class Hamiltonian(object):
def __init__(self, system, terms, grid=None, idiot_proof=True):
'''
**Arguments:**
system
The System object for which the energy must be computed.
terms
The terms in the Hamiltonian.
**Optional arguments:**
grid
The integration grid, in case some terms need one.
idiot_proof
When set to False, the kinetic energy, external potential and
Hartree terms are not added automatically and a error is raised
when no exchange is present.
'''
# check arguments:
if len(terms) == 0:
raise ValueError('At least one term must be present in the Hamiltonian.')
for term in terms:
if term.require_grid and grid is None:
raise TypeError('The term %s requires a grid, but not grid is given.' % term)
# Assign attributes
self.system = system
self.terms = list(terms)
self.grid = grid
if idiot_proof:
# Check if an exchange term is present
if not any(term.exchange for term in self.terms):
raise ValueError('No exchange term is given and idiot_proof option is set to True.')
# Add standard terms if missing
# 1) Kinetic energy
if sum(isinstance(term, KineticEnergy) for term in terms) == 0:
self.terms.append(KineticEnergy())
# 2) Hartree (or HatreeFock, which is a subclass of Hartree)
if sum(isinstance(term, Hartree) for term in terms) == 0:
self.terms.append(Hartree())
# 3) External Potential
if sum(isinstance(term, ExternalPotential) for term in terms) == 0:
self.terms.append(ExternalPotential())
# Create a cache for shared intermediate results. This cache should only
# be used for derived quantities that depend on the wavefunction and
# need to be updated at each SCF cycle.
self.cache = Cache()
# bind the terms to this hamiltonian such that certain shared
# intermediated results can be reused for the sake of efficiency.
for term in self.terms:
term.set_hamiltonian(self)
def add_term(self, term):
'''Add a new term to the hamiltonian'''
self.terms.append(term)
term.set_hamiltonian(self)
def clear(self):
'''Mark the properties derived from the wfn as outdated.
This method does not recompute anything, but just marks operators
as outdated. They are recomputed as they are needed.
'''
self.cache.clear()
def compute(self):
'''Compute the energy.
**Returns:**
The total energy, including nuclear-nuclear repulsion.
'''
total = 0.0
for term in self.terms:
energy = term.compute()
self.system.extra['energy_%s' % term.label] = energy
total += energy
energy = self.system.compute_nucnuc()
self.system.extra['energy_nn'] = energy
total += energy
self.system.extra['energy'] = total
# Store result in chk file
self.system.update_chk('extra')
return total
def log_energy(self):
'''Write an overview of the last energy computation on screen'''
log('Contributions to the energy:')
log.hline()
log(' Energy term Value')
log.hline()
for term in self.terms:
energy = self.system.extra['energy_%s' % term.label]
log('%50s %20.12f' % (term.label, energy))
log('%50s %20.12f' % ('nn', self.system.extra['energy_nn']))
log('%50s %20.12f' % ('total', self.system.extra['energy']))
log.hline()
log.blank()
def compute_fock(self, fock_alpha, fock_beta):
'''Compute alpha (and beta) Fock matrix(es).
**Arguments:**
fock_alpha
A One-Body operator output argument for the alpha fock matrix.
fock_alpha
A One-Body operator output argument for the beta fock matrix.
In the case of a closed-shell computation, the argument fock_beta is
``None``.
'''
# Loop over all terms and add contributions to the Fock matrix. Some
# terms will actually only evaluate potentials on grids and add these
# results to the total potential on a grid.
for term in self.terms:
term.add_fock_matrix(fock_alpha, fock_beta, postpone_grid=True)
# Collect all the total potentials and turn them into contributions
# for the fock matrix/matrices.
# Collect potentials for alpha electrons
# d = density
if 'dpot_total_alpha' in self.cache:
dpot = self.cache.load('dpot_total_alpha')
self.system.compute_grid_density_fock(self.grid.points, self.grid.weights, dpot, fock_alpha)
# g = gradient
if 'gpot_total_alpha' in self.cache:
gpot = self.cache.load('gpot_total_alpha')
self.system.compute_grid_gradient_fock(self.grid.points, self.grid.weights, gpot, fock_alpha)
if isinstance(self.system.wfn, UnrestrictedWFN):
# Colect potentials for beta electrons
# d = density
if 'dpot_total_beta' in self.cache:
dpot = self.cache.load('dpot_total_beta')
self.system.compute_grid_density_fock(self.grid.points, self.grid.weights, dpot, fock_beta)
# g = gradient
if 'gpot_total_beta' in self.cache:
gpot = self.cache.load('gpot_total_beta')
self.system.compute_grid_gradient_fock(self.grid.points, self.grid.weights, gpot, fock_beta)
class Part(JustOnceClass):
name = None
linear = False # whether the populations are linear in the density matrix.
def __init__(self, coordinates, numbers, pseudo_numbers, grid, moldens, spindens, local, lmax):
'''
**Arguments:**
coordinates
An array (N, 3) with centers for the atom-centered grids.
numbers
An array (N,) with atomic numbers.
pseudo_numbers
An array (N,) with effective charges. When set to None, this
defaults to``numbers.astype(float)``.
grid
The integration grid
moldens
The spin-summed electron density on the grid.
spindens
The spin difference density on the grid. (Can be None)
local
Whether or not to use local (non-periodic) subgrids for atomic
integrals.
lmax
The maximum angular momentum in multipole expansions.
'''
# Init base class
JustOnceClass.__init__(self)
# Some type checking for first three arguments
natom, coordinates, numbers, pseudo_numbers = typecheck_geo(coordinates, numbers, pseudo_numbers)
self._natom = natom
self._coordinates = coordinates
self._numbers = numbers
self._pseudo_numbers = pseudo_numbers
# Assign remaining arguments as attributes
self._grid = grid
self._moldens = moldens
self._spindens = spindens
self._local = local
self._lmax = lmax
# Caching stuff, to avoid recomputation of earlier results
self._cache = Cache()
# Initialize the subgrids
if local:
self._init_subgrids()
# Some screen logging
self._init_log_base()
self._init_log_scheme()
self._init_log_memory()
if log.do_medium:
log.blank()
def __getitem__(self, key):
return self.cache.load(key)
def _get_natom(self):
return self._natom
natom = property(_get_natom)
def _get_coordinates(self):
return self._coordinates
coordinates = property(_get_coordinates)
def _get_numbers(self):
return self._numbers
numbers = property(_get_numbers)
def _get_pseudo_numbers(self):
return self._pseudo_numbers
pseudo_numbers = property(_get_pseudo_numbers)
def _get_grid(self):
return self.get_grid()
grid = property(_get_grid)
def _get_local(self):
return self._local
local = property(_get_local)
def _get_lmax(self):
return self._lmax
lmax = property(_get_lmax)
def _get_cache(self):
return self._cache
cache = property(_get_cache)
def __clear__(self):
self.clear()
def clear(self):
'''Discard all cached results, e.g. because wfn changed'''
JustOnceClass.clear(self)
self.cache.clear()
def get_grid(self, index=None):
'''Return an integration grid
**Optional arguments:**
index
The index of the atom. If not given, a grid for the entire
system is returned. If self.local is False, a full system grid
is always returned.
'''
if index is None or not self.local:
return self._grid
else:
return self._subgrids[index]
def get_moldens(self, index=None, output=None):
result = self.to_atomic_grid(index, self._moldens)
if output is not None:
output[:] = result
return result
def get_spindens(self, index=None, output=None):
result = self.to_atomic_grid(index, self._spindens)
if output is not None:
output[:] = result
return result
def get_wcor(self, index):
'''Return the weight corrections on a grid
See get_grid for the meaning of the optional arguments
'''
raise NotImplementedError
def _init_subgrids(self):
raise NotImplementedError
def _init_log_base(self):
raise NotImplementedError
def _init_log_scheme(self):
raise NotImplementedError
def _init_log_memory(self):
if log.do_medium:
# precompute arrays sizes for certain grids
nbyte_global = self.grid.size*8
nbyte_locals = np.array([self.get_grid(i).size*8 for i in xrange(self.natom)])
# compute and report usage
estimates = self.get_memory_estimates()
nbyte_total = 0
log('Coarse estimate of memory usage for the partitioning:')
log(' Label Memory[GB]')
log.hline()
for label, nlocals, nglobal in estimates:
nbyte = np.dot(nlocals, nbyte_locals) + nglobal*nbyte_global
log('%30s %10.3f' % (label, nbyte/1024.0**3))
nbyte_total += nbyte
log('%30s %10.3f' % ('Total', nbyte_total/1024.0**3))
log.hline()
log.blank()
def get_memory_estimates(self):
return [
('Atomic weights', np.ones(self.natom), 0),
('Promolecule', np.zeros(self.natom), 1),
('Working arrays', np.zeros(self.natom), 2),
]
def to_atomic_grid(self, index, data):
raise NotImplementedError
def compute_pseudo_population(self, index):
grid = self.get_grid(index)
dens = self.get_moldens(index)
at_weights = self.cache.load('at_weights', index)
wcor = self.get_wcor(index)
return grid.integrate(at_weights, dens, wcor)
@just_once
def do_partitioning(self):
self.update_at_weights()
do_partitioning.names = []
def update_at_weights(self):
'''Updates the at_weights arrays in the case (and all related arrays)'''
raise NotImplementedError
@just_once
def do_populations(self):
populations, new = self.cache.load('populations', alloc=self.natom, tags='o')
if new:
self.do_partitioning()
pseudo_populations = self.cache.load('pseudo_populations', alloc=self.natom, tags='o')[0]
if log.do_medium:
log('Computing atomic populations.')
for i in xrange(self.natom):
pseudo_populations[i] = self.compute_pseudo_population(i)
populations[:] = pseudo_populations
populations += self.numbers - self.pseudo_numbers
@just_once
def do_charges(self):
charges, new = self._cache.load('charges', alloc=self.natom, tags='o')
if new:
self.do_populations()
populations = self._cache.load('populations')
if log.do_medium:
log('Computing atomic charges.')
