# type 2 = H+
N0 = 1 # number of titratable units
K_diss = 0.0088
system.part.add(id=0, pos=[0, 0, 0] * system.box_l, type=3)
system.part.add(id=1, pos=[1.0, 1.0, 1.0] * system.box_l / 2.0, type=1)
system.part.add(id=2, pos=np.random.random() * system.box_l, type=2)
system.part.add(id=3, pos=np.random.random() * system.box_l, type=2)
# create a harmonic bond between the two reacting particles => the potential energy is quadratic in the elongation of the bond and therefore the density of states is known as the one of the harmonic oscillator
h = HarmonicBond(r_0=0, k=1)
system.bonded_inter[0] = h
system.part[0].add_bond((h, 1))
RE = reaction_ensemble.WangLandauReactionEnsemble(temperature=1,
exclusion_radius=0)
RE.add_reaction(gamma=K_diss,
reactant_types=[0],
reactant_coefficients=[1],
product_types=[1, 2],
product_coefficients=[1, 1],
default_charges={
0: 0,
1: -1,
2: +1
})
print(RE.get_status())
system.setup_type_map([0, 1, 2, 3])
# initialize wang_landau
# generate preliminary_energy_run_results here, this should be done in a seperate simulation without energy reweighting using the update energy functions
class ReactionEnsembleTest(ut.TestCase):
"""Test the core implementation of the wang_landau reaction ensemble.
Create a harmonic bond between the two reacting particles. Therefore the
potential energy is quadratic in the elongation of the bond and
therefore the density of states is known as the one of the harmonic
oscillator
"""
# System parameters
#
box_l = 6 * np.sqrt(2)
temperature = 1.0
# Integration parameters
#
system = espressomd.System(box_l=[box_l, box_l, box_l])
system.seed = system.cell_system.get_state()['n_nodes'] * [1234]
np.random.seed(seed=system.seed)
system.time_step = 0.01
system.cell_system.skin = 0
system.cell_system.set_n_square(use_verlet_lists=False)
#
# Setup System
#
N0 = 1 # number of titratable units
K_diss = 0.0088
system.part.add(id=0, pos=[0, 0, 0] * system.box_l, type=3)
system.part.add(id=1, pos=[1.0, 1.0, 1.0] * system.box_l / 2.0, type=1)
system.part.add(id=2, pos=np.random.random() * system.box_l, type=2)
system.part.add(id=3, pos=np.random.random() * system.box_l, type=2)
h = HarmonicBond(r_0=0, k=1)
system.bonded_inter[0] = h
system.part[0].add_bond((h, 1))
WLRE = reaction_ensemble.WangLandauReactionEnsemble(
temperature=temperature, exclusion_radius=0, seed=69)
WLRE.add_reaction(gamma=K_diss,
reactant_types=[0],
reactant_coefficients=[1],
product_types=[1, 2],
product_coefficients=[1, 1],
default_charges={
0: 0,
1: -1,
2: +1
})
system.setup_type_map([0, 1, 2, 3])
# initialize wang_landau
# generate preliminary_energy_run_results here, this should be done in a
# separate simulation without energy reweighting using the update energy
# functions
np.savetxt("energy_boundaries.dat",
np.c_[[0, 1], [0, 0], [9, 9]],
delimiter='\t',
header="nbar E_potmin E_potmax")
WLRE.add_collective_variable_degree_of_association(
associated_type=0, min=0, max=1, corresponding_acid_types=[0, 1])
WLRE.set_wang_landau_parameters(
final_wang_landau_parameter=1e-2,
do_not_sample_reaction_partition_function=True,
full_path_to_output_filename="WL_potential_out.dat")
def test_wang_landau_energy_recording(self):
self.WLRE.update_maximum_and_minimum_energies_at_current_state()
self.WLRE.write_out_preliminary_energy_run_results()
nbars, E_mins, E_maxs = np.loadtxt("preliminary_energy_run_results",
unpack=True)
npt.assert_almost_equal(nbars, [0, 1])
npt.assert_almost_equal(E_mins, [27.0, -10])
npt.assert_almost_equal(E_maxs, [27.0, -10])
def test_wang_landau_output(self):
self.WLRE.add_collective_variable_potential_energy(
filename="energy_boundaries.dat", delta=0.05)
while True:
try:
self.WLRE.reaction()
for i in range(2):
self.WLRE.displacement_mc_move_for_particles_of_type(3)
except reaction_ensemble.WangLandauHasConverged: # only catch my exception
break
# test as soon as wang_landau has converged (throws exception then)
nbars, Epots, WL_potentials = np.loadtxt("WL_potential_out.dat",
unpack=True)
mask_nbar_0 = np.where(np.abs(nbars - 1.0) < 0.0001)
Epots = Epots[mask_nbar_0]
Epots = Epots[1:]
WL_potentials = WL_potentials[mask_nbar_0]
WL_potentials = WL_potentials[1:]
expected_canonical_potential_energy = np.sum(
np.exp(WL_potentials) * Epots *
np.exp(-Epots / self.temperature)) / np.sum(
np.exp(WL_potentials) * np.exp(-Epots / self.temperature))
expected_canonical_squared_potential_energy = np.sum(
np.exp(WL_potentials) * Epots**2 *
np.exp(-Epots / self.temperature)) / np.sum(
np.exp(WL_potentials) * np.exp(-Epots / self.temperature))
expected_canonical_configurational_heat_capacity = expected_canonical_squared_potential_energy - \
expected_canonical_potential_energy**2
# for the calculation regarding the analytical results which are
# compared here, see Master Thesis Jonas Landsgesell p. 72
self.assertAlmostEqual(
expected_canonical_potential_energy - 1.5,
0.00,
places=1,
msg=
"difference to analytical expected canonical potential energy too big"
)
self.assertAlmostEqual(
expected_canonical_configurational_heat_capacity - 1.5,
0.00,
places=1,
msg=
"difference to analytical expected canonical configurational heat capacity too big"
)
def _wang_landau_output_checkpoint(self, filename):
# write first checkpoint
self.WLRE.write_wang_landau_checkpoint()
old_checkpoint = np.loadtxt(filename)
# modify old_checkpoint in memory and in file (this destroys the
# information contained in the checkpoint, but allows for testing of
# the functions)
modified_checkpoint = old_checkpoint
modified_checkpoint[0] = 1
np.savetxt(filename, modified_checkpoint)
# check whether changes are carried out correctly
self.WLRE.load_wang_landau_checkpoint()
self.WLRE.write_wang_landau_checkpoint()
new_checkpoint = np.loadtxt(filename)
npt.assert_almost_equal(new_checkpoint, modified_checkpoint)
def test_wang_landau_output_checkpoint(self):
filenames = [
"checkpoint_wang_landau_potential_checkpoint",
"checkpoint_wang_landau_histogram_checkpoint"
]
for filename in filenames:
self._wang_landau_output_checkpoint(filename)
class ReactionEnsembleTest(ut.TestCase):
"""Test the core implementation of the wang_landau reaction ensemble.
