def get_cross_pairwise_ksz(df1, df2, params):
'''
Wrapper for calling pairwise_cross_ksz using a cross_dataset
object.
params: params object from paramTools.py
'''
dT1 = df1.dT.values
dT2 = df2.dT.values
z = df1.z.values # this requires Dc and z values to be the same
Dc = df1.Dc.values
if params.GET_TZAV_FAST:
tzav1 = get_tzav_fast(dT1, z, params.SIGMA_Z)
tzav2 = get_tzav_fast(dT2, z, params.SIGMA_Z)
else:
tzav1 = get_tzav(dT1, z, params.SIGMA_Z)
tzav2 = get_tzav(dT2, z, params.SIGMA_Z)
# more common values
ra_deg = df1.ra.values
dec_deg = df1.dec.values
assert params.UNEVEN_BINS # only uneven bins supported for this
bin_edges = params.BIN_EDGES
if params.DO_VARIANCE_WEIGHTED:
assert False # not implemented
else:
r_sep, p_uk = cross_pairwise_ksz_uneven_bins(Dc, ra_deg, dec_deg,
tzav1, tzav2, dT1, dT2,
bin_edges)
return r_sep, p_uk
def test_get_tzav_fast():
N_gals = 15000
sigma_z = 0.01
z = np.random.uniform(size=N_gals)
dT = np.random.normal(size=N_gals)
tzav = pairwiser.get_tzav(dT, z, sigma_z)
tzav_fast = pairwiser.get_tzav_fast(dT, z, sigma_z)
chisq = np.sum((tzav - tzav_fast)**2)
assert chisq < 1e-8
def get_pairwise_ksz_massboosted_debiased(df, params, multithreading=True):
'''Wrapper for calling pairwise_ksz using only a preprocessed dataframe
and a parameters container.
Arguments:
df: preprocessed catalog
params: paramTols.params object.'''
dT = df.dT.values
z = df.z.values
Dc = df.Dc.values
mass = df.mass.values
tzav = get_tzav_fast(dT, z, params.SIGMA_Z) # use fast implementation
ra_deg = df.ra.values
dec_deg = df.dec.values
bin_edges = params.BIN_EDGES
r_sep, p_uk = pairwise_ksz_massboosted_debiased(Dc, ra_deg, dec_deg, tzav,
dT, mass, bin_edges)
return r_sep, p_uk
def correct_tsz_decrement(df, sigma_z, N_in_sigma=10,
gaussian_or_square='gaussian',
mean_or_median='median'):
'''Takes dTs from aperture photometry, computes wiggly tee
for each redshift and substracts wiggly tee.
Results are appended in df['dT_kSZ']'''
if 'gaussian_conventional' not in gaussian_or_square: # new method
print('Applying new wtee correction')
zs_to_eval, w_tee, f_wiggly_tee = get_wiggly_tee(df.z.values,
df.dT.values,
sigma_z,
N_in_sigma,
gaussian_or_square=gaussian_or_square, # noqa
mean_or_median=mean_or_median) # noqa
df['wiggly_tee'] = f_wiggly_tee(df.z.values)
else: # usual method
assert 'gaussian_conventional' in gaussian_or_square
print('Applying convetional wiggly tee correction')
tzav = pairwiser.get_tzav_fast(df.dT.values, df.z.values, sigma_z)
df['wiggly_tee'] = tzav
df['dT_kSZ'] = df['dT'].values - df['wiggly_tee']
import scipy.stats
fnames = [
"params_disjoint_bin_lum_gt_04p3_and_06p1_jk.ini",
"params_disjoint_bin_lum_gt_06p1_and_07p9_jk.ini",
"params_lum_gt_07p9_jk.ini"
]
for j in range(len(fnames)):
fname = fnames[j]
p = paramTools.params(fname)
df = catalogTools.preProcessedCat(howMany=None, query=p.CAT_QUERY).df
dT = df.dT.values
z = df.z.values
tzav = pairwiser.get_tzav_fast(dT, z, p.SIGMA_Z)
dT_ksz = dT - tzav
mean, std = np.mean(dT_ksz), np.std(dT_ksz)
plt.figure(figsize=[8, 4.5])
plt.hist(dT_ksz,
normed=True,
histtype='step',
color='black',
lw=2,
bins=200)
x = np.linspace(dT_ksz.min(), dT_ksz.max(), 1000)
plt.plot(x,
scipy.stats.norm.pdf(x, mean, std),