno, subdir))
if a.success:
E.append(a.total_energy.magnitude)
dirs.append(subdir)
#print "%s/%s"%(icsdno,subdir), a.total_energy
# if len(E) > 0: print icsdno, min(E)
if len(E) > 1:
minidx = np.argsort(E)[0]
minE = min(E)
minEdir = dirs[minidx]
E.pop(minidx)
dirs.pop(minidx)
if minEdir == "non-magnetic":
if np.mean(
E
) - minE < 1: # print the ones with energy difference smaller than 1 eV
from pylada.crystal import read
jobdir = os.getcwd()
allcifs = commands.getoutput("ls %s/cifs/*.cif" % (jobdir)).split('\n')
print "number of cif files", len(allcifs)
for cif in allcifs:
icsdno = "icsd_" + cif[-10:-4]
if not os.path.exists('%s/%s' % (jobdir, icsdno)):
continue
subdirs = os.listdir('%s/%s' % (jobdir, icsdno))
E = []
dirs = []
for subdir in subdirs:
a = vasp.Extract('%s/%s/%s' % (jobdir, icsdno, subdir))
if a.success:
E.append(a.total_energy.magnitude)
dirs.append(subdir)
if "non-magnetic" not in dirs: continue
# print "%6s/%20s"%(icsdno,subdir), a.total_energy
# print icsdno, len(E)
if len(E) > 1:
minidx = np.argsort(E)[0]
minE = min(E)
minEdir = dirs[minidx]
E.pop(minidx)
dirs.pop(minidx)
if minEdir == "non-magnetic":
if np.mean(E) - minE < 1 and np.std(
E
elif band_type == "conduction":
if np.all(eig[ix, iy, iz, iband] <= E_nghs):
Nb += 1
bminidx.append([ix, iy, iz, iband])
#print eig[ix,iy,iz,iband], min(E_nghs), max(E_nghs)
else:
XX
return Nb, bminidx
#def get_effective_mass()
if __name__ == "__main__":
dirname = sys.argv[1]
calc = vasp.Extract(dirname)
eigenvalues = calc.eigenvalues.magnitude
fermi = calc.fermi_energy.magnitude
nspin = calc.ispin
nk = read_nk(dirname)
fraction = 0.5
n_neighbors = np.array([int(np.ceil(nk0 * fraction)) for nk0 in nk])
# get vbm and cbm
vbm = max([x for x in eigenvalues.flatten() if float(x) <= fermi])
cbm = min([x for x in eigenvalues.flatten() if float(x) > fermi])
gap = cbm - vbm
print "fermi", fermi, vbm, cbm, gap
print "nspin", nspin
print "nk, number of neighbors", nk, n_neighbors