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Original file line number | Diff line number | Diff line change |
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@@ -1,90 +1,2 @@ | ||
import numpy as np | ||
import pandas as pd | ||
from astropy import units as u | ||
|
||
from radvel.basis import Basis | ||
from radvel.utils import Msini | ||
from orbitize.basis import tp_to_tau | ||
from orbitize.kepler import calc_orbit | ||
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||
def compute_sep( | ||
df, epochs, basis, m0, m0_err, plx, plx_err, n_planets=1, pl_num=1 | ||
): | ||
""" | ||
Computes a sky-projected angular separation posterior given a | ||
RadVel-computed DataFrame. | ||
Args: | ||
df (pd.DataFrame): Radvel-computed posterior (in any orbital basis) | ||
epochs (np.array of astropy.time.Time): epochs at which to compute | ||
separations | ||
basis (str): basis string of input posterior (see | ||
radvel.basis.BASIS_NAMES` for the full list of possibilities). | ||
m0 (float): median of primary mass distribution (assumed Gaussian). | ||
m0_err (float): 1sigma error of primary mass distribution | ||
(assumed Gaussian). | ||
plx (float): median of parallax distribution (assumed Gaussian). | ||
plx_err: 1sigma error of parallax distribution (assumed Gaussian). | ||
n_planets (int): total number of planets in RadVel posterior | ||
pl_num (int): planet number used in RadVel fits (e.g. a RadVel label of | ||
'per1' implies `pl_num` == 1) | ||
Example: | ||
>> df = pandas.read_csv('sample_radvel_chains.csv.bz2', index_col=0) | ||
>> epochs = astropy.time.Time([2022, 2024], format='decimalyear') | ||
>> seps, df_orb = compute_sep( | ||
df, epochs, 'per tc secosw sesinw k', 0.82, 0.02, 312.22, 0.47 | ||
) | ||
Returns: | ||
tuple of: | ||
np.array of size (len(epochs) x len(df)): sky-projected angular | ||
separations [mas] at each input epoch | ||
pd.DataFrame: corresponding orbital posterior in orbitize basis | ||
""" | ||
|
||
myBasis = Basis(basis, n_planets) | ||
df = myBasis.to_synth(df) | ||
chain_len = len(df) | ||
tau_ref_epoch = 58849 | ||
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||
# convert RadVel posteriors -> orbitize posteriors | ||
m_st = np.random.normal(m0, m0_err, size = chain_len) | ||
semiamp = df['k{}'.format(pl_num)].values | ||
per_day = df['per{}'.format(pl_num)].values | ||
period_yr = per_day / 365.25 | ||
ecc = df['e{}'.format(pl_num)].values | ||
msini = ( | ||
Msini(semiamp, per_day, m_st, ecc, Msini_units='Earth') * | ||
(u.M_earth / u.M_sun).to('') | ||
) | ||
cosi = (2. * np.random.random(size = chain_len)) - 1. | ||
inc = np.arccos(cosi) | ||
m_pl = msini / np.sin(inc) | ||
mtot = m_st + m_pl | ||
sma = (period_yr**2 * mtot)**(1/3) | ||
omega_st_rad = df['w{}'.format(pl_num)].values | ||
omega_pl_rad = omega_st_rad + np.pi | ||
parallax = np.random.normal(plx, plx_err, size = chain_len) | ||
lan = np.random.random_sample(size = chain_len) * 2. * np.pi | ||
tp_mjd = df['tp{}'.format(pl_num)].values - 2400000.5 | ||
tau = tp_to_tau(tp_mjd, tau_ref_epoch, period_yr) | ||
|
||
# compute projected separation in mas | ||
raoff, deoff, _ = calc_orbit( | ||
epochs.mjd, sma, ecc, inc, | ||
omega_pl_rad, lan, tau, | ||
parallax, mtot, tau_ref_epoch=tau_ref_epoch | ||
) | ||
seps = np.sqrt(raoff**2 + deoff**2) | ||
|
||
df_orb = pd.DataFrame( | ||
np.transpose([sma, ecc, inc, omega_pl_rad, lan, tau, parallax, m_st, m_pl]), | ||
columns=[ | ||
'sma', 'ecc', 'inc_rad', 'omega_pl_rad', 'lan_rad', 'tau_58849', | ||
'plx', 'm_st', 'mp' | ||
] | ||
) | ||
|
||
return seps, df_orb | ||
def compute_sep(df, epochs, basis, m0, m0_err, plx, plx_err, n_planets=1, pl_num=1): | ||
raise ModuleNotFoundError("This functionaity is deprecated. See sblunt/orbitize_radvel_utils.") |
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Original file line number | Diff line number | Diff line change |
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@@ -10,7 +10,6 @@ deprecation | |
cython | ||
pytest | ||
pandas | ||
radvel | ||
pyerfa | ||
astroquery | ||
sphinx | ||
|
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