charges[:] = self.numbers - populations
@just_once
def do_spin_charges(self):
if self._spindens is not None:
spin_charges, new = self._cache.load('spin_charges', alloc=self.natom, tags='o')
self.do_partitioning()
if log.do_medium:
log('Computing atomic spin charges.')
for index in xrange(self.natom):
grid = self.get_grid(index)
spindens = self.get_spindens(index)
at_weights = self.cache.load('at_weights', index)
wcor = self.get_wcor(index)
spin_charges[index] = grid.integrate(at_weights, spindens, wcor)
@just_once
def do_moments(self):
ncart = get_ncart_cumul(self.lmax)
cartesian_multipoles, new1 = self._cache.load('cartesian_multipoles', alloc=(self.natom, ncart), tags='o')
npure = get_npure_cumul(self.lmax)
pure_multipoles, new1 = self._cache.load('pure_multipoles', alloc=(self.natom, npure), tags='o')
nrad = self.lmax+1
radial_moments, new2 = self._cache.load('radial_moments', alloc=(self.natom, nrad), tags='o')
if new1 or new2:
self.do_partitioning()
if log.do_medium:
log('Computing cartesian and pure AIM multipoles and radial AIM moments.')
for i in xrange(self.natom):
# 1) Define a 'window' of the integration grid for this atom
center = self.coordinates[i]
grid = self.get_grid(i)
# 2) Compute the AIM
aim = self.get_moldens(i)*self.cache.load('at_weights', i)
# 3) Compute weight corrections
wcor = self.get_wcor(i)
# 4) Compute Cartesian multipole moments
# The minus sign is present to account for the negative electron
# charge.
cartesian_multipoles[i] = -grid.integrate(aim, wcor, center=center, lmax=self.lmax, mtype=1)
cartesian_multipoles[i, 0] += self.pseudo_numbers[i]
# 5) Compute Pure multipole moments
# The minus sign is present to account for the negative electron
# charge.
pure_multipoles[i] = -grid.integrate(aim, wcor, center=center, lmax=self.lmax, mtype=2)
pure_multipoles[i, 0] += self.pseudo_numbers[i]
# 6) Compute Radial moments
# For the radial moments, it is not common to put a minus sign
# for the negative electron charge.
radial_moments[i] = grid.integrate(aim, wcor, center=center, lmax=self.lmax, mtype=3)
def do_all(self):
'''Computes all properties and return a list of their keys.'''
for attr_name in dir(self):
attr = getattr(self, attr_name)
if callable(attr) and attr_name.startswith('do_') and attr_name != 'do_all':
attr()
return list(self.cache.iterkeys(tags='o'))
class Geminal(object):
'''A collection of geminals and optimization routines.
This is just a base class that serves as a template for
specific implementations.
'''
def __init__(self, lf, occ_model, npairs=None, nvirt=None):
'''
**Arguments:**
lf
A LinalgFactory instance.
occ_model
Occupation model
**Optional arguments:**
npairs
Number of electron pairs, if not specified,
npairs = number of occupied orbitals
nvirt
Number of virtual orbitals, if not specified,
nvirt = (nbasis-npairs)
'''
check_type('pairs', npairs, int, type(None))
check_type('virtuals', nvirt, int, type(None))
self._lf = lf
self._nocc = occ_model.noccs[0]
self._nbasis = lf.default_nbasis
if npairs is None:
npairs = occ_model.noccs[0]
elif npairs >= lf.default_nbasis:
raise ValueError('Number of electron pairs (%i) larger than number of basis functions (%i)' %(npairs, self.nbasis))
if nvirt is None:
nvirt = (lf.default_nbasis-npairs)
elif nvirt >= lf.default_nbasis:
raise ValueError('Number of virtuals (%i) larger than number of basis functions (%i)' %(nvirt, self.nbasis))
self._npairs = npairs
self._nvirt = nvirt
self._cache = Cache()
self._ecore = 0
self._geminal = lf.create_two_index(npairs, nvirt)
self._lagrange = lf.create_two_index(npairs, nvirt)
def __call__(self, one, two, core, orb, olp, scf, **kwargs):
'''Optimize geminal coefficients and---if required---find
optimal set of orbitals.
**Arguments:**
one, two
One- and two-body integrals (some Hamiltonian matrix elements).
core
The core energy (not included in 'one' and 'two').
orb
An expansion instance. It contains the MO coefficients
(orbitals).
olp
The AO overlap matrix. A TwoIndex instance.
scf
A boolean. If True: Initializes orbital optimization.
**Keywords:**
See :py:meth:`RAp1rog.solve`
and :py:meth:`RAp1rog.solve_scf`
'''
if scf:
return self.solve_scf(one, two, core, orb, olp, **kwargs)
else:
return self.solve(one, two, core, orb, olp, **kwargs)
def solve(self, one, two, core, orb, olp, **kwargs):
raise NotImplementedError
def solve_scf(self, one, two, core, orb, olp, **kwargs):
raise NotImplementedError
def _get_nbasis(self):
'''The number of basis functions'''
return self._nbasis
nbasis = property(_get_nbasis)
def _get_nocc(self):
'''The number of occupied orbitals'''
return self._nocc
nocc = property(_get_nocc)
def _get_nvirt(self):
'''The number of virtual orbitals'''
return self._nvirt
nvirt = property(_get_nvirt)
def _get_npairs(self):
'''The number of electron pairs'''
return self._npairs
npairs = property(_get_npairs)
def _get_lf(self):
'''The LinalgFactory instance'''
return self._lf
lf = property(_get_lf)
def _get_ecore(self):
'''The core energy'''
return self._ecore
ecore = property(_get_ecore)
def _get_dimension(self):
'''The number of unknowns (i.e. the number of geminal coefficients)'''
return self._npairs*self._nvirt
dimension = property(_get_dimension)
def _get_geminal(self):
'''The geminal coefficients'''
return self._geminal
geminal = property(_get_geminal)
def _get_lagrange(self):
'''The Lagrange multipliers'''
return self._lagrange
lagrange = property(_get_lagrange)
def __clear__(self):
self.clear()
def clear(self):
'''Clear all wavefunction information'''
self._cache.clear()
def clear_dm(self):
'''Clear RDM information'''
self._cache.clear(tags='d', dealloc=True)
def clear_geminal(self):
'''Clear geminal information'''
self._geminal.clear()
def clear_lagrange(self):
'''Clear lagrange information'''
self._lagrange.clear()
def update_ecore(self, new):
'''Update core energy'''
self._ecore = new
def update_geminal(self, geminal=None):
'''Update geminal matrix
**Optional arguments:**
geminal
When provided, this geminal matrix is stored.
'''
if geminal is None:
raise NotImplementedError
else:
self._geminal.assign(geminal)
def update_lagrange(self, lagrange=None, dim1=None, dim2=None):
'''Update Lagragne multipliers
**Optional arguments:**
lagrange
When provided, this set of Lagrange multipliers is stored.
'''
if lagrange is None:
raise NotImplementedError
else:
self.lagrange.assign(lagrange)
def update_auxmatrix(self, select, two_mo, one_mo=None):
'''Update auxiliary matrices'''
raise NotImplementedError
def get_auxmatrix(self, select):
'''Get auxiliary matrices'''
raise NotImplementedError
def init_one_dm(self, select):
'''Initialize 1-RDM as OneIndex object
The 1-RDM expressed in the natural orbital basis is diagonal and
only the diagonal elements are stored.
**Arguments**
select
'ps2' or 'response'.
'''
check_options('onedm', select, 'ps2', 'response')
dm, new = self._cache.load('one_dm_%s' % select, alloc=(self._lf.create_one_index, self.nbasis), tags='d')
if not new:
raise RuntimeError('The density matrix one_dm_%s already exists. Call one_dm_%s.clear prior to updating the 1DM.' % select)
return dm
def init_two_dm(self, select):
r'''Initialize 2-RDM as TwoIndex object
Only the symmetry-unique elements of the (response) 2-RDM are
stored. These are matrix elements of type
.. math::
Gamma_{p\bar{q}p\bar{q}}
(spin-up and spin-down (bar-sign)) or
.. math::
Gamma_{p\bar{p}q\bar{q}}
and are stored as elements :math:`{pq}` of two_dm_pqpq, and
two_dm_ppqq.
**Arguments**
select
'(r(esponse))ppqq', or '(r(esponse))pqpq'.
'''
check_options('twodm', select, 'ppqq', 'pqpq', 'rppqq', 'rpqpq')
dm, new = self._cache.load('two_dm_%s' % select, alloc=(self._lf.create_two_index, self.nbasis), tags='d')
if not new:
raise RuntimeError('The density matrix two_dm_%s already exists. Call two_dm_%s.clear prior to updating the 2DM.' % select)
return dm
def init_three_dm(self, select):
'''Initialize 3-RDM
**Arguments**
select
'''
raise NotImplementedError
def init_four_dm(self, select):
'''Initialize 4-RDM
**Arguments**
select
'''
raise NotImplementedError
def get_one_dm(self, select):
'''Get a density matrix (1-RDM). If not available, it will be created
(if possible)
**Arguments:**
select
'ps2', or 'response'.
'''
if not 'one_dm_%s' % select in self._cache:
self.update_one_dm(select)
return self._cache.load('one_dm_%s' % select)
def get_two_dm(self, select):
'''Get a density matrix (2-RDM). If not available, it will be created
(if possible)
**Arguments:**
select
'(r(esponse))ppqq', or '(r(esponse))pqpq'.
