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Please see the Using Virtual-Observatory interfaces for more information on the Virtual-Observatory interfaces to version 2.0 of the Chandra Source Catalog.

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Accessing release 2.0 of the Chandra Source Catalog with PyVO and CIAO tools, with basic science applications using AstroPy and scikit-learn¶

Original notebook by Doug Burke, expanded by Rafael Martínez-Galarza¶

The PyVo package allows users to query Virtual Observatory services from Python. In this notebook we show how you can use it to query release 2.0 of the Chandra Source Catalog using the VO interfaces for tabulated properties, and CIAO tools to retrieve catalog data products. We also show how to use AstroPy to perform cross-matches with other catalogs, and some basic applications in machine learning with scikit-learn.

This notebook assumes you have installed CIAO 4.13 using conda, but you should also be able to do this with a CIAO installed using the ciao-install script, or any other Python environment as we are just going to use AstroPy along with pyvo. The pyyaml package is needed to support writing out the file (it avoids a warning message you would otherwise get).

Packages to install:

pip install astropy pyvo pyyaml matplotlib

In [1]:

import numpy as np

from matplotlib import pyplot as plt
import matplotlib

import astropy
import pyvo as vo

import scipy

%matplotlib inline

Let's just check the versions (it is expected that the results should not depend on these versions):

In [2]:

print("Versions:")
print(f"  NumPy       - {np.__version__}")
print(f"  Matplotlib  - {matplotlib.__version__}")
print(f"  AstroPy     - {astropy.__version__}")
print(f"  PyVO        - {vo.__version__}")

Versions:
  NumPy       - 1.19.1
  Matplotlib  - 3.3.1
  AstroPy     - 4.0.1.post1
  PyVO        - 1.1

Aim¶

This document will show you how to:

Use a cone search - that is, find results around a given location - and then run a general "database query", which can be done over the whole sky. The Virtual Observatory cone-search interface is "simple", in that you give it a location, radius, and an optional "verbosity" parameter between 1 and 3, inclusive, that controls the number of columns that gets returned. The details can be found at the CSC 2.0 cone search page.
Use the Table Access Protocol (TAP) service, for performing specific queries of CSC2 tables, allowing much-more general queries. It is suggested that you use CSCView to create a query, and then view the query - using the View/Query/Show Language menu item - to see what query it has created: this is important for complex queries where you are trying to access data from multiple tables (e.g. observation-level fluxes mapped to a given master source), since there are many "hidden" traps in these queries which CSCView knows about.
Cross-match the CSC2 catalog with other catalogs, and visualize the results.
Download data products from the CSC2, such as light curves and spectra, and visualize them for science applications.
Input CSC2 data to a machine learning algorithm.

Cone Search¶

Let's start with using cone search in order to find CSC2 sources near the M33 galaxy. Cone search will only query the master level tables (you need to use TAP interfaces, see below, to query per-ObsID properties). We will use a search radius of 1 degree and use the name resolver from AstroPy. Here is a visualization of the CSC2 sources in this area, from ESASky:

M33

In [3]:

from astropy.coordinates import SkyCoord
from astropy import units as u

m33 = SkyCoord.from_name('M33')
maxrad = 1.0 * u.deg

In [4]:

m33

Out[4]:

<SkyCoord (ICRS): (ra, dec) in deg
    (23.4621, 30.65994167)>

We now need a Python object representing the CSC2 Virtual Observatory Cone Search Service, which has a base URL of http://cda.cfa.harvard.edu/csc2scs/coneSearch:

In [5]:

cone = vo.dal.SCSService('http://cda.cfa.harvard.edu/csc2scs/coneSearch')

To run the search we are going to use the search method, which (fortunately for this notebook) has a short help page, include herein:

In [6]:

help(cone.search)

Help on method search in module pyvo.dal.scs:

search(pos, radius=1.0, verbosity=2, **keywords) method of pyvo.dal.scs.SCSService instance
submit a simple Cone Search query that requests objects or observations
whose positions fall within some distance from a search position.

Parameters
----------
pos : astropy.coordinates.SkyCoord
a SkyCoord instance defining the position of the center of the
circular search region.
converted if it's a iterable containing scalars,
assuming icrs degrees.
radius : `~astropy.units.Quantity` or float
a Quantity instance defining the radius of the circular search
region, in degrees.
converted if it is another unit.
verbosity : int
an integer value that indicates the volume of columns
to return in the result table. 0 means the minimum
set of columns, 3 means as many columns as are available.
**keywords :
additional case insensitive parameters can be given via arbitrary
case insensitive keyword arguments. Where there is overlap
with the parameters set by the other arguments to
this function, these keywords will override.

Returns
-------
SCSResults
a container holding a table of matching catalog records

Raises
------
DALServiceError
for errors connecting to or communicating with the service
DALQueryError
if the service responds with an error,
including a query syntax error.

See Also
--------
SCSResults
pyvo.dal.query.DALServiceError
pyvo.dal.query.DALQueryError

We can then run the query with the following (the arguments don't need to be named but it makes things a bit clearer to be explicit):

In [7]:

results = cone.search(pos=m33, radius=maxrad, verbosity=3)

Hopefully this completes without error, and returns 750 results (note that it depends on the location returned by the name search and so can change when the SIMBAD or NED results change):

In [8]:

len(results)

Out[8]:

In [9]:

results

Out[9]:

<Table length=750>
         name                 ra         ... hrc_hetg_time hrc_letg_time
                             deg         ...       s             s      
        object             float64       ...    float64       float64   
--------------------- ------------------ ... ------------- -------------
2CXO J013159.9+303500  22.99991954305341 ...            --            --
2CXO J013202.9+303445  23.01219298372888 ...            --            --
2CXO J013203.1+303841 23.012944925258978 ...            --            --
2CXO J013205.9+302305  23.02473533826901 ...            --            --
2CXO J013215.7+304008  23.06568844810522 ...            --            --
2CXO J013217.5+302555  23.07332419514978 ...            --            --
2CXO J013217.9+301933 23.074814699111812 ...            --            --
2CXO J013220.0+303450  23.08344394730176 ...            --            --
2CXO J013220.0+303640 23.083592126140672 ...            --            --
                  ...                ... ...           ...           ...
2CXO J013517.0+304409 23.820940414580434 ...            --            --
2CXO J013517.5+304446  23.82316738827336 ...            --            --
2CXO J013518.6+302952  23.82774548052521 ...            --            --
2CXO J013521.7+303210 23.840531244373096 ...            --            --
2CXO J013522.8+302735 23.845361001171284 ...            --            --
2CXO J013526.9+304048 23.862273225646504 ...            --            --
2CXO J013528.3+304213 23.868126726976016 ...            --            --
2CXO J013530.8+303653 23.878469335874797 ...            --            --
2CXO J013533.6+303439 23.890200740260696 ...            --            --
2CXO J013547.5+303315 23.948226444178488 ...            --            --

The fact that the source names begin with '2CXO' means that we are indeed searching release 2.0 and not 1.1. Let's see what the default columns are (note that this search was run with the cone-search verbosity parameter set to 3, which http://cda.cfa.harvard.edu/cscvo/ tells us returns all CSC columns at the master level.

The results can be converted to an AstroPy table - using results.to_table(), which we use below - but they can also be used directly:

In [10]:

# Let's print the first 50 fields
results.fieldnames[0:50]

Out[10]:

('name',
 'ra',
 'dec',
 'err_ellipse_r0',
 'err_ellipse_r1',
 'err_ellipse_ang',
 'gal_b',
 'gal_l',
 'extent_flag',
 'conf_flag',
 'pileup_flag',
 'var_flag',
 'streak_src_flag',
 'sat_src_flag',
 'man_add_flag',
 'man_inc_flag',
 'man_reg_flag',
 'man_match_flag',
 'man_pos_flag',
 'var_inter_hard_flag',
 'major_axis_b',
 'major_axis_h',
 'major_axis_m',
 'major_axis_s',
 'major_axis_u',
 'major_axis_w',
 'major_axis_lolim_b',
 'major_axis_lolim_h',
 'major_axis_lolim_m',
 'major_axis_lolim_s',
 'major_axis_lolim_u',
 'major_axis_lolim_w',
 'major_axis_hilim_b',
 'major_axis_hilim_h',
 'major_axis_hilim_m',
 'major_axis_hilim_s',
 'major_axis_hilim_u',
 'major_axis_hilim_w',
 'minor_axis_b',
 'minor_axis_h',
 'minor_axis_m',
 'minor_axis_s',
 'minor_axis_u',
 'minor_axis_w',
 'minor_axis_lolim_b',
 'minor_axis_lolim_h',
 'minor_axis_lolim_m',
 'minor_axis_lolim_s',
 'minor_axis_lolim_u',
 'minor_axis_lolim_w')

In [11]:

# Print the first 50 entries
results['name'][0:50]

Out[11]:

masked_array(data=[b'2CXO J013159.9+303500', b'2CXO J013202.9+303445',
                   b'2CXO J013203.1+303841', b'2CXO J013205.9+302305',
                   b'2CXO J013215.7+304008', b'2CXO J013217.5+302555',
                   b'2CXO J013217.9+301933', b'2CXO J013220.0+303450',
                   b'2CXO J013220.0+303640', b'2CXO J013221.0+304500',
                   b'2CXO J013223.4+304746', b'2CXO J013224.4+303322',
                   b'2CXO J013224.9+304510', b'2CXO J013227.2+303520',
                   b'2CXO J013228.9+304516', b'2CXO J013229.2+303617',
                   b'2CXO J013230.2+304216', b'2CXO J013230.3+303547',
                   b'2CXO J013230.4+303618', b'2CXO J013231.6+301726',
                   b'2CXO J013232.5+302623', b'2CXO J013232.6+304028',
                   b'2CXO J013233.6+304728', b'2CXO J013233.9+302112',
                   b'2CXO J013236.8+303228', b'2CXO J013237.2+301807',
                   b'2CXO J013239.6+302126', b'2CXO J013240.6+303722',
                   b'2CXO J013240.8+303549', b'2CXO J013241.6+303214',
                   b'2CXO J013242.1+303329', b'2CXO J013242.3+304816',
                   b'2CXO J013242.6+302521', b'2CXO J013243.4+303505',
                   b'2CXO J013244.3+303557', b'2CXO J013244.5+302329',
                   b'2CXO J013244.8+303033', b'2CXO J013245.0+303911',
                   b'2CXO J013245.6+302019', b'2CXO J013246.8+303438',
                   b'2CXO J013247.6+304415', b'2CXO J013247.7+304711',
                   b'2CXO J013248.6+303441', b'2CXO J013248.9+304253',
                   b'2CXO J013249.2+304253', b'2CXO J013249.3+303829',
                   b'2CXO J013249.6+303249', b'2CXO J013249.8+303101',
                   b'2CXO J013249.8+305018', b'2CXO J013250.6+303143'],
             mask=[False, False, False, False, False, False, False, False,
                   False, False, False, False, False, False, False, False,
                   False, False, False, False, False, False, False, False,
                   False, False, False, False, False, False, False, False,
                   False, False, False, False, False, False, False, False,
                   False, False, False, False, False, False, False, False,
                   False, False],
       fill_value='?',
            dtype=object)

We can iterate through the sources, and print, for example, their aperture photometry fluxes in the 'b' band, and their limits, only for sources that have a significance (or signal-to-noise ratio) larger than 50:

In [12]:

for r in results:
    if (r['significance']>50.0):
        out = f"{r['name']} flux={r['flux_aper_b']:.3e}  "
        out += f"{r['flux_aper_lolim_b']:.3e} - {r['flux_aper_hilim_b']:.3e}"
        print(out)

b'2CXO J013311.7+303841' flux=1.467e-13  1.444e-13 - 1.488e-13
b'2CXO J013315.1+305318' flux=1.331e-12  1.318e-12 - 1.344e-12
b'2CXO J013324.4+304402' flux=7.953e-13  7.838e-13 - 8.061e-13
b'2CXO J013328.6+302723' flux=2.681e-12  2.660e-12 - 2.701e-12
b'2CXO J013334.1+303211' flux=7.751e-13  7.595e-13 - 7.908e-13
b'2CXO J013350.9+303936' flux=1.422e-11  1.417e-11 - 1.427e-11
b'2CXO J013425.8+305518' flux=3.005e-13  2.939e-13 - 3.072e-13
b'2CXO J013451.8+302909' flux=2.094e-12  2.071e-12 - 2.116e-12

Let's display the location on the sky of these sources, and color code by significance:

In [13]:

#plt.plot(results['ra'], results['dec'], 'o');

from matplotlib import cm
import matplotlib.colors as colors
cmap = cm.coolwarm
normalize = matplotlib.colors.Normalize(vmin=np.min(np.log10(results['significance'].data)), 
                                        vmax=np.max(np.log10(results['significance'].data)))
plt.scatter(results['ra'],results['dec'],cmap=cmap,norm=normalize,c=np.log10(results['significance'].data),s=15)
plt.xlabel('RA [deg]')
plt.ylabel('Dec [deg]')
#plt.ylim(-0.98,0.98)
cbar = plt.colorbar()
cbar.ax.get_yaxis().labelpad = 15
cbar.ax.set_ylabel('log(significance)', rotation=270)

Out[13]:

Text(0, 0.5, 'log(significance)')

No description has been provided for this image

Now let's simultaneously visualize the spectral and variability probabilities of these sources by plotting the hardness ratios, and color coding them by variability probability, both at short timescales (intra-observation variability), and long timescales (inter-observation variability):

In [15]:

from matplotlib import cm
import matplotlib.colors as colors

plt.subplots(figsize=(12, 4))
cmap = cm.coolwarm
normalize = matplotlib.colors.Normalize(vmin=0, vmax=1)
plt.subplot(1,2,1)
plt.scatter(results['hard_hm'],results['hard_ms'],cmap=cmap,norm=normalize,c=results['var_intra_prob_b'].data,s=15)
plt.xlim(-0.98,0.98)
plt.ylim(-0.98,0.98)
cbar = plt.colorbar()
plt.xlabel('hard_hm')
plt.ylabel('hard_ms')
cbar.ax.get_yaxis().labelpad = 15
cbar.ax.set_ylabel('var_intra_prob_b', rotation=270)
plt.title('Short term variability')
plt.subplot(1,2,2)
plt.scatter(results['hard_hm'],results['hard_ms'],cmap=cmap,norm=normalize,c=results['var_inter_prob_b'].data,s=15)
plt.xlim(-0.98,0.98)
plt.ylim(-0.98,0.98)
cbar = plt.colorbar()
plt.xlabel('hard_hm')
plt.ylabel('hard_ms')
cbar.ax.get_yaxis().labelpad = 15
cbar.ax.set_ylabel('var_inter_prob_b', rotation=270)
plt.title('Long term variability')

Out[15]:

Text(0.5, 1.0, 'Long term variability')

Saving data¶

Let's try to save the search results as a FITS table. Unfortunately for us the name column is being turned into a Python object rather than a string, which causes the conversion to FITS to fail. This appears to be a choice with how AstroPy handles string columns of unrestricted length, so one solution is to manually convert the name field to string values.

In [16]:

results.votable.get_field_by_id_or_name('name')

Out[16]:

<FIELD ID="name" arraysize="*" datatype="char" name="name"/>

In [17]:

tbl = results.to_table()

The name column starts out as having a data type of object (as the string names are normally 21 characters long but can be 22), as does the likelihood_class column.

A quick conversion is to just use str on all object-type keywords:

In [18]:

for key in tbl.keys():
    if tbl[key].dtype == 'O':
        tbl[key] = [str(n) for n in tbl[key]]

The output for a AstroPy Table is slightly different:

In [19]:

tbl

Out[19]:

