

In [1]:

    
import numpy as np
import sys; sys.path.insert(0, '../')
from gaia_tools.query import make_query, make_simple_query
import warnings; warnings.filterwarnings('ignore')









    



Created TAP+ (v1.0.1) - Connection:
	Host: gea.esac.esa.int
	Use HTTPS: False
	Port: 80
	SSL Port: 443

Simple Query

This is a simple single-level query.

Default Columns



In [2]:

    
circle = """
--Selections: Cluster RA
1=CONTAINS(POINT('ICRS',gaia.ra,gaia.dec),
           CIRCLE('ICRS',{ra:.4f},{dec:.4f},{rad:.2f}))
""".format(ra=230, dec=0, rad=4)



In [3]:

    
df = make_simple_query(
    WHERE=circle,        # The WHERE part of the SQL
    random_index=1e4,         # a shortcut to use the random_index in 'WHERE'
    ORDERBY='gaia.parallax',  # setting the data ordering
    pprint=True,              # print the query
    do_query=True,            # perform the query using gaia_tools.query
    local=False,              # whether to perform the query locally
    units=True                # to fill in missing units from 'defaults' file
)

df









    



--Data Columns:
SELECT
--GaiaDR2 Columns:
gaia.source_id AS id,
gaia.parallax AS prlx, gaia.parallax_error AS prlx_err,
gaia.ra, gaia.ra_error AS ra_err,
gaia.dec, gaia.dec_error AS dec_err,
gaia.pmra, gaia.pmra_error AS pmra_err,
gaia.pmdec, gaia.pmdec_error AS pmdec_err


--SOURCE:
FROM gaiadr2.gaia_source AS gaia

--Selections:
WHERE
--Selections: Cluster RA
1=CONTAINS(POINT('ICRS',gaia.ra,gaia.dec),
           CIRCLE('ICRS',230.0000,0.0000,4.00))

AND random_index < 10000

--Ordering:
ORDER BY
gaia.parallax


starting query @ m04d02h18m24s59
Query finished.
query finished @ m04d02h18m25s00






    Out[3]:




QTable masked=True length=5

id prlx prlx_err ra ra_err dec dec_err pmra pmra_err pmdec pmdec_err
mas mas deg deg deg deg mas / yr mas / yr mas / yr mas / yr
int64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64
4421854511258656512 1.2275574478067548 0.47971770270355213 228.8875273212023 0.47414322708214046 2.6766887835445843 0.4727195334587449 -1.086205413366316 0.8596805506822935 8.1358729037405 0.8617703124298011
4421967451718550400 1.3590008052478864 0.41067570449898166 229.30428207922225 0.32917441643105966 3.504297860472648 0.30141853305113375 -15.617658856828964 0.69020127163748 -11.519501862147981 0.6119762121388062
6336481490785920000 1.9709522874255738 1.4830800617599345 227.8835097013005 1.527311600754504 -3.008821326198841 1.2609050214395636 -18.116198677523734 4.053641595094551 -5.963363297817978 3.2724859878416246
4422008786483604992 2.8061159372664246 0.28322948843882695 230.96828886161552 0.2202753669467863 2.776882655709402 0.20585757806277802 -10.545282269206211 0.4348205679710938 3.626420510413496 0.40995909534972347
4422017415073107328 nan nan 231.11830942674368 0.8840346540361825 2.9373577587788855 0.9947966063839209 nan nan nan nan

The system also supports Pan-STARRS1 and 2MASS cross-matches using the panstarrs1 and twomass keywords



In [4]:

    
df = make_simple_query(
    WHERE=circle,        # The WHERE part of the SQL
    random_index=1e4,         # a shortcut to use the random_index in 'WHERE'
    ORDERBY='gaia.parallax',  # setting the data ordering
    panstarrs1=True, twomass=True,
    do_query=True,            # perform the query using gaia_tools.query
    local=False,              # whether to perform the query locally
    units=True                # to fill in missing units from 'defaults' file
)

df









    




starting query @ m04d02h18m25s00
Query finished.
query finished @ m04d02h18m25s01