'''
if not 'two_dm_%s' % select in self._cache:
self.update_two_dm(select)
return self._cache.load('two_dm_%s' % select)
def get_three_dm(self, select):
'''Get a density matrix (3-RDM). If not available, it will be created
(if possible)
**Arguments:**
select
'''
raise NotImplementedError
def get_four_dm(self, select):
'''Get a density matrix (4-RDM). If not available, it will be created
(if possible)
**Arguments:**
select
'''
raise NotImplementedError
one_dm_ps2 = PropertyHelper(get_one_dm, 'ps2', 'Alpha 1-RDM')
one_dm_response = PropertyHelper(get_one_dm, 'response', 'Alpha 1-RDM')
two_dm_ppqq = PropertyHelper(get_two_dm, 'ppqq', 'Alpha-beta PS2 (ppqq) 2-RDM')
two_dm_pqpq = PropertyHelper(get_two_dm, 'pqpq', 'Alpha-beta PS2 (pqpq) 2-RDM')
two_dm_rppqq = PropertyHelper(get_two_dm, 'rppqq', 'Alpha-beta (ppqq) 2-RDM')
two_dm_rpqpq = PropertyHelper(get_two_dm, 'rpqpq', 'Alpha-beta (pqpq) 2-RDM')
def update_one_dm(self, one_dm=None):
'''Update 1-RDM
**Optional arguments:**
one_dm
When provided, this 1-RDM is stored.
'''
raise NotImplementedError
def update_two_dm(self, two_dm=None):
'''Update 2-RDM
**Optional arguments:**
two_dm
When provided, this 2-RDM is stored.
'''
raise NotImplementedError
def update_three_dm(self, three_dm=None):
'''Update 3-RDM
**Optional arguments:**
three_dm
When provided, this 3-RDM is stored.
'''
raise NotImplementedError
def update_four_dm(self, four_dm=None):
'''Update 2-RDM
**Optional arguments:**
four_dm
When provided, this 4-RDM is stored.
'''
raise NotImplementedError
# Initial guess generators:
def generate_guess(self, guess, dim=None):
'''Generate a guess of type 'guess'.
**Arguments:**
guess
A dictionary, containing the type of guess.
**Optional arguments:**
dim
Length of guess.
'''
check_options('guess.type', guess['type'], 'random', 'const')
check_type('guess.factor', guess['factor'], int, float)
if guess['factor'] == 0:
raise ValueError('Scaling factor must be different from 0.')
if dim is None:
dim = self.dimension
if guess['type'] == 'random':
return np.random.random(dim)*guess['factor']
elif guess['type'] == 'const':
return np.ones(dim)*guess['factor']
def compute_rotation_matrix(self, coeff):
'''Compute orbital rotation matrix'''
raise NotImplementedError
# Check convergence:
def check_convergence(self, e0, e1, gradient, thresh):
'''Check convergence.
**Arguements:**
e0, e1
Used to calculate energy difference e0-e1
gradient
The gradient, a OneIndex instance
thresh
Dictionary containing threshold parameters ('energy', 'gradientmax',
'gradientnorm')
**Returns:**
True if energy difference, norm of orbital gradient, largest
element of orbital gradient are smaller than some threshold
values.
'''
return abs(e0-e1) < thresh['energy'] and \
gradient.get_max() < thresh['gradientmax'] and \
gradient.norm() < thresh['gradientnorm']
def check_stepsearch(self, linesearch):
'''Check trustradius. Abort calculation if trustradius is smaller than
1e-8
'''
return linesearch.method == 'trust-region' and \
linesearch.trustradius < 1e-8
def prod(self, lst):
return reduce(mul, lst)
def perm(self, a):
'''Calculate the permament of a matrix
**Arguements**
a
A np array
'''
check_type('matrix', a, np.ndarray)
n = len(a)
r = range(n)
s = permutations(r)
import math # FIXME: fsum really needed for accuracy?
return math.fsum(self.prod(a[i][sigma[i]] for i in r) for sigma in s)
class MeanFieldWFN(object):
def __init__(self, lf, nbasis, occ_model=None, norb=None):
"""
**Arguments:**
lf
A LinalgFactory instance.
nbasis
The number of basis functions.
**Optional arguments:**
occ_model
A model to assign new occupation numbers when the orbitals are
updated by a diagonalization of a Fock matrix.
norb
the number of orbitals (occupied + virtual). When not given,
it is set to nbasis.
"""
self._lf = lf
self._nbasis = nbasis
self._occ_model = occ_model
if norb is None:
self._norb = nbasis
else:
self._norb = norb
# The cache is used to store different representations of the
# wavefunction, i.e. as expansion, as density matrix or both.
self._cache = Cache()
# Write some screen log
self._log_init()
@classmethod
def from_hdf5(cls, grp, lf):
# make the wfn object
from horton.checkpoint import load_hdf5_low
occ_model = load_hdf5_low(grp['occ_model'],
lf) if 'occ_model' in grp else None
result = cls(lf, grp['nbasis'][()], occ_model, grp['norb'][()])
# load stuff into cache
for spin in 'alpha', 'beta':
if 'exp_%s' % spin in grp:
exp = result.init_exp(spin)
exp.read_from_hdf5(grp['exp_%s' % spin])
if 'dm_%s' % spin in grp:
dm = result.init_dm(spin)
dm.read_from_hdf5(grp['dm_%s' % spin])
return result
def to_hdf5(self, grp):
grp.attrs['class'] = self.__class__.__name__
grp['nbasis'] = self._nbasis
grp['norb'] = self._norb
if self.occ_model is not None:
tmp = grp.create_group('occ_model')
self.occ_model.to_hdf5(tmp)
for spin in 'alpha', 'beta':
if 'exp_%s' % spin in self._cache:
tmp = grp.create_group('exp_%s' % spin)
self._cache.load('exp_%s' % spin).to_hdf5(tmp)
if 'dm_%s' % spin in self._cache:
tmp = grp.create_group('dm_%s' % spin)
self._cache.load('dm_%s' % spin).to_hdf5(tmp)
def _get_nbasis(self):
'''The number of basis functions.'''
return self._nbasis
nbasis = property(_get_nbasis)
def _get_norb(self):
'''The number of orbitals in the expansion(s)'''
return self._norb
norb = property(_get_norb)
def _get_occ_model(self):
'''The model for the orbital occupations'''
return self._occ_model
def _set_occ_model(self, occ_model):
self._occ_model = occ_model
occ_model = property(_get_occ_model, _set_occ_model)
def _get_temperature(self):
'''The electronic temperature used for the Fermi smearing'''
if self._occ_model is None:
return 0
else:
return self._occ_model.temperature
temperature = property(_get_temperature)
def _get_cache(self):
'''The cache object in which the main attributes are stored'''
return self._cache
cache = property(_get_cache)
def _log_init(self):
'''Write a summary of the wavefunction to the screen logger'''
if log.do_medium:
log('Initialized: %s' % self)
if self.occ_model is not None:
self.occ_model.log()
log.blank()
def _iter_expansions(self):
'''Iterate over all expansion in the cache'''
for spin in 'alpha', 'beta':
if 'exp_%s' % spin in self._cache:
yield self._cache.load('exp_%s' % spin)
def _iter_density_matrices(self):
'''Iterate over all density matrices in the cache'''
for select in 'alpha', 'beta', 'full', 'spin':
if 'dm_%s' % select in self._cache:
yield self._cache.load('dm_%s' % select)
def _assign_dm_full(self, dm):
raise NotImplementedError
def _assign_dm_spin(self, dm):
raise NotImplementedError
def __clear__(self):
self.clear()
def clear(self):
'''Clear all wavefunction information'''
self._cache.clear()
def clear_exp(self):
'''Clear the wavefunction expansions'''
self._cache.clear(tags='e')
def clear_dm(self):
'''Clear the density matrices'''
self._cache.clear(tags='d')
def init_exp(self, spin, norb=None):
if spin not in ['alpha', 'beta']:
raise ValueError('The select argument must be alpha or beta')
if norb is None:
norb = self._norb
exp, new = self._cache.load('exp_%s' % spin,
alloc=(self._lf.create_expansion,
self._nbasis, norb),
tags='e')
if not new:
raise RuntimeError(
'The expansion exp_%s already exists. Call wfn.clear prior to updating the wfn.'
% spin)
return exp
def init_dm(self, select):
if select not in ['alpha', 'beta', 'full', 'spin']:
raise ValueError(
'The select argument must be one of alpha, beta, full or spin.'
)
dm, new = self._cache.load('dm_%s' % select,
alloc=(self._lf.create_one_body,
self.nbasis),
tags='d')
if not new:
raise RuntimeError(
'The density matrix dm_%s already exists. Call wfn.clear prior to updating the wfn.'
% select)
return dm
def update_dm(self, select, dm=None):
"""Derive the density matrix from the expansion(s) and store in cache
**Arguments:**
select
'alpha', 'beta', 'full' or 'spin'.
**Optional arguments:**
dm
When provided, this density matrix is stored instead of one
derived from the orbitals.
"""
cached_dm = self.init_dm(select)
if dm is None:
if select == 'alpha':
self.exp_alpha.compute_density_matrix(cached_dm)
elif select == 'beta':
self.exp_beta.compute_density_matrix(cached_dm)
elif select == 'full':
self._assign_dm_full(cached_dm)
elif select == 'spin':
self._assign_dm_spin(cached_dm)
else:
cached_dm.assign(dm)
return cached_dm
def get_dm(self, select):
'''Get a density matrix. If not available, it will be created (if possible)
**Arguments:**
select
'alpha', 'beta', 'full' or 'spin'.
'''
if not 'dm_%s' % select in self._cache:
self.update_dm(select)
return self._cache.load('dm_%s' % select)
def get_exp(self, spin):
'''Return an expansion of the wavefunction, if available.
**Arguments:**
select
the spin component: 'alpha' or 'beta'.
'''
return self._cache.load('exp_%s' % spin)
def get_level_shift(self, spin, overlap):
'''Return a level shift operator for the given spin component.
**Arguments:**
select
the spin component: 'alpha' or 'beta'.
