Matching catalogs based on membership (detailed)
================================================

Here we show the specific steps of matching two catalogs based on
proximity

.. code:: ipython3

    %load_ext autoreload
    %autoreload 2

ClCatalogs
----------

Given some input data

.. code:: ipython3

    import numpy as np
    from astropy.table import Table
    input1 = Table({'ID': ['CL0a', 'CL1a', 'CL2a', 'CL3a', 'CL4a']})
    input1['MASS'] = 1e14*np.arange(1, 6)*10
    input2 = Table({'ID': ['CL0b', 'CL1b', 'CL2b', 'CL3b']})
    input2['MASS'] = 1e14*np.arange(1, 5)*10
    display(input1)
    display(input2)
    input1_mem = Table(
        {'ID':[
            'MEM0', 'MEM1', 'MEM2', 'MEM3', 'MEM4',
            'MEM5', 'MEM6', 'MEM7', 'MEM8', 'MEM9',
            'MEM10', 'MEM11', 'MEM12', 'MEM13', 'MEM14'],
         'ID_CLUSTER': [
             'CL0a', 'CL0a', 'CL0a', 'CL0a', 'CL0a',
             'CL1a', 'CL1a', 'CL1a', 'CL1a', 'CL2a',
             'CL2a', 'CL2a', 'CL3a', 'CL3a', 'CL4a'],
        })
    input2_mem = Table(
        {'ID':[
            'MEM0', 'MEM1', 'MEM2', 'MEM3', 'MEM4',
            'MEM5', 'MEM6', 'MEM7', 'MEM8', 'MEM9',
            'MEM10', 'MEM11', 'MEM12', 'MEM13'],
         'ID_CLUSTER': [
             'CL3b', 'CL0b', 'CL0b', 'CL0b', 'CL0b',
             'CL1b', 'CL1b', 'CL1b', 'CL1b', 'CL2b',
             'CL2b', 'CL2b', 'CL3b', 'CL3b'],
        })
    input1_mem['RA'] = np.arange(len(input1_mem))*10.0
    input2_mem['RA'] = np.arange(len(input2_mem))*10.0
    input1_mem['DEC'] = 0.0
    input2_mem['DEC'] = 0.0
    input1_mem['Z'] = 0.1
    input2_mem['Z'] = 0.1
    input1_mem['PMEM'] = 1.0
    input2_mem['PMEM'] = 1.0
    display(input1_mem)
    display(input2_mem)



.. raw:: html

    <div><i>Table length=5</i>
    <table id="table139651809206528" class="table-striped table-bordered table-condensed">
    <thead><tr><th>ID</th><th>MASS</th></tr></thead>
    <thead><tr><th>str4</th><th>float64</th></tr></thead>
    <tr><td>CL0a</td><td>1000000000000000.0</td></tr>
    <tr><td>CL1a</td><td>2000000000000000.0</td></tr>
    <tr><td>CL2a</td><td>3000000000000000.0</td></tr>
    <tr><td>CL3a</td><td>4000000000000000.0</td></tr>
    <tr><td>CL4a</td><td>5000000000000000.0</td></tr>
    </table></div>



.. raw:: html

    <div><i>Table length=4</i>
    <table id="table139651809206576" class="table-striped table-bordered table-condensed">
    <thead><tr><th>ID</th><th>MASS</th></tr></thead>
    <thead><tr><th>str4</th><th>float64</th></tr></thead>
    <tr><td>CL0b</td><td>1000000000000000.0</td></tr>
    <tr><td>CL1b</td><td>2000000000000000.0</td></tr>
    <tr><td>CL2b</td><td>3000000000000000.0</td></tr>
    <tr><td>CL3b</td><td>4000000000000000.0</td></tr>
    </table></div>



.. raw:: html

    <div><i>Table length=15</i>
    <table id="table139651809206768" class="table-striped table-bordered table-condensed">
    <thead><tr><th>ID</th><th>ID_CLUSTER</th><th>RA</th><th>DEC</th><th>Z</th><th>PMEM</th></tr></thead>
    <thead><tr><th>str5</th><th>str4</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th></tr></thead>
    <tr><td>MEM0</td><td>CL0a</td><td>0.0</td><td>0.0</td><td>0.1</td><td>1.0</td></tr>
    <tr><td>MEM1</td><td>CL0a</td><td>10.0</td><td>0.0</td><td>0.1</td><td>1.0</td></tr>
    <tr><td>MEM2</td><td>CL0a</td><td>20.0</td><td>0.0</td><td>0.1</td><td>1.0</td></tr>
    <tr><td>MEM3</td><td>CL0a</td><td>30.0</td><td>0.0</td><td>0.1</td><td>1.0</td></tr>
    <tr><td>MEM4</td><td>CL0a</td><td>40.0</td><td>0.0</td><td>0.1</td><td>1.0</td></tr>
    <tr><td>MEM5</td><td>CL1a</td><td>50.0</td><td>0.0</td><td>0.1</td><td>1.0</td></tr>
    <tr><td>MEM6</td><td>CL1a</td><td>60.0</td><td>0.0</td><td>0.1</td><td>1.0</td></tr>
    <tr><td>MEM7</td><td>CL1a</td><td>70.0</td><td>0.0</td><td>0.1</td><td>1.0</td></tr>
    <tr><td>MEM8</td><td>CL1a</td><td>80.0</td><td>0.0</td><td>0.1</td><td>1.0</td></tr>
    <tr><td>MEM9</td><td>CL2a</td><td>90.0</td><td>0.0</td><td>0.1</td><td>1.0</td></tr>
    <tr><td>MEM10</td><td>CL2a</td><td>100.0</td><td>0.0</td><td>0.1</td><td>1.0</td></tr>
    <tr><td>MEM11</td><td>CL2a</td><td>110.0</td><td>0.0</td><td>0.1</td><td>1.0</td></tr>
    <tr><td>MEM12</td><td>CL3a</td><td>120.0</td><td>0.0</td><td>0.1</td><td>1.0</td></tr>
    <tr><td>MEM13</td><td>CL3a</td><td>130.0</td><td>0.0</td><td>0.1</td><td>1.0</td></tr>
    <tr><td>MEM14</td><td>CL4a</td><td>140.0</td><td>0.0</td><td>0.1</td><td>1.0</td></tr>
    </table></div>



