def readmaps(directory,
tes,
asic,
az,
el,
p,
proj_name,
nside=None,
azmin=None,
azmax=None,
remove=None,
verbose=False):
"""
directory: path to flat maps (get_flatmap method used to read maps)
npix: array of number of pixels
el, az: coordinates from data
proj_name: projection you want. It could be 'flat' or 'healpix'
nside: healpix parameter needed
p: pointing"""
if proj_name == 'flat':
filemaps = np.zeros((len(tes), len(el), len(az)))
for i in range(len(tes)):
filename = directory + 'flat_ASIC{}TES{}.fits'.format(
asic[i], tes[i])
if verbose:
print("[FlatMapsFile]: Reading {} file".format(filename))
#filemaps[i], _, _ = sbfit.get_flatmap(npix[i], directory,
# azmin=azmin, azmax=azmax, remove=remove)
filemaps[i] = FitsArray(filename)
elif proj_name == 'healpix':
warn(
"HEALPix projections: At the moment, HEALPix projection comes from the readout of flat maps \
In future this should be change and read directly from TOD")
if nside == None:
nside = hp.get_nside(
FitsArray(directory +
'hp_ASIC{}TES{}.fits'.format(asic[0], tes[0])))
filemaps = np.zeros((len(tes), 12 * nside**2))
for i in range(len(tes)):
filename = directory + 'hp_ASIC{}TES{}.fits'.format(
asic[i], tes[i])
if verbose:
print("[FlatMapsFile]: Reading {} file".format(filename))
#auxmap, _, _ = sbfit.get_flatmap(npix[i], directory,
# azmin=azmin, azmax=azmax, remove=remove)
filemaps[i] = FitsArray(filename)
else:
raise ValueError(
"You didn't specified the projection you want: flat or healpix.")
return filemaps
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 maps_from_files(files, silent=False):
"""allmost equivalent to get_all_maps,
used in the functions that compute spectra
"""
if not silent: print('Reading Files')
nn = len(files)
mm = FitsArray(files[0])
sh = np.shape(mm)
maps = np.zeros((nn, sh[0], sh[1], sh[2]))
for i in xrange(nn):
maps[i,:,:,:] = FitsArray(files[i])
totmap = np.sum(np.sum(np.sum(maps, axis=0), axis=0),axis=1)
seenmap = totmap > -1e20
return maps, seenmap
def read_all_maps(files, nsub, seenmap):
"""
Read all realisations of I,Q,U maps for one number of subbands
Only seen pixels are stored in order to save memory.
nsub : number of subbands
seenmap : list of booleans for pixels observed or not
mapsout : array of shape (nreals, nsub, npixok, 3)
"""
print('\nReading all maps')
nfiles = len(files)
print(files, nfiles)
npixok = np.sum(seenmap)
mapsout = np.zeros((nfiles, nsub, npixok, 3))
for ifile in xrange(nfiles):
print('Doing: ' + files[ifile], 'nsub=' + str(nsub))
sys.stdout.flush()
sys.stdout.write('\r Reading: ' + files[ifile] + ' ({:3.1f} %)'.format(100 * (ifile + 1) * 1. / nfiles))
mm = FitsArray(files[ifile])
mapsout[ifile, :, :, :] = mm[:, seenmap, :]
sys.stdout.flush()
return mapsout
def coverage(dictionaries, regions, bands, makeCov = False, filename = None):
"""
Read or make the coverage maps for the bands and regions
Assumes one config: FI or TD (info will be read it from dictionatries[0])
Parameters:
dictionaries:
array of Qubic dictionaries
regions:
sky regions where coverage will be computed or read
bands:
150 or 220GHz
filename:
Array of filenames of coverages maps. #filenames = #regions + #bands. Default: read this 4 files
["doc/FastSimulator/Data/DataFastSimulator_FI150Q_coverage.fits'",
"doc/FastSimulator/Data/DataFastSimulator_FI220Q_coverage.fits'",
"doc/FastSimulator/Data/DataFastSimulator_FI150G_coverage.fits'",
"doc/FastSimulator/Data/DataFastSimulator_FI220G_coverage.fits'" ]
Return:
coveragesmaps:
maps with format priority: region (in regions) and then band (in bands ascendent order).
It take the first letter in regions to use in name when reading maps from files.
Shape: (len(regions) +len(bands), npix)
"""
#regions = ['Qubic_field', 'GalCen_field']
#bands = ['150', '220']
global_dir = Qubic_DataDir(datafile='instrument.py', datadir=os.environ['QUBIC_DATADIR'])
if filename == None:
import itertools
config = dictionaries[0]['config']
filename = [global_dir + '/doc/FastSimulator/Data/DataFastSimulator_{}{}{}_coverage.fits'.format(config, jband, ireg[0]) \
for ireg, jband in itertools.product(regions, bands)]
else:
filename = filename
coveragesmaps = np.zeros((len(regions) * len(bands), 12 * dictionaries[0]['nside'] ** 2, ))
if makeCov:
#Save coverage maps with format priority: region (in regions) and then band (in bands ascendent order)
cov = np.shape((len(regions)+len(bands),
dictionaries[0]['nf_recon'],
12 * dictionaries[0]['nside'] ** 2))
for jr, region in enumerate(regions):
for jb, band in enumerate(bands):
index = len(bands) * jr + jb
cov[index] = make_cov[dictionaries[index]]
coveragesmaps[index] = np.sum(cov[index], axis = 0) # Average the bands
coveragesmaps[index] /= np.max(coveragesmaps[index]) # Normalize by the max
else:
for jr, region in enumerate(regions):
for jb, band in enumerate(bands):
index = len(bands) * jr + jb
#print(index, len(coveragesmaps), len(regions), len(bands))
coveragesmaps[index] = FitsArray(filename[index])
return coveragesmaps
def func(lmax, n):
class XpolDummy(Xpol):
def __init__(self):
self.lmax = lmax
self.wl = np.ones(max(n+1, 1))
xpol = XpolDummy()
mll = xpol._get_Mll(binning=False)
expected = FitsArray('test/data/xpol_mll_{}_{}.fits'.format(lmax, n))
assert_same(mll, expected, atol=200)
def save_azel_mymap(self, mymap, TESNum):
repository = os.getcwd() + '/Fits/Flat'
try:
os.makedirs(repository)
except OSError:
if not os.path.isdir(repository):
raise
FitsArray(mymap).save(repository +
'/imgflat_TESNum_{}.fits'.format(TESNum))
def get_seenmap(files):
"""
Return a list of len the number of pixels,
True or False if the pixel is observed or not in files.