Table length=750

name	ra	dec	err_ellipse_r0	err_ellipse_r1	err_ellipse_ang	gal_b	gal_l	extent_flag	conf_flag	pileup_flag	var_flag	streak_src_flag	sat_src_flag	man_add_flag	man_inc_flag	man_reg_flag	man_match_flag	man_pos_flag	var_inter_hard_flag	major_axis_b	major_axis_h	major_axis_m	major_axis_s	major_axis_u	major_axis_w	major_axis_lolim_b	major_axis_lolim_h	major_axis_lolim_m	major_axis_lolim_s	major_axis_lolim_u	major_axis_lolim_w	major_axis_hilim_b	major_axis_hilim_h	major_axis_hilim_m	major_axis_hilim_s	major_axis_hilim_u	major_axis_hilim_w	minor_axis_b	minor_axis_h	minor_axis_m	minor_axis_s	minor_axis_u	minor_axis_w	minor_axis_lolim_b	minor_axis_lolim_h	minor_axis_lolim_m	minor_axis_lolim_s	minor_axis_lolim_u	minor_axis_lolim_w	minor_axis_hilim_b	minor_axis_hilim_h	minor_axis_hilim_m	minor_axis_hilim_s	minor_axis_hilim_u	minor_axis_hilim_w	pos_angle_b	pos_angle_h	pos_angle_m	pos_angle_s	pos_angle_u	pos_angle_w	pos_angle_lolim_b	pos_angle_lolim_h	pos_angle_lolim_m	pos_angle_lolim_s	pos_angle_lolim_u	pos_angle_lolim_w	pos_angle_hilim_b	pos_angle_hilim_h	pos_angle_hilim_m	pos_angle_hilim_s	pos_angle_hilim_u	pos_angle_hilim_w	src_area_b	src_area_h	src_area_m	src_area_s	src_area_u	src_area_w	phot_nsrcs	photflux_aper_b	photflux_aper_h	photflux_aper_m	photflux_aper_s	photflux_aper_u	photflux_aper_w	photflux_aper_lolim_b	photflux_aper_lolim_h	photflux_aper_lolim_m	photflux_aper_lolim_s	photflux_aper_lolim_u	photflux_aper_lolim_w	photflux_aper_hilim_b	photflux_aper_hilim_h	photflux_aper_hilim_m	photflux_aper_hilim_s	photflux_aper_hilim_u	photflux_aper_hilim_w	photflux_aper90_b	photflux_aper90_h	photflux_aper90_m	photflux_aper90_s	photflux_aper90_u	photflux_aper90_w	photflux_aper90_lolim_b	photflux_aper90_lolim_h	photflux_aper90_lolim_m	photflux_aper90_lolim_s	photflux_aper90_lolim_u	photflux_aper90_lolim_w	photflux_aper90_hilim_b	photflux_aper90_hilim_h	photflux_aper90_hilim_m	photflux_aper90_hilim_s	photflux_aper90_hilim_u	photflux_aper90_hilim_w	flux_aper_b	flux_aper_h	flux_aper_m	flux_aper_s	flux_aper_u	flux_aper_w	flux_aper_lolim_b	flux_aper_lolim_h	flux_aper_lolim_m	flux_aper_lolim_s	flux_aper_lolim_u	flux_aper_lolim_w	flux_aper_hilim_b	flux_aper_hilim_h	flux_aper_hilim_m	flux_aper_hilim_s	flux_aper_hilim_u	flux_aper_hilim_w	flux_aper90_b	flux_aper90_h	flux_aper90_m	flux_aper90_s	flux_aper90_u	flux_aper90_w	flux_aper90_lolim_b	flux_aper90_lolim_h	flux_aper90_lolim_m	flux_aper90_lolim_s	flux_aper90_lolim_u	flux_aper90_lolim_w	flux_aper90_hilim_b	flux_aper90_hilim_h	flux_aper90_hilim_m	flux_aper90_hilim_s	flux_aper90_hilim_u	flux_aper90_hilim_w	photflux_aper_avg_b	photflux_aper_avg_h	photflux_aper_avg_m	photflux_aper_avg_s	photflux_aper_avg_u	photflux_aper_avg_w	photflux_aper_avg_lolim_b	photflux_aper_avg_lolim_h	photflux_aper_avg_lolim_m	photflux_aper_avg_lolim_s	photflux_aper_avg_lolim_u	photflux_aper_avg_lolim_w	photflux_aper_avg_hilim_b	photflux_aper_avg_hilim_h	photflux_aper_avg_hilim_m	photflux_aper_avg_hilim_s	photflux_aper_avg_hilim_u	photflux_aper_avg_hilim_w	photflux_aper90_avg_b	photflux_aper90_avg_h	photflux_aper90_avg_m	photflux_aper90_avg_s	photflux_aper90_avg_u	photflux_aper90_avg_w	photflux_aper90_avg_lolim_b	photflux_aper90_avg_lolim_h	photflux_aper90_avg_lolim_m	photflux_aper90_avg_lolim_s	photflux_aper90_avg_lolim_u	photflux_aper90_avg_lolim_w	photflux_aper90_avg_hilim_b	photflux_aper90_avg_hilim_h	photflux_aper90_avg_hilim_m	photflux_aper90_avg_hilim_s	photflux_aper90_avg_hilim_u	photflux_aper90_avg_hilim_w	flux_aper_avg_b	flux_aper_avg_h	flux_aper_avg_m	flux_aper_avg_s	flux_aper_avg_u	flux_aper_avg_w	flux_aper_avg_lolim_b	flux_aper_avg_lolim_h	flux_aper_avg_lolim_m	flux_aper_avg_lolim_s	flux_aper_avg_lolim_u	flux_aper_avg_lolim_w	flux_aper_avg_hilim_b	flux_aper_avg_hilim_h	flux_aper_avg_hilim_m	flux_aper_avg_hilim_s	flux_aper_avg_hilim_u	flux_aper_avg_hilim_w	flux_aper90_avg_b	flux_aper90_avg_h	flux_aper90_avg_m	flux_aper90_avg_s	flux_aper90_avg_u	flux_aper90_avg_w	flux_aper90_avg_lolim_b	flux_aper90_avg_lolim_h	flux_aper90_avg_lolim_m	flux_aper90_avg_lolim_s	flux_aper90_avg_lolim_u	flux_aper90_avg_lolim_w	flux_aper90_avg_hilim_b	flux_aper90_avg_hilim_h	flux_aper90_avg_hilim_m	flux_aper90_avg_hilim_s	flux_aper90_avg_hilim_u	flux_aper90_avg_hilim_w	flux_powlaw_aper_b	flux_powlaw_aper_h	flux_powlaw_aper_m	flux_powlaw_aper_s	flux_powlaw_aper_u	flux_powlaw_aper_w	flux_powlaw_aper_lolim_b	flux_powlaw_aper_lolim_h	flux_powlaw_aper_lolim_m	flux_powlaw_aper_lolim_s	flux_powlaw_aper_lolim_u	flux_powlaw_aper_lolim_w	flux_powlaw_aper_hilim_b	flux_powlaw_aper_hilim_h	flux_powlaw_aper_hilim_m	flux_powlaw_aper_hilim_s	flux_powlaw_aper_hilim_u	flux_powlaw_aper_hilim_w	flux_powlaw_aper90_b	flux_powlaw_aper90_h	flux_powlaw_aper90_m	flux_powlaw_aper90_s	flux_powlaw_aper90_u	flux_powlaw_aper90_w	flux_powlaw_aper90_lolim_b	flux_powlaw_aper90_lolim_h	flux_powlaw_aper90_lolim_m	flux_powlaw_aper90_lolim_s	flux_powlaw_aper90_lolim_u	flux_powlaw_aper90_lolim_w	flux_powlaw_aper90_hilim_b	flux_powlaw_aper90_hilim_h	flux_powlaw_aper90_hilim_m	flux_powlaw_aper90_hilim_s	flux_powlaw_aper90_hilim_u	flux_powlaw_aper90_hilim_w	flux_bb_aper_b	flux_bb_aper_h	flux_bb_aper_m	flux_bb_aper_s	flux_bb_aper_u	flux_bb_aper_w	flux_bb_aper_lolim_b	flux_bb_aper_lolim_h	flux_bb_aper_lolim_m	flux_bb_aper_lolim_s	flux_bb_aper_lolim_u	flux_bb_aper_lolim_w	flux_bb_aper_hilim_b	flux_bb_aper_hilim_h	flux_bb_aper_hilim_m	flux_bb_aper_hilim_s	flux_bb_aper_hilim_u	flux_bb_aper_hilim_w	flux_bb_aper90_b	flux_bb_aper90_h	flux_bb_aper90_m	flux_bb_aper90_s	flux_bb_aper90_u	flux_bb_aper90_w	flux_bb_aper90_lolim_b	flux_bb_aper90_lolim_h	flux_bb_aper90_lolim_m	flux_bb_aper90_lolim_s	flux_bb_aper90_lolim_u	flux_bb_aper90_lolim_w	flux_bb_aper90_hilim_b	flux_bb_aper90_hilim_h	flux_bb_aper90_hilim_m	flux_bb_aper90_hilim_s	flux_bb_aper90_hilim_u	flux_bb_aper90_hilim_w	flux_brems_aper_b	flux_brems_aper_h	flux_brems_aper_m	flux_brems_aper_s	flux_brems_aper_u	flux_brems_aper_w	flux_brems_aper_lolim_b	flux_brems_aper_lolim_h	flux_brems_aper_lolim_m	flux_brems_aper_lolim_s	flux_brems_aper_lolim_u	flux_brems_aper_lolim_w	flux_brems_aper_hilim_b	flux_brems_aper_hilim_h	flux_brems_aper_hilim_m	flux_brems_aper_hilim_s	flux_brems_aper_hilim_u	flux_brems_aper_hilim_w	flux_brems_aper90_b	flux_brems_aper90_h	flux_brems_aper90_m	flux_brems_aper90_s	flux_brems_aper90_u	flux_brems_aper90_w	flux_brems_aper90_lolim_b	flux_brems_aper90_lolim_h	flux_brems_aper90_lolim_m	flux_brems_aper90_lolim_s	flux_brems_aper90_lolim_u	flux_brems_aper90_lolim_w	flux_brems_aper90_hilim_b	flux_brems_aper90_hilim_h	flux_brems_aper90_hilim_m	flux_brems_aper90_hilim_s	flux_brems_aper90_hilim_u	flux_brems_aper90_hilim_w	flux_apec_aper_b	flux_apec_aper_h	flux_apec_aper_m	flux_apec_aper_s	flux_apec_aper_u	flux_apec_aper_w	flux_apec_aper_lolim_b	flux_apec_aper_lolim_h	flux_apec_aper_lolim_m	flux_apec_aper_lolim_s	flux_apec_aper_lolim_u	flux_apec_aper_lolim_w	flux_apec_aper_hilim_b	flux_apec_aper_hilim_h	flux_apec_aper_hilim_m	flux_apec_aper_hilim_s	flux_apec_aper_hilim_u	flux_apec_aper_hilim_w	flux_apec_aper90_b	flux_apec_aper90_h	flux_apec_aper90_m	flux_apec_aper90_s	flux_apec_aper90_u	flux_apec_aper90_w	flux_apec_aper90_lolim_b	flux_apec_aper90_lolim_h	flux_apec_aper90_lolim_m	flux_apec_aper90_lolim_s	flux_apec_aper90_lolim_u	flux_apec_aper90_lolim_w	flux_apec_aper90_hilim_b	flux_apec_aper90_hilim_h	flux_apec_aper90_hilim_m	flux_apec_aper90_hilim_s	flux_apec_aper90_hilim_u	flux_apec_aper90_hilim_w	likelihood	likelihood_class	significance	flux_powlaw	flux_powlaw_lolim	flux_powlaw_hilim	powlaw_gamma	powlaw_gamma_lolim	powlaw_gamma_hilim	powlaw_gamma_rhat	powlaw_nh	powlaw_nh_lolim	powlaw_nh_hilim	powlaw_nh_rhat	powlaw_ampl	powlaw_ampl_lolim	powlaw_ampl_hilim	powlaw_ampl_rhat	powlaw_stat	flux_bb	flux_bb_lolim	flux_bb_hilim	bb_kt	bb_kt_lolim	bb_kt_hilim	bb_kt_rhat	bb_nh	bb_nh_lolim	bb_nh_hilim	bb_nh_rhat	bb_ampl	bb_ampl_lolim	bb_ampl_hilim	bb_ampl_rhat	bb_stat	flux_brems	flux_brems_lolim	flux_brems_hilim	brems_kt	brems_kt_lolim	brems_kt_hilim	brems_kt_rhat	brems_nh	brems_nh_lolim	brems_nh_hilim	brems_nh_rhat	brems_norm	brems_norm_lolim	brems_norm_hilim	brems_norm_rhat	brems_stat	flux_apec	flux_apec_lolim	flux_apec_hilim	apec_kt	apec_kt_lolim	apec_kt_hilim	apec_kt_rhat	apec_abund	apec_abund_lolim	apec_abund_hilim	apec_abund_rhat	apec_z	apec_z_lolim	apec_z_hilim	apec_z_rhat	apec_nh	apec_nh_lolim	apec_nh_hilim	apec_nh_rhat	apec_norm	apec_norm_lolim	apec_norm_hilim	apec_norm_rhat	apec_stat	nh_gal	hard_hm	hard_hm_lolim	hard_hm_hilim	var_inter_hard_prob_hm	var_inter_hard_sigma_hm	hard_hs	hard_hs_lolim	hard_hs_hilim	var_inter_hard_prob_hs	var_inter_hard_sigma_hs	hard_ms	hard_ms_lolim	hard_ms_hilim	var_inter_hard_prob_ms	var_inter_hard_sigma_ms	ks_intra_prob_b	ks_intra_prob_h	ks_intra_prob_m	ks_intra_prob_s	ks_intra_prob_u	ks_intra_prob_w	kp_intra_prob_b	kp_intra_prob_h	kp_intra_prob_m	kp_intra_prob_s	kp_intra_prob_u	kp_intra_prob_w	var_intra_prob_b	var_intra_prob_h	var_intra_prob_m	var_intra_prob_s	var_intra_prob_u	var_intra_prob_w	var_intra_index_b	var_intra_index_h	var_intra_index_m	var_intra_index_s	var_intra_index_u	var_intra_index_w	var_inter_prob_b	var_inter_prob_h	var_inter_prob_m	var_inter_prob_s	var_inter_prob_u	var_inter_prob_w	var_inter_index_b	var_inter_index_h	var_inter_index_m	var_inter_index_s	var_inter_index_u	var_inter_index_w	var_inter_sigma_b	var_inter_sigma_h	var_inter_sigma_m	var_inter_sigma_s	var_inter_sigma_u	var_inter_sigma_w	dither_warning_flag	acis_num	acis_hetg_num	acis_letg_num	acis_time	acis_hetg_time	acis_letg_time	hrc_num	hrc_hetg_num	hrc_letg_num	hrc_time	hrc_hetg_time	hrc_letg_time
	deg	deg	arcsec	arcsec	deg	deg	deg													arcsec	arcsec	arcsec	arcsec	arcsec	arcsec	arcsec	arcsec	arcsec	arcsec	arcsec	arcsec	arcsec	arcsec	arcsec	arcsec	arcsec	arcsec	arcsec	arcsec	arcsec	arcsec	arcsec	arcsec	arcsec	arcsec	arcsec	arcsec	arcsec	arcsec	arcsec	arcsec	arcsec	arcsec	arcsec	arcsec	deg	deg	deg	deg	deg	deg	deg	deg	deg	deg	deg	deg	deg	deg	deg	deg	deg	deg	arcsec^2	arcsec^2	arcsec^2	arcsec^2	arcsec^2	arcsec^2		photon/s/cm^2	photon/s/cm^2	photon/s/cm^2	photon/s/cm^2	photon/s/cm^2	photon/s/cm^2	photon/s/cm^2	photon/s/cm^2	photon/s/cm^2	photon/s/cm^2	photon/s/cm^2	photon/s/cm^2	photon/s/cm^2	photon/s/cm^2	photon/s/cm^2	photon/s/cm^2	photon/s/cm^2	photon/s/cm^2	photon/s/cm^2	photon/s/cm^2	photon/s/cm^2	photon/s/cm^2	photon/s/cm^2	photon/s/cm^2	photon/s/cm^2	photon/s/cm^2	photon/s/cm^2	photon/s/cm^2	photon/s/cm^2	photon/s/cm^2	photon/s/cm^2	photon/s/cm^2	photon/s/cm^2	photon/s/cm^2	photon/s/cm^2	photon/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	photon/s/cm^2	photon/s/cm^2	photon/s/cm^2	photon/s/cm^2	photon/s/cm^2	photon/s/cm^2	photon/s/cm^2	photon/s/cm^2	photon/s/cm^2	photon/s/cm^2	photon/s/cm^2	photon/s/cm^2	photon/s/cm^2	photon/s/cm^2	photon/s/cm^2	photon/s/cm^2	photon/s/cm^2	photon/s/cm^2	photon/s/cm^2	photon/s/cm^2	photon/s/cm^2	photon/s/cm^2	photon/s/cm^2	photon/s/cm^2	photon/s/cm^2	photon/s/cm^2	photon/s/cm^2	photon/s/cm^2	photon/s/cm^2	photon/s/cm^2	photon/s/cm^2	photon/s/cm^2	photon/s/cm^2	photon/s/cm^2	photon/s/cm^2	photon/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	erg/s/cm^2				erg/s/cm^2	erg/s/cm^2	erg/s/cm^2					10^20/cm^2	10^20/cm^2	10^20/cm^2							erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	keV	keV	keV		10^20/cm^2	10^20/cm^2	10^20/cm^2							erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	keV	keV	keV		10^20/cm^2	10^20/cm^2	10^20/cm^2							erg/s/cm^2	erg/s/cm^2	erg/s/cm^2	keV	keV	keV										10^20/cm^2	10^20/cm^2	10^20/cm^2							10^20/cm^2																																																				photon/s/cm^2	photon/s/cm^2	photon/s/cm^2	photon/s/cm^2	photon/s/cm^2	photon/s/cm^2					s	s	s				s	s	s
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b'2CXO 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b'2CXO J013202.9+303445'	23.01219298372888	30.57933853498285	9.940394936885975	7.8069837868582725	149.52782967363248	-31.482799	133.182443	1	1	0	1	0	0	0	0	0	0	0	0	--	--	--	18.54112852818911	--	--	--	--	--	15.57432176898459	--	--	--	--	--	21.50793528739363	--	--	--	--	--	18.54112852818911	--	--	--	--	--	15.57432176898459	--	--	--	--	--	21.50793528739363	--	--	--	--	--	0.0	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	1079.9961359049175	--	--	2	5.4678466021834e-06	1.8084637824433e-06	7.6053719112355e-07	1.6056415471433e-06	0.0	--	4.1555634176593e-06	9.4040116687052e-07	3.9611312037685e-07	1.1363001718245e-06	0.0	--	6.7801297867074e-06	2.6765263980161e-06	1.1091167370552e-06	2.0749829224621e-06	1.1800476475746e-06	--	3.8842219382247e-06	5.2736736969752e-07	6.4829392894802e-07	1.5280406656124e-06	0.0	--	2.8447822646153e-06	0.0	3.6154853729793e-07	1.1357059001174e-06	0.0	--	4.8689542605915e-06	1.0758294341829e-06	9.225721296568e-07	1.8997262329236e-06	8.2151557698449e-07	--	5.138182005086e-14	4.7172720467491e-14	2.0069249187846e-15	2.7399135701826e-15	--	--	3.782272864855e-14	2.9482950292182e-14	1.064898936498e-15	1.8836905795005e-15	--	--	6.4940911453171e-14	6.4020120634452e-14	2.9489509010713e-15	3.5533254113305e-15	--	--	2.3932517494116e-14	1.6355331660537e-14	1.6170904637569e-15	2.5854505490755e-15	0.0	--	1.3998264949388e-14	4.5431476834825e-15	8.8781437225866e-16	1.9031788764028e-15	0.0	--	3.3415213104992e-14	2.8167515637592e-14	2.3146588991029e-15	3.2677222217482e-15	5.6559746355326e-15	--	5.4678466021834e-06	1.8084637824433e-06	7.6053719112355e-07	1.6056415471433e-06	0.0	--	4.1555634176593e-06	9.4040116687052e-07	3.9611312037685e-07	1.1363001718245e-06	0.0	--	6.7801297867074e-06	2.6765263980161e-06	1.1091167370552e-06	2.0749829224621e-06	1.1800476475746e-06	--	3.8842219382247e-06	5.2736736969752e-07	6.4829392894802e-07	1.5280406656124e-06	0.0	--	2.8447822646153e-06	0.0	3.6154853729793e-07	1.1357059001174e-06	0.0	--	4.8689542605915e-06	1.0758294341829e-06	9.225721296568e-07	1.8997262329236e-06	8.2151557698449e-07	--	5.138182005086e-14	4.7172720467491e-14	2.0069249187846e-15	2.7399135701826e-15	--	--	3.782272864855e-14	2.9482950292182e-14	1.064898936498e-15	1.8836905795005e-15	--	--	6.4940911453171e-14	6.4020120634452e-14	2.9489509010713e-15	3.5533254113305e-15	--	--	2.3932517494116e-14	1.6355331660537e-14	1.6170904637569e-15	2.5854505490755e-15	0.0	--	1.3998264949388e-14	4.5431476834825e-15	8.8781437225866e-16	1.9031788764028e-15	0.0	--	3.3415213104992e-14	2.8167515637592e-14	2.3146588991029e-15	3.2677222217482e-15	5.6559746355326e-15	--	1.36550516728e-14	--	--	2.95163907044e-15	--	--	1.06403000048e-14	--	--	2.12691638899e-15	--	--	1.64924650074e-14	--	--	3.77636175188e-15	--	--	9.61604551579e-15	--	--	2.71319282253e-15	--	--	7.308194592e-15	--	--	2.02585064082e-15	--	--	1.19238964396e-14	--	--	3.36435909994e-15	--	--	1.70652639304e-14	--	--	2.66577039029e-15	--	--	1.32976082574e-14	--	--	1.92092278124e-15	--	--	2.0611292799e-14	--	--	3.41061799934e-15	--	--	1.20175564784e-14	--	--	2.4504178583e-15	--	--	9.1333429236e-15	--	--	1.8296453342e-15	--	--	1.49017700332e-14	--	--	3.03851814429e-15	--	--	1.35390634262e-14	--	--	2.88607505868e-15	--	--	1.05499195529e-14	--	--	2.07967173346e-15	--	--	1.63523753069e-14	--	--	3.6924783839e-15	--	--	9.53436524936e-15	--	--	2.65292535694e-15	--	--	7.24611758951e-15	--	--	1.98085093318e-15	--	--	1.18226129092e-14	--	--	3.2896274426e-15	--	--	1.70021410751e-14	--	--	2.85485124686e-15	--	--	1.32484216169e-14	--	--	2.05717222201e-15	--	--	2.05350535063e-14	--	--	3.65253027172e-15	--	--	1.19731046327e-14	--	--	2.62422394051e-15	--	--	9.09955952088e-15	--	--	1.95942054225e-15	--	--	1.48466497446e-14	--	--	3.25403768623e-15	--	--	25.67892962074734	MARGINAL	4.166666666666559	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	6.02	0.46221111805122	0.17051842598376	0.7001873828857	--	--	0.11992504684572	-0.16926920674578	0.3579013116802	--	--	-0.34353529044347	-0.579013116802	-0.11680199875078	--	--	0.14096963123043005	--	--	0.39108920561601	--	--	0.40806929648507995	--	--	0.57444754889095	--	--	0.054569386005344	--	--	0.67577897561155	--	--	0	--	--	2	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	0	1	0	0	97852.244593881	--	--	0	0	0	--	--	--
