def compY1Y3():
f1 = fitsio.read('/Users/ashleyross/Dropbox/DESY3/Y1mag22.fits')
fs_1 = fitsio.read('/Users/ashleyross/Dropbox/DESY3/Y3red_1.fits')
fs_2 = fitsio.read('/Users/ashleyross/Dropbox/DESY3/Y3red_2.fits')
maskf = open(
'/Users/ashleyross/DESY1/mask_Y1redBAO_mean_z_bpz_VFF_4096ring.dat')
npix = 12 * 4096 * 4096
mask = []
pixl1 = np.zeros(f1.size)
pixl3_1 = np.zeros(fs_1.size)
pixl3_2 = np.zeros(fs_2.size)
for i in range(0, npix):
mask.append(0)
for line in maskf:
pix = int(float(line.split()[0]))
mask[pix] = 1
print 'mask done'
ng = 0
ng3 = 0
for i in range(0, f1.size):
ra, dec = f1[i]['RA'], f1[i]['DEC']
th, phi = radec2thphi(ra, dec)
p = hp.ang2pix(4096, th, phi)
if mask[int(p)] == 1:
pixl1[i] = 1
print 'f1 done'
for i in range(0, fs_1.size):
ra, dec = fs_1[i]['RA'], fs_1[i]['DEC']
th, phi = radec2thphi(ra, dec)
p = hp.ang2pix(4096, th, phi)
if mask[int(p)] == 1:
pixl3_1[i] = 1
print 'f3_1 done'
for i in range(0, fs_2.size):
ra, dec = fs_2[i]['RA'], fs_2[i]['DEC']
th, phi = radec2thphi(ra, dec)
p = hp.ang2pix(4096, th, phi)
if mask[int(p)] == 1:
pixl3_2[i] = 1
print 'f3_2 done'
w1 = (pixl1 == 1) & (f1['MAG_AUTO_I'] - f1['MAG_AUTO_Z'] + 2. *
(f1['MAG_AUTO_R'] - f1['MAG_AUTO_I']) >
1.7) & (f1['MAG_AUTO_I'] > 17.5)
f1m = f1[w1]
w3_1 = (pixl3_1 == 1)
f3_1m = fs_1[w3_1]
w3_2 = (pixl3_2 == 1)
f3_2m = fs_2[w3_2]
print f1m.size, f3_1m.size + f3_2m.size
def mkgalmapY3ac(res,zr,gz='.gz',md='',fore='',wm='',syscut=''): gl = [] for i in range(0,12*res*res): gl.append(0) #f = fitsio.read(dir+'dr1_lss_red_'+zr+'_v0_redux.fits.gz',ext=1) f = fitsio.read(dir+'test'+zr+mask+'.fits'+gz,ext=1) ngt = 0 w = 1. zem = 0 fw = '' if fore == 'fore': fw = '_fore' #if fore == 'auto': # fw = '_auto' if md == 'nodepth': md = '_none' #else: # md = '_'+md for i in range(0,len(f)): ra,dec = f[i]['RA'],f[i]['DEC'] #if f[i]['v0'+md+fw] == 1.: #if wm != '': # w = float(ln[4]) #if z > zmin and z < zmax: th,phi = radec2thphi(ra,dec) p = hp.ang2pix(res,th,phi,nest=True) gl[p] += w ngt += w print len(gl),ngt return gl
def mkgalmapY3ac(res, zr, gz='.gz', md='', fore='', wm='', syscut=''):
gl = []
for i in range(0, 12 * res * res):
gl.append(0)
#f = fitsio.read(dir+'dr1_lss_red_'+zr+'_v0_redux.fits.gz',ext=1)
f = fitsio.read(dir + 'test' + zr + mask + '.fits' + gz, ext=1)
ngt = 0
w = 1.
zem = 0
fw = ''
if fore == 'fore':
fw = '_fore'
#if fore == 'auto':
# fw = '_auto'
if md == 'nodepth':
md = '_none'
#else:
# md = '_'+md
for i in range(0, len(f)):
ra, dec = f[i]['RA'], f[i]['DEC']
#if f[i]['v0'+md+fw] == 1.:
#if wm != '':
# w = float(ln[4])
#if z > zmin and z < zmax:
th, phi = radec2thphi(ra, dec)
p = hp.ang2pix(res, th, phi, nest=True)
gl[p] += w
ngt += w
print len(gl), ngt
return gl
def compY1Y3():
f1 = fitsio.read('/Users/ashleyross/Dropbox/DESY3/Y1mag22.fits')
fs_1 = fitsio.read('/Users/ashleyross/Dropbox/DESY3/Y3red_1.fits')
fs_2 = fitsio.read('/Users/ashleyross/Dropbox/DESY3/Y3red_2.fits')
maskf = open('/Users/ashleyross/DESY1/mask_Y1redBAO_mean_z_bpz_VFF_4096ring.dat')
npix = 12*4096*4096
mask = []
pixl1 = np.zeros(f1.size)
pixl3_1 = np.zeros(fs_1.size)
pixl3_2 = np.zeros(fs_2.size)
for i in range(0,npix):
mask.append(0)
for line in maskf:
pix = int(float(line.split()[0]))
mask[pix] = 1
print 'mask done'
ng = 0
ng3 = 0
for i in range(0,f1.size):
ra,dec = f1[i]['RA'],f1[i]['DEC']
th,phi = radec2thphi(ra,dec)
p = hp.ang2pix(4096,th,phi)
if mask[int(p)] == 1:
pixl1[i] = 1
print 'f1 done'
for i in range(0,fs_1.size):
ra,dec = fs_1[i]['RA'],fs_1[i]['DEC']
th,phi = radec2thphi(ra,dec)
p = hp.ang2pix(4096,th,phi)
if mask[int(p)] == 1:
pixl3_1[i] = 1
print 'f3_1 done'
for i in range(0,fs_2.size):
ra,dec = fs_2[i]['RA'],fs_2[i]['DEC']
th,phi = radec2thphi(ra,dec)
p = hp.ang2pix(4096,th,phi)
if mask[int(p)] == 1:
pixl3_2[i] = 1
print 'f3_2 done'
w1 = (pixl1 == 1) & (f1['MAG_AUTO_I']-f1['MAG_AUTO_Z'] +2.*(f1['MAG_AUTO_R']-f1['MAG_AUTO_I'])>1.7) & (f1['MAG_AUTO_I'] >17.5)
f1m = f1[w1]
w3_1 = (pixl3_1 == 1)
f3_1m = fs_1[w3_1]
w3_2 = (pixl3_2 == 1)
f3_2m = fs_2[w3_2]
print f1m.size, f3_1m.size+f3_2m.size
def ngvext(file, mask, res=256, mc=.8, extmax=.15, nbin=10):
extmap = np.loadtxt('maps/healSFD_r_256_fullsky.dat')
extc = 2.751
h = healpix()
ml = zeros((4096 * 4096 * 12))
mask = fitsio.read(dir + mask + '.fits.gz')
for i in range(0, len(mask)):
ml[mask[i]['PIXEL']] = mask[i]['SIGNAL']
data = fitsio.read(dir + file + '.fits.gz')
ngl = np.zeros((12. * res * res))
ngt = 0
for i in range(0, len(data)):
if data[i]['HPIX_4096'] > mc:
th, phi = radec2thphi(data[i]['RA'], data[i]['DEC'])
pix = h.ang2pix_nest(res, th, phi)
ngl[pix] += 1.