    Out[4]:




QTable masked=True length=2

id prlx prlx_err ra ra_err dec dec_err pmra pmra_err pmdec pmdec_err g g_err r r_err i i_err z z_err j h k
mas mas deg deg deg deg mas / yr mas / yr mas / yr mas / yr mag mag mag mag mag mag mag mag mag mag mag
int64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float32 float32 float32
4421854511258656512 1.2275574478067548 0.47971770270355213 228.8875273212023 0.47414322708214046 2.6766887835445843 0.4727195334587449 -1.086205413366316 0.8596805506822935 8.1358729037405 0.8617703124298011 20.7679004669189 0.0411490015685558 19.5503005981445 0.00723499990999699 18.4057998657227 0.00405299989506602 17.9002990722656 0.00269200000911951 16.5049991607666 15.90999984741211 15.385000228881836
4422008786483604992 2.8061159372664246 0.28322948843882695 230.96828886161552 0.2202753669467863 2.776882655709402 0.20585757806277802 -10.545282269206211 0.4348205679710938 3.626420510413496 0.40995909534972347 20.4762001037598 0.0156880002468824 19.2593002319336 0.00898800045251846 17.861400604248 0.00322599988430738 17.2089996337891 0.0047889999113977 15.788000106811523 15.204000473022461 14.838000297546387

Different Defaults

If you want fewer default columns, this is an option through the defaults keyword.



In [5]:

    
df = make_simple_query(
    WHERE=circle, random_index=1e4, ORDERBY='gaia.parallax',
    do_query=True, local=False, units=True,
    defaults='empty', 
)

df









    




starting query @ m04d02h18m25s01
Query finished.
query finished @ m04d02h18m25s01






    Out[5]:




QTable masked=True length=5

id
int64
4421854511258656512
4421967451718550400
6336481490785920000
4422008786483604992
4422017415073107328

Likewise, there's an option for much greater detail.



In [6]:

    
df = make_simple_query(
    WHERE=circle, random_index=1e4, ORDERBY='gaia.parallax',
    do_query=True, local=False, units=True,
    defaults='full'
)

df









    




starting query @ m04d02h18m25s01
Query finished.
query finished @ m04d02h18m25s02






    Out[6]:




QTable masked=True length=5

id ref_epoch prlx prlx_err ra ra_err dec dec_err pmra pmra_err pmdec pmdec_err rvel rvel_err l b ecl_lon ecl_lat
yr mas mas deg deg deg deg mas / yr mas / yr mas / yr mas / yr km / s km / s deg deg deg deg
int64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64
4421854511258656512 2015.5 1.2275574478067548 0.47971770270355213 228.8875273212023 0.47414322708214046 2.6766887835445843 0.4727195334587449 -1.086205413366316 0.8596805506822935 8.1358729037405 0.8617703124298011 nan nan 3.848132547687008 47.75429241848735 225.65099212892665 20.01174691229745
4421967451718550400 2015.5 1.3590008052478864 0.41067570449898166 229.30428207922225 0.32917441643105966 3.504297860472648 0.30141853305113375 -15.617658856828964 0.69020127163748 -11.519501862147981 0.6119762121388062 nan nan 5.199902605104458 47.94185070637154 225.83207814826355 20.92242336503168
6336481490785920000 2015.5 1.9709522874255738 1.4830800617599345 227.8835097013005 1.527311600754504 -3.008821326198841 1.2609050214395636 -18.116198677523734 4.053641595094551 -5.963363297817978 3.2724859878416246 nan nan 356.7225516888186 44.73931290031377 226.28760395945173 14.270443215122377
4422008786483604992 2015.5 2.8061159372664246 0.28322948843882695 230.96828886161552 0.2202753669467863 2.776882655709402 0.20585757806277802 -10.545282269206211 0.4348205679710938 3.626420510413496 0.40995909534972347 nan nan 5.846380077001391 46.18235257154016 227.75425415624437 20.675724379475792
4422017415073107328 2015.5 nan nan 231.11830942674368 0.8840346540361825 2.9373577587788855 0.9947966063839209 nan nan nan nan nan nan 6.161884327807021 46.16076695196851 227.86275770664682 20.87043033203375

Complex Nested Query

A complex query like this one shows the real utility of this package. Instead of keeping track of the complex SQL, we only need to pay close attention to the custom calculated columns.

