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cat_tools.py
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cat_tools.py
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from astropy import units as u
from astropy.io import ascii
from astroquery.vizier import Vizier
from astropy import table
from astropy import coordinates as coord
from astropy import units as u
from misc import bcolors
import numpy as np
import os
from scipy.spatial import cKDTree
# Catalogue properties
#http://wise2.ipac.caltech.edu/docs/release/allsky/expsup/sec4_4h.html#Table8
#https://asd.gsfc.nasa.gov/archive/galex/FAQ/counts_background.html
#http://www.galex.caltech.edu/researcher/techdoc-ch4.html#1
catalog_zp = {}
catalog_zp['WISE_W1'] = 20.500 + 2.699
catalog_zp['WISE_W2'] = 19.500 + 3.339
catalog_zp['WISE_W3'] = 18.000 + 5.174
catalog_zp['WISE_W4'] = 13.000 + 6.620
catalog_zp['GALEX_FUV'] = 18.82 + 0
catalog_zp['GALEX_NUV'] = 20.08 + 0
catalog_prop = {}
catalog_prop['2MASS'] = {}
catalog_prop['2MASS']['KEYWORDS'] = ["RAJ2000", "DEJ2000", "Jmag", "e_Jmag", "Hmag", "e_Hmag", "Kmag", "e_Kmag"]
catalog_prop['2MASS']['CATID'] = "II/246"
catalog_prop['2MASS']['CATID_OUT'] = catalog_prop['2MASS']['CATID'] + '/out'
catalog_prop['2MASS']['FILTER'] = ['J', 'H', 'K']
catalog_prop['2MASS']['SIGMA_HIGH'] = 0
catalog_prop['2MASS']['SIGMA_LOW'] = 0.3
catalog_prop['SDSS'] = {}
catalog_prop['SDSS']['KEYWORDS'] = ["RA_ICRS", "DE_ICRS",
"class", "mode",
"umag", "e_umag",
"gmag", "e_gmag",
"rmag", "e_rmag",
"imag", "e_imag",
"zmag", "e_zmag"]
catalog_prop['SDSS']['CATID'] = "V/147"
catalog_prop['SDSS']['CATID_OUT'] = catalog_prop['SDSS']['CATID'] + '/sdss12'
catalog_prop['SDSS']['FILTER'] = ['u', 'g', 'r', 'i', 'z']
catalog_prop['SDSS']['SIGMA_HIGH'] = 0
catalog_prop['SDSS']['SIGMA_LOW'] = 0.2
catalog_prop['PanSTARRS'] = {}
catalog_prop['PanSTARRS']['KEYWORDS'] = ['RAJ2000', 'DEJ2000',
'o_gmag', 'gPSFf', 'gmag', 'e_gmag',
'o_rmag', 'rPSFf', 'rmag', 'e_rmag',
'o_imag', 'iPSFf', 'imag', 'e_imag', 'iKmag',
'o_zmag', 'zPSFf', 'zmag', 'e_zmag',
'o_ymag', 'yPSFf', 'ymag', 'e_ymag']
catalog_prop['PanSTARRS']['CATID'] = "II/349"
catalog_prop['PanSTARRS']['CATID_OUT'] = catalog_prop['PanSTARRS']['CATID'] + '/ps1'
catalog_prop['PanSTARRS']['FILTER'] = ['g', 'r', 'i', 'z', 'y']
catalog_prop['PanSTARRS']['SIGMA_HIGH'] = 0
catalog_prop['PanSTARRS']['SIGMA_LOW'] = 0.2
catalog_prop['GAIA'] = {}
catalog_prop['GAIA']['KEYWORDS'] = ["RAJ2000", "DEJ2000", "Plx", "e_Plx", "pmRA", "e_pmRA", "pmDE", "e_pmDE"]
catalog_prop['GAIA']['CATID'] = "I/345"
catalog_prop['GAIA']['CATID_OUT'] = catalog_prop['GAIA']['CATID'] + '/gaia2'
catalog_prop['UKIDSS'] = {}
catalog_prop['UKIDSS']['KEYWORDS'] = ['RAJ2000', 'DEJ2000',
'cl',
'Ymag', 'e_Ymag',
'Jmag1', 'e_Jmag1',
'Jmag2', 'e_Jmag2',
'Hmag', 'e_Hmag',
'Kmag', 'e_Kmag']
catalog_prop['UKIDSS']['CATID'] = "II/319"
catalog_prop['UKIDSS']['CATID_OUT'] = catalog_prop['UKIDSS']['CATID'] + '/las9'
catalog_prop['UKIDSS']['FILTER'] = ['Y', 'J', 'H', 'K']
catalog_prop['UKIDSS']['SIGMA_HIGH'] = 0
catalog_prop['UKIDSS']['SIGMA_LOW'] = 0.2
catalog_prop['WISE'] = {}
catalog_prop['WISE']['KEYWORDS'] = ['RAJ2000', 'DEJ2000',
'W1mag', 'e_W1mag',
'W2mag', 'e_W2mag']
catalog_prop['WISE']['CATID'] = "II/328"
catalog_prop['WISE']['CATID_OUT'] = catalog_prop['WISE']['CATID'] + '/allwise'
catalog_prop['WISE']['FILTER'] = ['W1', 'W2']
catalog_prop['WISE']['SIGMA_HIGH'] = 0
catalog_prop['WISE']['SIGMA_LOW'] = 0.2
def PS1_to_SDSS(DATA):