ngt += 1.
print ngt, len(data)
mlr = zeros((12. * res * res))
for i in range(0, len(mask)):
if mask[i]['SIGNAL'] > mc:
th, phi = h.pix2ang_nest(4096, mask[i]['PIXEL'])
pix = h.ang2pix_nest(res, th, phi)
mlr[pix] += (res / 4096.)**2.
ave = sum(ngl) / sum(mlr)
print ave
bing = zeros((nbin))
binr = zeros((nbin))
for i in range(0, 12 * res * res):
extv = extmap[i] / extc
if extv < extmax:
bin = int(extv / extmax * nbin)
bing[bin] += ngl[i]
binr[bin] += mlr[i]
print bing, binr, bing / (binr * ave)
fo = open(dir + file + 'vsext.dat', 'w')
for i in range(0, nbin):
extv = i * extmax / float(nbin) + extmax / float(2. * nbin)
fo.write(
str(extv) + ' ' + str(bing[i] / (binr[i] * ave)) + ' ' +
str(sqrt(bing[i]) / (binr[i] * ave)) + '\n')
fo.close()
return True
def ngvext(file,mask,res=256,mc=.8,extmax=.15,nbin=10):
extmap = np.loadtxt('maps/healSFD_r_256_fullsky.dat')
extc = 2.751
h = healpix()
ml = zeros((4096*4096*12))
mask = fitsio.read(dir+mask+'.fits.gz')
for i in range(0,len(mask)):
ml[mask[i]['PIXEL']] = mask[i]['SIGNAL']
data = fitsio.read(dir+file+'.fits.gz')
ngl = np.zeros((12.*res*res))
ngt = 0
for i in range(0,len(data)):
if data[i]['HPIX_4096'] > mc:
th,phi = radec2thphi(data[i]['RA'],data[i]['DEC'])
pix = h.ang2pix_nest(res,th,phi)
ngl[pix] += 1.
ngt += 1.
print ngt, len(data)
mlr = zeros((12.*res*res))
for i in range(0,len(mask)):
if mask[i]['SIGNAL'] > mc:
th,phi = h.pix2ang_nest(4096,mask[i]['PIXEL'])
pix = h.ang2pix_nest(res,th,phi)
mlr[pix] += (res/4096.)**2.
ave = sum(ngl)/sum(mlr)
print ave
bing = zeros((nbin))
binr = zeros((nbin))
for i in range(0,12*res*res):
extv = extmap[i]/extc
if extv < extmax:
bin = int(extv/extmax*nbin)
bing[bin] += ngl[i]
binr[bin] += mlr[i]
print bing, binr,bing/(binr*ave)
fo = open(dir+file+'vsext.dat','w')
for i in range(0,nbin):
extv = i*extmax/float(nbin)+extmax/float(2.*nbin)
fo.write(str(extv)+' '+str(bing[i]/(binr[i]*ave))+' '+str(sqrt(bing[i])/(binr[i]*ave))+'\n')
fo.close()
return True
def plotMaghist2obs(band,ndraw = 1e5,nbin=100):
#This randomly takes two ccd observations and find the coadded depth base on 1/noise^2tot = 1/(1/noise1^2+1/noise2^2)
import fitsio
from matplotlib import pyplot as plt
from numpy import zeros,array
from random import random
readnoise = 10. # e-; 7.0 to 15.0 according to DECam Data Handbook
p = 1.15 #value given in imaging requirements
gain = 4.0 #from Dustin
f = fitsio.read(dir+'/legacypipe-dir/decals-ccds.fits.gz')
nr = len(f)
NTl = []
emin = 1000
emax = 0
msee = 0
n = 0
arcsec2pix = 1./.262 #from Dustin
if band == 'g':
zp0 = 25.08
recm = 24.
cor = 0.08
extc = 3.303/2.751
if band == 'r':
zp0 = 25.29
recm = 23.4
cor = .16
extc = 2.285/2.751
if band == 'z':
zp0 = 24.92
recm = 22.5
extc = 1.263/2.751
cor = .29
nd = 0
nbr = 0
#for i in range(0,len(f)):
while nd < ndraw:
i = int(random()*nr)
j = int(random()*nr)
#j = i #this is used to test result when conditions are exactly the same on each ccd
pid2 = f[j]['propid']
pid = f[i]['propid']
DS = 0
DS2 = 0
year = int(f[i]['date_obs'].split('-')[0])
if year > 2014:
if pid == '2014B-0404' or pid == '2013A-0741':
DS = 1 #enforce 2015 data
year2 = int(f[j]['date_obs'].split('-')[0])
if year2 > 2014:
if pid2 == '2014B-0404' or pid2 == '2013A-0741':
DS2 = 1 #enforce 2015 data
if f[i]['filter'] == band and f[j]['filter'] == band and DS == 1 and DS2 == 1:
if f[i]['seeing'] != 99 and f[i]['ccdzpt'] != 99 and f[i]['fwhm'] != 99:
if f[j]['seeing'] != 99 and f[j]['ccdzpt'] != 99 and f[j]['fwhm'] != 99:
if f[j]['dec'] > -20 and f[j]['exptime'] >=30 and f[j]['ccdnmatch'] >= 20 and abs(f[j]['zpt'] - f[j]['ccdzpt']) <= 0.1 and f[j]['zpt'] >= zp0-.5 and f[j]['zpt'] <=zp0+.25:
if f[i]['dec'] > -20 and f[i]['exptime'] >=30 and f[i]['ccdnmatch'] >= 20 and abs(f[i]['zpt'] - f[i]['ccdzpt']) <= 0.1 and f[i]['zpt'] >= zp0-.5 and f[i]['zpt'] <=zp0+.25:
nd += 1
ra,dec = f[i]['ra'],f[i]['dec']
th,phi = radec2thphi(ra,dec)
pix = hpix.ang2pix_nest(256,th,phi)
ext = extmap[pix]*extc
ra2,dec2 = f[j]['ra'],f[j]['dec']
th,phi = radec2thphi(ra2,dec2)
pix2 = hpix.ang2pix_nest(256,th,phi)
ext2 = extmap[pix2]*extc
avsky = f[i]['avsky']
skysig = sqrt(avsky * gain + readnoise**2) / gain
zpscale = zeropointToScale(f[i]['ccdzpt'] + 2.5*log(f[i]['exptime'],10.))
skysig /= zpscale
psf_sigma = f[i]['fwhm'] / 2.35
avsky2 = f[j]['avsky']
skysig2 = sqrt(avsky2 * gain + readnoise**2) / gain
zpscale2 = zeropointToScale(f[j]['ccdzpt'] + 2.5*log(f[j]['exptime'],10.))
skysig2 /= zpscale2
psf_sigma2 = f[j]['fwhm'] / 2.35
# point-source depth
#psfnorm = 1./(2. * sqrt(pi) * psf_sigma) #1/Neff #for point source
#detsig1 = skysig / psfnorm
Np = ((4.*pi*psf_sigma**2.)**(1./p) + (8.91*(.45*arcsec2pix)**2. )**(1./p))**p #Neff in requirements doc
Np2 = ((4.*pi*psf_sigma2**2.)**(1./p) + (8.91*(.45*arcsec2pix)**2. )**(1./p))**p #Neff in requirements doc
Np = sqrt(Np) #square root necessary because Np gives sum of noise squared
Np2 = sqrt(Np2)
signalext = 1./10.**(-ext/2.5)
signalext2 = 1./10.**(-ext2/2.5)
detsig1 = skysig*Np #total noise
detsig2 = skysig2*Np2
detsigtot = sqrt(1./(1./(detsig1*signalext)**2.+1./(detsig2*signalext2)**2))
m = nanomaggiesToMag(detsigtot * 5.)-cor#-(ext+ext2)/2.
