#
# @BEGIN LICENSE
#
# Psi4: an open-source quantum chemistry software package
#
# Copyright (c) 2007-2017 The Psi4 Developers.
#
# The copyrights for code used from other parties are included in
# the corresponding files.
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# This file is part of Psi4.
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# Psi4 is free software; you can redistribute it and/or modify
# it under the terms of the GNU Lesser General Public License as published by
# the Free Software Foundation, version 3.
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# Psi4 is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Lesser General Public License for more details.
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# You should have received a copy of the GNU Lesser General Public License along
# with Psi4; if not, write to the Free Software Foundation, Inc.,
# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
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# @END LICENSE
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from __future__ import print_function
from __future__ import absolute_import
import math
import numpy as np
import itertools as it
from psi4 import core
# Import driver helpers
from psi4.driver import p4util
from psi4.driver import constants
from psi4.driver.p4util.exceptions import *
### Math helper functions
def nCr(n, r):
f = math.factorial
return f(n) / f(r) / f(n-r)
### Begin CBS gufunc data
def _sum_cluster_ptype_data(ptype, ptype_dict, compute_list, fragment_slice_dict, fragment_size_dict, ret, vmfc=False):
"""
Sums gradient and hessian data from compute_list.
compute_list comes in as a tuple(frag, basis)
"""
if len(compute_list) == 0:
return
sign = 1
# Do ptype
if ptype == 'gradient':
for fragn, basisn in compute_list:
start = 0
grad = np.asarray(ptype_dict[(fragn, basisn)])
if vmfc:
sign = ((-1) ** (n - len(fragn)))
for bas in basisn:
end = start + fragment_size_dict[bas]
ret[fragment_slice_dict[bas]] += sign * grad[start:end]
start += fragment_size_dict[bas]
elif ptype == 'hessian':
for fragn, basisn in compute_list:
hess = np.asarray(ptype_dict[(fragn, basisn)])
if vmfc:
raise Exception("VMFC for hessian NYI")
# Build up start and end slices
abs_start, rel_start = 0, 0
abs_slices, rel_slices = [], []
for bas in basisn:
rel_end = rel_start + 3 * fragment_size_dict[bas]
rel_slices.append(slice(rel_start, rel_end))
rel_start += 3 * fragment_size_dict[bas]
tmp_slice = fragment_slice_dict[bas]
abs_slices.append(slice(tmp_slice.start * 3, tmp_slice.end * 3))
for abs_sl1, rel_sl1 in zip(abs_slices, rel_slices):
for abs_sl2, rel_sl2 in zip(abs_slices, rel_slices):
ret[abs_sl1, abs_sl2] += hess[rel_sl1, rel_sl2]
else:
raise KeyError("ptype can only be gradient or hessian How did you end up here?")
def _print_nbody_energy(energy_body_dict, header):
core.print_out("""\n ==> N-Body: %s energies <==\n\n""" % header)
core.print_out(""" n-Body Total Energy [Eh] I.E. [kcal/mol] Delta [kcal/mol]\n""")
previous_e = energy_body_dict[1]
nbody_range = list(energy_body_dict)
nbody_range.sort()
for n in nbody_range:
delta_e = (energy_body_dict[n] - previous_e)
delta_e_kcal = delta_e * constants.hartree2kcalmol
int_e_kcal = (energy_body_dict[n] - energy_body_dict[1]) * constants.hartree2kcalmol
core.print_out(""" %4s %20.12f %20.12f %20.12f\n""" %
(n, energy_body_dict[n], int_e_kcal, delta_e_kcal))
previous_e = energy_body_dict[n]
core.print_out("\n")
[docs]def nbody_gufunc(func, method_string, **kwargs):
"""
Computes the nbody interaction energy, gradient, or Hessian depending on input.
This is a generalized univeral function for computing interaction quantities.
:returns: *return type of func* |w--w| The interaction data.
:returns: (*float*, :py:class:`~psi4.core.Wavefunction`) |w--w| interaction data and wavefunction with energy/gradient/hessian set appropriately when **return_wfn** specified.
:type func: function
:param func: ``energy`` || etc.
Python function that accepts method_string and a molecule. Returns a
energy, gradient, or Hessian as requested.
:type method_string: string
:param method_string: ``'scf'`` || ``'mp2'`` || ``'ci5'`` || etc.
First argument, lowercase and usually unlabeled. Indicates the computational
method to be passed to func.
:type molecule: :ref:`molecule <op_py_molecule>`
:param molecule: ``h2o`` || etc.
The target molecule, if not the last molecule defined.
:type return_wfn: :ref:`boolean <op_py_boolean>`
:param return_wfn: ``'on'`` || |dl| ``'off'`` |dr|
Indicate to additionally return the :py:class:`~psi4.core.Wavefunction`
calculation result as the second element of a tuple.
