def check_scatter_pdf(value):
"""
Check two processor, algorithm pairs against each other for PDF calculation
:param value:
:return:
"""
# set everything up
atoms, exp = value[:2]
scat = ElasticScatter(exp_dict=exp, verbose=True)
proc1, alg1 = value[-1][0]
proc2, alg2 = value[-1][1]
# run algorithm 1
scat.set_processor(proc1, alg1)
ans1 = scat.get_pdf(atoms)
# run algorithm 2
scat.set_processor(proc2, alg2)
ans2 = scat.get_pdf(atoms)
# test
stats_check(ans1, ans2, rtol, atol)
assert_allclose(ans1, ans2, rtol=rtol, atol=atol)
# make certain we did not give back the same pointer
assert ans1 is not ans2
def check_scatter_consistancy(value):
atoms, exp = value[0:2]
proc, alg = value[-1]
scat = ElasticScatter(exp_dict=exp, verbose=True)
scat.set_processor(proc, alg)
ans = scat.get_pdf(atoms)
ans1 = scat.get_fq(atoms)
print(len(ans1))
print(scat.get_scatter_vector().shape)
ans2 = scat.get_sq(atoms)
print(len(ans2))
ans3 = scat.get_iq(atoms)
def check_dynamics(value):
"""
Test classical dynamics simulation, symplectic dynamics are look the same
forward as reversed
Parameters
----------
value: list or tuple
The values to use in the tests
"""
ideal_atoms, _ = value[0]
ideal_atoms.set_velocities(np.zeros((len(ideal_atoms), 3)))
if isinstance(value[1], str):
s = ElasticScatter(verbose=True)
target_data = None
exp_func = None
exp_grad = None
if value[1] == 'PDF':
target_data = s.get_pdf(ideal_atoms)
exp_func = s.get_pdf
exp_grad = s.get_grad_pdf
elif value[1] == 'FQ':
target_data = s.get_fq(ideal_atoms)
exp_func = s.get_fq
exp_grad = s.get_grad_fq
calc = Calc1D(target_data=target_data,
exp_function=exp_func, exp_grad_function=exp_grad,
potential='rw', conv=30)
else:
calc = value[1]
ideal_atoms.positions *= 1.02
ideal_atoms.set_calculator(calc)
start_pe = ideal_atoms.get_potential_energy()
e = value[2]
traj = classical_dynamics(ideal_atoms, e, 5)
pe_list = []
for atoms in traj:
pe_list.append(atoms.get_potential_energy())
min_pe = np.min(pe_list)
print(min_pe, start_pe, len(traj))
print(pe_list)
if start_pe != 0.0:
assert min_pe < start_pe
def check_n_forces(value):
"""
Test numerical vs analytical forces
Parameters
----------
value: list or tuple
The values to use in the tests
"""
rtol = 1e-6
atol = 6e-5
ideal_atoms = value[0]
ideal_atoms.set_velocities(np.zeros((len(ideal_atoms), 3)))
if isinstance(value[1], str):
s = ElasticScatter(verbose=True)
target_data = None
exp_func = None
exp_grad = None
if value[1] == 'PDF':
target_data = s.get_pdf(ideal_atoms)
exp_func = s.get_pdf
exp_grad = s.get_grad_pdf
elif value[1] == 'FQ':
target_data = s.get_fq(ideal_atoms)
exp_func = s.get_fq
exp_grad = s.get_grad_fq
calc = Calc1D(target_data=target_data,
exp_function=exp_func, exp_grad_function=exp_grad,
potential='rw', conv=1)
else:
calc = value[1]
ideal_atoms.positions *= 1.02
ideal_atoms.set_calculator(calc)
ans1 = ideal_atoms.get_forces()
ans2 = calc.calculate_numerical_forces(ideal_atoms, d=5e-5)
stats_check(ans2, ans1,
rtol=rtol,
atol=atol
)
def check_scatter_pdf(value):
"""
Smoke test for PDF
:param value:
:return:
"""
atoms, exp = value[0:2]
proc, alg = value[-1]
scat = ElasticScatter(exp_dict=exp, verbose=True)
scat.set_processor(proc, alg)
# Test a set of different sized ensembles
ans = scat.get_pdf(atoms)
# Check that Scatter gave back something
assert ans is not None
# Check that all the values are not zero
assert np.any(ans)
del atoms, exp, proc, alg, scat, ans
return
def check_n_forces(value):
"""
Test numerical vs analytical forces
Parameters
----------
value: list or tuple
The values to use in the tests
"""
rtol = 1e-6
atol = 6e-5
ideal_atoms = value[0]
ideal_atoms.set_velocities(np.zeros((len(ideal_atoms), 3)))
if isinstance(value[1], str):
s = ElasticScatter(verbose=True)
