Source code for grizli.pipeline.photoz

"""
Helpers for working with ``eazy-py``.
"""
import numpy as np


[docs]def fix_aperture_corrections(tab, verbose=True, ref_filter=None): """ June 2020: Reapply total corrections using fixed bug for the kron total corrections where the necessary pixel scale wasn't used. """ from grizli import prep, utils pixel_scale = tab.meta['ASEC_0']/tab.meta['APER_0'] if ('TOTCFILT' not in tab.meta) & (ref_filter is None): raise IOError('No ref_filter specified and TOTCFILT not in tab.meta') if ref_filter is None: ref_filter = np.atleast_1d(tab.meta['TOTCFILT'])[0].lower() keys = list(tab.meta.keys()) for k in keys: if k.endswith('_PLAM'): filt = k.split('_PLAM')[0].lower() if verbose: print('Fix aperture corrections: ', filt) tot_corr = prep.get_kron_tot_corr(tab, filt, pixel_scale=pixel_scale) rescale = tot_corr / tab[filt + '_tot_corr'] tab[f'{filt}_tot_corr'] = tot_corr # Loop through apertures and fix corr and tot columns if found for ka in keys: if ka.startswith('APER_'): ap = ka.split('_')[1] if f'{filt}_tot_{ap}' in tab.colnames: aper_radius = tab.meta[f'ASEC_{ap}']/2. msg = f' aper #{ap} (R={aper_radius:.2f}")' if f'EE_{filt}_{ap}' not in tab.meta: # compute filter EE correction if not in header ee_ratio = prep.get_filter_ee_ratio(filt, ref_filter=ref_filter, aper_radius=aper_radius) tab.meta[f'EE_{filt}_{ap}'] = 1./ee_ratio msg += ' + ee_filter' for col in ['corr', 'ecorr', 'tot', 'etot']: tab[f'{filt}_{col}_{ap}'] *= 1./ee_ratio if verbose: print(msg) # Rescale total correction tab[f'{filt}_tot_{ap}'] *= rescale tab[f'{filt}_etot_{ap}'] *= rescale return tab
[docs]def apply_catalog_corrections(root, total_flux='flux_auto', auto_corr=True, get_external_photometry=False, aperture_indices='all', suffix='_apcorr', verbose=True, apply_background=True, ee_correction=False): """ Aperture and background corrections to photometric catalog First correct fluxes in individual filters scaled by the ratio of ``total_flux`` / aper_flux in the detection band, where ``total_flux`` is ``flux_auto`` by default. >>> apcorr = {total_flux} / flux_aper_{ap} >>> {filt}_corr_{ap} = {filt}_flux_aper_{ap} * apcorr If ``ee_correction = True``, then apply an encircled energy correction for the relative fraction for the relative flux between the target filter and detection image for a *point source* flux within a photometric aperture. The correction function is `~grizli.prep.get_filter_ee_ratio`. >>> ee_{filt} = prep.get_filter_ee_ratio({filt}, ref_filter=ref_filter, aper_radius={aper_arcsec}) >>> {filt}_corr_{ap} = {filt}_corr_{ap} / ee_{filt} If `auto_corr`, compute total fluxes with a correction for flux outside the Kron aperture derived for point sources using `~grizli.prep.get_kron_tot_corr`. >>> {filt}_tot_{ap} = {filt}_corr_{ap} * {filt}_tot_corr Note that any background has already been subtracted from {filt}_flux_aper_{ap}.in the SEP catalog. """ import os import eazy import numpy as np from grizli import utils, prep import mastquery.utils cat = utils.read_catalog('{0}_phot.fits'.format(root)) filters = [] for c in cat.meta: if c.endswith('_ZP'): filters.append(c.split('_ZP')[0].lower()) if get_external_photometry: print('Get external photometry from Vizier') try: