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)

    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
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
320.104167	81.590000	+81.0010	YPC	21 20 25.0	+81 35 24	0	0	0	809	10.30	T
328.960000	81.319167	+81.0032	HPM	21 55 50.4	+81 19 09	1	0	0	811	12.50	T
328.960000	81.319167	+81.0032	HPM	21 55 50.4	+81 19 09	1	0	0	813	12.40	T
330.534583	81.635556	+81.0040	HPM	22 02 08.3	+81 38 08	1	0	0	811	17.70
331.742917	81.140556	+81.0041	RED	22 06 58.3	+81 08 26	0	0	0	155	10.10	T
343.107083	81.618611	+81.0071	HPM	22 52 25.7	+81 37 07	1	0	0	811	14.80
343.396250	81.512500	+81.0073	HPM	22 53 35.1	+81 30 45	1	0	0	811	13.30	T
343.396250	81.512500	+81.0073	HPM	22 53 35.1	+81 30 45	1	0	0	813	13.70	T
343.396250	81.512500	+81.0073	CPM	22 53 35.1	+81 30 45	1	0	0	4	13.70	T
345.737083	81.855833	+81.0082	UNK	23 02 56.9	+81 51 21	0	0	0	816	8.00	T
351.081250	81.493889	+81.0098	HPM	23 24 19.5	+81 29 38	1	1	0	811	17.40
352.878333	81.411667	+81.0102	HPM	23 31 30.8	+81 24 42	1	0	0	811	11.00	T
352.878333	81.411667	+81.0102	HPM	23 31 30.8	+81 24 42	1	0	0	813	11.50	T
355.546250	81.093889	+81.0111	HPM	23 42 11.1	+81 05 38	1	0	0	811	16.00
324.736667	80.677778	+80.0020	HPM	21 38 56.8	+80 40 40	1	0	0	811	9.80	T
355.632917	80.638333	+80.0109	HPM	23 42 31.9	+80 38 18	1	0	0	811	11.90	T
356.260000	80.949722	+80.0112	WD	23 45 02.4	+80 56 59	1	0	0	141	14.00
356.260000	80.949722	+80.0112	PNB	23 45 02.4	+80 56 59	1	0	0	1011	--
321.720417	79.308889	+79.0005	BV	21 26 52.9	+79 18 32	0	0	0	608	9.46	T
322.253333	79.009167	+79.0008	BV	21 29 00.8	+79 00 33	0	0	0	608	10.24	T
322.254167	79.290278	+79.0009	BV	21 29 01.0	+79 17 25	0	0	0	608	12.17	T
326.260833	79.289167	+79.0028	HPM	21 45 02.6	+79 17 21	1	1	0	811	17.10
330.195833	79.823611	+79.0045	HPM	22 00 47.0	+79 49 25	1	0	0	811	15.90
332.768750	79.430833	+79.0055	HPM	22 11 04.5	+79 25 51	1	0	0	811	17.90
...	...	...	...	...	...	...	...	...	...	...	...