Create a harmonic bond between the two reacting particles. Therefore the
potential energy is quadratic in the elongation of the bond and
therefore the density of states is known as the one of the harmonic
oscillator
"""
# System parameters
#
box_l = 6 * np.sqrt(2)
temperature = 1.0
# Integration parameters
#
system = espressomd.System(box_l=[box_l, box_l, box_l])
system.time_step = 0.01
system.cell_system.skin = 0.4
#
# Setup System
#
N0 = 1 # number of titratable units
K_diss = 0.0088
system.part.add(id=0, pos=[0, 0, 0] * system.box_l, type=3)
system.part.add(id=1, pos=[1.0, 1.0, 1.0] * system.box_l / 2.0, type=1)
system.part.add(id=2, pos=np.random.random() * system.box_l, type=2)
system.part.add(id=3, pos=np.random.random() * system.box_l, type=2)
h = HarmonicBond(r_0=0, k=1)
system.bonded_inter[0] = h
system.part[0].add_bond((h, 1))
RE = reaction_ensemble.WangLandauReactionEnsemble(temperature=temperature,
exclusion_radius=0)
RE.add_reaction(gamma=K_diss,
reactant_types=[0],
reactant_coefficients=[1],
product_types=[1, 2],
product_coefficients=[1, 1],
default_charges={
0: 0,
1: -1,
2: +1
})
system.setup_type_map([0, 1, 2, 3])
# initialize wang_landau
# generate preliminary_energy_run_results here, this should be done in a
# seperate simulation without energy reweighting using the update energy
# functions
np.savetxt("energy_boundaries.dat",
np.c_[[0, 1], [0, 0], [9, 9]],
delimiter='\t',
header="nbar E_potmin E_potmax")
RE.add_collective_variable_degree_of_association(
associated_type=0, min=0, max=1, corresponding_acid_types=[0, 1])
RE.add_collective_variable_potential_energy(
filename="energy_boundaries.dat", delta=0.05)
RE.set_wang_landau_parameters(
final_wang_landau_parameter=1e-2,
do_not_sample_reaction_partition_function=True,
full_path_to_output_filename="WL_potential_out.dat")
def test_wang_landau_output(self):
while True:
try:
self.RE.reaction()
for i in range(7):
self.RE.displacement_mc_move_for_particles_of_type(3)
except reaction_ensemble.WangLandauHasConverged: # only catch my exception
break
# test as soon as wang_landau has converged (throws exception then)
nbars, Epots, WL_potentials = np.loadtxt("WL_potential_out.dat",
unpack=True)
mask_nbar_0 = np.where(np.abs(nbars - 1.0) < 0.0001)
Epots = Epots[mask_nbar_0]
Epots = Epots[1:]
WL_potentials = WL_potentials[mask_nbar_0]
WL_potentials = WL_potentials[1:]
expected_canonical_potential_energy = np.sum(
np.exp(WL_potentials) * Epots *
np.exp(-Epots / self.temperature)) / np.sum(
np.exp(WL_potentials) * np.exp(-Epots / self.temperature))
expected_canonical_squared_potential_energy = np.sum(
np.exp(WL_potentials) * Epots**2 *
np.exp(-Epots / self.temperature)) / np.sum(
np.exp(WL_potentials) * np.exp(-Epots / self.temperature))
expected_canonical_configurational_heat_capacity = expected_canonical_squared_potential_energy - \
expected_canonical_potential_energy**2
print(expected_canonical_potential_energy,
expected_canonical_configurational_heat_capacity)
# for the calculation regarding the analytical results which are
# compared here, see Master Thesis Jonas Landsgesell p. 72
self.assertAlmostEqual(
expected_canonical_potential_energy - 1.5,
0.00,
places=1,
msg=
"difference to analytical expected canonical potential energy too big"
)
self.assertAlmostEqual(
expected_canonical_configurational_heat_capacity - 1.5,
0.00,
places=1,
msg=
"difference to analytical expected canonical configurational heat capacity too big"
)