external_limits = 3 external_timeout = 300 external_sys_err = 0.03 ext = get_external_catalog(cat, external_limits=external_limits, timeout=external_timeout, sys_err=external_sys_err) for c in ext.colnames: if c not in cat.colnames: cat[c] = ext[c] for k in ext.meta: cat.meta[k] = ext.meta[k] except: print(' - External catalog FAILED') pass # Fix: Take flux_auto when flag==0, flux otherwise if (total_flux == 'flux_auto_fix') & (total_flux not in cat.colnames): flux = cat['flux_auto']*1. flagged = (cat['flag'] > 0) flux[flagged] = cat['flux'][flagged] cat['flux_auto_fix'] = flux*1. # Additional auto correction cat.meta['TOTALCOL'] = total_flux, 'Column for total flux' #cat.meta['HASTOT'] = (auto_corr & ('tot_corr' in cat.colnames), 'Catalog has full total flux') apcorr = {} for NAPER in range(100): if 'APER_{0}'.format(NAPER) not in cat.meta: break if aperture_indices == 'all': aperture_indices = range(NAPER) # EE correction cat.meta['EE_CORR'] = ee_correction if ee_correction: for f in filters: ref_filter = np.atleast_1d(cat.meta['TOTCFILT'])[0] _ = prep.get_filter_ee_ratio(cat, f.lower(), ref_filter=ref_filter.lower()) for i in aperture_indices: if verbose: print('Compute aperture corrections: i={0}, D={1:.2f}" aperture'.format(i, cat.meta['ASEC_{0}'.format(i)])) if 'flux_aper_{0}'.format(i) in cat.colnames: cat['apcorr_{0}'.format(i)] = cat[total_flux]/cat['flux_aper_{0}'.format(i)] for f in filters: bkgc = '{0}_bkg_aper_{1}'.format(f, i) # if (bkgc in cat.colnames) & apply_background: # bkg = cat[bkgc] # else: # bkg = 0. # background already subtracted from flux columns bkg = 0. cat['{0}_corr_{1}'.format(f, i)] = (cat['{0}_flux_aper_{1}'.format(f, i)]-bkg)*cat['apcorr_{0}'.format(i)] cat['{0}_ecorr_{1}'.format(f, i)] = cat['{0}_fluxerr_aper_{1}'.format(f, i)]*cat['apcorr_{0}'.format(i)] if ee_correction: cat[f'{f}_corr_{i}'] *= cat[f'{f}_ee_{i}'] #cat.meta['EE_{0}_{1}'.format(f, i)] = 1./ee_ratio #cat['{0}_corr_{1}'.format(f, i)] /= ee_ratio #cat['{0}_ecorr_{1}'.format(f, i)] /= ee_ratio aper_area = np.pi*(cat.meta['APER_{0}'.format(i)]/2)**2 mask_thresh = aper_area bad = cat['{0}_mask_aper_{1}'.format(f, i)] > 0.2*mask_thresh cat['{0}_corr_{1}'.format(f, i)][bad] = -99 cat['{0}_ecorr_{1}'.format(f, i)][bad] = -99 tot_col = '{0}_tot_corr'.format(f.lower()) if auto_corr and (tot_col in cat.colnames): cat['{0}_tot_{1}'.format(f, i)] = cat['{0}_corr_{1}'.format(f, i)]*cat[tot_col] cat['{0}_etot_{1}'.format(f, i)] = cat['{0}_ecorr_{1}'.format(f, i)]*cat[tot_col] cat['{0}_tot_{1}'.format(f, i)][bad] = -99 cat['{0}_etot_{1}'.format(f, i)][bad] = -99 if 'id' not in cat.colnames: cat.rename_column('number', 'id') if 'z_spec' not in cat.colnames: cat['z_spec'] = cat['id']*0.-1 # Spurious sources, sklearn SVM model trained for a single field morph_model = os.path.join(os.path.dirname(utils.