b'2CXO J013203.1+303841'	23.012944925258978	30.644821460096193	15.056586680708355	6.997260877221328	164.07193014010852	-31.418314	133.169072	1	0	0	1	0	0	0	0	0	0	0	0	--	--	--	21.79529877602101	--	--	--	--	--	18.447244652216586	--	--	--	--	--	25.143352899825434	--	--	--	--	--	21.79529877602101	--	--	--	--	--	18.447244652216586	--	--	--	--	--	25.143352899825434	--	--	--	--	--	0.0	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	1492.3666193067588	--	--	1	4.3533727795305e-06	1.2892360795421e-06	6.5984051278646e-07	1.6627626311398e-06	0.0	--	3.0657554785426e-06	5.5252974837519e-07	3.4311706664896e-07	1.2008841224899e-06	0.0	--	5.5796749709476e-06	2.025942410709e-06	9.6336714866824e-07	2.1015472143573e-06	2.598616123521e-06	--	4.1236481802077e-06	8.3185932636066e-07	9.5418979988395e-07	1.3964864127162e-06	0.0	--	3.2293630326928e-06	2.9227489845104e-07	6.8540394076171e-07	1.0518988563317e-06	0.0	--	5.0179333277226e-06	1.3714437542703e-06	1.2095363660501e-06	1.7410739691008e-06	1.6265070291267e-06	--	2.146030228156e-14	1.7501125867404e-14	1.5836710880821e-15	2.2824590125078e-15	--	--	1.073015114078e-14	5.6760408218609e-15	7.9183554404107e-16	1.4854098335368e-15	--	--	3.1761247376709e-14	2.8853207511126e-14	2.3425134844548e-15	3.0432786833437e-15	--	--	1.1320541649326e-14	5.7287817860751e-15	2.2194687147038e-15	2.1845772605862e-15	--	--	5.0313518441448e-15	0.0	1.5439782363157e-15	1.5692033843647e-15	--	--	1.7358163862299e-14	1.1978361916339e-14	2.8627929798353e-15	2.7691824429966e-15	--	--	4.3533727795305e-06	1.2892360795421e-06	6.5984051278646e-07	1.6627626311398e-06	0.0	--	3.0657554785426e-06	5.5252974837519e-07	3.4311706664896e-07	1.2008841224899e-06	0.0	--	5.5796749709476e-06	2.025942410709e-06	9.6336714866824e-07	2.1015472143573e-06	2.598616123521e-06	--	4.1236481802077e-06	8.3185932636066e-07	9.5418979988395e-07	1.3964864127162e-06	0.0	--	3.2293630326928e-06	2.9227489845104e-07	6.8540394076171e-07	1.0518988563317e-06	0.0	--	5.0179333277226e-06	1.3714437542703e-06	1.2095363660501e-06	1.7410739691008e-06	1.6265070291267e-06	--	2.146030228156e-14	1.7501125867404e-14	1.5836710880821e-15	2.2824590125078e-15	--	--	1.073015114078e-14	5.6760408218609e-15	7.9183554404107e-16	1.4854098335368e-15	--	--	3.1761247376709e-14	2.8853207511126e-14	2.3425134844548e-15	3.0432786833437e-15	--	--	1.1320541649326e-14	5.7287817860751e-15	2.2194687147038e-15	2.1845772605862e-15	--	--	5.0313518441448e-15	0.0	1.5439782363157e-15	1.5692033843647e-15	--	--	1.7358163862299e-14	1.1978361916339e-14	2.8627929798353e-15	2.7691824429966e-15	--	--	--	--	--	3.11697783665e-15	--	--	--	--	--	2.35771400465e-15	--	--	--	--	--	3.83628041434e-15	--	--	--	--	--	2.47803781968e-15	--	--	--	--	--	1.88205404026e-15	--	--	--	--	--	3.04265403176e-15	--	--	--	--	--	2.81241936139e-15	--	--	--	--	--	2.12734285028e-15	--	--	--	--	--	3.46143921402e-15	--	--	--	--	--	2.23590988052e-15	--	--	--	--	--	1.69815940293e-15	--	--	--	--	--	2.7453577014e-15	--	--	--	--	--	3.04688517305e-15	--	--	--	--	--	2.304695195e-15	--	--	--	--	--	3.75001252067e-15	--	--	--	--	--	2.42231324273e-15	--	--	--	--	--	1.83973157676e-15	--	--	--	--	--	2.97423271576e-15	--	--	--	--	--	3.01307424069e-15	--	--	--	--	--	2.27912025899e-15	--	--	--	--	--	3.70839906547e-15	--	--	--	--	--	2.39543311285e-15	--	--	--	--	--	1.81931628824e-15	--	--	--	--	--	2.94122799932e-15	--	--	20.72416822743528	MARGINAL	3.6923076923076574	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	5.96	0.42473454091193	0.059337913803873	0.69768894440974	--	--	-0.027482823235478	-0.37039350405996	0.23297938788257	--	--	-0.420986883198	-0.64647095565272	-0.20174890693317	--	--	--	--	--	0.901770452153998	--	--	--	--	--	0.81856520156533	--	--	--	--	--	0.80819166138719	--	--	--	--	--	2	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	0	1	0	0	97852.244593881	--	--	0	0	0	--	--	--
b'2CXO J013205.9+302305'	23.02473533826901	30.384816785501236	9.749317643492928	7.058213836836468	141.4480326506573	-31.671998	133.236985	1	0	0	0	0	0	0	0	0	0	0	1	163.05188582413206	--	--	--	--	--	161.39793000618982	--	--	--	--	--	164.7058416420743	--	--	--	--	--	163.05188582413206	--	--	--	--	--	161.39793000618982	--	--	--	--	--	164.7058416420743	--	--	--	--	--	0.0	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	83522.12301522802	--	--	--	--	--	1	7.1360014417056e-06	1.9836747647619e-06	1.978392665417e-06	1.1428776649558e-06	5.4983529234983e-05	--	5.5884348639863e-06	1.0315108776762e-06	1.6046962730605e-06	5.3157100695617e-07	3.6956142600562e-05	--	8.5975920984405e-06	2.8961651565524e-06	2.3301069170467e-06	1.7276057726075e-06	7.3010915869404e-05	--	7.9000162399273e-06	2.4959174945634e-06	2.0962479045685e-06	1.0967497368084e-06	3.0086879162381e-05	--	6.6381504030531e-06	1.7279428808516e-06	1.7640558119876e-06	5.9055755058915e-07	1.5645177164438e-05	--	9.0830154619969e-06	3.2254933775897e-06	2.4088992858211e-06	1.5818505819352e-06	4.4528581160324e-05	--	3.2452095681197e-14	2.5598594316969e-14	4.8736557160671e-15	0.0	4.4105967000782e-13	--	2.1634730454131e-14	1.3060507304576e-14	3.9427327141217e-15	0.0	2.8668878550508e-13	--	4.2754348278402e-14	3.7614261037179e-14	5.8045787180125e-15	1.5258415412382e-15	5.8807956001042e-13	--	3.344426449969e-14	2.8343700332197e-14	5.0326257520932e-15	1.8545946788279e-15	3.8688646396696e-13	--	2.5083198374768e-14	1.7951010210391e-14	4.2096856349851e-15	7.3207684690576e-16	2.5147620157852e-13	--	4.1365274512775e-14	3.8263995448466e-14	5.8071576270185e-15	2.9771125107501e-15	5.2229672635539e-13	--	8.5382024269014e-06	2.9179061373082e-06	2.117668115772e-06	1.2875931577921e-06	5.5096160050034e-05	--	7.1117300111701e-06	2.0425342961157e-06	1.7855966929819e-06	7.3216081521511e-07	3.9354400035738e-05	--	9.8807647005309e-06	3.7515936051105e-06	2.4497395385622e-06	1.8430255003691e-06	7.0050832063614e-05	--	8.3435588619129e-06	2.7223246212546e-06	2.0868737369625e-06	1.2316964690506e-06	2.710112814619e-05	--	7.2475803015703e-06	2.041743465941e-06	1.7911359351485e-06	7.874780703766e-07	1.5635266238187e-05	--	9.4395374222554e-06	3.4029057765683e-06	2.3652151974933e-06	1.6557231223303e-06	3.8566990054194e-05	--	3.4076026543122e-14	2.8405669178778e-14	5.3372246056021e-15	8.9724611486605e-16	2.339620079974e-13	--	2.4740128860075e-14	1.7043401507267e-14	4.4942711377594e-15	0.0	1.169810039987e-13	--	4.3411924226169e-14	3.9251470137948e-14	6.1801780734448e-15	1.8760600583563e-15	3.4585688138747e-13	--	2.7918981289289e-14	2.2843425083506e-14	5.1716505860123e-15	1.9866909565598e-15	2.9985985938656e-13	--	2.1029882010114e-14	1.3869222372129e-14	4.42889665147e-15	9.4819341108536e-16	1.874124121166e-13	--	3.4808080568465e-14	3.1409709489821e-14	5.8707131126403e-15	3.0251885020342e-15	4.1230730665652e-13	--	1.23369368533e-14	--	--	--	--	--	8.83840252178e-15	--	--	--	--	--	1.58354711849e-14	--	--	--	--	--	1.54701791385e-14	--	--	--	--	--	1.24816218049e-14	--	--	--	--	--	1.84587364721e-14	--	--	--	--	--	1.34017022811e-14	--	--	--	--	--	9.60121954467e-15	--	--	--	--	--	1.72021850175e-14	--	--	--	--	--	1.68053656685e-14	--	--	--	--	--	1.35588745735e-14	--	--	--	--	--	2.00518567636e-14	--	--	--	--	--	1.18376862265e-14	--	--	--	--	--	8.48073043091e-15	--	--	--	--	--	1.51946420221e-14	--	--	--	--	--	1.48441325984e-14	--	--	--	--	--	1.19765160737e-14	--	--	--	--	--	1.77117491231e-14	--	--	--	--	--	1.43956648927e-14	--	--	--	--	--	1.03133121619e-14	--	--	--	--	--	1.84780176235e-14	--	--	--	--	--	1.80517674165e-14	--	--	--	--	--	1.45644941656e-14	--	--	--	--	--	2.15390406674e-14	--	--	--	--	--	74.40186198049321	TRUE	6.3771465118328345	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	6.2	0.072454715802623	-0.20424734540912	0.27795128044972	0.32939994352340995	0.2260411076672	0.29356652092442	-0.044347282948157	0.6052467207995	0.41072784102154003	0.56353079639426	0.20362273579013	-0.015615240474703	0.48282323547783	0.09817340888523995	0.8749901250754	0.025036039916280006	--	--	--	--	--	0.06011189264139005	--	--	--	--	--	0.053576332429928	--	--	--	--	--	0	--	--	--	--	--	0.72522207855082	0.66062591072124	0.11198516125032998	0.48070028295589995	0.9912261641606296	--	5	4	0	0	7	--	5.4395058734952e-06	3.3409305026501e-06	5.2895507630088e-07	6.8683802988934e-07	7.9844739083968e-05	--	0	4	0	0	190832.45010656	--	--	0	0	0	--	--	--
b'2CXO J013215.7+304008'	23.06568844810522	30.669130877640722	4.549652723778539	3.393878527391884	178.9596832009697	-31.386059	133.216071	0	0	0	1	0	0	0	0	0	0	0	0	13.798594427889922	--	--	--	--	--	9.308478729357255	--	--	--	--	--	18.28871012642259	--	--	--	--	--	13.798594427889922	--	--	--	--	--	9.308478729357255	--	--	--	--	--	18.28871012642259	--	--	--	--	--	0.0	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	598.1630368698571	--	--	--	--	--	1	0.0	0.0	0.0	0.0	0.0	--	0.0	0.0	0.0	0.0	0.0	--	1.6988426512658e-06	5.9304214000462e-07	3.6162998972541e-07	2.3644853171919e-06	8.7875387609284e-05	--	0.0	0.0	0.0	0.0	0.0	--	0.0	0.0	0.0	0.0	0.0	--	1.5131488349388e-06	5.9304214000462e-07	3.4506983851298e-07	2.1301734376369e-06	8.1115570796111e-05	--	0.0	0.0	0.0	0.0	0.0	--	0.0	0.0	0.0	0.0	0.0	--	8.517233287244e-15	8.0241789529299e-15	6.1146704642662e-16	6.0739214822603e-15	2.1727861615106e-12	--	0.0	0.0	0.0	0.0	0.0	--	0.0	0.0	0.0	0.0	0.0	--	5.6637876689412e-15	8.0241789529299e-15	6.3459730827951e-16	5.6820015948024e-15	1.4674587683922e-12	--	1.9218383262047e-06	0.0	2.6400658938299e-07	1.7295800335198e-06	6.5075057765144e-06	--	1.2491949120331e-06	0.0	1.1220280048777e-07	1.4064716756095e-06	2.6697459595956e-06	--	2.5624511016063e-06	2.9548466949841e-07	4.0921021354363e-07	2.0336820174353e-06	1.0178406470958e-05	--	2.0233026764406e-06	0.0	2.6637928129013e-07	1.6124091804628e-06	4.6162837460575e-06	--	1.5107326650756e-06	0.0	1.359077965766e-07	1.3348349846576e-06	1.8714663835368e-06	--	2.5088953187863e-06	3.2148730181614e-07	3.9685076600366e-07	1.8736554823971e-06	7.3611011085781e-06	--	0.0	0.0	5.8970150811534e-16	2.2312807426835e-15	0.0	--	0.0	0.0	2.2680827235205e-16	1.6362725446346e-15	0.0	--	1.8052284376108e-15	2.3925602502302e-15	9.5259474387863e-16	2.8262889407324e-15	1.2877087373149e-14	--	0.0	0.0	5.6987457593052e-16	2.3289959551712e-15	0.0	--	0.0	0.0	2.6408821811414e-16	1.8114412984665e-15	0.0	--	2.3295074712382e-15	3.6739230475708e-15	8.6176155384615e-16	2.8177975753923e-15	1.2125984393073e-14	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	33.58268426751727	TRUE	3.9999999999997224	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	5.91	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	0.24402989167386002	--	--	0.08551671203431999	--	--	0.09237205057050002	--	--	0.11348795440769999	--	--	0.078823660214155	--	--	0.4088922986418	--	--	0	--	--	0	--	--	0.88761568562767	0.46863619455890004	0.63902611362677	0.71754863746818	0.14128659237684005	--	6	0	4	5	0	--	8.0465222619297e-07	2.9294306853388e-07	1.634344912164e-07	1.1353261649015e-07	2.1587675097642e-06	--	0	3	0	0	286125.475723213	--	--	0	0	0	--	--	--
b'2CXO J013217.5+302555'	23.07332419514978	30.431960075923463	4.04772863896093	2.16431947825693	139.0492500123097	-31.617918	133.275118	1	0	0	0	0	0	0	0	0	0	0	1	19.87671747897246	--	15.470521040154297	17.289111382724514	--	--	18.38519735750283	--	11.868368374688874	14.105796854640147	--	--	21.36823760044209	--	19.07267370561972	20.47242591080888	--	--	19.87671747897246	--	15.470521040154297	17.289111382724514	--	--	18.38519735750283	--	11.868368374688874	14.105796854640147	--	--	21.36823760044209	--	19.07267370561972	20.47242591080888	--	--	0.0	--	0.0	0.0	--	--	--	--	--	--	--	--	--	--	--	--	--	--	1241.1926706881156	--	751.8994277031808	939.0640548049555	--	--	1	1.1283728758286e-05	2.6462942301354e-06	2.125683501256e-06	3.4362929189864e-06	5.8229628088944e-06	--	9.9359231095217e-06	1.9053318456975e-06	1.8043709519913e-06	2.8520449075054e-06	0.0	--	1.2552251721829e-05	3.3872566145733e-06	2.4469960505206e-06	4.0205409304674e-06	1.1645925617789e-05	--	1.2106375776155e-05	2.4177181075246e-06	2.4679226861538e-06	3.6245201206894e-06	0.0	--	1.0948786661769e-05	1.7977903876465e-06	2.1534996078509e-06	3.0842422987656e-06	0.0	--	1.326396489054e-05	3.0376458274027e-06	2.7823457644567e-06	4.1647979426132e-06	4.3187756998564e-06	--	1.7463740559626e-14	5.7973068197027e-15	4.4661128942729e-15	7.6646711395025e-15	0.0	--	1.0359846094694e-14	0.0	3.7224819757966e-15	6.1969681553425e-15	0.0	--	2.4271639421854e-14	1.1594613639405e-14	5.2097438127491e-15	9.1323741236626e-15	2.4591100099597e-14	--	2.21570232911e-14	5.2903818429535e-15	5.4856821783281e-15	7.9885043444676e-15	0.0	--	1.668615334268e-14	2.5192294490255e-16	4.7558738934087e-15	6.7053908877554e-15	0.0	--	2.7354349742099e-14	1.0076917796102e-14	6.2154904632475e-15	9.2716178011797e-15	2.4866232300096e-15	--	1.269250187201e-05	2.8832681932523e-06	2.7218124345393e-06	3.4375150020924e-06	4.5935882917033e-06	--	1.1611518432808e-05	2.265425008984e-06	2.4203526998834e-06	2.9574148110914e-06	0.0	--	1.3773485311212e-05	3.4667867561724e-06	3.0055392436272e-06	3.9176151930935e-06	9.1871765834067e-06	--	1.3529763174061e-05	3.0896806222001e-06	2.7977907980208e-06	3.6406910514241e-06	0.0	--	1.2559098547184e-05	2.530908594781e-06	2.5136768194729e-06	3.1739275228497e-06	0.0	--	1.4500427800938e-05	3.6155837068299e-06	3.0651921895953e-06	4.0799979018471e-06	2.4000861178282e-06	--	2.3825918877704e-14	1.030429338338e-14	5.9833904241302e-15	6.5465371896094e-15	0.0	--	1.7946796037751e-14	3.9254450984304e-15	5.2782172099015e-15	5.4439625050436e-15	0.0	--	2.9705041717657e-14	1.6683141668329e-14	6.6470828610513e-15	7.6491118741752e-15	1.2792144029845e-14	--	3.320824456513e-14	1.6980486935578e-14	6.2743633383211e-15	7.3963919650501e-15	0.0	--	2.8541384539845e-14	1.1050793085059e-14	5.6144685316745e-15	6.3746931619891e-15	0.0	--	3.7875104590414e-14	2.2640649247438e-14	6.8954408034003e-15	8.3579908385193e-15	1.5795318170294e-15	--	2.53699449246e-14	--	5.0540712231e-15	6.08384512489e-15	--	--	2.22751767327e-14	--	4.28461031021e-15	5.049474656e-15	--	--	2.82826679288e-14	--	5.82353213599e-15	7.11821559378e-15	--	--	2.73816841165e-14	--	5.87199643673e-15	6.40032115297e-15	--	--	2.47289933572e-14	--	5.12059475384e-15	5.44541845577e-15	--	--	3.00343748758e-14	--	6.62339811962e-15	7.35522385016e-15	--	--	2.80410141651e-14	--	5.32144068427e-15	4.83499516577e-15	--	--	2.46204139642e-14	--	4.51127390465e-15	4.01295316535e-15	--	--	3.12604025895e-14	--	6.1316074639e-15	5.65703716619e-15	--	--	3.02645588887e-14	--	6.18263561335e-15	5.08650716754e-15	--	--	2.73325801486e-14	--	5.39148342949e-15	4.32762034023e-15	--	--	3.31965376288e-14	--	6.97378779721e-15	5.84539399486e-15	--	--	2.44545667853e-14	--	5.09578790113e-15	5.77344985578e-15	--	--	2.14714615535e-14	--	4.3199758009e-15	4.7918525416e-15	--	--	2.72621952387e-14	--	5.87160000136e-15	6.75504716996e-15	--	--	2.63937200064e-14	--	5.92046433002e-15	6.07377940743e-15	--	--	2.38367415216e-14	--	5.16286052202e-15	5.16759545202e-15	--	--	2.89506984913e-14	--	6.67806813803e-15	6.97996336284e-15	--	--	2.9786279547e-14	--	5.15151219764e-15	5.58942131636e-15	--	--	2.61527820849e-14	--	4.36721630956e-15	4.63911238687e-15	--	--	3.32060418643e-14	--	5.93580808573e-15	6.53973024585e-15	--	--	3.21482171122e-14	--	5.98520676362e-15	5.88017787264e-15	--	--	2.90337527827e-14	--	5.21931828207e-15	5.00287850338e-15	--	--	3.52626814417e-14	--	6.75109524518e-15	6.7574772419e-15	--	--	296.8874315952047	TRUE	11.741624722449183	2.33726228515e-14	2.0257128386e-14	2.77003111079e-14	1.71611	1.4987	2.1983	--	0.116965	--	13.477762281	--	4.96102e-06	4.3758e-06	7.2104e-06	--	0.70957820372	1.11397914508e-14	6.38565369119e-15	1.41431037397e-14	0.431012	0.35596	0.53593	--	4.82658e-06	--	3.19038521709	--	4.07632e-05	1.9872e-05	8.0575e-05	--	0.920127515792	2.31910703044e-14	1.97133092223e-14	2.63248892598e-14	4.76893	2.7552	9.457699999999999	--	1.3629e-05	--	5.93599653571	--	6.35659e-06	5.7084e-06	7.6311e-06	--	0.711793082964	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	6.12	0.13741411617739	-0.029356652092442	0.27670206121174	0.52814077041512	0.23577916464356	-0.11492816989382	-0.2792004996877	0.0368519675203	0.053249546189710006	0.11355820588245	-0.2423485321674	-0.34540911930044	-0.13304184884447	0.87916415308576	0.31759872324456	0.8594023903199299	--	0.980565647407224	0.84090994937885	--	--	0.66142261933028	--	0.944673213449756	0.6246203204184	--	--	0.15362003438119	--	0.7747703259446	0.55675645939657	--	--	0	--	2	1	--	--	0.72346212364198	0.15182368834176996	0.984536836837158	0.31270240121391	0.36636029291644	--	5	0	6	0	0	--	4.1561389025952e-06	1.4151636078317e-06	1.8401481059497e-06	1.4456052013707e-06	2.8652192877194e-05	--	0	4	0	0	190832.45010656	--	--	0	0	0	--	--	--
b'2CXO J013217.9+301933'	23.074814699111812	30.326018507915876	9.17592893125802	6.832057912029376	158.06100253389477	-31.721784	133.299686	1	0	0	0	0	0	0	0	0	0	0	1	65.00187108220067	--	--	--	--	--	61.116298058420895	--	--	--	--	--	68.88744410598044	--	--	--	--	--	65.00187108220067	--	--	--	--	--	61.116298058420895	--	--	--	--	--	68.88744410598044	--	--	--	--	--	0.0	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	13273.993135567895	--	--	--	--	--	1	1.4723883987544e-05	4.0094081619081e-06	3.0203421513281e-06	4.2650602246823e-06	0.00017000128381041	--	1.2143409474263e-05	2.5143746100101e-06	2.3731259760435e-06	3.0803212733817e-06	0.00013026072395862	--	1.7304358500825e-05	5.504441713806e-06	3.6675583266126e-06	5.3905622284179e-06	0.00020974184366219	--	1.2563161922077e-05	3.1937412638613e-06	2.9125645713564e-06	3.6171435664518e-06	0.00014963985056626	--	1.0469301601731e-05	1.905038648619e-06	2.3226780758918e-06	2.6211185264144e-06	0.0001112188078533	--	1.4657022242423e-05	4.4824438791035e-06	3.502451066821e-06	4.6131686064893e-06	0.00018603873313643	--	1.4964060936555e-14	0.0	7.7604441862396e-15	5.2209017462932e-15	3.379105061502e-13	--	3.3253468747899e-15	0.0	6.1170560056242e-15	2.7148689080725e-15	1.7288444500708e-13	--	2.6048550519188e-14	1.4699659009367e-14	9.4038323668551e-15	7.7269345845139e-15	4.9507818342936e-13	--	1.3680283870539e-14	0.0	6.9227289475304e-15	6.4490930467771e-15	3.8985412941647e-13	--	4.5600946235131e-15	0.0	5.5026819839344e-15	4.1140765988061e-15	2.3391247764988e-13	--	2.2800473117565e-14	1.2886811305563e-14	8.3427759111264e-15	8.672918235321e-15	5.3870752428458e-13	--	1.282006737007e-05	3.5572534636074e-06	2.6011018894164e-06	3.9885972790255e-06	0.00013202085730418	--	1.0843543555882e-05	2.4456117562301e-06	2.1151817562287e-06	3.1130515348492e-06	0.00010338982800929	--	1.4673058445872e-05	4.6688951709847e-06	3.0584384853578e-06	4.8641430232019e-06	0.00015906127386045	--	1.25183589284e-05	3.2918714634678e-06	2.4784052838529e-06	4.0593895158084e-06	0.00011496997939041	--	1.088756576777e-05	2.3092232654177e-06	2.0516537283286e-06	3.2661754724896e-06	8.9101734027569e-05	--	1.4047227516491e-05	4.2253872516154e-06	2.8800538066994e-06	4.8059439095203e-06	0.00013940110001087	--	8.0793762742616e-15	0.0	6.3871274999471e-15	5.8764024567682e-15	3.4363996932981e-13	--	0.0	0.0	5.1806700832904e-15	3.8533786601758e-15	1.8802941718046e-13	--	1.6525996924626e-14	1.0089050122094e-14	7.522616833271e-15	7.8994262533605e-15	4.9276674847293e-13	--	1.8104091793332e-14	0.0	5.6346932585905e-15	7.0098727403556e-15	4.6674191298262e-13	--	9.9395013767311e-15	0.0	4.6151448388768e-15	5.3275032826703e-15	3.2088506517555e-13	--	2.6268682209932e-14	1.1294233925161e-14	6.5942682418504e-15	8.692242198041e-15	6.1259876078969e-13	--	2.77754118661e-14	--	6.61344308815e-15	--	--	--	2.14171850534e-14	--	5.02286816822e-15	--	--	--	3.37989951624e-14	--	8.20401800809e-15	--	--	--	2.45629803481e-14	--	6.45658605486e-15	--	--	--	1.9480984414e-14	--	5.01234970048e-15	--	--	--	2.93626431748e-14	--	7.81586732957e-15	--	--	--	2.99954099867e-14	--	6.86141989374e-15	--	--	--	2.31289908331e-14	--	5.21120498258e-15	--	--	--	3.65004386585e-14	--	8.51163480489e-15	--	--	--	2.65262192903e-14	--	6.69868136944e-15	--	--	--	2.10380359888e-14	--	5.20029211575e-15	--	--	--	3.17095035194e-14	--	8.10893007879e-15	--	--	--	2.66216639886e-14	--	6.65003878413e-15	--	--	--	2.05275481358e-14	--	5.05066236769e-15	--	--	--	3.23950369018e-14	--	8.24941520057e-15	--	--	--	2.35426719337e-14	--	6.49231377749e-15	--	--	--	1.86717742922e-14	--	5.04008569569e-15	--	--	--	2.81429641506e-14	--	7.85911667802e-15	--	--	--	3.23191843714e-14	--	6.74398594395e-15	--	--	--	2.49208168647e-14	--	5.12201464098e-15	--	--	--	3.93281641146e-14	--	8.36595724693e-15	--	--	--	2.85812320051e-14	--	6.58403270725e-15	--	--	--	2.26678736592e-14	--	5.11128854905e-15	--	--	--	3.41660704429e-14	--	7.97014485614e-15	--	--	--	75.8776433341377	TRUE	6.535894698923717	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	6.23	0.18613366645846	-0.043098063710181	0.36914428482199	0.19888729841601005	0.19119050352704	-0.0099937539038102	-0.25046845721424	0.20674578388507	0.53791098438015	0.31711388207653	-0.19237976264834	-0.34790755777639	-0.01686445971268	0.22160731486964003	0.11133338268094	0.6986037289203499	--	0.6862313995927001	--	--	--	0.913104635226648	--	0.51743004922751	--	--	--	0.04647215253096	--	0.11827624336335	--	--	--	0	--	0	--	--	--	0.49282234680519	0.55179685864146	0.42770802220697	0.36269310180087	0.900370287138686	--	0	3	0	0	6	--	2.4266433414266e-06	1.9093732005293e-06	5.436961299221e-07	1.6134213419087e-06	0.00011383378225651	--	0	3	0	0	100834.79339426299	--	--	0	0	0	--	--	--
