def TES_Instru2coord(TES, ASIC, q, frame='ONAFP', verbose=True):
"""
From (TES, ASIC) numbering on the instrument to (x,y) coordinates in ONAFP or GRF frame.
Returns also the focal plane index.
!!! If q is a TD instrument, only ASIC 1 and 2 are acceptable.
Parameters
----------
TES: TES number as defined on the instrument
ASIC: ASIC number
q: QubicInstrument()
frame: str
'GRF' or 'ONAFP' only
Returns
-------
x, y: TES center coordinates.
FP_index: Focal Plane index, as used in Qubic soft.
index_q: position index of the FP_index in q.detector.index()
"""
if TES in [4, 36, 68, 100]:
raise ValueError('This is a thermometer !')
FP_index = tes2index(TES, ASIC)
if verbose:
print('FP_index =', FP_index)
index_q = np.where(q.detector.index == FP_index)[0][0]
if verbose:
print('Index_q =', index_q)
centerGRF = q.detector.center[q.detector.index == FP_index][0]
xGRF = centerGRF[0]
yGRF = centerGRF[1]
if frame not in ['GRF', 'ONAFP']:
raise ValueError('The frame is not valid.')
elif frame == 'GRF':
if verbose:
print('X_GRF = {:.3f} mm, Y_GRF = {:.3f} mm'.format(
xGRF * 1e3, yGRF * 1e3))
return xGRF, yGRF, FP_index, index_q
elif frame == 'ONAFP':
xONAFP = -yGRF
yONAFP = xGRF
if verbose:
print('X_ONAFP = {:.3f} mm, Y_ONAFP = {:.3f} mm'.format(
xONAFP * 1e3, yONAFP * 1e3))
return xONAFP, yONAFP, FP_index, index_q
def tes_signal2image_fp(tes_signal, asics):
"""
tes_signal : array of shape (128, #ASICS)
Signal on each TES, for each ASIC.
asics : list
Indices of the asics used between 1 and 8.
"""
thermos = [4, 36, 68, 100]
image_fp = np.empty((34, 34))
image_fp[:] = np.nan
for ASIC in asics:
for TES in range(128):
if TES + 1 not in thermos:
index = tes2index(TES + 1, ASIC)
image_fp[index // 34, index % 34] = tes_signal[TES, ASIC - 1]
return image_fp
def _init_id(self, ident_focalplane, num, asic=None):
"""
Generates focal plane identifications from user input
Parameter:
num: is the detector or pixel number.
asic: ASIC number if ident_focalplane = TESName and num has to be the tes number.
"""
if ident_focalplane == 'FileName':
self.npix = num
self.tes, self.asic = (self.npix,
1) if (self.npix < 128) else (self.npix -
128, 2)
self.qpix = tes2pix(self.tes, self.asic) - 1
elif ident_focalplane == 'qsName':
FPidentity = make_id_focalplane()
self.qpix = num
det_index = self.instrument.detector[self.qpix].index[0]
self.tes = FPidentity[det_index].TES
self.asic = FPidentity[det_index].ASIC
self.npix = self.tes if self.asic == 1 else self.tes + 128
elif ident_focalplane == 'TESName':
if num > 128:
raise ValueError(
"Wrong TES value. You gave a TES number greater than 128.")
else:
if asic == None:
raise ValueError(
"You choose {} identification but ASIC number is missing."
.format(ident_focalplane))
else:
self.tes = num
self.asic = asic
self.npix = self.tes if self.asic == 1 else self.tes + 128
self.qpix = tes2pix(self.tes, self.asic) - 1
if self.verbose:
print("You are running fitting in healpix maps.")
print("========================================")
print("TES number {} asic number {}".format(self.tes, self.asic))
print("In FileName format the number of tes is {}".format(
self.npix))
print("Index number: qpack {} qsoft {} ".format(\
tes2index(self.tes, self.asic), self.instrument.detector[self.qpix].index[0] ))
print("qubicsoft number: {}".format(self.qpix))
return
def get_tes_xycoords_radial_dist(q):
tes_xy = np.zeros((256, 2))
tes_radial_dist = np.zeros(256)
for i in range(256):
if i < 128:
tes = i + 1
asic = 1
else:
tes = i - 128 + 1
asic = 2
index = tes2index(tes, asic)
# None are the thermometers
if index is not None:
index_place = np.where(q.detector.index == index)[0][0]
x = q.detector.center[index_place, 0]
y = q.detector.center[index_place, 1]
tes_radial_dist[i] = np.sqrt(x**2 + y**2)
print(tes, index, tes_radial_dist[i])
tes_xy[i, :] = ([x, y])
return tes_xy, tes_radial_dist
def get_FL_perTES(tes_xy,
alpha,
rdist=None,
npeaks=9,
ntes=256,
nsig=3,
goodtes=None,
approx=True,
doplot=True):
tes_dist = cdist(tes_xy, tes_xy, 'euclidean')
print('TES dist:', tes_dist[0, 1])
print(tes_dist.shape)
tanalpha = np.tan(alpha)
if goodtes is not None:
for i in range(ntes):
if i < 128:
tes = i + 1
asic = 1
else:
tes = i - 128 + 1
asic = 2
index = tes2index(tes, asic)
if index not in goodtes:
print(i, index)
tes_dist[i, :] = np.nan
tes_dist[:, i] = np.nan
fl_mean = np.zeros((npeaks, ntes))
fl_std = np.zeros((npeaks, ntes))
for peak in range(npeaks):
print('Peak ', peak, '\n')
if approx:
fl = tes_dist / tanalpha[peak]
else:
fl = np.zeros((ntes, ntes))
for tes1 in range(ntes):
for tes2 in range(ntes):
print('TES', tes1)
# Compute k = Drcos(phi)
k = (tes_xy[tes2, 0] - tes_xy[tes1, 0]) * tes_xy[tes1, 0] \
+ (tes_xy[tes2, 1] - tes_xy[tes1, 1]) * tes_xy[tes1, 1]
D = tes_dist[tes1, tes2]
tg = tanalpha[peak, tes1, tes2]
Delta = D**4 - 4 * tg**2 * k**2 * (1 + D**2 / k)
Xplus = (-2 * k * tg**2 + D**2 + np.sqrt(Delta)) / (2 *
tg**2)
fl[tes1, tes2] = np.sqrt(Xplus - rdist[tes1]**2)
print('fl', fl.shape)
np.fill_diagonal(fl, np.nan)
# fl = fl[~np.isnan(fl)]
# fl_clip, mini, maxi = sigmaclip(fl, low=nsig, high=nsig)
# print(mini, maxi)
# print('fl_clip', fl_clip)
# Mean and STD for each TES
fl_mean[peak, :] = np.nanmean(fl, axis=0)
fl_std[peak, :] = np.nanstd(fl, axis=0)
# Global mean and std
fl_global_mean = np.nanmean(fl)
fl_global_std = np.nanstd(fl)
if doplot:
plt.subplots(122)
plt.suptitle('Peak {}'.format(peak))
plt.subplot(121)
plt.hist(np.ravel(fl),
bins=100,
label='mean = {:.5f} \n STD = {:.5f}'.format(
fl_global_mean, fl_global_std))
plt.xlabel('Focal length [m]')
plt.legend()
plt.subplot(122)
plt.imshow(fl)
plt.colorbar()
plt.xlabel('TES index')
plt.ylabel('TES index')
plt.show()
return fl_mean, fl_std