.. raw:: html

    <div><i>Table length=14</i>
    <table id="table139650895385792" class="table-striped table-bordered table-condensed">
    <thead><tr><th>ID</th><th>ID_CLUSTER</th><th>RA</th><th>DEC</th><th>Z</th><th>PMEM</th></tr></thead>
    <thead><tr><th>str5</th><th>str4</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th></tr></thead>
    <tr><td>MEM0</td><td>CL3b</td><td>0.0</td><td>0.0</td><td>0.1</td><td>1.0</td></tr>
    <tr><td>MEM1</td><td>CL0b</td><td>10.0</td><td>0.0</td><td>0.1</td><td>1.0</td></tr>
    <tr><td>MEM2</td><td>CL0b</td><td>20.0</td><td>0.0</td><td>0.1</td><td>1.0</td></tr>
    <tr><td>MEM3</td><td>CL0b</td><td>30.0</td><td>0.0</td><td>0.1</td><td>1.0</td></tr>
    <tr><td>MEM4</td><td>CL0b</td><td>40.0</td><td>0.0</td><td>0.1</td><td>1.0</td></tr>
    <tr><td>MEM5</td><td>CL1b</td><td>50.0</td><td>0.0</td><td>0.1</td><td>1.0</td></tr>
    <tr><td>MEM6</td><td>CL1b</td><td>60.0</td><td>0.0</td><td>0.1</td><td>1.0</td></tr>
    <tr><td>MEM7</td><td>CL1b</td><td>70.0</td><td>0.0</td><td>0.1</td><td>1.0</td></tr>
    <tr><td>MEM8</td><td>CL1b</td><td>80.0</td><td>0.0</td><td>0.1</td><td>1.0</td></tr>
    <tr><td>MEM9</td><td>CL2b</td><td>90.0</td><td>0.0</td><td>0.1</td><td>1.0</td></tr>
    <tr><td>MEM10</td><td>CL2b</td><td>100.0</td><td>0.0</td><td>0.1</td><td>1.0</td></tr>
    <tr><td>MEM11</td><td>CL2b</td><td>110.0</td><td>0.0</td><td>0.1</td><td>1.0</td></tr>
    <tr><td>MEM12</td><td>CL3b</td><td>120.0</td><td>0.0</td><td>0.1</td><td>1.0</td></tr>
    <tr><td>MEM13</td><td>CL3b</td><td>130.0</td><td>0.0</td><td>0.1</td><td>1.0</td></tr>
    </table></div>


Create two ``ClCatalog`` objects, they have the same properties of
``astropy`` tables with additional functionality. For the membership
matching, the main columns to be included are: - ``id`` - must
correspond to ``id_cluster`` in the cluster member catalog. - ``mass``
(or mass proxy) - necessary for proxity matching if
``shared_member_fraction`` used as preference criteria for unique
matches, default use.

All of the columns can be added when creating the ``ClCatalog`` object
passing them as keys:

::

   cat = ClCatalog('Cat', ra=[0, 1])

or can also be added afterwards:

::

   cat = ClCatalog('Cat')
   cat['ra'] = [0, 1]

.. code:: ipython3

    from clevar.catalog import ClCatalog
    c1 = ClCatalog('Cat1', id=input1['ID'], mass=input1['MASS'])
    c2 = ClCatalog('Cat2', id=input2['ID'], mass=input2['MASS'])
    
    # Format for nice display
    c1['mass'].info.format = '.2e'
    c2['mass'].info.format = '.2e'
    
    display(c1)
    display(c2)



.. raw:: html

    <b>Cat1</b><br></b><b>tags:</b> id(id), mass(mass)<br><b>Radius unit:</b> None<br><div><i>ClData length=5</i>
    <table id="table139650257059792" class="table-striped table-bordered table-condensed">
    <thead><tr><th>id</th><th>mass</th><th>mt_self</th><th>mt_other</th><th>mt_multi_self</th><th>mt_multi_other</th></tr></thead>
    <thead><tr><th>str4</th><th>float64</th><th>object</th><th>object</th><th>object</th><th>object</th></tr></thead>
    <tr><td>CL0a</td><td>1.00e+15</td><td>None</td><td>None</td><td>[]</td><td>[]</td></tr>
    <tr><td>CL1a</td><td>2.00e+15</td><td>None</td><td>None</td><td>[]</td><td>[]</td></tr>
    <tr><td>CL2a</td><td>3.00e+15</td><td>None</td><td>None</td><td>[]</td><td>[]</td></tr>
    <tr><td>CL3a</td><td>4.00e+15</td><td>None</td><td>None</td><td>[]</td><td>[]</td></tr>
    <tr><td>CL4a</td><td>5.00e+15</td><td>None</td><td>None</td><td>[]</td><td>[]</td></tr>
    </table></div>



.. raw:: html

    <b>Cat2</b><br></b><b>tags:</b> id(id), mass(mass)<br><b>Radius unit:</b> None<br><div><i>ClData length=4</i>
    <table id="table139650257095936" class="table-striped table-bordered table-condensed">
    <thead><tr><th>id</th><th>mass</th><th>mt_self</th><th>mt_other</th><th>mt_multi_self</th><th>mt_multi_other</th></tr></thead>
    <thead><tr><th>str4</th><th>float64</th><th>object</th><th>object</th><th>object</th><th>object</th></tr></thead>
    <tr><td>CL0b</td><td>1.00e+15</td><td>None</td><td>None</td><td>[]</td><td>[]</td></tr>
    <tr><td>CL1b</td><td>2.00e+15</td><td>None</td><td>None</td><td>[]</td><td>[]</td></tr>
    <tr><td>CL2b</td><td>3.00e+15</td><td>None</td><td>None</td><td>[]</td><td>[]</td></tr>
    <tr><td>CL3b</td><td>4.00e+15</td><td>None</td><td>None</td><td>[]</td><td>[]</td></tr>
    </table></div>


The members can be added to the cluster object using the ``add_members``
function. It has a similar instanciating format of a ``ClCatalog``
object, where the columns are added by keyword arguments (the key
``id_cluster`` is always necessary and must correspond to ``id`` in the
main cluster catalog).

For details see XXX:

.. code:: ipython3

    from clevar.catalog import MemCatalog
    c1.add_members(id=input1_mem['ID'], id_cluster=input1_mem['ID_CLUSTER'],
                    ra=input1_mem['RA'], dec=input1_mem['DEC'], z=input1_mem['Z'],
                    pmem=input1_mem['PMEM'])
    c2.add_members(id=input2_mem['ID'], id_cluster=input2_mem['ID_CLUSTER'],
                    ra=input2_mem['RA'], dec=input2_mem['DEC'], z=input2_mem['Z'],
                    pmem=input2_mem['PMEM'])
    
    display(c1.members)
    display(c2.members)