target_data = None
exp_func = None
exp_grad = None
if value[1] == 'PDF':
target_data = s.get_pdf(ideal_atoms)
exp_func = s.get_pdf
exp_grad = s.get_grad_pdf
elif value[1] == 'FQ':
target_data = s.get_fq(ideal_atoms)
exp_func = s.get_fq
exp_grad = s.get_grad_fq
calc = Calc1D(target_data=target_data,
exp_function=exp_func,
exp_grad_function=exp_grad,
potential='rw',
conv=1)
else:
calc = value[1]
ideal_atoms.positions *= 1.02
ideal_atoms.set_calculator(calc)
ans1 = ideal_atoms.get_forces()
ans2 = calc.calculate_numerical_forces(ideal_atoms, d=5e-5)
stats_check(ans2, ans1, rtol=rtol, atol=atol)
s = ElasticScatter({'qmin': 3., 'rmax': 20.})
displacement = atoms.get_positions() - atoms.get_center_of_mass()
distance = np.sqrt(np.sum(displacement**2, axis=1))
print(np.max(distance))
norm_displacement = (displacement.T / distance).T
adp_tensor = norm_displacement * .01
print(np.max(adp_tensor))
adp_tensor_target = adp_tensor.copy()
target_atoms = atoms.copy()
target_adps = ADP(atoms, adps=adp_tensor_target)
target_atoms.info['adps'] = target_adps
target_pdf = s.get_pdf(target_atoms)
adp_calc = Calc1D(target_data=target_pdf,
exp_grad_function=s.get_grad_adp_pdf,
exp_function=s.get_pdf,
conv=100)
calc = Calc1D(target_data=target_pdf,
exp_grad_function=null_func,
exp_function=s.get_pdf,
conv=100)
starting_atoms = atoms.copy()
starting_adps = ADP(atoms, adps=adp_tensor * np.random.random(adp_tensor.shape))
starting_adps.set_calculator(adp_calc)
def check_nuts(self, value):
"""
Test NUTS simulation
Parameters
----------
value: list or tuple
The values to use in the tests
"""
print(self.traj_file)
ideal_atoms, _ = value[0]
ideal_atoms.set_velocities(np.zeros((len(ideal_atoms), 3)))
s = ElasticScatter(verbose=True)
if value[1] == 'PDF':
target_data = s.get_pdf(ideal_atoms)
exp_func = s.get_pdf
exp_grad = s.get_grad_pdf
calc = Calc1D(target_data=target_data,
exp_function=exp_func, exp_grad_function=exp_grad,
potential='rw', conv=30)
elif value[1] == 'FQ':
target_data = s.get_pdf(ideal_atoms)
exp_func = s.get_pdf
exp_grad = s.get_grad_pdf
calc = Calc1D(target_data=target_data,
exp_function=exp_func, exp_grad_function=exp_grad,
potential='rw', conv=30)
else:
calc = value[1]
ideal_atoms.positions *= 1.02
ideal_atoms.set_calculator(calc)
start_pe = ideal_atoms.get_potential_energy()
if value[2]:
traj_name = self.traj_file.name
else:
traj_name = None
nuts = NUTSCanonicalEnsemble(ideal_atoms, escape_level=4, verbose=True,
seed=seed, trajectory=traj_name)
traj, metadata = nuts.run(5)
print(traj[0].get_momenta())
pe_list = []
for atoms in traj:
pe_list.append(atoms.get_potential_energy())
min_pe = np.min(pe_list)
print(len(traj))
print(min_pe, start_pe)
if start_pe != 0.0:
if not min_pe < start_pe:
view(traj)
assert min_pe < start_pe
self.traj_file.close()
if value[2]:
assert os.path.exists(self.traj_file.name)
read_traj = TrajectoryReader(self.traj_file.name)
print(len(traj), len(read_traj))
assert len(traj) == len(read_traj)
for i, (atoms1, atoms2) in enumerate(zip(read_traj, traj)):
for att in ['get_positions', 'get_potential_energy',
'get_forces', 'get_momenta']:
print(i, att)
assert_allclose(*[getattr(a, att)() for a in [atoms1, atoms2]])
del traj
from pyiid.calc.pdfcalc import PDFCalc
from pyiid.utils import build_sphere_np
import matplotlib.pyplot as plt
from pprint import pprint
import time
from copy import deepcopy as dc
from collections import OrderedDict
import pickle
import traceback
from pyiid.experiments.elasticscatter import ElasticScatter
exp = None
scat = ElasticScatter()
atoms = Atoms('Au4', [[0,0,0], [3,0,0], [0,3,0], [3,3,0]])
pdf = scat.get_pdf(atoms)
type_list = []
time_list = []
benchmarks = [
('CPU', 'flat'),
('Multi-GPU', 'flat')
]
colors=['b', 'r']
sizes = range(10, 80, 5)
