def molpro_mcscf(filename, select_run=0, threshold=0.0, **kwargs):
'''Reads MOLPRO MCSCF output.
**Parameters:**
filename : str
Specifies the filename for the input file.
select_run : (list of) int
Specifies the MCSCF calculation (1PROGRAM * MULTI) to be read.
For the selected MCSCF calculation, all electronic states will be read.
threshold : float, optional
Specifies a read threshold for the CI coefficients.
**Returns:**
ci : list of CIinfo class instances
See :ref:`Central Variables` for details.
..ATTENTION: Changed return value to list of CI classes
'''
display('\nReading data of MCSCF calculation from MOLPRO...')
method = 'mcscf'
available = {'mcscf': 'MULTI'}
single_run_selected = isinstance(select_run, int)
count = 0
numci = []
# Go through the file line by line
with open(filename) as fileobject:
for line in fileobject:
if '1PROGRAM * %s' % available[method] in line:
count += 1
numci.append(0)
if '!%s state' % method in line.lower() and 'energy' in line.lower(
):
numci[-1] += 1
if count == 0:
display(
'The input file %s does not contain any DETCI calculations!\n' %
(filename) +
'It does not contain the keyword:\n\t1PROGRAM * MULTI')
raise IOError('Not a valid input file')
else:
string = ', '.join(map(str, numci)).rsplit(', ', 1)
string = ' and '.join(string) if len(
string[0].split(',')) < 2 else ', and '.join(string)
display('The input file %s contains' % (filename))
display('%d MCSCF calculation(s) with %s root(s)%s.' %
(count, string, ', respectively' if len(numci) > 1 else ''))
if select_run is None:
select_run = numpy.arange(count)
if isinstance(select_run, int) and 0 <= select_run < count:
select_run = [select_run]
ci = [[]]
elif isinstance(select_run, (list, numpy.ndarray)):
ci = []
for i in select_run:
ci.append([])
if not isinstance(i, int):
raise IOError(
str(i) + ' is not a valid selection for select_run')
else:
raise IOError(
str(select_run) + ' is a not valid selection for select_run')
select_run = numpy.array(select_run)
display('\tYour selection (counting from zero): %s' %
', '.join(map(str, select_run)))
general_information = {'fileinfo': filename, 'read_threshold': threshold}
ci_skip = 0
count = 0
count_runs = 0
min_c = 1
start_reading = False
sec_flag = False
info_flag = False
info_sep = False
info_split = False
occ_types = ['core', 'closed', 'active', 'external']
with open(filename) as fileobject:
for line in fileobject:
thisline = line.split() # The current line split into segments
if '1PROGRAM *' in line:
start_reading = False
#--- Number of IRREPs
if 'Point group' in line or '_PGROUP' in line:
nIRREP = point_groups()[thisline[-1].lower()]
rhf_occ = numpy.zeros(nIRREP, dtype=numpy.intc)
#--- RHF occupation
elif 'Final occupancy:' in line:
c_occ = line.split()[2:]
for ii in range(len(c_occ)):
rhf_occ[ii] = int(c_occ[ii])
#--- A MCSCF Calculation starts ---
elif '1PROGRAM * %s' % available[method] in line:
occ_info = {}
for i in occ_types:
occ_info[i] = numpy.zeros(nIRREP, dtype=numpy.intc)
state_info = []
i = numpy.argwhere(select_run == count_runs)
if len(i) > 0:
index_run = int(i)
start_reading = True
count = 0
old = 0
count_runs += 1
elif start_reading:
#--- Active space ---
if 'Number of ' in line and 'orbitals:' in line:
line = line.replace('(',
'').replace(')',
'').replace('-shell', '')
c_occ = numpy.array(line.split()[-nIRREP:],
dtype=numpy.intc)
occ_info[line.split()[2]] += c_occ
elif 'State symmetry' in line:
fileobject.next()
thisline = fileobject.next()
if 'State symmetry' in thisline:
fileobject.next()
thisline = fileobject.next()
thisline = thisline.replace('=', ' ').split()
data = {
'nel': thisline[3],
'spin': thisline[6],
'sym': thisline[9]
}
thisline = fileobject.next().split()
state_info.extend([data for i in range(int(thisline[-1]))])
elif '!%s state' % method in line.lower(
) and 'energy' in line.lower():
ci[index_run].append(CIinfo(method=method))
info = state_info[count]
thisline = line.lower().replace('state', 'state ').split()
ci[index_run][count].info = copy(general_information)
ci[index_run][count].info['fileinfo'] += '@%d' % index_run
ci[index_run][count].info['state'] = thisline[2]
ci[index_run][count].info['energy'] = float(thisline[4])
ci[index_run][count].info['spin'] = info['spin']
ci[index_run][count].info['nel'] = info['nel']
ci[index_run][count].info['occ_info'] = occ_info
count += 1
elif 'CI vector' in line:
sec_flag = 'mcscf'
info_split = ' '
ci_skip = 3
info = thisline[-1]
count = old
first = True
if not ci_skip:
if line == '\n' or '/EOF' in line:
sec_flag = False
elif sec_flag != False:
split_line = filter(None, line.split(info_split))
if len(split_line) > 1:
occupation = numpy.zeros(
(numpy.sum(occ_info['active']), 2),
dtype=numpy.intc)
for i, j in enumerate(split_line[0].replace(
' ', '')):
if j == '2':
occupation[i, :] = 1
elif j == 'a':
occupation[i, 0] = 1
elif j == 'b':
occupation[i, 1] = 1
c0 = 0
coeffs = split_line[-1].split()
for i, j in enumerate(coeffs):
if first:
old += 1
min_c = min(min_c, abs(float(j)))
if abs(float(j)) > threshold:
ci[index_run][count + c0 + i].coeffs.append(
float(j))
ci[index_run][count + c0 +
i].occ.append(occupation)
c0 += len(coeffs)
first = False
else:
ci_skip -= 1
#--- Calculating norm of CI states
display('\nIn total, %d states have been read.' % sum([len(i)
for i in ci]))
display('Norm of the states:')
for i in range(len(ci)):
for j in range(len(ci[i])):
ci[i][j].coeffs = numpy.array(ci[i][j].coeffs)
ci[i][j].occ = numpy.array(ci[i][j].occ, dtype=numpy.intc)
norm = sum(ci[i][j].coeffs**2)
# Write Norm to log-file
display('\tState %s (%s):\tNorm = %0.8f (%d Coefficients)' %
(ci[i][j].info['state'], ci[i][j].info['spin'], norm,
len(ci[i][j].coeffs)))
display('')
if min_c > threshold:
display(
'\nInfo:' +
'\n\tSmallest coefficient (|c|=%f) larger than the read threshold (%f).'
% (min_c, threshold) +
'\n\tUse `gthresh, printci=0.0` in the MOLPRO input file to print '
+ 'all CI coefficients.\n')
#if single_run_selected and len(ci) == 1:
#ci = ci[0]
ci_new = []
for i in ci:
ci_new.extend(i)
return ci_new
def gaussian_tddft(fname,select_state=None,threshold=0.0,**kwargs):
'''Reads Gaussian16 TDDFT output.
**Parameters:**
fname: str, file descriptor
Specifies the filename for the input file.
fname can also be used with a file descriptor instad of a filename.
select_state : None or list of int, optional
If not None, specifies the states to be read (0 corresponds to the ground
state), else read all electronic states.
threshold : float, optional
Specifies a read threshold for the CI coefficients.
**Returns:**
ci : list of CIinfo class instances
See :ref:`Central Variables` for details.
'''
display('\nReading data of TDDFT calculation from Gaussian...')