This ADQL queries for data within a rectangular area on a sky rotated by a rotation matrix and specified North Galactic Pole angles. The specifics aren't important -- the real takeaway is that the sky rotation and calculation are written in a clear format, with all the parts of the query close together. Running the query is trivial after that.



In [7]:

    
###########
# Custom Calculations

# Innermost Level
l0cols = """
--Rotation Matrix
{K00}*cos(radians(dec))*cos(radians(ra))+
{K01}*cos(radians(dec))*sin(radians(ra))+
{K02}*sin(radians(dec)) AS cosphi1cosphi2,

{K10}*cos(radians(dec))*cos(radians(ra))+
{K11}*cos(radians(dec))*sin(radians(ra))+
{K12}*sin(radians(dec)) AS sinphi1cosphi2,

{K20}*cos(radians(dec))*cos(radians(ra))+
{K21}*cos(radians(dec))*sin(radians(ra))+
{K22}*sin(radians(dec)) AS sinphi2,

--c1, c2
{sindecngp}*cos(radians(dec)){mcosdecngp:+}*sin(radians(dec))*cos(radians(ra{mrangp:+})) as c1,
{cosdecngp}*sin(radians(ra{mrangp:+})) as c2
"""

# Inner Level
l1cols = """
gaia.cosphi1cosphi2, gaia.sinphi1cosphi2, gaia.sinphi2,
gaia.c1, gaia.c2,

atan2(sinphi1cosphi2, cosphi1cosphi2) AS phi1,
atan2(sinphi2, sinphi1cosphi2 / sin(atan2(sinphi1cosphi2, cosphi1cosphi2))) AS phi2"""

# Inner Level
l2cols = """
gaia.sinphi1cosphi2, gaia.cosphi1cosphi2, gaia.sinphi2,
gaia.phi1, gaia.phi2,
gaia.c1, gaia.c2,

( c1*pmra+c2*pmdec)/cos(phi2) AS pmphi1,
(-c2*pmra+c1*pmdec)/cos(phi2) AS pmphi2"""

# Outer Level
l3cols = """
gaia.phi1, gaia.phi2,
gaia.pmphi1, gaia.pmphi2"""

###########
# Custom Selection

l3sel = """
    phi1 > {phi1min:+}
AND phi1 < {phi1max:+}
AND phi2 > {phi2min:+}
AND phi2 < {phi2max:+}
"""

###########
# Custom substitutions

l3userasdict = {
    'K00': .656, 'K01': .755, 'K02': .002,
    'K10': .701, 'K11': .469, 'K12': .537,
    'K20': .53, 'K21': .458, 'K22': .713,
    'sindecngp': -0.925, 'cosdecngp': .382, 'mcosdecngp': -.382,
    'mrangp': -0,
    'phi1min': -0.175, 'phi1max': 0.175,
    'phi2min': -0.175, 'phi2max': 0.175}

###########
# Making Query
df = make_query(
    gaia_mags=True,
    panstarrs1=True,  # doing a Pan-STARRS1 crossmatch
    user_cols=l3cols,
    use_AS=True, user_ASdict=l3userasdict,

    # Inner Query
    FROM=make_query(
        gaia_mags=True,
        user_cols=l2cols,
    
        # Inner Query
        FROM=make_query(
            gaia_mags=True,
            user_cols=l1cols,
        
            # Innermost Query
            FROM=make_query(
                gaia_mags=True,
                inmostquery=True, # telling system this is the innermost level
                user_cols=l0cols,
                random_index=1e4  # quickly specifying random index
            )
        )
    ),
    WHERE=l3sel,
    ORDERBY='gaia.source_id',
    pprint=True,
    # doing query
    do_query=True, local=False, units=True
)

df









    