:type bsse_type: string or list
:param bsse_type: ``'cp'`` || ``['nocp', 'vmfc']`` || |dl| ``None`` |dr| || etc.
Type of BSSE correction to compute: CP, NoCP, or VMFC. The first in this
list is returned by this function. By default, this function is not called.
:type max_nbody: int
:param max_nbody: ``3`` || etc.
Maximum n-body to compute, cannot exceed the number of fragments in the moleucle.
:type ptype: string
:param ptype: ``'energy'`` || ``'gradient'`` || ``'hessian'``
Type of the procedure passed in.
:type return_total_data: :ref:`boolean <op_py_boolean>`
:param return_total_data: ``'on'`` || |dl| ``'off'`` |dr|
If True returns the total data (energy/gradient/etc) of the system,
otherwise returns interaction data.
"""
### ==> Parse some kwargs <==
kwargs = p4util.kwargs_lower(kwargs)
return_wfn = kwargs.pop('return_wfn', False)
ptype = kwargs.pop('ptype', None)
return_total_data = kwargs.pop('return_total_data', False)
molecule = kwargs.pop('molecule', core.get_active_molecule())
molecule.update_geometry()
core.clean_variables()
if ptype not in ['energy', 'gradient', 'hessian']:
raise ValidationError("""N-Body driver: The ptype '%s' is not regonized.""" % ptype)
# Figure out BSSE types
do_cp = False
do_nocp = False
do_vmfc = False
return_method = False
# Must be passed bsse_type
bsse_type_list = kwargs.pop('bsse_type')
if bsse_type_list is None:
raise ValidationError("N-Body GUFunc: Must pass a bsse_type")
if not isinstance(bsse_type_list, list):
bsse_type_list = [bsse_type_list]
for num, btype in enumerate(bsse_type_list):
if btype.lower() == 'cp':
do_cp = True
if (num == 0): return_method = 'cp'
elif btype.lower() == 'nocp':
do_nocp = True
if (num == 0): return_method = 'nocp'
elif btype.lower() == 'vmfc':
do_vmfc = True
if (num == 0): return_method = 'vmfc'
else:
raise ValidationError("N-Body GUFunc: bsse_type '%s' is not recognized" % btype.lower())
max_nbody = kwargs.get('max_nbody', -1)
max_frag = molecule.nfragments()
if max_nbody == -1:
max_nbody = molecule.nfragments()
else:
max_nbody = min(max_nbody, max_frag)
# What levels do we need?
nbody_range = range(1, max_nbody + 1)
fragment_range = range(1, max_frag + 1)
# Flip this off for now, needs more testing
# If we are doing CP lets save them integrals
#if 'cp' in bsse_type_list and (len(bsse_type_list) == 1):
# # Set to save RI integrals for repeated full-basis computations
# ri_ints_io = core.get_global_option('DF_INTS_IO')
# # inquire if above at all applies to dfmp2 or just scf
# core.set_global_option('DF_INTS_IO', 'SAVE')
# psioh = core.IOManager.shared_object()
# psioh.set_specific_retention(97, True)
bsse_str = bsse_type_list[0]
if len(bsse_type_list) >1:
bsse_str = str(bsse_type_list)
core.print_out("\n\n")
core.print_out(" ===> N-Body Interaction Abacus <===\n")
core.print_out(" BSSE Treatment: %s\n" % bsse_str)
cp_compute_list = {x:set() for x in nbody_range}
nocp_compute_list = {x:set() for x in nbody_range}
vmfc_compute_list = {x:set() for x in nbody_range}
vmfc_level_list = {x:set() for x in nbody_range} # Need to sum something slightly different
# Build up compute sets
if do_cp:
# Everything is in dimer basis
basis_tuple = tuple(fragment_range)
for nbody in nbody_range:
for x in it.combinations(fragment_range, nbody):
cp_compute_list[nbody].add( (x, basis_tuple) )
if do_nocp:
# Everything in monomer basis
for nbody in nbody_range:
for x in it.combinations(fragment_range, nbody):
nocp_compute_list[nbody].add( (x, x) )
if do_vmfc:
# Like a CP for all combinations of pairs or greater
for nbody in nbody_range:
for cp_combos in it.combinations(fragment_range, nbody):
basis_tuple = tuple(cp_combos)
for interior_nbody in nbody_range:
for x in it.combinations(cp_combos, interior_nbody):
combo_tuple = (x, basis_tuple)