# Initialize variables
ci = []
ci_flag = False
prttol = False
init_state = False
rhfspin = 0
spin = 'Unknown'
nel = 0
deex = []
if isinstance(select_state,int): select_state = [select_state]
if isinstance(fname, str):
filename = fname
fname = descriptor_from_file(filename, index=0, ci_descriptor=True)
else:
filename = fname.name
for line in fname:
thisline = line.split() # The current line split into segments
#--- Check the file for keywords ---
# Initialize Hartree-Fock ground state
if ' SCF Done: E(' in line:
ci.append(CIinfo(method='tddft'))
ci[-1].info = []
ci[-1].coeffs = []
ci[-1].occ = []
ci[-1].occ.append([0,0])
ci[-1].coeffs.append(1.0)
ci[-1].info = {'state': '0',
'energy': float(thisline[4]),
'energy_nm': 0.0,
'fileinfo': filename,
'read_threshold': threshold,
'spin': spin,
'f_0i': 0.0}
# Initialize new excited state
elif ' Excited State' in line and 'eV' in line and 'nm' in line:
if select_state is None or int(thisline[2].replace(':',' ')) in select_state:
init_state = True
tddft_skip = 1
ci.append(CIinfo(method='tddft'))
ci[-1].info = []
ci[-1].coeffs = []
ci[-1].occ = []
ci[-1].info = {'state': thisline[2][:-1],
'energy': float(thisline[-6])*ev_to_ha + ci[0].info['energy'],
'energy_nm': float(thisline[-4]),
'fileinfo': filename,
'read_threshold': threshold,
'spin': thisline[3].split('-')[0],
'f_0i': float(thisline[8].replace('=',' ').split()[-1])}
deex.append([])
if init_state == True:
if not tddft_skip:
if thisline == [] or '->' not in line and '<-' not in line:
init_state = False
else:
if '->' in line:
thisline = line.replace('->','-> ').split()
if abs(float(thisline[-1])) > threshold:
tmp_occ = [thisline[0],thisline[2]]
ci[-1].occ.append(tmp_occ)
ci[-1].coeffs.append(float(thisline[-1])*numpy.sqrt(2))
elif '<-' in line:
deex[-1].append(float(thisline[-1])*numpy.sqrt(2))
elif tddft_skip:
tddft_skip -= 1
fname.close()
deex = numpy.array(deex)
#--- Calculating norm of CI states
display('\nIn total, %d states have been read.' % len(ci))
display('Norm of the states:')
for i in range(len(ci)):
j = numpy.array(ci[i].coeffs,dtype=float)
norm = numpy.sum(j**2)
ci[i].coeffs = j
# Write Norm to log-file
display('\tState %s:\tNorm = %0.8f (%d Coefficients)' % (ci[i].info['state'],norm, len(ci[i].coeffs)))
# Transform to numpy arrays
ci[i].occ = numpy.array([s for s in ci[i].occ],dtype=numpy.intc)-1
return ci
def gamess_cis(filename, select_state=None, threshold=0.0, **kwargs):
'''Reads GAMESS-US CIS output.
**Parameters:**
filename : str
Specifies the filename for the input file.
select_state : None or list of int, optional
If not None, specifies the states to be read (0 corresponds to the ground
state), else read all electronic states.
threshold : float, optional
Specifies a read threshold for the CI coefficients.
**Returns:**
ci : list of CIinfo class instances
See :ref:`Central Variables` for details.
'''
display('\nReading data of CIS calculation from GAMESS-US...')
# Initialize variables
ci = []
ci_flag = False
prttol = False
init_state = False
rhfspin = 0
min_c = -1
if isinstance(select_state, int): select_state = [select_state]
with open(filename) as fileobject:
for line in fileobject:
thisline = line.split() # The current line split into segments
#--- Check the file for keywords ---
# Initialize Hartree-Fock ground state
if 'NUMBER OF ELECTRONS' in line and "=" in line:
nel = int(thisline[-1])
elif 'SPIN MULTIPLICITY' in line:
rhfspin = int(thisline[-1])
elif ' FINAL RHF ENERGY IS' in line and (select_state is None
or 0 in select_state):
ci.append(CIinfo(method='cis'))
ci[-1].info = []
ci[-1].coeffs = []
ci[-1].occ = []
ci[-1].occ.append([0, 0])
ci[-1].coeffs.append(1.0)
ci[-1].info = {
'state': '0',
'energy': float(thisline[4]),
'fileinfo': filename,
'read_threshold': threshold,
'spin': multiplicity()[rhfspin],
'nel': nel
}
# Printing parameter
elif ' PRINTING CIS COEFFICIENTS LARGER THAN' in line:
min_c = float(thisline[-1])
# Initialize new excited state
elif ' EXCITED STATE ' in line and 'ENERGY=' and 'SPACE SYM' in line:
if select_state is None or int(thisline[2]) in select_state:
init_state = True
cis_skip = 6
ci.append(CIinfo(method='cis'))
ci[-1].info = []
ci[-1].coeffs = []
ci[-1].occ = []
ci[-1].info = {
'state': thisline[2],
'energy': float(thisline[4]),
'fileinfo': filename,
'read_threshold': threshold,
'spin':
multiplicity()[int(2 * float(thisline[7]) + 1)],
'nel': nel
}
if init_state == True:
if not cis_skip:
if '----------------------------------------------' in line:
init_state = False
else:
if abs(float(thisline[2])) > threshold:
ci[-1].occ.append(thisline[:2])
ci[-1].coeffs.append(thisline[2])
elif cis_skip:
cis_skip -= 1
#--- Calculating norm of CI states
display('\nIn total, %d states have been read.' % len(ci))
display('Norm of the states:')
for i in range(len(ci)):
j = numpy.array(ci[i].coeffs, dtype=float)
norm = numpy.sum(j**2)
ci[i].coeffs = j
# Write Norm to log-file
display('\tState %s:\tNorm = %0.8f (%d Coefficients)' %
(ci[i].info['state'], norm, len(ci[i].coeffs)))
# Transform to numpy arrays
ci[i].occ = numpy.array([s for s in ci[i].occ], dtype=numpy.intc) - 1
display('')
if min_c > threshold:
display(
'\nInfo:' +
'\n\tSmallest coefficient (|c|=%f) is larger than the read threshold (%f).'