print sizes
for proc, alg in benchmarks:
print proc, alg
number_of_atoms = []
scat.set_processor(proc, alg)
type_list.append((proc, alg))
from pyiid.experiments.elasticscatter import ElasticScatter
from pyiid.calc.calc_1d import Calc1D
from pyiid.sim.nuts_hmc import NUTSCanonicalEnsemble
from ase.cluster import Octahedron
import matplotlib.pyplot as plt
from ase.visualize import view
# Lets set up the atoms
# We use the Atomic Simulation Environment to take care of our atoms
# atoms = Atoms('Au4', [[0, 0, 0], [3, 0, 0], [0, 3, 0], [3, 3, 0]])
atoms = Octahedron('Au', 2)
view(atoms)
# we can view the atoms by importing ASE's gui
scat = ElasticScatter()
pdf = scat.get_pdf(atoms)
r = scat.get_r()
# Now lets dilate the atoms so that they don't match the pdf
atoms2 = dc(atoms)
atoms2.positions *= 1.05
pdf2 = scat.get_pdf(atoms2)
r = scat.get_r()
# Now we need to define the potential energy surface
calc = Calc1D(
target_data=pdf, # The target or experimental data
exp_function=scat.get_pdf,
# The function which takes in atoms and produces
# data like the experiment
exp_grad_function=scat.get_grad_pdf, # the function which produces the
# gradient of the calculated data
def check_nuts(self, value):
"""
Test NUTS simulation
Parameters
----------
value: list or tuple
The values to use in the tests
"""
print(self.traj_file)
ideal_atoms, _ = value[0]
ideal_atoms.set_velocities(np.zeros((len(ideal_atoms), 3)))
s = ElasticScatter(verbose=True)
if value[1] == 'PDF':
target_data = s.get_pdf(ideal_atoms)
exp_func = s.get_pdf
exp_grad = s.get_grad_pdf
calc = Calc1D(target_data=target_data,
exp_function=exp_func,
exp_grad_function=exp_grad,
potential='rw',
conv=30)
elif value[1] == 'FQ':
target_data = s.get_pdf(ideal_atoms)
exp_func = s.get_pdf
exp_grad = s.get_grad_pdf
calc = Calc1D(target_data=target_data,
exp_function=exp_func,
exp_grad_function=exp_grad,
potential='rw',
conv=30)
else:
calc = value[1]
ideal_atoms.positions *= 1.02
ideal_atoms.set_calculator(calc)
start_pe = ideal_atoms.get_potential_energy()
if value[2]:
traj_name = self.traj_file.name
else:
traj_name = None
nuts = NUTSCanonicalEnsemble(ideal_atoms,
escape_level=4,
verbose=True,
seed=seed,
trajectory=traj_name)
traj, metadata = nuts.run(5)
print(traj[0].get_momenta())
pe_list = []
for atoms in traj:
pe_list.append(atoms.get_potential_energy())
min_pe = np.min(pe_list)
print(len(traj))
print(min_pe, start_pe)
if start_pe != 0.0:
if not min_pe < start_pe:
view(traj)
assert min_pe < start_pe
self.traj_file.close()
if value[2]:
assert os.path.exists(self.traj_file.name)
read_traj = TrajectoryReader(self.traj_file.name)
print(len(traj), len(read_traj))
assert len(traj) == len(read_traj)
for i, (atoms1, atoms2) in enumerate(zip(read_traj, traj)):
for att in [
'get_positions', 'get_potential_energy', 'get_forces',
'get_momenta'
]:
print(i, att)
assert_allclose(
*[getattr(a, att)() for a in [atoms1, atoms2]])
del traj
displacement = atoms.get_positions() - atoms.get_center_of_mass()
distance = np.sqrt(np.sum(displacement**2, axis=1))
print(np.max(distance))
norm_displacement = (displacement.T / distance).T
adp_tensor = norm_displacement * .01
print(np.max(adp_tensor))
adp_tensor_target = adp_tensor.copy()
adp_tensor_target[:, :1] = 0.0
target_atoms = atoms.copy()
target_adps = ADP(atoms, adps=adp_tensor_target)
target_atoms.info['adps'] = target_adps
target_pdf = s.get_pdf(target_atoms)
adp_calc = Calc1D(target_data=target_pdf,
exp_grad_function=s.get_grad_adp_pdf,
exp_function=s.get_pdf,
conv=100)
calc = Calc1D(target_data=target_pdf,
exp_grad_function=null_func,
exp_function=s.get_pdf,
conv=100)
starting_atoms = atoms.copy()
starting_adps = ADP(atoms, adps=adp_tensor * adp_tensor_target * 1.5)
starting_adps.set_calculator(adp_calc)