'''
level_shift, new = self._cache.load('level_shift_%s' % spin,
alloc=(self._lf.create_one_body,
self.nbasis))
if not new:
level_shift.assign(overlap)
level_shift.idot(self.get_dm(spin))
level_shift.idot(overlap)
return level_shift
dm_alpha = PropertyHelper(get_dm, 'alpha', 'Alpha density matrix')
dm_beta = PropertyHelper(get_dm, 'beta', 'Beta density matrix')
dm_full = PropertyHelper(get_dm, 'full', 'Full density matrix')
dm_spin = PropertyHelper(get_dm, 'spin', 'Spin density matrix')
exp_alpha = PropertyHelper(get_exp, 'alpha', 'Alpha orbital expansion')
exp_beta = PropertyHelper(get_exp, 'beta', 'Beta orbital expansion')
def apply_basis_permutation(self, permutation):
"""Reorder the expansion coefficients and the density matrices"""
for exp in self._iter_expansions():
exp.apply_basis_permutation(permutation)
for dm in self._iter_density_matrices():
dm.apply_basis_permutation(permutation)
def apply_basis_signs(self, signs):
"""Fix the signs of the expansion coefficients and the density matrices"""
for exp in self._iter_expansions():
exp.apply_basis_signs(signs)
for dm in self._iter_density_matrices():
dm.apply_basis_signs(signs)
def check_normalization(self, olp, eps=1e-4):
'''Run an internal test to see if the orbitals are normalized
**Arguments:**
olp
The overlap one_body operators
**Optional arguments:**
eps
The allowed deviation from unity, very loose by default.
'''
for exp in self._iter_expansions():
exp.check_normalization(olp, eps)
class Geminal(object):
'''A collection of geminals and optimization routines.
This is just a base class that serves as a template for
specific implementations.
'''
def __init__(self, lf, occ_model, npairs=None, nvirt=None):
'''
**Arguments:**
lf
A LinalgFactory instance.
occ_model
Occupation model
**Optional arguments:**
npairs
Number of electron pairs, if not specified,
npairs = number of occupied orbitals
nvirt
Number of virtual orbitals, if not specified,
nvirt = (nbasis-npairs)
'''
check_type('pairs', npairs, int, type(None))
check_type('virtuals', nvirt, int, type(None))
self._lf = lf
self._nocc = occ_model.noccs[0]
self._nbasis = lf.default_nbasis
if npairs is None:
npairs = occ_model.noccs[0]
elif npairs >= lf.default_nbasis:
raise ValueError(
'Number of electron pairs (%i) larger than number of basis functions (%i)'
% (npairs, self.nbasis))
if nvirt is None:
nvirt = (lf.default_nbasis - npairs)
elif nvirt >= lf.default_nbasis:
raise ValueError(
'Number of virtuals (%i) larger than number of basis functions (%i)'
% (nvirt, self.nbasis))
self._npairs = npairs
self._nvirt = nvirt
self._cache = Cache()
self._ecore = 0
self._geminal = lf.create_two_index(npairs, nvirt)
self._lagrange = lf.create_two_index(npairs, nvirt)
def __call__(self, one, two, core, orb, olp, scf, **kwargs):
'''Optimize geminal coefficients and---if required---find
optimal set of orbitals.
**Arguments:**
one, two
One- and two-body integrals (some Hamiltonian matrix elements).
core
The core energy (not included in 'one' and 'two').
orb
An expansion instance. It contains the MO coefficients
(orbitals).
olp
The AO overlap matrix. A TwoIndex instance.
scf
A boolean. If True: Initializes orbital optimization.
**Keywords:**
See :py:meth:`RAp1rog.solve`
and :py:meth:`RAp1rog.solve_scf`
'''
if scf:
return self.solve_scf(one, two, core, orb, olp, **kwargs)
else:
return self.solve(one, two, core, orb, olp, **kwargs)
def solve(self, one, two, core, orb, olp, **kwargs):
raise NotImplementedError
def solve_scf(self, one, two, core, orb, olp, **kwargs):
raise NotImplementedError
def _get_nbasis(self):
'''The number of basis functions'''
return self._nbasis
nbasis = property(_get_nbasis)
def _get_nocc(self):
'''The number of occupied orbitals'''
return self._nocc
nocc = property(_get_nocc)
def _get_nvirt(self):
'''The number of virtual orbitals'''
return self._nvirt
nvirt = property(_get_nvirt)
def _get_npairs(self):
'''The number of electron pairs'''
return self._npairs
npairs = property(_get_npairs)
def _get_lf(self):
'''The LinalgFactory instance'''
return self._lf
lf = property(_get_lf)
def _get_ecore(self):
'''The core energy'''
return self._ecore
ecore = property(_get_ecore)
def _get_dimension(self):
'''The number of unknowns (i.e. the number of geminal coefficients)'''
return self._npairs * self._nvirt
dimension = property(_get_dimension)
def _get_geminal(self):
'''The geminal coefficients'''
return self._geminal
geminal = property(_get_geminal)
def _get_lagrange(self):
'''The Lagrange multipliers'''
return self._lagrange
lagrange = property(_get_lagrange)
def __clear__(self):
self.clear()
def clear(self):
'''Clear all wavefunction information'''
self._cache.clear()
def clear_dm(self):
'''Clear RDM information'''
self._cache.clear(tags='d', dealloc=True)
def clear_geminal(self):
'''Clear geminal information'''
self._geminal.clear()
def clear_lagrange(self):
'''Clear lagrange information'''
self._lagrange.clear()
def update_ecore(self, new):
'''Update core energy'''
self._ecore = new
def update_geminal(self, geminal=None):
'''Update geminal matrix
**Optional arguments:**
geminal
When provided, this geminal matrix is stored.
'''
if geminal is None:
raise NotImplementedError
else:
self._geminal.assign(geminal)
def update_lagrange(self, lagrange=None, dim1=None, dim2=None):
'''Update Lagragne multipliers
**Optional arguments:**
lagrange
When provided, this set of Lagrange multipliers is stored.
'''
if lagrange is None:
raise NotImplementedError
else:
self.lagrange.assign(lagrange)
def update_auxmatrix(self, select, two_mo, one_mo=None):
'''Update auxiliary matrices'''
raise NotImplementedError
def get_auxmatrix(self, select):
'''Get auxiliary matrices'''
raise NotImplementedError
def init_one_dm(self, select):
'''Initialize 1-RDM as OneIndex object
The 1-RDM expressed in the natural orbital basis is diagonal and
only the diagonal elements are stored.
**Arguments**
select
'ps2' or 'response'.
'''
check_options('onedm', select, 'ps2', 'response')
dm, new = self._cache.load('one_dm_%s' % select,
alloc=(self._lf.create_one_index,
self.nbasis),
tags='d')
if not new:
raise RuntimeError(
'The density matrix one_dm_%s already exists. Call one_dm_%s.clear prior to updating the 1DM.'
% select)
return dm
def init_two_dm(self, select):
r'''Initialize 2-RDM as TwoIndex object
Only the symmetry-unique elements of the (response) 2-RDM are
stored. These are matrix elements of type
.. math::
Gamma_{p\bar{q}p\bar{q}}
(spin-up and spin-down (bar-sign)) or
.. math::
Gamma_{p\bar{p}q\bar{q}}
and are stored as elements :math:`{pq}` of two_dm_pqpq, and
two_dm_ppqq.
**Arguments**
select
'(r(esponse))ppqq', or '(r(esponse))pqpq'.
'''
check_options('twodm', select, 'ppqq', 'pqpq', 'rppqq', 'rpqpq')
dm, new = self._cache.load('two_dm_%s' % select,
alloc=(self._lf.create_two_index,
self.nbasis),
tags='d')
if not new:
raise RuntimeError(
'The density matrix two_dm_%s already exists. Call two_dm_%s.clear prior to updating the 2DM.'
% select)
return dm
def init_three_dm(self, select):
'''Initialize 3-RDM
**Arguments**
select
'''
raise NotImplementedError
def init_four_dm(self, select):
'''Initialize 4-RDM
**Arguments**
select
'''
raise NotImplementedError
def get_one_dm(self, select):
'''Get a density matrix (1-RDM). If not available, it will be created
(if possible)
**Arguments:**
select
'ps2', or 'response'.
'''
if not 'one_dm_%s' % select in self._cache:
self.update_one_dm(select)
return self._cache.load('one_dm_%s' % select)
def get_two_dm(self, select):
'''Get a density matrix (2-RDM). If not available, it will be created
(if possible)
**Arguments:**
select
'(r(esponse))ppqq', or '(r(esponse))pqpq'.
'''
if not 'two_dm_%s' % select in self._cache:
self.update_two_dm(select)
return self._cache.load('two_dm_%s' % select)
def get_three_dm(self, select):
'''Get a density matrix (3-RDM). If not available, it will be created
(if possible)
**Arguments:**
select
'''
raise NotImplementedError
def get_four_dm(self, select):
'''Get a density matrix (4-RDM). If not available, it will be created
(if possible)
**Arguments:**
select
'''
raise NotImplementedError
one_dm_ps2 = PropertyHelper(get_one_dm, 'ps2', 'Alpha 1-RDM')
one_dm_response = PropertyHelper(get_one_dm, 'response', 'Alpha 1-RDM')
two_dm_ppqq = PropertyHelper(get_two_dm, 'ppqq',
'Alpha-beta PS2 (ppqq) 2-RDM')
two_dm_pqpq = PropertyHelper(get_two_dm, 'pqpq',
'Alpha-beta PS2 (pqpq) 2-RDM')
two_dm_rppqq = PropertyHelper(get_two_dm, 'rppqq',
'Alpha-beta (ppqq) 2-RDM')
two_dm_rpqpq = PropertyHelper(get_two_dm, 'rpqpq',
'Alpha-beta (pqpq) 2-RDM')
def update_one_dm(self, one_dm=None):
'''Update 1-RDM
**Optional arguments:**
one_dm
When provided, this 1-RDM is stored.