% (min_c, threshold) +
'\n\tUse `PRTTOL=0.0` in the `$CIS` input card to print ' +
'all CI coefficients.\n')
return ci
def gamess_tddft(fname, select_state=None, threshold=0.0, **kwargs):
'''Reads GAMESS-US TDDFT output.
**Parameters:**
fname: str, file descriptor
Specifies the filename for the input file.
fname can also be used with a file descriptor instad of a filename.
select_state : None or list of int, optional
If not None, specifies the states to be read (0 corresponds to the ground
state), else read all electronic states.
threshold : float, optional
Specifies a read threshold for the CI coefficients.
**Returns:**
ci : list of CIinfo class instances
See :ref:`Central Variables` for details.
'''
display('\nReading data of TDDFT calculation from GAMESS-US...')
# Initialize variables
ci = []
ci_flag = False
prttol = False
init_state = False
rhfspin = 0
spin = 'Unknown'
if isinstance(select_state, int): select_state = [select_state]
if isinstance(fname, str):
filename = fname
fname = descriptor_from_file(filename, index=0, ci_descriptor=True)
else:
filename = fname.name
for line in fname:
thisline = line.split() # The current line split into segments
#--- Check the file for keywords ---
# Initialize Hartree-Fock ground state
if 'NUMBER OF ELECTRONS' in line:
nel = int(thisline[-1])
elif 'SPIN MULTIPLICITY' in line:
rhfspin = int(thisline[-1])
elif 'SINGLET EXCITATIONS' in line:
spin = 'Singlet'
#elif ' FINAL RHF ENERGY IS' in line and (select_state is None or 0 in select_state):
elif ' FINAL' in line and ' ENERGY IS' in line and (
select_state is None or 0 in select_state):
ci.append(CIinfo(method='tddft'))
ci[-1].info = []
ci[-1].coeffs = []
ci[-1].occ = []
ci[-1].occ.append([0, 0])
ci[-1].coeffs.append(1.0)
ci[-1].info = {
'state': '0',
'energy': float(thisline[4]),
'fileinfo': filename,
'read_threshold': threshold,
'spin': spin,
'nel': nel
}
# Initialize new excited state
elif 'STATE #' in line and 'ENERGY =' in line:
if select_state is None or int(thisline[2]) in select_state:
init_state = True
tddft_skip = 8
ci.append(CIinfo(method='cis'))
ci[-1].info = []
ci[-1].coeffs = []
ci[-1].occ = []
ci[-1].info = {
'state': thisline[2],
'energy':
float(thisline[-2]) * ev_to_ha + ci[0].info['energy'],
'fileinfo': filename,
'read_threshold': threshold,
'spin': 'Unknown',
'nel': nel
}
if init_state == True and line != '\n' and 'WARNING:' not in line:
if not tddft_skip:
if 'NON-ABELIAN' in line or 'SUMMARY' in line or 'SYMMETRY' in line or 'STATE #' in line:
init_state = False
else:
if abs(float(thisline[2])) > threshold:
ci[-1].occ.append(thisline[:2])
ci[-1].coeffs.append(thisline[2])
elif tddft_skip:
tddft_skip -= 1
fname.close()
#--- Calculating norm of CI states
display('\nIn total, %d states have been read.' % len(ci))
display('Norm of the states:')
for i in range(len(ci)):
j = numpy.array(ci[i].coeffs, dtype=float)
norm = numpy.sum(j**2)
ci[i].coeffs = j
# Write Norm to log-file
display('\tState %s:\tNorm = %0.8f (%d Coefficients)' %
(ci[i].info['state'], norm, len(ci[i].coeffs)))
# Transform to numpy arrays
ci[i].occ = numpy.array([s for s in ci[i].occ], dtype=numpy.intc) - 1
return ci
def psi4_detci(filename,select_run=None,threshold=0.0,**kwargs):
'''Reads PSI4 DETCI output.