# Based on http://adsabs.harvard.edu/abs/2016ApJ...822...66F
# The equations are valid for main-sequence stars with 0.4 < x < 2.7.
# Coefficients are provided for gP1 - usdss and yP1 - zsdss for
# much less reliable than the griz transformations. In particular,
# the extrapolation from PS1 colors to u band is strongly
# metallicity dependent, and should be used with caution. The
# corrections are typically 0.01 mag in r and i, up to 0.1 mag in z,
# and up to 0.25 in g.
# After colour transformation differences between the PS1 and SDSS
# u(SDSS - PS1) = -26.29 mmag
# g(SDSS - PS1) = -2.27 mmag
# r(SDSS - PS1) = -4.85 mmag
# i(SDSS - PS1) = -7.86 mmag
# z(SDSS - PS1) = -12.66 mmag
coefficients = {}
coefficients['u'] = [ 0.04438, -2.26095, -0.13387, 0.27099]
coefficients['g'] = [-0.01808, -0.13595, 0.01941, -0.00183]
coefficients['r'] = [-0.01836, -0.03577, 0.02612, -0.00558]
coefficients['i'] = [ 0.01170, -0.00400, 0.00066, -0.00058]
coefficients['z'] = [-0.01062, 0.07529, -0.03592, 0.00890]
coefficients['y'] = [ 0.08924, -0.20878, 0.10360, -0.02441]
x = DATA['g_PS1'] - DATA['i_PS1']
x_err = np.sqrt(DATA['g_PS1_ERR']**2 + DATA['i_PS1_ERR']**2)
# Select objects with 0.4 < x < 2.7
mask_good = np.where((x >= 0.4) & (x <= 2.7))[0]
x = x [mask_good]
x_err = x_err[mask_good]
DATA = DATA[mask_good]
# Compute SDSS photometry
for filter in ['u', 'g', 'r', 'i', 'z']:
if filter not in ['u', 'y']:
mag_SDSS = DATA[filter+'_PS1'] - (coefficients[filter][0] + coefficients[filter][1] * x + coefficients[filter][2] * x**2 + coefficients[filter][3] * x**3)
mag_SDSS_err= np.sqrt(DATA[filter+'_PS1_ERR']**2 +
( x_err * coefficients[filter][1]) ** 2 +
(2 * x * x_err * coefficients[filter][2]) ** 2 +
(3 * x**2 * x_err * coefficients[filter][3]) ** 2
)
elif filter == 'u':
mag_SDSS = DATA['g_PS1'] - (coefficients[filter][0] + coefficients[filter][1] * x + coefficients[filter][2] * x**2 + coefficients[filter][3] * x**3)
mag_SDSS_err= np.sqrt(DATA['g_PS1_ERR']**2 +
( x_err * coefficients[filter][1]) ** 2 +
(2 * x * x_err * coefficients[filter][2]) ** 2 +
(3 * x**2 * x_err * coefficients[filter][3]) ** 2
)
mag_SDSS = np.array(mag_SDSS)
mag_SDSS_err = np.array(mag_SDSS_err)
DATA[filter+'_SDSS'] = mag_SDSS
DATA[filter+'_SDSS_ERR'] = mag_SDSS_err
return DATA
def SDSS_to_Bessel(DATA):
# http://www.sdss3.org/dr8/algorithms/sdssUBVRITransform.php
DATA['B_BESSEL'] = DATA['g_SDSS'] + 0.3130*(DATA['g_SDSS'] - DATA['r_SDSS']) + 0.2271
DATA['B_BESSEL_ERR']= np.sqrt(0.0107**2 +
DATA['g_SDSS_ERR']**2 +
(0.3130 * DATA['g_SDSS_ERR'])**2 +
(0.3130 * DATA['r_SDSS_ERR'])**2
)
DATA['V_BESSEL'] = DATA['g_SDSS'] - 0.5784*(DATA['g_SDSS'] - DATA['r_SDSS']) - 0.0038