.. raw:: html

    <b>members</b><br><b>tags:</b> id(id), id_cluster(id_cluster), ra(ra), dec(dec), z(z), pmem(pmem)<br><div><i>ClData length=15</i>
    <table id="table139651808688400" class="table-striped table-bordered table-condensed">
    <thead><tr><th>id</th><th>id_cluster</th><th>ra</th><th>dec</th><th>z</th><th>pmem</th><th>ind_cl</th></tr></thead>
    <thead><tr><th>str5</th><th>str4</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>int64</th></tr></thead>
    <tr><td>MEM0</td><td>CL0a</td><td>0.0</td><td>0.0</td><td>0.1</td><td>1.0</td><td>0</td></tr>
    <tr><td>MEM1</td><td>CL0a</td><td>10.0</td><td>0.0</td><td>0.1</td><td>1.0</td><td>0</td></tr>
    <tr><td>MEM2</td><td>CL0a</td><td>20.0</td><td>0.0</td><td>0.1</td><td>1.0</td><td>0</td></tr>
    <tr><td>MEM3</td><td>CL0a</td><td>30.0</td><td>0.0</td><td>0.1</td><td>1.0</td><td>0</td></tr>
    <tr><td>MEM4</td><td>CL0a</td><td>40.0</td><td>0.0</td><td>0.1</td><td>1.0</td><td>0</td></tr>
    <tr><td>MEM5</td><td>CL1a</td><td>50.0</td><td>0.0</td><td>0.1</td><td>1.0</td><td>1</td></tr>
    <tr><td>MEM6</td><td>CL1a</td><td>60.0</td><td>0.0</td><td>0.1</td><td>1.0</td><td>1</td></tr>
    <tr><td>MEM7</td><td>CL1a</td><td>70.0</td><td>0.0</td><td>0.1</td><td>1.0</td><td>1</td></tr>
    <tr><td>MEM8</td><td>CL1a</td><td>80.0</td><td>0.0</td><td>0.1</td><td>1.0</td><td>1</td></tr>
    <tr><td>MEM9</td><td>CL2a</td><td>90.0</td><td>0.0</td><td>0.1</td><td>1.0</td><td>2</td></tr>
    <tr><td>MEM10</td><td>CL2a</td><td>100.0</td><td>0.0</td><td>0.1</td><td>1.0</td><td>2</td></tr>
    <tr><td>MEM11</td><td>CL2a</td><td>110.0</td><td>0.0</td><td>0.1</td><td>1.0</td><td>2</td></tr>
    <tr><td>MEM12</td><td>CL3a</td><td>120.0</td><td>0.0</td><td>0.1</td><td>1.0</td><td>3</td></tr>
    <tr><td>MEM13</td><td>CL3a</td><td>130.0</td><td>0.0</td><td>0.1</td><td>1.0</td><td>3</td></tr>
    <tr><td>MEM14</td><td>CL4a</td><td>140.0</td><td>0.0</td><td>0.1</td><td>1.0</td><td>4</td></tr>
    </table></div>



.. raw:: html

    <b>members</b><br><b>tags:</b> id(id), id_cluster(id_cluster), ra(ra), dec(dec), z(z), pmem(pmem)<br><div><i>ClData length=14</i>
    <table id="table139650256700560" class="table-striped table-bordered table-condensed">
    <thead><tr><th>id</th><th>id_cluster</th><th>ra</th><th>dec</th><th>z</th><th>pmem</th><th>ind_cl</th></tr></thead>
    <thead><tr><th>str5</th><th>str4</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>int64</th></tr></thead>
    <tr><td>MEM0</td><td>CL3b</td><td>0.0</td><td>0.0</td><td>0.1</td><td>1.0</td><td>3</td></tr>
    <tr><td>MEM1</td><td>CL0b</td><td>10.0</td><td>0.0</td><td>0.1</td><td>1.0</td><td>0</td></tr>
    <tr><td>MEM2</td><td>CL0b</td><td>20.0</td><td>0.0</td><td>0.1</td><td>1.0</td><td>0</td></tr>
    <tr><td>MEM3</td><td>CL0b</td><td>30.0</td><td>0.0</td><td>0.1</td><td>1.0</td><td>0</td></tr>
    <tr><td>MEM4</td><td>CL0b</td><td>40.0</td><td>0.0</td><td>0.1</td><td>1.0</td><td>0</td></tr>
    <tr><td>MEM5</td><td>CL1b</td><td>50.0</td><td>0.0</td><td>0.1</td><td>1.0</td><td>1</td></tr>
    <tr><td>MEM6</td><td>CL1b</td><td>60.0</td><td>0.0</td><td>0.1</td><td>1.0</td><td>1</td></tr>
    <tr><td>MEM7</td><td>CL1b</td><td>70.0</td><td>0.0</td><td>0.1</td><td>1.0</td><td>1</td></tr>
    <tr><td>MEM8</td><td>CL1b</td><td>80.0</td><td>0.0</td><td>0.1</td><td>1.0</td><td>1</td></tr>
    <tr><td>MEM9</td><td>CL2b</td><td>90.0</td><td>0.0</td><td>0.1</td><td>1.0</td><td>2</td></tr>
    <tr><td>MEM10</td><td>CL2b</td><td>100.0</td><td>0.0</td><td>0.1</td><td>1.0</td><td>2</td></tr>
    <tr><td>MEM11</td><td>CL2b</td><td>110.0</td><td>0.0</td><td>0.1</td><td>1.0</td><td>2</td></tr>
    <tr><td>MEM12</td><td>CL3b</td><td>120.0</td><td>0.0</td><td>0.1</td><td>1.0</td><td>3</td></tr>
    <tr><td>MEM13</td><td>CL3b</td><td>130.0</td><td>0.0</td><td>0.1</td><td>1.0</td><td>3</td></tr>
    </table></div>


The catalogs can also be read directly from files, for more details see
catalogs.ipynb.

Matching
--------

Import the ``MembershipMatch`` and create a object for matching

.. code:: ipython3

    from clevar.match import MembershipMatch
    mt = MembershipMatch()