b'2CXO J013220.0+303450'	23.08344394730176	30.58080251270294	5.951785664656966	3.7648597519006373	170.66214417324494	-31.470037	133.252815	1	0	0	1	0	0	0	0	0	0	0	0	15.326424778991163	--	--	--	--	--	13.024782933348135	--	--	--	--	--	17.62806662463419	--	--	--	--	--	15.326424778991163	--	--	--	--	--	13.024782933348135	--	--	--	--	--	17.62806662463419	--	--	--	--	--	0.0	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	737.9579042368936	--	--	--	--	--	1	2.1651735589947e-06	0.0	7.001260758443e-07	6.1164794394365e-07	1.2204959889844e-05	--	1.3968861670934e-06	0.0	4.5945773727282e-07	3.3703049972405e-07	7.8031710771136e-06	--	2.9334609508961e-06	5.3444043360161e-07	9.2985494448072e-07	8.8626538816324e-07	1.6406667392905e-05	--	2.7292433568129e-06	7.7006156107895e-07	6.6579440865593e-07	6.2702886425724e-07	1.1707979761618e-05	--	2.0378350397537e-06	3.4025975954651e-07	4.6000340961682e-07	3.8166974346093e-07	7.8053198410784e-06	--	3.4206516738722e-06	1.1998633626114e-06	8.7158540769503e-07	8.7238798505356e-07	1.5433246049405e-05	--	9.6975718916609e-15	6.8118789941016e-15	1.7255338647943e-15	1.0629444090062e-15	8.2431837590552e-14	--	3.4811796534167e-15	0.0	1.113247654706e-15	5.3147220450311e-16	5.1694542217804e-14	--	1.5665308440375e-14	1.3623757988203e-14	2.309988883515e-15	1.571309126357e-15	1.131691329633e-13	--	2.0835460152347e-14	1.6346405022093e-14	1.6983777793577e-15	1.1477292182627e-15	1.0647038815444e-13	--	1.4304942791163e-14	8.5001306114882e-15	1.1322518529051e-15	6.7820362897343e-16	7.6478729519386e-14	--	2.7055000496331e-14	2.3865751332255e-14	2.2330522654518e-15	1.617254807552e-15	1.3646204678949e-13	--	2.1651735589947e-06	0.0	7.001260758443e-07	6.1164794394365e-07	1.2204959889844e-05	--	1.3968861670934e-06	0.0	4.5945773727282e-07	3.3703049972405e-07	7.8031710771136e-06	--	2.9334609508961e-06	5.3444043360161e-07	9.2985494448072e-07	8.8626538816324e-07	1.6406667392905e-05	--	2.7292433568129e-06	7.7006156107895e-07	6.6579440865593e-07	6.2702886425724e-07	1.1707979761618e-05	--	2.0378350397537e-06	3.4025975954651e-07	4.6000340961682e-07	3.8166974346093e-07	7.8053198410784e-06	--	3.4206516738722e-06	1.1998633626114e-06	8.7158540769503e-07	8.7238798505356e-07	1.5433246049405e-05	--	9.6975718916609e-15	6.8118789941016e-15	1.7255338647943e-15	1.0629444090062e-15	8.2431837590552e-14	--	3.4811796534167e-15	0.0	1.113247654706e-15	5.3147220450311e-16	5.1694542217804e-14	--	1.5665308440375e-14	1.3623757988203e-14	2.309988883515e-15	1.571309126357e-15	1.131691329633e-13	--	2.0835460152347e-14	1.6346405022093e-14	1.6983777793577e-15	1.1477292182627e-15	1.0647038815444e-13	--	1.4304942791163e-14	8.5001306114882e-15	1.1322518529051e-15	6.7820362897343e-16	7.6478729519386e-14	--	2.7055000496331e-14	2.3865751332255e-14	2.2330522654518e-15	1.617254807552e-15	1.3646204678949e-13	--	5.43381279491e-15	--	--	--	--	--	3.56593964666e-15	--	--	--	--	--	7.30168594315e-15	--	--	--	--	--	6.82408140367e-15	--	--	--	--	--	5.14021716121e-15	--	--	--	--	--	8.41932121232e-15	--	--	--	--	--	6.49644729983e-15	--	--	--	--	--	4.26329354052e-15	--	--	--	--	--	8.72960105915e-15	--	--	--	--	--	8.15859634515e-15	--	--	--	--	--	6.14543620803e-15	--	--	--	--	--	1.00658006856e-14	--	--	--	--	--	5.33698803913e-15	--	--	--	--	--	3.50239840068e-15	--	--	--	--	--	7.17157767758e-15	--	--	--	--	--	6.70248354224e-15	--	--	--	--	--	5.04862396688e-15	--	--	--	--	--	8.26929787679e-15	--	--	--	--	--	6.62958915251e-15	--	--	--	--	--	4.35066788133e-15	--	--	--	--	--	8.90851042368e-15	--	--	--	--	--	8.32580321723e-15	--	--	--	--	--	6.27138424155e-15	--	--	--	--	--	1.02720948784e-14	--	--	--	--	--	27.648466369083646	TRUE	2.9767441860464423	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	5.98	-0.59837601499063	-1.0	-0.10056214865709	--	--	-0.49219237976265	-1.0	-0.02685821361649	--	--	0.034978138663335	-0.2367270455965	0.32667083073079	--	--	0.19234460230962003	--	--	--	--	--	0.41652128172342995	--	--	--	--	--	0.077669061558529	--	--	--	--	--	0	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	0	1	0	0	97852.244593881	--	--	0	0	0	--	--	--
b'2CXO J013220.0+303640'	23.083592126140672	30.61121201049678	5.389185280559116	2.770478894308565	139.23513994820468	-31.44013	133.24636	1	0	0	0	0	0	0	0	0	0	0	0	12.99319353253489	--	1.9203412037707983	--	--	--	11.543472216466991	--	0.0	--	--	--	14.44291484860279	--	6.820538821788982	--	--	--	12.99319353253489	--	1.9203412037707983	--	--	--	11.543472216466991	--	0.0	--	--	--	14.44291484860279	--	6.820538821788982	--	--	--	0.0	--	0.0	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	530.37334214756	--	11.585283709254984	--	--	--	1	3.0827500714551e-06	7.8205124249405e-07	8.1423615871766e-07	8.8250872409146e-07	8.2430233542204e-06	--	2.3318237719981e-06	3.4555752575318e-07	5.8487386048733e-07	5.827887800604e-07	4.2271914637028e-06	--	3.7941539340985e-06	1.2185449592349e-06	1.043598456948e-06	1.1655775601208e-06	1.2047495671553e-05	--	3.2490324295683e-06	8.8975026703545e-07	8.5400234545812e-07	8.9358189745732e-07	8.3436113556228e-06	--	2.5610020327186e-06	4.4487513351773e-07	6.3361464340441e-07	6.1738385642506e-07	4.4770597517976e-06	--	3.9370628264181e-06	1.3160889366566e-06	1.0743900475118e-06	1.1697799384896e-06	1.2006660243457e-05	--	0.0	0.0	1.6467464257075e-15	1.6418256263432e-15	0.0	--	0.0	0.0	1.1227816538915e-15	1.0735013710705e-15	0.0	--	1.7747532474001e-15	2.3022287549329e-15	2.1707111975236e-15	2.1785763118785e-15	9.9056760407622e-15	--	0.0	0.0	1.8004382323349e-15	1.4062993048655e-15	0.0	--	0.0	0.0	1.2727235780298e-15	9.099583737365e-16	0.0	--	1.8121341105771e-15	2.4774140463834e-15	2.2971108481514e-15	1.8750657398207e-15	1.5075412127283e-14	--	3.0827500714551e-06	7.8205124249405e-07	8.1423615871766e-07	8.8250872409146e-07	8.2430233542204e-06	--	2.3318237719981e-06	3.4555752575318e-07	5.8487386048733e-07	5.827887800604e-07	4.2271914637028e-06	--	3.7941539340985e-06	1.2185449592349e-06	1.043598456948e-06	1.1655775601208e-06	1.2047495671553e-05	--	3.2490324295683e-06	8.8975026703545e-07	8.5400234545812e-07	8.9358189745732e-07	8.3436113556228e-06	--	2.5610020327186e-06	4.4487513351773e-07	6.3361464340441e-07	6.1738385642506e-07	4.4770597517976e-06	--	3.9370628264181e-06	1.3160889366566e-06	1.0743900475118e-06	1.1697799384896e-06	1.2006660243457e-05	--	0.0	0.0	1.6467464257075e-15	1.6418256263432e-15	0.0	--	0.0	0.0	1.1227816538915e-15	1.0735013710705e-15	0.0	--	1.7747532474001e-15	2.3022287549329e-15	2.1707111975236e-15	2.1785763118785e-15	9.9056760407622e-15	--	0.0	0.0	1.8004382323349e-15	1.4062993048655e-15	0.0	--	0.0	0.0	1.2727235780298e-15	9.099583737365e-16	0.0	--	1.8121341105771e-15	2.4774140463834e-15	2.2971108481514e-15	1.8750657398207e-15	1.5075412127283e-14	--	5.57051289498e-15	--	1.62541941605e-15	--	--	--	3.8751394052e-15	--	1.12345165521e-15	--	--	--	7.18515431381e-15	--	2.12738717689e-15	--	--	--	6.22748232586e-15	--	1.73179928673e-15	--	--	--	4.69083084286e-15	--	1.23280627191e-15	--	--	--	7.76413380887e-15	--	2.20143977127e-15	--	--	--	6.58355536387e-15	--	1.6867203465e-15	--	--	--	4.57986460096e-15	--	1.16582141596e-15	--	--	--	8.49183228094e-15	--	2.20761927704e-15	--	--	--	7.35999995742e-15	--	1.79711221863e-15	--	--	--	5.54389607182e-15	--	1.27930022343e-15	--	--	--	9.17610384302e-15	--	2.2844646847e-15	--	--	--	5.45533041072e-15	--	1.63551749757e-15	--	--	--	3.79501245963e-15	--	1.13043121156e-15	--	--	--	7.03658560223e-15	--	2.14060378359e-15	--	--	--	6.09871556803e-15	--	1.74255826389e-15	--	--	--	4.59383770059e-15	--	1.2404652048e-15	--	--	--	7.60359343546e-15	--	2.21511643715e-15	--	--	--	6.7607110326e-15	--	1.64982827568e-15	--	--	--	4.70310332702e-15	--	1.14032248466e-15	--	--	--	8.72033741886e-15	--	2.15933406669e-15	--	--	--	7.55804882953e-15	--	1.7578056487e-15	--	--	--	5.69307574172e-15	--	1.25131927535e-15	--	--	--	9.42302191733e-15	--	2.23449870597e-15	--	--	--	30.363035256579877	TRUE	4.216216216216492	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	5.95	0.076202373516552	-0.262960649594	0.33041848844472	--	--	0.023735165521549	-0.32292317301686	0.30168644597127	--	--	-0.051217988757027	-0.26046221111805	0.17301686445971	--	--	0.13266755596421997	--	0.22703835392017002	--	--	--	0.41891976655040997	--	0.46479917383132996	--	--	--	0.56655583525398	--	0.58580946153991	--	--	--	1	--	1	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	0	1	0	0	97852.244593881	--	--	0	0	0	--	--	--
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b'2CXO J013517.0+304409'	23.820940414580434	30.73595300522553	4.297419632631618	2.7390769083124034	89.70456355199792	-31.19625	133.947008	0	0	0	0	0	0	0	0	0	0	0	0	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	1	0.0	0.0	0.0	0.0	0.0	--	0.0	0.0	0.0	0.0	0.0	--	4.7896705526204e-06	3.0573714341216e-06	5.3264503408509e-07	8.1472186845985e-07	2.0469312133883e-05	--	0.0	0.0	0.0	0.0	0.0	--	0.0	0.0	0.0	0.0	0.0	--	4.7451905819737e-06	2.7949679411456e-06	5.3270424937276e-07	6.5691650334149e-07	2.0469312133883e-05	--	0.0	0.0	0.0	0.0	0.0	--	0.0	0.0	0.0	0.0	0.0	--	3.8140239813978e-14	3.7612412471679e-14	1.6399912460615e-15	1.8553967001671e-15	1.8152275857208e-14	--	0.0	0.0	0.0	0.0	0.0	--	0.0	0.0	0.0	0.0	0.0	--	3.7909031886722e-14	3.7066153571153e-14	1.6401457361139e-15	1.944964611889e-15	1.8152275857208e-14	--	1.6561905908183e-06	6.51193662937e-07	3.2991124300731e-07	1.3570068308306e-07	0.0	--	1.1896580300244e-06	3.8088685945372e-07	2.0257707903957e-07	0.0	0.0	--	2.1227231516122e-06	9.2150046642028e-07	4.5145749043105e-07	2.7140136616613e-07	4.0171267947644e-06	--	1.2279729288306e-06	5.0367499605864e-07	3.4421000336242e-07	1.0427346141518e-07	0.0	--	8.5585992009403e-07	2.9213149771401e-07	2.2551689875469e-07	0.0	0.0	--	1.5814802871303e-06	7.1521849440327e-07	4.5696845273977e-07	2.0358151990583e-07	4.4453533691509e-06	--	6.7751625941012e-15	4.787697783876e-15	1.1105464400432e-15	9.1062774084328e-16	0.0	--	3.9628309512668e-15	1.9641837062055e-15	7.2822717379885e-16	2.2765693521082e-16	0.0	--	9.4596609804432e-15	7.4884503799086e-15	1.4746600269427e-15	1.5610761271599e-15	2.2557213563041e-15	--	4.4294654945813e-15	3.9159536962879e-15	1.0744244948088e-15	0.0	0.0	--	2.6248684412333e-15	1.7303051216156e-15	7.3419007145267e-16	0.0	0.0	--	6.2340625479292e-15	6.0105335803489e-15	1.4146589181649e-15	2.9121456395247e-16	3.9107026118442e-15	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	16.229791058870717	MARGINAL	3.238095238095188	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	5.43	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	0.49246636009858	--	--	--	--	--	0.81997584488244	--	--	--	--	--	0.67412946222978	--	--	--	--	--	2	--	--	--	0.58982973145992	0.46369657485418003	0.76400718543328	0.39166550023069	--	--	3	0	5	0	--	--	5.3902968092618e-08	1.7862603947449e-08	3.6238639751885e-08	1.162349261747e-08	--	--	0	2	0	0	181878.205323112	--	--	0	0	0	--	--	--
b'2CXO J013517.5+304446'	23.82316738827336	30.74638400686761	1.7420970948765375	1.4528996509544438	80.40576240156145	-31.185646	133.946789	0	0	0	0	0	0	0	0	0	0	0	0	1.624422547351468	1.2960832254142154	--	--	--	--	0.6883450575966487	0.0	--	--	--	--	2.560500037106287	2.8501214926980123	--	--	--	--	1.624422547351468	1.2960832254142154	--	--	--	--	0.6883450575966487	0.0	--	--	--	--	2.560500037106287	2.8501214926980123	--	--	--	--	0.0	0.0	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	8.289873255209644	5.277347013438937	--	--	--	--	1	0.0	0.0	0.0	0.0	0.0	--	0.0	0.0	0.0	0.0	0.0	--	3.8252947186583e-06	3.3387729911916e-06	3.988797464262e-07	3.2900078512867e-07	2.5903711392778e-05	--	0.0	0.0	0.0	0.0	0.0	--	0.0	0.0	0.0	0.0	0.0	--	3.7997324795014e-06	3.2149756290048e-06	4.1408397406319e-07	3.2900078512867e-07	2.6643080708752e-05	--	0.0	0.0	0.0	0.0	0.0	--	0.0	0.0	0.0	0.0	0.0	--	2.3673889802505e-14	2.3847293264079e-14	1.1893282037541e-15	1.105878838223e-15	4.0661044735391e-14	--	0.0	0.0	0.0	0.0	--	--	0.0	0.0	0.0	0.0	--	--	2.3848549195948e-14	2.2924202629699e-14	1.1984875680096e-15	1.105878838223e-15	--	--	3.8312308516626e-06	2.0714035581015e-06	5.3443967227076e-07	0.0	4.4686330997323e-06	--	3.2802697660063e-06	1.7232396646759e-06	3.9425877462597e-07	0.0	0.0	--	4.349782461692e-06	2.3990872225019e-06	6.6585926381275e-07	1.4385659675453e-07	8.7020749836892e-06	--	4.3918013755567e-06	2.1825931708383e-06	6.2933465810626e-07	1.1616811470733e-07	3.8349568506226e-06	--	3.8306129664755e-06	1.8489346261482e-06	4.8843883912724e-07	1.9361352451222e-08	0.0	--	4.9199787017507e-06	2.5162517155285e-06	7.7023047708527e-07	2.1297487696345e-07	7.6699137012451e-06	--	1.7296535121644e-14	1.3513544449491e-14	1.5683281176893e-15	1.4225780860891e-15	0.0	--	1.4365475494928e-14	1.0685128169365e-14	1.1700225639904e-15	5.2410771592758e-16	0.0	--	2.00551794762e-14	1.6341960729617e-14	1.941739574282e-15	2.2836121908273e-15	4.8360406933885e-15	--	1.9541331636344e-14	1.550877525e-14	1.8084016056849e-15	5.8566150451892e-16	0.0	--	1.677798460197e-14	1.2771932558823e-14	1.4094894867838e-15	1.1262721240748e-16	0.0	--	2.2142128845166e-14	1.8245617941176e-14	2.2073137245859e-15	1.0361703541489e-15	2.1918208660054e-15	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	141.9787940287403	TRUE	6.940325866628899	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	5.42	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	0.63612680573767	0.89917423128923	0.18170899557266995	--	--	--	0.5194670687365299	0.66842387543239	0.015622739387330054	--	--	--	0.20171316121616	0.25403673604377	0.36986553885713	--	--	--	0	0	0	--	--	--	0.86176269641749	0.73440989080541	0.927635840792914	0.55214788950068	0.43012729823796003	--	5	5	6	3	0	--	9.6461941523104e-08	3.8074431753959e-08	6.3492175340448e-08	2.4024424948716e-08	4.7064415924986e-07	--	0	2	0	0	181878.205323112	--	--	0	0	0	--	--	--
b'2CXO J013518.6+302952'	23.82774548052521	30.49780624248648	7.917745115935952	4.86189309618582	2.1259526387768517	-31.428515	134.008958	0	0	0	1	0	0	0	0	0	0	0	0	10.970780783463132	--	--	9.283338407623557	--	--	7.683077235880078	--	--	5.26713700875755	--	--	14.258484331046187	--	--	13.299539806489562	--	--	10.970780783463132	--	--	9.283338407623557	--	--	7.683077235880078	--	--	5.26713700875755	--	--	14.258484331046187	--	--	13.299539806489562	--	--	0.0	--	--	0.0	--	--	--	--	--	--	--	--	--	--	--	--	--	--	378.115905986375	--	--	270.74362352886055	--	--	1	2.0111964775007e-05	0.0	1.6614394906775e-06	1.2953163911959e-05	0.0	--	1.3990932017396e-05	0.0	3.8766921449142e-07	1.0242036581549e-05	0.0	--	2.5941519782256e-05	2.787461552777e-06	2.8798284505077e-06	1.5513673057347e-05	1.4764865728497e-05	--	2.7665192256697e-05	2.3250480476093e-06	3.9488532114531e-06	1.460873723564e-05	2.9277059652476e-05	--	2.31798622217e-05	4.810444236433e-07	2.7282985824585e-06	1.2090337197642e-05	1.7566235791486e-05	--	3.1886679348459e-05	4.088877600968e-06	5.1694078404477e-06	1.7127137273639e-05	4.0987883513466e-05	--	0.0	0.0	2.6461512272071e-15	2.162216784747e-14	--	--	0.0	0.0	0.0	1.698884616587e-14	--	--	3.4096798436818e-14	4.0611828955028e-14	5.4025587555477e-15	2.6255489529071e-14	--	--	1.1927809486308e-14	0.0	8.453942859218e-15	2.4784809535882e-14	1.6964894359826e-14	--	0.0	0.0	5.5827924542006e-15	2.0469960272162e-14	0.0	--	2.3855618972616e-14	1.3971149148521e-14	1.1165584908401e-14	2.9099658799602e-14	3.4778033437644e-14	--	2.0111964775007e-05	0.0	1.6614394906775e-06	1.2953163911959e-05	0.0	--	1.3990932017396e-05	0.0	3.8766921449142e-07	1.0242036581549e-05	0.0	--	2.5941519782256e-05	2.787461552777e-06	2.8798284505077e-06	1.5513673057347e-05	1.4764865728497e-05	--	2.7665192256697e-05	2.3250480476093e-06	3.9488532114531e-06	1.460873723564e-05	2.9277059652476e-05	--	2.31798622217e-05	4.810444236433e-07	2.7282985824585e-06	1.2090337197642e-05	1.7566235791486e-05	--	3.1886679348459e-05	4.088877600968e-06	5.1694078404477e-06	1.7127137273639e-05	4.0987883513466e-05	--	0.0	0.0	2.6461512272071e-15	2.162216784747e-14	--	--	0.0	0.0	0.0	1.698884616587e-14	--	--	3.4096798436818e-14	4.0611828955028e-14	5.4025587555477e-15	2.6255489529071e-14	--	--	1.1927809486308e-14	0.0	8.453942859218e-15	2.4784809535882e-14	1.6964894359826e-14	--	0.0	0.0	5.5827924542006e-15	2.0469960272162e-14	0.0	--	2.3855618972616e-14	1.3971149148521e-14	1.1165584908401e-14	2.9099658799602e-14	3.4778033437644e-14	--	4.09839514224e-14	--	--	2.17863357656e-14	--	--	2.79436032426e-14	--	--	1.7278818021e-14	--	--	5.34033306414e-14	--	--	2.60434358578e-14	--	--	5.70598737416e-14	--	--	2.32622532862e-14	--	--	4.75520307482e-14	--	--	1.92406399468e-14	--	--	6.6008431853e-14	--	--	2.72838666257e-14	--	--	5.15944182208e-14	--	--	1.9769289969e-14	--	--	3.51780124233e-14	--	--	1.56790920444e-14	--	--	6.72290904089e-14	--	--	2.36322546756e-14	--	--	7.18322876949e-14	--	--	2.1108562518e-14	--	--	5.98629286958e-14	--	--	1.74592824782e-14	--	--	8.30975667529e-14	--	--	2.47578425579e-14	--	--	4.07100835009e-14	--	--	2.13291387184e-14	--	--	2.77568751143e-14	--	--	1.69162134663e-14	--	--	5.30464724406e-14	--	--	2.54969014565e-14	--	--	5.66785813459e-14	--	--	2.27740833788e-14	--	--	4.72342728119e-14	--	--	1.88368655873e-14	--	--	6.5567342319e-14	--	--	2.67113011703e-14	--	--	5.1153931354e-14	--	--	2.11189177232e-14	--	--	3.48776804687e-14	--	--	1.67494864701e-14	--	--	6.66551226734e-14	--	--	2.52456027955e-14	--	--	7.12190202053e-14	--	--	2.25496209409e-14	--	--	5.93518494975e-14	--	--	1.86512085533e-14	--	--	8.23881220479e-14	--	--	2.64480333286e-14	--	--	74.51427633191923	TRUE	4.914285714285031	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	5.6	0.1211742660837	-0.62773266708307	0.55153029356652	--	--	-0.99937539038101	-1.0	-0.64522173641474	--	--	-0.76077451592755	-0.90381011867583	-0.60399750156152	--	--	0.57401143819678	--	--	0.950531748206129	--	--	0.46379405384446004	--	--	0.79950640537117	--	--	0.058192690270282	--	--	0.50994530070035	--	--	0	--	--	1	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	0	1	0	0	11509.938621634	--	--	0	0	0	--	--	--
