def check_scatter_fq(value):
"""
Smoke test for FQ
Parameters
----------
value: list or tuple
The values to use in the tests
"""
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_fq(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_nrg(value):
"""
Check for PDF energy against known value
:param value:
:return:
"""
# setup
atoms1, atoms2 = value[0]
exp_dict = value[1]
p, thresh = value[2]
proc1, alg1 = value[3]
scat = ElasticScatter(verbose=True)
scat.update_experiment(exp_dict)
scat.set_processor(proc1, alg1)
if value[4] == 'FQ':
exp_func = scat.get_fq
exp_grad = scat.get_grad_fq
elif value[4] == 'PDF':
exp_func = scat.get_pdf
exp_grad = scat.get_grad_pdf
else:
exp_func = None
exp_grad = None
target_data = exp_func(atoms1)
calc = Calc1D(target_data=target_data,
exp_function=exp_func, exp_grad_function=exp_grad,
potential=p)
atoms2.set_calculator(calc)
ans = atoms2.get_potential_energy()
assert ans >= thresh
del atoms1, atoms2, proc1, alg1, p, thresh, scat, target_data, calc, ans
def check_del_atom(value):
atoms, exp = value[0:2]
proc, alg = value[-1]
scat = ElasticScatter(exp_dict=exp, verbose=True)
scat.set_processor(proc, alg)
assert scat._check_wrap_atoms_state(atoms) == False
# Test a set of different sized ensembles
ans1 = scat.get_fq(atoms)
assert scat._check_wrap_atoms_state(atoms) == True
# Check that Scatter gave back something
assert ans1 is not None
assert np.any(ans1)
atoms2 = dc(atoms)
del atoms2[np.random.choice(len(atoms2))]
assert scat._check_wrap_atoms_state(atoms2) == False
ans2 = scat.get_fq(atoms2)
assert scat._check_wrap_atoms_state(atoms2) == True
# Check that Scatter gave back something
assert ans2 is not None
assert np.any(ans2)
assert not np.allclose(ans1, ans2)
# make certain we did not give back the same pointer
assert ans1 is not ans2
# Check that all the values are not zero
del atoms, exp, proc, alg, scat, ans1
return
def test_consistency():
outs = [[] for i in range(len(test_atoms))]
s = ElasticScatter()
for i, atoms in enumerate(test_data):
fq = s.get_fq(atoms)
outs[i % len(test_atoms)].append(fq)
for j in range(len(test_atoms)):
for a, b in permutations(outs[j], 2):
stats_check(a, b, rtol=2e-7, atol=1e-7)
def test_consistency():
outs = [[] for i in range(len(test_atoms))]
s = ElasticScatter()
for i, atoms in enumerate(test_data):
fq = s.get_fq(atoms)
outs[i % len(test_atoms)].append(fq)
for j in range(len(test_atoms)):
for a, b in permutations(outs[j], 2):
stats_check(a, b, rtol=2e-7, atol=1e-7)
def check_del_atom(value):
atoms, exp = value[0:2]
proc, alg = value[-1]
scat = ElasticScatter(exp_dict=exp, verbose=True)
scat.set_processor(proc, alg)
assert scat._check_wrap_atoms_state(atoms) == False
# Test a set of different sized ensembles
ans1 = scat.get_fq(atoms)
assert scat._check_wrap_atoms_state(atoms) == True
# Check that Scatter gave back something
assert ans1 is not None
assert np.any(ans1)
atoms2 = dc(atoms)
del atoms2[np.random.choice(len(atoms2))]
assert scat._check_wrap_atoms_state(atoms2) == False
ans2 = scat.get_fq(atoms2)
assert scat._check_wrap_atoms_state(atoms2) == True
# Check that Scatter gave back something
assert ans2 is not None
assert np.any(ans2)
assert not np.allclose(ans1, ans2)
# make certain we did not give back the same pointer
assert ans1 is not ans2
# Check that all the values are not zero
del atoms, exp, proc, alg, scat, ans1
return
def finite_difference_grad(atoms, exp_dict):
s = ElasticScatter(exp_dict, verbose=True)
start_fq = s.get_fq(atoms)
finite_difference_grad_fq = np.zeros((len(atoms), 3, len(start_fq)))
for i in range(len(atoms)):
for w in range(3):
atoms2 = dc(atoms)
atoms2[i].position[w] += dq
fq2 = s.get_fq(atoms2)
finite_difference_grad_fq[i, w, :] = (fq2 - start_fq) / dq
return finite_difference_grad_fq
def start(value):
atoms, exp = value[:2]
e = ElasticScatter(exp)
e._wrap_atoms(atoms)
q = atoms.get_positions().astype(np.float32)
if value[2] == 'fq':
scatter_array = atoms.get_array('F(Q) scatter')
else:
scatter_array = atoms.get_array('PDF scatter')
n, qmax_bin = scatter_array.shape
k_max = n * (n - 1) / 2.