Prepare the matching object
~~~~~~~~~~~~~~~~~~~~~~~~~~~

Before matching the clusters it is necessary to match the members
catalogs and then filling the clusters with information about of the
shared members.

The matching of members can be done by ``id`` if both member catalogs
share the same ``id``\ s or by angular proximity.

The first step is to prepare each catalog with the matching
configuration:

-  ``delta_z``: Defines redshift window for matching. The possible
   values are:

   -  ``'cat'``: uses redshift properties of the catalog
   -  ``'spline.filename'``: interpolates data in ``'filename'``
      assuming (z, zmin, zmax) format
   -  ``float``: uses ``delta_z*(1+z)``
   -  ``None``: does not use z

-  ``match_radius``: Radius of the catalog to be used in the matching.
   If ``'cat'`` uses the radius in the catalog, else must be in format
   ``'value unit'``. (ex: ``'1 arcsec'``, ``'1 Mpc'``)

In this case, because one of the configuraion radius has physical units,
we also need a cosmology (``cosmo``) object to convert it to angular
size (this is done internally).

To match the members by ``id``, just run the function:

.. code:: ipython3

    %%time
    mt.match_members(c1.members, c2.members, method='id')


.. parsed-literal::

    28 members were matched.
    CPU times: user 404 µs, sys: 634 µs, total: 1.04 ms
    Wall time: 1.03 ms


To match the members by angular proximity you also have to provide: -
``radius``\ (``str``, ``None``). Radius for matching, with format
``'value unit'`` (ex: ``1 arcsec``, ``1 Mpc``). -
``cosmo``\ (``clevar.Cosmology``, ``None``). Cosmology object for when
radius has physical units. Then call the same function with these
arguments

.. code:: ipython3

    from clevar.cosmology import AstroPyCosmology
    mt.match_members(c1.members, c2.members, method='angular_distance',
                     radius='0.1 kpc', cosmo=AstroPyCosmology())


.. parsed-literal::

    
    ## ClCatalog 1
    ## Prep mt_cols
    * zmin|zmax set to -1|10
    * ang radius from set scale
    
    ## ClCatalog 2
    ## Prep mt_cols
    * zmin|zmax set to -1|10
    * ang radius from set scale
    
    ## Multiple match (catalog 1)
    Finding candidates (members)
    * 14/15 objects matched.
    
    ## Multiple match (catalog 2)
    Finding candidates (members)
    * 14/14 objects matched.
    
    ## Finding unique matches of catalog 1
    Unique Matches (members)
    * 14/15 objects matched.
    
    ## Finding unique matches of catalog 2
    Unique Matches (members)
    * 14/14 objects matched.
    Cross Matches (members)
    * 14/15 objects matched.
    Cross Matches (members)
    * 14/14 objects matched.
    28 members were matched.


This function adds a ``matched_mems`` attribute to your matching object
(``mt.matched_mems`` in this case) that contains the indices of the
matched members. This attribute can be saved and loaded so you don’t
have to redo this step. Just use the functions:

.. code:: ipython3

    mt.save_matched_members(filename='mem_mt.txt', overwrite=False)
    mt.load_matched_members(filename='mem_mt.txt')

Now we fill the catalogs with the information regarding the matched
members. In this step, each cluster catalog will have a ``ClData`` table
in its ``mt_input`` attibute with the number of members in each cluster
(``nmem``) and a dictionary containing the number of shaded objects with
the clusters of the other catalog (``shared_mems``).

If ``pmem`` is provided to the members, these quantities are computed
as:

.. raw:: html

   <center>

:math:`nmem=\sum_{cluster\;members} Pmem_i`

.. raw:: html

   </center>

.. raw:: html

   <center>

:math:`shared\_mems=\sum_{shared\;members} Pmem_i`

.. raw:: html

   </center>

.. code:: ipython3

    mt.fill_shared_members(c1, c2)

.. code:: ipython3

    display(c1.mt_input)
    display(c2.mt_input)



.. raw:: html

    <div><i>ClData length=5</i>
    <table id="table139650256475808" class="table-striped table-bordered table-condensed">
    <thead><tr><th>share_mems</th><th>nmem</th></tr></thead>
    <thead><tr><th>object</th><th>float64</th></tr></thead>
    <tr><td>{&apos;CL3b&apos;: 1.0, &apos;CL0b&apos;: 4.0}</td><td>5.0</td></tr>
    <tr><td>{&apos;CL1b&apos;: 4.0}</td><td>4.0</td></tr>
    <tr><td>{&apos;CL2b&apos;: 3.0}</td><td>3.0</td></tr>
    <tr><td>{&apos;CL3b&apos;: 2.0}</td><td>2.0</td></tr>
    <tr><td>{}</td><td>1.0</td></tr>
    </table></div>



.. raw:: html

    <div><i>ClData length=4</i>
    <table id="table139651809205072" class="table-striped table-bordered table-condensed">
    <thead><tr><th>share_mems</th><th>nmem</th></tr></thead>
    <thead><tr><th>object</th><th>float64</th></tr></thead>
    <tr><td>{&apos;CL0a&apos;: 4.0}</td><td>4.0</td></tr>
    <tr><td>{&apos;CL1a&apos;: 4.0}</td><td>4.0</td></tr>
    <tr><td>{&apos;CL2a&apos;: 3.0}</td><td>3.0</td></tr>
    <tr><td>{&apos;CL3a&apos;: 2.0, &apos;CL0a&apos;: 1.0}</td><td>3.0</td></tr>
    </table></div>


Again, these results can be saved and loaded so you don’t have to redo
this step. Just use the functions:

.. code:: ipython3

    mt.save_shared_members(c1, c2, fileprefix='mem_share')
    mt.load_shared_members(c1, c2, fileprefix='mem_share')

Once this step is done, you can actually start matching the clusters.

Multiple matching
~~~~~~~~~~~~~~~~~

The next step is to match the catalogs and store all candidates that
pass the matching criteria.

.. code:: ipython3

    %%time
    mt.multiple(c1, c2)
    mt.multiple(c2, c1)


.. parsed-literal::

    Finding candidates (Cat1)
    * 4/5 objects matched.
    Finding candidates (Cat2)
    * 4/4 objects matched.
    CPU times: user 1.98 ms, sys: 0 ns, total: 1.98 ms
    Wall time: 1.86 ms


This will fill the ``mt_multi_self`` and ``mt_multi_other`` columns:

.. code:: ipython3

    display(c1)
    display(c2)



.. raw:: html

    <b>Cat1</b><br></b><b>tags:</b> id(id), mass(mass)<br><b>Radius unit:</b> None<br><div><i>ClData length=5</i>
    <table id="table139650256483952" class="table-striped table-bordered table-condensed">
    <thead><tr><th colspan=6></th><th colspan=2 style='text-align:left; background-color:grey; color:white'>mt_input</th></tr></thread><thead><tr><th>id</th><th>mass</th><th>mt_self</th><th>mt_other</th><th>mt_multi_self</th><th>mt_multi_other</th><th>share_mems</th><th>nmem</th></tr></thead>
    <thead><tr><th>str4</th><th>float64</th><th>object</th><th>object</th><th>object</th><th>object</th><th>object</th><th>float64</th></tr></thead>
    <tr><td>CL0a</td><td>1.00e+15</td><td>None</td><td>None</td><td>[&apos;CL0b&apos;, &apos;CL3b&apos;]</td><td>[&apos;CL0b&apos;, &apos;CL3b&apos;]</td><td>{&apos;CL3b&apos;: 1.0, &apos;CL0b&apos;: 4.0}</td><td>5.0</td></tr>
    <tr><td>CL1a</td><td>2.00e+15</td><td>None</td><td>None</td><td>[&apos;CL1b&apos;]</td><td>[&apos;CL1b&apos;]</td><td>{&apos;CL1b&apos;: 4.0}</td><td>4.0</td></tr>
    <tr><td>CL2a</td><td>3.00e+15</td><td>None</td><td>None</td><td>[&apos;CL2b&apos;]</td><td>[&apos;CL2b&apos;]</td><td>{&apos;CL2b&apos;: 3.0}</td><td>3.0</td></tr>
    <tr><td>CL3a</td><td>4.00e+15</td><td>None</td><td>None</td><td>[&apos;CL3b&apos;]</td><td>[&apos;CL3b&apos;]</td><td>{&apos;CL3b&apos;: 2.0}</td><td>2.0</td></tr>
    <tr><td>CL4a</td><td>5.00e+15</td><td>None</td><td>None</td><td>[]</td><td>[]</td><td>{}</td><td>1.0</td></tr>
    </table></div>



