def save_hdf5(fid,
variables=[
'geo_info', 'geo_spec_all', 'ao_spec', 'ao_spherical',
'mo_coeff_all', 'mo_energy_all', 'mo_occ_all', 'sym',
'index_list'
],
hdf5mode='w',
**kwargs):
'''Writes all global variables specified in :literal:`variables` and all
additional :literal:`kwargs` to an HDF5 file.
'''
from orbkit.output import hdf5_open, hdf5_append
# Save HDF5 File
display('Saving Hierarchical Data Format file (HDF5) to %s...' % fid)
data_stored = []
for HDF5_file in hdf5_open(fid, mode=hdf5mode):
for i in variables:
if i in globals():
data = globals()[i]
if not (data == [] or data is None):
if i == 'sym':
data = numpy.array([[k, l] for k, l in data.items()])
hdf5_append(data, HDF5_file, name=i)
data_stored.append(i)
elif i not in kwargs:
raise ValueError(
'Variable `%s` is not in globals() or in **kwargs' % i)
for j in kwargs.keys():
hdf5_append(kwargs[j], HDF5_file, name=j)
data_stored.append(j)
display('\tContent: ' + ', '.join(data_stored))
def read_hdf5(fid,
variables=[
'geo_info', 'geo_spec_all', 'ao_spec', 'ao_spherical',
'mo_coeff_all', 'mo_energy_all', 'mo_occ_all', 'sym',
'index_list'
]):
'''Reads all variables specified in :literal:`variables` from an HDF5 file
created with :literal:`write_hdf5` and appends this data to the globals() of
this module.
'''
from orbkit.output import hdf5_open, hdf52dict
# Read HDF5 File
display('Reading Hierarchical Data Format file (HDF5) File from %s' % fid)
data_stored = []
for HDF5_file in hdf5_open(fid, mode='r'):
for i in variables:
try:
globals()[i] = hdf52dict(i, HDF5_file)
data_stored.append(i)
if i == 'sym':
s = dict(globals()[i])
globals()[i] = {}
for k, l in s.items():
globals()[i][k] = int(l)
except KeyError:
pass
if not data_stored:
raise IOError(
'Could not find any data in `%s` for the selected `variables`.' %
fid)
display('\tFound: ' + ', '.join(data_stored))
def fermions_stda(fname, 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 simplified TDA calculation from FermiONs++...')
return fermions_tdscf(fname,
td_typ='stda',
st_typ='singlet',
select_state=select_state,
threshold=threshold,
**kwargs)
def hx_network_creator(rho, filename):
'''Creates a ZIBAmira hx-network file including a colormap file (.cmap)
adjusted to the density for the easy depiction of the density.
'''
from .hx_network_draft import hx_network
filename += '.hx' if not filename.endswith('.hx') else ''
# Create a .cmap colormap file using the default values
display('\tCreating ZIBAmira colormap file...\n\t\t' +
filename.replace('.hx', '.cmap'))
assert (rho.shape != tuple(grid.N_)), 'The grid does not fit the data.'
colormap_creator(rho, filename.replace('.hx', '.cmap'))
# Create a .hx network file based on the file orbkit.hx_network_draft.py
# Open an empty file
fid = open(filename, 'w')
# Copy the content of the draft file and replace the keywords
fid.write(
hx_network.replace("FILENAME",
os.path.splitext(os.path.basename(filename))[0]))
# Close the file
fid.close()
def hx_network_creator(rho, filename):
'''Creates a ZIBAmira hx-network file including a colormap file (.cmap)
adjusted to the density for the easy depiction of the density.
'''
from orbkit.hx_network_draft import hx_network
# Create a .cmap colormap file using the default values
display('\tCreating ZIBAmira colormap file...\n\t\t%(f)s.cmap' %
{'f': filename})
AssertionError(
rho.shape != tuple(grid.N_)), 'The grid does not fit the data.'
colormap_creator(rho, filename)
# Create a .hx network file based on the file orbkit.hx_network_draft.py
display('\tCreating ZIBAmira network file...\n\t\t%(f)s.hx' %
{'f': filename})
# Open an empty file
fid = open('%(f)s.hx' % {'f': filename}, 'w')
filename = filename.split('/')[-1]
# Copy the content of the draft file and replace the keywords
fid.write(hx_network.replace("FILENAME", filename))
# Close the file
fid.close()
def main_read(fname,
all_mo=False,
spin=None,
itype='auto',
check_norm=False,
**kwargs):
'''
This is the high-lever interface for the
orbkit reading routines.
**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.
all_mo : bool, optional
If True, all molecular orbitals are returned.
spin : {None, 'alpha', or 'beta'}, optional
If not None, returns exclusively 'alpha' or 'beta' molecular orbitals.
itype : str, optional
Can be used to manually specify the input filetype.
check_norm : bool, optional
If True, ORBKIT verifies that molecular orbitals are orthonormal.
**Note:**
All additional keyword arguments are forwarded to the reading functions.
**Returns:**
qc (class QCinfo) with attributes geo_spec, geo_info, ao_spec, mo_spec, etot :
See :ref:`Central Variables` for details.
'''
if isinstance(fname, str):
filename = fname
else:
filename = fname.name
if itype == 'auto':
itype = find_itype(fname)
display('Loading data from {0} type file {1}\n'.format(itype, filename))
qc = readers[itype](fname, all_mo=all_mo, spin=spin, **kwargs)
if check_norm:
deviation = check_mo_norm(qc)
if deviation >= 1e-5:
raise ValueError(
'Bad molecular orbital norm: {0:%4e}'.format(deviation))
return qc
def check_sel(count,i,interactive=False):
if count == 0:
raise IndexError
elif count == 1:
return 0
message = '\tPlease give an integer from 0 to %d: ' % (count-1)
try:
if interactive:
i = int(raw_input(message))
i = range(count)[i]
except (IndexError,ValueError):
raise IOError(message.replace(':','!'))
else:
display('\tSelecting the %s' %
('last element.' if (i == count-1) else 'element %d.' % i))
return i
def hdf5_attributes(filename, gname='', **attrs):
warning = []
for f in hdf5_open(filename,mode='a'):
for key,value in attrs.items():
if isinstance(value,dict):
for k,v in value.items():
if k in f[gname].keys():
f[os.path.join(gname,key)].attrs[k] = v
else:
warning.append(key)
else:
if key in f[gname].keys():
f[os.path.join(gname,'data')].attrs[key] = value
else:
warning.append('data')
if warning:
display('Warning: The following attributes are not datasets in '+filename)
display(', '.join([os.path.join(gname,i) for i in data]))
def order_pm(x, y, backward=True, mu=1e-1, use_factor=False):
'''Outdated function to order exclusively the sign of a data set.
'''
if backward:
st = [-2, 1, -1]
else:
st = [1, -2, 1]
if numpy.ndim(y) == 1:
diff = numpy.zeros(2)
for rr in range(len(y))[st[0]:st[1]:st[2]]:
m = (y[rr + st[2]] - y[rr]) / (x[rr + st[2]] - x[rr])
epol = m * (x[rr + 2 * st[2]] - x[rr]) + y[rr]
for ii_d in range(2):
diff[ii_d] = (((-1)**ii_d * y[rr + 2 * st[2]]) - epol)**2
if numpy.argmin(numpy.abs(diff)) == 1:
y[rr + 2 * st[2]] = -y[rr + 2 * st[2]]
elif numpy.ndim(y) == 2:
y = numpy.array(y)
shape = numpy.shape(y)
for rr in range(shape[0])[st[0]:st[1]:st[2]]:
for ii_s, sign in [(0, -1), (1, +1)]:
f = numpy.ones(shape[1])
if use_factor:
f[numpy.abs(y[rr, :]) > mu] = 1 / numpy.abs(
y[rr, numpy.abs(y[rr, :]) > mu])
m = (y[rr + st[2], :] - y[rr, :]) / (x[rr + st[2]] - x[rr])
epol = f[:] * (m * (x[rr + 2 * st[2]] - x[rr]) + y[rr, :])
# Euclidean norm (2 norm)
current = numpy.sum(
(f[:] * sign * y[rr + 2 * st[2], :] - epol[:])**2)
# Current value
if ii_s == 0:
diff = current
new_sign = sign
elif current < diff:
new_sign = sign
y[rr + 2 * st[2], :] *= new_sign
else:
display('Function order_pm only works for vectors and 2D matrices')
return y
def test():
ts = unittest.TestSuite()
display(
'----------------------------------------------------------------------'
)
display(
' Testing ORBKIT functionality '
)
display(
'----------------------------------------------------------------------\n'
)
tsrun = unittest.TextTestRunner(verbosity=2)
tests_home = __file__.split('__')[0] + 'test/'
for test in tests:
ts.addTest(ScriptTestCase(filename=os.path.abspath(tests_home + test)))
testdir = tests_home + 'test_tmp'
clean(testdir)
os.mkdir(testdir)
os.chdir(testdir)
results = tsrun.run(ts)
clean(testdir)
sys.exit(len(results.errors + results.failures))
def plot(self,mo_matrix,symmetry='1',title='All',x_label='index',
y_label='MO coefficients',output_format='png',
plt_dir='Plots',ylim=None,thresh=0.1,x0=0,grid=True,x_grid=None,**kwargs):
'''Plots all molecular orbital coefficients of one self.symmetry.'''
import pylab as plt
from matplotlib.ticker import MultipleLocator
import os
display('Plotting data of self.symmetry %s to %s/' % (symmetry,plt_dir))
if not os.path.exists(plt_dir):
os.makedirs(plt_dir)
if numpy.ndim(mo_matrix) == 2:
mo_matrix = mo_matrix[:,numpy.newaxis,:]
shape = numpy.shape(mo_matrix)
def plot_mo(i):
fig=plt.figure()
plt.rc('xtick', labelsize=16)
plt.rc('ytick', labelsize=16)
ax = plt.subplot(111)
curves=[]
for ij in range(shape[2]):
Y = mo_matrix[:,i,ij]
if x_grid is None:
X = numpy.arange(len(Y))+x0
else:
X = x_grid
if max(numpy.abs(Y)) > thresh:
curves.append(ax.plot(X,Y, '.-' ,linewidth=1.5))
plt.xlabel(x_label, fontsize=16);
plt.ylabel(y_label, fontsize=16);
plt.title('%s: %d.%s'% (title,i+1,symmetry))
plt.ylim(ylim)
plt.tight_layout()
return fig
if output_format == 'pdf':
from matplotlib.backends.backend_pdf import PdfPages
output_fid = '%s.%s.pdf'% (title,symmetry.replace(' ','_'))
display('\t%s' % output_fid)
with PdfPages(os.path.join(plt_dir,output_fid)) as pdf:
for i in range(shape[1]):
fig = plot_mo(i)
pdf.savefig(fig,**kwargs)
plt.close()
elif output_format == 'png':
for i in range(shape[1]):
fig = plot_mo(i)
output_fid = '%d.%s.png' % (i+1,symmetry.replace(' ','_'))
display('\t%s' % output_fid)
fig.savefig(os.path.join(plt_dir, output_fid),format='png',**kwargs)
plt.close()
else:
raise ValueError('output_format `%s` is not supported' % output_format)
def spin_check(spin, restricted, has_alpha, has_beta):
'''Check if `spin` keyword is valid.
'''
if spin is not None:
if restricted:
raise IOError(
'The keyword `spin` is only supported for unrestricted calculations.'
