def __init__(self,expnum=None,ccdname=None,ccdwcs=None): """Initialize the class with either the exposure number *and* CCD name, or directly with the WCS of the CCD of interest. """ self.ps1dir = os.getenv('PS1CAT_DIR') self.nside = 32 if ccdwcs is None: from legacypipe.common import Decals, DecamImage decals = Decals() ccd = decals.find_ccds(expnum=expnum,ccdname=ccdname)[0] im = DecamImage(decals,ccd) self.ccdwcs = im.get_wcs() else: self.ccdwcs = ccdwcs
def __init__(self,expnum=None,ccdname=None,ccdwcs=None): """Initialize the class with either the exposure number *and* CCD name, or directly with the WCS of the CCD of interest. """ self.ps1dir = os.getenv('PS1CAT_DIR') if self.ps1dir is None: raise ValueError('You must have the PS1CAT_DIR environment variable set to point to Pan-STARRS1 catalogs') self.nside = 32 if ccdwcs is None: from legacypipe.common import Decals decals = Decals() ccd = decals.find_ccds(expnum=expnum,ccdname=ccdname)[0] im = decals.get_image_object(ccd) self.ccdwcs = im.get_wcs() else: self.ccdwcs = ccdwcs
def __init__(self, expnum=None, ccdname=None, ccdwcs=None): """Initialize the class with either the exposure number *and* CCD name, or directly with the WCS of the CCD of interest. """ self.ps1dir = os.getenv('PS1CAT_DIR') if self.ps1dir is None: raise ValueError( 'You must have the PS1CAT_DIR environment variable set to point to Pan-STARRS1 catalogs' ) self.nside = 32 if ccdwcs is None: from legacypipe.common import Decals decals = Decals() ccd = decals.find_ccds(expnum=expnum, ccdname=ccdname)[0] im = decals.get_image_object(ccd) self.ccdwcs = im.get_wcs() else: self.ccdwcs = ccdwcs
from astrometry.util.plotutils import * from legacyanalysis.ps1cat import ps1cat from legacypipe.common import Decals from tractor import Image, PointSource, PixPos, NanoMaggies, Tractor ps = PlotSequence('rewcs') expnum, ccdname = 431109, 'N14' cat = ps1cat(expnum=expnum, ccdname=ccdname) stars = cat.get_stars() print len(stars), 'stars' decals = Decals() ccd = decals.find_ccds(expnum=expnum,ccdname=ccdname)[0] im = decals.get_image_object(ccd) wcs = im.get_wcs() tim = im.get_tractor_image(pixPsf=True, splinesky=True) margin = 15 ok,stars.xx,stars.yy = wcs.radec2pixelxy(stars.ra, stars.dec) stars.xx -= 1. stars.yy -= 1. W,H = wcs.get_width(), wcs.get_height() stars.ix = np.round(stars.xx).astype(int) stars.iy = np.round(stars.yy).astype(int) stars.cut((stars.ix >= margin) * (stars.ix < (W-margin)) * (stars.iy >= margin) * (stars.iy < (H-margin))) plt.clf()
def psf_residuals(expnum, ccdname, stampsize=35, nstar=30, magrange=(13, 17), verbose=0, splinesky=False): # Set the debugging level. if verbose == 0: lvl = logging.INFO else: lvl = logging.DEBUG logging.basicConfig(level=lvl, format='%(message)s', stream=sys.stdout) pngprefix = 'qapsf-{}-{}'.format(expnum, ccdname) # Gather all the info we need about this CCD. decals = Decals() ccd = decals.find_ccds(expnum=expnum, ccdname=ccdname)[0] band = ccd.filter ps1band = dict(g=0, r=1, i=2, z=3, Y=4) print('Band {}'.format(band)) #scales = dict(g=0.0066, r=0.01, z=0.025) #vmin, vmax = np.arcsinh(-1), np.arcsinh(100) #print(scales[band]) im = decals.get_image_object(ccd) iminfo = im.get_image_info() H, W = iminfo['dims'] wcs = im.get_wcs() # Choose a uniformly selected subset of PS1 stars on this CCD. ps1 = ps1cat(ccdwcs=wcs) cat = ps1.get_stars(band=band, magrange=magrange) rand = np.random.RandomState(seed=expnum * ccd.ccdnum) these = rand.choice(len(cat) - 1, nstar, replace=False) #these = rand.random_integers(0,len(cat)-1,nstar) cat = cat[these] cat = cat[np.argsort(cat.median[:, ps1band[band]])] # sort by magnitude #print(cat.nmag_ok) get_tim_kwargs = dict(const2psf=True, splinesky=splinesky) # Make a QAplot of the