"""
Author: Marusa Zerjal, 2019 - 07 - 15

Take Sco-Cen components fitted to 6D data and make overlaps
(using covariance matrix) with stars missing radial velocities
in order to find more Sco-Cen candidates.

MZ: It fails in python2 (cannot import emcee).

"""

import numpy as np
import sys
sys.path.insert(0, '..')
from chronostar.component import SphereComponent
from chronostar import tabletool
from chronostar import expectmax
from astropy.table import Table, vstack, join



c = np.load('../all_nonbg_scocen_comps.npy') # including LCC
print('components', c.shape)
print('Are there duplicate components?')

datafile = 'scocen_members_with_artificially_broken_radial_velocities_for_comparison.fits'
data_table = tabletool.read(datafile)

print('DATA READ', len(data_table))
historical = 'c_XU' in data_table.colnames


# Read backgrounf overlaps
ln_bg_ols = np.loadtxt('bgols_scocen_with_artificially_broken_radial_velocities_multiprocessing.dat')
print('len bg_ols', len(ln_bg_ols), 'len data_table', len(data_table))

bg_lnol_colname = 'background_log_overlap'
print('Background overlaps: insert column')
tabletool.insert_column(data_table, ln_bg_ols, bg_lnol_colname, filename=datafile)

print('Print bg ols to cartesian table')
data_table.write('data_table_cartesian_with_bg_ols_artificially_broken_radial_velocities.fits', overwrite=True, format='fits')

exit(0)
############################################################################
############ COMPONENT OVERLAPS ############################################
############################################################################

print('Create data dict')
# Create data dict for real
data_dict = tabletool.build_data_dict_from_table(
        data_table,
        get_background_overlaps=True,
        historical=historical,
)

# Create components
comps = [SphereComponent(pars=x) for x in c]

# COMPONENT OVERLAPS
overlaps = expectmax.get_all_lnoverlaps(data_dict, comps)
print('overlaps.shape', overlaps.shape, len(comps))

# MEMBERSHIP PROBABILITIES
membership_probabilities = np.array([expectmax.calc_membership_probs(ol) for ol in overlaps])

# Create a table
for i in range(membership_probabilities.shape[1]-1):
    data_table['comp_overlap_%d' % (i + 1)] = membership_probabilities[:, i]
data_table['comp_overlap_bg'] = membership_probabilities[:, -1]

# Print data
data_table.write('data_table_cartesian_with_bg_ols_and_component_overlaps_lalala.fits', format='fits', overwrite=True)
print(data_table)

@@ -697,16 +697,105 @@
 n.fits'%0A
+datafile = 'data_table_cartesian_with_bg_ols_artificially_broken_radial_velocities.fits'%0A
 data_tab
@@ -905,16 +905,19 @@
 lnames%0A%0A
+%22%22%22
 %0A# Read 
@@ -1453,16 +1453,11 @@
 s')%0A
-%0Aexit(0)
+%22%22%22
 %0A###
@@ -2436,32 +2436,39 @@
 ta_table.write('
+scocen_
 data_table_carte
@@ -2511,14 +2511,50 @@
 aps_
-lalala
+with_artificially_broken_radial_velocities
 .fit

determine bg ols