)
if spin != 'alpha' and spin != 'beta':
raise IOError('`spin=%s` is not a valid option' % spin)
elif spin == 'alpha' and has_alpha:
display('Reading only molecular orbitals of spin alpha.')
elif spin == 'beta' and has_beta:
display('Reading only molecular orbitals of spin beta.')
elif (not has_alpha) and (not has_beta):
raise IOError(
'Molecular orbitals in the input file do not contain `Spin=` keyword'
)
elif ((spin == 'alpha' and not has_alpha)
or (spin == 'beta' and not has_beta)):
raise IOError(
'You requested `%s` orbitals, but None of them are present.' %
spin)
def mo_transition_flux_density(i,j,qc,drv='x',
ao_list=None,mo_list=None,
delta_ao_list=None,delta_mo_list=None):
'''Calculate one component (e.g. drv='x') of the
transition electoronic flux density between the
molecular orbitals i and j.
.. math::
moTEFD_{i,j}(r) = <mo_i|\delta(r-r')\\nabla_x|mo_j>
**Parameters:**
i: int
index of the first mo (Bra)
j: int
index of the second mo (Ket)
drv: {0,1,2,'x','y','z'}
The desired component of the vector field of the
transition electoronic flux density
**Returns:**
mo_tefd : numpy.ndarray
'''
if mo_list is None:
if ao_list is None:
display('\tComputing ao_list and ' +
'mo #%d, since it is not given.' % i)
ao_list = core.ao_creator(qc.geo_spec,qc.ao_spec)
else:
display('\tComputing mo #%d, since it is not given.' % i)
mo = core.mo_creator(ao_list,[qc.mo_spec[i]])[0]
else:
mo = mo_list[i]
if delta_mo_list is None:
if delta_ao_list is None:
display('\tComputing delta_ao_list and the derivative of ' +
'mo #%d, since it is not given.' % j)
delta_ao_list = core.ao_creator(qc.geo_spec,qc.ao_spec,drv=drv)
else:
display('\tComputing mo #%d, since it is not given.' % j)
delta_mo = core.mo_creator(delta_ao_list,[qc.mo_spec[j]])[0]
else:
delta_mo = delta_mo_list[i]
return mo*delta_mo
def numerical_mulliken_charges(atom,
qc,
ao_list=None,
mo_list=None,
rho_atom=None):
r'''Compute the Mulliken charges and gross atomic populations of the selected
atoms *numerically* using the respective gross atomic densities.
.. math::
P^a = \int \rho^a(x,y,z) {\rm d}^3r
**Parameters:**
atom : 'all' or int or list of int
Specifies the atom (numbering starting from one) to which the numerical
Mulliken charges and gross populations will be computed.
If (atom == 'all') or (atom == -1), computes the charges and populations
for all atoms.
**Returns:**
rho_atom : list of numpy.ndarrays, shape=(len(atoms,) + N)
Contains the atom gross atomic density on a grid.
mulliken_num : dict, (present if not is_vector)
Contains information of Mulliken charge analysis and has following members:
:population: - Mulliken population for each atom.
:charges: - Mulliken charges for each atom.
'''
if (atom == 'all' or atom == -1 or atom == [-1]):
atom = range(1, len(qc.geo_info) + 1)
atom, index = atom2index(atom, geo_info=qc.geo_info)
rho_atom = gross_atomic_density(atom, qc, ao_list=ao_list, mo_list=mo_list)
if grid.is_vector:
display('Warning: You have applied a vector grid.\n' +
'The integration is not implemented for such a grid!\n' +
'\nReturning only the gross atomic densities...')
return rho_atom
GP_A = numpy.array([core.integration(i) for i in rho_atom])
mulliken_num = {
'population': GP_A,
'charge': numpy.array(qc.geo_info[:, 2], dtype=float) - GP_A
}
display('\nNumerical Mulliken Charges Q and Gross Atomic Populations GP:')
for i, a in enumerate(index):
a = int(a)
display('\tAtom %s (%s):\tQ = %+0.4f ( GP = %0.4f )' %
(qc.geo_info[a][1], qc.geo_info[a][0],
mulliken_num['charge'][i], mulliken_num['population'][i]))
return rho_atom, mulliken_num
def order_using_analytical_overlap(fid_list,
itype='molden',
deg=0,
numproc=1,
**kwargs):
'''Performs an ordering routine using analytical overlap integrals between
molecular orbitals. Set fid_list to None to omit the reading of input files.
If :literal:`deg` is set to a value larger than zero, the molecular orbital
coefficients are extrapolated with a polynomial of degree :literal:`deg`,
before computing the molecular orbital overlap matrix.
**Paramerters:**
fid_list : list of str or None
If not None, it contains the list of input file names.
itype : str, choices={'molden', 'gamess', 'gaussian.log', 'gaussian.fchk'}
Specifies the type of the input files.
deg : int, optional
If greater than zero, specifies the degree of the extrapolation polynomial
for the molecular orbital coefficients.
**Returns:**
index_list : numpy.ndarray, shape=(Nfiles,NMO)
Contains the new indices of the molecular orbitals. If index < 0, the
molecular orbital changes its sign.
mo_overlap : numpy.ndarray, shape=((Nfiles - 1),NMO,NMO)
Contains the overlap matrix between the molecular orbitals of two neighboring
geometries, i.e., mo_overlap[i,j,k] corresponds to overlap between the
jth molecular orbital at geometry i to the kth molecular orbital at
geometry (i+1).
**Global Variables:**
geo_spec_all, geo_info, ao_spec, ao_spherical, mo_coeff_all, mo_energy_all, mo_occ_all, sym, index_list
'''
global geo_spec_all, geo_info, ao_spec, ao_spherical, mo_coeff_all, mo_energy_all, mo_occ_all, sym, index_list
if fid_list is not None:
read(fid_list, itype=itype, **kwargs)
display(
'\nStarting the ordering routine using the molecular orbital overlap...'
)
iterate = list(range(1, len(geo_spec_all)))
if deg > 0:
display('\tThe molecular orbital coefficients will be extrapolated')
display('\tusing a least squares polynomial fit of degree %d.' % deg)
std = numpy.array(
[numpy.std(i - geo_spec_all[0]) for i in geo_spec_all])
mo_overlap = [[] for i in sym.keys()]
index_list = [[] for i in sym.keys()]
for s in sym.values():
shape = numpy.shape(mo_coeff_all[s])
index_list[s] = numpy.ones((shape[0], shape[1]), dtype=int)
index_list[s] *= numpy.arange(shape[1], dtype=int)
c = 0
t = time()
for rr in iterate:
r1 = rr - 1
r2 = rr
if (deg is None) or (deg > 0 and r1 >= deg):
ao_overlap = get_ao_overlap(geo_spec_all[r2],
geo_spec_all[r2],
ao_spec,
ao_spherical=ao_spherical)
else:
ao_overlap = get_ao_overlap(geo_spec_all[r1],
geo_spec_all[r2],
ao_spec,
ao_spherical=ao_spherical)
cs = 0
for s in sym.values():
mo_coeff = mo_coeff_all[s]
shape = numpy.shape(mo_coeff)
if deg is not None and deg > 0 and r1 >= deg:
mo_r1 = get_extrapolation(r1,
r2,
mo_coeff,
grid1d=std,
deg=deg)
else:
mo_r1 = mo_coeff[r1]
overlap = get_mo_overlap_matrix(mo_r1,
mo_coeff[r2],
ao_overlap,
numproc=numproc)
for i in range(shape[1]):
# Iterate the rows of the overlap matrix
line_max = None # variable for maximum value in the current row
line_sort = numpy.argsort(numpy.abs(
overlap[i, :]))[::-1] # sort the row
for k in line_sort[::-1]:
# Is this value the maximum in the current column?
col_max = numpy.argmax(numpy.abs(overlap[:, k]))
if i == col_max:
line_max = k
break
if line_max is not None:
# Interchange the coefficients
mo_coeff[r2, [i, line_max], :] = mo_coeff[r2,
[line_max, i], :]
overlap[:, [i, line_max]] = overlap[:, [line_max, i]]
index_list[s][r2,
[i, line_max]] = index_list[s][r2,
[line_max, i]]
for i in range(shape[1]):
# Change the signs
mo_coeff[r2, i, :] *= numpy.sign(overlap[i, i])
overlap[:, i] *= numpy.sign(overlap[i, i])
index_list[s][r2, i] *= numpy.sign(overlap[i, i])
mo_overlap[cs].append(overlap)
cs += 1
mo_coeff_all[s] = mo_coeff
index = numpy.abs(index_list[s])[r2, :]
mo_energy_all[s][r2, :] = mo_energy_all[s][r2, index]
mo_occ_all[s][r2, :] = mo_occ_all[s][r2, index]
c += 1
#if not c % int(numpy.ceil(len(iterate)/10.)):
display('\tFinished %d of %d geometries (%.1f s)' %
(c, len(iterate), time() - t))
t = time()
tmp = []
for i in mo_overlap:
tmp.append(numpy.array(i))
mo_overlap = tmp
return index_list, mo_overlap
def read_gamess(fname,
all_mo=False,
spin=None,
read_properties=False,
**kwargs):
'''Reads all information desired from a Gamess-US output file.
**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.
all_mo : bool, optional
If True, all molecular orbitals are returned.
**Returns:**
qc (class QCinfo) with attributes geo_spec, geo_info, ao_spec, mo_spec, etot :
See :ref:`Central Variables` for details.
'''
if isinstance(fname, str):
filename = fname
fname = descriptor_from_file(filename, index=0)
else:
filename = fname.name
from io import TextIOWrapper
if isinstance(fname, TextIOWrapper):
flines = fname.readlines() # Read the WHOLE file into RAM
else:
magic = 'This is an Orbkit magic string'
text = fname.read().decode("iso-8859-1").replace(
'\n', '\n{}'.format(magic))
flines = text.split(magic)
flines.pop()
# Initialize the variables
qc = QCinfo()
qc.ao_spec = AOClass([])
qc.mo_spec = MOClass([])
has_alpha = False # Flag for alpha electron set
has_beta = False # Flag for beta electron set
restricted = True # Flag for restricted calculation
sec_flag = None # A Flag specifying the current section
is_pop_ana = True # Flag for population analysis for ground state
keyword = [' ATOM ATOMIC COORDINATES', '']
# Keywords for single point calculation and
# geometry optimization
mokey = 'EIGENVECTORS' # Keyword for MOs
unrestopt = False # Flag for unrestricted optimization
bopt = False # Flag for geometry optimization
sym = {} # Symmetry of MOs
geo_skip = 1 # Number of lines to skip in geometry section
for il in range(len(flines)):
line = flines[il] # The current line as string
thisline = line.split() # The current line split into segments
# Check the file for keywords
if 'RUNTYP=OPTIMIZE' in line:
keyword = [
' COORDINATES OF ALL ATOMS ARE',
'***** EQUILIBRIUM GEOMETRY LOCATED *****'
]
geo_skip = 2
bopt = True
if 'SCFTYP=UHF' in line:
mokey = ' SET ****'
restricted = False
else:
mokey = 'EIGENVECTORS'
elif keyword[0] in line and keyword[1] in flines[il - 1]:
# The section containing information about
# the molecular geometry begins
sec_flag = 'geo_info'
atom_count = 0 # Counter for Atoms
angstrom = not '(BOHR)' in line
elif 'ATOMIC BASIS SET' in line:
# The section containing information about
# the atomic orbitals begins
sec_flag = 'ao_info'
ao_skip = 6 # Number of lines to skip
AO = [] # Atomic orbitals
elif '----- ALPHA SET ' in line:
# The section for alpha electrons
has_alpha = True
has_beta = False
restricted = False
elif '----- BETA SET ' in line:
# The section for alpha electrons
restricted = False
has_alpha = False
has_beta = True
elif mokey in line and len(thisline) < 3:
# The section containing information about
# the molecular orbitals begins
sec_flag = 'mo_info'
mo_skip = 1
len_mo = 0 # Number of MOs
init_mo = False # Initialize new MO section
info_key = None # A Flag specifying the energy and symmetry section
lxlylz = []
if 'ALPHA' in line:
has_alpha = True
mo_skip = 0
elif 'BETA' in line:
has_beta = True
has_alpha = False
mo_skip = 0
elif 'NATURAL ORBITALS' in line and len(thisline) <= 3:
display('The natural orbitals are not extracted.')