positions of all the stars. tim = im.get_tractor_image(**get_tim_kwargs) img = tim.getImage() #img = tim.getImage()/scales[band] fig = plt.figure(figsize=(5, 10)) ax = fig.gca() ax.get_xaxis().get_major_formatter().set_useOffset(False) #ax.imshow(np.arcsinh(img),cmap='gray',interpolation='nearest', # origin='lower',vmin=vmax,vmax=vmax) ax.imshow(img, **tim.ima) ax.axis('off') ax.set_title('{}: {}/{} AM={:.2f} Seeing={:.3f}"'.format( band, expnum, ccdname, ccd.airmass, ccd.seeing)) for istar, ps1star in enumerate(cat): ra, dec = (ps1star.ra, ps1star.dec) ok, xpos, ypos = wcs.radec2pixelxy(ra, dec) ax.text(xpos, ypos, '{:2d}'.format(istar + 1), color='red', horizontalalignment='left') circ = plt.Circle((xpos, ypos), radius=30, color='g', fill=False, lw=1) ax.add_patch(circ) #radec = wcs.radec_bounds() #ax.scatter(cat.ra,cat.dec) #ax.set_xlim([radec[1],radec[0]])#*[1.0002,0.9998]) #ax.set_ylim([radec[2],radec[3]])#*[0.985,1.015]) #ax.set_xlabel('$RA\ (deg)$',fontsize=18) #ax.set_ylabel('$Dec\ (deg)$',fontsize=18) fig.savefig(pngprefix + '-ccd.png', bbox_inches='tight') # Initialize the many-stamp QAplot ncols = 3 nrows = np.ceil(nstar / ncols).astype('int') inchperstamp = 2.0 fig = plt.figure(figsize=(inchperstamp * 3 * ncols, inchperstamp * nrows)) irow = 0 icol = 0 for istar, ps1star in enumerate(cat): ra, dec = (ps1star.ra, ps1star.dec) mag = ps1star.median[ps1band[band]] # r-band ok, xpos, ypos = wcs.radec2pixelxy(ra, dec) ix, iy = int(xpos), int(ypos) # create a little tractor Image object around the star slc = (slice(max(iy - stampsize, 0), min(iy + stampsize + 1, H)), slice(max(ix - stampsize, 0), min(ix + stampsize + 1, W))) # The PSF model 'const2Psf' is the one used in DR1: a 2-component # Gaussian fit to PsfEx instantiated in the image center. tim = im.get_tractor_image(slc=slc, **get_tim_kwargs) stamp = tim.getImage() ivarstamp = tim.getInvvar() # Initialize a tractor PointSource from PS1 measurements flux = NanoMaggies.magToNanomaggies(mag) star = PointSource(RaDecPos(ra, dec), NanoMaggies(**{band: flux})) # Fit just the source RA,Dec,flux. tractor = Tractor([tim], [star]) tractor.freezeParam('images') print('2-component MOG:', tim.psf) tractor.printThawedParams() for step in range(50): dlnp, X, alpha = tractor.optimize() if dlnp < 0.1: break print('Fit:', star) model_mog = tractor.getModelImage(0) chi2_mog = -2.0 * tractor.getLogLikelihood() mag_mog = NanoMaggies.nanomaggiesToMag(star.brightness)[0] # Now change the PSF model to a pixelized PSF model from PsfEx instantiated # at this place in the image. psf = PixelizedPsfEx(im.psffn) tim.psf = psf.constantPsfAt(xpos, ypos) #print('PSF model:', tim.psf) #tractor.printThawedParams() for step in range(50): dlnp, X, alpha = tractor.optimize() if dlnp < 0.1: break print('Fit:', star) model_psfex = tractor.getModelImage(0) chi2_psfex = -2.0 * tractor.getLogLikelihood() mag_psfex = NanoMaggies.nanomaggiesToMag(star.brightness)[0] #mn, mx = np.percentile((stamp-model_psfex)[ivarstamp>0],[1,95]) sig = np.std((stamp - model_psfex)[ivarstamp > 0]) mn, mx = [-2.0 * sig, 5 * sig] # Generate a QAplot. if (istar > 0) and (istar % (ncols) == 0): irow = irow + 1 icol = 3 * istar - 3 * ncols * irow #print(istar, irow, icol, icol+1, icol+2) ax1 = plt.subplot2grid((nrows, 3 * ncols), (irow, icol), aspect='equal') ax1.axis('off') #ax1.imshow(stamp, **tim.ima) ax1.imshow(stamp, cmap='gray', interpolation='nearest', origin='lower', vmin=mn, vmax=mx) ax1.text(0.1, 0.9, '{:2d}'.format(istar + 1), color='white', horizontalalignment='left', verticalalignment='top', transform=ax1.transAxes) ax2 = plt.subplot2grid((nrows, 3 * ncols), (irow, icol + 1), aspect='equal') ax2.axis('off') #ax2.imshow(stamp-model_mog, **tim.ima) ax2.imshow(stamp - model_mog, cmap='gray', interpolation='nearest', origin='lower', vmin=mn, vmax=mx) ax2.text(0.1, 0.9, 'MoG', color='white', horizontalalignment='left', verticalalignment='top', transform=ax2.transAxes) ax2.text(0.08, 0.08, '{:.3f}'.format(mag_mog), color='white', horizontalalignment='left', verticalalignment='bottom', transform=ax2.transAxes) #ax2.set_title('{:.3f}, {:.2f}'.format(mag_psfex,chi2_psfex),fontsize=14) #ax2.set_title('{:.3f}, $\chi^{2}$={:.2f}'.format(mag_psfex,chi2_psfex)) ax3 = plt.subplot2grid((nrows, 3 * ncols), (irow, icol + 2), aspect='equal') ax3.axis('off') #ax3.imshow(stamp-model_psfex, **tim.ima) ax3.imshow(stamp - model_psfex, cmap='gray', interpolation='nearest', origin='lower', vmin=mn, vmax=mx) ax3.text(0.1, 0.9, 'PSFEx', color='white', horizontalalignment='left', verticalalignment='top', transform=ax3.transAxes) ax3.text(0.08, 0.08, '{:.3f}'.format(mag_psfex), color='white', horizontalalignment='left', verticalalignment='bottom', transform=ax3.transAxes) if istar == (nstar - 1): break fig.savefig(pngprefix + '-stargrid.png', bbox_inches='tight')
def psf_residuals(expnum,ccdname,stampsize=35,nstar=30, magrange=(13,17),verbose=0): # Set the debugging level. if verbose==0: lvl = logging.INFO else: lvl = logging.DEBUG logging.basicConfig(level=lvl,format='%(message)s',stream=sys.stdout) pngprefix = 'qapsf-{}-{}'.format(expnum,ccdname) # Gather all the info we need about this CCD. decals = Decals() ccd = decals.find_ccds(expnum=expnum,ccdname=ccdname)[0] band = ccd.filter ps1band = dict(g=0,r=1,i=2,z=3,Y=4) print('Band {}'.format(band)) #scales = dict(g=0.0066, r=0.01, z=0.025) #vmin, vmax = np.arcsinh(-1), np.arcsinh(100) #print(scales[band]) im = DecamImage(decals,ccd) iminfo = im.get_image_info() H,W = iminfo['dims'] wcs = im.get_wcs() # Choose a uniformly selected subset of PS1 stars on this CCD. ps1 = ps1cat(ccdwcs=wcs) cat = ps1.get_stars(band=band,magrange=magrange) rand = np.random.RandomState(seed=expnum*ccd.ccdnum) these = rand.choice(len(cat)-1,nstar,replace=False) #these = rand.random_integers(0,len(cat)-1,nstar) cat = cat[these] cat = cat[np.argsort(cat.median[:,ps1band[band]])] # sort by magnitude #print(cat.nmag_ok) # Make a QAplot of the positions of all the stars. tim = im.get_tractor_image(const2psf=True) img = tim.getImage() #img = tim.getImage()/scales[band] fig = plt.figure(figsize=(5,10)) ax = fig.gca() ax.get_xaxis().get_major_formatter().set_useOffset(False) #ax.imshow(np.arcsinh(img),cmap='gray',interpolation='nearest', # origin='lower',vmin=vmax,vmax=vmax) ax.imshow(img, **tim.ima) ax.axis('off') ax.set_title('{}: {}/{} AM={:.2f} Seeing={:.3f}"'. format(band,expnum,ccdname,ccd.airmass,ccd.seeing)) for istar, ps1star in enumerate(cat): ra, dec = (ps1star.ra, ps1star.dec) ok, xpos, ypos = wcs.radec2pixelxy(ra, dec) ax.text(xpos,ypos,'{:2d}'.format(istar+1),color='red', horizontalalignment='left') circ = plt.Circle((xpos,ypos),radius=30,color='g',fill=False,lw=1) ax.add_patch(circ) #radec = wcs.radec_bounds() #ax.scatter(cat.ra,cat.dec) #ax.set_xlim([radec[1],radec[0]])#*[1.0002,0.9998]) #ax.set_ylim([radec[2],radec[3]])#*[0.985,1.015]) #ax.set_xlabel('$RA\ (deg)$',fontsize=18) #ax.set_ylabel('$Dec\ (deg)$',fontsize=18) fig.savefig(pngprefix+'-ccd.png',bbox_inches='tight') # Initialize the many-stamp QAplot ncols = 3 nrows = np.ceil(nstar/ncols).astype('int') inchperstamp = 2.0 fig = plt.figure(figsize=(inchperstamp*3*ncols,inchperstamp*nrows)) irow = 0 icol = 0 for istar, ps1star in enumerate(cat): ra, dec = (ps1star.ra, ps1star.dec) mag = ps1star.median[ps1band[band]] # r-band ok, xpos, ypos = wcs.radec2pixelxy(ra, dec) ix,iy = int(xpos), int(ypos) # create a little tractor Image object around the star slc = (slice(max(iy-stampsize, 