def get_angular_profile(maps, thmax=25, nbins=20, label='', center=np.array([316.44761929, -58.75808063]),
allstokes=False, fontsize=None, doplot=False, separate=False):
vec0 = hp.ang2vec(center[0], center[1], lonlat=True)
sh = np.shape(maps)
ns = hp.npix2nside(sh[0])
vecpix = hp.pix2vec(ns, np.arange(12 * ns ** 2))
angs = np.degrees(np.arccos(np.dot(vec0, vecpix)))
rng = np.array([0, thmax])
xx, yyI, dx, dyI, _ = ft.profile(angs, maps[:, 0], nbins=nbins, plot=False, rng=rng)
xx, yyQ, dx, dyQ, _ = ft.profile(angs, maps[:, 1], nbins=nbins, plot=False, rng=rng)
xx, yyU, dx, dyU, _ = ft.profile(angs, maps[:, 2], nbins=nbins, plot=False, rng=rng)
avg = np.sqrt((dyI ** 2 + dyQ ** 2 / 2 + dyU ** 2 / 2) / 3)
if doplot:
plot(xx, avg, 'o', label=label)
if allstokes:
plot(xx, dyI, label=label + ' I', alpha=0.3)
plot(xx, dyQ / np.sqrt(2), label=label + ' Q/sqrt(2)', alpha=0.3)
plot(xx, dyU / np.sqrt(2), label=label + ' U/sqrt(2)', alpha=0.3)
xlabel('Angle [deg.]')
ylabel('RMS')
legend(fontsize=fontsize)
if separate:
return xx, dyI, dyQ, dyU
else:
return xx, avg
def get_flatmap(TESNum,
directory,
azmin=None,
azmax=None,
elmin=None,
elmax=None,
remove=None,
fitted_directory=None):
themap = np.array(
FitsArray(directory + '/Flat/imgflat_TESNum_{}.fits'.format(TESNum)))
az = np.array(FitsArray(directory + '/Flat/azimuth.fits'.format(TESNum)))
el = np.array(FitsArray(directory + '/Flat/elevation.fits'.format(TESNum)))
if azmin is None:
azmin = np.min(az)
if azmax is None:
azmax = np.max(az)
if elmin is None:
elmin = np.min(el)
if elmax is None:
elmax = np.max(el)
okaz = (az >= azmin) & (az <= azmax)
az = az[okaz]
okel = (el >= elmin) & (el <= elmax)
el = el[okel]
themap = themap[:, okaz][okel, :]
if remove is not None:
mm = np.mean(remove)
ss = np.std(remove)
xc, yval, dx, dy, others = ft.profile(remove,
themap,
rng=[mm - 2 * ss, mm + 2 * ss],
mode=True,
nbins=20,
cutbad=True,
plot=False,
dispersion=False,
clip=3)
bla = np.polyfit(xc, yval, 1, w=1. / dy**2)
pp = np.poly1d(bla)
themap -= pp(remove)
if fitted_directory is None:
return themap, az, el
else:
### Read fitted synthesized beam
thefile = open(fitted_directory + '/fit-TES{}.pk'.format(TESNum), 'rb')
fitpars = pickle.load(thefile, encoding='latin1')
thefile.close()
sbfitmodel = SbModelIndepPeaks(nrings=2,
common_fwhm=True,
no_xy_shift=False,
distortion=False)
x = np.meshgrid(az * np.cos(np.radians(50)), np.flip(el))
fitmap, newxxyy = sbfitmodel(x, fitpars, return_peaks=True)
return themap, az, el, fitmap, newxxyy
def find_right_period(guess, t_data, data_oneTES):
ppp = np.linspace(guess - 1.5, guess + 1.5, 250)
rms = np.zeros(len(ppp))
for i in range(len(ppp)):
xin = t_data % ppp[i]
yin = data_oneTES
xx, yy, dx, dy, o = ft.profile(xin, yin, nbins=100, plot=False)
rms[i] = np.std(yy)
period = ppp[np.argmax(rms)]
return ppp, rms, period
def get_noise_invcov_profile(maps, coverage, covcut=0.1, nbins=100, fit=True, label='',
norm=False, allstokes=False, fitlim=None, doplot=False, QUsep=True):
seenpix = coverage > (covcut * np.max(coverage))
covnorm = coverage / np.max(coverage)
xx, yyI, dx, dyI, _ = ft.profile(np.sqrt(1. / covnorm[seenpix]), maps[seenpix, 0], nbins=nbins, plot=False)
xx, yyQ, dx, dyQ, _ = ft.profile(np.sqrt(1. / covnorm[seenpix]), maps[seenpix, 1], nbins=nbins, plot=False)
xx, yyU, dx, dyU, _ = ft.profile(np.sqrt(1. / covnorm[seenpix]), maps[seenpix, 2], nbins=nbins, plot=False)
avg = np.sqrt((dyI ** 2 + dyQ ** 2 / 2 + dyU ** 2 / 2) / 3)
avgQU = np.sqrt((dyQ ** 2 / 2 + dyU ** 2 / 2) / 2)
if norm:
fact = xx[0] / avg[0]
else:
fact = 1.