elif ' NUMBER OF OCCUPIED ORBITALS (ALPHA) =' in line:
occ = [] # occupation number of molecular orbitals
occ.append(int(thisline[-1]))
elif ' NUMBER OF OCCUPIED ORBITALS (BETA ) =' in line:
occ.append(int(thisline[-1]))
# elif 'ECP POTENTIALS' in line:
# sec_flag = 'ecp_info'
# ecp = ''
elif ' NUMBER OF OCCUPIED ORBITALS (ALPHA) KEPT IS =' in line:
occ = [] # occupation number of molecular orbitals
occ.append(int(thisline[-1]))
elif ' NUMBER OF OCCUPIED ORBITALS (BETA ) KEPT IS =' in line:
occ.append(int(thisline[-1]))
elif 'NUMBER OF STATES REQUESTED' in line and read_properties:
# get the number of excited states and initialize variables for
# transition dipole moment and energies
exc_states = int(line.split('=')[1]) # Number of excited states
# Dipole moments matrix: Diagonal elements -> permanent dipole moments
# Off-diagonal elements -> transition dipole moments
qc.dipole_moments = numpy.zeros(
((exc_states + 1), (exc_states + 1), 3))
# Multiplicity of ground and excited states
qc.states['multiplicity'] = numpy.zeros(exc_states + 1)
# Energies of ground and excited states
qc.states['energy'] = numpy.zeros(exc_states + 1)
qc.states['energy'][0] = qc.etot
qc.states['multiplicity'][0] = gs_multi
dm_flag = None # Flag specifying the dipole moments section
elif 'TRANSITION DIPOLE MOMENTS' in line and read_properties:
# Section containing energies of excited states
sec_flag = 'dm_info'
# Energy and Multiplicity for ground state
elif 'SPIN MULTIPLICITY' in line and read_properties:
# odd way to get gound state multiplicity
gs_multi = int(line.split()[3])
elif 'FINAL' in line and read_properties:
# get (last) energy
qc.etot = float(line.split()[4])
elif 'TOTAL MULLIKEN AND LOWDIN ATOMIC POPULATIONS' in line and is_pop_ana == True and read_properties:
# Read Mulliken and Lowdin Atomic Populations
sec_flag = 'pop_info'
pop_skip = 1
is_pop_ana == False
qc.pop_ana['Lowdin'] = []
qc.pop_ana['Mulliken'] = []
else:
# Check if we are in a specific section
if sec_flag == 'geo_info':
if not geo_skip:
if len(line) < 2:
sec_flag = None
else:
qc.geo_info.append(
[thisline[0], atom_count + 1, thisline[1]])
qc.geo_spec.append([float(ii) for ii in thisline[2:]])
atom_count += 1
elif geo_skip:
geo_skip -= 1
elif sec_flag == 'ao_info':
if not ao_skip:
if ' TOTAL NUMBER OF BASIS SET SHELLS' in line:
sec_flag = None
else:
if len(thisline) == 1:
# Read atom type
at_type = thisline[0]
AO.append([])
new_ao = False
elif len(thisline) == 0 and new_ao == False:
new_ao = True
else:
coeffs = [float(ii) for ii in thisline[3:]]
if new_ao:
ao_type = thisline[1].lower().replace(
'l', 'sp')
for i_ao, t_ao in enumerate(ao_type):
AO[-1].append({
'atom_type':
at_type,
'type':
t_ao,
'pnum':
1,
'coeffs':
[[coeffs[0], coeffs[1 + i_ao]]]
})
new_ao = False
else:
for i_ao in range(len(ao_type)):
AO[-1][-len(ao_type) +
i_ao]['coeffs'].append(
[coeffs[0], coeffs[1 + i_ao]])
AO[-1][-len(ao_type) + i_ao]['pnum'] += 1
elif ao_skip:
ao_skip -= 1
elif sec_flag == 'mo_info':
if not mo_skip:
if 'END OF' in line and 'CALCULATION' in line or '-----------' in line:
sec_flag = None
has_alpha = False
has_beta = False
else:
if thisline == []:
info_key = None
init_mo = True
try:
int(flines[il + 1].split()[0])
except ValueError:
sec_flag = None
init_mo = False
elif init_mo:
init_len = len(thisline)
lxlylz = []
for ii in range(len(thisline)):
if has_alpha == True or has_beta == True:
qc.mo_spec.append({
'coeffs': [],
'energy': 0.0,
'occ_num': 0.0,
'sym': '',
'spin': ''
})
else:
qc.mo_spec.append({
'coeffs': [],
'energy': 0.0,
'occ_num': 0.0,
'sym': ''
})
init_mo = False
info_key = 'energy'
elif len(
thisline) == init_len and info_key == 'energy':
for ii in range(init_len, 0, -1):
qc.mo_spec[-ii]['energy'] = float(
thisline[init_len - ii])
info_key = 'symmetry'
elif len(thisline
) == init_len and info_key == 'symmetry':
for ii in range(init_len, 0, -1):
len_mo += 1
a = thisline[init_len - ii]
if a not in sym.keys(): sym[a] = 1
else: sym[a] = len_mo
if has_alpha:
qc.mo_spec[-ii]['sym'] = '%d.%s_a' % (
sym[a], thisline[init_len - ii])
qc.mo_spec[-ii]['spin'] = 'alpha'
elif has_beta:
qc.mo_spec[-ii]['sym'] = '%d.%s_b' % (
sym[a], thisline[init_len - ii])
qc.mo_spec[-ii]['spin'] = 'beta'
else:
qc.mo_spec[-ii]['sym'] = '%d.%s' % (
sym[a], thisline[init_len - ii])
info_key = 'coeffs'
elif thisline != [] and info_key == 'coeffs':
lxlylz.append((line[11:17]))
for ii, m in enumerate(
re.finditer('-?\d+\.\d+', line[16:])):
qc.mo_spec[-init_len + ii]['coeffs'].append(
float(m.group()))
elif mo_skip:
mo_skip -= 1
elif sec_flag == 'ecp_info':
if 'THE ECP RUN REMOVES' in line:
sec_flag = None
elif 'PARAMETERS FOR' in line:
if line[17:25].split()[0] != ecp:
ecp = line[17:25].split()[0]
zcore = float(line[51:55].split()[0])
ii_geo = int(line[35:41].split()[0]) - 1
qc.geo_info[ii_geo][2] = str(
float(qc.geo_info[ii_geo][2]) - zcore)
else:
ii_geo = int(line[35:41].split()[0]) - 1
qc.geo_info[ii_geo][2] = str(
float(qc.geo_info[ii_geo][2]) - zcore)
elif sec_flag == 'dm_info':
# instead of giving the output in a useful human and machine readable
# way, gamess output syntax differs for transitions involving the
# ground state compared to transitions between excited states...
if 'GROUND STATE (SCF) DIPOLE=' in line:
# ground state dipole is in debye...convert to atomic units
for ii in range(3):
qc.dipole_moments[0][0][ii] = float(
thisline[ii + 4]) * 0.393430307
if 'EXPECTATION VALUE DIPOLE MOMENT FOR EXCITED STATE' in line:
state = (int(line.replace('STATE', 'STATE ').split()[7]))
dm_flag = 'state_info'
if 'TRANSITION FROM THE GROUND STATE TO EXCITED STATE' in line:
state = [
0, int(line.replace('STATE', 'STATE ').split()[8])
]
dm_flag = 'transition_info'
if 'TRANSITION BETWEEN EXCITED STATES' in line:
state = [
int(thisline[4]),
int(line.replace('AND', 'AND ').split()[6])
]
dm_flag = 'transition_info'
if 'NATURAL ORBITAL OCCUPATION NUMBERS FOR EXCITED STATE' in line:
sec_flag = None
dm_flag = None
if dm_flag == 'state_info':
if 'STATE MULTIPLICITY' in line:
qc.states['multiplicity'][state] = int(
line.split('=')[1])
if 'STATE ENERGY' in line:
qc.states['energy'][state] = float(line.split('=')[1])
if 'STATE DIPOLE' and 'E*BOHR' in line:
for ii in range(3):
qc.dipole_moments[state][state][ii] = float(
thisline[ii + 3])
elif dm_flag == 'transition_info':
if 'TRANSITION DIPOLE' and 'E*BOHR' in line:
for ii in range(3):
qc.dipole_moments[state[0]][state[1]][ii] = float(
thisline[ii + 3])
qc.dipole_moments[state[1]][state[0]][ii] = float(
thisline[ii + 3])
elif sec_flag == 'pop_info':
if not pop_skip:
if line == '\n':
sec_flag = None
else:
qc.pop_ana = {}
qc.pop_ana['Lowdin'].append(float(thisline[5]))
qc.pop_ana['Mulliken'].append(float(thisline[3]))
elif pop_skip:
pop_skip -= 1
# Check usage of same atomic basis sets
basis_set = {}
for ii in range(len(AO)):
if not AO[ii][0]['atom_type'] in basis_set.keys():
basis_set[AO[ii][0]['atom_type']] = AO[ii]
else:
for jj in range(len(AO[ii])):
if AO[ii][jj]['coeffs'] != basis_set[
AO[ii][0]['atom_type']][jj]['coeffs']:
raise IOError('Different basis sets for the same atom.')
# Numpy array
for ii in basis_set.keys():
for jj in range(len(basis_set[ii])):
basis_set[ii][jj]['coeffs'] = numpy.array(
basis_set[ii][jj]['coeffs'])
for kk in range(len(qc.mo_spec)):
qc.mo_spec[kk]['coeffs'] = numpy.array(qc.mo_spec[kk]['coeffs'])
# Complement atomic basis sets
for kk in range(len(qc.geo_info)):
for ll in range(len(basis_set[qc.geo_info[kk][0]])):
qc.ao_spec.append({
'atom':
qc.geo_info[kk][1] - 1,
'type':
basis_set[qc.geo_info[kk][0]][ll]['type'],
'pnum':
basis_set[qc.geo_info[kk][0]][ll]['pnum'],
'coeffs':
basis_set[qc.geo_info[kk][0]][ll]['coeffs'],
'lxlylz':
None
})
# Reconstruct exponents list for ao_spec
count = 0
for i, j in enumerate(qc.ao_spec):
l = l_deg(lquant[j['type']])
j['lxlylz'] = []
for i in range(l):
j['lxlylz'].append((lxlylz[count].lower().count('x'),
lxlylz[count].lower().count('y'),
lxlylz[count].lower().count('z')))
count += 1
j['lxlylz'] = numpy.array(j['lxlylz'], dtype=numpy.int64)
if restricted:
for ii in range(len(qc.mo_spec)):
if occ[0] and occ[1]:
qc.mo_spec[ii]['occ_num'] += 2.0
occ[0] -= 1
occ[1] -= 1
if not occ[0] and occ[1]:
qc.mo_spec[ii]['occ_num'] += 1.0
occ[1] -= 1
if not occ[1] and occ[0]:
qc.mo_spec[ii]['occ_num'] += 1.0
occ[0] -= 1
if restricted == False:
for ii in range(len(qc.mo_spec)):
if qc.mo_spec[ii]['spin'] == 'alpha' and occ[0] > 0:
qc.mo_spec[ii]['occ_num'] += 1.0
occ[0] -= 1
has_alpha = True
elif qc.mo_spec[ii]['spin'] == 'beta' and occ[1] > 0:
qc.mo_spec[ii]['occ_num'] += 1.0
occ[1] -= 1
has_beta = True
if spin is not None:
if restricted:
raise IOError(
'The keyword `spin` is only supported for unrestricted calculations.'
