def check_scatter_grad_fq(value):
"""
Check two processor, algorithm pairs against each other for gradient FQ
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_grad_fq(atoms)
# run algorithm 2
scat.set_processor(proc2, alg2)
ans2 = scat.get_grad_fq(atoms)
# test
if not stats_check(ans1, ans2, rtol, atol):
print(value)
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_grad_fq(value):
"""
Smoke test for grad FQ
: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_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
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)
