def do_check(self, pot, **kwargs):
cgd = CGDescent(_xrand, pot, **kwargs)
res = cgd.run()
self.assertAlmostEqual(res.energy, _emin, 4)
self.assertTrue(res.success)
self.assertLess(np.max(np.abs(res.coords - _xmin)), 1e-2)
self.assertGreater(res.nfev, 0)
def test_reset(self):
cgd1 = CGDescent(self.x0, self.pot)
cgd1.run()
res1 = cgd1.get_result()
x2 = self.x0.copy()
x2[1] = 2.
cgd2 = CGDescent(x2, self.pot)
cgd2.reset(self.x0)
cgd2.run()
res2 = cgd2.get_result()
self.assertEqual(res1.rms, res2.rms)
self.assertEqual(res1.nfev, res2.nfev)
self.assertEqual(res1.nsteps, res2.nsteps)
self.assertTrue(np.all(res1.coords == res2.coords))
def quench_pycg_descent(pot, x0, nsteps=2000, **kwargs):
""" "subroutine" for quenching using cvode.
Parameters
----------
pot: BasePotential
potential to quench
x0: ndarray[ndim*nparticles]
initial coordinates
nsteps: int
maximum number of steps
"""
cgd = CGDescent(pot, x0, **kwargs)
res = cgd.run(nsteps)
return res_to_dict(res)
def test_result(self):
cgd = CGDescent(_xrand, _EG())
res = cgd.run()
self.assertIn("gnorm", res)
self.assertIn("itersub", res)
self.assertIn("numsub", res)
self.assertIn("nfunc", res)
self.assertIn("ngrad", res)
self.assertIn("energy", res)
self.assertIn("grad", res)
self.assertIn("success", res)
self.assertIn("coords", res)
self.assertIn("rms", res)
self.assertIn("nsteps", res)
self.assertIn("nfev", res)
def test_raises(self):
pot = _lj_cpp._ErrorPotential()
with self.assertRaises(RuntimeError):
cgd = CGDescent(_xrand, pot)
cgd.run()
def test_run_niter(self):
cgd1 = CGDescent(_xrand, _EG())
res1 = cgd1.run()
cgd2 = CGDescent(_xrand, _EG())
res2 = cgd2.run(res1.nsteps)
self.assert_same(res1, res2)
def test_raises(self):
with self.assertRaises(NotImplementedError):
cgd = CGDescent(_xrand, _Raise())
cgd.run()