--Data Columns:
SELECT
--GaiaDR2 Columns:
gaia.source_id AS id,
gaia.parallax AS prlx, gaia.parallax_error AS prlx_err,
gaia.ra, gaia.ra_error AS ra_err,
gaia.dec, gaia.dec_error AS dec_err,
gaia.pmra, gaia.pmra_error AS pmra_err,
gaia.pmdec, gaia.pmdec_error AS pmdec_err,
--GaiaDR2 Magnitudes and Colors:
gaia.phot_bp_mean_mag AS Gbp,
gaia.phot_bp_mean_flux_over_error AS Gbpfluxfracerr,
gaia.phot_rp_mean_mag AS Grp,
gaia.phot_rp_mean_flux_over_error AS Grpfluxfracerr,
gaia.phot_g_mean_mag AS Gg,
gaia.phot_g_mean_flux_over_error AS Ggfluxfracerr,
gaia.bp_rp AS Gbp_rp, gaia.bp_g AS Gbp_g, gaia.g_rp AS Gg_rp,
--Adding Pan-STARRS1 Columns:
panstarrs1.g_mean_psf_mag AS g, panstarrs1.g_mean_psf_mag_error AS g_err,
panstarrs1.r_mean_psf_mag AS r, panstarrs1.r_mean_psf_mag_error AS r_err,
panstarrs1.i_mean_psf_mag AS i, panstarrs1.i_mean_psf_mag_error AS i_err,
panstarrs1.z_mean_psf_mag AS z, panstarrs1.z_mean_psf_mag_error AS z_err,

--Custom Selection & Assignement:
gaia.phi1, gaia.phi2,
gaia.pmphi1, gaia.pmphi2

--SOURCE:
FROM (
    --Data Columns:
    SELECT
    --GaiaDR2 Columns:
    gaia.source_id,
    gaia.parallax, gaia.parallax_error,
    gaia.ra, gaia.ra_error,
    gaia.dec, gaia.dec_error,
    gaia.pmra, gaia.pmra_error,
    gaia.pmdec, gaia.pmdec_error,
    --GaiaDR2 Magnitudes and Colors:
    gaia.phot_bp_mean_mag,
    gaia.phot_bp_mean_flux_over_error,
    gaia.phot_rp_mean_mag,
    gaia.phot_rp_mean_flux_over_error,
    gaia.phot_g_mean_mag,
    gaia.phot_g_mean_flux_over_error,
    gaia.bp_rp, gaia.bp_g, gaia.g_rp,
    
    --Custom Selection & Assignement:
    gaia.sinphi1cosphi2, gaia.cosphi1cosphi2, gaia.sinphi2,
    gaia.phi1, gaia.phi2,
    gaia.c1, gaia.c2,
    
    ( c1*pmra+c2*pmdec)/cos(phi2) AS pmphi1,
    (-c2*pmra+c1*pmdec)/cos(phi2) AS pmphi2
    
    --SOURCE:
    FROM (
        --Data Columns:
        SELECT
        --GaiaDR2 Columns:
        gaia.source_id,
        gaia.parallax, gaia.parallax_error,
        gaia.ra, gaia.ra_error,
        gaia.dec, gaia.dec_error,
        gaia.pmra, gaia.pmra_error,
        gaia.pmdec, gaia.pmdec_error,
        --GaiaDR2 Magnitudes and Colors:
        gaia.phot_bp_mean_mag,
        gaia.phot_bp_mean_flux_over_error,
        gaia.phot_rp_mean_mag,
        gaia.phot_rp_mean_flux_over_error,
        gaia.phot_g_mean_mag,
        gaia.phot_g_mean_flux_over_error,
        gaia.bp_rp, gaia.bp_g, gaia.g_rp,
        