__file__), 'data/sep_catalog_junk.pkl') # Only apply if detection pixel scale is 0.06" if 'ASEC_0' in cat.meta: try: detection_pscale = cat.meta['ASEC_0'][0]/cat.meta['APER_0'][0] except: detection_pscale = cat.meta['ASEC_0']/cat.meta['APER_0'] run_morph_model = np.isclose(detection_pscale, 0.06, atol=0.005) else: run_morph_model = True if os.path.exists(morph_model) & run_morph_model: if verbose: print('Apply morphological validity class') try: from sklearn.externals import joblib clf = joblib.load(morph_model) X = np.hstack([[cat['peak']/cat['flux'], cat['cpeak']/cat['peak']]]).T # Predict labels, which where generated for # bad_bright, bad_faint, stars, big_galaxies, small_galaxies pred = clf.predict_proba(X) # Should be >~ 0.9 for valid sources, stars & galaxies # in "ir" image cat['class_valid'] = pred[:, -3:].sum(axis=1) cat['class_valid'].format = '.2f' except: cat['class_valid'] = 99. cat['class_valid'].format = '.2f' msg = "Couldn't run morph classification from {0}" print(msg.format(morph_model)) cat['dummy_err'] = 10**(-0.4*(8-23.9)) cat['dummy_flux'] = cat[total_flux] # detection band if suffix: if verbose: print('Write {0}_phot{1}.fits'.format(root, suffix)) cat.write('{0}_phot{1}.fits'.format(root, suffix), overwrite=True) return cat
FILTER_TRANS = {'f098m': 201, 'f105w': 202, 'f110w': 241, 'f125w': 203, 'f140w': 204, 'f160w': 205, 'f435w': 233, 'f475w': 234, 'f555w': 235, 'f606w': 236, 'f625w': 237, 'f775w': 238, 'f814w': 239, 'f850lp': 240, 'f702w': 15, 'f600lpu': 243, 'f225wu': 207, 'f275wu': 208, 'f336wu': 209, 'f350lpu': 339, 'f438wu': 211, 'f475wu': 212, 'f475xu': 242, 'f555wu': 213, 'f606wu': 214, 'f625wu': 215, 'f775wu': 216, 'f814wu': 217, 'f390wu': 210, 'ch1': 18, 'ch2': 19, 'f336w':209, 'f350lp':339, 'f115w': 309, 'f150w': 310, 'f200w': 311}
[docs]def eazy_photoz(root, force=False, object_only=True, apply_background=True, aper_ix=1, apply_prior=False, beta_prior=True, get_external_photometry=False, external_limits=3, external_sys_err=0.3, external_timeout=300, sys_err=0.05, z_step=0.01, z_min=0.01, z_max=12, total_flux='flux_auto', auto_corr=True, compute_residuals=False, dummy_prior=False, extra_rf_filters=[], quiet=True, aperture_indices='all', zpfile='zphot.zeropoint', extra_params={}, filter_trans=FILTER_TRANS, extra_translate={}, force_apcorr=False, ebv=None, absmag_filters=[], **kwargs): import os import eazy import numpy as np from grizli import utils import mastquery.utils if (os.path.exists('{0}.eazypy.self.npy'.format(root))) & (not force): self = np.load('{0}.eazypy.self.npy'.format(root), allow_pickle=True)[0] zout = utils.read_catalog('{0}.eazypy.zout.fits'.format(root)) cat = utils.read_catalog('{0}_phot_apcorr.fits'.format(root)) return self, cat, zout # trans.pop('f814w') if (not os.path.exists('{0}_phot_apcorr.fits'.format(root))) | force_apcorr: print('Apply catalog corrections') apply_catalog_corrections(root, suffix='_apcorr', aperture_indices=aperture_indices) cat = utils.read_catalog('{0}_phot_apcorr.fits'.format(root)) filters = [] for c in cat.meta: if c.endswith('_ZP'): filters.append(c.split('_ZP')[0].lower()) if len(filters) == 0: for f in filter_trans: if f'{f}_tot_{aper_ix}' in cat.colnames: filters.append(f.lower()) # Translate fp = open('zphot.translate', 'w') for f in filters: if f in filter_trans: fp.write('{0}_tot_{1} F{2}\n'.format(f, aper_ix, filter_trans[f])) fp.write('{0}_etot_{1} E{2}\n'.format(f, aper_ix, filter_trans[f])) extra_filters = {'hscg':314, 'hscr':315, 'hsci':316, 'hscz':317, 'hscy':318, 'irac_ch1':18, 'irac_ch2':19, 'irac_ch3':20, 'irac_ch4':21} for c in extra_filters: if f'{c}_flux' in cat.colnames: fp.write(f'{c}_flux