b'2CXO J013521.7+303210'	23.840531244373096	30.536338649237354	3.46758190619622	1.9514825885503169	2.557786909956434	-31.388562	134.012644	0	0	0	1	0	0	0	0	0	0	0	1	6.789113581382417	--	--	6.9579473842407165	--	--	5.257168177143424	--	--	4.71254853851562	--	--	8.32105898562141	--	--	9.203346229965813	--	--	6.789113581382417	--	--	6.9579473842407165	--	--	5.257168177143424	--	--	4.71254853851562	--	--	8.32105898562141	--	--	9.203346229965813	--	--	0.0	--	--	0.0	--	--	--	--	--	--	--	--	--	--	--	--	--	--	144.8024872036109	--	--	152.0940250467394	--	--	1	6.7255050583436e-06	3.0649947007346e-07	1.4013693432072e-06	4.3386404057502e-06	7.3759911037573e-06	--	5.6221772739773e-06	0.0	1.1046087764104e-06	3.6499275639731e-06	0.0	--	7.7639312083355e-06	6.4364888715426e-07	1.6816432118486e-06	4.9868407274228e-06	1.5161759491057e-05	--	7.6068598518027e-06	4.5652022881537e-07	1.3999333230674e-06	4.3652729497942e-06	8.6849742973271e-06	--	6.5847433190285e-06	8.1521469431316e-08	1.1135833251672e-06	3.6927627372457e-06	0.0	--	8.568851882649e-06	8.1521469431316e-07	1.6703749877508e-06	4.9982237380751e-06	1.7369948594654e-05	--	8.5140552474553e-15	0.0	3.419730895353e-15	8.0654816357692e-15	0.0	--	3.8868513086209e-15	0.0	2.6955525881018e-15	6.6873796527155e-15	0.0	--	1.2956171028736e-14	4.160290855307e-15	4.1036770744237e-15	9.3625187962903e-15	2.4240153880264e-15	--	1.056827751613e-14	0.0	3.3539774264493e-15	6.7916887013984e-15	0.0	--	6.8777679073227e-15	0.0	2.667936589221e-15	5.6511488369829e-15	0.0	--	1.4091036688173e-14	2.9198456898963e-15	4.0019048838316e-15	7.932228565814e-15	2.7147646542184e-15	--	6.9758399570497e-06	3.3474406712045e-07	1.2685345924361e-06	4.5548094194913e-06	1.632435476718e-05	--	5.9266711275586e-06	0.0	1.001474678239e-06	3.9271202145712e-06	7.4201612578089e-06	--	8.0250087865408e-06	6.840422241157e-07	1.5355945066331e-06	5.1824986244113e-06	2.485754021366e-05	--	7.7330612288663e-06	5.1171780841929e-07	1.285979892223e-06	4.5031824252675e-06	1.7741542117033e-05	--	6.7806731872042e-06	1.3266757996056e-07	1.0287839137784e-06	3.8968752481274e-06	9.4621557957507e-06	--	8.6854492705285e-06	8.7181552545509e-07	1.5431758706676e-06	5.1094896024076e-06	2.6020928438315e-05	--	7.7357437109438e-15	0.0	3.0396171358073e-15	8.380813988266e-15	0.0	--	3.3719908483601e-15	0.0	2.3911654801684e-15	7.125121796792e-15	0.0	--	1.2099496573527e-14	4.465358269491e-15	3.6880687914462e-15	9.63650617974e-15	2.8440314976646e-15	--	1.0051320686643e-14	0.0	3.0287036726356e-15	7.1709164908216e-15	0.0	--	6.4847230236407e-15	0.0	2.4152953338739e-15	6.0814449963937e-15	0.0	--	1.3617918349645e-14	2.6471683596428e-15	3.6421120113972e-15	8.1963014267537e-15	3.0812715949911e-15	--	1.39664317848e-14	--	2.95686755267e-15	7.10547987191e-15	--	--	1.14134281252e-14	--	2.30779906549e-15	5.91525167532e-15	--	--	1.63692587585e-14	--	3.60593603984e-15	8.22569464516e-15	--	--	1.64938850235e-14	--	3.01746780311e-15	7.21834011097e-15	--	--	1.41625998263e-14	--	2.3931641197e-15	6.07036679344e-15	--	--	1.86880357974e-14	--	3.64177148651e-15	8.2987855863e-15	--	--	1.47535278594e-14	--	3.13677321173e-15	5.41320789161e-15	--	--	1.20566464228e-14	--	2.44821323842e-15	4.50644961733e-15	--	--	1.72917692116e-14	--	3.82533318504e-15	6.26662744387e-15	--	--	1.74234189488e-14	--	3.20106058302e-15	5.49918884543e-15	--	--	1.49607512011e-14	--	2.53877218653e-15	4.62462184449e-15	--	--	1.97412238877e-14	--	3.8633489795e-15	6.32231072866e-15	--	--	1.33455758293e-14	--	2.98656205234e-15	6.67876703804e-15	--	--	1.0906061968e-14	--	2.33097526037e-15	5.56001686347e-15	--	--	1.56415888752e-14	--	3.64214884432e-15	7.73170837881e-15	--	--	1.57606750739e-14	--	3.04777088402e-15	6.78484956282e-15	--	--	1.35330235263e-14	--	2.41719759767e-15	5.70581669074e-15	--	--	1.78572882952e-14	--	3.67834417037e-15	7.80040991301e-15	--	--	1.60348207511e-14	--	3.00998822158e-15	6.4400923263e-15	--	--	1.31037244848e-14	--	2.34925909977e-15	5.36132219205e-15	--	--	1.879349959e-14	--	3.67071734339e-15	7.45540539384e-15	--	--	1.89365826517e-14	--	3.07167716666e-15	6.54238384955e-15	--	--	1.62600407237e-14	--	2.43615775286e-15	5.50191166663e-15	--	--	2.1455680937e-14	--	3.70719658045e-15	7.52165178642e-15	--	--	93.35179049158563	TRUE	6.490534350869882	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	5.56	-0.54465958775765	-0.81261711430356	-0.24047470331043	0.9940364301762245	1.2726783646928	-0.85821361648969	-0.96502186133666	-0.71143035602748	0.86624745364133	0.54690530039349	-0.51842598376015	-0.60899437851343	-0.42036227357901	0.966005938508695	0.43323570421066	0.951729701281236	--	0.944688708461032	0.5161709613754	--	--	0.942239355009891	--	0.89776842875401	0.72208389065609	--	--	0.36794959366539	--	0.54324847048303	0.70884678567527	--	--	0	--	1	2	--	--	0.7436093384500799	0.77311064851197	0.95419454552295	0.70916942466013	0.85286563019727	--	5	5	6	5	5	--	4.2865982617655e-06	2.4963124858023e-06	1.1937348236179e-07	1.7270049557628e-06	1.5357849517789e-05	--	0	2	0	0	107346.342603636	--	--	0	0	0	--	--	--
b'2CXO J013522.8+302735'	23.845361001171284	30.45985646121493	8.363377734353387	4.808624863354854	13.755085838887112	-31.462692	134.035235	1	0	0	1	0	0	0	0	0	0	0	1	6.866432652920134	42.78385012861307	--	--	--	--	1.3522755787520806	35.10533532631925	--	--	--	--	12.380589727088186	50.46236493090689	--	--	--	--	6.866432652920134	42.78385012861307	--	--	--	--	1.3522755787520806	35.10533532631925	--	--	--	--	12.380589727088186	50.46236493090689	--	--	--	--	0.0	0.0	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	148.11948803206457	5750.552877175566	--	--	--	--	1	1.4954023234986e-05	4.9154477498725e-06	2.305073324837e-06	4.9001877192994e-06	--	--	1.2764710521724e-05	3.5869483580151e-06	1.8051779049928e-06	3.8863557773754e-06	--	--	1.7014552847468e-05	6.24394714173e-06	2.8049687446811e-06	5.8576956644499e-06	--	--	1.0267237341471e-05	3.3372214004725e-06	1.6295105623168e-06	3.2934177934377e-06	1.3858345770164e-05	--	8.9305130947121e-06	2.5863465853662e-06	1.2923704459754e-06	2.6197641538709e-06	1.9114959682984e-06	--	1.1525330750186e-05	4.0880962155788e-06	1.9666506786582e-06	3.9296462308064e-06	2.5327321579954e-05	--	6.8472608428816e-14	6.1349044617236e-14	4.9677301170926e-15	5.2473727713731e-15	--	--	5.397252664389e-14	4.430764333467e-14	3.757642011647e-15	3.0530168851625e-15	--	--	8.2972690213741e-14	7.8390445899801e-14	6.1141293748832e-15	7.3463218799224e-15	--	--	4.3754713839204e-14	3.8916380221923e-14	3.4244055437839e-15	4.1194462578381e-15	--	--	3.6080020639626e-14	2.931363705028e-14	2.6498376231661e-15	2.959038861264e-15	--	--	5.0977954497631e-14	4.8519123393566e-14	4.1582067317375e-15	5.2798536544122e-15	--	--	1.6762494748981e-05	4.7935436365732e-06	2.5477852342572e-06	5.9249780168596e-06	0.0	--	1.4775364677495e-05	3.6099526151971e-06	2.070075502834e-06	5.0436928333611e-06	0.0	--	1.8632734816262e-05	5.9179551068805e-06	2.9989555361569e-06	6.8062632003582e-06	8.1765040918776e-06	--	1.1583998645613e-05	3.4790814351021e-06	1.8355364021088e-06	3.9613318943345e-06	1.0695263833755e-05	--	1.0320991394714e-05	2.759271483012e-06	1.5055523298195e-06	3.3185915673292e-06	2.9504176093118e-06	--	1.2847005896513e-05	4.1988913871922e-06	2.165520474398e-06	4.5683644253951e-06	1.8071307857035e-05	--	6.8219060405207e-14	5.7162061332171e-14	5.5563064519958e-15	8.713106533345e-15	--	--	5.5015371294522e-14	4.1572408241579e-14	4.4572568241285e-15	6.853005138586e-15	--	--	8.0689211231965e-14	7.2009349989878e-14	6.655356079863e-15	1.0475307854696e-14	--	--	4.4605970046662e-14	3.7758102779571e-14	3.8936285087051e-15	5.4769579733425e-15	0.0	--	3.7496670391487e-14	2.85488094187e-14	3.1239577569843e-15	4.3334172975896e-15	0.0	--	5.1715269701836e-14	4.6506931472399e-14	4.6180245103247e-15	6.5603122977399e-15	1.6618656137346e-15	--	--	2.73049595505e-14	--	--	--	--	--	1.89947544699e-14	--	--	--	--	--	3.56151646311e-14	--	--	--	--	--	1.8657658634e-14	--	--	--	--	--	1.39932439755e-14	--	--	--	--	--	2.33220732925e-14	--	--	--	--	--	2.35594158039e-14	--	--	--	--	--	1.63891588201e-14	--	--	--	--	--	3.07296727877e-14	--	--	--	--	--	1.60983039317e-14	--	--	--	--	--	1.20737279487e-14	--	--	--	--	--	2.01228799146e-14	--	--	--	--	--	2.53229200004e-14	--	--	--	--	--	1.76159443481e-14	--	--	--	--	--	3.30298956527e-14	--	--	--	--	--	1.73033179599e-14	--	--	--	--	--	1.29774884699e-14	--	--	--	--	--	2.16291474498e-14	--	--	--	--	--	3.08678468821e-14	--	--	--	--	--	2.14732847876e-14	--	--	--	--	--	4.02624089767e-14	--	--	--	--	--	2.10922030054e-14	--	--	--	--	--	1.5819152254e-14	--	--	--	--	--	2.63652537567e-14	--	--	--	--	37.419022811426856	TRUE	7.037448492806409	8.12962272699e-15	5.35432710005e-15	1.07740309158e-14	6.97368	4.5137	10.467	--	90.4141	41.823	160.32	--	7.02072e-05	1.798e-05	0.00043399000000000004	--	1.32378761645	7.14610210109e-15	4.41210001176e-15	9.34692520663e-15	0.193877	0.16761	0.2635700000000001	--	28.9322	0.27719000000000094	53.32699999999999	--	0.00246275	0.00027289999999999997	0.058487	--	1.31874530016	5.48898448112e-15	7.24234098299e-16	9.07003948144e-15	0.309984	0.19175	0.5357399999999999	--	50.6556	20.302	98.61600000000001	--	0.000332669	4.206300000000001e-05	0.00708	--	1.32026144961	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	5.62	0.38475952529669	0.22173641474079	0.51905059337914	0.53283133441428	0.14244186511633	0.012492192379763	-0.16427232979388	0.17176764522174	0.60703997976085	0.1480371659532	-0.37101811367895	-0.49281698938164	-0.2367270455965	0.00667048166177997	0.0010589261464841	0.64582871082155	0.31913291456781	--	--	--	--	0.5529385732811001	0.70877611583323	--	--	--	--	0.12958467901013	0.067121637114178	--	--	--	--	0	0	--	--	--	--	0.949699794336109	0.17122102496738	0.84969341085537	0.9602551814949	0.75104602479562	--	6	0	5	6	5	--	8.178575195991e-06	4.9795606866911e-07	1.891928406439e-06	3.7184987304821e-06	7.9750696720368e-06	--	0	2	0	0	107346.342603636	--	--	0	0	0	--	--	--
b'2CXO J013526.9+304048'	23.862273225646504	30.68024239734238	2.465383536971316	1.8194694572008274	164.84697036213132	-31.243809	134.000653	1	0	0	1	0	0	0	0	0	0	0	0	15.849017402407942	--	2.1906027534624894	3.549644454461706	--	--	14.653958724993085	--	0.0	0.6110062126473639	--	--	17.044076079822798	--	6.0884773126061225	6.488282696276048	--	--	15.849017402407942	--	2.1906027534624894	3.549644454461706	--	--	14.653958724993085	--	0.0	0.6110062126473639	--	--	17.044076079822798	--	6.0884773126061225	6.488282696276048	--	--	0.0	--	0.0	0.0	--	--	--	--	--	--	--	--	--	--	--	--	--	--	789.1409080420236	--	15.0756876608811	39.58399126131939	--	--	1	5.9926033148496e-06	1.1496545581249e-06	1.6943056549015e-06	1.5986752498289e-06	0.0	--	4.9827627323321e-06	5.625969114228e-07	1.381227436061e-06	1.2141837340473e-06	0.0	--	6.9430415101602e-06	1.7367122048269e-06	2.0073838737419e-06	1.9629303700431e-06	4.8767167998224e-06	--	6.9229333685038e-06	1.4592256855434e-06	1.7121590962458e-06	1.955601951464e-06	2.4725654216446e-06	--	6.0740936734354e-06	9.4143592615702e-07	1.4312791710143e-06	1.6019327118629e-06	0.0	--	7.7218413168034e-06	1.9534795467758e-06	1.9930390214774e-06	2.2884671181473e-06	4.9451308432893e-06	--	1.3751866541178e-14	6.474677040017e-15	4.3293310413145e-15	2.5303197303861e-15	3.623005365202e-14	--	6.7356081018013e-15	0.0	3.5293459575933e-15	1.8274531386122e-15	7.0122684487781e-15	--	2.0487474642979e-14	1.3512369474818e-14	5.1293161250356e-15	3.1980429925713e-15	6.5447838855262e-14	--	1.2033083495271e-14	0.0	4.2287099501252e-15	3.1578481407903e-15	1.8180780736419e-14	--	7.4960192265623e-15	0.0	3.5248793806934e-15	2.5546636644596e-15	0.0	--	1.6372884100123e-14	5.025968122773e-15	4.932540519557e-15	3.761032617121e-15	3.5604028942154e-14	--	5.9926033148496e-06	1.1496545581249e-06	1.6943056549015e-06	1.5986752498289e-06	0.0	--	4.9827627323321e-06	5.625969114228e-07	1.381227436061e-06	1.2141837340473e-06	0.0	--	6.9430415101602e-06	1.7367122048269e-06	2.0073838737419e-06	1.9629303700431e-06	4.8767167998224e-06	--	6.9229333685038e-06	1.4592256855434e-06	1.7121590962458e-06	1.955601951464e-06	2.4725654216446e-06	--	6.0740936734354e-06	9.4143592615702e-07	1.4312791710143e-06	1.6019327118629e-06	0.0	--	7.7218413168034e-06	1.9534795467758e-06	1.9930390214774e-06	2.2884671181473e-06	4.9451308432893e-06	--	1.3751866541178e-14	6.474677040017e-15	4.3293310413145e-15	2.5303197303861e-15	3.623005365202e-14	--	6.7356081018013e-15	0.0	3.5293459575933e-15	1.8274531386122e-15	7.0122684487781e-15	--	2.0487474642979e-14	1.3512369474818e-14	5.1293161250356e-15	3.1980429925713e-15	6.5447838855262e-14	--	1.2033083495271e-14	0.0	4.2287099501252e-15	3.1578481407903e-15	1.8180780736419e-14	--	7.4960192265623e-15	0.0	3.5248793806934e-15	2.5546636644596e-15	0.0	--	1.6372884100123e-14	5.025968122773e-15	4.932540519557e-15	3.761032617121e-15	3.5604028942154e-14	--	1.50014345992e-14	--	4.263302792e-15	2.87575943081e-15	--	--	1.25922779852e-14	--	3.49227994664e-15	2.18412108669e-15	--	--	1.72688761183e-14	--	5.03432563736e-15	3.53099575682e-15	--	--	1.68268650974e-14	--	4.23193592004e-15	3.52360604401e-15	--	--	1.48011445253e-14	--	3.54435873255e-15	2.89051321809e-15	--	--	1.87334256358e-14	--	4.91951310754e-15	4.11945811547e-15	--	--	1.81334502073e-14	--	4.43137280404e-15	2.5155083733e-15	--	--	1.5221307291e-14	--	3.6299543182e-15	1.91051268858e-15	--	--	2.08742905991e-14	--	5.23279128987e-15	3.08866217987e-15	--	--	2.03399960431e-14	--	4.39876937188e-15	3.0821981884e-15	--	--	1.7891343357e-14	--	3.68408617953e-15	2.52841392967e-15	--	--	2.26446103359e-14	--	5.11345256423e-15	3.6034069025e-15	--	--	1.47636103524e-14	--	4.29078593862e-15	2.79079455875e-15	--	--	1.23926471427e-14	--	3.51479273696e-15	2.11959080412e-15	--	--	1.6995105138e-14	--	5.06677914029e-15	3.42667179999e-15	--	--	1.65601015094e-14	--	4.25921686185e-15	3.41950041769e-15	--	--	1.45664955639e-14	--	3.56720724589e-15	2.80511244252e-15	--	--	1.8436436517e-14	--	4.95122647782e-15	3.99774792373e-15	--	--	1.83686800078e-14	--	4.3296526371e-15	2.74562613645e-15	--	--	1.54187603425e-14	--	3.54663035167e-15	2.08528567325e-15	--	--	2.11450749869e-14	--	5.11267492253e-15	3.37121183842e-15	--	--	2.06038494828e-14	--	4.29779760205e-15	3.36415652344e-15	--	--	1.81234325115e-14	--	3.5995196405e-15	2.75971228828e-15	--	--	2.29383595734e-14	--	4.99607556359e-15	3.93304521535e-15	--	--	117.26576682248776	TRUE	6.114031721028103	1.19301130271e-14	8.89056581495e-15	1.54903508011e-14	2.58544	2.0038	3.3290000000000006	--	40.058	21.876	65.018	--	8.02733e-06	4.9208e-06	1.4369999999999998e-05	--	0.737374218089	9.26277395663e-15	5.241831705e-15	1.22210567676e-14	0.523579	0.43175	0.5993199999999999	--	0.677754	--	16.1588197262	--	1.53544e-05	9.9526e-06	3.382e-05	--	0.66439382229	1.06187279908e-14	6.22686601103e-15	1.33772999929e-14	1.87253	1.1368	3.4303999999999997	--	28.1174	14.869	46.733999999999995	--	8.43245e-06	5.4359e-06	1.5428999999999997e-05	--	0.706171477688	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	5.44	-0.11492816989382	-0.40037476577139	0.10306058713304	--	--	-0.087445346658339	-0.38538413491568	0.14928169893816	--	--	0.026233603997502	-0.11680199875078	0.17926296064959	--	--	0.946123774341105	--	0.24049966323645	0.9960250645830955	--	--	0.88506607347396	--	0.35883302427798003	0.974898134079393	--	--	0.32996571358618	--	0.11431328381278	0.68922431926232	--	--	0	--	0	2	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	0	1	0	0	95839.563442184	--	--	0	0	0	--	--	--