files : list of .fits files
seenmap : list of len npix
"""
print('\nGetting Observed pixels map')
m = FitsArray(files[0])
npix = np.shape(m)[1]
seenmap = np.zeros(npix) == 0
for i in xrange(len(files)):
sys.stdout.flush()
sys.stdout.write('\r Reading: '+files[i]+' ({:3.1f} %)'.format(100*(i+1)*1./len(files)))
m = FitsArray(files[i])
bla = np.mean(m, axis=(0,2)) != hp.UNSEEN
seenmap *= bla
sys.stdout.flush()
return seenmap
def run_asic(idnum,
Vtes,
fff,
dc,
theasicfile,
asic,
reselect_ok=False,
lowcut=0.5,
highcut=15.,
nbins=50,
nointeractive=False,
doplot=True,
notch=None,
lastpassallfree=False,
name='fib',
okfile=None,
initpars=None,
timerange=None,
removesat=False,
stop_each=False,
rangepars=None):
"""
Parameters
----------
idnum
Vtes
fff : float
Modulation frequency of the external source
dc : float
Duty cycle of the modulation
theasicfile
asic
reselect_ok : bool
If true, you will select the good TES one by one, if False, you will use the file created before.
lowcut
highcut
nbins
nointeractive
doplot
notch
lastpassallfree
name
okfile
initpars
timerange
removesat
stop_each
rangepars
Returns
-------
"""
fib = idnum
### Read data
# GOING TO TEST PASSING IN THESE VARS
FREQ_SAMPLING = (2e6 / 128 / 100)
time, dd, a = qs2array(theasicfile, FREQ_SAMPLING, timerange=timerange)
ndet, nsamples = np.shape(dd)
### Fold the data at the modulation period of the fibers
### Signal is also badpass filtered before folding
folded, tt, folded_nonorm = fold_data(time,
dd,
1. / fff,
lowcut,
highcut,
nbins,
notch=notch)
if nointeractive:
reselect_ok = False
answer = 'n'
else:
if reselect_ok:
print('\n\n')
answer = raw_input(
'This will overwrite the file for OK TES. Are you sure you want to proceed [y/n]'
)
else:
answer = 'n'
if answer == 'y':
print(
'Now going to reselect the OK TES and overwrite the corresponding file'
)
#### Pass 1 - allows to obtain good values for t0 basically
#### Now perform the fit on the median folded data
print('')
print('FIRST PASS')
print('First Pass is only to have a good guess of the t0, '
'your selection should be very conservative - only high S/N')
# if initpars == Noy
# ne:
# initpars = [dc, 0.06, 0., 0.6]
av, params, err = fit_average(tt,
folded,
fff,
dc,
fib,
Vtes,
initpars=initpars,
fixpars=[0, 0, 0, 0],
doplot=True,
name=name)
#### And the fit on all data with this as a first guess forcing some parameters
#### it returns the list of OK detectorsy
allparams, allerr, allchi2, ndf, ok = fit_all(
tt,
folded,
av,
initpars=[dc, params[1], params[2], params[3]],
fixpars=[1, 0, 1, 0],
rangepars=rangepars,
stop_each=True)
#### Pass 2
#### Refit with only the above selected ones in order to have good t0
#### Refit the median of the OK detectors
print('')
print('SECOND PASS')
print(
'Second pass is the final one, please select the pixels that seem OK'
)
av, params, err = fit_average(tt,
folded[ok, :],
fff,
dc,
fib,
Vtes,
initpars=initpars,
fixpars=[0, 0, 0, 0],
doplot=True,
name=name)
#### And the fit on all data with this as a first guess forcing some parameters
#### it returns the list of OK detectors
allparams, allerr, allchi2, ndf, ok = fit_all(
tt,
folded,
av,
initpars=[dc, params[1], params[2], params[3]],
rangepars=rangepars,
fixpars=[1, 0, 1, 0],
stop_each=True)
#### Final Pass
#### The refit them all with only tau and amp as free parameters
#### also do not normalize amplitudes of folded
allparams, allerr, allchi2, ndf, ok_useless = fit_all(
tt,
folded_nonorm * 1e9,
av,
initpars=[dc, params[1], params[2], params[3]],
rangepars=rangepars,
fixpars=[1, 0, 1, 0],
functname=simsig_nonorm)
okfinal = ok * (allparams[:, 1] < 1.)
### Make sure no thermometer is included
okfinal[[3, 35, 67, 99]] = False
# Save the list of OK bolometers
if okfile is None:
FitsArray(okfinal.astype(int)).save(
'TES-OK-{}{}-asic{}.fits'.format(name, fib, asic))
else:
FitsArray(okfinal.astype(int)).save(okfile)
else:
# if initpars is None:
# initpars = [dc, 0.06, 0., 0.6]
if okfile is None:
okfinal = np.array(
FitsArray('TES-OK-{}{}-asic{}.fits'.format(name, fib,
asic))).astype(bool)
else:
okfinal = np.array(FitsArray(okfile)).astype(bool)
if removesat:
#### remove pixels looking saturated
saturated = (np.min(folded_nonorm, axis=1) < removesat)
okfinal = (okfinal * ~saturated).astype(bool)
if doplot is False:
### Now redo the fits one last time
av, params, err = fit_average(tt,
folded[okfinal, :],
fff,
dc,
fib,
Vtes,
initpars=initpars,
fixpars=[0, 0, 0, 0],
doplot=False,
clear=False,
name=name)
allparams, allerr, allchi2, ndf, ok_useless = fit_all(
tt,
folded_nonorm * 1e9,
av,
initpars=[dc, params[1], params[2], params[3]],