F{extra_filters[c]}\n') fp.write(f'{c}_err E{extra_filters[c]}\n') # fp.write('irac_ch1_flux F18\n') # fp.write('irac_ch1_err E18\n') # # fp.write('irac_ch2_flux F19\n') # fp.write('irac_ch2_err E19\n') # # fp.write('irac_ch3_flux F20\n') # fp.write('irac_ch3_err E20\n') # # fp.write('irac_ch4_flux F21\n') # fp.write('irac_ch4_err E21\n') for k in extra_translate: fp.write('{0} {1}\n'.format(k, extra_translate[k])) # For zeropoint if dummy_prior: fp.write('dummy_flux F205x\n') fp.write('dummy_err E205x\n') fp.close() params = {} params['CATALOG_FILE'] = '{0}_phot_apcorr.fits'.format(root) params['Z_STEP'] = z_step params['MAIN_OUTPUT_FILE'] = '{0}.eazypy'.format(root) params['Z_MAX'] = z_max if ebv is None: try: ebv = mastquery.utils.get_mw_dust(np.median(cat['ra']), np.median(cat['dec'])) except: try: ebv = mastquery.utils.get_irsa_dust(np.median(cat['ra']), np.median(cat['dec'])) except: print("Couldn't get EBV, fall back to ebv=0.0") ebv = 0. params['MW_EBV'] = ebv params['PRIOR_ABZP'] = 23.9 params['SYS_ERR'] = sys_err params['CAT_HAS_EXTCORR'] = False # Pick prior filter, starting from reddest for f in ['f435w', 'f606w', 'f814w', 'f105w', 'f110w', 'f125w', 'f140w', 'f160w','f150w','f200w','f277w'][::-1]: if f in filters: if dummy_prior: params['PRIOR_FILTER'] = 'dummy_flux' else: params['PRIOR_FILTER'] = filter_trans[f] mag = 23.9-2.5*np.log10(cat['{0}_tot_{1}'.format(f, aper_ix)]) break if os.path.exists('templates/fsps_full/tweak_fsps_QSF_11_v3_noRed.param.fits'): params['TEMPLATES_FILE'] = 'templates/fsps_full/tweak_fsps_QSF_11_v3_noRed.param' else: params['TEMPLATES_FILE'] = 'templates/fsps_full/tweak_fsps_QSF_12_v3.param' for k in extra_params: params[k] = extra_params[k] if zpfile is not None: if not os.path.exists(zpfile): zpfile = None load_products = False if ((not os.path.exists('FILTER.RES.latest')) | (not os.path.exists('templates'))): try: # should work with eazy-py >= 0.2.0 eazy.symlink_eazy_inputs() #(path=None) except: print(""" The filter file ``FILTER.RES.latest`` and ``templates`` directory were not found in the working directory and the automatic command to retrieve them failed: >>> import eazy; eazy.symlink_eazy_inputs(path=None) Run it with ``path`` pointing to the location of the ``eazy-photoz`` repository.""") return False self = eazy.photoz.PhotoZ(param_file=None, translate_file='zphot.translate', zeropoint_file=zpfile, params=params, load_prior=True, load_products=load_products) if quiet: self.param.params['VERBOSITY'] = 1. if object_only: return self idx = np.arange(self.NOBJ) # sample = (mag < 27) #& (self.cat['star_flag'] != 1) #sample |= (self.cat['z_spec'] > 0) sample = np.isfinite(self.cat['ra']) # mag) for iter in range(1+(get_external_photometry & compute_residuals)*1): self.fit_catalog(idx[sample], n_proc=10) if compute_residuals: self.error_residuals() self.fit_phoenix_stars() self.standard_output(prior=apply_prior, beta_prior=beta_prior, extra_rf_filters=extra_rf_filters, absmag_filters=absmag_filters) zout = utils.read_catalog('{0}.eazypy.zout.fits'.format(root)) np.save('{0}.eazypy.self.npy'.format(root), [self]) return self, cat, zout