.. raw:: html

    <b>Cat2</b><br></b><b>tags:</b> id(id), mass(mass)<br><b>Radius unit:</b> None<br><div><i>ClData length=4</i>
    <table id="table139650256483040" class="table-striped table-bordered table-condensed">
    <thead><tr><th colspan=6></th><th colspan=2 style='text-align:left; background-color:grey; color:white'>mt_input</th></tr></thread><thead><tr><th>id</th><th>mass</th><th>mt_self</th><th>mt_other</th><th>mt_multi_self</th><th>mt_multi_other</th><th>share_mems</th><th>nmem</th></tr></thead>
    <thead><tr><th>str4</th><th>float64</th><th>object</th><th>object</th><th>object</th><th>object</th><th>object</th><th>float64</th></tr></thead>
    <tr><td>CL0b</td><td>1.00e+15</td><td>None</td><td>None</td><td>[&apos;CL0a&apos;]</td><td>[&apos;CL0a&apos;]</td><td>{&apos;CL0a&apos;: 4.0}</td><td>4.0</td></tr>
    <tr><td>CL1b</td><td>2.00e+15</td><td>None</td><td>None</td><td>[&apos;CL1a&apos;]</td><td>[&apos;CL1a&apos;]</td><td>{&apos;CL1a&apos;: 4.0}</td><td>4.0</td></tr>
    <tr><td>CL2b</td><td>3.00e+15</td><td>None</td><td>None</td><td>[&apos;CL2a&apos;]</td><td>[&apos;CL2a&apos;]</td><td>{&apos;CL2a&apos;: 3.0}</td><td>3.0</td></tr>
    <tr><td>CL3b</td><td>4.00e+15</td><td>None</td><td>None</td><td>[&apos;CL3a&apos;, &apos;CL0a&apos;]</td><td>[&apos;CL3a&apos;, &apos;CL0a&apos;]</td><td>{&apos;CL3a&apos;: 2.0, &apos;CL0a&apos;: 1.0}</td><td>3.0</td></tr>
    </table></div>


Unique matching
~~~~~~~~~~~~~~~

Once all candidates are stored in each catalog, we can find the best
candidates. You can also pass the argument: - ``preference``: In cases
where there are multiple matched, how the best candidate will be chosen.
Options are: ``'more_massive'``, ``'angular_proximity'``,
``'redshift_proximity'``, ``'shared_member_fraction'`` (default value).

.. code:: ipython3

    %%time
    mt.unique(c1, c2, preference='shared_member_fraction')
    mt.unique(c2, c1, preference='shared_member_fraction')


.. parsed-literal::

    Unique Matches (Cat1)
    * 4/5 objects matched.
    Unique Matches (Cat2)
    * 4/4 objects matched.
    CPU times: user 695 µs, sys: 919 µs, total: 1.61 ms
    Wall time: 1.56 ms


This will fill the matching columns: - ``mt_self``: Best candidate found
- ``mt_other``: Best candidate found by the other catalog -
``mt_frac_self``: Fraction of shared members with the best candidate
found - ``mt_frac_other``: Fraction of shared members by the best
candidate found by the other catalog, relative to the other catalog

If ``pmem`` is present in the members catalogs, the shared fractions are
computed by:

.. raw:: html

   <h1>

.. raw:: html

   <center>

:math:`\frac{\sum_{shared\;members}Pmem_i}{\sum_{cluster\;members}Pmem_i}`

.. raw:: html

   </center>

.. raw:: html

   </h1>

.. code:: ipython3

    display(c1)
    display(c2)



.. raw:: html

    <b>Cat1</b><br></b><b>tags:</b> id(id), mass(mass)<br><b>Radius unit:</b> None<br><div><i>ClData length=5</i>
    <table id="table139650256371136" class="table-striped table-bordered table-condensed">
    <thead><tr><th colspan=8></th><th colspan=2 style='text-align:left; background-color:grey; color:white'>mt_input</th></tr></thread><thead><tr><th>id</th><th>mass</th><th>mt_self</th><th>mt_other</th><th>mt_multi_self</th><th>mt_multi_other</th><th>mt_frac_self</th><th>mt_frac_other</th><th>share_mems</th><th>nmem</th></tr></thead>
    <thead><tr><th>str4</th><th>float64</th><th>object</th><th>object</th><th>object</th><th>object</th><th>float64</th><th>float64</th><th>object</th><th>float64</th></tr></thead>
    <tr><td>CL0a</td><td>1.00e+15</td><td>CL0b</td><td>CL0b</td><td>[&apos;CL0b&apos;, &apos;CL3b&apos;]</td><td>[&apos;CL0b&apos;, &apos;CL3b&apos;]</td><td>0.8</td><td>1.0</td><td>{&apos;CL3b&apos;: 1.0, &apos;CL0b&apos;: 4.0}</td><td>5.0</td></tr>
    <tr><td>CL1a</td><td>2.00e+15</td><td>CL1b</td><td>CL1b</td><td>[&apos;CL1b&apos;]</td><td>[&apos;CL1b&apos;]</td><td>1.0</td><td>1.0</td><td>{&apos;CL1b&apos;: 4.0}</td><td>4.0</td></tr>
    <tr><td>CL2a</td><td>3.00e+15</td><td>CL2b</td><td>CL2b</td><td>[&apos;CL2b&apos;]</td><td>[&apos;CL2b&apos;]</td><td>1.0</td><td>1.0</td><td>{&apos;CL2b&apos;: 3.0}</td><td>3.0</td></tr>
    <tr><td>CL3a</td><td>4.00e+15</td><td>CL3b</td><td>CL3b</td><td>[&apos;CL3b&apos;]</td><td>[&apos;CL3b&apos;]</td><td>1.0</td><td>0.6666666666666666</td><td>{&apos;CL3b&apos;: 2.0}</td><td>2.0</td></tr>
    <tr><td>CL4a</td><td>5.00e+15</td><td>None</td><td>None</td><td>[]</td><td>[]</td><td>0.0</td><td>0.0</td><td>{}</td><td>1.0</td></tr>
    </table></div>



.. raw:: html

    <b>Cat2</b><br></b><b>tags:</b> id(id), mass(mass)<br><b>Radius unit:</b> None<br><div><i>ClData length=4</i>
    <table id="table139650256440528" class="table-striped table-bordered table-condensed">
    <thead><tr><th colspan=8></th><th colspan=2 style='text-align:left; background-color:grey; color:white'>mt_input</th></tr></thread><thead><tr><th>id</th><th>mass</th><th>mt_self</th><th>mt_other</th><th>mt_multi_self</th><th>mt_multi_other</th><th>mt_frac_other</th><th>mt_frac_self</th><th>share_mems</th><th>nmem</th></tr></thead>
    <thead><tr><th>str4</th><th>float64</th><th>object</th><th>object</th><th>object</th><th>object</th><th>float64</th><th>float64</th><th>object</th><th>float64</th></tr></thead>
    <tr><td>CL0b</td><td>1.00e+15</td><td>CL0a</td><td>CL0a</td><td>[&apos;CL0a&apos;]</td><td>[&apos;CL0a&apos;]</td><td>0.8</td><td>1.0</td><td>{&apos;CL0a&apos;: 4.0}</td><td>4.0</td></tr>
    <tr><td>CL1b</td><td>2.00e+15</td><td>CL1a</td><td>CL1a</td><td>[&apos;CL1a&apos;]</td><td>[&apos;CL1a&apos;]</td><td>1.0</td><td>1.0</td><td>{&apos;CL1a&apos;: 4.0}</td><td>4.0</td></tr>
    <tr><td>CL2b</td><td>3.00e+15</td><td>CL2a</td><td>CL2a</td><td>[&apos;CL2a&apos;]</td><td>[&apos;CL2a&apos;]</td><td>1.0</td><td>1.0</td><td>{&apos;CL2a&apos;: 3.0}</td><td>3.0</td></tr>
    <tr><td>CL3b</td><td>4.00e+15</td><td>CL3a</td><td>CL3a</td><td>[&apos;CL3a&apos;, &apos;CL0a&apos;]</td><td>[&apos;CL3a&apos;, &apos;CL0a&apos;]</td><td>1.0</td><td>0.6666666666666666</td><td>{&apos;CL3a&apos;: 2.0, &apos;CL0a&apos;: 1.0}</td><td>3.0</td></tr>
    </table></div>


Cross matching
~~~~~~~~~~~~~~