fixpars=[1, 0, 1, 0],
functname=simsig_nonorm,
rangepars=rangepars)
else:
figure(figsize=(6, 8))
subplot(3, 1, 1)
### Now redo the fits one last time
av, params, err = fit_average(tt,
folded[okfinal, :],
fff,
dc,
fib,
Vtes,
initpars=initpars,
fixpars=[0, 0, 0, 0],
doplot=True,
clear=False,
name=name)
print(params)
print(err)
if lastpassallfree:
fixed = [0, 0, 0, 0]
else:
fixed = [1, 0, 1, 0]
allparams, allerr, allchi2, ndf, ok_useless = fit_all(
tt,
folded_nonorm * 1e9,
av,
initpars=[dc, params[1], params[2], params[3]],
fixpars=fixed,
functname=simsig_nonorm,
stop_each=stop_each,
rangepars=rangepars)
subplot(3, 2, 3)
mmt, sst = meancut(allparams[okfinal, 1], 3)
hist(allparams[okfinal, 1],
range=[0, mmt + 4 * sst],
bins=10,
label=statstr(allparams[okfinal, 1], cut=3))
xlabel('Tau [sec]')
legend()
title('Asic {} - {} {}'.format(name, asic, fib))
subplot(3, 2, 4)
mma, ssa = meancut(allparams[okfinal, 3], 3)
hist(allparams[okfinal, 3],
range=[0, mma + 4 * ssa],
bins=10,
label=statstr(allparams[okfinal, 3], cut=3))
legend()
xlabel('Amp [nA]')
pars = allparams
tau = pars[:, 1]
tau[~okfinal] = np.nan
amp = pars[:, 3]
amp[~okfinal] = np.nan
if asic == 1:
tau1 = tau
tau2 = None
amp1 = amp
amp2 = None
else:
tau1 = None
tau2 = tau
amp1 = None
amp2 = amp
subplot(3, 2, 5)
imtau = image_asics(data1=tau1, data2=tau2)
imshow(imtau,
vmin=0,
vmax=mmt + 4 * sst,
cmap='viridis',
interpolation='nearest')
title('Tau - {} {} - asic {}'.format(name, fib, asic))
colorbar()
subplot(3, 2, 6)
imamp = image_asics(data1=amp1, data2=amp2)
imshow(imamp,
vmin=0,
vmax=mma + 6 * ssa,
cmap='viridis',
interpolation='nearest')
colorbar()
title('Amp - {} {} - asic {}'.format(name, fib, asic))
tight_layout()
return tt, folded, okfinal, allparams, allerr, allchi2, ndf
centers = [centerQ, centerQ, centerG, centerG]
dictionaries = [d150Q, d220Q, d150G, d220G]
regions = ['Qubic_field', 'GalCen_field']
bands = ['150', '220']
nf_recon = d150Q['nf_recon']
print(" =========== DONE ======== ")
print("# Read coverage maps")
coveragesmaps = fsed.coverage(dictionaries, regions, bands)
print("# Read FullMap (noise+foreground with nside = 256)")
maps = FitsArray("DataSED/SED_FullMaps_nside{}_nreals{}.fits".format(nside_new,nreals))
print("# Read sky (dust + synch) down graded to nside {}".format(nside_new))
fgr_map_ud = FitsArray("DataSED/SED_ForegMaps_nside{}_nreals{}.fits".format(nside_new,nreals))
print("# Read noise down graded to nside {}".format(nside_new))
noise_ud_i = FitsArray("DataSED/SED_NoiseMaps_nside{}_nreals{}.fits".format(nside_new,nreals))
print("# Read covariance matrix")
bandreg = ["FI150Q", "FI220Q", "FI150G", "FI220G"]
Cp_prime = []
for j, idict in enumerate(bandreg):
Cp_prime.append(FitsArray("DataSED/SED_CovarMatrix_nside{}_nreals{}_{}.fits".format(nside_new,nreals, bandreg[j])))
#Cp_prime = FitsArray("SED_CovarMatrix_nside{}_nreals{}.fits".format(nside_new,nreals))
print("# Compute mask for coverage map and compute downgraded coverage map")
allfiles_ang = np.sort(glob.glob(dirfiles + 'angles*_' + names + '_*.fits'))
allfiles_ang = np.sort(allfiles_ang)
allels = np.zeros(len(allfiles))
for i in range(len(allfiles)):
allels[i] = str.split(allfiles[i], '_')[-1][:-5]
print 'Found {} Files'.format(len(allfiles))
#### Read files
alldata = []
allaz = []
allel = []
allang_az = []
for j in range(len(allfiles)):
data = np.array(FitsArray(allfiles[j]))
sh = np.shape(data)
alldata.append((data.T - np.median(data, axis=1)).T)
bla = np.array(FitsArray(allfiles_ang[j]))
allaz.append(bla[0, :])
allel.append(bla[1, :] + 124.35)
allang_az.append(bla[2, :])
#### Make a fake TOD for healpix mapping
tod = dl.make_tod(alldata)
az = dl.make_tod(allaz, axis=0)
el = dl.make_tod(allel, axis=0)
#### Make directories
dir_files = '/Volumes/Data/Qubic/Calib-TD/Synthesized_Beams_Files/' + freq
dir_img = '/Volumes/Data/Qubic/Calib-TD/Synthesized_Beams_Images/' + freq
def do_some_dets(detnums,
d,
p,
directory,
fittedpeakfile,
az,
el,
proj_name,
custom=False,
q=None,
nside=None,
tol=5e-3,
refit=False,
resample=False,
newsize=70,
doplot=True,
verbose=True,
sbfitmodel=None,
angs=None,
usepeaks=None,
azmin=None,
azmax=None,
remove=None,
fitted_directory=None,
weighted=False,
nf_sub_rec=1,
lowcut=1e-3,
highcut=0.3):
if nside is not None:
d['nside'] = nside
s = qubic.QubicScene(d)
if q == None:
q = qubic.QubicMultibandInstrument(d)
if len(q) == 1:
xgrf, ygrf, FP_index, index_q = sc.get_TES_Instru_coords(q,
frame='GRF',
verbose=False)
else:
xgrf, ygrf, FP_index, index_q = sc.get_TES_Instru_coords(q[0],
frame='GRF',
verbose=False)
# Create TES, index and ASIC numbers assuming detnums is continuos TES number (1-248).
tes, asic = np.zeros((len(detnums), ), dtype=int), np.zeros(
(len(detnums), ), dtype=int)
qpix = np.zeros((len(detnums), ), dtype=int)
for j, npix in enumerate(detnums):
tes[j], asic[j] = (npix, 1) if (npix < 128) else (npix - 128, 2)
qpix[j] = tes2pix(tes[j], asic[j]) - 1
if verbose:
print("DETNUM{} TES{} ASIC{} QPIX{}".format(
npix, tes[j], asic[j], qpix[j]))
#Center of FOV
azcen_fov = np.mean(az)
elcen_fov = np.mean(el)
#Directory where the maps are:
mapsdir = directory
#File where the fitted peaks are:
peaksdir = fittedpeakfile
if not custom:
if verbose:
print('')
print('Normal Reconstruction')
qcut = select_det(qubic.QubicMultibandInstrument(d), detnums)
#qcut = select_det(qubic.QubicMultibandInstrument(d),[145])
else:
if verbose:
print('')
print('Custom Reconstruction')
### Refit or not the locations of the peaks
### from the synthesized beam images
### First instantiate a jchinstrument (modified from instrument
### to be able to read peaks from a file)
print("Generating jchinstrument")
qcut = select_det(
jcinst.QubicMultibandInstrument(d,
peakfile=fittedpeakfile,
tes=tes,
asic=asic), qpix)
print("LEN(qcut) and detector", len(qcut),
np.shape(qcut[0].detector.center))
print("Generating jchinstrument"[::-1])
### In the present case, we use the peak measurements at 150 GHz
### So we assume its index is len(qcut)//2
id150 = len(qcut) // 2
nu = qcut[id150].filter.nu
synthbeam = qcut[id150].synthbeam
horn = getattr(qcut[id150], 'horn', None)
primary_beam = getattr(qcut[id150], 'primary_beam', None)
# Cosine projection with elevation center of the FOV considering symetric scan in azimuth for each elevation step
thecos = np.cos(np.radians(elcen_fov))
#Read map (flat or healpy) for each detector
print("TES ASIC", tes, asic)
filemaps = readmaps(directory,
tes,
asic,
az,
el,
p,
proj_name=proj_name,
nside=d['nside'],
verbose=verbose)
# Compute measured coordenates
allphis_M, allthetas_M, allvals_M = get_data_Mrefsyst(
detnums,
filemaps,
az,
el,
fitted_directory,
fittedpeakfile,
proj_name,
resample=resample,
newsize=newsize,
azmin=azmin,
azmax=azmax,
remove=remove,
sbfitmodel=sbfitmodel,
refit=refit,
verbose=verbose)
if doplot:
_plot_onemap(filemaps,
az,
el, [allphis_M, allthetas_M],
proj_name,
makerotation=False)
### Now we want to perform the rotation to go to boresight
### reference frame (used internally by QubicSoft)
if verbose:
print("Solid angles synthbeam = {:.3e}, QubicScene {:.3e}".format(
synthbeam.peak150.solid_angle, s.solid_angle))
allphis_Q, allthetas_Q, allvals_Q, numpeak = convert_M2Q(
detnums,
allphis_M,
allthetas_M,
allvals_M,
elcen_fov,
solid_angle=synthbeam.peak150.solid_angle,
nu=nu,
horn=horn,
solid_angle_s=s.solid_angle,
angs=angs,
verbose=verbose)
print("allphis_Q , allvals_Q shapes", np.shape(allphis_Q),
np.shape(allvals_Q))
if doplot:
plt.title("xy coord. of peaks in M and Q ref system")
plt.plot(np.cos(allphis_M[0]) * np.sin(allthetas_M[0]),
np.sin(allphis_M[0]) * np.sin(allthetas_M[0]),
'r+',
ms=10,
label="M ref syst")
plt.plot(np.cos(allphis_Q[0]) * np.sin(allthetas_Q[0]),
np.sin(allphis_Q[0]) * np.sin(allthetas_Q[0]),
'b+',
ms=10,
label="Q ref syst")
plt.legend()
plt.show()
if doplot:
# Plot peaks in measured reference frame.
_plot_grfRF(detnums, xgrf, ygrf, qcut, numpeak)
for idet in range(len(detnums)):
position = np.ravel(qcut[0].detector[idet].center)
position = -position / np.sqrt(np.sum(position**2))
theta_center = np.arcsin(np.sqrt(position[0]**2 + position[1]**2))
phi_center = np.arctan2(position[1], position[0])
rav_thQ = np.ravel(allthetas_Q[idet, :])
rav_phQ = np.ravel(allphis_Q[idet, :])
## Now we identify the nearest peak to the theoretical Line Of Sight
angdist = np.zeros(len(rav_phQ))
for k in range(len(rav_phQ)):
angdist[k] = sbfit.ang_dist([theta_center, phi_center],
[rav_thQ[k], rav_phQ[k]])
print(k, np.degrees(angdist[k]))
idxmin = np.argmin(angdist)
numpeak[idet] = idxmin
### We nowwrite the temporary file that contains the peaks locations to be used
if usepeaks is None:
peaknums = np.arange(9)
else:
peaknums = usepeaks
data = [
allthetas_Q[:, peaknums], allphis_Q[:, peaknums] - np.pi,
allvals_Q[:, peaknums], numpeak
]
file = open(os.environ['QUBIC_PEAKS'] + 'peaks.pk', 'wb')
pickle.dump(data, file)
file.close()
### Make the TODs from the measured synthesized beams
realTOD = np.zeros((len(detnums), len(p)))
sigmaTOD = np.zeros(len(detnums))
if weighted:
sumweight = 0.
allimg = []
for i in range(len(detnums)):
if proj_name == "flat":
filename = directory + 'flat_ASIC{}TES{}.fits'.format(
asic[i], tes[i])
img = FitsArray(filename)
elif proj_name == "healpix":
filename = directory + '../../Flat/synth_beam/flat_ASIC{}TES{}.fits'.format(
asic[i], tes[i])
img = FitsArray(filename)
allimg.append(img)
fact = 1 #5e-28
realTOD[i, :] = np.ravel(img) * fact
if weighted: ## Not to be used - old test...
