# set remaining stars as members of background
if using_bg:
z[stars_so_far:,-1] = 1.
return z
assoc_name = sys.argv[1]
try:
label = sys.argv[2]
except IndexError:
label = None
star_pars_file = '../data/{}_xyzuvw.fits'.format(assoc_name)
if not os.path.isfile(star_pars_file):
banyan_data = '../data/gagne_bonafide_full_kinematics_with_lit' \
'_and_best_radial_velocity_comb_binars.fits'
star_pars_file = dt.loadDictFromTable(banyan_data, assoc_name)
print("Loaded association {} from banyan data".format(assoc_name))
print("with total of {} stars".format(len(star_pars_file['xyzuvw'])))
rdir_suffix = 'em_fit/{}'.format(assoc_name.replace(' ','_'))
if label:
rdir_suffix += '_{}/'.format(label)
else:
rdir_suffix += '/'
rdir = '/data/mash/tcrun/' + rdir_suffix
if not os.path.isdir(rdir):
rdir = '../results/' + rdir_suffix
print("Operating in directory: {}".format(rdir))
if os.path.isfile(rdir + 'bg_hists.npy'):
bg_hists = np.load(rdir + 'bg_hists.npy')
else:
import pandas as pd
from astropy.table import Table
import sys
sys.path.insert(0, '..')
import chronostar.retired2.datatool as dt
if __name__ == '__main__':
input_file = '../data/beta_Pictoris_with_gaia_small_everything.fits'
output_file = '../data/beta_Pictoris_with_gaia_small_everything_final.fits'
my_banyan_table_file = '../data/gagne_bonafide_full_kinematics_with_lit_' \
'and_best_radial_velocity_comb_binars_with_banyan_' \
'radec.fits'
banyan_file = '../data/banyan_data.txt'
banyan_data = Table.read(banyan_file, format='ascii')
sp = dt.loadDictFromTable(input_file)
master_sp = dt.loadDictFromTable(my_banyan_table_file)
master_table = master_sp['table']
beta_table = sp['table']
# insert new columns into master table
new_cols = [
'r_RVel',
'r_SpT',
]
for col_name in new_cols:
master_table[col_name] = banyan_data[col_name]
# use a join to get the references
beta_table['r_RVel'] = np.array(len(beta_table) * '', dtype='S10')
beta_table['r_SpT'] = np.array(len(beta_table) * '', dtype='S3')
# coding: utf-8
get_ipython().magic(u'ls ')
import numpy as np
z = np.load('final_membership.npy')
fmerrs = np.load('final_med_errs.npy')
fmerrs
fmerrs[0]
import sys
sys.path.insert(0, '/home/tcrun/chronostar/')
import chronostar.retired2.datatool as dt
sp = dt.loadDictFromTable('../../../data/marusa_galah_li_strong_stars_xyzuvw.fits')
get_ipython().magic(u'ls ')
sp['table'].info
comp_a = sp['table'][np.where(z[:,0] > 0.5)]
len(comp_a)
comp_a['X']
comp_a['U']
comp_a['V']
import matplotlib.pyplot as plt
plt.plot(comp_a['X'], comp_a['U'], '.')
plt.show()
plt.clf()
plt.plot(comp_a['U'], comp_a['V'], '.')
plt.show()
np.mean(comp_a['U'])
np.mean(comp_a['V'])
import chronostar.coordinate as coord
coord.lsr
u_mean = np.mean(comp_a['U'])
v_mean = np.mean(comp_a['V'])
xyzuvw = sp['xyzuvw'][np.where(z[:,0] > 0.5)]
import pandas as pd
from astropy.table import Table
import sys
sys.path.insert(0, '..')