0), min(iy+stampsize+1, H)), slice(max(ix-stampsize, 0), min(ix+stampsize+1, W))) # The PSF model 'const2Psf' is the one used in DR1: a 2-component # Gaussian fit to PsfEx instantiated in the image center. tim = im.get_tractor_image(slc=slc, const2psf=True) stamp = tim.getImage() ivarstamp = tim.getInvvar() # Initialize a tractor PointSource from PS1 measurements flux = NanoMaggies.magToNanomaggies(mag) star = PointSource(RaDecPos(ra,dec), NanoMaggies(**{band: flux})) # Fit just the source RA,Dec,flux. tractor = Tractor([tim], [star]) tractor.freezeParam('images') print('2-component MOG:', tim.psf) tractor.printThawedParams() for step in range(50): dlnp,X,alpha = tractor.optimize() if dlnp < 0.1: break print('Fit:', star) model_mog = tractor.getModelImage(0) chi2_mog = -2.0*tractor.getLogLikelihood() mag_mog = NanoMaggies.nanomaggiesToMag(star.brightness)[0] # Now change the PSF model to a pixelized PSF model from PsfEx instantiated # at this place in the image. psf = PixelizedPsfEx(im.psffn) tim.psf = psf.constantPsfAt(xpos, ypos) #print('PSF model:', tim.psf) #tractor.printThawedParams() for step in range(50): dlnp,X,alpha = tractor.optimize() if dlnp < 0.1: break print('Fit:', star) model_psfex = tractor.getModelImage(0) chi2_psfex = -2.0*tractor.getLogLikelihood() mag_psfex = NanoMaggies.nanomaggiesToMag(star.brightness)[0] #mn, mx = np.percentile((stamp-model_psfex)[ivarstamp>0],[1,95]) sig = np.std((stamp-model_psfex)[ivarstamp>0]) mn, mx = [-2.0*sig,5*sig] # Generate a QAplot. if (istar>0) and (istar%(ncols)==0): irow = irow+1 icol = 3*istar - 3*ncols*irow #print(istar, irow, icol, icol+1, icol+2) ax1 = plt.subplot2grid((nrows,3*ncols), (irow,icol), aspect='equal') ax1.axis('off') #ax1.imshow(stamp, **tim.ima) ax1.imshow(stamp,cmap='gray',interpolation='nearest', origin='lower',vmin=mn,vmax=mx) ax1.text(0.1,0.9,'{:2d}'.format(istar+1),color='white', horizontalalignment='left',verticalalignment='top', transform=ax1.transAxes) ax2 = plt.subplot2grid((nrows,3*ncols), (irow,icol+1), aspect='equal') ax2.axis('off') #ax2.imshow(stamp-model_mog, **tim.ima) ax2.imshow(stamp-model_mog,cmap='gray',interpolation='nearest', origin='lower',vmin=mn,vmax=mx) ax2.text(0.1,0.9,'MoG',color='white', horizontalalignment='left',verticalalignment='top', transform=ax2.transAxes) ax2.text(0.08,0.08,'{:.3f}'.format(mag_mog),color='white', horizontalalignment='left',verticalalignment='bottom', transform=ax2.transAxes) #ax2.set_title('{:.3f}, {:.2f}'.format(mag_psfex,chi2_psfex),fontsize=14) #ax2.set_title('{:.3f}, $\chi^{2}$={:.2f}'.format(mag_psfex,chi2_psfex)) ax3 = plt.subplot2grid((nrows,3*ncols), (irow,icol+2), aspect='equal') ax3.axis('off') #ax3.imshow(stamp-model_psfex, **tim.ima) ax3.imshow(stamp-model_psfex,cmap='gray',interpolation='nearest', origin='lower',vmin=mn,vmax=mx) ax3.text(0.1,0.9,'PSFEx',color='white', horizontalalignment='left',verticalalignment='top', transform=ax3.transAxes) ax3.text(0.08,0.08,'{:.3f}'.format(mag_psfex),color='white', horizontalalignment='left',verticalalignment='bottom', transform=ax3.transAxes) if istar==(nstar-1): break fig.savefig(pngprefix+'-stargrid.png',bbox_inches='tight')
def main(decals=None, opt=None): '''Driver function for forced photometry of individual DECam images. ''' if opt is None: parser = get_parser() opt = parser.parse_args() Time.add_measurement(MemMeas) t0 = Time() if os.path.exists(opt.outfn): print('Ouput file exists:', opt.outfn) sys.exit(0) if not opt.forced: opt.apphot = True zoomslice = None if opt.zoom is not None: (x0,x1,y0,y1) = opt.zoom zoomslice = (slice(y0,y1), slice(x0,x1)) ps = None if opt.plots is not None: from astrometry.util.plotutils import PlotSequence ps = PlotSequence(opt.plots) # Try parsing filename as exposure