Create a MOC from a filtered astropy Table¶

[1]:

import astropy.units as u
from astropy.visualization.wcsaxes.frame import EllipticalFrame
from astroquery.vizier import Vizier
from mocpy import MOC

[2]:

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

[3]:

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

WARNING: Wrong depth_max 29. Reset to 13

[4]:

def plot(moc, title=""):
    """Create a plot of a MOC."""
    import matplotlib.pyplot as plt

    fig = plt.figure(figsize=(15, 10))

    from astropy.wcs import WCS

    wcs = WCS(naxis=2)
    wcs.wcs.ctype = ["GLON-AIT", "GLAT-AIT"]
    wcs.wcs.crval = [110.0, 0.0]
    wcs.wcs.cdelt = [-0.675, 0.675]
    wcs.wcs.crpix = [240.5, 120.5]

    ax = fig.add_subplot(1, 1, 1, projection=wcs, frame_class=EllipticalFrame)

    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)

    ax.set_aspect(1.0)

    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")

../../_images/_collections_notebooks_filtering_astropy_table_4_0.png

[5]:

table

[5]:

Table length=46887

_RAJ2000	_DEJ2000	NPM2	KLA	RAJ2000	DEJ2000	ID	IQ	IN	r_Star	mag	tyc
deg	deg									mag
float64	float64	str8	str3	str10	str9	uint8	uint8	uint8	int16	float32	str1
339.638333	83.166667	+83.0016	HPM	22 38 33.2	+83 10 00	1	0	0	811	14.20
339.638333	83.166667	+83.0016	HPM	22 38 33.2	+83 10 00	1	0	0	813	14.20
349.530833	83.010556	+83.0022	VAR	23 18 07.4	+83 00 38	0	0	0	843	--	T
349.530833	83.010556	+83.0022	SRA	23 18 07.4	+83 00 38	0	0	0	1	9.60	T
323.854167	82.997222	+82.0030	UBV	21 35 25.0	+82 59 50	0	0	0	5	10.08	T
332.107917	82.747500	+82.0055	WD	22 08 25.9	+82 44 51	0	0	0	1012	16.00
332.933750	82.607778	+82.0059	HPM	22 11 44.1	+82 36 28	1	0	0	811	16.70
335.088750	82.970833	+82.0065	HPM	22 20 21.3	+82 58 15	1	0	0	811	11.00	T
337.119167	82.763889	+82.0074	UBV	22 28 28.6	+82 45 50	0	0	0	5	10.42	T
...	...	...	...	...	...	...	...	...	...	...	...
257.227500	-23.094167	-23.0151	ELS	17 08 54.6	-23 05 39	0	0	0	511	13.50
267.989583	-23.012222	-23.0162	CMC	17 51 57.5	-23 00 44	1	0	0	817	11.13	T
267.989583	-23.012222	-23.0162	ELS	17 51 57.5	-23 00 44	1	0	0	252	--	T
270.244583	-23.032222	-23.0164	CMC	18 00 58.7	-23 01 56	1	0	0	817	10.52	T
270.244583	-23.032222	-23.0164	EA	18 00 58.7	-23 01 56	0	0	0	1	10.20	T
270.337500	-23.000556	-23.0165	COM	18 01 21.0	-23 00 02	0	0	0	819	11.20	T
270.442917	-23.020000	-23.0166	UNK	18 01 46.3	-23 01 12	1	0	0	816	14.00
270.474167	-23.028889	-23.0167	COM	18 01 53.8	-23 01 44	0	1	0	819	11.60	T
271.357083	-23.005556	-23.0168	ELS	18 05 25.7	-23 00 20	0	0	0	252	--	T
271.357083	-23.005556	-23.0168	OB	18 05 25.7	-23 00 20	0	0	0	393	9.70	T

[6]:

indexes = galex.contains_lonlat(
    table["_RAJ2000"].T * u.deg,
    table["_DEJ2000"].T * u.deg,
)
filtered_table = table[indexes]
filtered_table

[6]:

Table length=10714

_RAJ2000	_DEJ2000	NPM2	KLA	RAJ2000	DEJ2000	ID	IQ	IN	r_Star	mag	tyc
deg	deg									mag
float64	float64	str8	str3	str10	str9	uint8	uint8	uint8	int16	float32	str1
339.638333	83.166667	+83.0016	HPM	22 38 33.2	+83 10 00	1	0	0	811	14.20
339.638333	83.166667	+83.0016	HPM	22 38 33.2	+83 10 00	1	0	0	813	14.20
349.530833	83.010556	+83.0022	VAR	23 18 07.4	+83 00 38	0	0	0	843	--	T
349.530833	83.010556	+83.0022	SRA	23 18 07.4	+83 00 38	0	0	0	1	9.60	T
323.854167	82.997222	+82.0030	UBV	21 35 25.0	+82 59 50	0	0	0	5	10.08	T
335.088750	82.970833	+82.0065	HPM	22 20 21.3	+82 58 15	1	0	0	811	11.00	T
337.119167	82.763889	+82.0074	UBV	22 28 28.6	+82 45 50	0	0	0	5	10.42	T
337.274583	82.662222	+82.0075	HPM	22 29 05.9	+82 39 44	1	1	0	811	17.40
338.337917	82.369167	+82.0079	HPM	22 33 21.1	+82 22 09	1	0	0	811	16.20
...	...	...	...	...	...	...	...	...	...	...	...
292.318750	-22.677778	-22.2070	ELS	19 29 16.5	-22 40 40	0	0	0	520	13.50
292.665000	-22.236667	-22.2079	CMC	19 30 39.6	-22 14 12	1	0	0	817	12.57
292.892917	-22.538056	-22.2084	CMC	19 31 34.3	-22 32 17	1	0	0	817	11.12	T
293.529583	-22.156389	-22.2101	CMC	19 34 07.1	-22 09 23	1	1	0	817	12.69
293.727083	-22.121667	-22.2108	CMC	19 34 54.5	-22 07 18	1	0	0	817	13.35
294.078333	-22.048333	-22.2118	CMC	19 36 18.8	-22 02 54	1	0	0	817	12.63
294.845417	-22.401667	-22.2128	CMC	19 39 22.9	-22 24 06	1	0	0	817	12.32
295.010833	-22.548333	-22.2133	HPM	19 40 02.6	-22 32 54	1	0	0	811	14.10
297.006250	-22.252500	-22.2166	CMC	19 48 01.5	-22 15 09	1	0	0	817	10.95	T
297.245417	-22.472778	-22.2169	HPM	19 48 58.9	-22 28 22	1	2	0	811	15.60

[7]:

m1 = MOC.from_lonlat(
    table["_RAJ2000"].T * u.deg,
    table["_DEJ2000"].T * u.deg,
    max_norder=6,
)
m2 = MOC.from_lonlat(
    filtered_table["_RAJ2000"].T * u.deg,
    filtered_table["_DEJ2000"].T * u.deg,
    max_norder=6,
)
plot(moc=m1, title="MOC from table")
plot(moc=m2, title="MOC from filtered table")

../../_images/_collections_notebooks_filtering_astropy_table_7_0.png

../../_images/_collections_notebooks_filtering_astropy_table_7_1.png

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Create a MOC from a filtered astropy Table¶