# select a volume complete sample of SDSS galaxies down to a given luminosity ()
Mlim = -17.0; Dmax = 70.
sdata = sdata[(M_r < Mlim) & (d_A < Dmax)]
M_rs = M_r[(M_r < Mlim) & (d_A < Dmax)]
from random import randint
import numpy as np
# select a random galaxy from sdata
iran = randint(0,np.size(sdata)-1)
randobj = sdata[iran]
# plot its image and spectrum
plot_image_spec_sdss_galaxy(randobj)
# print some key properties from the SDSS catalog
print "SDSS objID=", randobj['objID']
print "r-band absolute magnitude, M_r=", M_rs[iran]
r50s = randobj['petroR50_r']
print "light concentration in the r-band, c_r=", randobj['petroR90_r']/r50s
print "fraction of light profile fit by the de Vaucouleurs (spheroidal) component in the r-band, fracdeV_r=",randobj['fracdeV_r']
sbs = randobj['modelMag_r'] - 2.5*np.log10(0.5) + 2.5*np.log10(np.pi*(r50s)**2)
print "r-band surface brightness, mu_r=", sbs, " mag/arcsec^2"
print "r-band axis ratio q=b/a of ellipsoidal fit to light distribution=", randobj['expAB_r']
import py_compile
from setup import setup
py_compile.compile(os.path.join(setup.code_home_dir(),'fetch_sdss_image.py'))
[197.505, 0, 0, 0],
],
'z_uz': [
[0, 0, 0, 0],
[0.623441, -0.293199, 0.16293, -0.0134639],
[-21.567, 5.93194, -1.41235, 0.0714143],
[82.8481, -0.245694, 0.849976, 0],
[-185.812, -7.9729, 0, 0],
[168.691, 0, 0, 0],
],
}
c = coeff[filter_name + '_' + colour_name.replace(' - ', '')]
kcor = 0.0
for x, a in enumerate(c):
for y, b in enumerate(c[x]):
kcor += c[x][y] * redshift**x * colour_value**y
return kcor
if __name__ == "__main__":
import doctest
doctest.testmod()
import py_compile
from setup import setup
import os
py_compile.compile(os.path.join(setup.code_home_dir(), 'calc_kcor.py'))
third row: sensitivity to extended source, airmass 1.3
fourth row: sensitivity to extended source, airmass 0.0
fifth row: assumed atmospheric extinction, airmass 1.0
"""
if fname not in 'ugriz':
raise ValueError("Unrecognized filter name '%s'" % fname)
data_home = setup.sdss_filter_dir()
if not os.path.exists(data_home):
os.makedirs(data_home)
archive_file = os.path.join(data_home, '%s.dat' % fname)
if not os.path.exists(archive_file):
raise ValueError(
"Error in fetch_sdss_filter: filter file '%s' does not exist!" %
archive_file)
F = open(archive_file)
return np.loadtxt(F, unpack=True)
if __name__ == '__main__':
for f in 'ugriz':
fetch_sdss_filters(f)
import py_compile
py_compile.compile(
os.path.join(setup.code_home_dir(), 'fetch_sdss_filters.py'))
sdsslist['logMsun'] = np.array(dummy)
return sdsslist
def alfalfa_sdss_crossmatch(aalist, sdsslist):
# crossmatch them
# match sdsslist to aalist
imatch = np.in1d(aalist['AGCnr'],sdsslist['AGCnr'])
# match aalist to sdsslist
imatch2 = np.in1d(sdsslist['AGCnr'],aalist['AGCnr'])
aamatch = aalist[imatch]
sdssmatch = sdsslist[imatch2]
# carry out some basic cuts
aatf = aamatch[aamatch['Dist']>1.0]
sdsstf = sdssmatch[aamatch['Dist']>1.0]
aatf = aamatch[aamatch['Dist']<500.0]
sdsstf = sdssmatch[aamatch['Dist']<500.0]
aatf = aatf[sdsstf['zsdss']<10.]
sdsstf = sdsstf[sdsstf['zsdss']<10.]
aatf = aatf[sdsstf['zsdss']>0.0001]
sdsstf = sdsstf[sdsstf['zsdss']>0.0001]
return aatf, sdsstf
if __name__ == '__main__':
import py_compile
py_compile.compile(os.path.join(setup.code_home_dir(),'read_data.py'))
sd, md, mnpd, phot_r, mdg, mnpdg, morph = read_meert_catalog(phot_type=3)