import chronostar.retired2.datatool as dt
if __name__ == '__main__':
input_file = '../data/beta_Pictoris_with_gaia_small_everything.fits'
output_file = '../data/beta_Pictoris_with_gaia_small_everything_final.fits'
my_banyan_table_file = '../data/gagne_bonafide_full_kinematics_with_lit_' \
'and_best_radial_velocity_comb_binars_with_banyan_' \
'radec.fits'
banyan_file = '../data/banyan_data.txt'
banyan_data = Table.read(banyan_file, format='ascii')
sp = dt.loadDictFromTable(input_file)
master_sp = dt.loadDictFromTable(my_banyan_table_file)
master_table = master_sp['table']
beta_table = sp['table']
# insert new columns into master table
new_cols = [
'r_RVel',
'r_SpT',
]
for col_name in new_cols:
master_table[col_name] = banyan_data[col_name]
# use a join to get the references
beta_table['r_RVel'] = np.array(len(beta_table) * '', dtype='S10')
beta_table['r_SpT'] = np.array(len(beta_table) * '', dtype='S3')
other_name = row[slice(*other_name_range)].strip()
star_labels[gaia_id] = (two_mass_id, primary_name or other_name)
return star_labels
if __name__ == '__main__':
rdir = '../results/em_fit/beta_Pictoris_wgs_inv2_5B_res/'
final_memb_file = rdir + 'final_membership.npy'
# bp_star_pars_file = '../data/beta_Pictoris_with_gaia_small_xyzuvw.fits'
bp_star_pars_file = '../data/bpmg_w_nearby_gaia_memberships_magnitudes.fits'
gagne_star_pars_file = '../data/gagne_bonafide_full_kinematics_with_lit_and' \
'_best_radial_velocity_comb_binars.fits'
z = np.load(final_memb_file)
bp_sp = dt.loadDictFromTable(bp_star_pars_file)
gg_sp = dt.loadDictFromTable(gagne_star_pars_file)
banyan_data = bp.readBanyanTxtFile()
banyan_upper_ix = 38
new_chronostar_membs = (np.where(z[banyan_upper_ix:,0]>0.5)[0] + banyan_upper_ix,)
# -- insert ra dec for stars not matched with Gaia --
# first incorporate BANYAN radec into all members table
bd_ras = []
bd_des = []
assert len(banyan_data) == len(gg_sp['table'])
for row in banyan_data:
if row[3] == '':
# coding: utf-8
get_ipython().magic(u'ls ')
import numpy as np
z = np.load('final_membership.npy')
fmerrs = np.load('final_med_errs.npy')
fmerrs
fmerrs[0]
import sys
sys.path.insert(0, '/home/tcrun/chronostar/')
import chronostar.retired2.datatool as dt
sp = dt.loadDictFromTable(
'../../../data/marusa_galah_li_strong_stars_xyzuvw.fits')
get_ipython().magic(u'ls ')
sp['table'].info
comp_a = sp['table'][np.where(z[:, 0] > 0.5)]
len(comp_a)
comp_a['X']
comp_a['U']
comp_a['V']
import matplotlib.pyplot as plt
plt.plot(comp_a['X'], comp_a['U'], '.')
plt.show()
plt.clf()
plt.plot(comp_a['U'], comp_a['V'], '.')
plt.show()
np.mean(comp_a['U'])
np.mean(comp_a['V'])
import chronostar.coordinate as coord
coord.lsr
u_mean = np.mean(comp_a['U'])
v_mean = np.mean(comp_a['V'])
if PLOT_BPMG_REAL:
for iteration in ['5B']: #, '6C']:
star_pars_file = '../../data/beta_Pictoris_with_gaia_small_xyzuvw.fits'
star_pars = dt.loadXYZUVW(star_pars_file)
fit_name = 'bpmg_and_nearby'
rdir = '../../results/em_fit/beta_Pictoris_wgs_inv2_{}_res/'.format(iteration)
memb_file = rdir + 'final_membership.npy'
groups_file = rdir + 'final_groups.npy'
z = np.load(memb_file)
groups = dt.loadGroups(groups_file)
# Assign markers based on BANYAN membership
gt_sp = dt.loadDictFromTable('../../data/banyan_with_gaia_near_bpmg_xyzuvw.fits')
banyan_membership = len(star_pars['xyzuvw']) * ['N/A']
for i in range(len(star_pars['xyzuvw'])):
master_table_ix = np.where(gt_sp['table']['source_id']==star_pars['gaia_ids'][i])
banyan_membership[i] = gt_sp['table']['Moving group'][master_table_ix[0][0]]
# assign markers based on present moving groups, keep track of
# assoc -> marker relationship incase a legend is called for
banyan_membership=np.array(banyan_membership)
banyan_markers = np.array(len(banyan_membership) * ['.'])