[docs]def show_from_ds9(ds9, self, zout, use_sky=True, **kwargs): import numpy as np if use_sky: xy = np.cast[float](ds9.get('pan fk5').split()) cosd = np.cos(xy[1]/180*np.pi) r = np.sqrt((self.cat['ra']-xy[0])**2*cosd**2 + (self.cat['dec']-xy[1])**2)*3600 runit = 'arcsec' if (not use_sky) | (xy.sum() == 0): xy = np.cast[float](ds9.get('pan image').split()) r = np.sqrt((self.cat['x_image']-xy[0])**2 + (self.cat['y_image']-xy[1])**2) runit = 'pix' ix = np.argmin(r) print('ID: {0}, r={1:.1f} {2}'.format(self.cat['id'][ix], r[ix], runit)) print(' z={0:.2f} logM={1:.2f}'.format(zout['z_phot'][ix], np.log10(zout['mass'][ix]))) fig = self.show_fit(ix, id_is_idx=True, **kwargs) return fig, self.cat['id'][ix], ix, zout['z_phot'][ix]
[docs]class EazyPhot(object): def __init__(self, photoz, grizli_templates=None, zgrid=None, apcorr=None, include_photometry=True, include_pz=False, source_text='unknown'): """ Parameters ---------- photoz : `~eazypy.photoz.PhotoZ` Photoz object. apcorr : `~numpy.ndarray` Aperture correction applied to the photometry to match the grism spectra. For the internal grizli catalogs, this should generally be something like >>> apcorr = 'flux_iso' / 'flux_auto' """ try: from .. import utils except: from grizli import utils not_obs_mask = (photoz.fnu < -90) | (photoz.efnu < 0) self.zgrid = photoz.zgrid if apcorr is None: self.apcorr = np.ones(photoz.NOBJ) else: self.apcorr = apcorr self.source_text = source_text self.ext_corr = photoz.ext_corr self.flam = photoz.fnu*photoz.to_flam*photoz.zp*photoz.ext_corr self.flam[not_obs_mask] = -99 self.eflam = photoz.efnu*photoz.to_flam*photoz.zp*photoz.ext_corr self.eflam[not_obs_mask] = -99 self.include_photometry = include_photometry #self.rdcat = utils.GTable(photoz.cat['ra','dec']) self.ra_cat = photoz.cat['ra'].data self.dec_cat = photoz.cat['dec'].data self.filters = photoz.filters self.f_numbers = photoz.f_numbers self.param = photoz.param if 'z_spec' in photoz.cat.colnames: self.z_spec = photoz.cat['z_spec']*1 else: self.z_spec = np.zeros(photoz.N)-1 self.include_pz = include_pz if include_pz: try: self.pz = photoz.pz*1 self.zgrid = photoz.zgrid except: self.pz = None else: self.pz = None if grizli_templates is None: self.tempfilt = None self.grizli_templates = None else: self.tempfilt = self.initialize_templates(grizli_templates, zgrid=zgrid) self.grizli_templates = grizli_templates
[docs] def initialize_templates(self, grizli_templates, zgrid=None): from eazy import templates as templates_module from eazy.photoz import TemplateGrid if zgrid is None: zgrid = self.zgrid template_list = [templates_module.Template(arrays=(grizli_templates[k].wave, grizli_templates[k].flux), name=k) for k in grizli_templates] tempfilt = TemplateGrid(zgrid, template_list, RES=self.param['FILTERS_RES'], f_numbers=self.f_numbers, add_igm=True, galactic_ebv=self.param['MW_EBV'], Eb=self.param['SCALE_2175_BUMP']) return tempfilt
