initpars=None,
lowcut=0.05,
highcut=15.,
rangepars=[[0., 1.], [0., 0.5], [0., 1. / fff], [0., 10000.]],
okfile='ScanAz2019-01-30_OK_Asic2.fits')
params[~okfinal, :] = np.nan
amps[128:, i] = params[:, 3]
erramps[128:, i] = err[:, 3]
taus[128:, i] = params[:, 1]
errtaus[128:, i] = err[:, 1]
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()
asic,
2,
name,
initpars=None,
lowcut=0.05,
highcut=15.,
okfile='ScanAz2019-01-30_OK_Asic2.fits')
amps[128:, i] = params[:, 3]
erramps[128:, i] = err[:, 3]
taus[128:, i] = params[:, 1]
errtaus[128:, i] = err[:, 1]
cutval = 200
allimg = np.zeros((len(as1), 17, 17))
for i in xrange(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=200)
colorbar()
title('$\Delta$az={}'.format(az[i]))
show()
savefig('imgscan_az_{}.png'.format(1000 + az[i]))
raw_input('Press a key')
thepix = 93
clf()
plot(az, amps[thepix, :])
rangepars=[[0., 1.], [0., 0.5], [0., 1. / fff], [0., 5000.]],
initpars=initpars,
okfile='TES_OK_ExtSrc150GHz_asic1.fits')
pars.append(allparams1)
img_amp = np.zeros((128, len(infos))) + np.nan
img_tau = np.zeros((128, len(infos))) + np.nan
for i in range(len(infos)):
img_amp[okfinal1, i] = pars[i][okfinal1, 3]
img_tau[okfinal1, i] = pars[i][okfinal1, 1]
# Time response tau
clf()
for i in range(len(infos)):
subplot(2, 2, i + 1)
imshow(ft.image_asics(data1=img_tau[:, i]),
vmin=0,
vmax=0.2,
interpolation='nearest')
title('tau {} {} GHz'.format(infos[i]['name'], infos[i]['fnum']))
colorbar()
tight_layout()
# Amplitude
clf()
for i in range(len(infos)):
subplot(2, 2, i + 1)
imshow(ft.image_asics(data1=img_amp[:, i]),
vmin=0,
vmax=500,
interpolation='nearest')
from qubicpack import qubicpack as qp
########### Read Maynooth Files
num, power1 = np.loadtxt('TESPowOfile150newCF2_CF.qb.txt', skiprows=1).T
num, power2 = np.loadtxt('TESPowOfile150newCF_CF.qb.txt', skiprows=1).T
#num, power = np.loadtxt('TESPowerOutfile.txt',skiprows=1).T
aa = qp()
pixnums = np.ravel(aa.TES2PIX)
pow_maynooth = np.zeros(256)
for i in range(len(pixnums)):
if pixnums[i] < 992:
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)))
lastpassallfree=False,
okfile='TES_OK_ExtSrc150GHz_asic1.fits',
name=name,
initpars=initpars)
pars.append(allparams1)
img_amp = np.zeros((128, len(infos))) + np.nan
img_tau = np.zeros((128, len(infos))) + np.nan
for i in xrange(len(infos)):
img_amp[okfinal1, i] = pars[i][okfinal1, 3]
img_tau[okfinal1, i] = pars[i][okfinal1, 1]
clf()
for i in xrange(len(infos)):
subplot(2, 2, i + 1)
imshow(ft.image_asics(data1=img_tau[:, i]), vmin=0, vmax=0.2)
title('tau {} {} GHz'.format(infos[i]['name'], infos[i]['fnum']))
colorbar()
plt.tight_layout()
clf()
for i in xrange(len(infos)):
subplot(2, 2, i + 1)
imshow(ft.image_asics(data1=img_amp[:, i]), vmin=0, vmax=60)
title('Amp {} {} GHz'.format(infos[i]['name'], infos[i]['fnum']))
colorbar()
plt.tight_layout()
clf()
for i in xrange(len(infos)):
subplot(2, 2, i + 1)
# Ref, data 2019-02-14
dir = basedir + '2019-02-14/2019-02-14_16.53.38__Test_int_sphere150Ghz_150mHz'
amps = make_amp(dir)
# img_ref = ft.image_asics(all1=amps / np.nanmean(amps))
amps_all = [amps]
# Test with different positions
allimg = np.zeros((4, 17, 17)) # contains datas from 2019-02-15
for pos in range(1, 5):
dir = basedir + '2019-02-15/*_pos{}'.format(pos)
amps = make_amp(dir, fff=0.333)
amps_all.append(amps)
img = ft.image_asics(all1=amps_all[pos])
allimg[pos - 1, :, :] = img
# Plot for different positions compared to the ref
figure('Integration Sphere, divided the mean of 2019-02-15 measurements ')
for pos in range(4):
# img = ft.image_asics(all1=amps_all[pos] / np.nanmean(amps_all[pos]))
subplot(2, 2, pos + 1)
imshow(allimg[pos] / np.nanmean(allimg, axis=0),
cmap='viridis',
vmin=0,
vmax=3,
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')