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')