'''
raise NotImplementedError
def update_two_dm(self, two_dm=None):
'''Update 2-RDM
**Optional arguments:**
two_dm
When provided, this 2-RDM is stored.
'''
raise NotImplementedError
def update_three_dm(self, three_dm=None):
'''Update 3-RDM
**Optional arguments:**
three_dm
When provided, this 3-RDM is stored.
'''
raise NotImplementedError
def update_four_dm(self, four_dm=None):
'''Update 2-RDM
**Optional arguments:**
four_dm
When provided, this 4-RDM is stored.
'''
raise NotImplementedError
# Initial guess generators:
def generate_guess(self, guess, dim=None):
'''Generate a guess of type 'guess'.
**Arguments:**
guess
A dictionary, containing the type of guess.
**Optional arguments:**
dim
Length of guess.
'''
check_options('guess.type', guess['type'], 'random', 'const')
check_type('guess.factor', guess['factor'], int, float)
if guess['factor'] == 0:
raise ValueError('Scaling factor must be different from 0.')
if dim is None:
dim = self.dimension
if guess['type'] == 'random':
return np.random.random(dim) * guess['factor']
elif guess['type'] == 'const':
return np.ones(dim) * guess['factor']
def compute_rotation_matrix(self, coeff):
'''Compute orbital rotation matrix'''
raise NotImplementedError
# Check convergence:
def check_convergence(self, e0, e1, gradient, thresh):
'''Check convergence.
**Arguements:**
e0, e1
Used to calculate energy difference e0-e1
gradient
The gradient, a OneIndex instance
thresh
Dictionary containing threshold parameters ('energy', 'gradientmax',
'gradientnorm')
**Returns:**
True if energy difference, norm of orbital gradient, largest
element of orbital gradient are smaller than some threshold
values.
'''
return abs(e0-e1) < thresh['energy'] and \
gradient.get_max() < thresh['gradientmax'] and \
gradient.norm() < thresh['gradientnorm']
def check_stepsearch(self, linesearch):
'''Check trustradius. Abort calculation if trustradius is smaller than
1e-8
'''
return linesearch.method == 'trust-region' and \
linesearch.trustradius < 1e-8
def prod(self, lst):
return reduce(mul, lst)
def perm(self, a):
'''Calculate the permament of a matrix
**Arguements**
a
A np array
'''
check_type('matrix', a, np.ndarray)
n = len(a)
r = range(n)
s = permutations(r)
import math # FIXME: fsum really needed for accuracy?
return math.fsum(self.prod(a[i][sigma[i]] for i in r) for sigma in s)
class Part(JustOnceClass):
name = None
linear = False # whether the populations are linear in the density matrix.
def __init__(self, coordinates, numbers, pseudo_numbers, grid, moldens, spindens, local, lmax):
'''
**Arguments:**
coordinates
An array (N, 3) with centers for the atom-centered grids.
numbers
An array (N,) with atomic numbers.
pseudo_numbers
An array (N,) with effective charges. When set to None, this
defaults to``numbers.astype(float)``.
grid
The integration grid
moldens
The spin-summed electron density on the grid.
spindens
The spin difference density on the grid. (Can be None)
local
Whether or not to use local (non-periodic) subgrids for atomic
integrals.
lmax
The maximum angular momentum in multipole expansions.
'''
# Init base class
JustOnceClass.__init__(self)
# Some type checking for first three arguments
natom, coordinates, numbers, pseudo_numbers = typecheck_geo(coordinates, numbers, pseudo_numbers)
self._natom = natom
self._coordinates = coordinates
self._numbers = numbers
self._pseudo_numbers = pseudo_numbers
# Assign remaining arguments as attributes
self._grid = grid
self._moldens = moldens
self._spindens = spindens
self._local = local
self._lmax = lmax
# Caching stuff, to avoid recomputation of earlier results
self._cache = Cache()
# Initialize the subgrids
if local:
self._init_subgrids()
# Some screen logging
self._init_log_base()
self._init_log_scheme()
self._init_log_memory()
if log.do_medium:
log.blank()
def __getitem__(self, key):
return self.cache.load(key)
def _get_natom(self):
return self._natom
natom = property(_get_natom)
def _get_coordinates(self):
return self._coordinates
coordinates = property(_get_coordinates)
def _get_numbers(self):
return self._numbers
numbers = property(_get_numbers)
def _get_pseudo_numbers(self):
return self._pseudo_numbers
pseudo_numbers = property(_get_pseudo_numbers)
def _get_grid(self):
return self.get_grid()
grid = property(_get_grid)
def _get_local(self):
return self._local
local = property(_get_local)
def _get_lmax(self):
return self._lmax
lmax = property(_get_lmax)
def _get_cache(self):
return self._cache
cache = property(_get_cache)
def __clear__(self):
self.clear()
def clear(self):
'''Discard all cached results, e.g. because wfn changed'''
JustOnceClass.clear(self)
self.cache.clear()
def get_grid(self, index=None):
'''Return an integration grid
**Optional arguments:**
index
The index of the atom. If not given, a grid for the entire
system is returned. If self.local is False, a full system grid
is always returned.
'''
if index is None or not self.local:
return self._grid
else:
return self._subgrids[index]
def get_moldens(self, index=None, output=None):
result = self.to_atomic_grid(index, self._moldens)
if output is not None:
output[:] = result
return result
def get_spindens(self, index=None, output=None):
result = self.to_atomic_grid(index, self._spindens)
if output is not None:
output[:] = result
return result
def get_wcor(self, index):
'''Return the weight corrections on a grid
See get_grid for the meaning of the optional arguments
'''
raise NotImplementedError
def _init_subgrids(self):
raise NotImplementedError
def _init_log_base(self):
raise NotImplementedError
def _init_log_scheme(self):
raise NotImplementedError
def _init_log_memory(self):
if log.do_medium:
# precompute arrays sizes for certain grids
nbyte_global = self.grid.size*8
nbyte_locals = np.array([self.get_grid(i).size*8 for i in xrange(self.natom)])
# compute and report usage
estimates = self.get_memory_estimates()
nbyte_total = 0
log('Coarse estimate of memory usage for the partitioning:')
log(' Label Memory[GB]')
log.hline()
for label, nlocals, nglobal in estimates:
nbyte = np.dot(nlocals, nbyte_locals) + nglobal*nbyte_global
log('%30s %10.3f' % (label, nbyte/1024.0**3))
nbyte_total += nbyte
log('%30s %10.3f' % ('Total', nbyte_total/1024.0**3))
log.hline()
log.blank()
def get_memory_estimates(self):
return [
('Atomic weights', np.ones(self.natom), 0),
('Promolecule', np.zeros(self.natom), 1),
('Working arrays', np.zeros(self.natom), 2),
]
def to_atomic_grid(self, index, data):
raise NotImplementedError
def compute_pseudo_population(self, index):
grid = self.get_grid(index)
dens = self.get_moldens(index)
at_weights = self.cache.load('at_weights', index)
wcor = self.get_wcor(index)
return grid.integrate(at_weights, dens, wcor)
@just_once
def do_partitioning(self):
self.update_at_weights()
do_partitioning.names = []
def update_at_weights(self):
'''Updates the at_weights arrays in the case (and all related arrays)'''
raise NotImplementedError
@just_once
def do_populations(self):
populations, new = self.cache.load('populations', alloc=self.natom, tags='o')
if new:
self.do_partitioning()
pseudo_populations = self.cache.load('pseudo_populations', alloc=self.natom, tags='o')[0]
if log.do_medium:
log('Computing atomic populations.')
for i in xrange(self.natom):
pseudo_populations[i] = self.compute_pseudo_population(i)
populations[:] = pseudo_populations
populations += self.numbers - self.pseudo_numbers
@just_once
def do_charges(self):
charges, new = self._cache.load('charges', alloc=self.natom, tags='o')
if new:
self.do_populations()
populations = self._cache.load('populations')
if log.do_medium:
log('Computing atomic charges.')
charges[:] = self.numbers - populations
@just_once
def do_spin_charges(self):
if self._spindens is not None:
spin_charges, new = self._cache.load('spin_charges', alloc=self.natom, tags='o')
self.do_partitioning()
if log.do_medium:
log('Computing atomic spin charges.')
for index in xrange(self.natom):
grid = self.get_grid(index)
spindens = self.get_spindens(index)
at_weights = self.cache.load('at_weights', index)
wcor = self.get_wcor(index)
spin_charges[index] = grid.integrate(at_weights, spindens, wcor)
@just_once
def do_moments(self):
ncart = get_ncart_cumul(self.lmax)
cartesian_multipoles, new1 = self._cache.load('cartesian_multipoles', alloc=(self.natom, ncart), tags='o')
npure = get_npure_cumul(self.lmax)
pure_multipoles, new1 = self._cache.load('pure_multipoles', alloc=(self.natom, npure), tags='o')
nrad = self.lmax+1
radial_moments, new2 = self._cache.load('radial_moments', alloc=(self.natom, nrad), tags='o')
if new1 or new2:
self.do_partitioning()
if log.do_medium:
log('Computing cartesian and pure AIM multipoles and radial AIM moments.')
for i in xrange(self.natom):
# 1) Define a 'window' of the integration grid for this atom
center = self.coordinates[i]
grid = self.get_grid(i)
# 2) Compute the AIM
aim = self.get_moldens(i)*self.cache.load('at_weights', i)
# 3) Compute weight corrections
wcor = self.get_wcor(i)
# 4) Compute Cartesian multipole moments
# The minus sign is present to account for the negative electron
# charge.
cartesian_multipoles[i] = -grid.integrate(aim, wcor, center=center, lmax=self.lmax, mtype=1)
cartesian_multipoles[i, 0] += self.pseudo_numbers[i]
# 5) Compute Pure multipole moments
# The minus sign is present to account for the negative electron
# charge.
pure_multipoles[i] = -grid.integrate(aim, wcor, center=center, lmax=self.lmax, mtype=2)
pure_multipoles[i, 0] += self.pseudo_numbers[i]
# 6) Compute Radial moments
# For the radial moments, it is not common to put a minus sign
# for the negative electron charge.
radial_moments[i] = grid.integrate(aim, wcor, center=center, lmax=self.lmax, mtype=3)
def do_all(self):
'''Computes all properties and return a list of their keys.'''
for attr_name in dir(self):
attr = getattr(self, attr_name)
if callable(attr) and attr_name.startswith('do_') and attr_name != 'do_all':
attr()
return list(self.cache.iterkeys(tags='o'))
class Part(JustOnceClass):
name = None
linear = False # whether the populations are linear in the density matrix.
def __init__(self, system, grid, local, slow, lmax, moldens=None):
'''
**Arguments:**
system
The system to be partitioned.
grid
The integration grid
local
Whether or not to use local (non-periodic) grids.
slow
When ``True``, also the AIM properties are computed that use the
AIM overlap operators.
lmax
The maximum angular momentum in multipole expansions.