b'2CXO J013528.3+304213'	23.868126726976016	30.703669407016164	2.7209096992704147	1.916804098203332	171.27433996990763	-31.219849	134.000979	0	0	0	1	0	0	0	0	0	0	0	0	4.469711664990523	--	--	--	--	--	2.637284567245949	--	--	--	--	--	6.3021387627350975	--	--	--	--	--	4.469711664990523	--	--	--	--	--	2.637284567245949	--	--	--	--	--	6.3021387627350975	--	--	--	--	--	0.0	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	62.763750782836084	--	--	--	--	--	1	8.049872117783e-06	2.3708150881777e-06	1.5370930904394e-06	2.4055974633476e-06	4.3337223137458e-06	--	7.0738712980621e-06	1.7537536268712e-06	1.2467532844675e-06	2.0021713787899e-06	9.4800175613189e-07	--	9.0258729375039e-06	2.9878765494842e-06	1.8103540842953e-06	2.7852926017548e-06	7.7194428713597e-06	--	8.4451524490553e-06	2.2540601312962e-06	1.6032169851508e-06	2.5109306475114e-06	2.6941701225187e-06	--	7.4965040596204e-06	1.6673869464383e-06	1.3160595158556e-06	2.1094122383839e-06	0.0	--	9.3938008384903e-06	2.8407333161542e-06	1.873482838605e-06	2.8888303266902e-06	5.3883402450374e-06	--	1.5779290704594e-14	7.5914986342494e-15	3.6689446246277e-15	3.9468461000024e-15	--	--	9.7305626011664e-15	1.1861716616015e-15	2.9601712312337e-15	3.2290800421687e-15	--	--	2.1565030629612e-14	1.3759591274577e-14	4.3777180180217e-15	4.6223906250224e-15	--	--	2.2704234132488e-14	6.8232142160463e-15	3.8234270746589e-15	4.1281044452324e-15	--	--	1.710189064525e-14	1.100518421943e-15	3.1091604782941e-15	3.4238085012117e-15	--	--	2.8306577619725e-14	1.254591001015e-14	4.4956779888847e-15	4.7909712160755e-15	--	--	8.049872117783e-06	2.3708150881777e-06	1.5370930904394e-06	2.4055974633476e-06	4.3337223137458e-06	--	7.0738712980621e-06	1.7537536268712e-06	1.2467532844675e-06	2.0021713787899e-06	9.4800175613189e-07	--	9.0258729375039e-06	2.9878765494842e-06	1.8103540842953e-06	2.7852926017548e-06	7.7194428713597e-06	--	8.4451524490553e-06	2.2540601312962e-06	1.6032169851508e-06	2.5109306475114e-06	2.6941701225187e-06	--	7.4965040596204e-06	1.6673869464383e-06	1.3160595158556e-06	2.1094122383839e-06	0.0	--	9.3938008384903e-06	2.8407333161542e-06	1.873482838605e-06	2.8888303266902e-06	5.3883402450374e-06	--	1.5779290704594e-14	7.5914986342494e-15	3.6689446246277e-15	3.9468461000024e-15	--	--	9.7305626011664e-15	1.1861716616015e-15	2.9601712312337e-15	3.2290800421687e-15	--	--	2.1565030629612e-14	1.3759591274577e-14	4.3777180180217e-15	4.6223906250224e-15	--	--	2.2704234132488e-14	6.8232142160463e-15	3.8234270746589e-15	4.1281044452324e-15	--	--	1.710189064525e-14	1.100518421943e-15	3.1091604782941e-15	3.4238085012117e-15	--	--	2.8306577619725e-14	1.254591001015e-14	4.4956779888847e-15	4.7909712160755e-15	--	--	1.85539318433e-14	1.51787784382e-14	3.6467201184e-15	4.13337617137e-15	--	--	1.62896842357e-14	1.10202090031e-14	2.95789520715e-15	3.43625339983e-15	--	--	2.08181794509e-14	1.91294194016e-14	4.33554502966e-15	4.78949172106e-15	--	--	1.9534766826e-14	1.45231829643e-14	3.79792361217e-15	4.32588981513e-15	--	--	1.73317947206e-14	1.07431764393e-14	3.11450878357e-15	3.63156163403e-15	--	--	2.17377389313e-14	1.83031894892e-14	4.4411375685e-15	4.97937516205e-15	--	--	2.25031117059e-14	1.2892346826e-14	3.78962710577e-15	3.63919614123e-15	--	--	1.97569219886e-14	9.36019701069e-15	3.07380865246e-15	3.02542028465e-15	--	--	2.52493014231e-14	1.62478891506e-14	4.50544555909e-15	4.21686753565e-15	--	--	2.36927161178e-14	1.23355059536e-14	3.94675593384e-15	3.80869315298e-15	--	--	2.10208443124e-14	9.12489481501e-15	3.23655957249e-15	3.19737776995e-15	--	--	2.63645879232e-14	1.55461170922e-14	4.61517603864e-15	4.3840488076e-15	--	--	1.82827733668e-14	1.39813250369e-14	3.67018768741e-15	4.01789850597e-15	--	--	1.60516168548e-14	1.01508250268e-14	2.97693001312e-15	3.34025184955e-15	--	--	2.05139298788e-14	1.76203000465e-14	4.36344536169e-15	4.65568359436e-15	--	--	1.92492738288e-14	1.33774494713e-14	3.82236421401e-15	4.2050337314e-15	--	--	1.70784973015e-14	9.89564755413e-15	3.13455143763e-15	3.53010359056e-15	--	--	2.14200503562e-14	1.68592513885e-14	4.46971741531e-15	4.84026209925e-15	--	--	2.2761668266e-14	1.72088141456e-14	3.70290217912e-15	3.95778574216e-15	--	--	1.99839253406e-14	1.24940705441e-14	3.00346510084e-15	3.29027752337e-15	--	--	2.55394111914e-14	2.16878205671e-14	4.4023392574e-15	4.58602877161e-15	--	--	2.3964941011e-14	1.64655382152e-14	3.85643514255e-15	4.14212119165e-15	--	--	2.12623698964e-14	1.21799871729e-14	3.16249149568e-15	3.47728884597e-15	--	--	2.66675121255e-14	2.07510892575e-14	4.5095585749e-15	4.76784575228e-15	--	--	146.50512518711835	TRUE	8.247812865654112	1.62819645751e-14	1.31507775187e-14	1.99813302577e-14	1.84317	1.4418	2.3331	--	10.843	0.125	24.021999999999995	--	4.20762e-06	2.9776e-06	6.1288e-06	--	1.22443493409	1.04893776757e-14	7.25870513496e-15	1.25814436309e-14	0.532076	0.44674	0.6211600000000002	--	5.43892e-06	--	2.81435049585	--	1.4995e-05	9.3133e-06	2.591e-05	--	1.39691601344	1.66536613108e-14	1.35220001866e-14	1.96497806383e-14	4.70101	2.441	14.086000000000002	--	5.63216	--	15.1169165037	--	4.75657e-06	3.8652e-06	6.3466e-06	--	1.20394344253	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	5.42	0.23360399750156	0.068082448469706	0.37289194253591	--	--	0.0087445346658337	-0.15552779512805	0.15302935665209	--	--	-0.2211118051218	-0.3366645846346	-0.10306058713304	--	--	0.8772605690554	0.78816126047072	0.84281597153313	0.08414746554418995	--	--	0.965584392108393	0.79565527200054	0.947222343714488	0.051580099614950026	--	--	0.41405555895313	0.085214630429942	0.75898808472765	0.27370894433706	--	--	0	0	2	0	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	0	1	0	0	95839.563442184	--	--	0	0	0	--	--	--
b'2CXO J013530.8+303653'	23.878469335874797	30.614852112908245	2.245083418420842	1.61674141937009	178.93834909695855	-31.305122	134.031846	1	0	0	1	0	0	0	0	0	0	0	0	10.914635469826157	--	7.797904745578632	9.287072782385435	--	--	9.835640415501569	--	5.9016889842608915	7.276218301128825	--	--	11.993630524150745	--	9.694120506896372	11.297927263642045	--	--	10.914635469826157	--	7.797904745578632	9.287072782385435	--	--	9.835640415501569	--	5.9016889842608915	7.276218301128825	--	--	11.993630524150745	--	9.694120506896372	11.297927263642045	--	--	0.0	--	0.0	0.0	--	--	--	--	--	--	--	--	--	--	--	--	--	--	374.2556314144844	--	191.0318248362788	270.96148944467325	--	--	1	1.0416694120263e-05	1.6808412241878e-06	2.7091502392808e-06	3.3370000561163e-06	0.0	--	9.2295491231414e-06	9.6048069953587e-07	2.3362007240429e-06	2.8564205780173e-06	0.0	--	1.1603839117385e-05	2.3711867269792e-06	3.0601615477401e-06	3.7893101531506e-06	1.3860191877151e-06	--	1.0731930195011e-05	1.9575223557872e-06	2.6644010554968e-06	3.2998853683182e-06	0.0	--	9.7542159895512e-06	1.3982302541337e-06	2.341043112875e-06	2.8759833279077e-06	0.0	--	1.1709644400472e-05	2.488849852358e-06	2.9877589981187e-06	3.6988519945869e-06	2.4978855726092e-06	--	2.6883121985559e-14	1.4383878028792e-14	6.7726087099408e-15	5.3928450841347e-15	--	--	1.8818185389891e-14	5.1634433949511e-15	5.8302382052227e-15	4.5228001536468e-15	--	--	3.4948058581226e-14	2.323549527728e-14	7.6595456555579e-15	6.2117109010645e-15	--	--	4.1046456353305e-14	2.8336130325568e-14	6.607743149666e-15	5.3227615897847e-15	--	--	3.5266997109646e-14	1.9712090661265e-14	5.7999903281725e-15	4.597395215078e-15	--	--	4.6485947406161e-14	3.6549501434428e-14	7.4154959711595e-15	6.0054593542145e-15	--	--	1.0416694120263e-05	1.6808412241878e-06	2.7091502392808e-06	3.3370000561163e-06	0.0	--	9.2295491231414e-06	9.6048069953587e-07	2.3362007240429e-06	2.8564205780173e-06	0.0	--	1.1603839117385e-05	2.3711867269792e-06	3.0601615477401e-06	3.7893101531506e-06	1.3860191877151e-06	--	1.0731930195011e-05	1.9575223557872e-06	2.6644010554968e-06	3.2998853683182e-06	0.0	--	9.7542159895512e-06	1.3982302541337e-06	2.341043112875e-06	2.8759833279077e-06	0.0	--	1.1709644400472e-05	2.488849852358e-06	2.9877589981187e-06	3.6988519945869e-06	2.4978855726092e-06	--	2.6883121985559e-14	1.4383878028792e-14	6.7726087099408e-15	5.3928450841347e-15	--	--	1.8818185389891e-14	5.1634433949511e-15	5.8302382052227e-15	4.5228001536468e-15	--	--	3.4948058581226e-14	2.323549527728e-14	7.6595456555579e-15	6.2117109010645e-15	--	--	4.1046456353305e-14	2.8336130325568e-14	6.607743149666e-15	5.3227615897847e-15	--	--	3.5266997109646e-14	1.9712090661265e-14	5.7999903281725e-15	4.597395215078e-15	--	--	4.6485947406161e-14	3.6549501434428e-14	7.4154959711595e-15	6.0054593542145e-15	--	--	2.5795532441e-14	--	6.69396062939e-15	6.1819171835e-15	--	--	2.31038818387e-14	--	5.80033119333e-15	5.35945042668e-15	--	--	2.84871830434e-14	--	7.53502362804e-15	6.95600354286e-15	--	--	2.52902988742e-14	--	6.48329544982e-15	5.85180779586e-15	--	--	2.30662756542e-14	--	5.7060714351e-15	5.12256249495e-15	--	--	2.75143220941e-14	--	7.26051946453e-15	6.58105309678e-15	--	--	3.18590992435e-14	--	6.96291849667e-15	5.52452117936e-15	--	--	2.85347420562e-14	--	6.03338376021e-15	4.78951699174e-15	--	--	3.51834564307e-14	--	7.83777471922e-15	6.21628982652e-15	--	--	3.1235104124e-14	--	6.74378896235e-15	5.2295162077e-15	--	--	2.84882960615e-14	--	5.93533672193e-15	4.57782014154e-15	--	--	3.39819121865e-14	--	7.55224120277e-15	5.88121227387e-15	--	--	2.552121551e-14	--	6.73769574511e-15	6.02909128124e-15	--	--	2.28581886755e-14	--	5.83822776458e-15	5.22695708801e-15	--	--	2.81842423444e-14	--	7.58425384442e-15	6.78404111017e-15	--	--	2.50213547386e-14	--	6.52565418068e-15	5.70714267343e-15	--	--	2.28209824057e-14	--	5.74335215847e-15	4.99592536735e-15	--	--	2.72217270716e-14	--	7.30795620288e-15	6.41835997951e-15	--	--	3.18936638052e-14	--	6.80303653175e-15	5.95261395526e-15	--	--	2.85656999577e-14	--	5.89484569587e-15	5.1606546085e-15	--	--	3.52216276528e-14	--	7.65780437729e-15	6.6979874581e-15	--	--	3.12689917012e-14	--	6.58893863187e-15	5.63474917492e-15	--	--	2.85192035728e-14	--	5.79905000566e-15	4.9325534602e-15	--	--	3.40187798297e-14	--	7.37882725808e-15	6.33694488965e-15	--	--	229.70565270790019	TRUE	8.774576101081156	2.22117911058e-14	1.88521721233e-14	2.62298804893e-14	2.06151	1.6929	2.4998000000000005	--	11.5722	2.0212000000000003	23.247999999999998	--	6.8631e-06	5.0563e-06	9.6244e-06	--	0.964790860205	1.49578528485e-14	1.23551487879e-14	1.7033044679e-14	0.456559	0.40824	0.5086300000000001	--	2.98263e-06	--	2.37340503371	--	3.79549e-05	2.6779e-05	5.461300000000001e-05	--	0.984881094385	1.98029219345e-14	1.29505082494e-14	2.30115274678e-14	2.89977	1.8695	5.337299999999999	--	5.77172	--	13.8283191635	--	7.88078e-06	6.1647e-06	1.0620999999999999e-05	--	0.929129025909	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	5.48	-0.18738288569644	-0.41411617738913	-0.0093691442848221	--	--	-0.29356652092442	-0.50655840099938	-0.12054965646471	--	--	-0.10618363522798	-0.20049968769519	-0.010618363522798	--	--	0.988546188904852	--	0.9941704437736896	0.957136621840564	--	--	0.945518658508582	--	0.984018497673073	0.88453085024399	--	--	0.093087822194806	--	0.54850920015242	0.19071139192291	--	--	0	--	1	0	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	0	1	0	0	95839.563442184	--	--	0	0	0	--	--	--
b'2CXO J013533.6+303439'	23.890200740260696	30.577708493124593	6.451189549369161	4.3514673695238	14.472927606149028	-31.339509	134.052093	1	0	0	0	0	0	0	0	0	0	0	0	11.474542036434528	--	--	--	--	--	9.990019863523063	--	--	--	--	--	12.959064209345993	--	--	--	--	--	11.474542036434528	--	--	--	--	--	9.990019863523063	--	--	--	--	--	12.959064209345993	--	--	--	--	--	0.0	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	413.63815784810464	--	--	--	--	--	1	2.2169114908154e-06	4.4863274250993e-07	0.0	1.182128519453e-06	0.0	--	9.8529399591793e-07	0.0	0.0	6.490117361703e-07	0.0	--	3.3992642859169e-06	9.3999241287795e-07	2.2112086546045e-07	1.7152453027358e-06	3.9182662873099e-06	--	3.5605598546912e-06	9.3036931416043e-07	4.7815833562405e-07	1.1906211673267e-06	0.0	--	2.6463620541624e-06	4.341723466082e-07	2.6687907104598e-07	7.7848307094438e-07	0.0	--	4.47475765522e-06	1.4265662817127e-06	6.7831763890854e-07	1.5798627027989e-06	4.6262661498356e-06	--	0.0	0.0	0.0	2.8225903864694e-15	0.0	--	0.0	0.0	0.0	1.5496574670813e-15	0.0	--	8.4333977811458e-15	9.996665860085e-15	5.1024189743659e-16	4.0955233058576e-15	2.4409777571116e-14	--	5.1269168033388e-15	0.0	1.0958632789569e-15	3.2062202005753e-15	6.7971534824713e-14	--	8.2692206505465e-16	0.0	5.880241984647e-16	2.2152066840339e-15	1.1821136491255e-14	--	9.426911541623e-15	5.1793008029159e-15	1.5769739867917e-15	4.1972337171168e-15	1.2412193315817e-13	--	2.2169114908154e-06	4.4863274250993e-07	0.0	1.182128519453e-06	0.0	--	9.8529399591793e-07	0.0	0.0	6.490117361703e-07	0.0	--	3.3992642859169e-06	9.3999241287795e-07	2.2112086546045e-07	1.7152453027358e-06	3.9182662873099e-06	--	3.5605598546912e-06	9.3036931416043e-07	4.7815833562405e-07	1.1906211673267e-06	0.0	--	2.6463620541624e-06	4.341723466082e-07	2.6687907104598e-07	7.7848307094438e-07	0.0	--	4.47475765522e-06	1.4265662817127e-06	6.7831763890854e-07	1.5798627027989e-06	4.6262661498356e-06	--	0.0	0.0	0.0	2.8225903864694e-15	0.0	--	0.0	0.0	0.0	1.5496574670813e-15	0.0	--	8.4333977811458e-15	9.996665860085e-15	5.1024189743659e-16	4.0955233058576e-15	2.4409777571116e-14	--	5.1269168033388e-15	0.0	1.0958632789569e-15	3.2062202005753e-15	6.7971534824713e-14	--	8.2692206505465e-16	0.0	5.880241984647e-16	2.2152066840339e-15	1.1821136491255e-14	--	9.426911541623e-15	5.1793008029159e-15	1.5769739867917e-15	4.1972337171168e-15	1.2412193315817e-13	--	3.29568137432e-15	--	--	--	--	--	6.99083927886e-16	--	--	--	--	--	5.89227882075e-15	--	--	--	--	--	8.24970999053e-15	--	--	--	--	--	5.92584802137e-15	--	--	--	--	--	1.04573788612e-14	--	--	--	--	--	3.62349438166e-15	--	--	--	--	--	7.68620020352e-16	--	--	--	--	--	6.47836874297e-15	--	--	--	--	--	9.07028756904e-15	--	--	--	--	--	6.51527698621e-15	--	--	--	--	--	1.14975476227e-14	--	--	--	--	--	3.17537003743e-15	--	--	--	--	--	6.73563341272e-16	--	--	--	--	--	5.67717673358e-15	--	--	--	--	--	7.94854809859e-15	--	--	--	--	--	5.70952046518e-15	--	--	--	--	--	1.00756243503e-14	--	--	--	--	--	3.85081203701e-15	--	--	--	--	--	8.16838916941e-16	--	--	--	--	--	6.88478515707e-15	--	--	--	--	--	9.63930639076e-15	--	--	--	--	--	6.92400881589e-15	--	--	--	--	--	1.22188390869e-14	--	--	--	--	--	29.764031037049644	MARGINAL	2.2173913043477245	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	5.5	0.99937539038101	0.45159275452842	1.0	--	--	-0.27982510930668	-0.69394128669582	0.098063710181137	--	--	-0.99937539038101	-1.0	-0.66770768269831	--	--	0.20382168918250998	--	--	--	--	--	0.14494875731493995	--	--	--	--	--	0.083207127370719	--	--	--	--	--	0	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	0	1	0	0	95836.403982002	--	--	0	0	0	--	--	--
b'2CXO J013547.5+303315'	23.948226444178488	30.554296126828916	8.351092102664134	8.351092102664134	0.0	-31.352461	134.114894	0	0	0	0	0	0	0	0	1	0	0	0	5.813896804643702	--	--	16.758104933482297	--	--	1.7353214895594853	--	--	12.364639228964439	--	--	9.892472119727918	--	--	21.151570638000155	--	--	5.813896804643702	--	--	16.758104933482297	--	--	1.7353214895594853	--	--	12.364639228964439	--	--	9.892472119727918	--	--	21.151570638000155	--	--	0.0	--	--	0.0	--	--	--	--	--	--	--	--	--	--	--	--	--	--	106.1902175276123	--	--	882.2662856266154	--	--	1	7.0699526640069e-06	5.7511094330733e-07	1.4465213025973e-06	3.3146836151391e-06	0.0	--	5.5727862175113e-06	0.0	1.084890976948e-06	2.6043942690379e-06	0.0	--	8.5671191105024e-06	1.1502218866147e-06	1.8081516282466e-06	3.9855124420125e-06	9.1418239151031e-06	--	7.4693313044237e-06	7.7676722278624e-07	1.4134016004339e-06	3.3493441490813e-06	0.0	--	6.1039696681312e-06	1.5034204311992e-07	1.0734695699498e-06	2.7023117566451e-06	0.0	--	8.7543775503461e-06	1.4031924024526e-06	1.7354424714188e-06	3.9963765415175e-06	1.1016753956025e-05	--	0.0	0.0	3.4482141911157e-15	5.5497495376294e-15	--	--	0.0	0.0	2.5507337852089e-15	3.941126483244e-15	--	--	7.30165310119e-15	6.7441337059533e-15	4.3456945970225e-15	7.1583725920147e-15	--	--	7.4854812182412e-15	0.0	3.2619998644654e-15	6.6187410976228e-15	--	--	1.5878293493239e-15	0.0	2.4570908069999e-15	5.1295243506576e-15	--	--	1.3383133087159e-14	6.487281974614e-15	4.0669089219309e-15	8.1079578445879e-15	--	--	7.0699526640069e-06	5.7511094330733e-07	1.4465213025973e-06	3.3146836151391e-06	0.0	--	5.5727862175113e-06	0.0	1.084890976948e-06	2.6043942690379e-06	0.0	--	8.5671191105024e-06	1.1502218866147e-06	1.8081516282466e-06	3.9855124420125e-06	9.1418239151031e-06	--	7.4693313044237e-06	7.7676722278624e-07	1.4134016004339e-06	3.3493441490813e-06	0.0	--	6.1039696681312e-06	1.5034204311992e-07	1.0734695699498e-06	2.7023117566451e-06	0.0	--	8.7543775503461e-06	1.4031924024526e-06	1.7354424714188e-06	3.9963765415175e-06	1.1016753956025e-05	--	0.0	0.0	3.4482141911157e-15	5.5497495376294e-15	--	--	0.0	0.0	2.5507337852089e-15	3.941126483244e-15	--	--	7.30165310119e-15	6.7441337059533e-15	4.3456945970225e-15	7.1583725920147e-15	--	--	7.4854812182412e-15	0.0	3.2619998644654e-15	6.6187410976228e-15	--	--	1.5878293493239e-15	0.0	2.4570908069999e-15	5.1295243506576e-15	--	--	1.3383133087159e-14	6.487281974614e-15	4.0669089219309e-15	8.1079578445879e-15	--	--	1.6282124736e-14	--	--	5.99567769331e-15	--	--	1.27930980069e-14	--	--	4.71088961617e-15	--	--	1.97711514652e-14	--	--	7.20908865505e-15	--	--	1.72900835855e-14	--	--	6.06268938226e-15	--	--	1.4095176836e-14	--	--	4.89148802432e-15	--	--	2.02970546438e-14	--	--	7.2338907402e-15	--	--	1.78922445137e-14	--	--	4.70827854776e-15	--	--	1.40581921179e-14	--	--	3.6993617161e-15	--	--	2.17262969094e-14	--	--	5.66114444433e-15	--	--	1.89998792043e-14	--	--	4.76090140605e-15	--	--	1.548903196e-14	--	--	3.84118181624e-15	--	--	2.23042060224e-14	--	--	5.68062099585e-15	--	--	1.56716589513e-14	--	--	5.67279772175e-15	--	--	1.23134463189e-14	--	--	4.45719820995e-15	--	--	1.90298715837e-14	--	--	6.82086392734e-15	--	--	1.66418263945e-14	--	--	5.736200689e-15	--	--	1.35667062999e-14	--	--	4.62807101044e-15	--	--	1.95360570719e-14	--	--	6.84433036756e-15	--	--	1.90467928102e-14	--	--	5.4680899588e-15	--	--	1.4965337208e-14	--	--	4.2963563962e-15	--	--	2.31282484123e-14	--	--	6.57472721237e-15	--	--	2.02259007999e-14	--	--	5.52920497569e-15	--	--	1.64885060869e-14	--	--	4.46106310539e-15	--	--	2.37434487652e-14	--	--	6.597346846e-15	--	--	33.75964290828637	MARGINAL	4.800000000000087	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	5.48	-0.21236727045597	-0.59025608994379	0.096814490943161	--	--	-0.63460337289194	-0.85509056839475	-0.39163023110556	--	--	-0.40599625234229	-0.53653966271081	-0.26545908806996	--	--	0.5045474907068099	--	--	0.933769801196785	--	--	0.45941895365247	--	--	0.953065566046522	--	--	0.042679814883972	--	--	0.3449781160157	--	--	0	--	--	0	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	0	1	0	0	95836.403982002	--	--	0	0	0	--	--	--