[docs] def get_phot_dict(self, ra, dec): """ Return catalog info for nearest object to supplied coordinates Returns: phot - photometry dictionary ix - index in catalog of nearest match dr - distance to nearest match """ from collections import OrderedDict try: from .. import utils except: from grizli import utils icat = utils.GTable() icat['ra'] = [ra] icat['dec'] = [dec] rdcat = utils.GTable() rdcat['ra'] = self.ra_cat rdcat['dec'] = self.dec_cat ix, dr = rdcat.match_to_catalog_sky(icat) phot = OrderedDict() apcorr_i = self.apcorr[ix[0]] try: phot['source'] = self.source_text except: phot['source'] = 'unknown' phot['flam'] = self.flam[ix[0], :]*1.e-19*apcorr_i phot['eflam'] = self.eflam[ix[0], :]*1.e-19*apcorr_i phot['filters'] = self.filters phot['tempfilt'] = self.tempfilt try: phot['ext_corr'] = self.ext_corr except: phot['ext_corr'] = 1 if self.include_pz & (self.pz is not None): pz = (self.zgrid, self.pz[ix[0], :].flatten()) else: pz = None phot['pz'] = pz try: phot['z_spec'] = self.z_spec[ix[0]] except: phot['z_spec'] = -1 if not self.include_photometry: phot['flam'] = None return phot, ix[0], dr[0]
[docs]def get_external_catalog(phot, filter_file='/usr/local/share/eazy-photoz/filters/FILTER.RES.latest', ZP=23.9, sys_err=0.3, verbose=True, external_limits=3, timeout=300): """ Fetch photometry from vizier """ import numpy as np import astropy.units as u try: from .. import utils except: from grizli import utils from eazy.filters import FilterFile res = FilterFile(filter_file) vizier_catalog = list(utils.VIZIER_VEGA.keys()) ra = np.median(phot['ra']) dec = np.median(phot['dec']) dr = np.sqrt((phot['ra']-ra)**2*np.cos(phot['dec']/180*np.pi)**2 + (phot['dec']-dec)**2)*60 radius = 1.5*dr.max() # arcmin tabs = utils.get_Vizier_photometry(ra, dec, templates=None, radius=radius*60, vizier_catalog=vizier_catalog, filter_file=filter_file, MW_EBV=0, convert_vega=False, raw_query=True, verbose=True, timeout=timeout) extern_phot = utils.GTable() N = len(phot) for t_i in tabs: # Match if 'RAJ2000' in t_i.colnames: other_radec = ('RAJ2000', 'DEJ2000') elif 'RA_ICRS' in t_i.colnames: other_radec = ('RA_ICRS', 'DE_ICRS') else: other_radec = ('ra', 'dec') idx, dr = phot.match_to_catalog_sky(t_i, other_radec=other_radec) if (dr < 2*u.arcsec).sum() == 0: continue tab = t_i[dr < 2*u.arcsec] idx = idx[dr < 2*u.arcsec] # Downweight PS1 if have SDSS # if (tab.meta['name'] == PS1_VIZIER) & (SDSS_DR12_VIZIER in viz_tables): # continue # err_scale = 1 # else: # err_scale = 1 err_scale = 1 if (tab.meta['name'] == utils.UKIDSS_LAS_VIZIER): flux_scale = 1.33 else: flux_scale = 1. convert_vega = utils.VIZIER_VEGA[tab.meta['name']] bands = utils.VIZIER_BANDS[tab.meta['name']] # if verbose: # print(tab.colnames) #filters += [res.filters[res.search(b, verbose=False)[0]] for b in bands] to_flam = 10**(-0.4*(48.6))*3.e18 # / pivot(Ang)**2 for ib, b in enumerate(bands): f_number = res.search(b, verbose=False)[0]+1 filt = res.filters[f_number-1] # filters.append(filt) if convert_vega: to_ab = filt.ABVega() else: to_ab = 0. fcol, ecol = bands[b] # pivot.append(filt.pivot()) fnu = 10**(-0.4*(tab[fcol]+to_ab-ZP)) efnu = tab[ecol]*np.log(10)/2.5*fnu*err_scale efnu = np.sqrt(efnu**2+(sys_err*fnu)**2) fnu.fill_value = -99 efnu.fill_value = -99 comment = 'Filter {0} from {1} (N={2})'.format(bands[b][0], t_i.meta['name'], len(idx)) if verbose: print(comment) if ((~efnu.mask).sum() > 4) & (external_limits > 0): fill = np.percentile(efnu.data[~efnu.mask], [90]) efill = external_limits * fill else: fill = -99 efill = -99 extern_phot.meta['F{0}'.format(f_number)] = b, comment extern_phot['F{0}'.format(f_number)] = fill*np.ones(N) extern_phot['F{0}'.format(f_number)][idx] = fnu.filled() extern_phot['E{0}'.format(f_number)] = efill*np.ones(N) extern_phot['E{0}'.format(f_number)][idx] = efnu.filled() return extern_phot