        --Custom Selection & Assignement:
        gaia.cosphi1cosphi2, gaia.sinphi1cosphi2, gaia.sinphi2,
        gaia.c1, gaia.c2,
        
        atan2(sinphi1cosphi2, cosphi1cosphi2) AS phi1,
        atan2(sinphi2, sinphi1cosphi2 / sin(atan2(sinphi1cosphi2, cosphi1cosphi2))) AS phi2
        
        --SOURCE:
        FROM (
            --Data Columns:
            SELECT
            --GaiaDR2 Columns:
            gaia.source_id,
            gaia.parallax, gaia.parallax_error,
            gaia.ra, gaia.ra_error,
            gaia.dec, gaia.dec_error,
            gaia.pmra, gaia.pmra_error,
            gaia.pmdec, gaia.pmdec_error,
            --GaiaDR2 Magnitudes and Colors:
            gaia.phot_bp_mean_mag,
            gaia.phot_bp_mean_flux_over_error,
            gaia.phot_rp_mean_mag,
            gaia.phot_rp_mean_flux_over_error,
            gaia.phot_g_mean_mag,
            gaia.phot_g_mean_flux_over_error,
            gaia.bp_rp, gaia.bp_g, gaia.g_rp,
            
            --Custom Selection & Assignement:
            --Rotation Matrix
            0.656*cos(radians(dec))*cos(radians(ra))+
            0.755*cos(radians(dec))*sin(radians(ra))+
            0.002*sin(radians(dec)) AS cosphi1cosphi2,
            
            0.701*cos(radians(dec))*cos(radians(ra))+
            0.469*cos(radians(dec))*sin(radians(ra))+
            0.537*sin(radians(dec)) AS sinphi1cosphi2,
            
            0.53*cos(radians(dec))*cos(radians(ra))+
            0.458*cos(radians(dec))*sin(radians(ra))+
            0.713*sin(radians(dec)) AS sinphi2,
            
            --c1, c2
            -0.925*cos(radians(dec))-0.382*sin(radians(dec))*cos(radians(ra+0)) as c1,
            0.382*sin(radians(ra+0)) as c2
            
            
            --SOURCE:
            FROM gaiadr2.gaia_source AS gaia
            
            --Selections:
            WHERE
            random_index <= 10000
        ) AS gaia
    ) AS gaia
) AS gaia

--Comparing to Pan-STARRS1
INNER JOIN gaiadr2.panstarrs1_best_neighbour AS panstarrs1_match ON panstarrs1_match.source_id = gaia.source_id
INNER JOIN gaiadr2.panstarrs1_original_valid AS panstarrs1 ON panstarrs1.obj_id = panstarrs1_match.original_ext_source_id

--Selections:
WHERE
    phi1 > -0.175
AND phi1 < +0.175
AND phi2 > -0.175
AND phi2 < +0.175


--Ordering:
ORDER BY
gaia.source_id


starting query @ m04d02h18m25s02
Query finished.
query finished @ m04d02h18m25s04






    Out[7]:




