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In [1]:

    
%matplotlib inline



In [2]:

    
import warnings 
warnings.filterwarnings('ignore')
from mocpy import MOC

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



In [3]:

    
from astroquery.vizier import Vizier

viz = Vizier(columns=['*', '_RAJ2000', '_DEJ2000'])
viz.ROW_LIMIT = -1
table = viz.get_catalogs('I/293/npm2cros')[0]



In [4]:

    
galex = MOC.from_fits('../resources/P-GALEXGR6-AIS-FUV.fits')



In [5]:

    
# A function creating all the matplotlib context for plotting a MOC with its perimeter
def plot(moc, title=''):
    from matplotlib import path, patches
    import matplotlib.pyplot as plt

    fig = plt.figure(figsize=(15, 10))
    from mocpy import WCS
    with WCS(fig, 
             fov=330 * u.deg,
             center=SkyCoord(0, 0, unit='deg', frame='galactic'),
             coordsys='galactic',
             rotation=Angle(0, u.degree),
             projection="AIT") as wcs:
        ax = fig.add_subplot(1, 1, 1, projection=wcs)

        moc.fill(ax=ax, wcs=wcs, edgecolor='r', facecolor='r', linewidth=1.0, fill=True, alpha=0.5)
        moc.border(ax=ax, wcs=wcs, color="black", alpha=0.5)
    
    plt.xlabel('ra')
    plt.ylabel('dec')
    if title:
        plt.title(title)
    plt.grid(color="black", linestyle="dotted")
    plt.show()
    plt.close()
    

plot(moc=galex, title='P-GALEXGR6-AIS-FUV')



In [6]:

    
print(table)









    



_RAJ2000 _DEJ2000   NPM2   KLA  RAJ2000    DEJ2000   ID  IQ  IN r_Star  mag  tyc
  deg      deg                  "h:m:s"    "d:m:s"                      mag     
-------- -------- -------- --- ---------- --------- --- --- --- ------ ----- ---
339.6383  83.1667 +83.0016 HPM 22 38 33.2 +83 10 00   1   0   0    811 14.20    
339.6383  83.1667 +83.0016 HPM 22 38 33.2 +83 10 00   1   0   0    813 14.20    
349.5308  83.0106 +83.0022 VAR 23 18 07.4 +83 00 38   0   0   0    843    --   T
349.5308  83.0106 +83.0022 SRA 23 18 07.4 +83 00 38   0   0   0      1  9.60   T
323.8542  82.9972 +82.0030 UBV 21 35 25.0 +82 59 50   0   0   0      5 10.08   T
332.1079  82.7475 +82.0055  WD 22 08 25.9 +82 44 51   0   0   0   1012 16.00    
332.9337  82.6078 +82.0059 HPM 22 11 44.1 +82 36 28   1   0   0    811 16.70    
335.0887  82.9708 +82.0065 HPM 22 20 21.3 +82 58 15   1   0   0    811 11.00   T
337.1192  82.7639 +82.0074 UBV 22 28 28.6 +82 45 50   0   0   0      5 10.42   T
337.2746  82.6622 +82.0075 HPM 22 29 05.9 +82 39 44   1   1   0    811 17.40    
     ...      ...      ... ...        ...       ... ... ... ...    ...   ... ...
257.2275 -23.0942 -23.0151 ELS 17 08 54.6 -23 05 39   0   0   0    511 13.50    
267.9896 -23.0122 -23.0162 CMC 17 51 57.5 -23 00 44   1   0   0    817 11.13   T
267.9896 -23.0122 -23.0162 ELS 17 51 57.5 -23 00 44   1   0   0    252    --   T
270.2446 -23.0322 -23.0164 CMC 18 00 58.7 -23 01 56   1   0   0    817 10.52   T
270.2446 -23.0322 -23.0164  EA 18 00 58.7 -23 01 56   0   0   0      1 10.20   T
270.3375 -23.0006 -23.0165 COM 18 01 21.0 -23 00 02   0   0   0    819 11.20   T
270.4429 -23.0200 -23.0166 UNK 18 01 46.3 -23 01 12   1   0   0    816 14.00    
270.4742 -23.0289 -23.0167 COM 18 01 53.8 -23 01 44   0   1   0    819 11.60   T
271.3571 -23.0056 -23.0168 ELS 18 05 25.7 -23 00 20   0   0   0    252    --   T
271.3571 -23.0056 -23.0168  OB 18 05 25.7 -23 00 20   0   0   0    393  9.70   T
Length = 46887 rows