)
if spin != 'alpha' and spin != 'beta':
raise IOError('`spin=%s` is not a valid option' % spin)
elif spin == 'alpha' and has_alpha == True:
display('Reading only molecular orbitals of spin alpha.')
elif spin == 'beta' and has_beta == True:
display('Reading only molecular orbitals of spin beta.')
elif (not has_alpha) and (not has_beta):
raise IOError('No spin molecular orbitals available')
elif ((spin == 'alpha' and not has_alpha)
or (spin == 'beta' and not has_beta)):
raise IOError(
'You requested `%s` orbitals, but None of them are present.' %
spin)
# Are all MOs requested for the calculation?
if not all_mo:
for i in range(len(qc.mo_spec))[::-1]:
if qc.mo_spec[i]['occ_num'] < 0.0000001:
del qc.mo_spec[i]
# Only molecular orbitals of one spin requested?
if spin is not None:
for i in range(len(qc.mo_spec))[::-1]:
if qc.mo_spec[i]['spin'] != spin:
del qc.mo_spec[i]
# Convert geo_info and geo_spec to numpy.ndarrays
qc.format_geo(is_angstrom=angstrom)
qc.mo_spec.update()
qc.ao_spec.update()
return qc
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 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 mo_set(qc,
fid_mo_list,
drv=None,
laplacian=None,
otype=None,
ofid=None,
return_all=True,
numproc=None,
slice_length=None):
'''Calculates and saves the density or the derivative thereof
using selected molecular orbitals.
**Parameters:**
qc.geo_spec, qc.geo_info, qc.ao_spec, qc.mo_spec :
See :ref:`Central Variables` for details.
fid_mo_list : str
Specifies the filename of the molecular orbitals list or list of molecular
orbital labels (cf. :mod:`orbkit.orbitals.MOClass.select` for details).
otype : str or list of str, optional
Specifies output file type. See :data:`otypes` for details.
ofid : str, optional
Specifies output file name. If None, the filename will be based on
:mod:`orbkit.options.outputname`.
drv : string or list of strings {None,'x','y', 'z', 'xx', 'xy', ...}, optional
If not None, a derivative calculation is requested.
return_all : bool
If False, no data will be returned.
numproc : int, optional
Specifies number of subprocesses for multiprocessing.
If None, uses the value from :mod:`options.numproc`.
slice_length : int, optional
Specifies the number of points per subprocess.
If None, uses the value from :mod:`options.slice_length`.
**Returns:**
datasets : numpy.ndarray, shape=((NSET,) + N)
Contains the NSET molecular orbital sets on a grid.
delta_datasets : numpy.ndarray, shape=((NSET,NDRV) + N)
Contains the NDRV NSET molecular orbital set on a grid. This is only
present if derivatives are requested.
'''
#Can be an mo_spec or a list of mo_spec
# For later iteration we'll make it into a list here if it is not
mo_info_list = qc.mo_spec.select(fid_mo_list, flatten_input=False)
drv = options.drv if drv is None else drv
laplacian = options.laplacian if laplacian is None else laplacian
slice_length = options.slice_length if slice_length is None else slice_length
numproc = options.numproc if numproc is None else numproc
if ofid is None:
ofid = options.outputname
datasets = []
datalabels = []
delta_datasets = []
delta_datalabels = []
cube_files = []
for i_file, mo_info in enumerate(mo_info_list):
qc_select = qc.copy()
qc_select.mo_spec = mo_info
label = 'mo_set:' + mo_info.selection_string
display('\nStarting with the molecular orbital list \n\t' + label +
'\n\t(Only regarding existing and occupied mos.)\n')
data = core.rho_compute(qc_select,
drv=drv,
laplacian=laplacian,
slice_length=slice_length,
numproc=numproc)
if drv is None:
rho = data
delta_datasets = numpy.zeros((0, ) + rho.shape)
elif laplacian:
rho, delta_rho, laplacian_rho = data
delta_datasets.extend(delta_rho)
delta_datasets.append(laplacian_rho)
delta_datalabels.extend(
['d^2/d%s^2 %s' % (i, label) for i in 'xyz'])
delta_datalabels.append('laplacian_of_' + label)
else:
rho, delta_rho = data
delta_datasets.extend(delta_rho)
delta_datalabels.extend(['d/d%s %s' % (i, label) for i in drv])
datasets.append(rho)
datalabels.append(label)
datasets = numpy.array(datasets)
delta_datasets = numpy.array(delta_datasets)
delta_datalabels.append('mo_set')
data = numpy.append(datasets, delta_datasets, axis=0)
if not options.no_output:
output_written = main_output(data,
qc,
outputname=ofid,
datalabels=datalabels + delta_datalabels,
otype=otype,
drv=None)
return data
def order_using_extrapolation(fid_list,
itype='molden',
deg=1,
use_mo_values=False,
matrix=None,
**kwargs):
'''Performs an ordering routine using extrapolation of quantities related to
the molecular orbitals. Set fid_list to None to omit the reading of input
files.
The molecular orbital coefficients (If use_mo_values is False) are
extrapolated with a polynomial of degree :literal:`deg` and ordered by
minimizing a selected norm (default: Euclidian norm).
**Paramerters:**
fid_list : list of str or None
If not None, it contains the list of input file names.
itype : str, choices={'molden', 'gamess', 'gaussian.log', 'gaussian.fchk'}
Specifies the type of the input files.
deg : int
Specifies the degree of the extrapolation polynomial.
use_mo_values : bool, optional
If True, some molecular orbital values and their derivatives are computed
at the nuclear positions. The ordering routine is applied for those values
instead.
matrix : None or numpy.ndarray with shape=(Nfiles,N,M)
If not None, contains the data to be ordered.
**Returns:**
:if matrix is None:
- index_list
:else:
- matrix, index_list
index_list : numpy.ndarray, shape=(Nfiles,NMO)
Contains the new indices of the molecular orbitals. If index < 0, the
molecular orbital changes its sign.
matrix : numpy.ndarray, shape=(Nfiles,N,M)
Contains the ordered matrix.
**Global Variables:**
geo_spec_all, geo_info, ao_spec, ao_spherical, mo_coeff_all, mo_energy_all, mo_occ_all, sym, index_list
'''
global geo_spec_all, geo_info, ao_spec, ao_spherical, mo_coeff_all, mo_energy_all, mo_occ_all, sym, index_list
# Read all input files
if fid_list is not None:
read(fid_list, itype=itype, **kwargs)
radius = range(len(geo_spec_all)) #: We assume an equally spaced grid
if deg < 2:
function = order_mo
else:
function = order_mo_higher_deg
if matrix is not None:
display('\tOdering backward')
matrix, index_list = function(matrix,
index_list=index_list[ii_s],
backward=True,
mu=mu,
deg=deg)
display('\tOdering forward')
matrix, index_list = function(matrix,
index_list=index_list[ii_s],
backward=False,
mu=mu,
deg=deg)
return matrix, index_list
index_list = [None for i in sym.keys()]
for s, ii_s in sym.items():
display('Starting ordering of MOs of symmetry %s' % s)
shape = numpy.shape(mo_coeff_all[ii_s])
mu = 5e-2
matrix = mo_coeff_all[ii_s]
if use_mo_values:
display('\tComputing molecular orbitals at the nuclear positions')
matrix = compute_mo_list(geo_spec_all,
ao_spec,
matrix,
ao_spherical=ao_spherical,
iter_drv=[None, 'x', 'y', 'z'])
display('\tOdering backward')
matrix, index_list[ii_s] = function(matrix,
index_list=index_list[ii_s],
backward=True,
mu=mu,
deg=deg)
display('\tOdering forward')
matrix, index_list[ii_s] = function(matrix,
index_list=index_list[ii_s],
backward=False,
mu=mu,
deg=deg)
for rr in range(shape[0]):
index = numpy.abs(index_list[ii_s])[rr, :]
sign = (-1)**(index_list[ii_s][rr] < 0)
mo_energy_all[ii_s][rr, :] = mo_energy_all[ii_s][rr, index]
mo_occ_all[ii_s][rr, :] = mo_occ_all[ii_s][rr, index]
mo_coeff_all[ii_s][
rr, :, :] = sign[:, numpy.newaxis] * mo_coeff_all[ii_s][
rr, index, :] # numpy.array(matrix,copy=True)
return index_list
def main_output(data,
geo_info,
geo_spec,
outputname='new',
otype='h5',
drv=None,
omit=[],
**kwargs):
'''Creates the requested output.
**Parameters:**
data : numpy.ndarray, shape=N or shape=((NDRV,) + N)
Contains the output data. The shape (N) depends on the grid and the data, i.e.,
3d for regular grid, 1d for vector grid.
geo_info, geo_spec :
See :ref:`Central Variables` for details.
outputname : str
Contains the base name of the output file.
otype : str or list of str
Contains the output file type. Possible options:
'h5', 'cb', 'am', 'hx', 'vmd', 'mayavi'
drv : None or list of str, optional
If not None, a 4d(regular)/2d(vector) input data array will be expected
with NDRV = len(drv).
omit : list of str, optional
If not empty, the input file types specified here are omitted.
**Note:**
All additional keyword arguments are forwarded to the output functions.
'''
print_waring = False
output_written = []
if isinstance(otype, str):
otype = [otype]
if 'vmd' in otype and not 'cb' in otype:
otype.append('cb')
otype = [i for i in otype if i not in omit]
if otype is None or otype == []:
return output_written
# Convert the data to a regular grid, if possible
output_not_possible = (grid.is_vector and not grid.is_regular)
is_regular_vector = (grid.is_vector and grid.is_regular)
if is_regular_vector:
display(
'\nConverting the regular 1d vector grid to a 3d regular grid.')
grid.vector2grid(*grid.N_)
data = grid.mv2g(data=data)
if 'mayavi' in otype:
if output_not_possible: print_waring = True
else:
view_with_mayavi(grid.x,
grid.y,
grid.z,
data,
geo_spec=geo_spec,
**kwargs)
if drv is not None:
fid = '%(f)s_d%(d)s'
it = enumerate(drv)
else:
fid = '%(f)s'
it = [(0, None)]
data = [data]
f = {'f': outputname}
for i, j in it:
f['d'] = j
d = data[i]
if 'h5' in otype:
display('\nSaving to Hierarchical Data Format file (HDF5)...' +
'\n\t%(o)s.h5' % {'o': fid % f})
HDF5_creator(d, (fid % f), geo_info, geo_spec, **kwargs)
output_written.append('%s.h5' % (fid % f))
if 'am' in otype or 'hx' in otype and not print_waring:
if output_not_possible: print_waring = True
else:
display('\nSaving to ZIBAmiraMesh file...' +
'\n\t%(o)s.am' % {'o': fid % f})
amira_creator(d, (fid % f))
output_written.append('%s.am' % (fid % f))
if 'hx' in otype and not print_waring:
if output_not_possible: print_waring = True
else:
# Create Amira network incl. Alphamap
display('\nCreating ZIBAmira network file...')
hx_network_creator(data, (fid % f))
output_written.append('%s.hx' % (fid % f))
if 'cb' in otype or 'vmd' in otype and not print_waring:
if output_not_possible: print_waring = True
else:
display('\nSaving to .cb file...' +
'\n\t%(o)s.cb' % {'o': fid % f})
cube_creator(d, (fid % f), geo_info, geo_spec, **kwargs)
output_written.append('%s.cb' % (fid % f))
#else: output_creator(d,(fid % f),geo_info,geo_spec) # Axel's cube files
if 'vmd' in otype and not print_waring:
if output_not_possible: print_waring = True
else:
# Create VMD network
display('\nCreating VMD network file...' +
'\n\t%(o)s.vmd' % {'o': fid % f})
vmd_network_creator((fid % f),
cube_files=['%s.cb' % (fid % f)],
**kwargs)
output_written.append('%s.vmd' % (fid % f))
if print_waring:
display(
'For a non-regular vector grid (`if grid.is_vector and not grid.is_regular`)'
)
display('only HDF5 is available as output format...')
display('Skipping all other formats...')
if is_regular_vector:
# Convert the data back to a regular vector grid
grid.grid2vector()
return output_written
def vmd_network_creator(filename,
cube_files=None,
render=False,
iso=(-0.01, 0.01),
abspath=False,
**kwargs):
'''Creates a VMD script file from a list of cube files provided.
**Parameters:**
filename : str
Contains the base name of the output file.
cube_files : None or list of str
Specifies the cube files which serve as input for the VMD script.
If None, searches the directory for '.cb' and '.cube' files.
render : bool
If True, the VMD script will automatically create '.tga' files for each
cube file.
iso : tuple
Specifies the isovalue for the blue and the red isosurface, respectively.
abspath : bool
If True, the paths of the cube files will be expanded to absolute file paths.
'''
from os import path, listdir
import linecache
from orbkit import vmd_network_draft
if cube_files is None:
display(
'No list of cube (.cb or .cube) filenames provided. Checking the directory'
+ ' of the outputfile...')
cube_files = []
for fid in listdir(path.dirname(filename)):
if fid.endswith('.cb') or fid.endswith('.cube'):
cube_files.append(fid)
if cube_files == []:
raise IOError('Could not find valid cube files in %s' %
path.dirname(filename))
elif isinstance(cube_files, str):
cube_files = [cube_files]
elif not isinstance(cube_files, list):
raise IOError('`cube_files` has to be a list of strings.')
title = []
mo = ''
for i, f in enumerate(cube_files):
title = linecache.getline(f, 2)
if title.split() == []:
title = path.splitext(path.basename(f))[0]
else:
title = title.replace('\n', '').replace(' ', '')
linecache.clearcache()
pid = path.abspath(f) if abspath else path.relpath(
f, path.dirname(filename))
mo += vmd_network_draft.mo_string % {
'c': i,
'n1': pid,
'n2': title,
'isored': iso[0],
'isoblue': iso[1],
'render': '' if render else '#'
}
f = open('%(f)s.vmd' % {'f': filename}, 'w')
f.write(vmd_network_draft.vmd_string % {'mo': mo})
f.close()
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 order_mo_higher_deg(mo,
index_list=None,
backward=True,
mu=1e-1,
deg=2,
**kwargs):
'''Orders a 3d-matrix (shape=(Nfiles,NMO,NAO)) by interchanging the axis=1,
i.e., NMO, applying an extrapolation a polynomial fit with a Vandermonde matrix
as implemented in numpy.'''
shape = numpy.shape(mo)
# Check if degree is correctly set
if not isinstance(deg, int) or deg < 1 or deg > (shape[0] - 1):
raise IOError('Wrong choice for degree of the fitting polynomial!')
display('\tusing a least squares polynomial fit of degree %d.' % deg)
if index_list == None:
index_list = numpy.ones((shape[0], shape[1]), dtype=int)
index_list *= numpy.arange(shape[1], dtype=int)
if 'criterion' in kwargs:
if kwargs['criterion'] == '1-norm':
test = lambda x, y: numpy.sum(numpy.abs(x)) < numpy.sum(
numpy.abs(y))
if kwargs['criterion'] == '2-norm':
test = lambda x, y: ((x**2).sum()) < ((y**2).sum())
elif kwargs['criterion'] == 'infty-norm':
test = lambda x, y: abs(x).max() < abs(y).max()
elif kwargs['criterion'] == 'perc':
test = lambda x, y: (
(x**2 < y**2).sum() / float(shape[2])) > 1. / 2.
else:
raise ValueError('creterion %s is not defined!' %
kwargs['criterion'])
else:
# Take 2-norm by default
test = lambda x, y: ((x**2).sum()) < ((y**2).sum())
if backward:
st = [-(deg + 1), 0, -1]
x = numpy.arange(0, deg + 1)
else:
st = [deg, -1, 1]
x = numpy.arange(-deg, 1)
for i, i_0 in enumerate(range(shape[1])[:-1]):
for rr in range(shape[0])[st[0]:st[1]:st[2]]:
epol = numpy.zeros(shape[2])
for k in range(shape[2]):
if mo[rr, i_0, k] != 0.:
xnew = rr + st[2]
y = mo[rr + x, i_0, k]
z = numpy.polyfit(rr + x, y, deg)
epol[k] = numpy.poly1d(z)(xnew)
cp = ((mo[rr + st[2], i_0, :] - epol[:])**2)
cm = ((-1 * mo[rr + st[2], i_0, :] - epol[:])**2)
is_smaller = test(cm, cp)
current = cm if is_smaller else cp
diff = current
i_1 = i_0
new_signs = [1, (-1)**is_smaller]
# Check other molecular orbitals
for ik_index, ik in enumerate(range(shape[1])[i + 1:]):
cp = ((mo[rr + st[2], ik, :] - epol[:])**2)
cm = ((-1 * mo[rr + st[2], ik, :] - epol[:])**2)
is_smaller = test(cm, cp)
current = cm if is_smaller else cp
if test(current, diff):
diff = current
i_1 = ik
new_signs = [1, (-1)**is_smaller]
if i_0 != i_1:
mo[rr + st[2], [i_0, i_1], :] = mo[rr + st[2], [i_1, i_0], :]
index_list[rr + st[2], [i_0, i_1]] = index_list[rr + st[2],
[i_1, i_0]]
mo[rr, i_0, :] *= new_signs[0]
mo[rr + st[2], i_0, :] *= new_signs[1]
index_list[rr, i_0] *= new_signs[0]
index_list[rr + st[2], i_0] *= new_signs[1]
return mo, index_list
def run_orbkit(use_qc=None, check_options=True, standalone=False):
'''Controls the execution of all computational tasks.
**Parameters:**
use_qc : QCinfo, optional
If not None, the reading of a quantum chemistry output is omitted and
the given QCinfo class is used for all computational tasks.
(See :ref:`Central Variables` in the manual for details on QCinfo.)
check_options : bool, optional
If True, the specified options will be validated.
**Returns:**
data : type and shape depend on the options.
Contains orbkit's output.
See :ref:`High-Level Interface` in the manual for details.
'''
# Set some global variables
global qc
# Display program information
display(lgpl_short)
# Check for the correctness of orbkit.options
if check_options:
display('Checking orbkit.options...\n')
options.check_options(display=display,
interactive=False,
info=True,
check_io=(use_qc is None))
# Measurement of required execution time
t = [time.time()]
# Do we need to read out the info of all MOs?
if (options.mo_set or options.calc_mo) is not False:
options.all_mo = True
if use_qc is None:
# Read the input file
qc = read.main_read(options.filename,
itype=options.itype,
all_mo=options.all_mo,
spin=options.spin,
cclib_parser=options.cclib_parser)
else:
# Use a user defined QCinfo class.
qc = use_qc
display('\nSetting up the grid...')
if options.grid_file is not None:
# Read the grid from an external file
grid.read(options.grid_file)
elif options.adjust_grid is not None:
# Adjust the grid to geo_spec
extend, step = options.adjust_grid
grid.adjust_to_geo(qc, extend=extend, step=step)
elif options.random_grid:
# Create a randomized grid
grid.random_grid(qc.geo_spec)
# Initialize grid
grid.grid_init(is_vector=options.is_vector)
if options.is_vector:
grid.is_regular = False
display(grid.get_grid()) # Display the grid
if not grid.is_regular and options.center_grid is not None:
raise IOError(
'The option --center is only supported for regular grids.')
elif options.center_grid is not None:
atom = grid.check_atom_select(options.center_grid,
qc.geo_info,
qc.geo_spec,
interactive=True,
display=display)
# Center the grid to a specific atom and (0,0,0) if requested
grid.center_grid(qc.geo_spec[atom - 1], display=display)
if check_options or standalone:
options.check_grid_output_compatibilty()
t.append(time.time()) # A new time step
# The calculation of all AOs (--calc_ao)
if options.calc_ao != False:
data = extras.calc_ao(qc, drv=options.drv, otype=options.otype)
t.append(time.time()) # Final time
good_bye_message(t)
return data
# The calculation of selected MOs (--calc_mo) or
# the density formed by selected MOs (--mo_set)
if (options.mo_set or options.calc_mo) != False:
