def read(self):
"""
"""
tree = os.walk(self.ps_name+'_df_run_full')
for dirs in tree:
file_name = os.path.join(dirs[0], 'deltadata.txt')
if os.path.isfile(file_name):
f = open(file_name, 'r')
lines = f.readlines()
try:
df = float(lines[0].split()[3])
except ValueError:
print('warning', lines[0].split()[3])
df = abs(complex(lines[0].split()[3]))
#print lines[0], df
#print dirs[0]
location = os.path.split(dirs[0])
#print location
base = os.path.join(*location[0:len(location)-1])
out_file = os.path.join(base, 't3', 'outdata', 'out_GSR.nc')
#print out_file
out = NetcdfReader(out_file)
if not isinstance(out.read_value('ecut'), collections.Iterable):
ecut = out.read_value('ecut')
etotal = out.read_value('etotal')
elif not isinstance(out.read_value('ecut')[0], collections.Iterable):
ecut = out.read_value('ecut')[0]
etotal = out.read_value('etotal')[0]
else:
raise Exception
self.etotal_data.update({ecut: etotal})
self.df_data.update({ecut: df})
self.pseudo.dojo_report['delta_factor'] = self.df_data
self.pseudo.dojo_report['total_energy'] = self.etotal_data
def read_convergence_data(self, data_dir):
gwrun = os.path.join(data_dir, 'out_SIGRES.nc')
scfrunout = os.path.join(data_dir, 'out_OUT.nc')
scfruneig = os.path.join(data_dir, 'out_EIG.nc')
results = {}
if os.path.isfile(gwrun):
# return the gap at gamma
data = NetcdfReader(gwrun)
data.print_tree()
if not isinstance(data.read_value('ecuteps'), collections.Iterable):
ecuteps = data.read_value('ecuteps')
elif not isinstance(data.read_value('ecuteps')[0], collections.Iterable):
ecuteps = data.read_value('ecuteps')[0]
else:
raise Exception
if not isinstance(data.read_value('sigma_nband'), collections.Iterable):
sigma_nband = data.read_value('sigma_nband')
elif not isinstance(data.read_value('sigma_nband')[0], collections.Iterable):
sigma_nband = data.read_value('sigma_nband')[0]
else:
raise Exception
gwgap = data.read_value('egwgap')[0][0]
#gwgap = min(data.read_value('egwgap')[0])
if not isinstance(gwgap, float):
raise Exception
results = {'ecuteps': float(Ha_to_eV * ecuteps),
'nbands': int(sigma_nband),
'gwgap': float(gwgap)}
data.close()
return results
if os.path.isfile(scfruneig):
# return the lowest and hightest eigenvalue at gamma
data = NetcdfReader(scfruneig)
out = NetcdfReader(scfrunout)
if data.read_value('Eigenvalues')[0][0][-1] < 2.0: # bad way to select only the scf results ..
if not isinstance(out.read_value('ecut'), collections.Iterable):
ecut = out.read_value('ecut')
elif not isinstance(out.read_value('ecut')[0], collections.Iterable):
ecut = out.read_value('ecut')[0]
else:
raise Exception
results = {'ecut': Ha_to_eV * ecut,
'min': data.read_value('Eigenvalues')[0][0][0]*Ha_to_eV,
'max': data.read_value('Eigenvalues')[0][0][-1]*Ha_to_eV,
'full_width': (data.read_value('Eigenvalues')[0][0][-1] -
data.read_value('Eigenvalues')[0][0][0])*Ha_to_eV}
data.close()
return results
def read_convergence_data(self, data_dir):
gwrun = os.path.join(data_dir, 'out_SIGRES.nc')
scfrunout = os.path.join(data_dir, 'out_OUT.nc')
scfruneig = os.path.join(data_dir, 'out_EIG.nc')
results = {}
if os.path.isfile(gwrun):
# return the gap at gamma
data = NetcdfReader(gwrun)
data.print_tree()
if not isinstance(data.read_value('ecuteps'),
collections.Iterable):
ecuteps = data.read_value('ecuteps')
elif not isinstance(
data.read_value('ecuteps')[0], collections.Iterable):
ecuteps = data.read_value('ecuteps')[0]
else:
raise Exception
if not isinstance(data.read_value('sigma_nband'),
collections.Iterable):
sigma_nband = data.read_value('sigma_nband')
elif not isinstance(
data.read_value('sigma_nband')[0], collections.Iterable):
sigma_nband = data.read_value('sigma_nband')[0]
else:
raise Exception
gwgap = data.read_value('egwgap')[0][0]
#gwgap = min(data.read_value('egwgap')[0])
if not isinstance(gwgap, float):
raise Exception
results = {
'ecuteps': float(Ha_to_eV * ecuteps),
'nbands': int(sigma_nband),
'gwgap': float(gwgap)
}
data.close()
return results
if os.path.isfile(scfruneig):
# return the lowest and hightest eigenvalue at gamma
data = NetcdfReader(scfruneig)
out = NetcdfReader(scfrunout)
if data.read_value('Eigenvalues')[0][0][
-1] < 2.0: # bad way to select only the scf results ..
if not isinstance(out.read_value('ecut'),
collections.Iterable):
ecut = out.read_value('ecut')
elif not isinstance(
out.read_value('ecut')[0], collections.Iterable):
ecut = out.read_value('ecut')[0]
else:
raise Exception
results = {
'ecut':
Ha_to_eV * ecut,
'min':
data.read_value('Eigenvalues')[0][0][0] * Ha_to_eV,
'max':
data.read_value('Eigenvalues')[0][0][-1] * Ha_to_eV,
'full_width':
(data.read_value('Eigenvalues')[0][0][-1] -
data.read_value('Eigenvalues')[0][0][0]) * Ha_to_eV
}
data.close()
return results