Finally the file can be written out as a FITS table:

In [22]:

tbl.write('m33.fits', format='fits', overwrite=True)

Moving to TAP queries (general queries)¶

For an all-sky search we are going to switch to the Table Access Protocol search. This has a base URL of http://cda.cfa.harvard.edu/csc2tap and requires us writing a query using the Astronomical Data Query Language (ADQL). In this particular case, we are going to do a simple search and return a subset of the columns that we got back from the cone search:

SELECT m.name, m.ra, m.dec, m.conf_flag, m.significance, m.extent_flag, m.sat_src_flag,
       m.flux_aper_b, m.flux_aper_lolim_b, m.flux_aper_hilim_b
FROM csc2.master_source m
WHERE m.name LIKE '%X'

In [20]:

tap = vo.dal.TAPService('http://cda.cfa.harvard.edu/csc2tap')

We can check what tables are available (although we are focused on the "master source" table, it's useful to know what else is available):

In [21]:

for i, tblname in enumerate(tap.tables.keys()):
    print(f"{i + 1:2d} - {tblname}")

 1 - TAP_SCHEMA.schemas
 2 - TAP_SCHEMA.tables
 3 - TAP_SCHEMA.columns
 4 - TAP_SCHEMA.keys
 5 - TAP_SCHEMA.key_columns
 6 - csc2.master_source
 7 - csc2.stack_source
 8 - csc2.observation_source
 9 - csc2.master_stack_assoc
10 - csc2.stack_observation_assoc
11 - csc2.detect_stack
12 - csc2.valid_stack
13 - csc2.likely_stack
14 - csc2.cross_match
15 - csc2.cone
16 - csc2.image
17 - ivoa.ObsCore

Extended sources¶

Let's try the simple query. This asks for master-source properties on all the "extended" sources in the catalog (where "extended" means large-diffuse emission, and not slightly-larger-than-a-point-source extended):

In [22]:

qry = """
SELECT m.name, m.ra, m.dec, m.conf_flag, m.significance, m.extent_flag, m.sat_src_flag,
       m.flux_aper_avg_b, m.flux_aper_avg_lolim_b, m.flux_aper_avg_hilim_b
FROM csc2.master_source m
WHERE m.name LIKE '%X'
"""

In [23]:

tresults = tap.search(qry)

In CSC2 there are 1299 extended master sources:

In [24]:

len(tresults)

Out[24]:

In [25]:

fluxes_ext = tresults['flux_aper_avg_b']
fluxes_ext1 = fluxes_ext[fluxes_ext>0]

Let's check the sky distribution and the fluxes for these extended sources, which should cover most of the sky (excluding poles and a band close to the plane):

In [26]:

#plt.plot(tresults['ra'], tresults['dec'], 'o')
plt.title('Extended sources in CSC2')
plt.xlabel('Right Ascension')
plt.ylabel('Declination')
plt.ylim(-90, 90)
plt.xlim(370, -10);



min_norm = np.min(np.log10(fluxes_ext1))
max_norm = np.max(np.log10(fluxes_ext1))

print(min_norm,max_norm)

normalize = matplotlib.colors.Normalize(vmin=min_norm, 
                                        vmax=max_norm)
plt.scatter(tresults['ra'][fluxes_ext>0],tresults['dec'][fluxes_ext>0],cmap=cmap,norm=normalize,
            c=np.log10(tresults['flux_aper_avg_b'].data[fluxes_ext>0]),s=15)
plt.xlabel('RA [deg]',size=13)
plt.ylabel('Dec [deg]',size=13)
plt.title('Extended sources in CSC2',size=15)
plt.ylim(-90, 90)
plt.xlim(370, -10);
cbar = plt.colorbar()
cbar.ax.get_yaxis().labelpad = 15
cbar.ax.set_ylabel('log(F$_X$) [erg/s/cm$^2$]', rotation=270,size=13)

-15.214243682432903 -7.35735093518183

Out[26]:

Text(0, 0.5, 'log(F$_X$) [erg/s/cm$^2$]')

Per-ObsID search¶

Let's now try a search of the entire catalog for source detections that have significance larger than 10, and that are not flagged for saturation or for streaks. Let's also select detections that are uniquely associated with a master source. We will explore the distributions of the short term variability for these sources

In [27]:

qry = """
SELECT DISTINCT m.name,m.ra,m.dec,m.err_ellipse_r0,m.err_ellipse_r1,m.err_ellipse_ang,
    m.likelihood_class,a.match_type,o.obsid,o.obi,o.region_id,o.theta,o.phi,o.flux_significance_b,
    o.likelihood_b,o.conf_code,o.dither_warning_flag,o.extent_code,o.sat_src_flag,o.streak_src_flag,
    o.var_code,o.flux_aper_b,o.flux_aper_lolim_b,o.flux_aper_hilim_b,o.var_index_b,o.var_index_h,
    o.var_index_m,o.var_index_s,o.var_index_u,o.var_prob_b,o.var_prob_h,o.var_prob_m,o.var_prob_s,
    o.var_prob_u,o.ks_prob_b,o.ks_prob_h,o.ks_prob_m,o.ks_prob_s,o.ks_prob_u,o.var_sigma_b,
    o.var_sigma_h,o.var_sigma_m,o.var_sigma_s,o.var_sigma_u,o.var_mean_b,o.var_mean_h,
    o.var_mean_m,o.var_mean_s,o.var_mean_u,o.var_min_b,o.var_min_h,o.var_min_m,o.var_min_s,
    o.var_min_u,o.var_max_b,o.var_max_h,o.var_max_m,o.var_max_s,o.var_max_u 
FROM csc2.master_source m , csc2.master_stack_assoc a , csc2.observation_source o , 
    csc2.stack_observation_assoc b , csc2.stack_source s 
WHERE ((a.match_type = 'u') AND (o.flux_significance_b > 10) AND (o.sat_src_flag = 0) 
    AND (o.streak_src_flag = 0)) AND (m.name = a.name) 
    AND (s.detect_stack_id = a.detect_stack_id and s.region_id = a.region_id) 
    AND (s.detect_stack_id = b.detect_stack_id and s.region_id = b.region_id) 
    AND (o.obsid = b.obsid and o.obi = b.obi and o.region_id = b.region_id)
ORDER BY name ASC
"""

In [28]:

%time cat = tap.search(qry)

CPU times: user 10.2 s, sys: 298 ms, total: 10.5 s
Wall time: 44.3 s

In [29]:

len(cat)

Out[29]:

We get about 31,000 results, which is fortunately less than the maximum number of rows you can make in a query against this service (as shown by the maxrec parameter):

In [30]:

tap.maxrec

Out[30]:

Let's have a look at the distributions of significance and variability for these sources.