If you want to make sure the same pair was found in both directions:

.. code:: ipython3

    c1.cross_match()
    c2.cross_match()

This will fill the ``mt_cross`` column:

.. code:: ipython3

    display(c1)
    display(c2)



.. raw:: html

    <b>Cat1</b><br></b><b>tags:</b> id(id), mass(mass)<br><b>Radius unit:</b> None<br><div><i>ClData length=5</i>
    <table id="table139651808692400" class="table-striped table-bordered table-condensed">
    <thead><tr><th colspan=9></th><th colspan=2 style='text-align:left; background-color:grey; color:white'>mt_input</th></tr></thread><thead><tr><th>id</th><th>mass</th><th>mt_self</th><th>mt_other</th><th>mt_multi_self</th><th>mt_multi_other</th><th>mt_frac_self</th><th>mt_frac_other</th><th>mt_cross</th><th>share_mems</th><th>nmem</th></tr></thead>
    <thead><tr><th>str4</th><th>float64</th><th>object</th><th>object</th><th>object</th><th>object</th><th>float64</th><th>float64</th><th>object</th><th>object</th><th>float64</th></tr></thead>
    <tr><td>CL0a</td><td>1.00e+15</td><td>CL0b</td><td>CL0b</td><td>[&apos;CL0b&apos;, &apos;CL3b&apos;]</td><td>[&apos;CL0b&apos;, &apos;CL3b&apos;]</td><td>0.8</td><td>1.0</td><td>CL0b</td><td>{&apos;CL3b&apos;: 1.0, &apos;CL0b&apos;: 4.0}</td><td>5.0</td></tr>
    <tr><td>CL1a</td><td>2.00e+15</td><td>CL1b</td><td>CL1b</td><td>[&apos;CL1b&apos;]</td><td>[&apos;CL1b&apos;]</td><td>1.0</td><td>1.0</td><td>CL1b</td><td>{&apos;CL1b&apos;: 4.0}</td><td>4.0</td></tr>
    <tr><td>CL2a</td><td>3.00e+15</td><td>CL2b</td><td>CL2b</td><td>[&apos;CL2b&apos;]</td><td>[&apos;CL2b&apos;]</td><td>1.0</td><td>1.0</td><td>CL2b</td><td>{&apos;CL2b&apos;: 3.0}</td><td>3.0</td></tr>
    <tr><td>CL3a</td><td>4.00e+15</td><td>CL3b</td><td>CL3b</td><td>[&apos;CL3b&apos;]</td><td>[&apos;CL3b&apos;]</td><td>1.0</td><td>0.6666666666666666</td><td>CL3b</td><td>{&apos;CL3b&apos;: 2.0}</td><td>2.0</td></tr>
    <tr><td>CL4a</td><td>5.00e+15</td><td>None</td><td>None</td><td>[]</td><td>[]</td><td>0.0</td><td>0.0</td><td>None</td><td>{}</td><td>1.0</td></tr>
    </table></div>



.. raw:: html

    <b>Cat2</b><br></b><b>tags:</b> id(id), mass(mass)<br><b>Radius unit:</b> None<br><div><i>ClData length=4</i>
    <table id="table139650256483328" class="table-striped table-bordered table-condensed">
    <thead><tr><th colspan=9></th><th colspan=2 style='text-align:left; background-color:grey; color:white'>mt_input</th></tr></thread><thead><tr><th>id</th><th>mass</th><th>mt_self</th><th>mt_other</th><th>mt_multi_self</th><th>mt_multi_other</th><th>mt_frac_other</th><th>mt_frac_self</th><th>mt_cross</th><th>share_mems</th><th>nmem</th></tr></thead>
    <thead><tr><th>str4</th><th>float64</th><th>object</th><th>object</th><th>object</th><th>object</th><th>float64</th><th>float64</th><th>object</th><th>object</th><th>float64</th></tr></thead>
    <tr><td>CL0b</td><td>1.00e+15</td><td>CL0a</td><td>CL0a</td><td>[&apos;CL0a&apos;]</td><td>[&apos;CL0a&apos;]</td><td>0.8</td><td>1.0</td><td>CL0a</td><td>{&apos;CL0a&apos;: 4.0}</td><td>4.0</td></tr>
    <tr><td>CL1b</td><td>2.00e+15</td><td>CL1a</td><td>CL1a</td><td>[&apos;CL1a&apos;]</td><td>[&apos;CL1a&apos;]</td><td>1.0</td><td>1.0</td><td>CL1a</td><td>{&apos;CL1a&apos;: 4.0}</td><td>4.0</td></tr>
    <tr><td>CL2b</td><td>3.00e+15</td><td>CL2a</td><td>CL2a</td><td>[&apos;CL2a&apos;]</td><td>[&apos;CL2a&apos;]</td><td>1.0</td><td>1.0</td><td>CL2a</td><td>{&apos;CL2a&apos;: 3.0}</td><td>3.0</td></tr>
    <tr><td>CL3b</td><td>4.00e+15</td><td>CL3a</td><td>CL3a</td><td>[&apos;CL3a&apos;, &apos;CL0a&apos;]</td><td>[&apos;CL3a&apos;, &apos;CL0a&apos;]</td><td>1.0</td><td>0.6666666666666666</td><td>CL3a</td><td>{&apos;CL3a&apos;: 2.0, &apos;CL0a&apos;: 1.0}</td><td>3.0</td></tr>
    </table></div>


Save and Load
-------------

The results of the matching can easily be saved and load using
``ClEvaR`` tools:

.. code:: ipython3

    mt.save_matches(c1, c2, out_dir='temp', overwrite=True)

.. code:: ipython3

    mt.load_matches(c1, c2, out_dir='temp')
    display(c1)
    display(c2)


.. parsed-literal::

    Cat1
    << ClEvar used in matching: 0.13.2 >>
     * Total objects:    5
     * multiple (self):  4
     * multiple (other): 4
     * unique (self):    4
     * unique (other):   4
     * cross:            4
    
