def test_high_ecc(self):
sim = rebound.Simulation()
sim.add(m=1.)
sim.add(m=1.e-5, P=1., e=0.9)
sim.add(m=1.e-5, P=2.)
sim.add(m=1.e-5, P=3.)
init_sim_parameters(sim)
self.assertEqual(sim.integrator, 'whfast')
def test_extreme_ecc(self):
sim = rebound.Simulation()
sim.add(m=1.)
sim.add(m=1.e-5, P=1., e=0.999)
sim.add(m=1.e-5, P=2.)
sim.add(m=1.e-5, P=3.)
init_sim_parameters(sim)
self.assertEqual(sim.integrator, 'ias15')
def test_descending_periods(self):
sim = rebound.Simulation()
sim.add(m=1.)
sim.add(m=1.e-5, P=3.)
sim.add(m=1.e-5, P=2.)
sim.add(m=1.e-5, P=1.)
init_sim_parameters(sim)
self.assertAlmostEqual(sim.dt, 0.05, delta=1e-15)
def test_negmass(self):
sim = rebound.Simulation()
sim.add(m=1.)
sim.add(m=1.e-5, P=1.)
sim.add(m=-1.e-5, P=2.)
sim.add(m=1.e-5, P=3.)
with self.assertRaises(AttributeError):
init_sim_parameters(sim)
def test_second_p_ecc(self):
sim = rebound.Simulation()
sim.add(m=1.)
sim.add(m=1.e-5, P=1.)
sim.add(m=1.e-5, P=10., e=0.99)
sim.add(m=1.e-5, P=3.)
init_sim_parameters(sim)
self.assertAlmostEqual(sim.dt,
0.05 * 10 * 0.01**1.5 / np.sqrt(1.99),
delta=1.e-8)
def test_same_trajectory(self):
sim = longstablesim()
init_sim_parameters(sim)
_, _ = get_tseries(sim, (1e4, 80, [[1,2,3]]))
x1 = sim.particles[1].x
sim = longstablesim()
nbody = NbodyRegressor()
nbody.predict_stable(sim, tmax=1e4, archive_filename='temp.bin', archive_interval=1.e4)
sa = rebound.SimulationArchive('temp.bin')
sim = sa[-1]
x2 = sim.particles[1].x
self.assertAlmostEqual(x1, x2, delta=1.e-5)
def test_same_traj_as_feature_classifier(self):
# procedure in featureclassifier
sim = unstablesim()
init_sim_parameters(sim)
trios = [[i, i + 1, i + 2] for i in range(1, sim.N_real - 2)]
featureargs = [10000, 80, trios]
triofeatures, stable = features(sim, featureargs)
tfc = sim.t
sim = unstablesim()
init_sim_parameters(sim)
try:
sim.integrate(1e4 * sim.particles[1].P, exact_finish_time=0)
except rebound.Collision:
tn = sim.t
self.assertEqual(tfc, tn)
def training_data(row, safolder, runfunc, args):
try:
sa = rebound.SimulationArchive(safolder + 'sa' + row['runstring'])
sim = sa[0]
except:
print("training_data_functions.py Error reading " + safolder + 'sa' +
row['runstring'])
return None
init_sim_parameters(sim)
try:
ret, stable = runfunc(sim, args)
except:
print('{0} failed'.format(row['runstring']))
return None
r = ret[
0] # all runfuncs return list of features for all adjacent trios (to not rerun for each). For training assume it's always 3 planets so list of 1 trio
return pd.Series(r, index=list(
r.keys())) # conert OrderedDict to pandas Series