# What should the program do?
if options.calc_mo != False:
fid_mo_list = options.calc_mo
elif options.mo_set != False:
fid_mo_list = options.mo_set
# Call the function for actual calculation
if options.calc_mo != False:
data = extras.calc_mo(qc,
fid_mo_list,
drv=options.drv,
otype=options.otype)
elif options.mo_set != False:
data = extras.mo_set(qc,
fid_mo_list,
drv=options.drv,
laplacian=options.laplacian,
otype=options.otype)
t.append(time.time()) # Final time
good_bye_message(t)
return data
if options.gross_atomic_density is not None:
atom = options.gross_atomic_density
rho_atom = extras.numerical_mulliken_charges(atom, qc)
if not grid.is_vector:
mulliken_num = rho_atom[1]
rho_atom = rho_atom[0]
if not options.no_output:
fid = '%s.h5' % options.outputname
display('\nSaving to Hierarchical Data Format file (HDF5)...' +
'\n\t%(o)s' % {'o': fid})
output.hdf5_write(fid,
mode='w',
gname='',
atom=core.numpy.array(atom),
geo_info=qc.geo_info,
geo_spec=qc.geo_spec,
gross_atomic_density=rho_atom,
x=grid.x,
y=grid.y,
z=grid.z)
if not options.is_vector:
output.hdf5_write(
fid,
mode='a',
gname='/numerical_mulliken_population_analysis',
**mulliken_num)
t.append(time.time())
good_bye_message(t)
return rho_atom
if options.mo_tefd is not None:
mos = options.mo_tefd
ao_list = core.ao_creator(qc.geo_spec, qc.ao_spec)
mo_tefd = []
index = []
for i, j in mos:
mo_tefd.append([])
index.append([])
for ii_d in options.drv:
display('\nMO-TEFD: %s->%s %s-component' % (i, j, ii_d))
tefd = extras.mo_transition_flux_density(i,
j,
qc,
drv=ii_d,
ao_list=ao_list)
mo_tefd[-1].append(tefd)
index[-1].append('%s->%s:%s' % (i, j, ii_d))
if not options.no_output:
from numpy import array
fid = '%s.h5' % options.outputname
display('\nSaving to Hierarchical Data Format file (HDF5)...' +
'\n\t%(o)s' % {'o': fid})
HDF5_File = output.hdf5_open(fid, mode='w')
data = {
'geo_info': array(qc.geo_info),
'geo_spec': array(qc.geo_spec),
'mo_tefd:info': array(index),
'mo_tefd': array(mo_tefd),
'x': grid.x,
'y': grid.y,
'z': grid.z
}
output.hdf5_append(data, HDF5_File, name='')
HDF5_File.close()
t.append(time.time())
good_bye_message(t)
return mo_tefd
t.append(time.time()) # A new time step
# Compute the (derivative of the) electron density
if options.no_slice:
data = core.rho_compute_no_slice(qc,
drv=options.drv,
laplacian=options.laplacian,
return_components=False)
else:
data = core.rho_compute(qc,
drv=options.drv,
slice_length=options.slice_length,
laplacian=options.laplacian,
numproc=options.numproc)
if options.drv is None:
rho = data
elif options.laplacian:
rho, delta_rho, laplacian_rho = data
else:
rho, delta_rho = data
# Compute the reduced electron density if requested
if options.z_reduced_density:
if grid.is_vector:
display('\nSo far, reducing the density is not supported for ' +
'vector grids.\nSkipping the reduction...\n')
elif options.drv is not None:
display(
'\nSo far, reducing the density is not supported for ' +
'the derivative of the density.\nSkipping the reduction...\n')
else:
from scipy import integrate
display('\nReducing the density with respect to the z-axis.\n')
rho = integrate.simps(rho,
grid.x,
dx=grid.delta_[0],
axis=0,
even='avg')
rho = integrate.simps(rho,
grid.y,
dx=grid.delta_[1],
axis=0,
even='avg')
t.append(time.time()) # A new time step
# Generate the output requested
if not options.no_output:
output_written = output.main_output(rho,
qc.geo_info,
qc.geo_spec,
outputname=options.outputname,
otype=options.otype,
data_id='rho',
omit=['vmd', 'mayavi'],
mo_spec=qc.mo_spec)
if options.drv is not None:
output_written.extend(
output.main_output(delta_rho,
qc.geo_info,
qc.geo_spec,
outputname=options.outputname,
otype=options.otype,
data_id='delta_rho',
omit=['vmd', 'mayavi'],
mo_spec=qc.mo_spec,
drv=options.drv))
if options.laplacian:
output_written.extend(
output.main_output(laplacian_rho,
qc.geo_info,
qc.geo_spec,
outputname=options.outputname +
'_laplacian',
otype=options.otype,
data_id='laplacian_rho',
omit=['vmd', 'mayavi'],
mo_spec=qc.mo_spec))
if 'vmd' in options.otype:
# Create VMD network
display('\nCreating VMD network file...' +
'\n\t%(o)s.vmd' % {'o': options.outputname})
cube_files = []
for i in output_written:
if i.endswith('.cb'):
cube_files.append(i)
output.vmd_network_creator(options.outputname,
cube_files=cube_files)
t.append(time.time()) # Final time
good_bye_message(t)
if 'mayavi' in options.otype:
plt_data = [rho]
datalabels = ['rho']
if options.drv is not None:
plt_data.extend(delta_rho)
datalabels.extend(
['d/d%s of %s' % (ii_d, 'rho') for ii_d in options.drv])
if options.laplacian:
plt_data.append(laplacian_rho)
datalabels.append('laplacian of rho')
output.main_output(plt_data,
qc.geo_info,
qc.geo_spec,
otype='mayavi',
datalabels=datalabels)
# Return the computed data, i.e., rho for standard, and (rho,delta_rho)
# for derivative calculations. For laplacian (rho,delta_rho,laplacian_rho)
return data
def read_gaussian_log(fname,
all_mo=False,
spin=None,
orientation='standard',
i_link=-1,
i_geo=-1,
i_ao=-1,
i_mo=-1,
interactive=True,
**kwargs):
'''Reads all information desired from a Gaussian .log file.
**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.
all_mo : bool, optional
If True, all molecular orbitals are returned.
spin : {None, 'alpha', or 'beta'}, optional
If not None, returns exclusively 'alpha' or 'beta' molecular orbitals.
orientation : string, choices={'input', 'standard'}, optional
Specifies orientation of the molecule in Gaussian nomenclature. [#first]_
i_link : int, default=-1
Selects the file for linked Gaussian jobs.
i_geo : int, default=-1
Selects the geometry section of the output file.
i_ao : int, default=-1
Selects the atomic orbital section of the output file.
i_mo : int, default=-1
Selects the molecular orbital section of the output file.
interactive : bool
If True, the user is asked to select the different sets.
**Returns:**
qc (class QCinfo) with attributes geo_spec, geo_info, ao_spec, ao_spherical, mo_spec, etot :
See :ref:`Central Variables` for details.
.. [#first] Attention: The MOs in the output are only valid for the standard orientation!
'''
if isinstance(fname, str):
filename = fname
fname = descriptor_from_file(filename, index=0)
else:
filename = fname.name
flines = fname.readlines() # Read the WHOLE file into RAM
if isinstance(fname, str):
fname.close() # Leave existing file descriptors alive
# Search the file the specific sections
count = {
'link': 0,
'geometry': 0,
'geometry_input': 0,
'atomic orbitals': 0,
'molecular orbitals': [],
'state': []
}
def check_sel(count, i, interactive=False, default=-1):
if count == 0:
raise IndexError
elif count == 1:
return 0
message = '\tPlease give an integer from 0 to {0} (default: {0}): '.format(
count - 1)
try:
if interactive:
i = raw_input(message)
i = default if i == '' else int(i)
i = range(count)[i]
except (IndexError, ValueError):
raise IOError(message.replace(':', '!'))
else:
display('\tSelecting the %s' %
('last element.' if
(i == count - 1) else 'element %d.' % i))
return i
# Go through the file line by line
for il in range(len(flines)):
line = flines[il] # The current line as string
# Check the file for keywords
if ' Entering Link 1' in line:
count['link'] += 1
try:
display('\tFound %d linked GAUSSIAN files.' % count['link'])
i_link = check_sel(count['link'], i_link, interactive=interactive)
except IndexError:
raise IOError('Found no `Entering Link 1` keyword!')
cartesian_basis = True
c_link = 0
# Go through the file line by line
for il in range(len(flines)):
line = flines[il] # The current line as string
thisline = line.split() # The current line split into segments
# Check the file for keywords
if ' Entering Link 1' in line:
c_link += 1
if i_link == (c_link - 1):
if ' orientation:' in line:
if '%s orientation:' % orientation in line.lower():
count['geometry'] += 1
if 'input orientation:' in line.lower():
count['geometry_input'] += 1
elif 'Standard basis:' in line or 'General basis read from cards:' in line:
# Check if a cartesian basis has been applied
if '(5D, 7F)' in line:
cartesian_basis = False
elif '(6D, 10F)' not in line:
raise IOError(
'Please apply a Spherical Harmonics (5D, 7F) or ' +
'a Cartesian Gaussian Basis Set (6D, 10F)!')
elif 'AO basis set' in line:
count['atomic orbitals'] += 1
elif 'The electronic state is ' in line:
count['state'].append(thisline[-1][:-1])
elif 'Orbital Coefficients:' in line:
mo_type = thisline[0]
if mo_type != 'Beta':
count['molecular orbitals'].append(mo_type)
else:
count['molecular orbitals'][-1] = 'Alpha&Beta'
display('\nContent of the GAUSSIAN .log file:')
display('\tFound %d geometry section(s). (%s orientation)' %
(count['geometry'], orientation))
try:
i_geo = check_sel(count['geometry'], i_geo, interactive=interactive)
except IndexError:
count['geometry'] = count['geometry_input']
orientation = 'input'
display('\Looking for "Input orientation": \n' +
'\tFound %d geometry section(s). (%s orientation)' %
(count['geometry'], orientation))
try:
i_geo = check_sel(count['geometry'],
i_geo,
interactive=interactive)
except IndexError:
raise IOError('Found no geometry section!' +
' Are you sure this is a GAUSSIAN .log file?')
try:
display('\tFound %d atomic orbitals section(s) %s.' %
(count['atomic orbitals'],
'(6D, 10F)' if cartesian_basis else '(5D, 7F)'))
i_ao = check_sel(count['atomic orbitals'],
i_ao,
interactive=interactive)
except IndexError:
raise IOError('Write GFINPUT in your GAUSSIAN route section to print' +
' the basis set information!')
try:
display('\tFound the following %d molecular orbitals section(s):' %
len(count['molecular orbitals']))
except IndexError:
raise IOError(
'Write IOP(6/7=3) in your GAUSSIAN route section to print\n' +
' all molecular orbitals!')
for i, j in enumerate(count['molecular orbitals']):
string = '\t\tSection %d: %s Orbitals' % (i, j)
try:
string += ' (electronic state: %s)' % count['state'][i]
except IndexError:
pass
display(string)
i_mo = check_sel(len(count['molecular orbitals']),
i_mo,
interactive=interactive)
if spin is not None:
if spin != 'alpha' and spin != 'beta':
raise IOError('`spin=%s` is not a valid option' % spin)
else:
display('Reading only molecular orbitals of spin %s.' % spin)
# Set a counter for the AOs
basis_count = 0
# Initialize some variables
sec_flag = None
skip = 0
c_link = 0
c_geo = 0
c_ao = 0
c_mo = 0
c_sao = 0
old_ao = -1
orb_sym = []
qc = QCinfo()
index = []
# Go through the file line by line
for il in range(len(flines)):
line = flines[il] # The current line as string
thisline = line.split() # The current line split into segments
# Check the file for keywords
if ' Entering Link 1' in line:
c_link += 1
if i_link == (c_link - 1):
if '%s orientation:' % orientation in line.lower():
# The section containing information about
# the molecular geometry begins
if i_geo == c_geo:
qc.geo_info = []
qc.geo_spec = []
sec_flag = 'geo_info'
c_geo += 1
skip = 4
elif 'Standard basis:' in line or 'General basis read from cards:' in line:
# Check if a cartesian basis has been applied
if '(5D, 7F)' in line:
cartesian_basis = False
elif '(6D, 10F)' not in line:
raise IOError(
'Please apply a Spherical Harmonics (5D, 7F) or ' +
'a Cartesian Gaussian Basis Sets (6D, 10F)!')
elif 'AO basis set' in line:
# The section containing information about
# the atomic orbitals begins
if i_ao == c_ao:
qc.ao_spec = []
if not cartesian_basis:
qc.ao_spherical = []
sec_flag = 'ao_info'
c_ao += 1
basis_count = 0
bNew = True # Indication for start of new AO section
elif 'Orbital symmetries:' in line:
sec_flag = 'mo_sym'
add = ''
orb_sym = []
elif 'Orbital Coefficients:' in line:
# The section containing information about
# the molecular orbitals begins
if (i_mo == c_mo):
sec_flag = 'mo_info'
mo_type = count['molecular orbitals'][i_mo]
qc.mo_spec = []
offset = 0
add = ''
orb_spin = []
if orb_sym == []:
if 'Alpha' in mo_type:
add = '_a'
orb_spin = ['alpha'] * basis_count
orb_sym = ['A1' + add] * basis_count
if 'Beta' in mo_type:
add = '_b'
orb_spin += ['beta'] * basis_count
orb_sym += ['A1' + add] * basis_count
for i in range(len(orb_sym)):
# for numpy version < 1.6
c = ((numpy.array(orb_sym[:i + 1]) == orb_sym[i]) !=
0).sum()
# for numpy version >= 1.6 this could be used:
#c = numpy.count_nonzero(numpy.array(orb_sym[:i+1]) == orb_sym[i])
qc.mo_spec.append({
'coeffs': numpy.zeros(basis_count),
'energy': 0.,
'sym': '%d.%s' % (c, orb_sym[i])
})
if orb_spin != []:
qc.mo_spec[-1]['spin'] = orb_spin[i]
if mo_type != 'Beta':
c_mo += 1
bNew = True # Indication for start of new MO section
elif 'E(' in line:
qc.etot = float(line.split('=')[1].split()[0])
else:
# Check if we are in a specific section
if sec_flag == 'geo_info':
if not skip:
qc.geo_info.append(
[thisline[1], thisline[0], thisline[1]])
qc.geo_spec.append([float(ij) for ij in thisline[3:]])
if '-----------' in flines[il + 1]:
sec_flag = None
else:
skip -= 1
if sec_flag == 'ao_info':
# Atomic orbital section
if ' ****' in line:
# There is a line with stars after every AO
bNew = True
# If there is an additional blank line, the AO section is complete
if flines[il + 1].split() == []:
sec_flag = None
elif bNew:
# The following AOs are for which atom?
bNew = False
at_num = int(thisline[0]) - 1
ao_num = 0
elif len(thisline) == 4:
# AO information section
# Initialize a new dict for this AO
ao_num = 0 # Initialize number of atomic orbiatls
ao_type = thisline[0].lower() # Type of atomic orbital
pnum = int(thisline[1]) # Number of primatives
for i_ao in ao_type:
# Calculate the degeneracy of this AO and increase basis_count
basis_count += l_deg(
lquant[i_ao], cartesian_basis=cartesian_basis)
qc.ao_spec.append({
'atom': at_num,
'type': i_ao,
'pnum': pnum,
'coeffs': numpy.zeros((pnum, 2))
})
else:
# Append the AO coefficients
coeffs = numpy.array(line.replace('D', 'e').split(),
dtype=numpy.float64)
for i_ao in range(len(ao_type)):
qc.ao_spec[-len(ao_type) +
i_ao]['coeffs'][ao_num, :] = [
coeffs[0], coeffs[1 + i_ao]
]
ao_num += 1
if sec_flag == 'mo_sym':
if 'electronic state' in line:
sec_flag = None
else:
info = line[18:].replace('(', '').replace(')',
'').split()
if 'Alpha' in line:
add = '_a'
elif 'Beta' in line:
add = '_b'
for i in info:
orb_sym.append(i + add)
if sec_flag == 'mo_info':
# Molecular orbital section
info = line[:21].split()
if info == []:
coeffs = line[21:].split()
if bNew:
index = [offset + i for i in range(len(coeffs))]
bNew = False
else:
for i, j in enumerate(index):
qc.mo_spec[j]['occ_num'] = int(
'O' in coeffs[i])
if mo_type not in 'Alpha&Beta':
qc.mo_spec[j]['occ_num'] *= 2
elif 'Eigenvalues' in info:
coeffs = line[21:].replace('-', ' -').split()
if mo_type == 'Natural':
key = 'occ_num'
else:
key = 'energy'
for i, j in enumerate(index):
qc.mo_spec[j][key] = float(coeffs[i])
else:
coeffs = line[21:].replace('-', ' -').split()
if not cartesian_basis and offset == 0:
if old_ao != line[:14].split()[-1] or len(
line[:14].split()) == 4:
old_ao = line[:14].split()[-1]
c_sao += 1
i = c_sao - 1
l = lquant[line[13].lower()]
m = line[14:21].replace(' ', '').lower()
p = 'yzx'.find(m) if len(m) == 1 else -1
if p != -1:
m = p - 1
elif m == '':
m = 0
else:
m = int(m)
qc.ao_spherical.append([i, (l, m)])
for i, j in enumerate(index):
qc.mo_spec[j]['coeffs'][int(info[0]) - 1] = float(
coeffs[i])
if int(info[0]) == basis_count:
bNew = True
offset = index[-1] + 1
if index[-1] + 1 == len(orb_sym):
sec_flag = None
orb_sym = []
# Are all MOs requested for the calculation?
if not all_mo:
for i in range(len(qc.mo_spec))[::-1]:
if qc.mo_spec[i]['occ_num'] < 0.0000001:
del qc.mo_spec[i]
if spin is not None:
if orb_spin == []:
raise IOError(
'You requested `%s` orbitals, but None of them are present.' %
spin)
else:
for i in range(len(qc.mo_spec))[::-1]:
if qc.mo_spec[i]['spin'] != spin:
del qc.mo_spec[i]
# Convert geo_info and geo_spec to numpy.ndarrays
qc.format_geo(is_angstrom=True)
return qc
def mo_select(mo_spec, fid_mo_list, strict=False):
'''Selects molecular orbitals from an external file or a list of molecular
orbital labels.
**Parameters:**
mo_spec :
See :ref:`Central Variables` for details.
strict : bool, optional
If True, orbkit will follow strictly the fid_mo_list, i.e., the order of
the molecular orbitals will be kept and multiple occurrences of items
will evoke multiple calculations of the respective molecular orbitals.
fid_mo_list : str, `'all_mo'`, or list
| If fid_mo_list is a str, specifies the filename of the molecular orbitals list.
| If fid_mo_list is 'all_mo', creates a list containing all molecular orbitals.
| If fid_mo_list is a list, provides a list (or a list of lists) of molecular
orbital labels.
**Supported Formats:**
Integer List (Counting from **ONE**!)::
1 2 3
5 4
h**o lumo+2:lumo+4
List with Symmetry Labels::
1.1 2.1 1.3
1.1 4.1
4.1 2.3 2.1
**Returns:**
Dictionary with following Members:
:mo: - List of molecular orbital labels.
:mo_ii: - List of molecular orbital indices.
:mo_spec: - Selected elements of mo_spec. See :ref:`Central Variables` for details.
:mo_in_file: - List of molecular orbital labels within the fid_mo_list file.
:sym_select: - If True, symmetry labels have been used.
..attention:
For **unrestricted** calculations, orbkit adds `_a` (alpha) or `_b` (beta) to
the symmetry labels, e.g., `1.1_a`.
If you have specified the option `spin=alpha` or `spin=beta`, only the
alpha or the beta orbitals are taken into account for the counting
within the Integer List.
'''
import re
display('\nProcessing molecular orbital list...')
mo_in_file = []
selected_mo = []
sym_select = False
def assign_selected_mo(selected_mo,
mo_spec,
strict=False,
what=lambda x, y: y[x]['sym']):
selected_mo_spec = []
selected_mo_ii = []
for i in selected_mo:
is_present = False
for k in range(len(mo_spec)):
if (what(k, mo_spec) == i):
is_present = True
if strict or (i not in selected_mo_ii):
selected_mo_spec.append(mo_spec[k])
selected_mo_ii.append(what(k, mo_spec))
if not is_present:
raise IOError('Cannot find %s in mo_spec' % i)
selected_mo_ii = numpy.array(selected_mo_ii)
return selected_mo_spec, selected_mo_ii
def expr2int(expr):
if isinstance(expr, int):
return expr
x = 0
for i in re.findall(r'\d+|[+-]\d+', expr):
x += int(i)
return x
def get_selection(selected_mo):
mo_occup = numpy.array([i['occ_num'] for i in mo_spec])
h**o = (mo_occup > 0.).nonzero()[0][-1] + 1 # molden numbering
lumo = (mo_occup > 0.).nonzero()[0][-1] + 1 + 1 # molden numbering
mo_energy = numpy.array([i['energy'] for i in mo_spec])
last_bound = sum(mo_energy <= 0.0) # molden numbering
sel = []
for i in selected_mo:
i = i.lower().replace('h**o', str(h**o)).replace('lumo', str(lumo))
i = i.replace('last_bound', str(last_bound))
if ':' in i:
k = [1, len(mo_spec) + 1, 1]
i = i.split(':')
for ik, j in enumerate(i):
if j != '': k[ik] = j
i = list(range(*[expr2int(j) for j in k]))
sel.extend(i)
else:
sel.append(int(i))
return sel
if isinstance(fid_mo_list, str) and fid_mo_list.lower() == 'all_mo':
selected_mo = numpy.array(numpy.arange(len(mo_spec)) + 1,
dtype=numpy.str)
mo_in_file = [selected_mo]
selected_mo_spec = mo_spec
selected_mo_ii = numpy.array([i['sym'] for i in selected_mo_spec])
else:
if isinstance(fid_mo_list, str) and not path.exists(fid_mo_list):
if ',' in fid_mo_list:
fid_mo_list = fid_mo_list.split(',')
else:
fid_mo_list = [fid_mo_list]
if isinstance(fid_mo_list, list):
for i in fid_mo_list:
if not isinstance(i, list):
i = i.split(',') if isinstance(i, str) else [i]
selected_mo.extend(list(map(str, i)))
mo_in_file.append(list(map(str, i)))
else:
try:
fid = open(fid_mo_list, 'r')
flines = fid.readlines()
fid.close()
for line in flines:
integer = line.replace(',', ' ').split()
mo_in_file.append(integer)
selected_mo.extend(integer)
except:
raise IOError('The selected mo-list (%(m)s) is not valid!' %
{'m': fid_mo_list} +
'\ne.g.\n\t1\t3\n\t2\t7\t9\n')
# Print some information
for i, j in enumerate(mo_in_file):
display('\tLine %d: %s' % (i + 1, ', '.join(j)))
# Check if the molecular orbitals are specified by symmetry
# (e.g. 1.1 in MOLPRO nomenclature) or
# by the number in the input file (e.g. 1)
try: # Try to convert selections into integer
for i in selected_mo:
if isinstance(i, int):
continue
i = i.replace('h**o',
'1').replace('lumo',
'2').replace('last_bound', '3')
for r in ['-', '+', ':']:
i = i.replace(r, '')
int(i)
except ValueError:
sym_select = True
errors = []
for i in range(len(selected_mo)):
if not '.' in selected_mo[i]:
errors.append(i)
if errors:
err = [selected_mo[i] for i in errors]
raise IOError(
'`%s` are no valid labels according ' % ', '.join(err) +
'to the MOLPRO nomenclature, e.g., `5.1` or `5.A1`.' +
'\n\tHint: You cannot mix integer numbering and MOLPRO\'s '
+ 'symmetry labels')
if sym_select:
what = lambda x, y: y[x]['sym']
selected_mo_spec, selected_mo_ii = assign_selected_mo(
selected_mo, mo_spec, strict=strict, what=what)
else:
selected_mo = get_selection(selected_mo)
if not strict:
selected_mo = list(map(int, selected_mo))
selected_mo.sort()
selected_mo = list(map(str, selected_mo))
what = lambda x, y: str(x + 1)
selected_mo_spec, selected_mo_ii = assign_selected_mo(
selected_mo, mo_spec, strict=strict, what=what)
selected_mo = selected_mo_ii
for i in range(len(mo_in_file)):
mo_in_file[i] = list(map(str, get_selection(mo_in_file[i])))
# Print some information
display('\nThe following orbitals will be considered...')
for i, j in enumerate(mo_in_file):
display('\tLine %d: %s' % (i + 1, ', '.join(j)))
display('')
return {
'mo': selected_mo,
'mo_ii': selected_mo_ii,
'mo_spec': selected_mo_spec,
'mo_in_file': mo_in_file,
'sym_select': sym_select
}
def main_output(data,
qc=None,
outputname='data',
otype='auto',
gname='',
drv=None,
omit=[],
datalabels='',
mode='w',
**kwargs):
'''Creates the requested output.
**Parameters:**
data : numpy.ndarray, shape=N, shape=((NDRV,) + N), shape=(n, (NDRV,) + N) or list of numpy.ndarrays
Contains the output data. The shape (N) depends on the grid and the data, i.e.,
3d for regular grid, 1d for vector grid.
qc : class or dict
QCinfo class or dictionary containing the following attributes/keys.