QTable masked=True length=71

id prlx prlx_err ra ra_err dec dec_err pmra pmra_err pmdec pmdec_err gbp gbpfluxfracerr grp grpfluxfracerr gg ggfluxfracerr gbp_rp gbp_g gg_rp g g_err r r_err i i_err z z_err phi1 phi2 pmphi1 pmphi2
mas mas deg deg deg deg mas / yr mas / yr mas / yr mas / yr mag mag mag mag mag mag mag mag mag mag mag mag mag mag rad rad mas / yr mas / yr
int64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float32 float32 float32 float32 float32 float32 float32 float32 float32 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64
2889564322129077376 0.3213177028021399 0.14088071404071692 89.852456363208 0.11092118907771961 -33.48788512061472 0.11905868588217561 -1.2001158428613647 0.2315703839344539 1.2068512160421232 0.2607473500694008 19.02077865600586 45.648506 17.74642562866211 108.068665 18.4609375 579.4508 1.27435302734375 0.5598411560058594 0.7145118713378906 nan nan nan nan 18.1292991638184 0.00695599988102913 nan nan 0.15177566486787875 -0.01615296804383597 1.3863772679241089 -0.4719919761842213
2889585036754358016 0.04112782798721501 0.3536404789521579 89.64273223729039 0.27550035439516946 -33.289729121056816 0.28568865747042466 3.6201024391883885 0.5441884680409558 8.104449717308881 0.5891796623791259 20.448328018188477 11.592497 18.59687614440918 50.61768 19.61026382446289 280.3479 1.8514518737792969 0.8380641937255859 1.013387680053711 nan nan nan nan 18.9641990661621 0.00823699962347746 nan nan 0.15803067899348286 -0.008958985863537603 0.30147490786345243 -7.639027705679244
2893531497522278528 0.2790765324906976 0.08843655423377134 95.69721841841746 0.06532838745037702 -30.50242870308282 0.0870745768550069 1.7096027371956273 0.14042939660904843 2.665564779495171 0.16040499468271435 17.70775032043457 156.32576 16.725849151611328 162.56195 17.290735244750977 1074.5438 0.9819011688232422 0.41701507568359375 0.5648860931396484 17.7226009368896 0.0102749997749925 17.2467002868652 0.00498000020161271 17.0774002075195 0.00384699995629489 17.0198993682861 0.00342700001783669 0.11734124245859556 -0.024506749257727892 -0.3823366779230443 -2.8264187687608446
2894277580582639616 -0.03565140127821627 0.38379135803464853 100.83588017948506 0.2746167229986943 -30.39519893204269 0.354504804621474 0.4595450174632992 0.5981503040659448 1.8037089064425966 0.6497080038713873 20.135364532470703 12.897309 19.291885375976562 18.578547 19.889904022216797 231.50648 0.8434791564941406 0.24546051025390625 0.5980186462402344 20.1900005340576 0.0153510002419353 19.7777004241943 0.0170040000230074 19.6713008880615 0.010308999568224 19.6107006072998 0.057473998516798 0.022452761935390815 -0.10979523300038331 0.29515634154001114 -1.687229325064532
2894301117005191936 -0.31822723307621215 0.596439798077345 100.55360000898901 0.37529189714934463 -30.06129384733782 0.5133368686502483 1.2125908967513244 0.7750190783329641 3.328302233757119 0.88877917171463 nan -- nan -- 20.133920669555664 161.91643 nan nan nan 20.9354000091553 0.0813969969749451 19.886100769043 0.00134299998171628 19.3973999023438 0.0328940004110336 19.2562999725342 0.0201140008866787 0.0352089498571001 -0.095456026639865 0.23771429243865633 -3.251390885692647
2918472063373495936 0.07540631742850838 0.018291002548211636 101.29860970041581 0.012077191412722404 -28.707030693978805 0.016833066274518332 -0.7198628622013465 0.024939448508541573 1.3504572377243913 0.031049207460582387 14.951737403869629 588.6317 13.43400764465332 949.3284 14.241029739379883 4129.2656 1.5177297592163086 0.7107076644897461 0.8070220947265625 15.145299911499 0.00218899990431964 14.2363004684448 0.00301899993792176 13.8430995941162 0.00734200002625585 13.6037998199463 0.00123199995141476 0.0466047869519749 -0.07376865860472206 1.1188268142430042 -0.8769080774380797
