def create_k_corr_grid(redshift):
"""
(Edited from original because of error)
Returns a grid of redshifts and K corrections on the
LSST Simulations AGN SED that can be used for K correction
interpolation.
"""
bp_dict = BandpassDict.loadTotalBandpassesFromFiles()
bp_i = bp_dict['i']
sed_dir = os.path.join(getPackageDir('sims_sed_library'), 'agnSED')
sed_name = os.path.join(sed_dir, 'agn.spec.gz')
if not os.path.exists(sed_name):
raise RuntimeError('\n\n%s\n\nndoes not exist\n\n' % sed_name)
base_sed = Sed()
base_sed.readSED_flambda(sed_name)
z_grid = np.arange(0.0, redshift.max(), 0.01)
k_grid = np.zeros(len(z_grid), dtype=float)
for i_z, zz in enumerate(z_grid):
ss = Sed(flambda=base_sed.flambda, wavelen=base_sed.wavelen)
ss.redshiftSED(zz, dimming=True)
k = k_correction(ss, bp_i, zz)
k_grid[i_z] = k
return z_grid, k_grid
def test_k_correction(self):
"""
Test that the K correction correctly converts absolute magnitude
to observed magnitude.
"""
bp_dict = BandpassDict.loadTotalBandpassesFromFiles()
rng = np.random.RandomState(41321)
sed_dir = os.path.join(getPackageDir('sims_sed_library'), 'galaxySED')
list_of_sed_files = os.listdir(sed_dir)
list_of_sed_files.sort()
sed_to_check = rng.choice(list_of_sed_files, size=10)
redshift_arr = rng.random_sample(len(sed_to_check)) * 2.0 + 0.1
bp = bp_dict['g']
for sed_name, zz in zip(sed_to_check, redshift_arr):
full_name = os.path.join(sed_dir, sed_name)
ss = Sed()
ss.readSED_flambda(full_name)
true_rest_mag = ss.calcMag(bp)
ss.redshiftSED(zz, dimming=True)
obs_mag = ss.calcMag(bp)
k_corr = k_correction(ss, bp, zz)
self.assertLess(np.abs(true_rest_mag - obs_mag + k_corr), 0.001)
bp_dict = BandpassDict.loadTotalBandpassesFromFiles()
bp_i = bp_dict['i']
sed_dir = os.path.join(getPackageDir('sims_sed_library'),
'agnSED')
sed_name = os.path.join(sed_dir, 'agn.spec.gz')
if not os.path.exists(sed_name):
raise RuntimeError('\n\n%s\n\nndoes not exist\n\n' % sed_name)
base_sed = Sed()
base_sed.readSED_flambda(sed_name)
z_grid = np.arange(0.0, dc2_data['redshift'].max(), 0.01)
k_grid = np.zeros(len(z_grid),dtype=float)
for i_z, zz in enumerate(z_grid):
ss = Sed(flambda=base_sed.flambda, wavelen=base_sed.wavelen)
ss.redshiftSED(zz, dimming=True)
k = k_correction(ss, bp_i, zz)
k_grid[i_z] = k
dc2_obs_mag_i = np.zeros(len(dc2_data), dtype=float)
k_arr = np.interp(dc2_data['redshift'], z_grid, k_grid)
print('calculating observed mag')
for i_obj in range(len(dc2_data)):
local_obs_mag = dc2_abs_mag_i[i_obj] + DM[i_obj] + k_arr[i_obj]
dc2_obs_mag_i[i_obj] = local_obs_mag
rng = np.random.RandomState(18231)
dc2_tau = tau_from_params(dc2_data['redshift'],
dc2_abs_mag_i,
dc2_data['bhmass'],
rng=rng)