number. try: expnum = int(opt.filename) opt.filename = None except: # make this 'None' for decals.find_ccds() expnum = None # Try parsing HDU number try: opt.hdu = int(opt.hdu) ccdname = None except: ccdname = opt.hdu opt.hdu = -1 if decals is None: decals = Decals() if opt.filename is not None and opt.hdu >= 0: # Read metadata from file T = exposure_metadata([opt.filename], hdus=[opt.hdu]) print('Metadata:') T.about() else: # Read metadata from decals-ccds.fits table T = decals.find_ccds(expnum=expnum, ccdname=ccdname) print(len(T), 'with expnum', expnum, 'and CCDname', ccdname) if opt.hdu >= 0: T.cut(T.image_hdu == opt.hdu) print(len(T), 'with HDU', opt.hdu) if opt.filename is not None: T.cut(np.array([f.strip() == opt.filename for f in T.image_filename])) print(len(T), 'with filename', opt.filename) assert(len(T) == 1) im = decals.get_image_object(T[0]) tim = im.get_tractor_image(slc=zoomslice, pixPsf=True, splinesky=True) print('Got tim:', tim) if opt.catfn in ['DR1', 'DR2']: if opt.catalog_path is None: opt.catalog_path = opt.catfn.lower() margin = 20 TT = [] chipwcs = tim.subwcs bricks = bricks_touching_wcs(chipwcs, decals=decals) for b in bricks: # there is some overlap with this brick... read the catalog. fn = os.path.join(opt.catalog_path, 'tractor', b.brickname[:3], 'tractor-%s.fits' % b.brickname) if not os.path.exists(fn): print('WARNING: catalog', fn, 'does not exist. Skipping!') continue print('Reading', fn) T = fits_table(fn) ok,xx,yy = chipwcs.radec2pixelxy(T.ra, T.dec) W,H = chipwcs.get_width(), chipwcs.get_height() I = np.flatnonzero((xx >= -margin) * (xx <= (W+margin)) * (yy >= -margin) * (yy <= (H+margin))) T.cut(I) print('Cut to', len(T), 'sources within image + margin') # print('Brick_primary:', np.unique(T.brick_primary)) T.cut(T.brick_primary) print('Cut to', len(T), 'on brick_primary') T.cut((T.out_of_bounds == False) * (T.left_blob == False)) print('Cut to', len(T), 'on out_of_bounds and left_blob') TT.append(T) T = merge_tables(TT) T._header = TT[0]._header del TT # Fix up various failure modes: # FixedCompositeGalaxy(pos=RaDecPos[240.51147402832561, 10.385488075518923], brightness=NanoMaggies: g=(flux -2.87), r=(flux -5.26), z=(flux -7.65), fracDev=FracDev(0.60177207), shapeExp=re=3.78351e-44, e1=9.30367e-13, e2=1.24392e-16, shapeDev=re=inf, e1=-0, e2=-0) # -> convert to EXP I = np.flatnonzero(np.array([((t.type == 'COMP') and (not np.isfinite(t.shapedev_r))) for t in T])) if len(I): print('Converting', len(I), 'bogus COMP galaxies to EXP') for i in I: T.type[i] = 'EXP' # Same thing with the exp component. # -> convert to DEV I = np.flatnonzero(np.array([((t.type == 'COMP') and (not np.isfinite(t.shapeexp_r))) for t in T])) if len(I): print('Converting', len(I), 'bogus COMP galaxies to DEV') for i in I: T.type[i] = 'DEV' if opt.write_cat: T.writeto(opt.write_cat) print('Wrote catalog to', opt.write_cat) else: T = fits_table(opt.catfn) T.shapeexp = np.vstack((T.shapeexp_r, T.shapeexp_e1, T.shapeexp_e2)).T T.shapedev = np.vstack((T.shapedev_r, T.shapedev_e1, T.shapedev_e2)).T cat = read_fits_catalog(T, ellipseClass=tractor.ellipses.EllipseE) # print('Got cat:', cat) print('Forced photom...') opti = None if opt.ceres: from tractor.ceres_optimizer import CeresOptimizer B = 8 opti = CeresOptimizer(BW=B, BH=B) tr = Tractor([tim], cat, optimizer=opti) tr.freezeParam('images') for src in cat: src.freezeAllBut('brightness') src.getBrightness().freezeAllBut(tim.band) F = fits_table() F.brickid = T.brickid F.brickname = T.brickname F.objid = T.objid F.filter = np.array([tim.band] * len(T)) F.mjd = np.array([tim.primhdr['MJD-OBS']] * len(T)) F.exptime = np.array([tim.primhdr['EXPTIME']] * len(T)) ok,x,y = tim.sip_wcs.radec2pixelxy(T.ra, T.dec) F.x = (x-1).astype(np.float32) F.y = (y-1).astype(np.float32) if opt.apphot: import photutils img = tim.getImage() ie = tim.getInvError() with np.errstate(divide='ignore'): imsigma = 1. / ie imsigma[ie == 0] = 0. apimg = [] apimgerr = [] # Aperture photometry locations xxyy = np.vstack([tim.wcs.positionToPixel(src.getPosition()) for src in cat]).T apxy = xxyy - 1. apertures = apertures_arcsec / tim.wcs.pixel_scale() print('Apertures:', apertures, 'pixels') for rad in apertures: aper = photutils.CircularAperture(apxy, rad) p = photutils.aperture_photometry(img, aper, error=imsigma) apimg.append(p.field('aperture_sum')) apimgerr.append(p.field('aperture_sum_err')) ap = np.vstack(apimg).T ap[np.logical_not(np.isfinite(ap))] = 0. F.apflux = ap ap = 1./(np.vstack(apimgerr).T)**2 ap[np.logical_not(np.isfinite(ap))] = 0. F.apflux_ivar = ap if opt.forced: kwa = {} if opt.plots is None: kwa.update(wantims=False) R = tr.optimize_forced_photometry(variance=True, fitstats=True, shared_params=False, **kwa) if opt.plots: (data,mod,ie,chi,roi) = R.ims1[0] ima = tim.ima imchi = dict(interpolation='nearest', origin='lower', vmin=-5, vmax=5) plt.clf() plt.imshow(data, **ima) plt.title('Data: %s' % tim.name) ps.savefig() plt.clf() plt.imshow(mod, **ima) plt.title('Model: %s' % tim.name) ps.savefig() plt.clf() plt.imshow(chi, **imchi) plt.title('Chi: %s' % tim.name) ps.savefig() F.flux = np.array([src.getBrightness().getFlux(tim.band) for src in cat]).astype(np.float32) F.flux_ivar = R.IV.astype(np.float32) F.fracflux = R.fitstats.profracflux.astype(np.float32) F.rchi2 = R.fitstats.prochi2 .astype(np.float32) program_name = sys.argv[0] version_hdr = get_version_header(program_name, decals.decals_dir) # HACK -- print only two directory names + filename of CPFILE. fname = os.path.basename(im.imgfn) d = os.path.dirname(im.imgfn) d1 = os.path.basename(d) d = os.path.dirname(d) d2 = os.path.basename(d) fname = os.path.join(d2, d1, fname) print('Trimmed filename to', fname) #version_hdr.add_record(dict(name='CPFILE', value=im.imgfn, comment='DECam comm.pipeline file')) version_hdr.add_record(dict(name='CPFILE', value=fname, comment='DECam comm.pipeline file')) version_hdr.add_record(dict(name='CPHDU', value=im.hdu, comment='DECam comm.pipeline ext')) version_hdr.add_record(dict(name='CAMERA', value='DECam', comment='Dark Energy Camera')) version_hdr.add_record(dict(name='EXPNUM', value=im.expnum, comment='DECam exposure num')) version_hdr.add_record(dict(name='CCDNAME', value=im.ccdname, comment='DECam CCD name')) version_hdr.add_record(dict(name='FILTER', value=tim.band, comment='Bandpass of this image')) version_hdr.add_record(dict(name='EXPOSURE', value='decam-%s-%s' % (im.expnum, im.ccdname), comment='Name of this image')) keys = ['TELESCOP','OBSERVAT','OBS-LAT','OBS-LONG','OBS-ELEV', 'INSTRUME'] for key in keys: if key in tim.primhdr: version_hdr.add_record(dict(name=key, value=tim.primhdr[key])) hdr = fitsio.FITSHDR() units = {'mjd':'sec', 'exptime':'sec', 'flux':'nanomaggy', 'flux_ivar':'1/nanomaggy^2'} columns = F.get_columns() for i,col in enumerate(columns): if col in units: hdr.add_record(dict(name='TUNIT%i' % (i+1), value=units[col])) outdir = os.path.dirname(opt.outfn) if len(outdir): trymakedirs(outdir) fitsio.write(opt.outfn, None, header=version_hdr, clobber=True) F.writeto(opt.outfn, header=hdr, append=True) print('Wrote', opt.outfn) print('Finished forced phot:', Time()-t0) return 0