return q, scatter_array, n, qmax_bin, k_max, 0
def start(value):
atoms, exp = value[:2]
e = ElasticScatter(exp)
e._wrap_atoms(atoms)
q = atoms.get_positions().astype(np.float32)
if value[2] == 'fq':
scatter_array = atoms.get_array('F(Q) scatter')
else:
scatter_array = atoms.get_array('PDF scatter')
n, qmax_bin = scatter_array.shape
k_max = int(n * (n - 1) / 2.)
return q, scatter_array, n, qmax_bin, k_max, 0
def test_consistency2():
outs = [[] for i in range(len(test_atoms))]
s = ElasticScatter(verbose=True)
for i, atoms in enumerate(test_data):
stru = convert_atoms_to_stru(atoms)
srfit_calc = DebyePDFCalculator()
srfit_calc.qmin = s.exp['qmin']
srfit_calc.qmax = s.exp['qmax']
srfit_calc.qstep = s.exp['qbin']
srfit_calc(stru)
stats_check(s.get_scatter_vector(), srfit_calc.qgrid)
fq = srfit_calc.fq
outs[i % len(test_atoms)].append(fq)
for j in range(len(test_atoms)):
for a, b in permutations(outs[j], 2):
stats_check(a, b)
def test_consistency2():
outs = [[] for i in range(len(test_atoms))]
s = ElasticScatter(verbose=True)
for i, atoms in enumerate(test_data):
stru = convert_atoms_to_stru(atoms)
srfit_calc = DebyePDFCalculator()
srfit_calc.qmin = s.exp['qmin']
srfit_calc.qmax = s.exp['qmax']
srfit_calc.qstep = s.exp['qbin']
srfit_calc(stru)
stats_check(s.get_scatter_vector(), srfit_calc.qgrid)
fq = srfit_calc.fq
outs[i % len(test_atoms)].append(fq)
for j in range(len(test_atoms)):
for a, b in permutations(outs[j], 2):
stats_check(a, b)
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_scatter_sq(value):
"""
Check two processor, algorithm pairs against each other for SQ 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_sq(atoms)
# run algorithm 2
scat.set_processor(proc2, alg2)
ans2 = scat.get_sq(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_forces(value):
"""
Check for PDF forces against known value
:param value:
:return:
"""
# setup
atoms1, atoms2 = value[0]
exp_dict = value[1]
p, thresh = value[2]
proc1, alg1 = value[3]
scat = ElasticScatter(verbose=True)
scat.update_experiment(exp_dict)
scat.set_processor(proc1, alg1)
if value[4] == 'FQ':
exp_func = scat.get_fq
exp_grad = scat.get_grad_fq
elif value[4] == 'PDF':
exp_func = scat.get_pdf
exp_grad = scat.get_grad_pdf
else:
exp_func = None
exp_grad = None
target_data = exp_func(atoms1)
calc = Calc1D(target_data=target_data,
exp_function=exp_func, exp_grad_function=exp_grad,
potential=p)
atoms2.set_calculator(calc)
# print atoms2.get_potential_energy()
forces = atoms2.get_forces()
print(forces)
com = atoms2.get_center_of_mass()
for i in range(len(atoms2)):
dist = atoms2[i].position - com
stats_check(np.cross(dist, forces[i]), np.zeros(3), atol=1e-7)
del dist
del atoms1, atoms2, proc1, alg1, p, thresh, scat, target_data, calc, \
forces, com
def check_scatter_grad_pdf(value):
"""
Smoke test for grad 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_grad_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
)
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_nrg(value):