**Parameters:**
filename : str
Specifies the filename for the input file.
select_run : (list of) int
Specifies the DETCI calculation (D E T C I) to be read.
For the selected DETCI calculation, all electronic states will be read.
threshold : float, optional
Specifies a read threshold for the CI coefficients.
**Returns:**
ci : list of CIinfo class instances
See :ref:`Central Variables` for details.
..#ATTENTION: Changed return value to list of CI classes
'''
display('\nReading data of DETCI calculation from PSI4...')
count = 0
numci = []
# Go through the file line by line
with open(filename) as fileobject:
for line in fileobject:
if 'D E T C I' in line:
count += 1
numci.append(0)
if '* ROOT' in line:
numci[-1] += 1
if count == 0:
display('The input file %s does not contain any DETCI calculations!\n' % (filename) +
'It does not contain the keyword:\n\tD E T C I')
raise IOError('Not a valid input file')
else:
string = ', '.join(map(str,numci)).rsplit(', ',1)
string = ' and '.join(string) if len(string[0].split(',')) < 2 else ', and '.join(string)
display('The input file %s contains' % (filename))
display('%d DETCI calculation(s) with %s root(s)%s.'%(count, string,
', respectively' if len(numci)>1 else ''))
if select_run is None:
select_run = numpy.arange(count)
if isinstance(select_run,int) and 0 <= select_run < count:
select_run = [select_run]
ci = [[]]
elif isinstance(select_run,(list,numpy.ndarray)):
ci = []
for i in select_run:
ci.append([])
if not isinstance(i,int):
raise IOError(str(i) + ' is not a valid selection for select_run')
else:
raise IOError(str(select_run) + ' is a not valid selection for select_run')
select_run = numpy.array(select_run)
display('\n\tYour selection (counting from zero): %s' %
', '.join(map(str,select_run)))
general_information = {'fileinfo': filename,
'read_threshold': threshold}
ci_skip = 0
count = 0
count_runs = 0
min_c = 1
orbs_in_ci = 0
start_reading = False
occ_types = ['core','closed','active','external']
synonyms = {'frozen docc': 'core',
'restricted docc': 'closed',
'ras ': 'active',
'active': 'active',
'restricted uocc': 'external',
'frozen uocc': 'external'}
irreps = []
fist_active_mo = []
index_active_mo = []
occ_symbols = {'A': numpy.array([1,0]),
'B': numpy.array([0,1]),
'X': numpy.array([1,1])}
with open(filename) as fileobject:
for line in fileobject:
thisline = line.split() # The current line split into segments
if 'Running in ' in line and 'symmetry.' in line:
nIRREP = point_groups()[thisline[2].lower()]
rhf_occ = numpy.zeros(nIRREP,dtype=numpy.intc)
#--- RHF occupation
elif 'Final Occupation by Irrep:' in line:
irreps = fileobject.next().split()
line = fileobject.next()
c_occ = line.replace(',','').split()[2:-1]
for ii in range(len(c_occ)):
rhf_occ[ii] = int(c_occ[ii])
#--- A DETCI Calculation starts ---
elif 'D E T C I' in line:
occ_info = {}
for i in occ_types:
occ_info[i] = numpy.zeros(nIRREP,dtype=numpy.intc)
info = {}
num_roots = 0
method = 'detci'
i = numpy.argwhere(select_run == count_runs)
if len(i) > 0:
index_run = int(i)
start_reading = True
count_runs += 1
elif start_reading:
if 'NUM ROOTS =' in line:
num_roots = int(thisline[3])
elif 'FCI =' in line and line.endswith('yes'):
method = 'fci'
elif 'REF SYM =' in line:
info['sym'] = thisline[3]
if info['sym'] != 'auto':
info['sym'] = irreps[int(info['sym'])]
elif 'S =' in line:
info['spin'] = multiplicity()[int(2*float(thisline[2])+1)]
elif 'NUM ALP =' in line:
info['nel'] = int(thisline[3]) + int(thisline[-1])
elif 'ORBS IN CI =' in line:
orbs_in_ci = int(thisline[-1])
elif '=' in line and any([i in line.lower() for i in synonyms.keys()]):
for i in synonyms.keys():
if i in line.lower():
occ_info[synonyms[i]] += numpy.array(thisline[-nIRREP:],
dtype=numpy.intc)
elif '* ROOT' in line and 'CI total energy' in line:
ci[index_run].append(CIinfo(method=method))
ci[index_run][-1].info = copy(general_information)
ci[index_run][-1].info['fileinfo'] += '@%d' % index_run
ci[index_run][-1].info['irreps'] = irreps
ci[index_run][-1].info['state'] = '%s.%s'%(thisline[2],info['sym'])
ci[index_run][-1].info['energy'] = float(thisline[7])
ci[index_run][-1].info['spin'] = info['spin']
ci[index_run][-1].info['nel'] = info['nel']
ci[index_run][-1].info['occ_info'] = occ_info
closed = []
active = {}
c = 0
for i in range(nIRREP):
a = occ_info['core'][i] + occ_info['closed'][i]
for b in range(occ_info['active'][i]):
active['%d%s' % (a+b+1,irreps[i])] = c
c += 1
elif 'most important determinants' in line:
num_det = int(thisline[1])
ci[index_run][-1].coeffs = [] # numpy.zeros(num_det)
ci[index_run][-1].occ = [] # numpy.zeros((numpy.sum(occ_info['active']),2),
#dtype=numpy.intc)
fileobject.next()
def rm(s,w='*(,)'):
for i in w:
s = s.replace(i,' ')
return s
for i in range(num_det):
thisline = rm(fileobject.next()).split()
if thisline == []:
break
min_c = min(min_c,abs(float(thisline[1])))
if abs(float(thisline[1])) > threshold:
ci[index_run][-1].coeffs.append(float(thisline[1]))
ci[index_run][-1].occ.append(numpy.zeros((numpy.sum(occ_info['active']),2),
dtype=numpy.intc))
for j in thisline[4:]:
ci[index_run][-1].occ[-1][active[j[:-1]]] = occ_symbols[j[-1]]
ci[index_run][-1].coeffs = numpy.array(ci[index_run][-1].coeffs)
ci[index_run][-1].occ = numpy.array(ci[index_run][-1].occ,dtype=numpy.intc)
elif 'A good bug is a dead bug' in line:
start_reading = False
#--- Calculating norm of CI states
display('\nIn total, %d states have been read.' % sum([len(i) for i in ci]))
display('Norm of the states:')
for i in range(len(ci)):
for j in range(len(ci[i])):
norm = sum(ci[i][j].coeffs**2)
display('\tState %s (%s):\tNorm = %0.8f (%d Coefficients)' %
(ci[i][j].info['state'],ci[i][j].info['spin'],
norm,len(ci[i][j].coeffs)))
display('')
if threshold and min_c > threshold:
display('\nInfo:'+
'\n\tSmallest coefficient (|c|=%f) larger than the read threshold (%f).'
%(min_c,threshold) +
'\n\tUse `set num_dets_print -1` in the PSI4 input file to print ' +
'all CI coefficients.\n')
ci_new = []
for i in ci:
ci_new.extend(i)
return ci_new
def fermions_tdscf(fname,
td_typ='rpa',
st_typ='singlet',
select_state=None,
threshold=0.0,
**kwargs):
'''Reads FermiONs++ TDDFT output.
**Parameters:**
fname: str, file descriptor
Specifies the filename for the input file.
fname can also be used with a file descriptor instad of a filename.
select_state : None or list of int, optional
If not None, specifies the states to be read (0 corresponds to the ground
state), else read all electronic states.
threshold : float, optional
Specifies a read threshold for the CI coefficients.
**Returns:**
ci : list of CIinfo class instances
See :ref:`Central Variables` for details.
'''
display('\nReading data of TDDFT calculation from FermiONs++...')
