def read(self, filename=None, fh=None):
"""Read myself from a file"""
if fh is None:
if filename.endswith('.gz'):
try:
import gzip
f = gzip.open(filename)
except:
f = open(filename, 'r')
else:
f = open(filename, 'r')
self.filename = filename
else:
f = fh
self.filename = None
# get my name
s = f.readline().replace('\n', '')
self.name = s.split()[1]
self.xc = f.readline().replace('\n', '').split()[0]
values = f.readline().split()
self.eps = float(values[0])
if len(values) > 1:
self.derivative_level = int(values[1])
self.numscale = float(values[2])
self.finegrid = int(values[3])
else:
# old writing style, use old defaults
self.numscale = 0.001
self.kss = KSSingles(filehandle=f)
if self.name == 'LrTDDFT':
self.Om = OmegaMatrix(kss=self.kss, filehandle=f, txt=self.txt)
else:
self.Om = ApmB(kss=self.kss, filehandle=f, txt=self.txt)
self.Om.Kss(self.kss)
# check if already diagonalized
p = f.tell()
s = f.readline()
if s != '# Eigenvalues\n':
# go back to previous position
f.seek(p)
else:
# load the eigenvalues
n = int(f.readline().split()[0])
for i in range(n):
l = f.readline().split()
E = float(l[0])
me = [float(l[1]), float(l[2]), float(l[3])]
self.append(LrTDDFTExcitation(e=E, m=me))
if fh is None:
f.close()
# update own variables
self.istart = self.Om.fullkss.istart
self.jend = self.Om.fullkss.jend
def read(self, filename=None, fh=None):
"""Read myself from a file"""
if fh is None:
if filename.endswith('.gz'):
try:
import gzip
f = gzip.open(filename, 'rt')
except:
f = open(filename, 'r')
else:
f = open(filename, 'r')
self.filename = filename
else:
f = fh
self.filename = None
# get my name
s = f.readline().replace('\n', '')
self.name = s.split()[1]
self.xc = f.readline().replace('\n', '').split()[0]
values = f.readline().split()
self.eps = float(values[0])
if len(values) > 1:
self.derivative_level = int(values[1])
self.numscale = float(values[2])
self.finegrid = int(values[3])
else:
# old writing style, use old defaults
self.numscale = 0.001
self.kss = KSSingles(filehandle=f)
if self.name == 'LrTDDFT':
self.Om = OmegaMatrix(kss=self.kss, filehandle=f,
txt=self.txt)
else:
self.Om = ApmB(kss=self.kss, filehandle=f,
txt=self.txt)
self.Om.Kss(self.kss)
# check if already diagonalized
p = f.tell()
s = f.readline()
if s != '# Eigenvalues\n':
# go back to previous position
f.seek(p)
else:
self.diagonalized = True
# load the eigenvalues
n = int(f.readline().split()[0])
for i in range(n):
self.append(LrTDDFTExcitation(string=f.readline()))
# load the eigenvectors
f.readline()
for i in range(n):
values = f.readline().split()
weights = [float(val) for val in values]
self[i].f = np.array(weights)
self[i].kss = self.kss
if fh is None:
f.close()
def read(self, filename=None, fh=None):
"""Read myself from a file"""
timer = self.timer
timer.start('name')
if fh is None:
if filename.endswith('.gz'):
try:
import gzip
f = gzip.open(filename, 'rt')
except:
f = open(filename, 'r')
else:
f = open(filename, 'r')
self.filename = filename
else:
f = fh
self.filename = None
timer.stop('name')
timer.start('header')
# get my name
s = f.readline().strip()
self.name = s.split()[1]
self.xc = XC(f.readline().strip().split()[0])
values = f.readline().split()
self.eps = float(values[0])
if len(values) > 1:
self.derivative_level = int(values[1])
self.numscale = float(values[2])
self.finegrid = int(values[3])
else:
# old writing style, use old defaults
self.numscale = 0.001
timer.stop('header')
timer.start('init_kss')
self.kss = KSSingles(filehandle=f)
timer.stop('init_kss')
timer.start('init_obj')
if self.name == 'LrTDDFT':
self.Om = OmegaMatrix(kss=self.kss, filehandle=f, txt=self.txt)
else:
self.Om = ApmB(kss=self.kss, filehandle=f, txt=self.txt)
self.Om.fullkss = self.kss
timer.stop('init_obj')
timer.start('read diagonalized')
# check if already diagonalized
p = f.tell()
s = f.readline()
if s != '# Eigenvalues\n':
# go back to previous position
f.seek(p)
else:
self.diagonalized = True
# load the eigenvalues
n = int(f.readline().split()[0])
for i in range(n):
self.append(LrTDDFTExcitation(string=f.readline()))
# load the eigenvectors
timer.start('read eigenvectors')
f.readline()
for i in range(n):
self[i].f = np.array([float(x) for x in f.readline().split()])
self[i].kss = self.kss
timer.stop('read eigenvectors')
timer.stop('read diagonalized')
if fh is None:
f.close()
