'line': 1,
'rv_offset': -21.0,
'data': ['emission_line_fits_1.p']
}, {
'line': 2,
'rv_offset': -25.0,
'data': ['emission_line_fits_2.p']
}]
# Optimise lines individually.
for o in OBSERVATIONS:
# Load observations.
obs = {}
label = 'EtaCar_CrossValidation_{}'.format(o['line'])
raw_obs = unpickle_observations(OBSERVATIONS_PATH, o['data'])
# Pre-set velocity offsets as per Kepler model.
raw_obs['Velocity'] -= o['rv_offset']
obs[o['line']] = raw_obs
# Load interpolation objects.
emissivity = Emissivity(setup_mode=False,
pickle_dir='interp_jar/rt_code_v1')
# Optimise.
op = MCMCOptimiser(kernel_type='RTInterp',
interp_dims='Point',
emissivity_obj=emissivity,
epoch_density=1000)
op.debug = DEBUG
import numpy as np
import matplotlib.pyplot as plt
from config import OBSERVATIONS_PATH
from ckm.core.motion import windy_star
from ckm.radiative.emissivity import Emissivity
from ckm.core.utils import unpickle_observations
# Setup
OBSERVATIONS = ['emission_line_fits_1.p', 'emission_line_fits_2.p']
# Load observations.
obs = unpickle_observations(OBSERVATIONS_PATH,
OBSERVATIONS,
time_of_periastron=2555555,
period=1000,
n_periods=2)
# Load interpolation objects.
emissivity = Emissivity(setup_mode=False, pickle_dir='interp_jar/rt_code_v1')
# Configure params and epochs and kernel.
windy_star.configure_params(period=1000,
eccentricity=0.7,
rv_semi_amplitude=60,
argument_of_periastron=250,
rv_offset=-20)
windy_star.configure_epochs(epochs=np.linspace(0, 2, 1000))
windy_star.configure_kernel(kernel_mode='RTInterp',
kernel_line=4,
interp_dims='Point',
from ckm.optimise.mcmc_kepler import MCMCOptimiser
from ckm.core.utils import unpickle_observations, pickle_mcmc_results
todo_list = [('emission_line_1', 'emission_line_fits_1.p'),
('emission_line_2', 'emission_line_fits_2.p')]
for task in todo_list:
# Setup
DEBUG = False
OBSERVATIONS_LABEL = task[0]
OBSERVATIONS = [task[1]]
# Load observations.
obs = unpickle_observations(OBSERVATIONS_PATH, OBSERVATIONS)
# Optimise.
op = MCMCOptimiser()
op.debug = DEBUG
simulation_chain, convergence = op.optimise_kepler_model(
observations=obs,
fixed_params={'Period': 1000},
guess=np.array([2555555, 0.60, 60, 250, 0]),
priors={'TimeOfPeriastron': (2555535, 2555575),
'Eccentricity': (0.10, 0.90),
'RVSemiAmplitude': (10, 120),
'ArgumentOfPeriastron': (180, 360),
'RVOffset': (-100, 100)},
walkers=128, burn=6000, run=6000)