    Cat2
    << ClEvar used in matching: 0.13.2 >>
     * Total objects:    4
     * multiple (self):  4
     * multiple (other): 4
     * unique (self):    4
     * unique (other):   4
     * cross:            4



.. raw:: html

    <b>Cat1</b><br></b><b>tags:</b> id(id), mass(mass)<br><b>Radius unit:</b> None<br><div><i>ClData length=5</i>
    <table id="table139651809204592" class="table-striped table-bordered table-condensed">
    <thead><tr><th colspan=9></th><th colspan=2 style='text-align:left; background-color:grey; color:white'>mt_input</th></tr></thread><thead><tr><th>id</th><th>mass</th><th>mt_self</th><th>mt_other</th><th>mt_multi_self</th><th>mt_multi_other</th><th>mt_frac_self</th><th>mt_frac_other</th><th>mt_cross</th><th>share_mems</th><th>nmem</th></tr></thead>
    <thead><tr><th>str4</th><th>float64</th><th>object</th><th>object</th><th>object</th><th>object</th><th>float64</th><th>float64</th><th>object</th><th>object</th><th>float64</th></tr></thead>
    <tr><td>CL0a</td><td>1.00e+15</td><td>CL0b</td><td>CL0b</td><td>[&apos;CL0b&apos;, &apos;CL3b&apos;]</td><td>[&apos;CL0b&apos;, &apos;CL3b&apos;]</td><td>0.8</td><td>1.0</td><td>CL0b</td><td>{&apos;CL3b&apos;: 1.0, &apos;CL0b&apos;: 4.0}</td><td>5.0</td></tr>
    <tr><td>CL1a</td><td>2.00e+15</td><td>CL1b</td><td>CL1b</td><td>[&apos;CL1b&apos;]</td><td>[&apos;CL1b&apos;]</td><td>1.0</td><td>1.0</td><td>CL1b</td><td>{&apos;CL1b&apos;: 4.0}</td><td>4.0</td></tr>
    <tr><td>CL2a</td><td>3.00e+15</td><td>CL2b</td><td>CL2b</td><td>[&apos;CL2b&apos;]</td><td>[&apos;CL2b&apos;]</td><td>1.0</td><td>1.0</td><td>CL2b</td><td>{&apos;CL2b&apos;: 3.0}</td><td>3.0</td></tr>
    <tr><td>CL3a</td><td>4.00e+15</td><td>CL3b</td><td>CL3b</td><td>[&apos;CL3b&apos;]</td><td>[&apos;CL3b&apos;]</td><td>1.0</td><td>0.6666666666666666</td><td>CL3b</td><td>{&apos;CL3b&apos;: 2.0}</td><td>2.0</td></tr>
    <tr><td>CL4a</td><td>5.00e+15</td><td>None</td><td>None</td><td>[]</td><td>[]</td><td>0.0</td><td>0.0</td><td>None</td><td>{}</td><td>1.0</td></tr>
    </table></div>



.. raw:: html

    <b>Cat2</b><br></b><b>tags:</b> id(id), mass(mass)<br><b>Radius unit:</b> None<br><div><i>ClData length=4</i>
    <table id="table139651809354320" class="table-striped table-bordered table-condensed">
    <thead><tr><th colspan=9></th><th colspan=2 style='text-align:left; background-color:grey; color:white'>mt_input</th></tr></thread><thead><tr><th>id</th><th>mass</th><th>mt_self</th><th>mt_other</th><th>mt_multi_self</th><th>mt_multi_other</th><th>mt_frac_other</th><th>mt_frac_self</th><th>mt_cross</th><th>share_mems</th><th>nmem</th></tr></thead>
    <thead><tr><th>str4</th><th>float64</th><th>object</th><th>object</th><th>object</th><th>object</th><th>float64</th><th>float64</th><th>object</th><th>object</th><th>float64</th></tr></thead>
    <tr><td>CL0b</td><td>1.00e+15</td><td>CL0a</td><td>CL0a</td><td>[&apos;CL0a&apos;]</td><td>[&apos;CL0a&apos;]</td><td>0.8</td><td>1.0</td><td>CL0a</td><td>{&apos;CL0a&apos;: 4.0}</td><td>4.0</td></tr>
    <tr><td>CL1b</td><td>2.00e+15</td><td>CL1a</td><td>CL1a</td><td>[&apos;CL1a&apos;]</td><td>[&apos;CL1a&apos;]</td><td>1.0</td><td>1.0</td><td>CL1a</td><td>{&apos;CL1a&apos;: 4.0}</td><td>4.0</td></tr>
    <tr><td>CL2b</td><td>3.00e+15</td><td>CL2a</td><td>CL2a</td><td>[&apos;CL2a&apos;]</td><td>[&apos;CL2a&apos;]</td><td>1.0</td><td>1.0</td><td>CL2a</td><td>{&apos;CL2a&apos;: 3.0}</td><td>3.0</td></tr>
    <tr><td>CL3b</td><td>4.00e+15</td><td>CL3a</td><td>CL3a</td><td>[&apos;CL3a&apos;, &apos;CL0a&apos;]</td><td>[&apos;CL3a&apos;, &apos;CL0a&apos;]</td><td>1.0</td><td>0.6666666666666666</td><td>CL3a</td><td>{&apos;CL3a&apos;: 2.0, &apos;CL0a&apos;: 1.0}</td><td>3.0</td></tr>
    </table></div>


Getting Matched Pairs
---------------------

There is functionality inbuilt in ``clevar`` to plot some results of the
matching, such as: - Recovery rates - Distances (anguar and redshift) of
cluster centers - Scaling relations (mass, redshift, …) for those cases,
check the match_metrics.ipynb and match_metrics_advanced.ipynb
notebooks.

If those do not provide your needs, you can get directly the matched
pairs of clusters:

.. code:: ipython3

    from clevar.match import get_matched_pairs
    mt1, mt2 = get_matched_pairs(c1, c2, 'cross')

These will be catalogs with the corresponding matched pairs:

.. code:: ipython3

    import pylab as plt
    plt.scatter(mt1['mass'], mt2['mass'])




.. parsed-literal::

    <matplotlib.collections.PathCollection at 0x7f02dbe02730>




.. image:: membership_matching_detailed_files/membership_matching_detailed_42_1.png