"""
Check for PDF energy against known value
:param value:
:return:
"""
# setup
atoms1, atoms2 = value[0]
exp_dict = value[1]
p, thresh = value[2]
proc1, alg1 = value[3]
scat = ElasticScatter(verbose=True)
scat.update_experiment(exp_dict)
scat.set_processor(proc1, alg1)
if value[4] == 'FQ':
exp_func = scat.get_fq
exp_grad = scat.get_grad_fq
elif value[4] == 'PDF':
exp_func = scat.get_pdf
exp_grad = scat.get_grad_pdf
else:
exp_func = None
exp_grad = None
target_data = exp_func(atoms1)
calc = Calc1D(target_data=target_data,
exp_function=exp_func,
exp_grad_function=exp_grad,
potential=p)
atoms2.set_calculator(calc)
ans = atoms2.get_potential_energy()
assert ans >= thresh
del atoms1, atoms2, proc1, alg1, p, thresh, scat, target_data, calc, ans
def test_fq_against_srfit():
# unpack the atoms and experiment
atoms = local_test_atoms
exp = None
# get the pyIID F(Q)
s = ElasticScatter(exp)
# s.set_processor('CPU', 'nxn')
# get the SrFit F(Q)
stru = convert_atoms_to_stru(atoms)
srfit_calc = DebyePDFCalculator()
srfit_calc.qmin = s.exp['qmin']
srfit_calc.qmax = s.exp['qmax']
srfit_calc.qstep = s.exp['qbin']
srfit_calc(stru)
stats_check(s.get_scatter_vector(), srfit_calc.qgrid)
ans1 = s.get_fq(atoms)
ans2 = srfit_calc.fq
stats_check(ans1, ans2, rtol=1e-4, atol=5e-6)
del srfit_calc
stats_check(ans1, ans2, rtol=1e-4, atol=5e-6)
def check_nrg(value):
"""
Check two processor, algorithm pairs against each other for PDF energy
Parameters
----------
value: list or tuple
The values to use in the tests
"""
rtol = 4e-6
atol = 9e-6
# setup
atoms1, atoms2 = value[0]
exp_dict = value[1]
p, thresh = value[2]
proc1, alg1 = value[3][0]
proc2, alg2 = value[3][1]
scat = ElasticScatter(verbose=True)
scat.update_experiment(exp_dict)
scat.set_processor(proc1, alg1)
if value[4] == 'FQ':
exp_func = scat.get_fq
exp_grad = scat.get_grad_fq
elif value[4] == 'PDF':
exp_func = scat.get_pdf
exp_grad = scat.get_grad_pdf
else:
exp_func = None
exp_grad = None
target_data = exp_func(atoms1)
calc = Calc1D(target_data=target_data,
exp_function=exp_func,
exp_grad_function=exp_grad,
potential=p)
atoms2.set_calculator(calc)
ans1 = atoms2.get_potential_energy()
scat.set_processor(proc2, alg2)
calc = Calc1D(target_data=target_data,
exp_function=exp_func,
exp_grad_function=exp_grad,
potential=p)
atoms2.set_calculator(calc)
ans2 = atoms2.get_potential_energy()
stats_check(ans2, ans1, rtol, atol)
def check_forces(value):
"""
Check two processor, algorithm pairs against each other for PDF forces
:param value:
:return:
"""
# setup
rtol = 1e-4
atol = 6e-5
atoms1, atoms2 = value[0]
exp_dict = value[1]
p, thresh = value[2]
proc1, alg1 = value[3][0]
proc2, alg2 = value[3][1]
scat = ElasticScatter(verbose=True)
scat.update_experiment(exp_dict)
scat.set_processor(proc1, alg1)
if value[4] == 'FQ':
exp_func = scat.get_fq
exp_grad = scat.get_grad_fq
elif value[4] == 'PDF':
exp_func = scat.get_pdf
exp_grad = scat.get_grad_pdf
else:
exp_func = None