realTOD[i, :] *= 1. / ss**2
sumweight += 1. / ss**2
print("All img", np.shape(allimg))
### new code multiband
plt.figure()
for i in range(len(detnums)):
plt.plot(realTOD[i, :], label='TES#{0:}'.format(detnums[i]))
plt.legend()
plt.xlabel('Samples')
plt.ylabel('TOD')
plt.show()
plt.figure()
for i in range(len(detnums)):
spectrum_f, freq_f = ft.power_spectrum(np.arange(len(realTOD[i, :])),
realTOD[i, :])
pl = plt.plot(freq_f,
spectrum_f,
label='TES#{0:}'.format(detnums[i]),
alpha=0.5)
plt.xscale('log')
plt.yscale('log')
plt.legend()
plt.xlabel('Fourier mode')
plt.ylabel('Power Spectrum')
plt.title('Before Filtering')
plt.show()
for i in range(len(detnums)):
realTOD[i, :] = ft.filter_data(np.arange(len(realTOD[i, :])),
realTOD[i, :], lowcut, highcut)
mm, ss = ft.meancut(realTOD[i, :], 3)
sigmaTOD[i] = ss
if doplot:
for i in range(len(detnums)):
plt.figure()
plt.subplot(1, 2, 1)
plt.imshow(allimg[i] * fact,
vmin=mm - 3 * ss,
vmax=mm + 3 * ss,
extent=[
np.max(az) * thecos,
np.min(az) * thecos,
np.min(el),
np.max(el)
],
aspect='equal')
plt.colorbar()
plt.title('Init - TOD {0:} RMS={1:5.2g}'.format(
detnums[i], sigmaTOD[i]))
plt.subplot(1, 2, 2)
plt.imshow(np.reshape(realTOD[i, :], np.shape(img)),
vmin=mm - 3 * ss,
vmax=mm + 3 * ss,
extent=[
np.max(az) * thecos,
np.min(az) * thecos,
np.min(el),
np.max(el)
],
aspect='equal')
plt.colorbar()
plt.title('Filtered - TOD {0:} RMS={1:5.2g}'.format(
detnums[i], sigmaTOD[i]))
plt.figure()
for i in range(len(detnums)):
spectrum_f, freq_f = ft.power_spectrum(np.arange(len(realTOD[i, :])),
realTOD[i, :])
pl = plt.plot(freq_f,
spectrum_f,
label='TES#{0:} Var*2pi={1:5.2g}'.format(
detnums[i], sigmaTOD[i]**2 * 2 * np.pi),
alpha=0.5)
plt.plot(freq_f,
freq_f * 0 + sigmaTOD[i]**2 * 2 * np.pi,
color=pl[0].get_color())
plt.xscale('log')
plt.yscale('log')
plt.ylim(np.min(sigmaTOD**2 * 2 * np.pi),
np.max(sigmaTOD**2 * 2 * np.pi) * 1e9)
plt.legend()
plt.xlabel('Fourier mode')
plt.ylabel('Power Spectrum')
plt.title('After Filtering')
if lowcut:
plt.axvline(x=lowcut, color='k')
if highcut:
plt.axvline(x=highcut, color='k')
plt.show()
plt.figure()
plt.clf()
print('%%%%%%%%%%%%%%%%%%%%%%')
ax = plt.subplot(111, projection='polar')
print("len qcut", len(qcut[0].detector.center))
print("=?===============================")
print(np.shape(realTOD), np.shape(p), nf_sub_rec, np.shape(qcut))
print("=?===============================")
maps_recon, cov, nus, nus_edge = si.reconstruct_maps(
realTOD,
d,
p,
nf_sub_rec,
x0=None,
instrument=qcut, #verbose=True,
forced_tes_sigma=sigmaTOD)
ax.set_rmax(0.5)
#legend(fontsize=8)
if weighted:
maps_recon /= sumweight / len(detnums)
return maps_recon, qcut, np.mean(cov, axis=0), nus, nus_edge
def __init__(self,
band,
scene,
true_sky=None,
factor=1,
fwhm=0,
mask=None,
convolution_operator=None):
"""
Parameters
----------
band : int
The band 150 or 220.
scene : Scene
The acquisition scene.
true_sky : array of shape (npixel,) or (npixel, 3)
The true CMB sky (temperature or polarized). The Planck observation
will be this true sky plus a random independent gaussian noise
realization.
factor : 1 or 3 floats, optional
The factor by which the Planck standard deviation is multiplied.
fwhm : float, optional, !not used!
The fwhm of the Gaussian used to smooth the map [radians].
mask : array of shape (npixel, ) or None
The boolean mask, which is equal True at pixels where we need Planck,
that is outside the QUBIC field.
"""
if band not in (150, 220):
raise ValueError("Invalid band '{}'.".format(band))
if true_sky is None:
raise ValueError('The Planck Q & U maps are not released yet.')
if scene.kind == 'IQU' and true_sky.shape[-1] != 3:
raise TypeError('The Planck sky shape is not (npix, 3).')
true_sky = np.array(hp.ud_grade(true_sky.T, nside_out=scene.nside),
copy=False).T
if scene.kind == 'IQU' and true_sky.shape[-1] != 3:
raise TypeError('The Planck sky shape is not (npix, 3).')
self.scene = scene
self.fwhm = fwhm
self._true_sky = true_sky
if mask is not None:
self.mask = mask
else:
self.mask = np.ones(scene.npixel, dtype=np.bool)
if band == 150:
filename = 'Variance_Planck143GHz_Kcmb2_ns256.fits'
else:
filename = 'Variance_Planck217GHz_Kcmb2_ns256.fits'
sigma = 1e6 * factor * np.sqrt(FitsArray(PATH + filename))
if scene.kind == 'I':
sigma = sigma[:, 0]
elif scene.kind == 'QU':
sigma = sigma[:, :2]
if self.scene.nside != 256:
sigma = np.array(hp.ud_grade(sigma.T, self.scene.nside, power=2),
copy=False).T
self.sigma = sigma
if convolution_operator is None:
self.C = IdentityOperator()
else:
self.C = convolution_operator
# Check that we do one simulation with only one reconstructed subband
if d['nf_recon'][0] != 1:
raise ValueError(
'You should do one simulation without spectroimaging as a reference.')
# Save the dictionary
shutil.copyfile(dictfilename, out_dir + name + '.dict')
# ==========================================
t0 = time.time()
# ===== Sky Creation or Reading =====
x0 = FitsArray(
dictmaps +
'nf_sub={}/nside{}_nfsub{}.fits'.format(nf_sub, d['nside'], nf_sub))
print('Input Map with shape:', np.shape(x0))
if x0.shape[1] % (12 * d['nside']**2) == 0:
print('Good size')
else:
y0 = np.ones((d['nf_sub'], 12 * d['nside']**2, 3))
for i in range(d['nf_sub']):
for j in range(3):
y0[i, :, j] = hp.ud_grade(x0[i, :, j], d['nside'])
# Put I = 0
# x0[:, :, 0] = 0.
# Multiply Q, U maps
####################### restore stuff ###################
import pickle
from pysimulators import FitsArray
## Pointing
infile=open('saved_ptg.dat', 'rb')
data=pickle.load(infile)
infile.close()
pointings=data['pointings']
mask=data['mask']
signoise=0.5
nbmc=10000
data=0
## Covariance matrix
covmc=FitsArray('covmc'+str(signoise)+'_'+str(nbmc)+'.dat')
cormc=FitsArray('cormc'+str(signoise)+'_'+str(nbmc)+'.dat')
###########################################################
##### build coverage
a=np.loadtxt('./cl_r=0.1bis2.txt')
ell=a[:,0]
ctt=np.concatenate([[0,0],a[:,1]*1e12*2*np.pi/(ell*(ell+1))])
cee=np.concatenate([[0,0],a[:,2]*1e12*2*np.pi/(ell*(ell+1))])
cte=np.concatenate([[0,0],a[:,4]*1e12*2*np.pi/(ell*(ell+1))])
cbb=np.concatenate([[0,0],a[:,7]*1e12*2*np.pi/(ell*(ell+1))])
ell=np.concatenate([[0,1],ell])
spectra=[ell,ctt,cte,cee,cbb]
nside=128
map_orig=hp.synfast(spectra[4],nside,fwhm=0,pixwin=True)
input_map=map_orig.copy()
kmax = 2
lowcut,
highcut,
nbins=nbins,
median=True,
method=method,
doplot=False,
rebin=False,
verbose=False)
all_az_el_azang.append(
np.array(
[unbinned['az'], unbinned['el'], unbinned['az_ang']]))
allsb.append(unbinned['sb'])
sh0 = allsb[0].shape
sh1 = allsb[1].shape
mini = np.min([sh0[1], sh1[1]])
sb = np.append(allsb[0][:, :mini], allsb[1][:, :mini], axis=0)
az_el_azang = np.array(all_az_el_azang[0][:, :mini])
print az_el_azang.shape
print sb.shape
FitsArray(sb).save(
savedir +
'alltes_unbinned_{}_{}.fits'.format(n, elevations[ii]))
FitsArray(az_el_azang).save(
savedir +
'angles_unbinned_{}_{}.fits'.format(n, elevations[ii]))
else:
if filesalreadydone:
print 'files already exist on disk and I was asked not to replace them so doing nothing'
else:
print 'This should not happen... There is a bug'
keywords_t.update({'mask': True})
keywords_types = (keywords_q, keywords_f, keywords_m, keywords_t)
dtypes = (bool, int, np.float32, np.float64, np.complex64, np.complex128)
def teardown():
for file in glob.glob(filename + '*'):
os.remove(file)
a = np.ones((4, 3))
a[1, 2] = 4
q = Quantity(a, unit='myunit', derived_units={'myunit': Quantity(2., 'Jy')})
header = create_fitsheader(fromdata=q, cdelt=0.5, crval=(4., 8.))