#
# future user-input...
#
sst = 'Singlets'
fspin = 1.0
if st_typ == 'triplet':
fspin = 3.0
sst = 'Triplets'
ss0 = ''
if td_typ == 'tda':
ss0 = 'Tamm-Dancoff Excitation Energies [%s]' % sst
elif td_typ == 'rpa':
ss0 = 'RPA Excitation Energies [%s]' % sst
elif td_typ == 'stda':
ss0 = 'Simplified Tamm-Dancoff Excitation Energies [%s]' % sst
elif td_typ == 'srpa':
ss0 = 'Simplified RPA Excitation Energies [%s]' % sst
nocca = -1
noccb = -1
# Initialize variables
ci = []
ci_flag = False
prttol = False
init_state = False
rhfspin = 0
spin = 'Unknown'
nel = 0
deex = []
if isinstance(select_state, int): select_state = [select_state]
if isinstance(fname, str):
filename = fname
fname = descriptor_from_file(filename, index=0, ci_descriptor=True)
else:
filename = fname.name
st = 0
for line in fname:
thisline = line.split() # The current line split into segments
#--- Check the file for keywords ---
# Initialize Hartree-Fock ground state
if 'No. of alpha-electrons:' in line:
nocca = int(line.split()[3])
elif 'No. of beta-electrons:' in line:
noccb = int(line.split()[3])
elif 'Final SCF energy:' in line:
ci.append(CIinfo(method='tddft'))
ci[-1].info = []
ci[-1].coeffs = []
ci[-1].occ = []
ci[-1].occ.append([0, 0])
ci[-1].coeffs.append(1.0)
ci[-1].info = {
'state': '0',
'energy': float(thisline[3]),
'energy_nm': 0.0,
'fileinfo': filename,
'read_threshold': threshold,
'spin': spin,
'f_0i': 0.0
}
# Initialize new excited state
if st == 0: # look for RPA
if ss0 in line:
st = 1
elif st == 1:
if 'Converged' in line and '==' in line:
st = 2
elif st == 2:
if 'Excited State' in line: # and 'eV' in line and 'nm' in line:
if select_state is None or int(thisline[2]) in select_state:
init_state = True
tddft_skip = 1
ci.append(CIinfo(method='tddft'))
ci[-1].info = []
ci[-1].coeffs = []
ci[-1].occ = []
ci[-1].info = {
'state': thisline[2],
'energy':
0.0, #float(thisline[-6])*ev_to_ha + ci[0].info['energy'],
'energy_nm': 0.0, #float(thisline[-4]),
'fileinfo': filename,
'read_threshold': threshold,
'spin': fspin, #thisline[3].split('-')[0],
'f_0i': 0.0
} #float(thisline[8].replace('=',' ').split()[-1])}
deex.append([])
if init_state == True:
if not tddft_skip:
if 'Excitation Energy:' in line:
ci[-1].info['energy'] = float(
thisline[2]) * ev_to_ha + ci[0].info['energy']
elif ' nm' in line:
ci[-1].info['energy_nm'] = float(thisline[0])
# 'spin': thisline[3].split('-')[0],
elif len(
line.strip()
) == 0: #thisline == [] or '->' not in line and '<-' not in line:
init_state = False
else:
if '<-->' in line:
if abs(float(thisline[-2])) > threshold:
ex = thisline[-2]
dex = thisline[-2]
thisline = line.split(
'<-->') #replace('->','-> ').split()
s0 = thisline[0].split('(')[1].split(
')')[0].strip()
nocc = nocca
if 'beta' in line:
nocc = noccb
s1 = int(thisline[1].split('(')[1].split(')')
[0].strip()) + nocc
tmp_occ = [s0, '%i' % s1]
ci[-1].occ.append(tmp_occ)
ci[-1].coeffs.append(float(ex) * numpy.sqrt(2))
deex[-1].append(float(dex) * numpy.sqrt(2))
elif tddft_skip:
tddft_skip -= 1
if '-------------------' in line or 'Excite Total:' in line:
st = 3
fname.close()
deex = numpy.array(deex)
#--- Calculating norm of CI states
display('\nIn total, %d states have been read.' % len(ci))
display('Norm of the states:')
for i in range(len(ci)):
j = numpy.array(ci[i].coeffs, dtype=float)
norm = numpy.sum(j**2)
ci[i].coeffs = j
# Write Norm to log-file
display('\tState %s:\tNorm = %0.8f (%d Coefficients)' %
(ci[i].info['state'], norm, len(ci[i].coeffs)))
# Transform to numpy arrays
ci[i].occ = numpy.array([s for s in ci[i].occ], dtype=numpy.intc) - 1
return ci
def tmol_tddft(fname,
nmoocc=None,
nforbs=0,
select_state=None,
threshold=0.0,
bortho=False,
**kwargs):
'''Reads Turbomole TD-DFT output (``sing_a`` file).