**Optional arguments:**
moldens
The all-electron density grid data.
'''
JustOnceClass.__init__(self)
self._system = system
self._grid = grid
self._local = local
self._slow = slow
self._lmax = lmax
# Caching stuff, to avoid recomputation of earlier results
self._cache = Cache()
# Caching of work arrays to avoid reallocation
if moldens is not None:
self._cache.dump('moldens', moldens)
# Initialize the subgrids
if local:
self._init_subgrids()
# Some screen logging
self._init_log_base()
self._init_log_scheme()
self._init_log_memory()
if log.do_medium:
log.blank()
def __getitem__(self, key):
return self.cache.load(key)
def _get_system(self):
return self._system
system = property(_get_system)
def _get_grid(self):
return self.get_grid()
grid = property(_get_grid)
def _get_local(self):
return self._local
local = property(_get_local)
def _get_slow(self):
return self._slow
slow = property(_get_slow)
def _get_lmax(self):
return self._lmax
lmax = property(_get_lmax)
def _get_cache(self):
return self._cache
cache = property(_get_cache)
def __clear__(self):
self.clear()
def clear(self):
'''Discard all cached results, e.g. because wfn changed'''
JustOnceClass.clear(self)
self.cache.clear()
def update_grid(self, grid):
'''Specify a new grid
**Arguments:**
grid
The new grid
When the new and old grid are the same, no action is taken. When
a really new grid is provided, the subgrids are updated and the
cache is cleared.
'''
if not (grid is self._grid):
self._grid = grid
if self.local:
self._init_subgrids()
self.clear()
def get_grid(self, index=None):
'''Return an integration grid
**Optional arguments:**
index
The index of the atom. If not given, a grid for the entire
system is returned. If self.local is False, a full system grid
is always returned.
'''
if index is None or not self.local:
return self._grid
else:
return self._subgrids[index]
def get_moldens(self, index=None, output=None):
self.do_moldens()
moldens = self.cache.load('moldens')
result = self.to_atomic_grid(index, moldens)
if output is not None:
output[:] = result
return result
def get_spindens(self, index=None, output=None):
self.do_spindens()
spindens = self.cache.load('spindens')
result = self.to_atomic_grid(index, spindens)
if output is not None:
output[:] = result
return result
def get_wcor(self, index):
'''Return the weight corrections on a grid
See get_grid for the meaning of the optional arguments
'''
raise NotImplementedError
def _init_subgrids(self):
raise NotImplementedError
def _init_log_base(self):
raise NotImplementedError
def _init_log_scheme(self):
raise NotImplementedError
def _init_log_memory(self):
if log.do_medium:
# precompute arrays sizes for certain grids
nbyte_global = self.grid.size * 8
nbyte_locals = np.array(
[self.get_grid(i).size * 8 for i in xrange(self.system.natom)])
# compute and report usage
estimates = self.get_memory_estimates()
nbyte_total = 0
log('Coarse estimate of memory usage for the partitioning:')
log(' Label Memory[GB]')
log.hline()
for label, nlocals, nglobal in estimates:
nbyte = np.dot(nlocals, nbyte_locals) + nglobal * nbyte_global
log('%30s %10.3f' % (label, nbyte / 1024.0**3))
nbyte_total += nbyte
log('%30s %10.3f' % ('Total', nbyte_total / 1024.0**3))
log.hline()
log.blank()
def get_memory_estimates(self):
return [
('Atomic weights', np.ones(self.system.natom), 0),
('Promolecule', np.zeros(self.system.natom), 1),
('Working arrays', np.zeros(self.system.natom), 2),
]
def to_atomic_grid(self, index, data):
raise NotImplementedError
def compute_pseudo_population(self, index):
grid = self.get_grid(index)
dens = self.get_moldens(index)
at_weights = self.cache.load('at_weights', index)
wcor = self.get_wcor(index)
return grid.integrate(at_weights, dens, wcor)
@just_once
def do_moldens(self):
raise NotImplementedError
@just_once
def do_spindens(self):
raise NotImplementedError
@just_once
def do_partitioning(self):
self.update_at_weights()
do_partitioning.names = []
def update_at_weights(self):
'''Updates the at_weights arrays in the case (and all related arrays)'''
raise NotImplementedError
@just_once
def do_populations(self):
populations, new = self.cache.load('populations',
alloc=self.system.natom,
tags='o')
if new:
self.do_partitioning()
self.do_moldens()
pseudo_populations = self.cache.load('pseudo_populations',
alloc=self.system.natom,
tags='o')[0]
if log.do_medium:
log('Computing atomic populations.')
for i in xrange(self.system.natom):
pseudo_populations[i] = self.compute_pseudo_population(i)
populations[:] = pseudo_populations
populations += self.system.numbers - self.system.pseudo_numbers
@just_once
def do_charges(self):
charges, new = self._cache.load('charges',
alloc=self.system.natom,
tags='o')
if new:
self.do_populations()
populations = self._cache.load('populations')
if log.do_medium:
log('Computing atomic charges.')
charges[:] = self.system.numbers - populations
@just_once
def do_spin_charges(self):
spin_charges, new = self._cache.load('spin_charges',
alloc=self.system.natom,
tags='o')
if new:
if isinstance(self.system.wfn, RestrictedWFN):
spin_charges[:] = 0.0
else:
try:
self.do_spindens()
except NotImplementedError:
self.cache.clear_item('spin_charges')
return
self.do_partitioning()
if log.do_medium:
log('Computing atomic spin charges.')
for index in xrange(self.system.natom):
grid = self.get_grid(index)
spindens = self.get_spindens(index)
at_weights = self.cache.load('at_weights', index)
wcor = self.get_wcor(index)
spin_charges[index] = grid.integrate(
at_weights, spindens, wcor)
@just_once
def do_moments(self):
if log.do_medium:
log('Computing cartesian and pure AIM multipoles and radial AIM moments.'
)
ncart = get_ncart_cumul(self.lmax)
cartesian_multipoles, new1 = self._cache.load(
'cartesian_multipoles',
alloc=(self._system.natom, ncart),
tags='o')
npure = get_npure_cumul(self.lmax)
pure_multipoles, new1 = self._cache.load('pure_multipoles',
alloc=(self._system.natom,
npure),
tags='o')
nrad = self.lmax + 1
radial_moments, new2 = self._cache.load('radial_moments',
alloc=(self._system.natom,
nrad),
tags='o')
if new1 or new2:
self.do_partitioning()
for i in xrange(self._system.natom):
# 1) Define a 'window' of the integration grid for this atom
center = self._system.coordinates[i]
grid = self.get_grid(i)
# 2) Compute the AIM
aim = self.get_moldens(i) * self.cache.load('at_weights', i)
# 3) Compute weight corrections (TODO: needs to be assessed!)
wcor = self.get_wcor(i)
# 4) Compute Cartesian multipole moments
# The minus sign is present to account for the negative electron
# charge.
cartesian_multipoles[i] = -grid.integrate(
aim, wcor, center=center, lmax=self.lmax, mtype=1)
cartesian_multipoles[i, 0] += self.system.pseudo_numbers[i]
# 5) Compute Pure multipole moments
# The minus sign is present to account for the negative electron
# charge.
pure_multipoles[i] = -grid.integrate(
aim, wcor, center=center, lmax=self.lmax, mtype=2)
pure_multipoles[i, 0] += self.system.pseudo_numbers[i]
# 6) Compute Radial moments
# For the radial moments, it is not common to put a minus sign
# for the negative electron charge.
radial_moments[i] = grid.integrate(aim,
wcor,
center=center,
lmax=self.lmax,
mtype=3)
def do_all(self):
'''Computes all properties and return a list of their names.'''
slow_methods = [
'do_overlap_operators', 'do_bond_order',
'do_noninteracting_response'
]
for attr_name in dir(self):
attr = getattr(self, attr_name)
if callable(attr) and attr_name.startswith(
'do_') and attr_name != 'do_all':
if self._slow or (not attr_name in slow_methods):
attr()
return list(self.cache.iterkeys(tags='o'))
class Part(JustOnceClass):
name = None
linear = False # whether the populations are linear in the density matrix.
def __init__(self, system, grid, local, slow, lmax, moldens=None):
'''
**Arguments:**
system
The system to be partitioned.
grid
The integration grid
local
Whether or not to use local (non-periodic) grids.
slow
When ``True``, also the AIM properties are computed that use the
AIM overlap operators.
lmax
The maximum angular momentum in multipole expansions.
**Optional arguments:**
moldens
The all-electron density grid data.