header['BUNIT'] = 'myunit'
f = FitsArray(q, header=header)
m = Map(f, origin='upper', error=a * 2, coverage=a * 3)
mask = np.zeros((4, 3), np.bool8)
mask[0, 2] = True
t = Tod(f, mask=mask)
del mask
def test_copy_false_subok_true():
def func(obj1, t):
obj2 = t(obj1, copy=False, subok=True)
if isinstance(obj, t):
assert obj1 is obj2
else:
assert obj1 is not obj2
assert_equal(obj1, obj2)
# Check that we do one simulation with only one reconstructed subband
if d['nf_recon'][0] != 1:
raise ValueError('You should do one simulation without spectroimaging as a reference.')
# Save the dictionary
shutil.copyfile(dictfilename, out_dir + name + '.dict')
# ==========================================
t0 = time.time()
# ===== Sky Creation or Reading =====
# x0 = FitsArray(dictmaps + 'nf_sub={}/nside{}_nfsub{}.fits'.format(nf_sub, d['nside'], nf_sub))
x0 = FitsArray(dictmaps + 'Dust_d1_nside128_nfsub15.fits')
print('Input Map with shape:', np.shape(x0))
if x0.shape[1] % (12 * d['nside'] ** 2) == 0:
print('Good size')
else:
y0 = np.ones((d['nf_sub'], 12 * d['nside'] ** 2, 3))
for i in range(d['nf_sub']):
for j in range(3):
y0[i, :, j] = hp.ud_grade(x0[i, :, j], d['nside'])
# Put I = 0
# x0[:, :, 0] = 0.
# Multiply Q, U maps
#x0[:, :, 1] *= multFactor
from qubic import SpectroImLib as si
from qubicpack.utilities import Qubic_DataDir
from pysimulators import FitsArray
# Get a dictionary
dictfilename = os.environ['QUBIC_DICT']+'spectroimaging.dict'
d = qubic.qubicdict.qubicDict()
d.read_from_file(dictfilename)
nf_sub = [15,]#[2, 4, 5, 10, 12, 14, 15, 16, 18, 20, 22, 24]
dirc = './'
os.makedirs(dirc, exist_ok=True)
for nf in nf_sub:
print(nf)
d['nf_sub'] = nf
sky_config = {'dust': models('d1', d['nside']), 'cmb': models('c1', d['nside'])}
# sky_config = {'cmb': models('c1', d['nside'])}
Qubic_sky = si.Qubic_sky(sky_config, d)
x0 = Qubic_sky.get_simple_sky_map()
dirc2 = dirc
try:
os.makedirs(dirc2)
except:
pass
FitsArray(x0).save(dirc2 + 'CMB_nside{}_nfsub{}.fits'.format(d['nside'], nf))
pow_maynooth[i] = 1000 * power1[pixnums[i] -
1] + 1000 * power2[pixnums[i] - 1]
img_maynooth = ft.image_asics(pow_maynooth, all1=True)
clf()
imshow(img_maynooth)
colorbar()
allfib = [2, 3, 4]
allcal = np.zeros(len(allfib))
allerrcal = np.zeros(len(allfib))
allnewok = []
for i in range(len(allfib)):
fib = allfib[i]
free = 'free13'
allok = np.array(FitsArray('listok_fib{}_{}.fits'.format(
fib, free))).astype(bool)
allparams = np.array(FitsArray('params_fib{}_{}.fits'.format(fib, free)))
allerr = np.array(FitsArray('err_fib{}_{}.fits'.format(fib, free)))
allok = allok * np.isfinite(np.sum(allparams, axis=1)) * np.isfinite(
np.sum(allerr, axis=1))
cal, errcal, newok = ft.calibrate(fib,
pow_maynooth,
allparams,
allerr,
allok,
cutparam=0.4,
cuterr=0.03,
bootstrap=10000)
savefig('Calibration-Fib{}_new250119.png'.format(fib))
delta_ell = int(sys.argv[8])
covcut = float(sys.argv[9])
is_spatial_corr = int(sys.argv[10])
### Initialize
global_dir = Qubic_DataDir(datafile='instrument.py',
datadir=os.environ['QUBIC_DATADIR'])
dictfilename = global_dir + '/dicts/BmodesNoDustNoSystPaper0_2020.dict'
# Read dictionary chosen
d = qubic.qubicdict.qubicDict()
d.read_from_file(dictfilename)
d['nside'] = 256
center = qubic.equ2gal(d['RA_center'], d['DEC_center'])
### Open Coverage File
cov = np.array(FitsArray(cov_file))
### Open Noise Profile
fit_n200k = pickle.load(open(file_noise_profile, "rb"))
### Open Cl for spatially correlated noise
if is_spatial_corr == 0:
print('No Spatial Correlations')
clth = None
else:
print('Spatial correlations from ' + clnoise_file)
clth = pickle.load(open(clnoise_file, "rb"))
alpha = 4.5 ### See notebook called "2pt-Correlation Function" for an empirical explanation of alpha
clth = (clth - 1) * alpha + 1
print('Spatial correlations:')
cutval = 2500
allimg = np.zeros((len(as1), 17, 17)) + np.nan
for i in range(len(as1)):
allimg[i, :, :] = ft.image_asics(all1=amps[:, i])
bad = allimg[i, :, :] > cutval
allimg[i, :, :][bad] = np.nan
clf()
imshow(allimg[i, :, :], vmin=0, vmax=1000, cmap='viridis')
colorbar()
title('$\Delta$az={}'.format(az[i]))
show()
savefig('imgscan01022019_az_{}.png'.format(1100 + az[i]))
#raw_input('Press a key')
FitsArray(allimg).save('allimg_scan_az.fits')
FitsArray(az).save('az_scan_az.fits')
thepix = 93
clf()
plot(az, amps[thepix, :])
#### Trying intercalibration from old fiber data
#### (probably worthless as at the time they were superconducting and now they are not)
calibration = FitsArray('/Users/hamilton/CMB/Qubic/Fibres/calibration.fits')
calibration_restrict = FitsArray(
'/Users/hamilton/CMB/Qubic/Fibres/calibration_restrict.fits')
cutval = 200