2918550644092742272 0.6273189056681521 0.782697310231922 101.576417046875 0.4883607934153599 -28.75376229315029 0.6028598697415664 2.6738991917543244 1.0275467338132094 6.671298858722015 0.965939827384999 21.0629825592041 5.76543 19.29847526550293 22.061457 20.290245056152344 153.97742 1.7645072937011719 0.7727375030517578 0.9917697906494141 21.4979991912842 0.0714500024914742 20.3416004180908 0.024184999987483 19.6637001037598 0.0140500003471971 19.3367004394531 0.00980699993669987 0.03974239084528264 -0.0803368883690285 0.230356235609403 -6.678244300414476
2921155417204886656 0.5746644074889986 0.08445267418651484 105.41909619530594 0.05031977808843377 -25.682389169566108 0.06735360382631876 -4.226561826565594 0.11833640014430594 5.09270444640058 0.14763444560101693 17.961925506591797 140.4744 16.735219955444336 299.11694 17.42011833190918 1250.0464 1.226705551147461 0.5418071746826172 0.6848983764648438 18.0634994506836 0.00478299986571074 17.3682994842529 0.00644299993291497 17.0844993591309 0.00258899992331862 16.9799995422363 0.00467999977990985 0.013578261560780708 -0.07636497260950607 5.601098258390017 -2.9216216680014737
2921254824222946560 nan nan 105.45364770475886 1.5353938308434325 -25.156187892381006 3.0021763612415984 nan nan nan nan nan -- nan -- 21.188934326171875 39.210197 nan nan nan 21.6951999664307 0.0274010002613068 21.1583003997803 0.0783889964222908 20.7777004241943 0.0526469983160496 20.3808994293213 0.0821679979562759 0.02366494168491069 -0.06263618141108063 nan nan
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
5617824614809288448 nan nan 110.32374750844698 6.951827359874592 -23.753915962728144 24.62950700482992 nan nan nan nan nan -- nan -- 21.25697898864746 35.90056 nan nan nan nan nan nan nan nan nan 19.9337005615234 0.0422349981963634 -0.08326974542837035 -0.14123429085914144 nan nan
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5717179264342566400 0.04494940409405784 0.15232524871360134 115.28270232626647 0.0813598890748721 -18.17982231998236 0.09326843839600313 -4.826272549307452 0.17394969453799566 3.152817779925323 0.19533820917480793 18.476722717285156 73.94697 17.296300888061523 164.27692 17.982465744018555 905.2205 1.1804218292236328 0.49425697326660156 0.6861648559570312 18.5611000061035 0.00507800001651049 17.9696998596191 0.00861299969255924 17.6774005889893 0.00585500011220574 17.5377998352051 0.0065080001950264 -0.12784860222106753 -0.1139531917901135 5.612530588623685 -1.2724774548585431
5718834171083071232 0.18630911416992119 0.08648532177028069 115.97190882725991 0.04666679406125647 -16.86914550398814 0.050611027236631245 -0.7766657497484148 0.10022556163760633 1.7603127769712652 0.1057769010471151 17.399930953979492 241.16924 16.71757698059082 307.42203 17.128801345825195 1853.4366 0.6823539733886719 0.2711296081542969 0.411224365234375 17.3780994415283 0.00627399981021881 17.1306991577148 0.00304000009782612 17.0604000091553 0.00302299996837974 17.046199798584 0.00451500015333295 -0.12267559450842516 -0.0925776910425794 1.3354535796158344 -1.3828769528247251
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id	prlx	prlx_err	ra	ra_err	dec	dec_err	pmra	pmra_err	pmdec	pmdec_err
	mas	mas	deg	deg	deg	deg	mas / yr	mas / yr	mas / yr	mas / yr
int64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64
4421854511258656512	1.2275574478067548	0.47971770270355213	228.8875273212023	0.47414322708214046	2.6766887835445843	0.4727195334587449	-1.086205413366316	0.8596805506822935	8.1358729037405	0.8617703124298011
4421967451718550400	1.3590008052478864	0.41067570449898166	229.30428207922225	0.32917441643105966	3.504297860472648	0.30141853305113375	-15.617658856828964	0.69020127163748	-11.519501862147981	0.6119762121388062
6336481490785920000	1.9709522874255738	1.4830800617599345	227.8835097013005	1.527311600754504	-3.008821326198841	1.2609050214395636	-18.116198677523734	4.053641595094551	-5.963363297817978	3.2724859878416246
4422008786483604992	2.8061159372664246	0.28322948843882695	230.96828886161552	0.2202753669467863	2.776882655709402	0.20585757806277802	-10.545282269206211	0.4348205679710938	3.626420510413496	0.40995909534972347
4422017415073107328	nan	nan	231.11830942674368	0.8840346540361825	2.9373577587788855	0.9947966063839209	nan	nan	nan	nan