'''
JustOnceClass.__init__(self)
self._system = system
self._grid = grid
self._local = local
self._slow = slow
self._lmax = lmax
# Caching stuff, to avoid recomputation of earlier results
self._cache = Cache()
# Caching of work arrays to avoid reallocation
if moldens is not None:
self._cache.dump('moldens', moldens)
# Initialize the subgrids
if local:
self._init_subgrids()
# Some screen logging
self._init_log_base()
self._init_log_scheme()
self._init_log_memory()
if log.do_medium:
log.blank()
def __getitem__(self, key):
return self.cache.load(key)
def _get_system(self):
return self._system
system = property(_get_system)
def _get_grid(self):
return self.get_grid()
grid = property(_get_grid)
def _get_local(self):
return self._local
local = property(_get_local)
def _get_slow(self):
return self._slow
slow = property(_get_slow)
def _get_lmax(self):
return self._lmax
lmax = property(_get_lmax)
def _get_cache(self):
return self._cache
cache = property(_get_cache)
def __clear__(self):
self.clear()
def clear(self):
'''Discard all cached results, e.g. because wfn changed'''
JustOnceClass.clear(self)
self.cache.clear()
def update_grid(self, grid):
'''Specify a new grid
**Arguments:**
grid
The new grid
When the new and old grid are the same, no action is taken. When
a really new grid is provided, the subgrids are updated and the
cache is cleared.
'''
if not (grid is self._grid):
self._grid = grid
if self.local:
self._init_subgrids()
self.clear()
def get_grid(self, index=None):
'''Return an integration grid
**Optional arguments:**
index
The index of the atom. If not given, a grid for the entire
system is returned. If self.local is False, a full system grid
is always returned.
'''
if index is None or not self.local:
return self._grid
else:
return self._subgrids[index]
def get_moldens(self, index=None, output=None):
self.do_moldens()
moldens = self.cache.load('moldens')
result = self.to_atomic_grid(index, moldens)
if output is not None:
output[:] = result
return result
def get_spindens(self, index=None, output=None):
self.do_spindens()
spindens = self.cache.load('spindens')
result = self.to_atomic_grid(index, spindens)
if output is not None:
output[:] = result
return result
def get_wcor(self, index):
'''Return the weight corrections on a grid
See get_grid for the meaning of the optional arguments
'''
raise NotImplementedError
def _init_subgrids(self):
raise NotImplementedError
def _init_log_base(self):
raise NotImplementedError
def _init_log_scheme(self):
raise NotImplementedError
def _init_log_memory(self):
if log.do_medium:
# precompute arrays sizes for certain grids
nbyte_global = self.grid.size*8
nbyte_locals = np.array([self.get_grid(i).size*8 for i in xrange(self.system.natom)])
# compute and report usage
estimates = self.get_memory_estimates()
nbyte_total = 0
log('Coarse estimate of memory usage for the partitioning:')
log(' Label Memory[GB]')
log.hline()
for label, nlocals, nglobal in estimates:
nbyte = np.dot(nlocals, nbyte_locals) + nglobal*nbyte_global
log('%30s %10.3f' % (label, nbyte/1024.0**3))
nbyte_total += nbyte
log('%30s %10.3f' % ('Total', nbyte_total/1024.0**3))
log.hline()
log.blank()
def get_memory_estimates(self):
return [
('Atomic weights', np.ones(self.system.natom), 0),
('Promolecule', np.zeros(self.system.natom), 1),
('Working arrays', np.zeros(self.system.natom), 2),
]
def to_atomic_grid(self, index, data):
raise NotImplementedError
def compute_pseudo_population(self, index):
grid = self.get_grid(index)
dens = self.get_moldens(index)
at_weights = self.cache.load('at_weights', index)
wcor = self.get_wcor(index)
return grid.integrate(at_weights, dens, wcor)
@just_once
def do_moldens(self):
raise NotImplementedError
@just_once
def do_spindens(self):
raise NotImplementedError
@just_once
def do_partitioning(self):
self.update_at_weights()
do_partitioning.names = []
def update_at_weights(self):
'''Updates the at_weights arrays in the case (and all related arrays)'''
raise NotImplementedError
@just_once
def do_populations(self):
populations, new = self.cache.load('populations', alloc=self.system.natom, tags='o')
if new:
self.do_partitioning()
self.do_moldens()
pseudo_populations = self.cache.load('pseudo_populations', alloc=self.system.natom, tags='o')[0]
if log.do_medium:
log('Computing atomic populations.')
for i in xrange(self.system.natom):
pseudo_populations[i] = self.compute_pseudo_population(i)
populations[:] = pseudo_populations
populations += self.system.numbers - self.system.pseudo_numbers
@just_once
def do_charges(self):
charges, new = self._cache.load('charges', alloc=self.system.natom, tags='o')
if new:
self.do_populations()
populations = self._cache.load('populations')
if log.do_medium:
log('Computing atomic charges.')
charges[:] = self.system.numbers - populations
@just_once
def do_spin_charges(self):
spin_charges, new = self._cache.load('spin_charges', alloc=self.system.natom, tags='o')
if new:
if isinstance(self.system.wfn, RestrictedWFN):
spin_charges[:] = 0.0
else:
try:
self.do_spindens()
except NotImplementedError:
self.cache.clear_item('spin_charges')
return
self.do_partitioning()
if log.do_medium:
log('Computing atomic spin charges.')
for index in xrange(self.system.natom):
grid = self.get_grid(index)
spindens = self.get_spindens(index)
at_weights = self.cache.load('at_weights', index)
wcor = self.get_wcor(index)
spin_charges[index] = grid.integrate(at_weights, spindens, wcor)
@just_once
def do_moments(self):
if log.do_medium:
log('Computing cartesian and pure AIM multipoles and radial AIM moments.')
ncart = get_ncart_cumul(self.lmax)
cartesian_multipoles, new1 = self._cache.load('cartesian_multipoles', alloc=(self._system.natom, ncart), tags='o')
npure = get_npure_cumul(self.lmax)
pure_multipoles, new1 = self._cache.load('pure_multipoles', alloc=(self._system.natom, npure), tags='o')
nrad = self.lmax+1
radial_moments, new2 = self._cache.load('radial_moments', alloc=(self._system.natom, nrad), tags='o')
if new1 or new2:
self.do_partitioning()
for i in xrange(self._system.natom):
# 1) Define a 'window' of the integration grid for this atom
center = self._system.coordinates[i]
grid = self.get_grid(i)
# 2) Compute the AIM
aim = self.get_moldens(i)*self.cache.load('at_weights', i)
# 3) Compute weight corrections (TODO: needs to be assessed!)
wcor = self.get_wcor(i)
# 4) Compute Cartesian multipole moments
# The minus sign is present to account for the negative electron
# charge.
cartesian_multipoles[i] = -grid.integrate(aim, wcor, center=center, lmax=self.lmax, mtype=1)
cartesian_multipoles[i, 0] += self.system.pseudo_numbers[i]
# 5) Compute Pure multipole moments
# The minus sign is present to account for the negative electron
# charge.
pure_multipoles[i] = -grid.integrate(aim, wcor, center=center, lmax=self.lmax, mtype=2)
pure_multipoles[i, 0] += self.system.pseudo_numbers[i]
# 6) Compute Radial moments
# For the radial moments, it is not common to put a minus sign
# for the negative electron charge.
radial_moments[i] = grid.integrate(aim, wcor, center=center, lmax=self.lmax, mtype=3)
def do_all(self):
'''Computes all properties and return a list of their names.'''
slow_methods = ['do_overlap_operators', 'do_bond_order', 'do_noninteracting_response']
for attr_name in dir(self):
attr = getattr(self, attr_name)
if callable(attr) and attr_name.startswith('do_') and attr_name != 'do_all':
if self._slow or (not attr_name in slow_methods):
attr()
return list(self.cache.iterkeys(tags='o'))
class MeanFieldWFN(object):
def __init__(self, lf, nbasis, occ_model=None, norb=None):
"""
**Arguments:**
lf
A LinalgFactory instance.
nbasis
The number of basis functions.
**Optional arguments:**
occ_model
A model to assign new occupation numbers when the orbitals are
updated by a diagonalization of a Fock matrix.
norb
the number of orbitals (occupied + virtual). When not given,
it is set to nbasis.
"""
self._lf = lf
self._nbasis = nbasis
self._occ_model = occ_model
if norb is None:
self._norb = nbasis
else:
self._norb = norb
# The cache is used to store different representations of the
# wavefunction, i.e. as expansion, as density matrix or both.
self._cache = Cache()
# Write some screen log
self._log_init()
@classmethod
def from_hdf5(cls, grp, lf):
# make the wfn object
from horton.checkpoint import load_hdf5_low
occ_model = load_hdf5_low(grp['occ_model'], lf) if 'occ_model' in grp else None
result = cls(lf, grp['nbasis'][()], occ_model, grp['norb'][()])
# load stuff into cache
for spin in 'alpha', 'beta':
if 'exp_%s' % spin in grp:
exp = result.init_exp(spin)
exp.read_from_hdf5(grp['exp_%s' % spin])
if 'dm_%s' % spin in grp:
dm = result.init_dm(spin)
dm.read_from_hdf5(grp['dm_%s' % spin])
return result
def to_hdf5(self, grp):
grp.attrs['class'] = self.__class__.__name__
grp['nbasis'] = self._nbasis
grp['norb'] = self._norb
if self.occ_model is not None:
tmp = grp.create_group('occ_model')
self.occ_model.to_hdf5(tmp)
for spin in 'alpha', 'beta':
if 'exp_%s' % spin in self._cache:
tmp = grp.create_group('exp_%s' % spin)
self._cache.load('exp_%s' % spin).to_hdf5(tmp)
if 'dm_%s' % spin in self._cache:
tmp = grp.create_group('dm_%s' % spin)
self._cache.load('dm_%s' % spin).to_hdf5(tmp)
def _get_nbasis(self):
'''The number of basis functions.'''