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
103.248333	-23.107500	-23.0010	HPM	06 52 59.6	-23 06 27	0	0	0	811	10.00	T
103.248333	-23.107500	-23.0010	RED	06 52 59.6	-23 06 27	0	0	0	258	9.10	T
103.553750	-23.105833	-23.0011	AP	06 54 12.9	-23 06 21	0	0	0	299	9.60	T
111.447500	-23.123611	-23.0032	UBV	07 25 47.4	-23 07 25	1	0	0	786	10.58	T
111.478333	-23.149722	-23.0033	UBV	07 25 54.8	-23 08 59	1	0	0	786	10.47	T
111.487917	-23.165833	-23.0034	UBV	07 25 57.1	-23 09 57	1	0	0	786	10.76	T
111.492917	-23.121389	-23.0035	UBV	07 25 58.3	-23 07 17	1	0	0	786	11.72	T
111.513333	-23.103889	-23.0036	UBV	07 26 03.2	-23 06 14	1	0	0	786	10.80	T
111.575833	-23.038611	-23.0037	UBV	07 26 18.2	-23 02 19	1	0	0	786	10.62	T
111.610000	-23.031111	-23.0038	UBV	07 26 26.4	-23 01 52	1	0	0	786	10.56	T
114.080417	-23.059444	-23.0047	OB	07 36 19.3	-23 03 34	0	0	0	393	11.20	T
114.796250	-23.153333	-23.0050	OB	07 39 11.1	-23 09 12	0	0	0	393	11.30	T
118.169583	-23.048333	-23.0064	OB	07 52 40.7	-23 02 54	0	0	0	393	11.30	T
119.790417	-23.114444	-23.0071	OB	07 59 09.7	-23 06 52	0	0	0	393	12.20	T
243.440833	-23.007778	-23.0109	INS	16 13 45.8	-23 00 28	1	0	0	971	12.50
244.380417	-23.060000	-23.0117	UNK	16 17 31.3	-23 03 36	0	0	0	1100	10.09	T
244.380417	-23.060000	-23.0117	RED	16 17 31.3	-23 03 36	0	0	0	1027	10.09	T
245.782500	-23.016667	-23.0123	ELS	16 23 07.8	-23 01 00	0	1	0	520	13.00
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
328.960000	81.319167	+81.0032	HPM	21 55 50.4	+81 19 09	1	0	0	811	12.50	T
328.960000	81.319167	+81.0032	HPM	21 55 50.4	+81 19 09	1	0	0	813	12.40	T
330.534583	81.635556	+81.0040	HPM	22 02 08.3	+81 38 08	1	0	0	811	17.70
331.742917	81.140556	+81.0041	RED	22 06 58.3	+81 08 26	0	0	0	155	10.10	T
343.107083	81.618611	+81.0071	HPM	22 52 25.7	+81 37 07	1	0	0	811	14.80
343.396250	81.512500	+81.0073	HPM	22 53 35.1	+81 30 45	1	0	0	811	13.30	T
343.396250	81.512500	+81.0073	HPM	22 53 35.1	+81 30 45	1	0	0	813	13.70	T
343.396250	81.512500	+81.0073	CPM	22 53 35.1	+81 30 45	1	0	0	4	13.70	T
345.737083	81.855833	+81.0082	UNK	23 02 56.9	+81 51 21	0	0	0	816	8.00	T
351.081250	81.493889	+81.0098	HPM	23 24 19.5	+81 29 38	1	1	0	811	17.40
352.878333	81.411667	+81.0102	HPM	23 31 30.8	+81 24 42	1	0	0	811	11.00	T
352.878333	81.411667	+81.0102	HPM	23 31 30.8	+81 24 42	1	0	0	813	11.50	T
355.546250	81.093889	+81.0111	HPM	23 42 11.1	+81 05 38	1	0	0	811	16.00
355.632917	80.638333	+80.0109	HPM	23 42 31.9	+80 38 18	1	0	0	811	11.90	T
356.260000	80.949722	+80.0112	WD	23 45 02.4	+80 56 59	1	0	0	141	14.00
356.260000	80.949722	+80.0112	PNB	23 45 02.4	+80 56 59	1	0	0	1011	--
326.260833	79.289167	+79.0028	HPM	21 45 02.6	+79 17 21	1	1	0	811	17.10
330.195833	79.823611	+79.0045	HPM	22 00 47.0	+79 49 25	1	0	0	811	15.90
332.768750	79.430833	+79.0055	HPM	22 11 04.5	+79 25 51	1	0	0	811	17.90
336.634583	79.814722	+79.0072	HPM	22 26 32.3	+79 48 53	1	0	0	811	10.30	T
343.067917	79.707500	+79.0091	HPM	22 52 16.3	+79 42 27	1	0	0	811	12.80
343.067917	79.707500	+79.0091	HPM	22 52 16.3	+79 42 27	1	0	0	813	12.90
354.310417	79.251944	+79.0134	HPM	23 37 14.5	+79 15 07	1	0	0	811	11.40	T
354.310417	79.251944	+79.0134	BY:	23 37 14.5	+79 15 07	1	0	0	1100	9.91	T
354.310417	79.251944	+79.0134	YPC	23 37 14.5	+79 15 07	1	0	0	809	10.30	T
354.310417	79.251944	+79.0134	HPM	23 37 14.5	+79 15 07	1	0	0	813	11.30	T
...	...	...	...	...	...	...	...	...	...	...	...