if m > 30 or m < 18:
print skysig,avsky,f[i]['fwhm'],f[i]['ccdzpt'],f[i]['exptime']
NTl.append(m)
if f[i]['exptime'] > emax:
emax = f[i]['exptime']
if f[i]['exptime'] < emin:
emin = f[i]['exptime']
n += 1.
msee += f[i]['seeing']
if m > recm:
nbr += 1.
msee = msee/n
print msee,n
print emin,emax
minN = min(NTl)
maxN = max(NTl)+.0001
print minN,maxN
hl = zeros((nbin))
for i in range(0,len(NTl)):
bin = int(nbin*(NTl[i]-minN)/(maxN-minN))
hl[bin] += 1
Nl = []
for i in range(0,len(hl)):
Nl.append(minN+i*(maxN-minN)/float(nbin)+0.5*(maxN-minN)/float(nbin))
NTl = array(NTl)
mean = sum(NTl)/float(len(NTl))
std = sqrt(sum(NTl**2.)/float(len(NTl))-mean**2.)
NTl.sort()
if len(NTl)/2. != len(NTl)/2:
med = NTl[len(NTl)/2+1]
else:
med = (NTl[len(NTl)/2+1]+NTl[len(NTl)/2])/2.
print mean,med,std
print 'percentage better than requirements '+str(nbr/float(nd))
from matplotlib.backends.backend_pdf import PdfPages
plt.clf()
pp = PdfPages(localdir+'validationplots/DR2DECaLS'+band+'2exposures.pdf')
plt.plot(Nl,hl,'k-')
plt.xlabel(r'5$\sigma$ '+band+ ' depth')
plt.ylabel('# of ccds')
plt.title('MC 2 exposure depth '+str(mean)[:5]+r'$\pm$'+str(std)[:4]+r', $f_{\rm pass}=$'+str(nbr/float(nd))[:5])
#plt.xscale('log')
pp.savefig()
pp.close()
return True
def plotMagMap(band,size=4):
import fitsio
from matplotlib import pyplot as plt
import matplotlib.cm as cm
from numpy import zeros,array
readnoise = 10. # e-; 7.0 to 15.0 according to DECam Data Handbook
p = 1.15 #value given in imaging requirements
gain = 4.0 #from Dustin
f = fitsio.read(dir+'/legacypipe-dir/decals-ccds.fits.gz')
NTl = []
emin = 1000
emax = 0
msee = 0
n = 0
arcsec2pix = 1./.262 #from Dustin
if band == 'g':
zp0 = 25.08
recm = 24.
cor = 0.08
extc = 3.303/2.751
if band == 'r':
zp0 = 25.29
recm = 23.4
cor = .16
extc = 2.285/2.751
if band == 'z':
zp0 = 24.92
recm = 22.5
extc = 1.263/2.751
cor = .29
nd = 0
nbr = 0
ral = []
decl = []
for i in range(0,len(f)):
pid = f[i]['propid']
#if DS == '2014B-0404' or DS == '2013A-0741': #enforce DECaLS only
# DS = 1
DS = 0
year = int(f[i]['date_obs'].split('-')[0])
if year > 2014:
if pid == '2014B-0404' or pid == '2013A-0741':
DS = 1 #enforce 2015 data
if f[i]['filter'] == band:
if f[i]['seeing'] != 99 and f[i]['ccdzpt'] != 99 and f[i]['fwhm'] != 99 and DS == 1:
if f[i]['dec'] > -20 and f[i]['exptime'] >=30 and f[i]['ccdnmatch'] >= 20 and abs(f[i]['zpt'] - f[i]['ccdzpt']) <= 0.1 and f[i]['zpt'] >= zp0-.5 and f[i]['zpt'] <=zp0+.25:
ra,dec = f[i]['ra'],f[i]['dec']
th,phi = radec2thphi(ra,dec)
pix = hpix.ang2pix_nest(256,th,phi)
ext = extmap[pix]*extc
#if ext > 0.5:
# print ext,extc,ra,dec,pix
#break
avsky = f[i]['avsky']
skysig = sqrt(avsky * gain + readnoise**2) / gain
zpscale = zeropointToScale(f[i]['ccdzpt'] + 2.5*log(f[i]['exptime'],10.))
skysig /= zpscale
psf_sigma = f[i]['fwhm'] / 2.35
# point-source depth
#psfnorm = 1./(2. * sqrt(pi) * psf_sigma) #1/Neff #for point source
#detsig1 = skysig / psfnorm
Np = ((4.*pi*psf_sigma**2.)**(1./p) + (8.91*(.45*arcsec2pix)**2. )**(1./p))**p #Neff in requirements doc
Np = sqrt(Np) #square root necessary because Np gives sum of noise squared
detsig1 = skysig*Np #total noise
m = nanomaggiesToMag(detsig1 * 5.)-cor-ext
#if m > 30 or m < 18:
# print skysig,avsky,f[i]['fwhm'],f[i]['ccdzpt'],f[i]['exptime']
NTl.append(m)
ral.append(ra)
decl.append(dec)
if f[i]['exptime'] > emax:
emax = f[i]['exptime']
if f[i]['exptime'] < emin:
emin = f[i]['exptime']
n += 1.
msee += f[i]['seeing']
if m > recm:
nbr += 1.
from matplotlib.backends.backend_pdf import PdfPages
plt.clf()
pp = PdfPages(localdir+'validationplots/DR2DECaLS'+band+'map.pdf')
#col = (NTl-min(NTl))/(max(NTl)-min(NTl))
col = NTl
map = plt.scatter(ral,decl,c=col,s=size,cmap=cm.rainbow,lw=0)
cbar = plt.colorbar(map)
cbar.set_label('depth', rotation=270)
plt.xlabel('r.a. (degrees)')
plt.ylabel('declination (degrees)')
plt.title('Map of 1 exposure depth for DR2 '+band+'-band')
#plt.show()
#plt.xscale('log')
pp.savefig()
pp.close()
return True
def plotMaghist(band,nbin=100):
import fitsio
from matplotlib import pyplot as plt
from numpy import zeros,array
readnoise = 10. # e-; 7.0 to 15.0 according to DECam Data Handbook
p = 1.15 #value given in imaging requirements
gain = 4.0 #from Dustin
f = fitsio.read(dir+'/legacypipe-dir/decals-ccds.fits.gz')
NTl = []
emin = 1000
emax = 0
msee = 0
n = 0
arcsec2pix = 1./.262 #from Dustin
if band == 'g':
zp0 = 25.08
recm = 24.