return self._nbasis
nbasis = property(_get_nbasis)
def _get_norb(self):
'''The number of orbitals in the expansion(s)'''
return self._norb
norb = property(_get_norb)
def _get_occ_model(self):
'''The model for the orbital occupations'''
return self._occ_model
def _set_occ_model(self, occ_model):
self._occ_model = occ_model
occ_model = property(_get_occ_model, _set_occ_model)
def _get_temperature(self):
'''The electronic temperature used for the Fermi smearing'''
if self._occ_model is None:
return 0
else:
return self._occ_model.temperature
temperature = property(_get_temperature)
def _get_cache(self):
'''The cache object in which the main attributes are stored'''
return self._cache
cache = property(_get_cache)
def _log_init(self):
'''Write a summary of the wavefunction to the screen logger'''
if log.do_medium:
log('Initialized: %s' % self)
if self.occ_model is not None:
self.occ_model.log()
log.blank()
def _iter_expansions(self):
'''Iterate over all expansion in the cache'''
for spin in 'alpha', 'beta':
if 'exp_%s' % spin in self._cache:
yield self._cache.load('exp_%s' % spin)
def _iter_density_matrices(self):
'''Iterate over all density matrices in the cache'''
for select in 'alpha', 'beta', 'full', 'spin':
if 'dm_%s' % select in self._cache:
yield self._cache.load('dm_%s' % select)
def _assign_dm_full(self, dm):
raise NotImplementedError
def _assign_dm_spin(self, dm):
raise NotImplementedError
def __clear__(self):
self.clear()
def clear(self):
'''Clear all wavefunction information'''
self._cache.clear()
def clear_exp(self):
'''Clear the wavefunction expansions'''
self._cache.clear(tags='e')
def clear_dm(self):
'''Clear the density matrices'''
self._cache.clear(tags='d')
def init_exp(self, spin, norb=None):
if spin not in ['alpha', 'beta']:
raise ValueError('The select argument must be alpha or beta')
if norb is None:
norb = self._norb
exp, new = self._cache.load('exp_%s' % spin, alloc=(self._lf.create_expansion, self._nbasis, norb), tags='e')
if not new:
raise RuntimeError('The expansion exp_%s already exists. Call wfn.clear prior to updating the wfn.' % spin)
return exp
def init_dm(self, select):
if select not in ['alpha', 'beta', 'full', 'spin']:
raise ValueError('The select argument must be one of alpha, beta, full or spin.')
dm, new = self._cache.load('dm_%s' % select, alloc=(self._lf.create_one_body, self.nbasis), tags='d')
if not new:
raise RuntimeError('The density matrix dm_%s already exists. Call wfn.clear prior to updating the wfn.' % select)
return dm
def update_dm(self, select, dm=None):
"""Derive the density matrix from the expansion(s) and store in cache
**Arguments:**
select
'alpha', 'beta', 'full' or 'spin'.
**Optional arguments:**
dm
When provided, this density matrix is stored instead of one
derived from the orbitals.
"""
cached_dm = self.init_dm(select)
if dm is None:
if select == 'alpha':
self.exp_alpha.compute_density_matrix(cached_dm)
elif select == 'beta':
self.exp_beta.compute_density_matrix(cached_dm)
elif select == 'full':
self._assign_dm_full(cached_dm)
elif select == 'spin':
self._assign_dm_spin(cached_dm)
else:
cached_dm.assign(dm)
return cached_dm
def get_dm(self, select):
'''Get a density matrix. If not available, it will be created (if possible)
**Arguments:**
select
'alpha', 'beta', 'full' or 'spin'.
'''
if not 'dm_%s' % select in self._cache:
self.update_dm(select)
return self._cache.load('dm_%s' % select)
def get_exp(self, spin):
'''Return an expansion of the wavefunction, if available.
**Arguments:**
select
the spin component: 'alpha' or 'beta'.
'''
return self._cache.load('exp_%s' % spin)
def get_level_shift(self, spin, overlap):
'''Return a level shift operator for the given spin component.
**Arguments:**
select
the spin component: 'alpha' or 'beta'.
'''
level_shift, new = self._cache.load('level_shift_%s' % spin, alloc=(self._lf.create_one_body, self.nbasis))
if not new:
level_shift.assign(overlap)
level_shift.idot(self.get_dm(spin))
level_shift.idot(overlap)
return level_shift
dm_alpha = PropertyHelper(get_dm, 'alpha', 'Alpha density matrix')
dm_beta = PropertyHelper(get_dm, 'beta', 'Beta density matrix')
dm_full = PropertyHelper(get_dm, 'full', 'Full density matrix')
dm_spin = PropertyHelper(get_dm, 'spin', 'Spin density matrix')
exp_alpha = PropertyHelper(get_exp, 'alpha', 'Alpha orbital expansion')
exp_beta = PropertyHelper(get_exp, 'beta', 'Beta orbital expansion')
def apply_basis_permutation(self, permutation):
"""Reorder the expansion coefficients and the density matrices"""
for exp in self._iter_expansions():
exp.apply_basis_permutation(permutation)
for dm in self._iter_density_matrices():
dm.apply_basis_permutation(permutation)
def apply_basis_signs(self, signs):
"""Fix the signs of the expansion coefficients and the density matrices"""
for exp in self._iter_expansions():
exp.apply_basis_signs(signs)
for dm in self._iter_density_matrices():
dm.apply_basis_signs(signs)
def check_normalization(self, olp, eps=1e-4):
'''Run an internal test to see if the orbitals are normalized
**Arguments:**
olp
The overlap one_body operators
**Optional arguments:**
eps
The allowed deviation from unity, very loose by default.
'''
for exp in self._iter_expansions():
exp.check_normalization(olp, eps)
class Hamiltonian(object):
def __init__(self, system, terms, grid=None, idiot_proof=True):
'''
**Arguments:**
system
The System object for which the energy must be computed.
terms
The terms in the Hamiltonian.
**Optional arguments:**
grid
The integration grid, in case some terms need one.
idiot_proof
When set to False, the kinetic energy, external potential and
Hartree terms are not added automatically and a error is raised
when no exchange is present.
'''
# check arguments:
if len(terms) == 0:
raise ValueError(
'At least one term must be present in the Hamiltonian.')
for term in terms:
if term.require_grid and grid is None:
raise TypeError(
'The term %s requires a grid, but not grid is given.' %
term)
# Assign attributes
self.system = system
self.terms = list(terms)
self.grid = grid
if idiot_proof:
# Check if an exchange term is present
if not any(term.exchange for term in self.terms):
raise ValueError(
'No exchange term is given and idiot_proof option is set to True.'
)
# Add standard terms if missing
# 1) Kinetic energy
if sum(isinstance(term, KineticEnergy) for term in terms) == 0:
self.terms.append(KineticEnergy())
# 2) Hartree (or HatreeFock, which is a subclass of Hartree)
if sum(isinstance(term, Hartree) for term in terms) == 0:
self.terms.append(Hartree())
# 3) External Potential
if sum(isinstance(term, ExternalPotential) for term in terms) == 0:
self.terms.append(ExternalPotential())
# Create a cache for shared intermediate results. This cache should only
# be used for derived quantities that depend on the wavefunction and
# need to be updated at each SCF cycle.
self.cache = Cache()
# bind the terms to this hamiltonian such that certain shared
# intermediated results can be reused for the sake of efficiency.
for term in self.terms:
term.set_hamiltonian(self)
def add_term(self, term):
'''Add a new term to the hamiltonian'''
self.terms.append(term)
term.set_hamiltonian(self)
def clear(self):
'''Mark the properties derived from the wfn as outdated.
This method does not recompute anything, but just marks operators
as outdated. They are recomputed as they are needed.
'''
self.cache.clear()
def compute(self):
'''Compute the energy.
**Returns:**
The total energy, including nuclear-nuclear repulsion.
'''
total = 0.0
for term in self.terms:
energy = term.compute()
self.system.extra['energy_%s' % term.label] = energy
total += energy
energy = self.system.compute_nucnuc()
self.system.extra['energy_nn'] = energy
total += energy
self.system.extra['energy'] = total
# Store result in chk file
self.system.update_chk('extra')
return total
def log_energy(self):
'''Write an overview of the last energy computation on screen'''
log('Contributions to the energy:')
log.hline()
log(' Energy term Value'
)
log.hline()
for term in self.terms:
energy = self.system.extra['energy_%s' % term.label]
log('%50s %20.12f' % (term.label, energy))
log('%50s %20.12f' % ('nn', self.system.extra['energy_nn']))
log('%50s %20.12f' % ('total', self.system.extra['energy']))
log.hline()
log.blank()
def compute_fock(self, fock_alpha, fock_beta):
'''Compute alpha (and beta) Fock matrix(es).
**Arguments:**
fock_alpha
A One-Body operator output argument for the alpha fock matrix.
fock_alpha
A One-Body operator output argument for the beta fock matrix.
In the case of a closed-shell computation, the argument fock_beta is
``None``.
'''
# Loop over all terms and add contributions to the Fock matrix. Some
# terms will actually only evaluate potentials on grids and add these
# results to the total potential on a grid.
for term in self.terms:
term.add_fock_matrix(fock_alpha, fock_beta, postpone_grid=True)
# Collect all the total potentials and turn them into contributions
# for the fock matrix/matrices.
# Collect potentials for alpha electrons
# d = density
if 'dpot_total_alpha' in self.cache:
dpot = self.cache.load('dpot_total_alpha')
self.system.compute_grid_density_fock(self.grid.points,
self.grid.weights, dpot,
fock_alpha)
# g = gradient
if 'gpot_total_alpha' in self.cache:
gpot = self.cache.load('gpot_total_alpha')
self.system.compute_grid_gradient_fock(self.grid.points,
self.grid.weights, gpot,
fock_alpha)
if isinstance(self.system.wfn, UnrestrictedWFN):
# Colect potentials for beta electrons
# d = density
if 'dpot_total_beta' in self.cache:
dpot = self.cache.load('dpot_total_beta')
self.system.compute_grid_density_fock(self.grid.points,
self.grid.weights, dpot,
fock_beta)
# g = gradient
if 'gpot_total_beta' in self.cache:
gpot = self.cache.load('gpot_total_beta')
self.system.compute_grid_gradient_fock(self.grid.points,
self.grid.weights, gpot,
fock_beta)