exp_grad = None
target_data = exp_func(atoms1)
calc = Calc1D(target_data=target_data,
exp_function=exp_func,
exp_grad_function=exp_grad,
potential=p)
atoms2.set_calculator(calc)
ans1 = atoms2.get_forces()
scat.set_processor(proc2, alg2)
calc = Calc1D(target_data=target_data,
exp_function=exp_func,
exp_grad_function=exp_grad,
potential=p)
atoms2.set_calculator(calc)
ans2 = atoms2.get_forces()
stats_check(ans2, ans1, rtol=rtol, atol=atol)
def omega_comparison(value):
s = ElasticScatter(value[1])
if value[-1] == 'fq':
qbin = s.exp['qbin']
else:
qbin = s.pdf_qbin
r1, r2, task = r_comparison(value)
norm1, norm2, task = norm_comparison(value)
q, scatter_array, n, qmax_bin, k_max, k_cov = task
omega1 = np.zeros((n, n, qmax_bin), np.float32)
nxn_omega(omega1, r1, qbin)
omega2 = np.zeros((k_max, qmax_bin), np.float32)
k_omega(omega2, r2, qbin)
stats_check(omega1, symmetric_reshape(omega2))
assert_allclose(omega1, symmetric_reshape(omega2))
print(np.max(np.abs(omega1 - symmetric_reshape(omega2))))
return omega1, omega2, task
def check_forces(value):
"""
Check for PDF forces against known value
:param value:
:return:
"""
# setup
atoms1, atoms2 = value[0]
exp_dict = value[1]
p, thresh = value[2]
proc1, alg1 = value[3]
scat = ElasticScatter(verbose=True)
scat.update_experiment(exp_dict)
scat.set_processor(proc1, alg1)
if value[4] == 'FQ':
exp_func = scat.get_fq
exp_grad = scat.get_grad_fq
elif value[4] == 'PDF':
exp_func = scat.get_pdf
exp_grad = scat.get_grad_pdf
else:
exp_func = None
exp_grad = None
target_data = exp_func(atoms1)
calc = Calc1D(target_data=target_data,
exp_function=exp_func,
exp_grad_function=exp_grad,
potential=p)
atoms2.set_calculator(calc)
# print atoms2.get_potential_energy()
forces = atoms2.get_forces()
print(forces)
com = atoms2.get_center_of_mass()
for i in range(len(atoms2)):
dist = atoms2[i].position - com
stats_check(np.cross(dist, forces[i]), np.zeros(3), atol=1e-7)
del dist
del atoms1, atoms2, proc1, alg1, p, thresh, scat, target_data, calc, \
forces, com
import numpy as np
from ase.atoms import Atoms
from pyiid.experiments.elasticscatter import ElasticScatter
import matplotlib.pyplot as plt
from matplotlib.colors import LogNorm
import ase.io
__author__ = 'christopher'
# atoms = Atoms('Au4', [[0, 0, 0], [3, 0, 0], [0, 3, 0], [3, 3, 0]])
atoms = ase.io.read('/mnt/work-data/dev/IID_data/db_test/PDF_LAMMPS_587.traj')
scat = ElasticScatter()
scat.update_experiment({'qmax': 8, 'qmin': .5})
s = scat.get_scatter_vector()
k = 100
i_max, j_max = k, k
pixel_array = np.zeros((i_max, j_max))
for i in range(i_max):
for j in range(j_max):
pixel_array[i, j] = np.sqrt(i ** 2 + j ** 2)
pixel_array /= np.max(pixel_array)
pixel_array *= np.max(s)
img = scat.get_2d_scatter(atoms, pixel_array)
print img.shape
# plt.imshow(pixel_array)
print np.max(img), np.min(img)
from copy import deepcopy as dc
from ase.visualize import view
from itertools import product
from scipy.signal import argrelmax