cor = 0.07
extc = 3.303/2.751
if band == 'r':
zp0 = 25.29
recm = 23.4
cor = .12
extc = 2.285/2.751
if band == 'z':
zp0 = 24.92
recm = 22.5
extc = 1.263/2.751
cor = .18
nd = 0
nbr = 0
for i in range(0,len(f)):
pid = f[i]['propid']
#if DS == '2014B-0404' or DS == '2013A-0741': #enforce DECaLS only
# DS = 1
DS = 0
year = int(f[i]['date_obs'].split('-')[0])
if year > 2014:
if pid == '2014B-0404' or pid == '2013A-0741':
DS = 1 #enforce 2015 data
if f[i]['filter'] == band:
if f[i]['seeing'] != 99 and f[i]['ccdzpt'] != 99 and f[i]['fwhm'] != 99 and DS == 1:
if f[i]['dec'] > -20 and f[i]['exptime'] >=30 and f[i]['ccdnmatch'] >= 20 and abs(f[i]['zpt'] - f[i]['ccdzpt']) <= 0.1 and f[i]['zpt'] >= zp0-.5 and f[i]['zpt'] <=zp0+.25:
ra,dec = f[i]['ra'],f[i]['dec']
th,phi = radec2thphi(ra,dec)
pix = hpix.ang2pix_nest(256,th,phi)
ext = extmap[pix]*extc
#if ext > 0.5:
# print ext,extc,ra,dec,pix
#break
avsky = f[i]['avsky']
skysig = sqrt(avsky * gain + readnoise**2) / gain
zpscale = zeropointToScale(f[i]['ccdzpt'] + 2.5*log(f[i]['exptime'],10.))
skysig /= zpscale
psf_sigma = f[i]['fwhm'] / 2.35
# point-source depth
#psfnorm = 1./(2. * sqrt(pi) * psf_sigma) #1/Neff #for point source
#detsig1 = skysig / psfnorm
Np = ((4.*pi*psf_sigma**2.)**(1./p) + (8.91*(.45*arcsec2pix)**2. )**(1./p))**p #Neff in requirements doc
Np = sqrt(Np) #square root necessary because Np gives sum of noise squared
detsig1 = skysig*Np #total noise
signalext = 1./10.**(-ext/2.5)
#signalext = 1.
m = nanomaggiesToMag(detsig1 * 5.*signalext)-cor#-ext
#if m > 30 or m < 18:
# print skysig,avsky,f[i]['fwhm'],f[i]['ccdzpt'],f[i]['exptime']
NTl.append(m)
if f[i]['exptime'] > emax:
emax = f[i]['exptime']
if f[i]['exptime'] < emin:
emin = f[i]['exptime']
n += 1.
msee += f[i]['seeing']
if m > recm:
nbr += 1.
msee = msee/n
print msee,n
print emin,emax
minN = min(NTl)
maxN = max(NTl)+.0001
print minN,maxN
hl = zeros((nbin))
for i in range(0,len(NTl)):
bin = int(nbin*(NTl[i]-minN)/(maxN-minN))
hl[bin] += 1
Nl = []
for i in range(0,len(hl)):
Nl.append(minN+i*(maxN-minN)/float(nbin)+0.5*(maxN-minN)/float(nbin))
NTl = array(NTl)
mean = sum(NTl)/float(len(NTl))
std = sqrt(sum(NTl**2.)/float(len(NTl))-mean**2.)
NTl.sort()
if len(NTl)/2. != len(NTl)/2:
med = NTl[len(NTl)/2+1]
else:
med = (NTl[len(NTl)/2+1]+NTl[len(NTl)/2])/2.
print mean,med,std
print 'percentage better than requirements '+str(nbr/n)
from matplotlib.backends.backend_pdf import PdfPages
plt.clf()
pp = PdfPages(localdir+'validationplots/DR2DECaLS'+band+'1exposure.pdf')
plt.plot(Nl,hl,'k-')
plt.xlabel(r'5$\sigma$ '+band+ ' depth')
plt.ylabel('# of ccds')
plt.title('1 exposure depth '+str(mean)[:5]+r'$\pm$'+str(std)[:4]+r', $f_{\rm pass}=$'+str(nbr/float(n))[:5])
#plt.xscale('log')
pp.savefig()
pp.close()
return True
def plotMaghist_survey(band,ndraw = 1e5,nbin=100,magmin=0):
#This takes random selections of 1,2,3,4 and 5 exposures in a fraction matching the expected coverage
import fitsio
from matplotlib import pyplot as plt
from numpy import zeros,array
from random import random
Ftiles = [0.02,0.26,0.76,0.98] #cumulative fraction of area
readnoise = 10. # e-; 7.0 to 15.0 according to DECam Data Handbook
p = 1.15 #value given in imaging requirements
gain = 4.0 #from Dustin
f = fitsio.read(dir+'/legacypipe-dir/decals-ccds.fits.gz')
nr = len(f)
NTl = []
emin = 1000
emax = 0
msee = 0
n = 0
arcsec2pix = 1./.262 #from Dustin
if band == 'g':
zp0 = 25.08
recm = 24.
cor = 0.08
extc = 3.303/2.751
if band == 'r':
zp0 = 25.29
recm = 23.4
cor = .16
extc = 2.285/2.751
if band == 'z':
zp0 = 24.92
recm = 22.5
extc = 1.263/2.751
cor = .29
nd = 0
#for i in range(0,len(f)):
nbr = 0
nl = [] #to speed things up, first make list of noise, then do draws
extl = [] #same for extinction
for i in range(0,len(f)):
pid = f[i]['propid']
#if DS == '2014B-0404' or DS == '2013A-0741': #enforce DECaLS only
# DS = 1
DS = 0
year = int(f[i]['date_obs'].split('-')[0])
if year > 2014:
if pid == '2014B-0404' or pid == '2013A-0741':
DS = 1 #enforce 2015 data
if f[i]['filter'] == band:
if f[i]['seeing'] != 99 and f[i]['ccdzpt'] != 99 and f[i]['fwhm'] != 99 and DS == 1:
if f[i]['dec'] > -20 and f[i]['exptime'] >=30 and f[i]['ccdnmatch'] >= 20 and abs(f[i]['zpt'] - f[i]['ccdzpt']) <= 0.1 and f[i]['zpt'] >= zp0-.5 and f[i]['zpt'] <=zp0+.25:
ra,dec = f[i]['ra'],f[i]['dec']
th,phi = radec2thphi(ra,dec)
pix = hpix.ang2pix_nest(256,th,phi)
ext = extmap[pix]*extc
extl.append(ext)
avsky = f[i]['avsky']
skysig = sqrt(avsky * gain + readnoise**2) / gain
zpscale = zeropointToScale(f[i]['ccdzpt'] + 2.5*log(f[i]['exptime'],10.))
skysig /= zpscale
psf_sigma = f[i]['fwhm'] / 2.35
# point-source depth
#psfnorm = 1./(2. * sqrt(pi) * psf_sigma) #1/Neff #for point source
#detsig1 = skysig / psfnorm
Np = ((4.*pi*psf_sigma**2.)**(1./p) + (8.91*(.45*arcsec2pix)**2. )**(1./p))**p #Neff in requirements doc
Np = sqrt(Np) #square root necessary because Np gives sum of noise squared
detsig1 = skysig*Np #total noise
signalext = 1./10.**(-ext/2.5)
m = nanomaggiesToMag(detsig1 * 5.)-cor-ext
if m > magmin:
nl.append(detsig1*signalext)
ng = len(nl)
print ng
nbr3 = 0
nbr6 = 0
for nd in range(0,int(ndraw)):
ran = random()
if ran < Ftiles[0]:
nexp = 1
if ran > Ftiles[0] and ran < Ftiles[1]:
nexp = 2
if ran > Ftiles[1] and ran < Ftiles[2]:
nexp = 3
if ran > Ftiles[2] and ran < Ftiles[3]:
nexp = 4
if ran > Ftiles[3]:
nexp = 5
detsigtoti = 0
extsum = 0
for j in range(0,nexp):
k = int(random()*ng)
detsigtoti += 1./nl[k]**2.
extsum += extl[k]
extcorr = extsum/float(nexp)
detsigtot = sqrt(1./detsigtoti)
m = nanomaggiesToMag(detsigtot * 5.)-cor#-extcorr
if m > recm:
nbr += 1.
if m > recm-.3:
nbr3 += 1.
if m > recm-.6:
nbr6 += 1.
