'e':
float(data[pmask]['pl_orbeccen'].iloc[i]),
'omega':
0
})
# create REBOUND simulation
sim = generate(objects)
print(objects)
# year long integrations, timestep = 1 hour
sim_data = integrate(sim, objects, 20 * objects[-1]['P'],
int(20 * objects[-1]['P'] * 24))
# collect the analytics from the simulation
ttv_data = analyze(sim_data)
#if name=='Kepler-30':
# import pdb; pdb.set_trace()
# save simulation data
for i in range(pmask.sum()):
iname = data[pmask].iloc[i].name
data.at[iname, 'ttv'] = np.percentile(
np.abs(ttv_data['planets'][i]['ttv']) * 24 * 60, 90)
mask = (data.ttv > 1) & (data.pl_ttvflag == 1)
print(data[mask].sort_values('ttv', ascending=False).get([
'pl_name', 'pl_orbper', 'pl_orbeccen', 'st_mass', 'st_optmag', 'ttv',
'pl_disc_refname', 'pl_def_refname', 'pl_ttvflag'
]))
def get_ttv(objects, ndays=60, ttvfast=True):
sim = generate(objects)
sim_data = integrate(sim, objects, ndays, ndays * 24 * 2) # dt=30 minutes
return analyze(sim_data, ttvfast=ttvfast)
'm': args.mass1,
'P': args.period1,
'inc': 3.14159 / 2,
'e': 0,
'omega': 0
},
{
'm': (ypred[i, 1]) * mearth / msun,
'P': ypred[i, 0],
'inc': 3.14159 / 2,
'e': ypred[i, 3],
'omega': ypred[i, 2]
},
]
sim_data = generate(pred, max(data[:, 1]), int(max(data[:, 1]) * 24))
ttv_pred = analyze(sim_data)
ml_error = lambda x: x # TODO
nlstats = {
'marginals': [
{
'median': 0,
'sigma': 0
}, # Tmid
{
'median': pred[1]['P'],
'sigma': 0
}, # P1
{
'median': pred[2]['P'],
def nbody_limits(newobj, nlstats, n=1):
# TODO create wrapper?
upper = np.copy(ttv)
lower = np.copy(ttv)
obj = copy.deepcopy(newobj)
obj[2]['m'] = nlstats['marginals'][3][
'median'] - n * nlstats['marginals'][3]['sigma']
obj[2]['omega'] = nlstats['marginals'][5][
'median'] - n * nlstats['marginals'][5]['sigma']
sim_data = generate(obj, newobj[1]['P'] * (len(ttv) + 1),
int((len(ttv) + 1) * newobj[1]['P'] * 24))
ttv_data = analyze(sim_data)
for i in range(len(ttv_data['planets'][0]['ttv'])):
try:
upper[i] = max(upper[i], ttv_data['planets'][0]['ttv'][i])
lower[i] = min(lower[i], ttv_data['planets'][0]['ttv'][i])
except:
pass
obj = copy.deepcopy(newobj)
obj[2]['m'] = nlstats['marginals'][3][
'median'] - n * nlstats['marginals'][3]['sigma']
sim_data = generate(obj, newobj[1]['P'] * (len(ttv) + 1),
int((len(ttv) + 1) * newobj[1]['P'] * 24))
ttv_data = analyze(sim_data)
for i in range(len(ttv_data['planets'][0]['ttv'])):
try:
upper[i] = max(upper[i], ttv_data['planets'][0]['ttv'][i])
lower[i] = min(lower[i], ttv_data['planets'][0]['ttv'][i])
except:
pass
obj = copy.deepcopy(newobj)
obj[2]['m'] = nlstats['marginals'][3][
'median'] + n * nlstats['marginals'][3]['sigma']
sim_data = generate(obj, newobj[1]['P'] * (len(ttv) + 1),
int((len(ttv) + 1) * newobj[1]['P'] * 24))
ttv_data = analyze(sim_data)
for i in range(len(ttv_data['planets'][0]['ttv'])):
try:
upper[i] = max(upper[i], ttv_data['planets'][0]['ttv'][i])
lower[i] = min(lower[i], ttv_data['planets'][0]['ttv'][i])
except:
pass
obj = copy.deepcopy(newobj)
obj[2]['m'] = nlstats['marginals'][3][
'median'] - n * nlstats['marginals'][3]['sigma']
obj[2]['omega'] = nlstats['marginals'][5][
'median'] + n * nlstats['marginals'][5]['sigma']
sim_data = generate(obj, newobj[1]['P'] * (len(ttv) + 1),
int((len(ttv) + 1) * newobj[1]['P'] * 24))
ttv_data = analyze(sim_data)
for i in range(len(ttv_data['planets'][0]['ttv'])):
try:
upper[i] = max(upper[i], ttv_data['planets'][0]['ttv'][i])
lower[i] = min(lower[i], ttv_data['planets'][0]['ttv'][i])
except:
pass
obj = copy.deepcopy(newobj)
obj[2]['m'] = nlstats['marginals'][3][
'median'] + n * nlstats['marginals'][3]['sigma']
obj[2]['omega'] = nlstats['marginals'][5][
'median'] - n * nlstats['marginals'][5]['sigma']
sim_data = generate(obj, newobj[1]['P'] * (len(ttv) + 1),
int((len(ttv) + 1) * newobj[1]['P'] * 24))
ttv_data = analyze(sim_data)
for i in range(len(ttv_data['planets'][0]['ttv'])):
try:
upper[i] = max(upper[i], ttv_data['planets'][0]['ttv'][i])
lower[i] = min(lower[i], ttv_data['planets'][0]['ttv'][i])
except:
pass
obj = copy.deepcopy(newobj)
obj[2]['m'] = nlstats['marginals'][3][
'median'] + n * nlstats['marginals'][3]['sigma']
obj[2]['omega'] = nlstats['marginals'][5][
'median'] + n * nlstats['marginals'][5]['sigma']
sim_data = generate(obj, newobj[1]['P'] * (len(ttv) + 1),
int((len(ttv) + 1) * newobj[1]['P'] * 24))
ttv_data = analyze(sim_data)
for i in range(len(ttv_data['planets'][0]['ttv'])):
try:
upper[i] = max(upper[i], ttv_data['planets'][0]['ttv'][i])
lower[i] = min(lower[i], ttv_data['planets'][0]['ttv'][i])
except:
pass
return upper, lower
# add longitude of ascending node
},
{
'm': [2 * mjup / msun, 10 * mjup / msun],
'P': [6, 9],
'inc': [np.pi / 2 * 0.8, np.pi / 2 * 0.9],
'e': [0.05, 0.075],
'omega': [np.pi / 4, np.pi / 2],
},
]
# run original simulation
sim = generate(objects)
sim_data = integrate(sim, objects, 180, 180 *
24) # year long integrations, timestep = 1 minute
ttv_data_og = analyze(sim_data)
# create a plot
f = plt.figure(figsize=(9, 13))
plt.subplots_adjust()
ax = [
plt.subplot2grid((6, 2), (0, 0)),
plt.subplot2grid((6, 2), (1, 0)),
plt.subplot2grid((6, 2), (2, 0)),
plt.subplot2grid((6, 2), (3, 0)),
plt.subplot2grid((6, 2), (4, 0)),
plt.subplot2grid((6, 2), (5, 0)),
plt.subplot2grid((6, 2), (1, 1)),
plt.subplot2grid((6, 2), (2, 1)),
plt.subplot2grid((6, 2), (3, 1)),
plt.subplot2grid((6, 2), (4, 1)),
