def test_QU_no_ids_no_patchy(self):
with suppress_stdout():
res = ilc(self.components, dict(Frequencies=self.freqs),
self.d[:, 1:])
aac(res.s[0], self.s[1:], atol=self.TOL)
aac(res.freq_cov, self.exp_freq_cov, atol=self.TOL)
def test_TQU_weights(self):
theta = hp.pix2ang(self.nside, np.arange(hp.nside2npix(self.nside)))[0]
weights = 1.0 / (1.0 + np.exp(-20.0 * (theta - 0.25 * np.pi)))
weights *= 1.0 / (1.0 + np.exp(20.0 * (theta - 0.65 * np.pi)))
bins = np.arange(1000) * self.BINS_WIDTH
with suppress_stdout():
res = harmonic_ilc(self.components,
dict(Frequencies=self.freqs),
self.d,
lbins=bins,
weights=weights,
iter=10)
lmax = res.cl_out.shape[-1] - 1
ell = np.arange(lmax + 1)
ref = self.cl[:, :lmax + 1]
norm_diff = (ref - res.cl_out[0, :4]) * np.sqrt((2 * ell + 1) / 2)
norm_diff[:3] /= ref[:3]
norm_diff = norm_diff[..., :int(2.5 * self.nside)]
if False: # Debug plots
import pylab as pl
pl.plot(norm_diff[..., 2:].T)
pl.show()
pl.plot(res.cl_in[0].T)
pl.plot(self.cl[:4].T, ls='--')
pl.show()
aac(norm_diff[..., 2:], np.zeros_like(norm_diff[..., 2:]), atol=5)
# This is a weak test:
# recovery is bad in polarization, mostly at small scales
aac(res.s[0], self.s * weights, atol=self.TOL * self.s.max())
def test_QU_ids_patchy(self):
patch_ids = np.arange(self.cov[1:].size).reshape(-1, 12) // 4
patch_ids = hp.ud_grade(patch_ids, self.NSIDE)
with suppress_stdout():
res = ilc(self.components, dict(Frequencies=self.freqs),
self.d_patchy[:, 1:], patch_ids)
aac(res.s[0], self.s_patchy[1:], atol=self.TOL)
def test_TQU_1_id_no_patchy(self):
# No patch and one patch has to give the same result
patch_ids = np.zeros(hp.nside2npix(self.NSIDE), dtype=int)
with suppress_stdout():
res = ilc(self.components, dict(Frequencies=self.freqs), self.d)
res_patch = ilc(self.components, dict(Frequencies=self.freqs),
self.d, patch_ids)
aac(res.s, res_patch.s)
aac(res.freq_cov, res_patch.freq_cov[0])
def test_QU_ids_patchy_mask(self):
mask_good = (np.arange(hp.nside2npix(self.NSIDE)) % 13).astype(bool)
patch_ids = np.arange(self.cov[1:].size).reshape(-1, 12) // 4
patch_ids = hp.ud_grade(patch_ids, self.NSIDE)
data = self.d_patchy[:, 1:].copy()
data[..., ~mask_good] = hp.UNSEEN
ref = self.s_patchy[1:].copy()
ref[..., ~mask_good] = hp.UNSEEN
with suppress_stdout():
res = ilc(self.components, dict(Frequencies=self.freqs), data,
patch_ids)
aac(res.s[0], ref, atol=self.TOL)
def test_TQU(self):
bins = np.arange(1000) * self.BINS_WIDTH
with suppress_stdout():
res = harmonic_ilc(self.components,
dict(Frequencies=self.freqs),
self.d,
lbins=bins,
iter=10)
lmax = res.cl_out.shape[-1] - 1
ell = np.arange(lmax + 1)
ref = self.cl[:, :lmax + 1]
norm_diff = (ref - res.cl_out[0, :4]) * np.sqrt((2 * ell + 1) / 2)
norm_diff[:3] /= ref[:3]