In [31]:

plt.subplots(figsize=(12, 4))
plt.subplot(1,2,1)
idx = (cat['flux_aper_b'] > 0) & (cat['flux_significance_b'] > 0)
plt.hexbin(cat['flux_aper_b'][idx], cat['flux_significance_b'][idx],
           xscale='log', yscale='log', mincnt=1)
plt.colorbar()
plt.xlabel('F$_X$ [erg/s/cm$^2$]',size=13)
plt.ylabel('Significance',size=13);
plt.subplot(1,2,2)
idx = (cat['flux_aper_b'] > 0) & (cat['var_sigma_b'] > 0)
plt.hexbin(cat['flux_aper_b'][idx], cat['var_sigma_b'][idx],
           xscale='log', yscale='log', mincnt=1)
plt.colorbar()
plt.xlabel('F$_X$ [erg/s/cm$^2$]',size=13)
plt.ylabel('$\sigma_{var}$',size=13);
plt.ylim(1e-11,1e1)

Out[31]:

(1e-11, 10.0)

So we see that at the level of individual observation detections, significance and flux are correlated. The dispersion in significance for a given flux has to do with the background level and the off-axis angle of the detection. We also see that at a given flux there is a range of variability sigmas. Those with larger values are those with larger variations in the light curve with respect to the mean flux.

Retrieving and displaying data products (light curves and spectra)¶

Let's now select a variable detection and obtain its light curve from the catalog. We can use CIAO tools for this, in particular, search_csc. Please note that using search_csc for bulk downloads (for example, download all catalog light curves) can saturate the server. For bulk downloads, users should issue a "Special Request" to the Chandra Data Archive.

In [32]:

from ciao_contrib.runtool import search_csc

First, let's print some detections (and the corresponding source name and coordinates) that have fluxes larger than $5 \times 10^{-13} \mathbb{erg/s/cm^{2}}$ and var_sigma_b larger than $10^{-3}$ . Additionally, let's select detections that are close to on-axis, using the off-axis angle theta, which is given in arcmin:

In [33]:

index = (cat['flux_aper_b']>5E-13) & (cat['flux_aper_b']<1E-12) & (cat['var_sigma_b']>1E-3) & (cat['theta'] < 3)
print('name,  obsid,  region_id, ra,  dec')
for i in range(20):
    print(cat['name'][index].data[i],
         cat['obsid'][index].data[i],
         cat['region_id'][index].data[i],
         cat['ra'][index].data[i],
         cat['dec'][index].data[i])

name,  obsid,  region_id, ra,  dec
b'2CXO J002404.2-720457' 78 58 6.01784253506969 -72.08276437373436
b'2CXO J002404.2-720457' 3384 129 6.01784253506969 -72.08276437373436
b'2CXO J002404.2-720457' 2735 129 6.01784253506969 -72.08276437373436
b'2CXO J004231.1+411621' 14197 115 10.629760440205644 41.27266631918941
b'2CXO J004231.1+411621' 15327 138 10.629760440205644 41.27266631918941
b'2CXO J004231.1+411621' 4720 138 10.629760440205644 41.27266631918941
b'2CXO J004231.1+411621' 8194 138 10.629760440205644 41.27266631918941
b'2CXO J004238.6+411603' 4722 478 10.66086581188398 41.26767223862725
b'2CXO J004239.5+411614' 1575 370 10.664987223146795 41.27062180613111
b'2CXO J004239.9+411547' 13300 111 10.666646569143893 41.263199005670494
b'2CXO J004242.5+405152' 1580 4 10.677263218723112 40.864543113379995
b'2CXO J004242.5+405152' 5690 23 10.677263218723112 40.864543113379995
b'2CXO J004245.1+411621' 14930 429 10.687979346386953 41.27268204164101
b'2CXO J004246.9+411615' 15267 487 10.695674817273016 41.27099019714981
b'2CXO J004247.1+411628' 1585 55 10.696564875665842 41.27454559893372
b'2CXO J004247.1+411628' 7138 295 10.696564875665842 41.27454559893372
b'2CXO J004247.1+411628' 8185 440 10.696564875665842 41.27454559893372
b'2CXO J004247.1+411628' 11276 440 10.696564875665842 41.27454559893372
b'2CXO J004247.1+411628' 8183 440 10.696564875665842 41.27454559893372
b'2CXO J004247.1+411628' 11277 440 10.696564875665842 41.27454559893372

search_csc can only perform a coordinate based search (or the name of a SIMBAD-recognized object). So, let's pick the first source, and do a search around its location with a search radius of 1 arcsec. We will request the light curve ('lc') for the broad band, as well as the spectral files (pha, arf, rmf). The command will also provide a table (source_search.tsv) with a summary of catalog properties for the retrieved sources (in this case, a single source).

In [34]:

search_csc('6.01784, -72.08276', '1', 'source_search.tsv', 'arcsec', 
           '', '', 'all', '/Users/juan/L3/tutorial', 'broad', 'lc,pha,rmf,arf', 'csc2','1','1')

Out[34]:

search_csc
             pos = 6.01784, -72.08276
          radius = 1
         outfile = source_search.tsv
         radunit = arcsec
         columns = 
     sensitivity = no
        download = all
            root = /Users/juan/L3/tutorial
           bands = broad
       filetypes = lc,pha,rmf,arf
         catalog = csc2
         verbose = 1
         clobber = yes
            mode = hl


13 rows returned by query
1 Different Master Source(s).
13 Different Observation(s).

name                	ra          	dec         	sepn	obsid	
 2CXO J002404.2-720457	6.017843e+00	-7.208276e+01	0.016"	   78	
 2CXO J002404.2-720457	6.017843e+00	-7.208276e+01	0.016"	  953	
 2CXO J002404.2-720457	6.017843e+00	-7.208276e+01	0.016"	  954	
 2CXO J002404.2-720457	6.017843e+00	-7.208276e+01	0.016"	  955	
 2CXO J002404.2-720457	6.017843e+00	-7.208276e+01	0.016"	  956	
 2CXO J002404.2-720457	6.017843e+00	-7.208276e+01	0.016"	 2735	
 2CXO J002404.2-720457	6.017843e+00	-7.208276e+01	0.016"	 2736	
 2CXO J002404.2-720457	6.017843e+00	-7.208276e+01	0.016"	 2737	
 2CXO J002404.2-720457	6.017843e+00	-7.208276e+01	0.016"	 2738	
 2CXO J002404.2-720457	6.017843e+00	-7.208276e+01	0.016"	 3384	
 2CXO J002404.2-720457	6.017843e+00	-7.208276e+01	0.016"	 3385	
 2CXO J002404.2-720457	6.017843e+00	-7.208276e+01	0.016"	 3386	
 2CXO J002404.2-720457	6.017843e+00	-7.208276e+01	0.016"	 3387	
Retrieving files for obsid_obi 00078_000
Retrieving files for obsid_obi 00953_000
Retrieving files for obsid_obi 00954_000
Retrieving files for obsid_obi 00955_000
Retrieving files for obsid_obi 00956_000
Retrieving files for obsid_obi 02735_000
Retrieving files for obsid_obi 02736_000
Retrieving files for obsid_obi 02737_000
Retrieving files for obsid_obi 02738_000
Retrieving files for obsid_obi 03384_000
Retrieving files for obsid_obi 03385_000
Retrieving files for obsid_obi 03386_000
Retrieving files for obsid_obi 03387_000

We have retrieved all the observations of the source. For now we are interested only in the detection of this source that we had identified as being variable a couple of cells above, which is the detection of this source in ObsID 78. We will use AstroPy to load the FITS file we have just downloaded. Note that the light curve file has both a source, and a background light curve.

In [35]:

from astropy.io import fits
hdul = fits.open('/Users/juan/L3/tutorial/2CXOJ002404.2-720457/00078_000/acisf00078_000N021_r0058b_lc3.fits.gz')

In [36]:

hdul.info()

Filename: /Users/juan/L3/tutorial/2CXOJ002404.2-720457/00078_000/acisf00078_000N021_r0058b_lc3.fits.gz
No.    Name      Ver    Type      Cards   Dimensions   Format
  0  PRIMARY       1 PrimaryHDU      29   ()      
  1  LIGHTCURVE    1 BinTableHDU    160   240R x 5C   [1D, 1D, 1D, 1D, 1D]   
  2  BKGLIGHTCURVE    1 BinTableHDU    164   240R x 5C   [1D, 1D, 1D, 1D, 1D]

Let's now plot the light curve, with its error bars.

In [37]:

lc3 = hdul[1].data
bg3 = hdul[2].data
plt.subplots(figsize=(12, 4))
plt.plot(lc3['Time'],lc3['COUNT_RATE'])
plt.errorbar(lc3['Time'],lc3['COUNT_RATE'],lc3['COUNT_RATE_ERR'])

plt.xlabel('Time [s]')
plt.ylabel('Count rate [counts/s]')
plt.title('2CXO J002404.2-720457, obsid 78')

Out[37]:

Text(0.5, 1.0, '2CXO J002404.2-720457, obsid 78')

This variable source has an interesting light curve (as determined using the Gregory-Loredo algorithm). The light curve shows clear flickering/flaring behavior. In fact, if we investigate, further, we realize that this source correspond to a magnetic cataclysmic variable in the globular cluster 47 Tuc.

We can also visualize the catalog spectra we downloaded for this source using Sherpa (and then do with it all the cool spectral fitting that you have seen in the BXA tutorials, and that you will see in the Sherpa tutorials):

In [38]:

from sherpa.astro import ui
ui.load_pha('/Users/juan/L3/tutorial/2CXOJ002404.2-720457/00078_000/acisf00078_000N022_r0058_pha3.fits.gz')

read ARF file /Users/juan/L3/tutorial/2CXOJ002404.2-720457/00078_000/acisf00078_000N021_r0058_arf3.fits
read RMF file /Users/juan/L3/tutorial/2CXOJ002404.2-720457/00078_000/acisf00078_000N021_r0058_rmf3.fits
read background file /Users/juan/L3/tutorial/2CXOJ002404.2-720457/00078_000/acisf00078_000N022_r0058_pha3.fits

Let's restrict the energy range, and also bin the counts in constructing the spectrum.

In [39]:

ui.notice_id(1,0.3,7.)
ui.group_counts(10)

In [40]:

ui.set_ylog()
ui.set_xlog()
ui.plot_data()

Cross-matching the CSC2 with other catalogs¶

Now we will explore how to cross-match the catalog with other catalogs for multi-wavelength analysis. For this section, I used inspiration from Adrian Price-Whelan's tutorial on AstroPy cross-matching. Here we will use the 2MASS catalog to explore the infrared and X-ray properties of sources in the Orion nebula. Let's start by doing a cone search to select CSC2 sources in the Orion nebula, just as we have done before with M33:

In [41]:

M42 = SkyCoord.from_name('M42')
maxrad = 5.0 * u.arcmin
results = cone.search(pos=M42, radius=maxrad)
print('Your query resulted in ',len(results),'CSC2 sources')

Your query resulted in  927 CSC2 sources

Now let's use the astroquery to search Vizier for 2MASS sources:

In [42]:

from astroquery.vizier import Vizier
# II/246 is the catalog name for the main 2MASS photometric catalog
v = Vizier(catalog="II/246", columns=['*', 'Date'])
v.ROW_LIMIT = -1

result = v.query_region(M42, radius=maxrad)
tmass_table = result[0]
print('Your query resulted in ',len(result[0]),'2MASS sources')

Your query resulted in  1368 2MASS sources

In [43]:

# The first 10 objects in the 2MASS query
tmass_table[0:10]

Out[43]:

Table length=10

RAJ2000	DEJ2000	_2MASS	Jmag	e_Jmag	Hmag	e_Hmag	Kmag	e_Kmag	Qflg	Rflg	Bflg	Cflg	Xflg	Aflg	Date
deg	deg		mag	mag	mag	mag	mag	mag
float64	float64	bytes17	float32	float32	float32	float32	float32	float32	bytes3	bytes3	bytes3	bytes3	uint8	uint8	bytes10
83.788731	-5.440881	05350929-0526271	14.772	--	15.353	0.257	12.972	--	UDU	020	010	0c0	2	0	2000-11-24
83.785823	-5.438737	05350859-0526194	13.240	0.032	12.317	0.033	11.764	0.024	AAA	222	111	000	1	0	2000-11-24
83.788837	-5.446417	05350932-0526471	14.318	--	13.601	--	14.812	0.184	UUC	002	001	000	2	0	2000-11-24
83.789403	-5.440359	05350945-0526252	13.423	--	11.918	--	14.070	0.113	UUB	002	001	00c	2	0	2000-11-24
83.789565	-5.463578	05350949-0527488	11.173	--	11.348	0.030	10.563	0.018	UEE	022	011	0cc	0	0	2000-11-24
83.789992	-5.466659	05350959-0527599	12.909	0.136	12.147	0.143	11.662	0.109	BBB	222	111	ccc	0	0	2000-11-24
83.790221	-5.439822	05350965-0526233	13.115	0.039	11.565	0.041	10.510	0.024	AAA	222	111	000	2	0	2000-11-24
83.790993	-5.464779	05350983-0527532	7.743	0.024	7.636	0.047	7.472	0.020	AAA	111	111	000	0	0	2000-11-24
83.777502	-5.447484	05350660-0526509	12.479	--	11.576	--	11.178	0.033	UUA	662	002	00c	2	0	2000-11-24
83.791884	-5.438272	05351005-0526177	13.130	--	14.956	0.684	13.955	0.394	UDD	022	011	0cc	2	0	2000-11-24

Let's get the coordinates from the 2MASS catalog

In [44]:

tmass_coords = SkyCoord(tmass_table['RAJ2000'],
                        tmass_table['DEJ2000'])

And the coordinates from the table of CSC2 sources:

In [45]:

M42_coords_csc2 = SkyCoord(
    ra=results['ra']*u.deg,
    dec=results['dec']*u.deg)

Now let's perform the cross-match using AstroPy's match_to_catalog_sky tool, which is based on a nearest neighbors search:

In [46]:

idx_, sep2d_, _ = M42_coords_csc2.match_to_catalog_sky(
    tmass_coords)

Let's visualize the separations between matched sources. Note that this is a crowded field, with many nearest neighbors being incorrect matches. To mitigate this, let's choose only matches that belong to the small separation peak (i.e., those at less than 1 arcsec in separation) in the following two-peaked distribution:

In [47]:

plt.hist(sep2d_.arcsec, histtype='step',
         bins=np.logspace(-2, 2., 64))
plt.xlabel('separation [arcsec]')
plt.xscale('log')
plt.yscale('log')
plt.tight_layout()

We will create a color-hardness diagram, and an X-ray flux-IR magnitude diagram. Select the relevant properties from each table for only those matches with separations less than 1 arcsec:

In [48]:

Jmag1 = tmass_table['Jmag'][idx_][sep2d_.arcsec < 1.0]
Kmag1 = tmass_table['Kmag'][idx_][sep2d_.arcsec < 1.0]
flux_b1 = results['flux_aper_b'][sep2d_.arcsec < 1.0]
hard_hm1 = results['hard_hm'][sep2d_.arcsec < 1.0]

Let's only select objects where the X-ray flux is larger than 0.

In [49]:

Jmag = Jmag1[flux_b1>0]
Kmag = Kmag1[flux_b1>0]
flux_b = flux_b1[flux_b1>0]
hard_hm = hard_hm1[flux_b1>0]

Finally, let's visualize our diagrams:

In [50]:

fig, axes = plt.subplots(1, 2, figsize=(10, 5))

ax = axes[0]
ax.scatter(Jmag - Kmag, hard_hm,
           marker='o', color='b',
           linewidth=0, alpha=0.5)
ax.set_xlabel('$J - K$',size=13)
ax.set_ylabel('Hardness ratio',size=13)
#ax.set_xlim(-1, 6)
#ax.set_ylim(19, 10) # backwards because magnitudes!

ax = axes[1]
ax.scatter(Jmag, flux_b,
           marker='o', color='r',
           linewidth=0, alpha=0.5)
ax.set_xlabel('$J$ [mag]',size=13)
ax.set_ylabel('$F_X$ [erg/s/cm$^2$]',size=13)
ax.set_yscale('log')
#ax.set_xlim(0.2, 1)
ax.set_ylim(5E-17, 1E-11) # backwards because magnitudes!

fig.tight_layout()

Looks like for young stellar objects, redder objects are also harder in X-rays. I am not an expert on this field, but perhaps hard X-ray flares resulting from magnetic reconnection in the surface of these stars are more common on YSO's where some of the redenning accretion disk is still detectable at near-IR wavelengths. Stars are also populating the expected locus of $F_x$ vs. $F_{NIR}$ .

Using your CSC2 data as input for a machine learning algorithm¶

Finally, let's see if we can input our cross-matched data to an algorithm for clustering using scikit-learn. We will use the four quantities we have just plotted as the features for a K-means clustering algorithm that will try to separate astrophysical sources into clusters based on their properties, without associating them with any particular class (this is an unsupervised machine learning method).

In [51]:

from sklearn.cluster import KMeans

We need to get an array 'X' that will be the input to the clustering algorithm. This array is a list of 4 features (log of the X-ray flux, J-K color, J magnitude and the CSC2 hm hardness ratio) for each of the sources in our cross-matched list:

In [52]:

X = []
for i in range(len(Jmag)):
    X.append([Jmag[i],Jmag[i]-Kmag[i],np.log10(flux_b[i]),hard_hm[i]])
    
print(len(X))

Note that this dataset contains only 554 objects, and we are also going to arbitrarily select n=3 as the number of clusters into which we will separate those objects. So, please do not over-interpret the results, as they are most likely meaningless, or at most just marginally indicative. But you can refine this by including more objects, more features, and also by carefully selecting the number of clusters.

Because some of the fluxes and magnitudes can be not defined, this means that our dataset has missing data, which usually does not go well with standard machine learning algorithms. A solution for this is data imputation, which replaces missing values (NaNs) with a certain value, for example, the mean of the distribution for the corresponding feature. You can do this data imputation easily with scikit-learn:

In [55]:

from sklearn.impute import SimpleImputer
imp_mean = SimpleImputer(missing_values=np.nan, strategy='mean')
imp_mean.fit(X)
X_imputed = imp_mean.transform(X)

Now, let's fit the K-means model to the imputed data:

In [57]:

y_pred = KMeans(n_clusters=3, random_state=None).fit_predict(X_imputed)

In [58]:

fig, axes = plt.subplots(1, 2, figsize=(12, 12))
plt.subplot(221)
plt.scatter(X_imputed[:, 0], X_imputed[:, 1], c=y_pred)
plt.xlabel('J mag')
plt.ylabel('J - K')
plt.subplot(222)
plt.scatter(X_imputed[:, 2], X_imputed[:, 3], c=y_pred)
plt.xlabel('log (Fx)')
plt.ylabel('hard_hm')
plt.subplot(223)
plt.scatter(X_imputed[:, 1], X_imputed[:, 3], c=y_pred)
plt.xlabel('J - K')
plt.ylabel('hard_hm')
plt.subplot(224)
plt.scatter(X_imputed[:, 0], X_imputed[:, 2], c=y_pred)
plt.xlabel('J mag')
plt.ylabel('log (Fx)')

Out[58]:

Text(0, 0.5, 'log (Fx)')

It looks like the K-means algorithm separated the sources based mainly on how bright they are in the near-IR and X-ray bands, but it also took into account some information about the IR color and X-ray hardness ratio.

Download the notebook.

/ csc / threads / pyvoaccess / notebook.html