DATA['V_BESSEL_ERR']= np.sqrt(0.0054**2 +
DATA['g_SDSS_ERR']**2 +
(0.5784 * DATA['g_SDSS_ERR'])**2 +
(0.5784 * DATA['r_SDSS_ERR'])**2
)
DATA['R_BESSEL'] = DATA['r_SDSS'] - 0.1837*(DATA['g_SDSS'] - DATA['r_SDSS']) - 0.0971
DATA['R_BESSEL_ERR']= np.sqrt(0.0106**2 +
DATA['r_SDSS_ERR']**2 +
(0.1837 * DATA['g_SDSS_ERR'])**2 +
(0.1837 * DATA['r_SDSS_ERR'])**2
)
DATA['I_BESSEL'] = DATA['r_SDSS'] - 1.2444*(DATA['r_SDSS'] - DATA['i_SDSS']) - 0.3820
DATA['I_BESSEL_ERR']= np.sqrt(0.0078**2 +
DATA['r_SDSS_ERR']**2 +
(1.2444 * DATA['r_SDSS_ERR'])**2 +
(1.2444 * DATA['i_SDSS_ERR'])**2
)
return DATA
def retrieve_photcat(OBJECT_PROP, PHOTCAT, CATPROP, FILENAME=None, ROW_LIMIT=-1, RADIUS=10. * u.arcmin, OUTDIR='photcat/'):
# Query VIZIER
v = Vizier(columns = ['all'], row_limit = ROW_LIMIT)
#result = v.query_region(coord.SkyCoord(OBJECT_PROP['RA'], OBJECT_PROP['DEC'], unit=(u.hour, u.deg)), radius = RADIUS, catalog=CATPROP[PHOTCAT]['CATID'])
result = v.query_region(coord.SkyCoord(OBJECT_PROP['RA'], OBJECT_PROP['DEC'], unit=u.deg), radius = RADIUS, catalog=CATPROP[PHOTCAT]['CATID'])
result = result[CATPROP[PHOTCAT]['CATID_OUT']]
result = result[CATPROP[PHOTCAT]['KEYWORDS']]
# Some formatting
for key in [x for x in result.keys() if 'mag' in x]:
result[key].format= '.4f'
# Check if output dir exists
if not os.path.isdir(OUTDIR) and OUTDIR != './':
os.system('mkdir %s' %OUTDIR)
# Filter output
if PHOTCAT == 'SDSS':
result = result[(result['class'] == 6) & (result['mode'] == 1)]
if PHOTCAT == 'PanSTARRS':
result = result[
(result['o_gmag'] >= 0.85) & (result['o_rmag'] >= 0.85) &
(result['o_imag'] >= 0.85) & (result['o_zmag'] >= 0.85) &
(result['o_ymag'] >= 0.85) &
(np.round(result['gPSFf'], 0) >= 1) & (np.round(result['rPSFf'], 0) >= 1) &
(np.round(result['iPSFf'], 0) >= 1) & (np.round(result['zPSFf'], 0) >= 1) &
(np.round(result['yPSFf'], 0) >= 1) &
(abs(result['imag'] - result['iKmag']) < 0.05) &
(result['imag'] > 14) & (result['imag'] < 21)
]
# Write result to file
for filter in OBJECT_PROP['FILTER']:
if filter in CATPROP[PHOTCAT]['FILTER']:
mask_good = np.where((result['e_'+filter+'mag'] > CATPROP[PHOTCAT]['SIGMA_HIGH']) &
(result['e_'+filter+'mag'] < CATPROP[PHOTCAT]['SIGMA_LOW']))[0]
filename = FILENAME
ascii.write(result[[CATPROP[PHOTCAT]['KEYWORDS'][0], CATPROP[PHOTCAT]['KEYWORDS'][1], filter+'mag', 'e_'+filter+'mag']][mask_good], filename, overwrite=True, format='no_header')
else:
print(bcolors.ERROR + 'Filter {filter} not in catalogue {catalog}.'.format(filter=filter, catalog=PHOTCAT) + bcolors.ENDC)
sys.exit()
return None
def crossmatch(X1, X2, max_distance=np.inf):
"""Cross-match the values between X1 and X2
By default, this uses a KD Tree for speed.