banyan_memb_set = set(banyan_membership)
for bassoc in set(gt_sp['table']['Moving group']):
if bassoc not in acronyms.keys():
acronyms[bassoc] = bassoc
banyan_markers[np.where(banyan_membership=='beta Pictoris')] = 'v'
# bpmg_range[1] = temp_range
# To ensure consistency, we now plot the BANYAN bpmg stars only,
# and use the ragnes from previous plot
# 2019-07-13 [TC]: not sure why this is here...
if False:
fit_name = 'banyan_bpmg'
rdir = '../../results/em_fit/beta_Pictoris/'
memb_file = rdir + 'final_membership.npy'
groups_file = rdir + 'final_best_groups.npy'
star_pars_file = '../../data/beta_Pictoris_with_gaia_small_xyzuvw.fits'
z = np.load(memb_file)
groups = dt.loadGroups(groups_file)
star_pars = dt.loadDictFromTable(star_pars_file, 'beta Pictoris')
nstars = len(star_pars['xyzuvw'])
# First do all, then just do possible membs of BPMG
for dim1, dim2 in DEFAULT_DIMS: #[(0,1), (0, 3), (1, 4), (2,5)]: #, (2, 5)]: # , 'yv', 'zw']:
# if dim1 == 0 and dim2 == 1 and debugging_circles:
# temp_range = bpmg_range[1]
# bpmg_range[1] = [-120, 80]
# import pdb; pdb.set_trace()
fp.plotPane(
dim1,
dim2,
groups=groups,
star_pars=star_pars,
group_now=True,
membership=z,
near BPMG, removes duplicates (especially binaries/ternaries absorbed
into centre of mass references), and packages up neatly for an EM run
"""
from astropy.table import Table
import numpy as np
import sys
sys.path.insert(0, '..')
import chronostar.retired2.datatool as dt
cart_gaia_table = '../data/gaia_cartesian_full_6d_table.fits'
gagne_table = '../data/gagne_bonafide_full_kinematics_with_lit_and_best_'\
'radial_velocity_comb_binars.fits'
assoc_name = 'beta Pictoris'
bp_star_pars = dt.loadDictFromTable(gagne_table, assoc_name=assoc_name)
print("Reading in gaia table...")
gt = Table.read(cart_gaia_table)
print("Gaia table read in")
MARGIN = 0.5
xyzuvw_file = "../data/{}_with_gaia_small_xyzuvw.fits".format(
assoc_name.replace(' ', '_'))
# construct box
kin_max = np.max(bp_star_pars['xyzuvw'], axis=0)
kin_min = np.min(bp_star_pars['xyzuvw'], axis=0)
span = kin_max - kin_min
upper_boundary = kin_max + MARGIN * span
lower_boundary = kin_min - MARGIN * span
from master_table and membership array
"""
import numpy as np
import sys
sys.path.insert(0, '..')
import chronostar.retired2.datatool as dt
save_file_name = '../results/tables/bpmg_members_astro_table.tex'
rdir = '../results/em_fit/beta_Pictoris_with_gaia_small_1.0/3/A/final/'
z_file = rdir + 'final_membership.npy'
table_file = '../data/beta_Pictoris_with_gaia_small_xyzuvw.fits'
star_pars = dt.loadDictFromTable(table_file)
bpmg_comp_ix = 0
bpmg_memb_probs = np.load(z_file)[:,bpmg_comp_ix]
threshold = 0.05
bpmg_table_mask = np.array(star_pars['indices'])[np.where(bpmg_memb_probs > threshold)]
# columns: Gaia DR2 id, main designation, ra, dec, parallax, pmra, pmdec, rv,
# membership prob, references
col_names = [
'source_id',
'Main designation',
'ra',
'ra_error',
'dec',