def main(decals=None, opt=None): '''Driver function for forced photometry of individual DECam images. ''' if opt is None: parser = get_parser() opt = parser.parse_args() Time.add_measurement(MemMeas) t0 = Time() if os.path.exists(opt.outfn): print('Ouput file exists:', opt.outfn) sys.exit(0) if not opt.forced: opt.apphot = True zoomslice = None if opt.zoom is not None: (x0, x1, y0, y1) = opt.zoom zoomslice = (slice(y0, y1), slice(x0, x1)) ps = None if opt.plots is not None: from astrometry.util.plotutils import PlotSequence ps = PlotSequence(opt.plots) # Try parsing filename as exposure number. try: expnum = int(opt.filename) opt.filename = None except: # make this 'None' for decals.find_ccds() expnum = None # Try parsing HDU number try: opt.hdu = int(opt.hdu) ccdname = None except: ccdname = opt.hdu opt.hdu = -1 if decals is None: decals = Decals() if opt.filename is not None and opt.hdu >= 0: # Read metadata from file T = exposure_metadata([opt.filename], hdus=[opt.hdu]) print('Metadata:') T.about() else: # Read metadata from decals-ccds.fits table T = decals.find_ccds(expnum=expnum, ccdname=ccdname) print(len(T), 'with expnum', expnum, 'and CCDname', ccdname) if opt.hdu >= 0: T.cut(T.image_hdu == opt.hdu) print(len(T), 'with HDU', opt.hdu) if opt.filename is not None: T.cut( np.array([f.strip() == opt.filename for f in T.image_filename])) print(len(T), 'with filename', opt.filename) assert (len(T) == 1) im = decals.get_image_object(T[0]) tim = im.get_tractor_image(slc=zoomslice, pixPsf=True, splinesky=True) print('Got tim:', tim) if opt.catfn in ['DR1', 'DR2']: if opt.catalog_path is None: opt.catalog_path = opt.catfn.lower() margin = 20 TT = [] chipwcs = tim.subwcs bricks = bricks_touching_wcs(chipwcs, decals=decals) for b in bricks: # there is some overlap with this brick... read the catalog. fn = os.path.join(opt.catalog_path, 'tractor', b.brickname[:3], 'tractor-%s.fits' % b.brickname) if not os.path.exists(fn): print('WARNING: catalog', fn, 'does not exist. Skipping!') continue print('Reading', fn) T = fits_table(fn) ok, xx, yy = chipwcs.radec2pixelxy(T.ra, T.dec) W, H = chipwcs.get_width(), chipwcs.get_height() I = np.flatnonzero((xx >= -margin) * (xx <= (W + margin)) * (yy >= -margin) * (yy <= (H + margin))) T.cut(I) print('Cut to', len(T), 'sources within image + margin') # print('Brick_primary:', np.unique(T.brick_primary)) T.cut(T.brick_primary) print('Cut to', len(T), 'on brick_primary') T.cut((T.out_of_bounds == False) * (T.left_blob == False)) print('Cut to', len(T), 'on out_of_bounds and left_blob') TT.append(T) T = merge_tables(TT) T._header = TT[0]._header del TT # Fix up various failure modes: # FixedCompositeGalaxy(pos=RaDecPos[240.51147402832561, 10.385488075518923], brightness=NanoMaggies: g=(flux -2.87), r=(flux -5.26), z=(flux -7.65), fracDev=FracDev(0.60177207), shapeExp=re=3.78351e-44, e1=9.30367e-13, e2=1.24392e-16, shapeDev=re=inf, e1=-0, e2=-0) # -> convert to EXP I = np.flatnonzero( np.array([((t.type == 'COMP') and (not np.isfinite(t.shapedev_r))) for t in T])) if len(I): print('Converting', len(I), 'bogus COMP galaxies to EXP') for i in I: T.type[i] = 'EXP' # Same thing with the exp component. # -> convert to DEV I = np.flatnonzero( np.array([((t.type == 'COMP') and (not np.isfinite(t.shapeexp_r))) for t in T])) if len(I): print('Converting', len(I), 'bogus COMP galaxies to DEV') for i in I: T.type[i] = 'DEV' if opt.write_cat: T.writeto(opt.write_cat) print('Wrote catalog to', opt.write_cat) else: T = fits_table(opt.catfn) T.shapeexp = np.vstack((T.shapeexp_r, T.shapeexp_e1, T.shapeexp_e2)).T T.shapedev = np.vstack((T.shapedev_r, T.shapedev_e1, T.shapedev_e2)).T cat = read_fits_catalog(T, ellipseClass=tractor.ellipses.EllipseE) # print('Got cat:', cat) print('Forced photom...') opti = None if opt.ceres: from tractor.ceres_optimizer import CeresOptimizer B = 8 opti = CeresOptimizer(BW=B, BH=B) tr = Tractor([tim], cat, optimizer=opti) tr.freezeParam('images') for src in cat: src.freezeAllBut('brightness') src.getBrightness().freezeAllBut(tim.band) F = fits_table() F.brickid = T.brickid F.brickname = T.brickname F.objid = T.objid F.filter = np.array([tim.band] * len(T)) F.mjd = np.array([tim.primhdr['MJD-OBS']] * len(T)) F.exptime = np.array([tim.primhdr['EXPTIME']] * len(T)) ok, x, y = tim.sip_wcs.radec2pixelxy(T.ra, T.dec) F.x = (x - 1).astype(np.float32) F.y = (y - 1).astype(np.float32) if opt.apphot: import photutils img = tim.getImage() ie = tim.getInvError() with np.errstate(divide='ignore'): imsigma = 1. / ie imsigma[ie == 0] = 0. apimg = [] apimgerr = [] # Aperture photometry locations xxyy = np.vstack( [tim.wcs.positionToPixel(src.getPosition()) for src in cat]).T apxy = xxyy - 1. apertures = apertures_arcsec / tim.wcs.pixel_scale() print('Apertures:', apertures, 'pixels') for rad in apertures: aper = photutils.CircularAperture(apxy, rad) p = photutils.aperture_photometry(img, aper, error=imsigma) apimg.append(p.field('aperture_sum')) apimgerr.append(p.field('aperture_sum_err')) ap = np.vstack(apimg).T ap[np.logical_not(np.isfinite(ap))] = 0. F.apflux = ap ap = 1. / (np.vstack(apimgerr).T)**2 ap[np.logical_not(np.isfinite(ap))] = 0. F.apflux_ivar = ap if opt.forced: kwa = {} if opt.plots is None: kwa.update(wantims=False) R = tr.optimize_forced_photometry(variance=True, fitstats=True, shared_params=False, **kwa) if opt.plots: (data, mod, ie, chi, roi) = R.ims1[0] ima = tim.ima imchi = dict(interpolation='nearest', origin='lower', vmin=-5, vmax=5) plt.clf() plt.imshow(data, **ima) plt.title('Data: %s' % tim.name) ps.savefig() plt.clf() plt.imshow(mod, **ima) plt.title('Model: %s' % tim.name) ps.savefig() plt.clf() plt.imshow(chi, **imchi) plt.title('Chi: %s' % tim.name) ps.savefig() F.flux = np.array([ src.getBrightness().getFlux(tim.band) for src in cat ]).astype(np.float32) F.flux_ivar = R.IV.astype(np.float32) F.fracflux = R.fitstats.profracflux.astype(np.float32) F.rchi2 = R.fitstats.prochi2.astype(np.float32) program_name = sys.argv[0] version_hdr = get_version_header(program_name, decals.decals_dir) # HACK -- print only two directory names + filename of CPFILE. fname = os.path.basename(im.imgfn) d = os.path.dirname(im.imgfn) d1 = os.path.basename(d) d = os.path.dirname(d) d2 = os.path.basename(d) fname = os.path.join(d2, d1, fname) print('Trimmed filename to', fname) #version_hdr.add_record(dict(name='CPFILE', value=im.imgfn, comment='DECam comm.pipeline file')) version_hdr.add_record( dict(name='CPFILE', value=fname, comment='DECam comm.pipeline file')) version_hdr.add_record( dict(name='CPHDU', value=im.hdu, comment='DECam comm.pipeline ext')) version_hdr.add_record( dict(name='CAMERA', value='DECam', comment='Dark Energy Camera')) version_hdr.add_record( dict(name='EXPNUM', value=im.expnum, comment='DECam exposure num')) version_hdr.add_record( dict(name='CCDNAME', value=im.ccdname, comment='DECam CCD name')) version_hdr.add_record( dict(name='FILTER', value=tim.band, comment='Bandpass of this image')) version_hdr.add_record( dict(name='EXPOSURE', value='decam-%s-%s' % (im.expnum, im.ccdname), comment='Name of this image')) keys = [ 'TELESCOP', 'OBSERVAT', 'OBS-LAT', 'OBS-LONG', 'OBS-ELEV', 'INSTRUME' ] for key in keys: if key in tim.primhdr: version_hdr.add_record(dict(name=key, value=tim.primhdr[key])) hdr = fitsio.FITSHDR() units = { 'mjd': 'sec', 'exptime': 'sec', 'flux': 'nanomaggy', 'flux_ivar': '1/nanomaggy^2' } columns = F.get_columns() for i, col in enumerate(columns): if col in units: hdr.add_record(dict(name='TUNIT%i' % (i + 1), value=units[col])) outdir = os.path.dirname(opt.outfn) if len(outdir): trymakedirs(outdir) fitsio.write(opt.outfn, None, header=version_hdr, clobber=True) F.writeto(opt.outfn, header=hdr, append=True) print('Wrote', opt.outfn) print('Finished forced phot:', Time() - t0) return 0