from workflow_analysis.analysis.plot import plot_pdf, colors
from pyiid.sim.dynamics import classical_dynamics
from pyiid.calc.calc_1d import Calc1D
from ase import Atoms
def null_func(atoms):
return np.zeros((len(atoms), 3))
atoms = Atoms(FaceCenteredCubic('Au', [[1, 0, 0], [1, 1, 0], [1, 1, 1]], (4, 6, 4)))
atoms.center()
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)
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
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.wrappers.elasticscatter import wrap_atoms
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 = []
from ase import Atoms
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
from copy import deepcopy as dc
from ase.visualize import view
from itertools import product
from scipy.signal import argrelmax
from workflow_analysis.analysis.plot import plot_pdf, colors
from pyiid.sim.dynamics import classical_dynamics
from pyiid.calc.calc_1d import Calc1D
from ase import Atoms
def null_func(atoms):
return np.zeros((len(atoms), 3))
atoms = FaceCenteredCubic('Au', [[1, 0, 0], [1, 1, 0], [1, 1, 1]], (4, 6, 4))
atoms.center()
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()
adp_tensor_target[:, :1] = 0.0
target_atoms = atoms.copy()
target_adps = ADP(atoms, adps=adp_tensor_target)
target_atoms.info['adps'] = target_adps
import numpy as np
from ase.atoms import Atoms
from pyiid.experiments.elasticscatter import ElasticScatter
import matplotlib.pyplot as plt
from matplotlib.colors import LogNorm
import ase.io
__author__ = 'christopher'
# atoms = Atoms('Au4', [[0, 0, 0], [3, 0, 0], [0, 3, 0], [3, 3, 0]])
atoms = ase.io.read('/mnt/work-data/dev/IID_data/db_test/PDF_LAMMPS_587.traj')
scat = ElasticScatter()
scat.update_experiment({'qmax': 8, 'qmin': .5})
s = scat.get_scatter_vector()
k = 100
i_max, j_max = k, k
pixel_array = np.zeros((i_max, j_max))
for i in range(i_max):
for j in range(j_max):
pixel_array[i, j] = np.sqrt(i**2 + j**2)
pixel_array /= np.max(pixel_array)
pixel_array *= np.max(s)
img = scat.get_2d_scatter(atoms, pixel_array)
print img.shape
# plt.imshow(pixel_array)
print np.max(img), np.min(img)
plt.imshow(img, aspect='auto')
finite_difference_grad_fq = np.zeros((len(atoms), 3, len(start_fq)))
for i in range(len(atoms)):
for w in range(3):
atoms2 = dc(atoms)
atoms2[i].position[w] += dq
fq2 = s.get_fq(atoms2)
finite_difference_grad_fq[i, w, :] = (fq2 - start_fq) / dq
return finite_difference_grad_fq
if __name__ == '__main__':
rt = 1e-5
at = 2e-2
import matplotlib.pyplot as plt
# atoms = setup_atomic_square()[0]
# atoms.adps = ADP(atoms, adps=np.random.random((len(atoms), 3)))
atoms = Atoms('Au2', [[0, 0, 0], [3, 0, 0]])
exp = None
s = ElasticScatter(exp)
# s.set_processor('CPU', 'nxn')
a = finite_difference_grad(atoms, exp)
b = s.get_grad_fq(atoms)
print(a[0, 0] / b[0, 0])
plt.plot(a[0, 0, :], label='fd')
plt.plot(b[0, 0, :], label='analytical')
plt.legend(loc='best')
plt.show()
# stats_check(a, b, rt, at)
# assert_allclose(a, b, rtol=rt, atol=at)