myY = (avg / xx) * fact
myYI = (dyI / xx) * fact
myYQU = (avgQU / xx) * fact
if doplot:
if QUsep is False:
p = plot(xx ** 2, myY, 'o', label=label + ' IQU')
if allstokes:
plot(xx ** 2, myYI, label=label + ' I', alpha=0.3)
plot(xx ** 2, myYQU, label=label + ' Average Q, U /sqrt(2)', alpha=0.3)
else:
pi = plot(xx ** 2, myYI, 'o', label=label + ' I')
pqu = plot(xx ** 2, myYQU, 'o', label=label + ' QU / sqrt(2)')
if fit:
ok = isfinite(myY)
if fitlim is not None:
print('Clipping fit from {} to {}'.format(fitlim[0], fitlim[1]))
ok = ok & (xx >= fitlim[0]) & (xx <= fitlim[1])
if QUsep is False:
mymodel = lambda x, a, b, c, d, e: (a + b * x + c * np.exp(-d * (x - e))) # /(a+b+c*np.exp(-d*(1-e)))
myfit = curve_fit(mymodel, xx[ok] ** 2, myY[ok], p0=[np.min(myY[ok]), 0.4, 0, 2, 1.5], maxfev=100000,
ftol=1e-7)
else:
mymodel = lambda x, a, b, c, d, e, f, g: (
a + b * x + f * x ** 2 + g * x ** 3 + c * np.exp(-d * (x - e))) # /(a+b+c*np.exp(-d*(1-e)))
myfitI = curve_fit(mymodel, xx[ok] ** 2, myYI[ok], p0=[np.min(myY[ok]), 0.4, 0, 2, 1.5, 0., 0.],
maxfev=100000, ftol=1e-7)
myfitQU = curve_fit(mymodel, xx[ok] ** 2, myYQU[ok], p0=[np.min(myY[ok]), 0.4, 0, 2, 1.5, 0., 0.],
maxfev=100000, ftol=1e-7)
if doplot:
if QUsep is False:
plot(xx ** 2, mymodel(xx ** 2, *myfit[0]), label=label + ' Fit', color=p[0].get_color())
else:
plot(xx ** 2, mymodel(xx ** 2, *myfitI[0]), label=label + ' Fit I', color=pi[0].get_color())
plot(xx ** 2, mymodel(xx ** 2, *myfitQU[0]), label=label + ' Fit QU / sqrt(2)',
color=pqu[0].get_color())
# print(myfit[0])
# Interpolation of the fit from invcov = 1 to 15
invcov_samples = np.linspace(1, 15, 1000)
if QUsep is False:
eff_v = mymodel(invcov_samples, *myfit[0]) ** 2
# Avoid extrapolation problem for pixels before the first bin or after the last one.
eff_v[invcov_samples < xx[0] ** 2] = mymodel(xx[0] ** 2, *myfit[0]) ** 2
eff_v[invcov_samples > xx[-1] ** 2] = mymodel(xx[-1] ** 2, *myfit[0]) ** 2
effective_variance_invcov = np.array([invcov_samples, eff_v])
else:
eff_vI = mymodel(invcov_samples, *myfitI[0]) ** 2
eff_vQU = mymodel(invcov_samples, *myfitQU[0]) ** 2
# Avoid extrapolation problem for pixels before the first bin or after the last one.
eff_vI[invcov_samples < xx[0] ** 2] = mymodel(xx[0] ** 2, *myfitI[0]) ** 2
eff_vQU[invcov_samples > xx[-1] ** 2] = mymodel(xx[-1] ** 2, *myfitQU[0]) ** 2
effective_variance_invcov = np.array([invcov_samples, eff_vI, eff_vQU])
if doplot:
xlabel('1./cov normed')
if norm:
add_yl = ' (Normalized to 1 at 1)'
else:
add_yl = ''
ylabel('RMS Ratio w.r.t linear scaling' + add_yl)
if fit:
return xx, myY, effective_variance_invcov
else:
return xx, myY, None