for nf_sub_rec in np.arange(noutmin, noutmax + 1):
print('-------------------------- Map-Making on {} sub-map(s)'.format(
nf_sub_rec))
maps_recon, cov, nus, nus_edge, maps_convolved = si.reconstruct_maps(
TOD, d, p, nf_sub_rec, x0=x0)
if nf_sub_rec == 1:
maps_recon = np.reshape(maps_recon, np.shape(maps_convolved))
cov = np.sum(cov, axis=0)
maxcov = np.max(cov)
unseen = cov < maxcov * 0.1
#diffmap = maps_convolved - maps_recon
maps_convolved[:, unseen, :] = hp.UNSEEN
maps_recon[:, unseen, :] = hp.UNSEEN
#diffmap[:,unseen,:] = hp.UNSEEN
#therms = np.std(diffmap[:,~unseen,:], axis = 1)
print('************************** Map-Making on {} sub-map(s)Done'.format(
nf_sub_rec))
FitsArray(nus).save(name + '_nf{0}'.format(nf_sub_rec) + '_nus.fits')
FitsArray(nus_edge).save(name + '_nf{0}'.format(nf_sub_rec) +
'_nus_edges.fits')
FitsArray(maps_convolved).save(name + '_nf{0}'.format(nf_sub_rec) +
'_maps_convolved.fits')
FitsArray(maps_recon).save(name + '_nf{0}'.format(nf_sub_rec) +
'_maps_recon.fits')
t1 = time.time()
print('************************** All Done in {} minutes'.format(
(t1 - t0) / 60))
cov = np.sum(cov, axis=0)
maxcov = np.max(cov)
unseen = cov < maxcov * 0.1
#diffmap = maps_convolved - maps_recon
maps_convolved[:, unseen, :] = hp.UNSEEN
maps_recon[:, unseen, :] = hp.UNSEEN
#diffmap[:,unseen,:] = hp.UNSEEN
#therms = np.std(diffmap[:,~unseen,:], axis = 1)
print('************************** Map-Making on {} sub-map(s)Done'.format(
nf_sub_rec))
#FitsArray(nus_edge).save(name + '_nf{0}_ptg{1}'.format(nf_sub_rec, ptg) + '_nus_edges.fits')
#FitsArray(nus).save(name + '_nf{0}_ptg{1}'.format(nf_sub_rec, ptg)+ '_nus.fits')
FitsArray(maps_convolved).save(rep_out + name +
'_nf{}'.format(nf_sub_rec) +
'_maps_convolved.fits')
FitsArray(maps_recon).save(rep_out + name + '_nf{}'.format(nf_sub_rec) +
'_maps_recon.fits')
t1 = time.time()
print('************************** All Done in {} minutes'.format(
(t1 - t0) / 60))
##### SIMU fix_hwp
# p = qubic.get_pointing(d)
# #I = np.array([0,2,4,6])
# pi_fraction = 6
# I = np.arange(pi_fraction/2)
# ptg_start = 500
# for simu in xrange(len(I)):
def GenerateMaps(d, nus_in, reso = 1.5, size=200, p=None, writemap = False, readmap = False ):
"""
Compute input maps to use in: calibration (for both methods Fit and Sigma) & QUBIC pipeline.
Number of maps == len(nus_in)
Input:
d: QUBIC dictionary
nus_in: frequencies where compute the point source map
Return:
input_maps: partition of the sky where the point source is.
m0: point source (already integrated over pixels) [RING ordered]
"""
if p:
if p.fix_az:
center = (d['fix_azimuth']['az'],d['fix_azimuth']['el'])
elif not p.fix_az:
center = qubic.equ2gal(d['RA_center'], d['DEC_center'])
else:
center = qubic.equ2gal(d['RA_center'], d['DEC_center'])
nsideLow, nsideHigh, _, _, sigma2fwhm = Parameters(d)
#center_gal = qubic.equ2gal(d['RA_center'], d['DEC_center'])
pixel = hp.pixelfunc.ang2pix(nsideHigh, np.deg2rad(90-center[1]), np.deg2rad(center[0]), nest = True)
vec_pix = hp.pix2vec(nsideHigh, pixel, nest = True)
vec_pixeles = hp.pix2vec(nsideHigh, np.arange(12*nsideHigh**2), nest = True )
ang_pixeles = np.arccos(np.dot(vec_pix,vec_pixeles))
if d['config'] == 'FI':
CteConfig = 61.347409
elif d['config'] == 'TD':
CteConfig = 153.36
#mask
print(nus_in)
if not readmap:
mask = np.rad2deg(ang_pixeles) < d['dtheta']
# Generate Gaussian maps - model of point source with FWHM (or Sigma) given by nus_in
c0 = np.zeros((len(nus_in),12*nsideHigh**2,3))
noise = np.zeros((len(nus_in), 12*nsideHigh**2,1))
T = d['temperature']
amplitude = 1e22
for i, n_i in enumerate(nus_in):
print('Map {}'.format(i))
fwhm_in = CteConfig/n_i # nus to fwhm
for j,each in enumerate(ang_pixeles):
if mask[j] == True:
c0[i,j,0] = amplitude*f(each, fwhm_in, sigma2fwhm)
c0[:,:,1] = c0[:,:,0]
c0[:,:,2] = c0[:,:,0]
m0 = np.empty((len(nus_in),12*nsideLow**2,3))
m0[:,:,0] = hp.ud_grade(c0[:,:,0], nsideLow, order_in = 'NESTED', order_out = 'RING')
input_maps = np.empty((len(nus_in),size,size))
for i, mapa in enumerate(m0):
input_maps[i] = hp.gnomview(mapa[:,0], rot = center,
reso = reso, xsize = size,
return_projected_map=True)
mp.close('all')
if writemap: FitsArray(m0).save('point-source-map-TD-150GHz.fits')
return input_maps, m0
elif readmap:
m0 = FitsArray('point-source-map-TD-150GHz.fits')
input_maps = np.empty((len(nus_in),size,size))
for i, mapa in enumerate(m0):
input_maps[i] = hp.gnomview(mapa[:,0], rot = center,
reso = reso, xsize = size,
return_projected_map=True)
mp.close('all')
return input_maps, m0
# Look at the coverage of the sky
coverage = np.sum(cov.copy(), axis=0)
maxcov = np.max(coverage)