NTl.append(m)
n += 1.
minN = min(NTl)
maxN = max(NTl)+.0001
print minN,maxN
hl = zeros((nbin))
for i in range(0,len(NTl)):
bin = int(nbin*(NTl[i]-minN)/(maxN-minN))
hl[bin] += 1
Nl = []
for i in range(0,len(hl)):
Nl.append(minN+i*(maxN-minN)/float(nbin)+0.5*(maxN-minN)/float(nbin))
NTl = array(NTl)
mean = sum(NTl)/float(len(NTl))
std = sqrt(sum(NTl**2.)/float(len(NTl))-mean**2.)
nc = 0
for i in range(0,len(hl)):
nc += hl[i]
if nc > .1*nd:
m9 = Nl[i]
break
NTl.sort()
if len(NTl)/2. != len(NTl)/2:
med = NTl[len(NTl)/2+1]
else:
med = (NTl[len(NTl)/2+1]+NTl[len(NTl)/2])/2.
print mean,med,std
print 'percentage better than requirements '+str(nbr/float(nd))
from matplotlib.backends.backend_pdf import PdfPages
plt.clf()
pp = PdfPages(localdir+'validationplots/DR2DECaLS'+band+'_simsurvey.pdf')
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(Nl,hl,'k-')
ax.set_xlabel(r'5$\sigma$ '+band+ ' depth')
ax.set_ylabel('# of ccds')
ax.set_title('MC survey depth '+str(mean)[:5]+r'$\pm$'+str(std)[:4]+r', $f_{r}=$'+str(nbr/float(nd))[:5]+r', $f_{r-0.3}=$'+str(nbr3/float(nd))[:5]+r', $f_{r-0.6}=$'+str(nbr6/float(nd))[:5])
ax.text(.4,.9,r'90% depth '+str(m9)[:5], verticalalignment='bottom', horizontalalignment='right', transform=ax.transAxes,fontsize=15)
#plt.xscale('log')
pp.savefig()
pp.close()
return True
def compSOFMA():
fma = fitsio.read('/Users/ashleyross/Dropbox/DESY3/allgal22magauto_1.fits')
fs = fitsio.read('/Users/ashleyross/Dropbox/DESY3/allgal22_1.fits')
f1 = fitsio.read('/Users/ashleyross/Dropbox/DESY3/Y1mag22.fits')
facs = 5.3
facm = 6.
fac1 = 3.
print np.mean(fma['SOF_CM_MAG_CORRECTED_I']),np.mean(fma['MAG_AUTO_I']-1.569*fma['EBV_SFD98'])
print fma.size*facm,fs.size*facs,f1.size*fac1,f1.size
wa = (fma['SOF_CM_MAG_CORRECTED_I']-fma['SOF_CM_MAG_CORRECTED_Z'] +2.*(fma['SOF_CM_MAG_CORRECTED_R']-fma['SOF_CM_MAG_CORRECTED_I'])>1.7) & (fma['SOF_CM_MAG_CORRECTED_I']>17.5)
ws = (fs['SOF_CM_MAG_CORRECTED_I']-fs['SOF_CM_MAG_CORRECTED_Z'] +2.*(fs['SOF_CM_MAG_CORRECTED_R']-fs['SOF_CM_MAG_CORRECTED_I'])>1.7) & (fs['SOF_CM_MAG_CORRECTED_I']>17.5)
w1 = (f1['MAG_AUTO_I']-f1['MAG_AUTO_Z'] +2.*(f1['MAG_AUTO_R']-f1['MAG_AUTO_I'])>1.7) & (f1['MAG_AUTO_I'] >17.5)
print fma[wa].size*6.,fs[ws].size*facs,f1[w1].size*fac1,f1[w1].size
wa = (fma['SOF_CM_MAG_CORRECTED_I']-fma['SOF_CM_MAG_CORRECTED_Z'] +2.*(fma['SOF_CM_MAG_CORRECTED_R']-fma['SOF_CM_MAG_CORRECTED_I'])>1.7) & (fma['SOF_CM_MAG_CORRECTED_I']>17.5)\
& ((fma['SOF_CM_MAG_CORRECTED_G'] - fma['SOF_CM_MAG_CORRECTED_R'])>-1.) & ((fma['SOF_CM_MAG_CORRECTED_G'] - fma['SOF_CM_MAG_CORRECTED_R'])<3.)
ws = (fs['SOF_CM_MAG_CORRECTED_I']-fs['SOF_CM_MAG_CORRECTED_Z'] +2.*(fs['SOF_CM_MAG_CORRECTED_R']-fs['SOF_CM_MAG_CORRECTED_I'])>1.7) & (fs['SOF_CM_MAG_CORRECTED_I']>17.5)\
& ((fs['SOF_CM_MAG_CORRECTED_G'] - fs['SOF_CM_MAG_CORRECTED_R'])>-1.) & ((fs['SOF_CM_MAG_CORRECTED_G'] - fs['SOF_CM_MAG_CORRECTED_R'])<3.)
w1 = (f1['MAG_AUTO_I']-f1['MAG_AUTO_Z'] +2.*(f1['MAG_AUTO_R']-f1['MAG_AUTO_I'])>1.7) & (f1['MAG_AUTO_I'] >17.5)\
& ((f1['MAG_AUTO_G'] - f1['MAG_AUTO_R'])>-1.) & ((f1['MAG_AUTO_G'] - f1['MAG_AUTO_R'])<3.)