In [7]:

    
indexes = galex.contains(table['_RAJ2000'].T * u.deg, table['_DEJ2000'].T * u.deg)
filtered_table = table[indexes]
print(filtered_table)









    



_RAJ2000 _DEJ2000   NPM2   KLA  RAJ2000    DEJ2000   ID  IQ  IN r_Star  mag  tyc
  deg      deg                  "h:m:s"    "d:m:s"                      mag     
-------- -------- -------- --- ---------- --------- --- --- --- ------ ----- ---
339.6383  83.1667 +83.0016 HPM 22 38 33.2 +83 10 00   1   0   0    811 14.20    
339.6383  83.1667 +83.0016 HPM 22 38 33.2 +83 10 00   1   0   0    813 14.20    
349.5308  83.0106 +83.0022 VAR 23 18 07.4 +83 00 38   0   0   0    843    --   T
349.5308  83.0106 +83.0022 SRA 23 18 07.4 +83 00 38   0   0   0      1  9.60   T
323.8542  82.9972 +82.0030 UBV 21 35 25.0 +82 59 50   0   0   0      5 10.08   T
335.0887  82.9708 +82.0065 HPM 22 20 21.3 +82 58 15   1   0   0    811 11.00   T
337.1192  82.7639 +82.0074 UBV 22 28 28.6 +82 45 50   0   0   0      5 10.42   T
337.2746  82.6622 +82.0075 HPM 22 29 05.9 +82 39 44   1   1   0    811 17.40    
338.3379  82.3692 +82.0079 HPM 22 33 21.1 +82 22 09   1   0   0    811 16.20    
328.9600  81.3192 +81.0032 HPM 21 55 50.4 +81 19 09   1   0   0    811 12.50   T
     ...      ...      ... ...        ...       ... ... ... ...    ...   ... ...
292.3187 -22.6778 -22.2070 ELS 19 29 16.5 -22 40 40   0   0   0    520 13.50    
292.6650 -22.2367 -22.2079 CMC 19 30 39.6 -22 14 12   1   0   0    817 12.57    
292.8929 -22.5381 -22.2084 CMC 19 31 34.3 -22 32 17   1   0   0    817 11.12   T
293.5296 -22.1564 -22.2101 CMC 19 34 07.1 -22 09 23   1   1   0    817 12.69    
293.7271 -22.1217 -22.2108 CMC 19 34 54.5 -22 07 18   1   0   0    817 13.35    
294.0783 -22.0483 -22.2118 CMC 19 36 18.8 -22 02 54   1   0   0    817 12.63    
294.8454 -22.4017 -22.2128 CMC 19 39 22.9 -22 24 06   1   0   0    817 12.32    
295.0108 -22.5483 -22.2133 HPM 19 40 02.6 -22 32 54   1   0   0    811 14.10    
297.0062 -22.2525 -22.2166 CMC 19 48 01.5 -22 15 09   1   0   0    817 10.95   T
297.2454 -22.4728 -22.2169 HPM 19 48 58.9 -22 28 22   1   2   0    811 15.60    
Length = 10714 rows



In [8]:

    
m1 = MOC.from_lonlat(table['_RAJ2000'].T * u.deg, table['_DEJ2000'].T * u.deg, 6)
m1.write("ttt.fits", format="fits")
m2 = MOC.from_lonlat(filtered_table['_RAJ2000'].T * u.deg, filtered_table['_DEJ2000'].T * u.deg, 6)

plot(moc=m1, title="MOC from table")
plot(moc=m2, title="MOC from filtered table")



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