unseen = coverage < maxcov * 0.1
print(maps_convolved.shape)
maps_convolved[:, unseen, :] = hp.UNSEEN
maps_recon[:, unseen, :] = hp.UNSEEN
# Wait for everyone to finish
MPI.COMM_WORLD.Barrier()
#### Save maps
if rank == 0:
rnd_name = qss.random_string(10)
directory = '/global/homes/h/hamilton/qubic/jc/EndToEndMaps/'
FitsArray(maps_recon).save(directory + namesim + '_maps_recon_seed_' +
str(seed) + '_' + rnd_name + '.fits')
FitsArray(maps_convolved).save(directory + namesim +
'_maps_convolved_seed_' + str(seed) + '_' +
rnd_name + '.fits')
FitsArray(coverage).save(directory + namesim + '_maps_coverage_' +
rnd_name + '.fits')
with open(directory + namesim + '_dictionary_' + rnd_name + '.pickle',
'wb') as handle:
pickle.dump(d, handle, protocol=2)
with open(directory + namesim + '_input_cell_' + rnd_name + '.pickle',
'wb') as handle:
pickle.dump(Qubic_sky.input_cmb_spectra, handle, protocol=2)
print(
def test_detector_indexing():
expected = FitsArray('test/data/detector_indexing.fits')
assert_same(q.detector.index, expected)
def get_data_Mrefsyst(detnums,
filemaps,
az,
el,
fitted_directory,
fittedpeakfile,
proj_name,
resample=None,
newsize=None,
azmin=None,
azmax=None,
remove=None,
sbfitmodel=None,
refit=False,
verbose=False):
"""
Compute theta, phi for all TES in the measured reference system
"""
thecos = np.cos(np.radians(np.mean(el)))
if refit:
if verbose: print('We refit the peak locations')
### We call the fitting function for the Synthesized beam
xypeaks = []
for i in range(len(detnums)):
if fitted_directory is None:
if sbfitmodel is None:
sbfitmodel = sbfit.SbModelIndepPeaks(nrings=2,
common_fwhm=True,
no_xy_shift=False,
distortion=False)
if verbose:
print('Using Fit Model {} for TES #{}'.format(
sbfitmodel.name, detnums[i]))
figure()
fit, thexypeaks = sbfit.fit_sb(filemaps,
az,
el,
sbfitmodel,
resample=resample,
newsize=newsize,
verbose=verbose,
doplot=True,
extra_title='TES #{}'.format(
detnums[i]))
print('FITING')
show()
else:
filemaps, az, el, fitmap, thexypeaks = sbfit.get_flatmap(
detnums[i],
directory,
azmin=azmin,
azmax=azmax,
remove=remove,
fitted_directory=fitted_directory)
xypeaks.append(thexypeaks)
### Convert to measurement coordinate system
xypeaks = np.array(xypeaks)
allthetas_M = np.radians(90 - (xypeaks[:, 1, :] - elcen_fov))
allphis_M = np.radians(-xypeaks[:, 0, :]) #*thecos)
allvals_M = xypeaks[:, 2, :]
else:
if verbose:
print('No refitting of the peak locations')
print("[FitFiles]: Reading {} file".format(fittedpeakfile))
### We just read them from the old peak file
peaks = np.array(FitsArray(fittedpeakfile))
#Thph indexes
if proj_name == "flat":
thphidx = [0, 1]
peaks[:, 1, :] = peaks[:, 1, :] / thecos
elif proj_name == "healpix":
thphidx = [0, 1]
peaks[:, 1, :] = peaks[:, 1, :]
### An put them in the expected format. Save TES of interest
mypeaks = peaks[np.array(detnums) - 1, :, :]
#Peaks in degrees
allphis_M = mypeaks[:, 1, :]
allthetas_M = mypeaks[:, 0, :]
allvals_M = mypeaks[:, 2, :]
return allphis_M, allthetas_M, allvals_M
grounding.append(bla[-2])
motor.append(bla[-1])
a1 = qp()
a1.read_qubicstudio_dataset(dirs[0], asic=1)
a2 = qp()
a2.read_qubicstudio_dataset(dirs[0], asic=2)
nsamples = len(a1.timeline(TES=66))
FREQ_SAMPLING = 1./a1.sample_period()
spectrum, freq = mlab.psd(2
a1.timeline(TES=66), Fs=FREQ_SAMPLING, NFFT=nsamples, window=mlab.window_hanning)
ok1 = np.array(FitsArray('/Users/hamilton/Qubic/ExternalSource/ScanAz2019-01-30_OK_Asic1.fits'), dtype=bool)
ok2 = np.array(FitsArray('/Users/hamilton/Qubic/ExternalSource/ScanAz2019-01-30_OK_Asic2.fits'), dtype=bool)
ok = np.append(ok1,ok2)
clf()
plot(freq, spectrum)
xscale('log')
yscale('log')
allspecs = []
allfreqs = []
medspec = []
for i in range(len(dirs)):
a1 = qp()
a1.read_qubicstudio_dataset(dirs[i], asic=1)
interpolation='nearest')
# imshow(img / img_ref, cmap='viridis', vmin=0, vmax=3, interpolation='nearest')
# imshow(img / np.nanmean(img), cmap='viridis', vmin=0, vmax=3, interpolation='nearest')
colorbar()
# if pos == 0:
# title('Int Sphere Ref 2019-02-14')
# else:
title('Int Sphere 2019-02-15 pos{}'.format(pos + 1))
# savefig(basedir + 'int_sphere_pos1-4_divided_ref')
# Try intercalibration
intercal = img / img[16, 0]
allimg = np.array(FitsArray('allimg_scan_az.fits'))
az = np.array(FitsArray('az_scan_az.fits'))
for i in range(len(az)):
clf()
subplot(1, 2, 1)
imshow(allimg[i, :, :], cmap='viridis', vmin=0, vmax=1000)
title(az[i])
subplot(1, 2, 2)
imshow(allimg[i, :, :] / intercal, cmap='viridis', vmin=0, vmax=1000)
title('Intercalibrated')
show()
savefig('scan_az_int_sphere_{}.png'.format(1100 + az[i]))
# raw_input('press a key')
pixnums = [2, 96, 67, 58 + 128]