print fma[wa].size*6.,fs[ws].size*facs,f1[w1].size*fac1,f1[w1].size
wa = (fma['SOF_CM_MAG_CORRECTED_I']-fma['SOF_CM_MAG_CORRECTED_Z'] +2.*(fma['SOF_CM_MAG_CORRECTED_R']-fma['SOF_CM_MAG_CORRECTED_I'])>1.7) & (fma['SOF_CM_MAG_CORRECTED_I']>17.5)\
& ((fma['SOF_CM_MAG_CORRECTED_G'] - fma['SOF_CM_MAG_CORRECTED_R'])>-1.) & ((fma['SOF_CM_MAG_CORRECTED_G'] - fma['SOF_CM_MAG_CORRECTED_R'])<3.)\
& ((fma['SOF_CM_MAG_CORRECTED_R'] - fma['SOF_CM_MAG_CORRECTED_I'])>-1.) & ((fma['SOF_CM_MAG_CORRECTED_R'] - fma['SOF_CM_MAG_CORRECTED_I'])<2.5)
ws = (fs['SOF_CM_MAG_CORRECTED_I']-fs['SOF_CM_MAG_CORRECTED_Z'] +2.*(fs['SOF_CM_MAG_CORRECTED_R']-fs['SOF_CM_MAG_CORRECTED_I'])>1.7) & (fs['SOF_CM_MAG_CORRECTED_I']>17.5)\
& ((fs['SOF_CM_MAG_CORRECTED_G'] - fs['SOF_CM_MAG_CORRECTED_R'])>-1.) & ((fs['SOF_CM_MAG_CORRECTED_G'] - fs['SOF_CM_MAG_CORRECTED_R'])<3.)\
& ((fs['SOF_CM_MAG_CORRECTED_R'] - fs['SOF_CM_MAG_CORRECTED_I'])>-1.) & ((fs['SOF_CM_MAG_CORRECTED_R'] - fs['SOF_CM_MAG_CORRECTED_I'])<2.5)
w1 = (f1['MAG_AUTO_I']-f1['MAG_AUTO_Z'] +2.*(f1['MAG_AUTO_R']-f1['MAG_AUTO_I'])>1.7) & (f1['MAG_AUTO_I'] >17.5)\
& ((f1['MAG_AUTO_G'] - f1['MAG_AUTO_R'])>-1.) & ((f1['MAG_AUTO_G'] - f1['MAG_AUTO_R'])<3.)\
& ((f1['MAG_AUTO_R'] - f1['MAG_AUTO_I'])>-1.) & ((f1['MAG_AUTO_R'] - f1['MAG_AUTO_I'])<2.5)
print fma[wa].size*6.,fs[ws].size*facs,f1[w1].size*fac1,f1[w1].size
wa = (fma['SOF_CM_MAG_CORRECTED_I']-fma['SOF_CM_MAG_CORRECTED_Z'] +2.*(fma['SOF_CM_MAG_CORRECTED_R']-fma['SOF_CM_MAG_CORRECTED_I'])>1.7) & (fma['SOF_CM_MAG_CORRECTED_I']>17.5)\
& ((fma['SOF_CM_MAG_CORRECTED_G'] - fma['SOF_CM_MAG_CORRECTED_R'])>-1.) & ((fma['SOF_CM_MAG_CORRECTED_G'] - fma['SOF_CM_MAG_CORRECTED_R'])<3.)\
& ((fma['SOF_CM_MAG_CORRECTED_R'] - fma['SOF_CM_MAG_CORRECTED_I'])>-1.) & ((fma['SOF_CM_MAG_CORRECTED_R'] - fma['SOF_CM_MAG_CORRECTED_I'])<2.5)\
& ((fma['SOF_CM_MAG_CORRECTED_I'] - fma['SOF_CM_MAG_CORRECTED_Z'])>-1.) & ((fma['SOF_CM_MAG_CORRECTED_I'] - fma['SOF_CM_MAG_CORRECTED_Z'])<2.)
ws = (fs['SOF_CM_MAG_CORRECTED_I']-fs['SOF_CM_MAG_CORRECTED_Z'] +2.*(fs['SOF_CM_MAG_CORRECTED_R']-fs['SOF_CM_MAG_CORRECTED_I'])>1.7) & (fs['SOF_CM_MAG_CORRECTED_I']>17.5)\
& ((fs['SOF_CM_MAG_CORRECTED_G'] - fs['SOF_CM_MAG_CORRECTED_R'])>-1.) & ((fs['SOF_CM_MAG_CORRECTED_G'] - fs['SOF_CM_MAG_CORRECTED_R'])<3.)\
& ((fs['SOF_CM_MAG_CORRECTED_R'] - fs['SOF_CM_MAG_CORRECTED_I'])>-1.) & ((fs['SOF_CM_MAG_CORRECTED_R'] - fs['SOF_CM_MAG_CORRECTED_I'])<2.5)\
& ((fs['SOF_CM_MAG_CORRECTED_I'] - fs['SOF_CM_MAG_CORRECTED_Z'])>-1.) & ((fs['SOF_CM_MAG_CORRECTED_I'] - fs['SOF_CM_MAG_CORRECTED_Z'])<2.)
f1 = fitsio.read('/Users/ashleyross/Dropbox/DESY3/Y1mag22_chbpz.fits')
w1 = (f1['mag_auto_i']-f1['mag_auto_z'] +2.*(f1['mag_auto_r']-f1['mag_auto_i'])>1.7) & (f1['mag_auto_i'] >17.5)\
& ((f1['mag_auto_g'] - f1['mag_auto_r'])>-1.) & ((f1['mag_auto_g'] - f1['mag_auto_r'])<3.)\
& ((f1['mag_auto_r'] - f1['mag_auto_i'])>-1.) & ((f1['mag_auto_r'] - f1['mag_auto_i'])<2.5)\
& ((f1['mag_auto_i'] - f1['mag_auto_z'])>-1.) & ((f1['mag_auto_i'] - f1['mag_auto_z'])<2.)
print fma[wa].size*6.,fs[ws].size*facs,f1[w1].size*fac1,f1[w1].size
fmaw = fma[wa]
fsw = fs[ws]
f1w = f1[w1]
maskf = open('/Users/ashleyross/DESY1/mask_Y1redBAO_mean_z_bpz_VFF_4096ring.dat')
npix = 12*4096*4096
mask = []
for i in range(0,npix):
mask.append(0)
for line in maskf:
pix = int(float(line.split()[0]))
mask[pix] = 1
ng = 0
ng3 = 0
for i in range(0,f1w.size):
ra,dec = f1w[i]['ra'],f1w[i]['dec']
th,phi = radec2thphi(ra,dec)
p = hp.ang2pix(4096,th,phi)
if mask[int(p)] == 1:
ng += 1.
for i in range(0,fsw.size):
ra,dec = fsw[i]['RA'],fsw[i]['DEC']
th,phi = radec2thphi(ra,dec)
p = hp.ang2pix(4096,th,phi)
if mask[int(p)] == 1:
ng3 += 1.
print ng,ng3
ws = (fsw['SOF_CM_MAG_CORRECTED_I']<19.+(3*fsw['DNF_ZMEAN_SOF']))
w1 = (f1w['mag_auto_i'] < 19.+(3.*f1w['mean_z_bpz']))
print fsw[ws].size*facs,f1w[w1].size*fac1,f1w[w1].size
ws = (fsw['SOF_CM_MAG_CORRECTED_I']<19.+(3*fsw['DNF_ZMEAN_SOF'])) & (fsw['DNF_ZMEAN_SOF'] > 0.6) & (fsw['DNF_ZMEAN_SOF'] < 1.)
w1 = (f1w['mag_auto_i'] < 19.+(3.*f1w['mean_z_bpz'])) & (f1w['mean_z_bpz']>0.6) & (f1w['mean_z_bpz']<1.)
