def __init__(self):
p = {
'aspectratio0': 0.035,
'sigma0': 3.8e-4 * 0.5 * 1.4,
'redge': 0.1,
'a0': 0.102,
'deltaredge': 0.001,
'alpha': 1.5,
'flaringindex': 2.0 / 7.0,
'ffudge': 1.0 / 100.0,
'tdep': 2e6 * keplertime,
'deltatdep': 2e6 * keplertime,
'pmass': 3.16227766e-05,
'nchain': 4,
'q_res': 1,
'p_res': 4,
'seq': 0,
'collision': 'direct',
'G': 1.0,
'starmass': 1.0,
'integrator': 'WHFAST',
'integrator_dt': 1e-2 * keplertime,
'snap_wall_interval': 15 * 60,
'incscatter': np.pi / 100.0,
'aspread': 0.01
}
p['physical_outputs'] = [p['tdep'], p['tdep'] + p['deltatdep']]
# try an empirical scaling of aspread
p['aspread'] = (0.04) * (np.log(p['pmass']) - np.log(1e-6)) / (
np.log(1e-5) - np.log(1e-6)) + 0.01
nmodels = 10
nchains = range(5, 11)
p_ress = range(3, 5)
p_resind = self.new_index()
counter = 0
for p_res in p_ress:
p['p_res'] = p_res
nchainind = self.new_index()
for nchain in nchains:
p['nchain'] = nchain
#make a bunch of realizations of the same model, with randomized phases
seqind = self.new_index()
for i in range(0, nmodels):
p['seq'] = i
print('{} p_res {}, nchain {} seq {}'.format(
counter, p['p_res'], p['nchain'], p['seq']))
c = chaincalc.Model(p)
self.add_model(c)
seqind[i] = c.hash
counter += 1
nchainind[nchain] = {'seq': seqind}
p_resind[p_res] = {'nchain': nchainind}
self.indexes['p_res'] = p_resind
def runtest(p):
c = chaincalc.Model(p, verbose=True)
c.init_rebound()
print(c.hash)
print(c.sim.status())
c.set_wall_start(wall_start)
try:
c.sim.integrate(10000.0)
except rebound.Collision:
c.mark_collision()
else:
c.manual_snapshot()
print('')
def __init__(self):
p = {
'aspectratio0': 0.035,
'sigma0': 3.8e-4,
'redge': 0.1,
'a0': 0.11,
'deltaredge': 0.001,
'alpha': 1.0,
'flaringindex': 2.0 / 7.0,
'ffudge': 1.0 / 100.0,
'tdep': 1e1,
'deltatdep': 1e1,
'pmass': 1e-5,
'nchain': 4,
'q_res': 1,
'p_res': 4,
'seq': 0,
'collision': 'line',
'G': 1.0,
'starmass': 1.0,
'integrator': 'WHFASTUNSAFE11',
'integrator_dt': 1e-2 * 2 * np.pi * 0.1**1.5,
'snap_wall_interval': 15,
'incscatter': np.pi / 100.0,
'aspread': 0.05
}
p['physical_outputs'] = [1e0, 1e1, 1e2]
#make a bunch of realizations of the same model, with randomized phases
nmodels = 10
nchains = range(5, 11)
nchainind = self.new_index()
for nchain in nchains:
seqind = self.new_index()
for i in range(0, nmodels):
p['seq'] = i
c = chaincalc.Model(p)
print(c.hash)
self.add_model(c)
seqind[i] = c.hash
nchainind[nchain] = seqind
self.indexes['nchain'] = nchainind