######## Selecting objects # def select_objects(): # import os # # from grizli.pipeline import photoz # import numpy as np # # total_flux = 'flux_auto_fix' # total_flux = 'flux_auto' # new segmentation masked SEP catalogs # object_only = False # # self, cat, zout = photoz.eazy_photoz(root, object_only=object_only, apply_prior=False, beta_prior=True, aper_ix=2, force=True, get_external_photometry=False, compute_residuals=False, total_flux=total_flux) # # if False: # args = np.load('fit_args.npy', allow_pickle=True)[0] # phot_obj = photoz.EazyPhot(self, grizli_templates=args['t0'], zgrid=self.zgrid, apcorr=self.idx*0.+1) # # flux = self.cat[total_flux]*1. # hmag = 23.9-2.5*np.log10(cat['f160w_tot_1']) # # # Reddest HST band # lc_clip = self.lc*1 # lc_clip[self.lc > 1.55e4] = 0 # in case ground-based / WISE red band # ixb = np.argmax(lc_clip) # sn_red = self.cat[self.flux_columns[ixb]]/self.cat[self.err_columns[ixb]] # # grad = np.gradient(self.zgrid) # cumpz = np.cumsum(self.pz*grad, axis=1) # cumpz = (cumpz.T/cumpz[:, -1]).T # # chi2 = (self.chi_best / self.nusefilt) # # iz6 = np.where(self.zgrid > 6.0)[0][0] # iz7 = np.where(self.zgrid > 7)[0][0] # iz8 = np.where(self.zgrid > 8)[0][0] # iz9 = np.where(self.zgrid > 9)[0][0] # # highz_sel = (hmag < 27.5) # & (self.cat['class_valid'] > 0.8) # #highz_sel |= (cumpz[:,iz6] < 0.3) & (self.cat['flux_radius'] > 2.5) # #highz_sel &= (self.cat['flux_radius'] > 2.5) # highz_sel &= chi2 < 3 # highz_sel &= (sn_red > 5) # highz_sel &= self.nusefilt >= 3 # # flux_ratio = cat['f160w_flux_aper_3']/cat['f160w_flux_aper_0'] # flux_ratio /= cat.meta['APER_3']**2/cat.meta['APER_0']**2 # # if False: # sel = highz_sel # so = np.argsort(cumpz[sel, iz7]) # ids = self.cat['id'][sel][so] # i = -1 # so = np.argsort(flux_ratio[sel])[::-1] # ids = self.cat['id'][sel][so] # i = -1 # # highz_sel &= (cumpz[:, iz6] > 0) & (flux_ratio < 0.45) & ((cumpz[:, iz6] < 0.3) | (cumpz[:, iz7] < 0.3) | (((cumpz[:, iz8] < 0.4) | (cumpz[:, iz9] < 0.5)) & (flux_ratio < 0.5))) # # # Big objects likely diffraction spikes # # big = (self.cat['flux_radius'] > 10) # # highz_sel &= ~big # #flux_ratio = self.cat['flux_aper_0']/self.cat['flux_aper_2'] # # sel = highz_sel # so = np.argsort(hmag[sel]) # ids = self.cat['id'][sel][so] # i = -1 # # # Red # uv = -2.5*np.log10(zout['restU']/zout['restV']) # red_sel = ((zout['z160'] > 1.) & (uv > 1.5)) | ((zout['z160'] > 1.5) & (uv > 1.1)) # red_sel &= (self.zbest < 4) & (hmag < 22) # & (~hmag.mask) # red_sel &= (zout['mass'] > 10**10.5) # & (self.cat['class_valid'] > 0.8) # red_sel &= (self.cat['flux_radius'] > 2.5) # red_sel &= (zout['restV']/zout['restV_err'] > 3) # red_sel &= (chi2 < 3) # #red_sel &= (sn_red > 20) # # sel = red_sel # # so = np.argsort(hmag[sel]) # ids = self.cat['id'][sel][so] # i = -1 # # ds9 = None # # for j in self.idx[sel][so]: # id_j, ra, dec = self.cat['id', 'ra', 'dec'][j] # # # Photo-z # fig, data = self.show_fit(id_j, ds9=ds9, show_fnu=True) # highz_sel[j]) # lab = '{0} {1}\n'.format(root, id_j) # lab += 'H={0:.1f} z={1:.1f}\n'.format(hmag[j], self.zbest[j]) # lab += 