def job_dump_hdf5(name, output, scfparams, dump_params):
"""Run a full calculation and dump the result as a yaml file"""
res = run_scf_calculation(name, scfparams)
# Remove keys which are given by the parameters
for key in dump_params.get("remove_keys", []):
if key in res:
del res[key]
molsturm.dump_state(res, output, type="hdf5")
def test_hdf5_serialisation(self):
for case in self.cases:
with self.subTest(label=case["testing"]["name"]):
hfres = case["hf"]
tmp = tempfile.mktemp(suffix=".hdf5")
molsturm.dump_state(hfres, tmp, type="hdf5")
back = molsturm.load_state(tmp)
os.remove(tmp)
self.assert_equal(hfres, back)
def job_orca_hf(name, params):
"""Run an orca HF calculation and extract reference values from it."""
orca_in_file = os.path.join(ORCADIR, name + ".orca_hf.in")
build_orca_input(params, ["hf"], orca_in_file)
res = run_orca(orca_in_file)
def parse_restricted(value):
return False if value == "UHF" else True
orca_patterns = [
{
"key": "energy_ground_state",
"regex": "FINAL SINGLE POINT ENERGY\s*(?P<value>" + FLPAT + ")",
"convert": float,
"default": None,
},
{
"key": "energy_1e",
"regex": "One Electron Energy\s*:\s*(?P<value>" + FLPAT + ") Eh",
"convert": float,
"default": None,
},
{
"key": "energy_2e",
"regex": "Two Electron Energy\s*:\s*(?P<value>" + FLPAT + ") Eh",
"convert": float,
"default": None,
},
{
"key": "energy_kinetic",
"regex": "Kinetic Energy\s*:\s*(?P<value>" + FLPAT + ") Eh",
"convert": float,
"default": None,
},
{
"key": "energy_potential",
"regex": "Potential Energy\s*:\s*(?P<value>" + FLPAT + ") Eh",
"convert": float,
"default": None,
},
{
"key": "energy_nuclear_repulsion",
"regex": "Nuclear Repulsion\s*:\s*(?P<value>" + FLPAT + ") Eh",
"convert": float,
"default": None,
},
{
"key": "restricted",
"regex": "Hartree-Fock type\s*HFTyp\s*\.*\s*(?P<value>\w+)",
"convert": lambda value: False if value == "UHF" else True,
"default": None,
},
{
"key": "spin_squared",
"regex":
"Expectation value of <S\*\*2>\s*:\s*(?P<value>" + FLPAT + ")",
"convert": float,
"default": 0,
},
]
reference = output_find_patterns(res, orca_patterns)
print("TODO: It would be nice if the MO coefficients would be extracted "
"from ORCA, too.")
# Extra parsing
reference["orben_f"] = orca_extract_orben(res)
with open(os.path.join(dir_of_this_script(), name + ".hf.yaml"), "w") as f:
# TODO It would be really nice if this was an actual hfres yaml
# data file and not just a faked one as it is now.
# This would require extracting a couple of more keys from
# the ORCA output.
f.write("# Data from ORCA calculation " +
os.path.relpath(orca_in_file) + "\n")
molsturm.dump_state(reference, f, type="yaml")
import molsturm.sturmian
l_max = 1
n_max = 5
for atom, mult in [("He", 1), ("Be", 1), ("C", 3), ("Ne", 1)]:
system = molsturm.System(atom)
system.multiplicity = mult
scfparams = molsturm.ScfParameters()
scfparams.system = system
k_guess = molsturm.sturmian.cs.empirical_kopt(scfparams.system)
scfparams.basis = molsturm.construct_basis("sturmian/atomic/cs_reference_pc",
scfparams.system, k_exp=k_guess,
n_max=n_max, l_max=l_max)
scfparams["scf/eigensolver/method"] = "lapack"
scfparams["guess/eigensolver/method"] = "lapack"
print("Running for " + atom + " please wait")
best = molsturm.sturmian.cs.find_kopt(scfparams, print_iterations=True)
k = best["input_parameters"]["discretisation"]["k_exp"]
print("kopt for " + atom + " is {0:.4g}".format(k) +
" with energy {0:.10g}".format(best["energy_ground_state"]))
out = atom + "_" + str(n_max) + "_" + str(l_max) + "_kopt.hdf5"
print("Dumping kopt solution for " + atom + " at " + out)
molsturm.dump_state(best, out)