def get_region(assoc_name, gagne_reference_data = None):
if gagne_reference_data is None:
gagne_reference_data =\
'../data/gagne_bonafide_full_kinematics_with_lit_and_best_radial_velocity' \
'_comb_binars_with_banyan_radec.fits'
gagne_table = tabletool.read(gagne_reference_data)
if assoc_name not in set(gagne_table['Moving group']):
raise UserWarning,\
'Association name must be one of:\n{}\nReceived: "{}"'.format(
list(set(gagne_table['Moving group'])), assoc_name
)
# Dummy comment
# Extract all stars
subtable = gagne_table[np.where(gagne_table['Moving group'] == assoc_name)]
star_means = tabletool.build_data_dict_from_table(subtable, only_means=True)
data_upper_bound = np.nanmax(star_means, axis=0)
data_lower_bound = np.nanmin(star_means, axis=0)
data_span = data_upper_bound - data_lower_bound
data_centre = 0.5 * (data_upper_bound + data_lower_bound)
# Set up boundaries of box that span double the association
box_lower_bound = data_centre - data_span
box_upper_bound = data_centre + data_span
return box_lower_bound, box_upper_bound
sys.path.insert(0, '../../')
from chronostar.component import SphereComponent
from chronostar import tabletool
from chronostar import expectmax
from astropy.table import Table, vstack, join
##### FILENAMES ############################################################
datafile = 'data/scocen_data_with_broken_radial_velocities.fits'
#datafile = 'data/scocen_data_with_original_radial_velocities.fits'
output_filename = datafile.replace('data/', 'results/').replace(
'.fits', '_with_membership.fits')
comps_filename = 'data/all_nonbg_scocen_comps_unique.npy'
############################################################################
data_table = tabletool.read(datafile)
# This table is masked. Unmask:
data_table = data_table.filled()
print('DATA READ', len(data_table))
historical = 'c_XU' in data_table.colnames
############################################################################
############ COMPONENT OVERLAPS ############################################
############################################################################
print('Create data dict')
# Create data dict
data_dict = tabletool.build_data_dict_from_table(
data_table,
# and the data prep has already been done
if (config.config['data_savefile'] != '' and
os.path.isfile(config.config['data_savefile'])):
log_message('Loading pre-prepared data (with background)')
data_table = tabletool.load(config.config['data_savefile'])
historical = 'c_XU' in data_table.colnames
# Otherwise, perform entire process
else:
# Construct synthetic data if required
datafile = config.config['data_loadfile']
assert os.path.exists(datafile)
# Read in data as table
log_message('Read data into table')
data_table = tabletool.read(datafile)
# historical = 'c_XU' in data_table.colnames # column names...
historical = False
log_message('Data table has {} rows'.format(len(data_table)))
# data_table['radial_velocity'] = data_table['radial_velocity_best']
# data_table['radial_velocity_error'] = data_table['radial_velocity_error_best']
#
# By the end of this, data will be a astropy table
# with cartesian data written in
# columns in default way.
if config.config['convert_to_cartesian']:
print('Converting to cartesian')
def get_region(assoc_name,
pos_margin=30.,
vel_margin=5.,
scale_margin=None,
gagne_reference_data=None):
"""
Get a 6D box surrounding a known association with members from BANYAN
Parameters
----------
assoc_name: str
Name of the association as listed in BANYAN table. One of:
{'118 Tau', '32 Orionis', 'AB Doradus', 'Carina', 'Carina-Near',
'Columba', 'Coma Ber', 'Corona Australis', 'Hyades', 'IC 2391',
'IC 2602', 'Lower Centaurus-Crux', 'Octans', 'Platais 8',
'Pleiades', 'TW Hya', 'Taurus', 'Tucana-Horologium',
'Upper Centaurus Lupus', 'Upper CrA', 'Upper Scorpius',
'Ursa Major', 'beta Pictoris', 'chi{ 1 For (Alessi 13)',
'epsilon Cha', 'eta Cha', 'rho Ophiuci'}
pos_margin: float {30.}
Margin in position space around known members from which new candidate
members are included
vel_margin: float {5.}
Margin in velocity space around known members from which new candidate
members are included
gagne_reference_data: str
filename to BANYAN table
Returns
-------
box_lower_bounds: [6] float array
The lower bounds of the 6D box [X,Y,Z,U,V,W]
box_upper_bounds: [6] float array
The upper bounds of the 6D box [X,Y,Z,U,V,W]
"""
if gagne_reference_data is None:
gagne_reference_data =\
'../data/gagne_bonafide_full_kinematics_with_lit_and_best_radial_velocity' \
'_comb_binars_with_banyan_radec.fits'
gagne_table = tabletool.read(gagne_reference_data)
if assoc_name not in set(gagne_table['Moving group']):
raise UserWarning(
'Association name must be one of:\n{}\nReceived: "{}"'.format(
list(set(gagne_table['Moving group'])), assoc_name))
# Extract all stars
subtable = gagne_table[np.where(gagne_table['Moving group'] == assoc_name)]
logging.info('Initial membership list has {} members'.format(
len(subtable)))
star_means = tabletool.build_data_dict_from_table(subtable,
only_means=True)
data_upper_bound = np.nanmax(star_means, axis=0)
data_lower_bound = np.nanmin(star_means, axis=0)
logging.info('Stars span from {} to {}'.format(np.round(data_lower_bound),
np.round(data_upper_bound)))
# First try and scale box margins on.
# scale_margin of 1 would double total span (1 + 1)
if scale_margin is not None:
data_span = data_upper_bound - data_lower_bound
box_margin = 0.5 * scale_margin * data_span
# Set up boundaries of box that span double the association
box_lower_bound = data_lower_bound - box_margin
box_upper_bound = data_upper_bound + box_margin
# Set margin based on provided (or default) constant amounts
else:
data_margin = np.array(3 * [pos_margin] + 3 * [vel_margin])
box_lower_bound = data_lower_bound - data_margin
box_upper_bound = data_upper_bound + data_margin
logging.info('Range extended.\nLower: {}\nUpper: {}'.format(
np.round(box_lower_bound), np.round(box_upper_bound)))
return box_lower_bound, box_upper_bound
"""
Add very large RV errors for stars with no known RVs.
Convert to cartesian.
"""
import numpy as np
import sys
sys.path.insert(0, '..')
from chronostar import tabletool
from astropy.table import Table
datafile = '../data/ScoCen_box_result.fits'
d = tabletool.read(datafile)
# Set missing radial velocities (nan) to 0
d['radial_velocity'] = np.nan_to_num(d['radial_velocity'])
# Set missing radial velocity errors (nan) to 1e+10
d['radial_velocity_error'][np.isnan(d['radial_velocity_error'])] = 1e+4
print('Convert to cartesian')
tabletool.convert_table_astro2cart(table=d, return_table=True)
d.write(
'/priv/mulga1/marusa/chronostar/data/ScoCen_box_result_15M_ready_for_bg_ols.fits'
)
print('Cartesian written.', len(d))