'U-V={0:.1f}, logM={1:4.1f}'.format(uv[j], np.log10(zout['mass'][j])) # # ax = fig.axes[0] # ax.text(0.95, 0.95, lab, ha='right', va='top', transform=ax.transAxes, fontsize=9, bbox=dict(facecolor='w', edgecolor='None', alpha=0.5)) # yl = ax.get_ylim() # ax.set_ylim(yl[0], yl[1]*1.1) # # fig.savefig('{0}_{1:05d}.eazy.png'.format(root, id_j), dpi=70) # plt.close() # # # Cutout # #from grizli_aws.aws_drizzle import drizzle_images # # #rgb_params = {'output_format': 'png', 'output_dpi': 75, 'add_labels': False, 'show_ir': False, 'suffix':'.rgb'} # rgb_params = None # # #aws_bucket = 's3://grizli/SelectedObjects/' # aws_bucket = None # # label = '{0}_{1:05d}'.format(root, id_j) # if not os.path.exists('{0}.rgb.png'.format(label)): # drizzle_images(label=label, ra=ra, dec=dec, pixscale=0.06, size=8, pixfrac=0.8, theta=0, half_optical_pixscale=False, filters=['f160w', 'f814w', 'f140w', 'f125w', 'f105w', 'f110w', 'f098m', 'f850lp', 'f775w', 'f606w', 'f475w'], remove=False, rgb_params=rgb_params, master='grizli-jan2019', aws_bucket=aws_bucket) # # show_all_thumbnails(label=label, filters=['f775w', 'f814w', 'f098m', 'f105w', 'f110w', 'f125w', 'f140w', 'f160w'], scale_ab=np.clip(hmag[j], 19, 22), close=True) ############
[docs]def show_all_thumbnails(label='j022708p4901_00273', filters=['f775w', 'f814w', 'f098m', 'f105w', 'f110w', 'f125w', 'f140w', 'f160w'], scale_ab=21, close=True): """ Show individual filter and RGB thumbnails """ import glob import matplotlib.pyplot as plt from astropy.visualization import make_lupton_rgb import astropy.io.fits as pyfits from grizli import utils from grizli.pipeline import auto_script #from PIL import Image ims = {} for filter in filters: drz_files = glob.glob('{0}-{1}*_dr*sci.fits'.format(label, filter)) if len(drz_files) > 0: im = pyfits.open(drz_files[0]) ims[filter] = im slx, sly, filts, fig = auto_script.field_rgb(root=label, xsize=4, output_dpi=None, HOME_PATH=None, show_ir=False, pl=1, pf=1, scl=1, rgb_scl=[1, 1, 1], ds9=None, force_ir=False, filters=ims.keys(), add_labels=False, output_format='png', rgb_min=-0.01, xyslice=None, pure_sort=False, verbose=True, force_rgb=None, suffix='.rgb', scale_ab=scale_ab) if close: plt.close() #rgb = np.array(Image.open('{0}.rgb.png'.format(label))) rgb = plt.imread('{0}.rgb.png'.format(label)) NX = (len(filters)+1) fig = plt.figure(figsize=[1.5*NX, 1.5]) ax = fig.add_subplot(1, NX, NX) ax.imshow(rgb, origin='upper', interpolation='nearest') ax.text(0.5, 0.95, label, ha='center', va='top', transform=ax.transAxes, fontsize=7, bbox=dict(facecolor='w', edgecolor='None', alpha=0.5)) for i, filter in enumerate(filters): if filter in ims: zp_i = utils.calc_header_zeropoint(ims[filter], ext=0) scl = 10**(-0.4*(zp_i-5-scale_ab)) img = ims[filter][0].data*scl image = make_lupton_rgb(img, img, img, stretch=0.1, minimum=-0.01) ax = fig.add_subplot(1, NX, i+1) ax.imshow(255-image, origin='lower', interpolation='nearest') ax.text(0.5, 0.95, filter, ha='center', va='top', transform=ax.transAxes, fontsize=7, bbox=dict(facecolor='w', edgecolor='None', alpha=0.5)) for ax in fig.axes: ax.set_xticklabels([]) ax.set_yticklabels([]) ax.set_xticks([]) ax.set_yticks([]) fig.tight_layout(pad=0.1) fig.savefig('{0}.thumb.png'.format(label)) if close: plt.close()