See :ref:`Central Variables` for details.
outputname : str or list of str
Contains the base name of the output file. If outputname contains @, string will be split and first
part interpreted as outputname and second as gname (cf. Parameters:gname).
otype : str or list of str, optional
Contains the output file type. Possible options:
'auto', 'h5', 'cb', 'am', 'hx', 'vmd', 'mayavi'
If otype='native', a native input file will be written, the type of which may be specifie by
ftype='numpy'.
gname : str, optional
For native, HDF5, or npz output, specifies the group, where the data will be stored.
drv : None, list of str or list of list of str, optional
If not None, a 4d(regular)/2d(vector) input data array will be expected
with NDRV = len(drv). Specifies the file labels, i.e. e.g., data_d{drv}.cube for 4d array.
For 5d arrays i.e., data_0_d{drv}.cube
datalabels : list of str, optional
If not empty, the output file types specified here are omitted.
omit : list of str, optional
If not empty, the output file types specified here are omitted.
mode : str={'r', 'w', 'a'}, optional
Specifies the mode used to open the file (native, HDF5, or npz).
**Note:**
All additional keyword arguments are forwarded to the output functions.
'''
if otype is None or otype == []:
return []
if isinstance(outputname, str):
if '@' in outputname:
outputname, gname = outputname.split('@')
if isinstance(otype, str):
if otype == 'auto':
outputname, otype = path.splitext(outputname)
otype = otype[1:]
otype = [otype]
elif isinstance(otype, list) and len(otype) == 1:
if otype[0] == 'auto':
outputname, otype = path.splitext(outputname)
otype = [otype[1:]]
else:
for iot in range(len(otype)):
if otype[iot] == 'auto':
outputname, tmp = path.splitext(outputname)
if tmp != '':
otype[iot] = tmp[1:]
# Catch our native format before all else
# We can't figure this out by the file ending alone
# as we support hdf5 for output of both grid-based data
# as well as our internal format
output_written = []
internals = [i for i in range(len(otype)) if otype[i] == 'native']
if len(internals) > 0:
if not isinstance(data, list):
data = [data]
if isinstance(outputname, str):
outputname = [outputname for _ in data]
if 'ftype' in kwargs.keys():
if isinstance(kwargs['ftype'], str):
ftype = [kwargs['ftype'] for _ in data]
else:
ftype = ['numpy' for _ in data]
if 'group' in kwargs.keys():
if isinstance(kwargs['group'], str):
group = [kwargs['group'] for _ in data]
else:
group = [i.__class__.__name__.lower() for i in data]
display('Writing native input file...')
for i, oname in enumerate(outputname):
output_written.append(
write_native(data[i],
oname,
ftype[i],
mode=mode,
gname=path.join(gname, group[i])))
display('\n'.join(['\t' + i for i in output_written]))
else:
print_waring = False
output_not_possible = (grid.is_vector and not grid.is_regular)
# Shape shall be (Ndrv,Ndata,Nx,Ny,Nz) or (Ndrv,Ndata,Nxyz)
data = numpy.array(data)
dims = 1 if grid.is_vector else 3
shape = data.shape
if drv is not None and isinstance(drv, str):
drv = [drv]
if data.ndim < dims:
output_not_possible = True
display('data.ndim < ndim of grid')
elif data.ndim == dims: # 3d data set
data = data[numpy.newaxis, numpy.newaxis]
elif data.ndim == dims + 1: # 4d data set
if drv is not None:
data = data[:, numpy.newaxis]
else:
data = data[numpy.newaxis]
elif data.ndim == dims + 2: # 5d data set check if drv matches Ndrv
if drv is None or len(drv) != data.shape[0]:
drv = list(range(data.shape[0]))
elif data.ndim > dims + 2:
output_not_possible = True
display('data.ndim > (ndim of grid) +2')
if 'vmd' in otype and not ('cb' in otype or 'cube' in otype):
otype.append('cube')
if 'hx' in otype and not 'am' in otype:
otype.append('am')
otype = [i for i in otype if i not in omit]
otype_synonyms = [synonyms[i] for i in otype]
otype_ext = dict(zip(otype_synonyms, otype))
# Convert the data to a regular grid, if possible
is_regular_vector = (grid.is_vector and grid.is_regular)
if is_regular_vector:
display(
'\nConverting the regular 1d vector grid to a 3d regular grid.'
)
grid.vector2grid(*grid.N_)
data = numpy.array(grid.mv2g(data))
isstr = isinstance(outputname, str)
if isinstance(datalabels, str):
if data.shape[1] > 1:
datalabels = numpy.array([
str(idata) + ',' + datalabels
for idata in range(data.shape[1])
])
else:
datalabels = numpy.array([datalabels])
elif isinstance(datalabels, list):
datalabels = numpy.array(datalabels)
if drv is not None:
fid = '%(f)s_d%(d)s.'
datalabel_id = 'd/d%(d)s %(f)s'
contents = {
'axis:0':
numpy.array(
['d/d%s' % i if i is not None else str(i) for i in drv]),
'axis:1':
datalabels
}
it = enumerate(drv)
elif data.shape[0] > 1:
fid = '%(f)s_%(d)s.'
datalabel_id = '%(d)s %(f)s'
it = enumerate(data.shape[0])
contents = {
'axis:0': numpy.arange(data.shape[0]).astype(str),
'axis:1': datalabels
}
else:
fid = '%(f)s.'
datalabel_id = '%(f)s'
it = [(0, None)]
if data.shape[1] > 1:
contents = {'axis:0': datalabels}
else:
contents = datalabels
cube_files = []
all_datalabels = []
for idrv, jdrv in it:
datasetlabels = []
for idata in range(data.shape[1]):
if isstr:
f = {
'f':
outputname + '_' +
str(idata) if data.shape[1] > 1 else outputname,
'd':
jdrv
}
else:
f = {'f': outputname[idata], 'd': jdrv}
c = {'f': datalabels[idata], 'd': jdrv}
datalabel = datalabel_id % c
datasetlabels.append(datalabel)
if 'am' in otype_synonyms and not print_waring:
if output_not_possible: print_waring = True
else:
filename = fid % f + otype_ext['am']
display('\nSaving to ZIBAmiraMesh file...\n\t' +
filename)
amira_creator(data[idrv, idata], filename)
output_written.append(filename)
if 'hx' in otype_synonyms and not print_waring:
if output_not_possible: print_waring = True
else:
filename = fid % f + otype_ext['hx']
display('\nCreating ZIBAmira network file...\n\t' +
filename)
hx_network_creator(data[idrv, idata], filename)
output_written.append(filename)
if 'cube' in otype_synonyms and not print_waring:
if output_not_possible: print_waring = True
elif qc is None:
display(
'\nFor cube file output `qc` is a required keyword parameter in `main_output`.'
)
else:
filename = fid % f + otype_ext['cube']
display('\nSaving to cube file...\n\t' + filename)
cube_creator(data[idrv, idata],
filename,
qc.geo_info,
qc.geo_spec,
comments=datalabel,
**kwargs)
output_written.append(filename)
cube_files.append(filename)
all_datalabels.extend(datasetlabels)
if 'vmd' in otype_synonyms and not print_waring:
if output_not_possible: print_waring = True
else:
filename = (outputname if isstr else
outputname[-1]) + '.' + otype_ext['vmd']
display('\nCreating VMD network file...\n\t' + filename)
vmd_network_creator(filename, cube_files=cube_files, **kwargs)
output_written.append(filename)
if 'h5' in otype_synonyms:
filename = (outputname
if isstr else outputname[-1]) + '.' + otype_ext['h5']
display('\nSaving to Hierarchical Data Format file (HDF5)...\n\t' +
filename)
hdf5_creator(data.reshape(shape),
filename,
qcinfo=qc,
gname=gname,
ftype='hdf5',
contents=contents,
mode=mode,
**kwargs)
output_written.append(filename)
if 'npz' in otype_synonyms:
filename = (outputname if isstr else outputname[-1])
display('\nSaving to a compressed .npz archive...\n\t' + filename +
'.npz')
hdf5_creator(data.reshape(shape),
filename,
qcinfo=qc,
gname=gname,
ftype='numpy',
contents=contents,
mode=mode,
**kwargs)
output_written.append(filename)
if 'mayavi' in otype_synonyms:
if output_not_possible: print_waring = True
else:
display('\nDepicting the results with MayaVi...\n\t')
if drv == ['x', 'y', 'z'] or drv == [0, 1, 2]:
is_vectorfield = True
data = numpy.swapaxes(data, 0, 1)
datalabels = datalabels
else:
is_vectorfield = False
data = data.reshape((-1, ) + grid.get_shape())
datalabels = all_datalabels
view_with_mayavi(grid.x,
grid.y,
grid.z,
data,
is_vectorfield=is_vectorfield,
geo_spec=qc.geo_spec,
datalabels=datalabels,
**kwargs)
if print_waring:
display(
'For a non-regular vector grid (`if grid.is_vector and not grid.is_regular`)'
)
display('only HDF5 is available as output format...')
display('Skipping all other formats...')
if is_regular_vector:
# Convert the data back to a regular vector grid
grid.grid2vector()
return output_written
def gross_atomic_density(atom,
qc,
bReturnmo=False,
ao_list=None,
mo_list=None,
drv=None):
r'''Computes the gross atomic density with respect to the selected atoms.
.. math::
\rho^a = \sum_i^{N_{\rm MO}} {\rm occ}_i \cdot \varphi_i^a \cdot \varphi_i
**Parameters:**
atom : 'all' or int or list of int
Specifies the atoms (counting from one) for which the gross atomic density
will be computed.
If (atom == 'all') or (atom == -1), computes the gross atomic density
for all atoms.
qc.geo_spec, qc.geo_info, qc.ao_spec, qc.mo_spec :
See :ref:`Central Variables` for details.
bReturnmo : bool, optional
If True, the gross atomic molecular orbitals are additionally returned.
**Returns:**
rho_atom : list of numpy.ndarrays, shape=(len(atoms,) + N)
Contains the atom gross atomic density on a grid.
mo_atom : list of numpy.ndarrays, shape=(len(atoms,NMO) + N)
Contains the NMO=len(mo_spec) gross atomic molecular orbitals on a grid.
'''
if (atom == 'all' or atom == -1):
atom = range(1, len(qc.geo_info) + 1)
atom, index = atom2index(atom, geo_info=qc.geo_info)
display('Computing the gross atomic density with respect to ' +
'the atom(s) (internal numbering)')
outp = '\t['
for i, a in enumerate(atom):
if not i % 10 and i != 0:
outp += '\n\t'
outp += '%d,\t' % a
display('%s]\n' % outp[:-2])
display('\tCalculating ao_list & mo_list')
if ao_list is None:
ao_list = core.ao_creator(qc.geo_spec, qc.ao_spec, drv=drv)
if mo_list is None:
mo_list = core.mo_creator(ao_list, qc.mo_spec)
display('\tCalculating the gross atomic density')
N = mo_list.shape[1:]
if bReturnmo: mo_atom = [[] for a in index]
rho_atom = [numpy.zeros(N) for a in index]
for i, a in enumerate(index):
display('\t\tFinished %d of %d' % (i + 1, len(index)))
ao_index = []
ao = []
ll = 0
for ii in qc.ao_spec:
for l in range(core.l_deg(l=ii['type'])):
if ii['atom'] == a:
ao_index.append(ll)
ao.append(ao_list[ll])
ll += 1
for ii_mo, spec in enumerate(qc.mo_spec):
mo_info = numpy.zeros(N)
for jj in range(len(ao_index)):
mo_info += spec['coeffs'][ao_index[jj]] * ao[jj]
rho_atom[i] += spec['occ_num'] * mo_list[ii_mo] * mo_info
if bReturnmo: mo_atom[i].append(mo_info)
string = 'Returning the gross atomic density'
if bReturnmo:
display(string + ' and\n\tthe gross atomic molecular orbitals')
return rho_atom, mo_atom
else:
display(string)
return rho_atom