print fsw[ws].size*facs,f1w[w1].size*fac1,f1w[w1].size
wa = (fma['DNF_ZMEAN_SOF'] > 0.6) & (fma['DNF_ZMEAN_SOF'] < 1.0)
ws = (fs['DNF_ZMEAN_SOF'] > 0.6) & (fs['DNF_ZMEAN_SOF'] < 1.0)
print fma[wa].size*6.,fs[ws].size*facs
wa = (fma['DNF_ZMEAN_SOF'] > 0.6) & (fma['DNF_ZMEAN_SOF'] < 1.0) & (fma['SOF_CM_MAG_CORRECTED_I']-fma['SOF_CM_MAG_CORRECTED_Z'] +2.*(fma['SOF_CM_MAG_CORRECTED_R']-fma['SOF_CM_MAG_CORRECTED_I'])>1.7)
ws = (fs['DNF_ZMEAN_SOF'] > 0.6) & (fs['DNF_ZMEAN_SOF'] < 1.0) & (fs['SOF_CM_MAG_CORRECTED_I']-fs['SOF_CM_MAG_CORRECTED_Z'] +2.*(fs['SOF_CM_MAG_CORRECTED_R']-fs['SOF_CM_MAG_CORRECTED_I'])>1.7)
print fma[wa].size*6.,fs[ws].size*facs
wa = (fma['DNF_ZMEAN_SOF'] > 0.6) & (fma['DNF_ZMEAN_SOF'] < 1.0) & (fma['SOF_CM_MAG_CORRECTED_I']-fma['SOF_CM_MAG_CORRECTED_Z'] +2.*(fma['SOF_CM_MAG_CORRECTED_R']-fma['SOF_CM_MAG_CORRECTED_I'])>1.7)\
& (fma['MAG_AUTO_I']-1.569*fma['EBV_SFD98'] <19.+(3*fma['DNF_ZMEAN_SOF']))
ws = (fs['DNF_ZMEAN_SOF'] > 0.6) & (fs['DNF_ZMEAN_SOF'] < 1.0) & (fs['SOF_CM_MAG_CORRECTED_I']-fs['SOF_CM_MAG_CORRECTED_Z'] +2.*(fs['SOF_CM_MAG_CORRECTED_R']-fs['SOF_CM_MAG_CORRECTED_I'])>1.7)\
& (fs['SOF_CM_MAG_CORRECTED_I']<19.+(3*fs['DNF_ZMEAN_SOF']))
print fma[wa].size*6.,fs[ws].size*facs
print np.mean(fma['SOF_CM_MAG_CORRECTED_I'][wa]),np.mean(fma['MAG_AUTO_I'][wa]-1.569*fma['EBV_SFD98'][wa])
def mkRMmap(zmin, zmax, res=4096, pixmin=0, pixmax=False, wm='', wo=''):
if pixmax == False:
npix = 12 * res * res - pixmin
else:
npix = 1 + pixmax - pixmin
pixl = np.zeros((npix))
f = fitsio.read(inputdir + samp + '_sample.fits.gz')
if wo != '':
fo = open('Y1redmagic' + str(zmin) + str(zmax) + wm + '.dat', 'w')
if wm != '':
sysb = wm.split('_')
if sysb[0] == 'seerw':
seemap = mkmap('r', 'FWHM_MEAN', pixmin=pixmin, pixmax=pixmax)
sys = sysb[1]
band = sysb[2]
b, m = findlinmb(band, sys, zmin, zmax)
if sys == 'maglimit3':
tp = '_'
pr = 1.
if sys == 'EXPTIME':
tp = '_total'
pr = 0.5
sysmap = mkmap(band, sys, tp, pixmin=pixmin, pixmax=pixmax, pr=pr)
else:
sys = sysb[0]
band = sysb[1]
b, m = findlinmb(band, sys, zmin, zmax)
if sys == 'maglimit3':
tp = '_'
pr = 1.
if sys == 'EXPTIME':
tp = '_total'
pr = 0.5
sysmap = mkmap(band, sys, tp, pixmin=pixmin, pixmax=pixmax, pr=pr)
A = 0.505
bs = 5.2
sig = .7
for i in range(0, len(f)):
z = f[i]['ZREDMAGIC']
w = 1.
if z > zmin and z <= zmax:
ra, dec = f[i]['RA'], f[i]['DEC']
th, phi = radec2thphi(ra, dec)
p = hp.ang2pix(res, th, phi) - pixmin
if wm != '':
if sysb[0] == 'seerw':
see = seemap[p]
sys = sysmap[p]
rsee = A * (1. - erf((see - bs) / sig))
rsys = m * sys + b
w = 1. / (rsee * rsys)
else:
sys = sysmap[p]
w = 1. / (m * sys + b)
pixl[p] += w
if wo != '':
fo.write(
str(f[i]['RA']) + ' ' + str(f[i]['DEC']) + ' ' + str(z) + ' ' +
str(w) + '\n')
fo.close()
return pixl
def mkRMmap(zmin,zmax,res=4096,pixmin=0,pixmax=False,wm='',wo=''):
if pixmax == False:
npix = 12*res*res- pixmin
else:
npix = 1+pixmax-pixmin
pixl = np.zeros((npix))
f = fitsio.read(inputdir+samp+'_sample.fits.gz')
if wo != '':
fo = open('Y1redmagic'+str(zmin)+str(zmax)+wm+'.dat','w')
if wm != '':
sysb = wm.split('_')
if sysb[0] == 'seerw':
seemap = mkmap('r','FWHM_MEAN',pixmin=pixmin,pixmax=pixmax)
sys = sysb[1]
band = sysb[2]
b,m = findlinmb(band,sys,zmin,zmax)
if sys == 'maglimit3':
tp = '_'
pr = 1.
if sys == 'EXPTIME':
tp = '_total'
pr = 0.5
sysmap = mkmap(band,sys,tp,pixmin=pixmin,pixmax=pixmax,pr=pr)
else:
sys = sysb[0]
band = sysb[1]
b,m = findlinmb(band,sys,zmin,zmax)
if sys == 'maglimit3':
tp = '_'
pr = 1.
if sys == 'EXPTIME':
tp = '_total'
pr = 0.5
sysmap = mkmap(band,sys,tp,pixmin=pixmin,pixmax=pixmax,pr=pr)
A = 0.505
bs = 5.2
sig = .7
for i in range(0,len(f)):
z = f[i]['ZREDMAGIC']
w = 1.
if z > zmin and z <= zmax:
ra,dec = f[i]['RA'],f[i]['DEC']
th,phi = radec2thphi(ra,dec)
p = hp.ang2pix(res,th,phi)-pixmin
if wm != '':
if sysb[0] == 'seerw':
see = seemap[p]
sys = sysmap[p]
rsee = A*(1.-erf((see-bs)/sig))
rsys = m*sys+b
w = 1./(rsee*rsys)
else:
sys = sysmap[p]
w = 1./(m*sys+b)
pixl[p] += w
if wo != '':
fo.write(str(f[i]['RA'])+' '+str(f[i]['DEC'])+' '+str(z)+' '+str(w)+'\n')
fo.close()
return pixl
def compSOFMA():
fma = fitsio.read('/Users/ashleyross/Dropbox/DESY3/allgal22magauto_1.fits')
fs = fitsio.read('/Users/ashleyross/Dropbox/DESY3/allgal22_1.fits')
f1 = fitsio.read('/Users/ashleyross/Dropbox/DESY3/Y1mag22.fits')
facs = 5.3
facm = 6.
fac1 = 3.
