In [64]:
import wisps
import numpy as np
import astroquery
import splat
from astropy.coordinates import SkyCoord
import astropy.units as u
import pandas as pd

In [37]:
from splat.database import queryVizier

In [58]:
@np.vectorize
def query_wise(coord):
    try:
        return queryVizier(coord, WISE=True).sort_values('sep').iloc[0]
    except:
        return

In [59]:
cands=wisps.datasets['candidates']

In [86]:
lts=cands[cands.spt.apply(splat.typeToNum)>=20]

In [87]:
coords=SkyCoord(ra=lts.RA.values*u.deg, dec=lts.DEC.values*u.deg)

In [88]:
%%capture
out=query_wise(coords)

In [89]:
pd.DataFrame.from_records([x for x in out if x is not None])


Out[89]:
_r WISE RAJ2000 DEJ2000 eeMaj eeMin eePA Im W1mag e_W1mag ... nW2 mW2 nW3 mW3 nW4 mW4 _2Mkey d2M _2M sep
0 6.867 J115451.66+194109.9 178.715280 19.686104 0.717 0.644 73 Im 16.684000 0.115 ... 13 1 13 0 13 0 0 NaN 2M 6.867
1 8.642 J113305.33+032843.4 173.272217 3.478731 0.706 0.627 80 Im 16.587999 0.113 ... 13 0 13 0 13 0 0 NaN 2M 8.642
2 12.829 J161850.72+334012.2 244.711369 33.670067 1.440 1.270 80 Im 18.521999 0.468 ... 18 1 18 0 18 0 0 NaN 2M 12.829
3 12.821 J115048.93-203347.7 177.703902 -20.563263 0.316 0.290 81 Im 15.604000 0.055 ... 14 1 14 0 12 0 1266598935 0.767 2M 12.821
4 14.762 J162523.11+572126.2 246.346302 57.357285 0.960 0.860 61 Im 18.097000 0.152 ... 86 1 86 0 82 0 0 NaN 2M 14.762
5 21.298 J130526.01-253808.5 196.358413 -25.635718 0.554 0.496 77 Im 16.760000 0.092 ... 25 0 24 4 22 1 0 NaN 2M 21.298
6 22.697 J100341.44+285426.7 150.922688 28.907439 0.650 0.573 78 Im 16.635000 0.110 ... 14 0 14 0 14 0 0 NaN 2M 22.697
7 0.109 J092757.42+602746.7 141.989262 60.462974 0.113 0.108 99 Im 13.832000 0.027 ... 18 18 18 0 18 0 534016176 0.308 2M 0.109
8 19.682 J112407.78+420253.1 171.032449 42.048087 0.570 0.502 72 Im 16.569000 0.090 ... 18 0 18 0 18 0 0 NaN 2M 19.682
9 26.653 J141850.40+524325.9 214.710002 52.723885 0.922 0.828 60 Im 17.337999 0.143 ... 27 0 27 0 23 0 0 NaN 2M 26.653
10 2.935 J033238.81-274956.5 53.161733 -27.832380 0.311 0.287 118 Im 16.350000 0.057 ... 43 6 42 1 40 0 0 NaN 2M 2.935
11 14.054 J033305.12-275130.1 53.271370 -27.858369 0.084 0.079 7 Im 12.414000 0.023 ... 39 39 39 1 37 0 181233443 0.194 2M 14.054

12 rows × 52 columns


In [95]:
wisps.datasets['schneider'].name.sort_values()


Out[95]:
9     WISE0325-5044
20    WISE0335+4310
1     WISE0350-5658
19    WISE0359-5401
11    WISE0404-6420
4     WISE0410+1502
6     WISE0535-7500
0     WISE0647-6232
17    WISE0734-7157
15    WISE0825+2805
5     WISE0943+3607
18    WISE1206+8401
16    WISE1405+5534
2     WISE1541-2250
10    WISE1542+2230
13    WISE1639-6847
3     WISE1738+2732
14    WISE2056+1459
8     WISE2209+2711
21    WISE2212-6931
7     WISE2220-3628
12    WISE2354+0240
Name: name, dtype: object

In [ ]: