def calculate_rho(data_fr,
coadd_fr,
freq_factor,
return_err=False,
use_t=False):
'''
Takes (un)weighted T/Q/U maps and an unweighted T/Q/U coadd,
as well as the weights from the map you want rho for,
and calculates the polarization rotation angle rho.
'''
maps.RemoveWeights(data_fr)
maps.RemoveWeights(coadd_fr)
q = data_fr['Q']
u = data_fr['U']
qc = coadd_fr['Q']
uc = coadd_fr['U']
weights = data_fr['Wpol']
qweight = weights.QQ
uweight = weights.UU
quweight = weights.QU
if use_t:
t = data_fr['T']
tc = coadd_fr['T']
tweight = weights.TT
tqweight = weights.TQ
tuweight = weights.TU
num = np.asarray(tuweight*qc*(t-tc) - tqweight*uc*(t-tc) - qweight*uc*(q-qc) + \
uweight*qc*(u-uc) + quweight*(qc*q - qc**2 + uc**2 - uc*u))
else:
num = np.asarray(qweight*uc*(qc-q) + uweight*qc*(u-uc) + \
quweight*(qc*q - qc**2 + uc**2 - uc*u))
den = np.asarray(qweight * uc**2 + uweight * qc**2 -
2 * quweight * qc * uc)
#num[num == np.inf] = 0
#den[den == np.inf] = 0
print(np.nansum(den))
rho = np.nansum(num) / np.nansum(den)
if return_err:
try:
err = get_errs(rho, data_fr, coadd_fr, freq_factor, use_t=use_t)
except:
err = np.nan
return rho, err
else:
return rho
def calculate_chi2(data_fr,
coadd_fr,
freq_factor,
rho,
use_t=False,
return_map=False):
'''
Takes (un)weighted T/Q/U maps and an (un)weighted T/Q/U coadd,
and calculates the chi^2 for a given input rotation
angle rho.
'''
maps.RemoveWeights(data_fr)
maps.RemoveWeights(coadd_fr)
q = data_fr['Q']
u = data_fr['U']
qc = coadd_fr['Q']
uc = coadd_fr['U']
weights = data_fr['Wpol']
qweight = weights.QQ
uweight = weights.UU
quweight = weights.QU
chiqq = qweight * (q - qc + rho * uc)**2
chiuu = uweight * (u - uc - rho * qc)**2
chiqu = 2 * quweight * (q - qc + rho * uc) * (u - uc - rho * qc)
if use_t:
t = data_fr['T']
tc = coadd_fr['T']
tweight = weights.TT
tqweight = weights.TQ
tuweight = weights.TU
chitt = tweight * (t - tc)**2
chitq = 2 * tqweight * ((t - tc) * (q - qc) + rho * uc * (t - tc))
chitu = 2 * tuweight * ((t - tc) * (u - uc) - rho * qc * (t - tc))
chi2_map = (chiqq + chiuu + chiqu + chitt + chitq +
chitu) / (freq_factor**2)
else:
chi2_map = (chiqq + chiuu + chiqu) / (freq_factor**2)
chi2 = np.nansum(chi2_map)
if return_map:
return chi2, chi2_map
else:
return chi2
def inject_signal_in_map(amp, data_fr, coadd_fr):
'''
Inject a map-space signal, rotating Q into U and vice-versa.
amp must be in radians.
'''
maps.RemoveWeights(data_fr)
maps.RemoveWeights(coadd_fr)
q = data_fr.pop('Q')
u = data_fr.pop('U')
qc = coadd_fr['Q']
uc = coadd_fr['U']
data_fr['Q'] = q - amp * uc
data_fr['U'] = u + amp * qc
return data_fr
def get_errs(min_rho, data_fr, coadd_fr, freq_factor, use_t=False):
maps.RemoveWeights(data_fr)
maps.RemoveWeights(coadd_fr)
chi2s = []
rhos = np.linspace(min_rho - 5. * (np.pi / 180),
min_rho + 5. * (np.pi / 180), 11)
for rho in rhos:
chi2s.append(
calculate_chi2(data_fr, coadd_fr, freq_factor, rho, use_t=use_t))
p = np.polyfit(rhos, np.array(chi2s), deg=2)
p[2] = p[2] - chi2s[5] - 1
err_1, err_2 = np.roots(p)
err_down = np.min([err_1, err_2])
err_up = np.max([err_1, err_2])
return np.mean((min_rho - err_down, err_up - min_rho))
def convert_to_fits_raw(g3file,
fitsfile=None,
outpath='',
hdr=None,
overwrite=True,
compress=False,
logger=None):
t0 = time.time()
if not logger:
logger = LOGGER
# Extract the relevante metadata
if not hdr:
hdr = get_metadata(g3file, logger=logger)
# Define fitsfile name only if undefined
if fitsfile is None:
basename = get_g3basename(g3file)
fitsfile = os.path.join(outpath, get_folder_date(hdr),
f"{basename}.fits")
# Skip if fitsfile exists and overwrite not True
if os.path.isfile(fitsfile) and not overwrite:
logger.warning(f"File exists, skipping: {fitsfile}")
return
# Populate additional metadata for DB
hdr['FITSNAME'] = (os.path.basename(fitsfile), 'Name of fits file')
# Second loop to write FITS
g3 = core.G3File(g3file)
logger.info(f"Loading 2: {g3file}")
for frame in g3:
# Convert to FITS
if frame.type == core.G3FrameType.Map:
logger.info(
f"Transforming to FITS: {frame.type} -- Id: {frame['Id']}")
maps.RemoveWeights(frame, zero_nans=True)
# Make sure that the folder exists:
dirname = os.path.dirname(fitsfile)
if not os.path.isdir(dirname):
logger.info(f"Creating directory {os.path.dirname(fitsfile)}")
os.mkdir(dirname)
maps.fitsio.save_skymap_fits(fitsfile,
frame['T'],
overwrite=overwrite,
compress=compress,
W=frame['Wunpol'],
hdr=hdr)
logger.info(f"Created: {fitsfile}")
logger.info(f"FITS creation time: {elapsed_time(t0)}")
return
def plot_map(frame):
if frame.type == core.G3FrameType.Map and 'Id' in frame and \
fnmatch(frame['Id'], 'combined_*'):
maps.RemoveWeights(frame)
for pol in ['T', 'Q', 'U']:
f = plt.figure(figsize=(12, 8))
if pol == 'T':
vmag = 50
else:
vmag = 10
plt.imshow(frame[pol] / (core.G3Units.microkelvin),
vmin=-1 * vmag,
vmax=vmag)
plt.colorbar()
plt.title(pol)
plt.tight_layout()
plt.savefig('{}_map_{}_{}.png'.format(
os.path.basename(args.output).split('.')[0], jmap,
pol),
dpi=200)
plt.close(f)
from spt3g import core, maps
from scipy.stats import norm
import os.path
coadd_dir = '/sptlocal/user/kferguson/full_daniel_maps/'
with open('weights_var_factors_backup.pkl', 'rb') as f:
data_kstest = pickle.load(f)
for field in data_kstest['ks_result']:
for obsid in data_kstest['ks_result'][field]:
for band in data_kstest['ks_result'][field][obsid]:
if data_kstest['ks_result'][field][obsid][band].pvalue < 0.01:
map_fname = '{}_{}GHz_{}_map.g3.gz'.format(field, band, obsid)
map_frame = list(core.G3File(os.path.join(coadd_dir, map_fname)))[0]
maps.RemoveWeights(map_frame)
q_arr = np.array(map_frame['Q'])
q_weight = np.array(map_frame['Wpol'].QQ)
q_arr_finite = q_arr[np.isfinite(q_arr)]
q_weight_finite = q_weight[np.isfinite(q_arr)]
factor = q_arr_finite * np.sqrt(q_weight_finite)
plt.figure(1)
plt.hist(factor / np.std(factor), bins=np.linspace(-4, 4, 101),
density=True)
plt.plot(np.linspace(-4, 4, 101), norm.pdf(np.linspace(-4, 4, 101)))
plt.xlim([-10,10])
plt.xlabel('$[Q_i \\times \sqrt{W_{qqi}}] / $std')
plt.ylabel('pixels')
field = result.group(1)
obsid = int(result.group(2))
if field not in factor:
factor[field] = {}
if field not in ks_test:
ks_test[field] = {}
if obsid not in factor[field]:
factor[field][obsid] = {}
if obsid not in ks_test[field]:
ks_test[field][obsid] = {}
if args.group == '2018ee':
real_data = list(core.G3File(fname))[0]
maps.RemoveWeights(real_data)
q_arr = np.array(real_data['Q'])
q_weight = np.array(real_data['Wpol'].QQ)
q_arr_finite = q_arr[np.isfinite(q_arr)]
q_weight_finite = q_weight[np.isfinite(q_arr)]
factor[field][obsid][band] = np.std(q_arr_finite *
np.sqrt(q_weight_finite))
ks_result = kstest(q_arr_finite * np.sqrt(q_weight_finite) /
factor[field][obsid][band],
cdf='norm')
ks_test[field][obsid][band] = ks_result
elif args.group == '2019':