Hint: Only implemented for singlet TD-DFT computations in C_1 symmetry, the
output data file of which is usually called ``sing_a``.
**Parameters:**
fname: str, file descriptor
Specifies the filename for the input file.
fname can also be used with a file descriptor instad of a filename.
nmoocc : int
Specifies the number of non-frozen occupied orbitals.
nforbs : int,optional
Specifies the number of frozen orbitals.
select_state : None or list of int, optional
If not None, specifies the states to be read (0 corresponds to the ground
state), else read all electronic states.
threshold : float, optional
Specifies a read threshold for the TD-DFT coefficients.
bortho : bool
If True, an orthonormalization of the TD-DFT coefficients is perfomed
with Gram-Schmidt.
**Returns:**
ci : list of CIinfo class instances
See :ref:`Central Variables` for details.
'''
display('\nReading data of TD-DFT calculation from Turbomole...')
nel = (nmoocc + nforbs) * 2
lumo = (nmoocc + nforbs)
states = []
if isinstance(select_state, int): select_state = [select_state]
if isinstance(fname, str):
filename = fname
fname = descriptor_from_file(filename, index=0, ci_descriptor=True)
else:
filename = fname.name
start = False
for i, line in enumerate(fname):
if '$tensor space dimension' in line:
tspace = int(line.split()[-1])
if 'eigenvalue' in line:
start = True
l = line.split()
states.append({
'eigenvalue': float(l[-1].replace('D', 'E')),
'coeffs': []
})
elif start:
for i in range((len(line) - 1) // 20):
states[-1]['coeffs'].append(
float(line[i * 20:(i + 1) * 20].replace('D', 'E')))
for i in range(len(states)):
if len(states[i]['coeffs']) == tspace * 2:
states[i]['coeffs'] = numpy.array(states[i]['coeffs']).reshape(
(2, nmoocc, -1))
states[i]['xia'] = states[i]['coeffs'][0]
elif len(states[i]['coeffs']) == tspace:
states[i]['xia'] = numpy.array(states[i]['coeffs']).reshape(
(nmoocc, -1))
else:
raise IOError(
'Shape of coefficient matrix does not match with tensor space.'
)
del states[i]['coeffs']
coeffs = numpy.zeros(
(len(states) + 1, numpy.prod(states[1]['xia'].shape) + 1))
for i in range(len(states)):
coeffs[i + 1][1:] = states[i]['xia'].reshape((-1, ))
coeffs[0, 0] = 1.0
# Set up configurations
ci = []
for i in range(len(states) + 1):
if select_state is None or i in select_state:
ci.append(CIinfo(method='tddft'))
ci[-1].coeffs = coeffs[i]
ci[-1].occ = []
ci[-1].info = {
'state': str(i),
'energy':
0.0 if i == 0 else numpy.abs(states[i - 1]['eigenvalue']),
'fileinfo': filename,
'read_threshold': threshold,
'spin': 'Unknown',
'nel': nel
}
if not i:
ci[-1].occ.append([-1, -1])
else:
ci[-1].occ.append([0, 0])
for jj in range(states[i - 1]['xia'].shape[0]):
for kk in range(states[i - 1]['xia'].shape[1]):
ci[-1].occ.append([jj + nforbs, kk + lumo])
ci[-1].occ = numpy.array(ci[-1].occ, dtype=numpy.intc)
# Gram-Schmidt
display('Orthonormalizing the TD-DFT coefficients with Gram-Schmidt...\n')
ci = orthonorm(ci, reorth=False)
if bortho:
for c in ci:
c.apply_threshold(threshold, keep_length=True)
ci = orthonorm(ci, reorth=True)
else:
for c in ci:
c.apply_threshold(threshold, keep_length=False)
for c in ci:
c.apply_threshold(0.0, keep_length=False)
#--- Calculating norm of CI states
display('\nIn total, %d states have been read.' % len(ci))
display('Norm of the states:')
for i in ci:
display(str(i))
return ci