def test(): # season = "s15" # patch = "deep56" # array = "pa3_f090" season = "s15" patch = "boss" array = "pa3_f150" dm = sints.ACTmr3() imap = dm.get_split(season, patch, array, 0, ncomp=None, srcfree=True) ivar = dm.get_split_ivar(season, patch, array, 0) fbeam = lambda x: dm.get_beam(x, season, patch, array) with bench.show("inpaint with plots"): inpainted = inpaint_map_white(imap, ivar, fbeam, plots=True) with bench.show("inpaint"): inpainted = inpaint_map_white(imap, ivar, fbeam, plots=False)
w2 = np.mean(mask**2.) modlmap = enmap.modlmap(map1.shape,map1.wcs) binned_power = bin(power/w2/beamf1(modlmap)/beamf2(modlmap),modlmap,bin_edges) return centers, binned_power else: ells,cls = pcalc.get_power_scalarXscalar(map1*mask, map2*mask,ret_dl=False) return ells,cls/beamf1(ells)/beamf2(ells) # # ACTxPlanck # coadded ACT x coadded Planck patch = args.region dma = interfaces.ACTmr3(region=mask) dmp = interfaces.PlanckHybrid(region=mask) pfreqs = ['030','044','070','100','143','217','353','545'] nfreqs = len(pfreqs) # we loop over all pairs of Planck x ACT # combs = [] # for planckfreq in ['030','044','070','100','143','217','353','545']: # no '857' # for actseason in ['s14','s15']: # for array in ['pa1_f150', 'pa2_f150', 'pa3_f090', 'pa3_f150']: # try: # actbeam = lambda x: dma.get_beam(x, actseason, # patch, array) # actbeam(100) # combs.append((planckfreq,actseason,array))
cols = catalogs.load_fits("AdvACT_S18dn_confirmed_clusters.fits", ['RAdeg', 'DECdeg', 'SNR']) ras = cols['RAdeg'] decs = cols['DECdeg'] sns = cols['SNR'] # Rough central frequencies freqs = {"pa3_f090": 97, "pa3_f150": 149} # Get a region mask mask = sints.get_act_mr3_crosslinked_mask(region) bmask = mask.copy() bmask[bmask < 0.99] = 0 # Map loader dm = sints.ACTmr3(region=mask, calibrated=True) modlmap = mask.modlmap() ells = np.arange(0, modlmap.max()) # 2D beam kbeam = dm.get_beam(modlmap, "s15", region, array, sanitize=True) # 1D beam lbeam = dm.get_beam(ells, "s15", region, array, sanitize=True) # beam and map files # Y-maps bfile = os.environ[ "WORK"] + "/data/depot/tilec/v1.2.0_20200324/map_v1.2.0_%s_%s/tilec_single_tile_%s_comptony_map_v1.2.0_%s_beam.txt" % ( cversion, region, region, cversion) yfile = os.environ[ "WORK"] + "/data/depot/tilec/v1.2.0_20200324/map_v1.2.0_%s_%s/tilec_single_tile_%s_comptony_map_v1.2.0_%s.fits" % ( cversion, region, region, cversion)
# iras = np.append(iras1,iras2) # idecs = np.append(idecs1,idecs2) fname = os.environ[ 'WORK'] + "/data/boss/sdss_dr8/redmapper_dr8_public_v6.3_catalog.fits" cols = catalogs.load_fits(fname, ['RA', 'DEC']) iras = cols['RA'] idecs = cols['DEC'] mask1 = sints.get_act_mr3_crosslinked_mask(region1) mask1[mask1 < 0.99] = 0 mask2 = sints.get_act_mr3_crosslinked_mask(region2) mask2[mask2 < 0.99] = 0 dm1 = sints.ACTmr3(region=mask1, calibrated=True) dm2 = sints.ACTmr3(region=mask2, calibrated=True) wt1 = dm1.get_coadd_ivar("s15", region1, "pa2_f150") wt2 = dm2.get_coadd_ivar("s15", region2, "pa2_f150") ras1, decs1 = catalogs.select_based_on_mask(iras, idecs, mask1) ras2, decs2 = catalogs.select_based_on_mask(iras, idecs, mask2) tdir = '/scratch/r/rbond/msyriac/data/depot/tilec/v1.0.0_rc_20190919' solution = 'tsz' yfile1 = tutils.get_generic_fname(tdir, region1, solution, None, cversion) cfile1 = tutils.get_generic_fname(tdir, region1, solution, 'cib', cversion) dfile1 = tutils.get_generic_fname(tdir, region1, solution, 'cmb', cversion) ybfile1 = tutils.get_generic_fname(tdir,
from enlib import bench from actsims import noise for season in ['s13', 's14', 's15']: for apatch in ['boss', 'deep1', 'deep5', 'deep6', 'deep8', 'deep56']: #for apatch in ['deep1','deep5','deep6','deep8','deep56','boss']: mask = sints.get_act_mr3_crosslinked_mask(apatch) ellcen = 5000 ellsig = 1000 modlmap = mask.modlmap() ells = np.arange(0, modlmap.max()) mfilter = maps.interp(ells, np.exp(-(ells - ellcen)**2. / 2. / ellsig**2.))(modlmap) for array in ['pa1_f150', 'pa2_f150', 'pa3_f150', 'pa3_f090']: dm = sints.ACTmr3(calibrated=False, region=mask) fname = "%s_%s_%s" % (season, apatch, array) try: splits = dm.get_splits(season=season, patch=apatch, arrays=[array], ncomp=3, srcfree=False)[0, :, :, ...] cmap = dm.get_coadd(season=season, patch=apatch, array=array, ncomp=3, srcfree=False) except: continue io.hplot(splits,