aac(norm_diff[..., 2:int(2.5 * self.nside)],
np.zeros_like(norm_diff[..., 2:int(2.5 * self.nside)]),
atol=5)
# This is a very weak test:
# recovery is bad at small scales at the poles, especially in Q and U
aac(res.s[0], self.s, atol=3 * self.TOL * self.s.max())
def test_d0s0(self):
# EXTERNAL_xFORECAST_RUN RESULTS
EXT_BETA = [1.54000015, 19.99999402, -3.00000035]
EXT_SIGMA_BETA = np.array(
[[1.07107731e+09, 1.02802459e+07, 3.05900728e+07],
[1.02802459e+07, 8.80751745e+05, 2.64258857e+05],
[3.05900728e+07, 2.64258857e+05, 8.80649611e+05]])
EXT_NOISE_POST_COMP_SEP = [
6.985360419978725e-07, 6.954968812329504e-07,
6.952971018678473e-07, 6.97621370087622e-07, 6.97915989771863e-07,
6.981532542428136e-07, 6.984690244398313e-07,
6.963706038663776e-07, 6.962958090983174e-07,
6.999793141962897e-07, 6.966029199088166e-07,
6.998332244730213e-07, 6.97245540450936e-07, 7.013469190449905e-07,
6.98145319051069e-07, 6.997552902541847e-07, 7.006828378883164e-07,
6.993357502111902e-07, 7.016843277673384e-07, 7.02276431905913e-07,
7.009651598790946e-07, 7.024327502574484e-07,
7.058590396724249e-07, 7.035637090541009e-07,
7.034402740635456e-07, 7.05326337473677e-07, 7.086905417607417e-07,
7.067287662339356e-07, 7.06396320822362e-07, 7.075857215168964e-07,
7.102089978543108e-07, 7.118461226661247e-07
]
EXT_CL_STAT_RES = [
3.43065437e-08, 2.13752688e-07, 2.50160994e-08, 4.39734801e-08,
1.75192647e-08, 2.10382699e-08, 9.55361360e-09, 8.80726572e-09,
7.34671936e-09, 4.24354505e-09, 3.50430309e-09, 3.21803173e-09,
3.62342203e-09, 1.83222822e-09, 2.40687985e-09, 1.76806752e-09,
2.57252032e-09, 1.19987889e-09, 1.71606507e-09, 1.01867261e-09,
1.11709059e-09, 1.05584166e-09, 8.37499498e-10, 1.04610499e-09,
7.27953346e-10, 7.55604710e-10, 5.50190292e-10, 6.38657310e-10,
4.82912230e-10, 5.21029442e-10, 4.77954181e-10
]
EXT_BIAS_R = 0.00100556
EXT_SIGMA_R = 0.0003163
nside = 16
# define sky and foregrounds simulations
sky = pysm.Sky(get_sky(nside, 'd0s0'))
# define instrument
instrument = pysm.Instrument(get_instrument('litebird', nside))
# get noiseless frequency maps
with suppress_stdout():
freq_maps = instrument.observe(sky, write_outputs=False)[0]
# take only the Q and U maps
freq_maps = freq_maps[:, 1:]
# define components used in the modeling
components = [CMB(), Dust(150.), Synchrotron(150.)]
# call for xForecast
with suppress_stdout():
res = xForecast(components,
instrument,
freq_maps,
2,
2 * nside - 1,
1.0,
make_figure=False)
# list of checks
aac(EXT_BETA, res.x, rtol=1e-03)
aac(np.diag(EXT_SIGMA_BETA), np.diag(res.Sigma_inv), rtol=5e-02)
aac(EXT_NOISE_POST_COMP_SEP[0], res.noise[0], rtol=1e-02)
aac(EXT_BIAS_R, res.cosmo_params['r'][0][0], rtol=1e-02)
aac(EXT_SIGMA_R, res.cosmo_params['r'][1][0], rtol=1e-02)