Members of matched pairs
~~~~~~~~~~~~~~~~~~~~~~~~

The members also carry the information on the matched clusters. The
column ``match`` shows to which clusters of the other catalog this
member also belongs. The column ``in_mt_sample`` says if those clusters
are presented in the matched sample:

.. code:: ipython3

    mt1.members




.. raw:: html

    <b>members</b><br><b>tags:</b> id(id), id_cluster(id_cluster), ra(ra), dec(dec), z(z), pmem(pmem)<br><div><i>ClData length=14</i>
    <table id="table139650256484720" class="table-striped table-bordered table-condensed">
    <thead><tr><th>id</th><th>id_cluster</th><th>ra</th><th>dec</th><th>z</th><th>pmem</th><th>ind_cl</th><th>match</th><th>mt_self</th><th>mt_other</th><th>mt_multi_self</th><th>mt_multi_other</th><th>mt_cross</th><th>in_mt_sample</th></tr></thead>
    <thead><tr><th>str5</th><th>str4</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>int64</th><th>object</th><th>object</th><th>object</th><th>object</th><th>object</th><th>object</th><th>bool</th></tr></thead>
    <tr><td>MEM0</td><td>CL0a</td><td>0.0</td><td>0.0</td><td>0.1</td><td>1.0</td><td>0</td><td>[&apos;CL3b&apos;]</td><td>MEM0</td><td>MEM0</td><td>[&apos;MEM0&apos;]</td><td>[&apos;MEM0&apos;]</td><td>MEM0</td><td>True</td></tr>
    <tr><td>MEM1</td><td>CL0a</td><td>10.0</td><td>0.0</td><td>0.1</td><td>1.0</td><td>0</td><td>[&apos;CL0b&apos;]</td><td>MEM1</td><td>MEM1</td><td>[&apos;MEM1&apos;]</td><td>[&apos;MEM1&apos;]</td><td>MEM1</td><td>True</td></tr>
    <tr><td>MEM2</td><td>CL0a</td><td>20.0</td><td>0.0</td><td>0.1</td><td>1.0</td><td>0</td><td>[&apos;CL0b&apos;]</td><td>MEM2</td><td>MEM2</td><td>[&apos;MEM2&apos;]</td><td>[&apos;MEM2&apos;]</td><td>MEM2</td><td>True</td></tr>
    <tr><td>MEM3</td><td>CL0a</td><td>30.0</td><td>0.0</td><td>0.1</td><td>1.0</td><td>0</td><td>[&apos;CL0b&apos;]</td><td>MEM3</td><td>MEM3</td><td>[&apos;MEM3&apos;]</td><td>[&apos;MEM3&apos;]</td><td>MEM3</td><td>True</td></tr>
    <tr><td>MEM4</td><td>CL0a</td><td>40.0</td><td>0.0</td><td>0.1</td><td>1.0</td><td>0</td><td>[&apos;CL0b&apos;]</td><td>MEM4</td><td>MEM4</td><td>[&apos;MEM4&apos;]</td><td>[&apos;MEM4&apos;]</td><td>MEM4</td><td>True</td></tr>
    <tr><td>MEM5</td><td>CL1a</td><td>50.0</td><td>0.0</td><td>0.1</td><td>1.0</td><td>1</td><td>[&apos;CL1b&apos;]</td><td>MEM5</td><td>MEM5</td><td>[&apos;MEM5&apos;]</td><td>[&apos;MEM5&apos;]</td><td>MEM5</td><td>True</td></tr>
    <tr><td>MEM6</td><td>CL1a</td><td>60.0</td><td>0.0</td><td>0.1</td><td>1.0</td><td>1</td><td>[&apos;CL1b&apos;]</td><td>MEM6</td><td>MEM6</td><td>[&apos;MEM6&apos;]</td><td>[&apos;MEM6&apos;]</td><td>MEM6</td><td>True</td></tr>
    <tr><td>MEM7</td><td>CL1a</td><td>70.0</td><td>0.0</td><td>0.1</td><td>1.0</td><td>1</td><td>[&apos;CL1b&apos;]</td><td>MEM7</td><td>MEM7</td><td>[&apos;MEM7&apos;]</td><td>[&apos;MEM7&apos;]</td><td>MEM7</td><td>True</td></tr>
    <tr><td>MEM8</td><td>CL1a</td><td>80.0</td><td>0.0</td><td>0.1</td><td>1.0</td><td>1</td><td>[&apos;CL1b&apos;]</td><td>MEM8</td><td>MEM8</td><td>[&apos;MEM8&apos;]</td><td>[&apos;MEM8&apos;]</td><td>MEM8</td><td>True</td></tr>
    <tr><td>MEM9</td><td>CL2a</td><td>90.0</td><td>0.0</td><td>0.1</td><td>1.0</td><td>2</td><td>[&apos;CL2b&apos;]</td><td>MEM9</td><td>MEM9</td><td>[&apos;MEM9&apos;]</td><td>[&apos;MEM9&apos;]</td><td>MEM9</td><td>True</td></tr>
    <tr><td>MEM10</td><td>CL2a</td><td>100.0</td><td>0.0</td><td>0.1</td><td>1.0</td><td>2</td><td>[&apos;CL2b&apos;]</td><td>MEM10</td><td>MEM10</td><td>[&apos;MEM10&apos;]</td><td>[&apos;MEM10&apos;]</td><td>MEM10</td><td>True</td></tr>
    <tr><td>MEM11</td><td>CL2a</td><td>110.0</td><td>0.0</td><td>0.1</td><td>1.0</td><td>2</td><td>[&apos;CL2b&apos;]</td><td>MEM11</td><td>MEM11</td><td>[&apos;MEM11&apos;]</td><td>[&apos;MEM11&apos;]</td><td>MEM11</td><td>True</td></tr>
    <tr><td>MEM12</td><td>CL3a</td><td>120.0</td><td>0.0</td><td>0.1</td><td>1.0</td><td>3</td><td>[&apos;CL3b&apos;]</td><td>MEM12</td><td>MEM12</td><td>[&apos;MEM12&apos;]</td><td>[&apos;MEM12&apos;]</td><td>MEM12</td><td>True</td></tr>
    <tr><td>MEM13</td><td>CL3a</td><td>130.0</td><td>0.0</td><td>0.1</td><td>1.0</td><td>3</td><td>[&apos;CL3b&apos;]</td><td>MEM13</td><td>MEM13</td><td>[&apos;MEM13&apos;]</td><td>[&apos;MEM13&apos;]</td><td>MEM13</td><td>True</td></tr>
    </table></div>



Outputing matched catalogs
--------------------------

To save the current catalogs, you can use the ``write`` inbuilt
function:

.. code:: ipython3

    c1.write('c1_temp.fits', overwrite=True)

This will allow you to save the catalog with its current labels and
matching information.

Outputing matching information to original catalogs
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Assuming your input data came from initial files, ``clevar`` also
provides functions create output files that combine all the information
on them with the matching results.

To add the matching information to an input catalog, use:

::

   from clevar.match import output_catalog_with_matching
   output_catalog_with_matching('input_catalog.fits', 'output_catalog.fits', c1)

-  note: ``input_catalog.fits`` must have the same number of rows that
   ``c1``.

To create a matched catalog containig all columns of both input
catalogs, use:

::

   from clevar.match import output_matched_catalog
   output_matched_catalog('input_catalog1.fits', 'input_catalog2.fits',
       'output_catalog.fits', c1, c2, matching_type='cross')

where ``matching_type`` must be ``cross``, ``cat1`` or ``cat2``.

-  note: ``input_catalog1.fits`` must have the same number of rows that
   ``c1`` (and the same for ``c2``).