vmfc_compute_list[interior_nbody].add( combo_tuple )
vmfc_level_list[len(basis_tuple)].add( combo_tuple )
# Build a comprehensive compute_range
compute_list = {x:set() for x in nbody_range}
for n in nbody_range:
compute_list[n] |= cp_compute_list[n]
compute_list[n] |= nocp_compute_list[n]
compute_list[n] |= vmfc_compute_list[n]
core.print_out(" Number of %d-body computations: %d\n" % (n, len(compute_list[n])))
# Build size and slices dictionaries
fragment_size_dict = {frag: molecule.extract_subsets(frag).natom() for
frag in range(1, max_frag+1)}
start = 0
fragment_slice_dict = {}
for k, v in fragment_size_dict.items():
fragment_slice_dict[k] = slice(start, start + v)
start += v
molecule_total_atoms = sum(fragment_size_dict.values())
# Now compute the energies
energies_dict = {}
ptype_dict = {}
for n in compute_list.keys():
core.print_out("\n ==> N-Body: Now computing %d-body complexes <==\n\n" % n)
total = len(compute_list[n])
for num, pair in enumerate(compute_list[n]):
core.print_out("\n N-Body: Computing complex (%d/%d) with fragments %s in the basis of fragments %s.\n\n" %
(num + 1, total, str(pair[0]), str(pair[1])))
ghost = list(set(pair[1]) - set(pair[0]))
current_mol = molecule.extract_subsets(list(pair[0]), ghost)
ptype_dict[pair] = func(method_string, molecule=current_mol, **kwargs)
energies_dict[pair] = core.get_variable("CURRENT ENERGY")
core.print_out("\n N-Body: Complex Energy (fragments = %s, basis = %s: %20.14f)\n" %
(str(pair[0]), str(pair[1]), energies_dict[pair]))
# Flip this off for now, needs more testing
#if 'cp' in bsse_type_list and (len(bsse_type_list) == 1):
# core.set_global_option('DF_INTS_IO', 'LOAD')
core.clean()
# Final dictionaries
cp_energy_by_level = {n: 0.0 for n in nbody_range}
nocp_energy_by_level = {n: 0.0 for n in nbody_range}
cp_energy_body_dict = {n: 0.0 for n in nbody_range}
nocp_energy_body_dict = {n: 0.0 for n in nbody_range}
vmfc_energy_body_dict = {n: 0.0 for n in nbody_range}
# Build out ptype dictionaries if needed
if ptype != 'energy':
if ptype == 'gradient':
arr_shape = (molecule_total_atoms, 3)
elif ptype == 'hessian':
arr_shape = (molecule_total_atoms * 3, molecule_total_atoms * 3)
else:
raise KeyError("N-Body: ptype '%s' not recognized" % ptype)
cp_ptype_by_level = {n: np.zeros(arr_shape) for n in nbody_range}
nocp_ptype_by_level = {n: np.zeros(arr_shape) for n in nbody_range}
vmfc_ptype_by_level = {n: np.zeros(arr_shape) for n in nbody_range}
cp_ptype_body_dict = {n: np.zeros(arr_shape) for n in nbody_range}
nocp_ptype_body_dict = {n: np.zeros(arr_shape) for n in nbody_range}
vmfc_ptype_body_dict = {n: np.zeros(arr_shape) for n in nbody_range}
else:
cp_ptype_by_level, cp_ptype_body_dict = None, None
nocp_ptype_by_level, nocp_ptype_body_dict = None, None
vmfc_ptype_body_dict = None
# Sum up all of the levels
for n in nbody_range:
# Energy
cp_energy_by_level[n] = sum(energies_dict[v] for v in cp_compute_list[n])
nocp_energy_by_level[n] = sum(energies_dict[v] for v in nocp_compute_list[n])
# Special vmfc case
if n > 1:
vmfc_energy_body_dict[n] = vmfc_energy_body_dict[n - 1]
for tup in vmfc_level_list[n]:
vmfc_energy_body_dict[n] += ((-1) ** (n - len(tup[0]))) * energies_dict[tup]
# Do ptype
if ptype != 'energy':
_sum_cluster_ptype_data(ptype, ptype_dict, cp_compute_list[n],
fragment_slice_dict, fragment_size_dict,
cp_ptype_by_level[n])
_sum_cluster_ptype_data(ptype, ptype_dict, nocp_compute_list[n],
fragment_slice_dict, fragment_size_dict,
nocp_ptype_by_level[n])
_sum_cluster_ptype_data(ptype, ptype_dict, vmfc_level_list[n],
fragment_slice_dict, fragment_size_dict,
vmfc_ptype_by_level[n], vmfc=True)
# Compute cp energy and ptype
if do_cp:
for n in nbody_range:
if n == max_frag:
cp_energy_body_dict[n] = cp_energy_by_level[n]