286.151667	-22.393056	-22.1920	CMC	19 04 36.4	-22 23 35	1	0	0	817	10.68	T
286.712917	-22.550000	-22.1934	CMC	19 06 51.1	-22 33 00	1	0	0	817	10.11	T
286.781667	-22.958889	-22.1936	CMC	19 07 07.6	-22 57 32	1	0	0	817	14.04
286.812917	-22.733056	-22.1937	DKM	19 07 15.1	-22 43 59	0	0	0	519	9.30	T
286.823333	-22.425556	-22.1938	CMC	19 07 17.6	-22 25 32	1	0	0	817	10.34	T
287.017917	-22.668889	-22.1944	ELS	19 08 04.3	-22 40 08	0	0	0	495	11.90	T
287.046667	-22.951389	-22.1945	CMC	19 08 11.2	-22 57 05	1	0	0	817	13.50
287.274167	-22.925000	-22.1949	CMC	19 09 05.8	-22 55 30	1	1	0	817	11.71	T
287.398333	-22.911389	-22.1952	CMC	19 09 35.6	-22 54 41	1	0	0	817	13.24
287.525417	-22.897500	-22.1954	CMC	19 10 06.1	-22 53 51	1	1	0	817	12.60
288.812500	-22.103056	-22.1999	RRA	19 15 15.0	-22 06 11	1	0	0	1	14.60
288.869583	-22.104722	-22.2004	RRA	19 15 28.7	-22 06 17	1	0	0	1	13.90
288.986667	-22.450000	-22.2009	CMC	19 15 56.8	-22 27 00	1	0	0	817	11.62
289.431667	-22.720278	-22.2017	RRA	19 17 43.6	-22 43 13	1	0	0	1	14.60
289.495000	-22.075000	-22.2019	SRA	19 17 58.8	-22 04 30	1	0	1	1	13.80
289.499583	-22.647222	-22.2020	CMC	19 17 59.9	-22 38 50	1	0	0	817	12.65
290.166667	-22.613889	-22.2027	CMC	19 20 40.0	-22 36 50	1	0	0	817	13.47
290.174583	-22.614167	-22.2028	CMC	19 20 41.9	-22 36 51	1	0	0	817	13.91
290.555417	-22.561111	-22.2036	CMC	19 22 13.3	-22 33 40	1	0	0	817	13.02
291.142500	-22.061944	-22.2047	HPM	19 24 34.2	-22 03 43	1	0	0	811	12.30	T
291.142500	-22.061944	-22.2047	YPC	19 24 34.2	-22 03 43	1	0	0	809	10.89	T
291.142500	-22.061944	-22.2047	CMC	19 24 34.2	-22 03 43	1	0	0	817	11.06	T
291.142500	-22.061944	-22.2047	DKM	19 24 34.2	-22 03 43	1	0	0	540	10.50	T
291.142500	-22.061944	-22.2047	CNS	19 24 34.2	-22 03 43	1	0	0	810	10.92	T
291.142500	-22.061944	-22.2047	UNK	19 24 34.2	-22 03 43	1	0	0	816	10.89	T
291.869167	-22.510278	-22.2062	RRA	19 27 28.6	-22 30 37	1	0	0	1	14.80
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¶