# pl._ax.get_xaxis().set_major_formatter(matplotlib.ticker.ScalarFormatter()) #pl._ax.get_xaxis().get_major_formatter().labelOnlyBase = False pl.legend(loc='upper right', labsize=ftsize) pl.done("fig_bandpass.pdf") sys.exit() comp = 'tSZ' mix = fg.get_mix_bandpassed(fnames, comp) mixp = fg.get_mix_bandpassed(fnames, comp, shifts=[2.4, 2.4, 1.5, 2.4] + [0] * 8) mixn = fg.get_mix_bandpassed(fnames, comp, shifts=[-2.4, -2.4, -1.5, -2.4] + [0] * 8) diff = (mixp - mix) * 100. / mix diff2 = (mixn - mix) * 100. / mix diff3 = (mixp - mixn) * 100. / mix print(diff, diff2, diff3) sys.exit() dm = sints.ACTmr3() for season in dm.cals.keys(): for patch in dm.cals[season].keys(): for array in dm.cals[season][patch].keys(): cal = dm.cals[season][patch][array]['cal'] cal_err = dm.cals[season][patch][array]['cal_err'] print(season, patch, array, cal_err * 100. / cal)
""" import argparse # Parse command line parser = argparse.ArgumentParser(description='Do a thing.') parser.add_argument("version", type=str, help='Region name.') parser.add_argument("region", type=str, help='Region name.') parser.add_argument("solution", type=str, help='Solution.') parser.add_argument("--lmin", type=int, default=80, help="lmin.") parser.add_argument("--lmax", type=int, default=6000, help="lmin.") args = parser.parse_args() save_path = sints.dconfig['tilec']['save_path'] savedir = save_path + args.version + "_" + args.region mask = enmap.read_map("%s/tilec_mask.fits" % savedir) dm = sints.ACTmr3(region=mask) fbeam = lambda x: dm.get_beam( x, "s15", "deep56", "pa3_f090", kind='normalized') name_map = {'CMB': 'cmb', 'tSZ': 'comptony', 'CIB': 'cib'} comps = "tilec_single_tile_" + args.region + "_" + name_map[ args.solution] + "_" + args.version lmin = args.lmin lmax = args.lmax w2 = np.mean(mask**2.) imap = enmap.read_map("%s/%s.fits" % (savedir, comps)) color = 'planck' if args.solution == 'CMB' else 'gray' io.hplot(imap, "map_%s_%s" % (args.solution, args.region), color='planck',
iname = "%s%s_%s_%s_split_%d_%d" % (tag, season, patch, array, s, sid) if not (skip_plots): plot_img(masked, "ivars_masked_%s.png" % iname) retvars[s, 0, modrmap < cutradius] = retvars[s, 0, modrmap >= cutradius].mean() if not (skip_plots): plot_img(retvars[s, 0], "ivars_filled_%s.png" % iname) return retvars ras, decs = np.loadtxt( "/home/r/rbond/sigurdkn/project/actpol/maps/mr3f_20190502/cat_bright_tot.txt", unpack=True, usecols=[0, 1]) dm = sints.ACTmr3(calibrated=False) for season in dm.seasons: for patch in dm.patches: if patch != apatch: continue for array in dm.arrays: if array != "pa3_f090": continue # !!! try: splits = dm.get_splits(season, patch, array, ncomp=None, srcfree=True)[0] print("Found %s %s %s" % (season, patch, array)) except: continue
(opath, region)) umap = enmap.read_map("%sdataCoadd_combined_U_s14&15_%s.fits" % (opath, region)) #oq_noise2d = pow(qmap) #ou_noise2d = pow(umap) _, oe_p2d, ob_p2d = get_pol_powers(tmap, qmap, umap, kbeam, w2) io.power_crop(oe_p2d, Nplot, "oe_noise2d.png", ftrans=True) #io.power_crop(ob_p2d,Nplot,"ob_noise2d.png",ftrans=True) dmask = sints.get_act_mr3_crosslinked_mask(region='deep6') dw2 = np.mean(dmask**2) dm = sints.ACTmr3(calibrated=True, region=dmask) imap = dm.get_coadd(season='s13', array='pa1_f150', patch='deep6', srcfree=True) tmap = imap[0] * dmask qmap = imap[1] * dmask umap = imap[2] * dmask dbeam = tutils.get_kbeam("d6", dmask.modlmap(), sanitize=False) _, de_p2d, db_p2d = get_pol_powers(tmap, qmap, umap, dbeam, dw2) io.power_crop(de_p2d, Nplot, "de_noise2d.png", ftrans=True) dbinner = stats.bin2D(dmask.modlmap(), bin_edges) cents, de_noise1d = dbinner.bin(de_p2d) cents, db_noise1d = dbinner.bin(db_p2d)