if ptype != 'energy':
cp_ptype_body_dict[n][:] = cp_ptype_by_level[n]
continue
for k in range(1, n + 1):
take_nk = nCr(max_frag - k - 1, n - k)
sign = ((-1) ** (n - k))
value = cp_energy_by_level[k]
cp_energy_body_dict[n] += take_nk * sign * value
if ptype != 'energy':
value = cp_ptype_by_level[k]
cp_ptype_body_dict[n] += take_nk * sign * value
_print_nbody_energy(cp_energy_body_dict, "Counterpoise Corrected (CP)")
cp_interaction_energy = cp_energy_body_dict[max_nbody] - cp_energy_body_dict[1]
core.set_variable('Counterpoise Corrected Total Energy', cp_energy_body_dict[max_nbody])
core.set_variable('Counterpoise Corrected Interaction Energy', cp_interaction_energy)
for n in nbody_range[1:]:
var_key = 'CP-CORRECTED %d-BODY INTERACTION ENERGY' % n
core.set_variable(var_key, cp_energy_body_dict[n] - cp_energy_body_dict[1])
# Compute nocp energy and ptype
if do_nocp:
for n in nbody_range:
if n == max_frag:
nocp_energy_body_dict[n] = nocp_energy_by_level[n]
if ptype != 'energy':
nocp_ptype_body_dict[n][:] = nocp_ptype_by_level[n]
continue
for k in range(1, n + 1):
take_nk = nCr(max_frag - k - 1, n - k)
sign = ((-1) ** (n - k))
value = nocp_energy_by_level[k]
nocp_energy_body_dict[n] += take_nk * sign * value
if ptype != 'energy':
value = nocp_ptype_by_level[k]
nocp_ptype_body_dict[n] += take_nk * sign * value
_print_nbody_energy(nocp_energy_body_dict, "Non-Counterpoise Corrected (NoCP)")
nocp_interaction_energy = nocp_energy_body_dict[max_nbody] - nocp_energy_body_dict[1]
core.set_variable('Non-Counterpoise Corrected Total Energy', nocp_energy_body_dict[max_nbody])
core.set_variable('Non-Counterpoise Corrected Interaction Energy', nocp_interaction_energy)
for n in nbody_range[1:]:
var_key = 'NOCP-CORRECTED %d-BODY INTERACTION ENERGY' % n
core.set_variable(var_key, nocp_energy_body_dict[n] - nocp_energy_body_dict[1])
# Compute vmfc energy and ptype
if do_vmfc:
_print_nbody_energy(vmfc_energy_body_dict, "Valiron-Mayer Function Couterpoise (VMFC)")
vmfc_interaction_energy = vmfc_energy_body_dict[max_nbody] - vmfc_energy_body_dict[1]
core.set_variable('Valiron-Mayer Function Couterpoise Total Energy', vmfc_energy_body_dict[max_nbody])
core.set_variable('Valiron-Mayer Function Couterpoise Interaction Energy', vmfc_interaction_energy)
for n in nbody_range[1:]:
var_key = 'VMFC-CORRECTED %d-BODY INTERACTION ENERGY' % n
core.set_variable(var_key, vmfc_energy_body_dict[n] - vmfc_energy_body_dict[1])
if return_method == 'cp':
ptype_body_dict = cp_ptype_body_dict
energy_body_dict = cp_energy_body_dict
elif return_method == 'nocp':
ptype_body_dict = nocp_ptype_body_dict
energy_body_dict = nocp_energy_body_dict
elif return_method == 'vmfc':
ptype_body_dict = vmfc_ptype_body_dict
energy_body_dict = vmfc_energy_body_dict
else:
raise ValidationError("N-Body Wrapper: Invalid return type. Should never be here, please post this error on github.")
# Figure out and build return types
if return_total_data:
ret_energy = energy_body_dict[max_nbody]
else:
ret_energy = energy_body_dict[max_nbody]
ret_energy -= energy_body_dict[1]
if ptype != 'energy':
if return_total_data:
np_final_ptype = ptype_body_dict[max_nbody].copy()
else:
np_final_ptype = ptype_body_dict[max_nbody].copy()
np_final_ptype -= ptype_body_dict[1]
ret_ptype = core.Matrix.from_array(np_final_ptype)
else:
ret_ptype = ret_energy
# Build and set a wavefunction
wfn = core.Wavefunction.build(molecule, 'sto-3g')
wfn.nbody_energy = energies_dict
wfn.nbody_ptype = ptype_dict
wfn.nbody_body_energy = energy_body_dict
wfn.nbody_body_ptype = ptype_body_dict
if ptype == 'gradient':
wfn.set_gradient(ret_ptype)
elif ptype == 'hessian':
wfn.set_hessian(ret_ptype)
core.set_variable("CURRENT ENERGY", ret_energy)
if return_wfn:
return (ret_ptype, wfn)
else:
return ret_ptype