Parameters
----------
X1 : array_like
first dataset, shape(N1, D)
X2 : array_like
second dataset, shape(N2, D)
max_distance : float (optional)
maximum radius of search. If no point is within the given radius,
then inf will be returned.
Returns
-------
dist, ind: ndarrays
The distance and index of the closest point in X2 to each point in X1
Both arrays are length N1.
Locations with no match are indicated by
dist[i] = inf, ind[i] = N2
Taken from astroML. Add multi-processing capabilities
"""
X1 = np.asarray(X1, dtype=float)
X2 = np.asarray(X2, dtype=float)
N1, D = X1.shape
N2, D2 = X2.shape
if D != D2:
raise ValueError('Arrays must have the same second dimension')
kdt = cKDTree(X2)
dist, ind = kdt.query(X1, k=1, distance_upper_bound=max_distance, n_jobs=-1)
return dist, ind
def crossmatch_angular(X1, X2, max_distance=np.inf):
"""Cross-match angular values between X1 and X2
by default, this uses a KD Tree for speed. Because the
KD Tree only handles cartesian distances, the angles
are projected onto a 3D sphere.
Parameters
----------
X1 : array_like
first dataset, shape(N1, 2). X1[:, 0] is the RA, X1[:, 1] is the DEC,
both measured in degrees
X2 : array_like
second dataset, shape(N2, 2). X2[:, 0] is the RA, X2[:, 1] is the DEC,
both measured in degrees
max_distance : float (optional)
maximum radius of search, measured in degrees.
If no point is within the given radius, then inf will be returned.
Returns
-------
dist, ind: ndarrays
The angular distance and index of the closest point in X2 to
each point in X1. Both arrays are length N1.
Locations with no match are indicated by
dist[i] = inf, ind[i] = N2
Taken from astroML.
"""
X1 = X1 * (np.pi / 180.)
X2 = X2 * (np.pi / 180.)
max_distance = max_distance * (np.pi / 180.)
# Convert 2D RA/DEC to 3D cartesian coordinates
Y1 = np.transpose(np.vstack([np.cos(X1[:, 0]) * np.cos(X1[:, 1]),
np.sin(X1[:, 0]) * np.cos(X1[:, 1]),
np.sin(X1[:, 1])]))
Y2 = np.transpose(np.vstack([np.cos(X2[:, 0]) * np.cos(X2[:, 1]),
np.sin(X2[:, 0]) * np.cos(X2[:, 1]),
np.sin(X2[:, 1])]))
# law of cosines to compute 3D distance
max_y = np.sqrt(2 - 2 * np.cos(max_distance))
dist, ind = crossmatch(Y1, Y2, max_y)
# convert distances back to angles using the law of tangents
not_inf = ~np.isinf(dist)
x = 0.5 * dist[not_inf]
dist[not_inf] = (180. / np.pi * 2 * np.arctan2(x,
np.sqrt(np.maximum(0, 1 - x ** 2))))
return dist, ind
def wrapper_crossmatch(FILE1, FILE2, RADIUS):
# # Load data file
data_1 = FILE1#np.loadtxt(FILE1)
data_2 = FILE2#np.loadtxt(FILE2)
# Make matricies of coordinates
dist, idx = crossmatch_angular(data_1[:,:2], data_2[:,:2], 1)
result = np.hstack([data_2[idx,:], data_1])
result = np.hstack([result, dist.reshape(-1,1)])
# Filter data
result = result[result[:,-1] < RADIUS/3600.,:]
return result#table.Table(result, names=('RA_1', 'DEC_1', 'MAG_1', 'MAGERR_1', 'RA_2', 'DEC_2', 'MAG_2', 'MAGERR_2', 'DIST'))