print np.mean(
fma['SOF_CM_MAG_CORRECTED_I']), np.mean(fma['MAG_AUTO_I'] -
1.569 * fma['EBV_SFD98'])
print fma.size * facm, fs.size * facs, f1.size * fac1, f1.size
wa = (fma['SOF_CM_MAG_CORRECTED_I'] - fma['SOF_CM_MAG_CORRECTED_Z'] + 2. *
(fma['SOF_CM_MAG_CORRECTED_R'] - fma['SOF_CM_MAG_CORRECTED_I']) >
1.7) & (fma['SOF_CM_MAG_CORRECTED_I'] > 17.5)
ws = (fs['SOF_CM_MAG_CORRECTED_I'] - fs['SOF_CM_MAG_CORRECTED_Z'] + 2. *
(fs['SOF_CM_MAG_CORRECTED_R'] - fs['SOF_CM_MAG_CORRECTED_I']) >
1.7) & (fs['SOF_CM_MAG_CORRECTED_I'] > 17.5)
w1 = (f1['MAG_AUTO_I'] - f1['MAG_AUTO_Z'] + 2. *
(f1['MAG_AUTO_R'] - f1['MAG_AUTO_I']) > 1.7) & (f1['MAG_AUTO_I'] >
17.5)
print fma[wa].size * 6., fs[ws].size * facs, f1[w1].size * fac1, f1[
w1].size
wa = (fma['SOF_CM_MAG_CORRECTED_I']-fma['SOF_CM_MAG_CORRECTED_Z'] +2.*(fma['SOF_CM_MAG_CORRECTED_R']-fma['SOF_CM_MAG_CORRECTED_I'])>1.7) & (fma['SOF_CM_MAG_CORRECTED_I']>17.5)\
& ((fma['SOF_CM_MAG_CORRECTED_G'] - fma['SOF_CM_MAG_CORRECTED_R'])>-1.) & ((fma['SOF_CM_MAG_CORRECTED_G'] - fma['SOF_CM_MAG_CORRECTED_R'])<3.)
ws = (fs['SOF_CM_MAG_CORRECTED_I']-fs['SOF_CM_MAG_CORRECTED_Z'] +2.*(fs['SOF_CM_MAG_CORRECTED_R']-fs['SOF_CM_MAG_CORRECTED_I'])>1.7) & (fs['SOF_CM_MAG_CORRECTED_I']>17.5)\
& ((fs['SOF_CM_MAG_CORRECTED_G'] - fs['SOF_CM_MAG_CORRECTED_R'])>-1.) & ((fs['SOF_CM_MAG_CORRECTED_G'] - fs['SOF_CM_MAG_CORRECTED_R'])<3.)
w1 = (f1['MAG_AUTO_I']-f1['MAG_AUTO_Z'] +2.*(f1['MAG_AUTO_R']-f1['MAG_AUTO_I'])>1.7) & (f1['MAG_AUTO_I'] >17.5)\
& ((f1['MAG_AUTO_G'] - f1['MAG_AUTO_R'])>-1.) & ((f1['MAG_AUTO_G'] - f1['MAG_AUTO_R'])<3.)
print fma[wa].size * 6., fs[ws].size * facs, f1[w1].size * fac1, f1[
w1].size
wa = (fma['SOF_CM_MAG_CORRECTED_I']-fma['SOF_CM_MAG_CORRECTED_Z'] +2.*(fma['SOF_CM_MAG_CORRECTED_R']-fma['SOF_CM_MAG_CORRECTED_I'])>1.7) & (fma['SOF_CM_MAG_CORRECTED_I']>17.5)\
& ((fma['SOF_CM_MAG_CORRECTED_G'] - fma['SOF_CM_MAG_CORRECTED_R'])>-1.) & ((fma['SOF_CM_MAG_CORRECTED_G'] - fma['SOF_CM_MAG_CORRECTED_R'])<3.)\
& ((fma['SOF_CM_MAG_CORRECTED_R'] - fma['SOF_CM_MAG_CORRECTED_I'])>-1.) & ((fma['SOF_CM_MAG_CORRECTED_R'] - fma['SOF_CM_MAG_CORRECTED_I'])<2.5)
ws = (fs['SOF_CM_MAG_CORRECTED_I']-fs['SOF_CM_MAG_CORRECTED_Z'] +2.*(fs['SOF_CM_MAG_CORRECTED_R']-fs['SOF_CM_MAG_CORRECTED_I'])>1.7) & (fs['SOF_CM_MAG_CORRECTED_I']>17.5)\
& ((fs['SOF_CM_MAG_CORRECTED_G'] - fs['SOF_CM_MAG_CORRECTED_R'])>-1.) & ((fs['SOF_CM_MAG_CORRECTED_G'] - fs['SOF_CM_MAG_CORRECTED_R'])<3.)\
& ((fs['SOF_CM_MAG_CORRECTED_R'] - fs['SOF_CM_MAG_CORRECTED_I'])>-1.) & ((fs['SOF_CM_MAG_CORRECTED_R'] - fs['SOF_CM_MAG_CORRECTED_I'])<2.5)
w1 = (f1['MAG_AUTO_I']-f1['MAG_AUTO_Z'] +2.*(f1['MAG_AUTO_R']-f1['MAG_AUTO_I'])>1.7) & (f1['MAG_AUTO_I'] >17.5)\
& ((f1['MAG_AUTO_G'] - f1['MAG_AUTO_R'])>-1.) & ((f1['MAG_AUTO_G'] - f1['MAG_AUTO_R'])<3.)\
& ((f1['MAG_AUTO_R'] - f1['MAG_AUTO_I'])>-1.) & ((f1['MAG_AUTO_R'] - f1['MAG_AUTO_I'])<2.5)
print fma[wa].size * 6., fs[ws].size * facs, f1[w1].size * fac1, f1[
w1].size
wa = (fma['SOF_CM_MAG_CORRECTED_I']-fma['SOF_CM_MAG_CORRECTED_Z'] +2.*(fma['SOF_CM_MAG_CORRECTED_R']-fma['SOF_CM_MAG_CORRECTED_I'])>1.7) & (fma['SOF_CM_MAG_CORRECTED_I']>17.5)\
& ((fma['SOF_CM_MAG_CORRECTED_G'] - fma['SOF_CM_MAG_CORRECTED_R'])>-1.) & ((fma['SOF_CM_MAG_CORRECTED_G'] - fma['SOF_CM_MAG_CORRECTED_R'])<3.)\
& ((fma['SOF_CM_MAG_CORRECTED_R'] - fma['SOF_CM_MAG_CORRECTED_I'])>-1.) & ((fma['SOF_CM_MAG_CORRECTED_R'] - fma['SOF_CM_MAG_CORRECTED_I'])<2.5)\
& ((fma['SOF_CM_MAG_CORRECTED_I'] - fma['SOF_CM_MAG_CORRECTED_Z'])>-1.) & ((fma['SOF_CM_MAG_CORRECTED_I'] - fma['SOF_CM_MAG_CORRECTED_Z'])<2.)
ws = (fs['SOF_CM_MAG_CORRECTED_I']-fs['SOF_CM_MAG_CORRECTED_Z'] +2.*(fs['SOF_CM_MAG_CORRECTED_R']-fs['SOF_CM_MAG_CORRECTED_I'])>1.7) & (fs['SOF_CM_MAG_CORRECTED_I']>17.5)\
& ((fs['SOF_CM_MAG_CORRECTED_G'] - fs['SOF_CM_MAG_CORRECTED_R'])>-1.) & ((fs['SOF_CM_MAG_CORRECTED_G'] - fs['SOF_CM_MAG_CORRECTED_R'])<3.)\
& ((fs['SOF_CM_MAG_CORRECTED_R'] - fs['SOF_CM_MAG_CORRECTED_I'])>-1.) & ((fs['SOF_CM_MAG_CORRECTED_R'] - fs['SOF_CM_MAG_CORRECTED_I'])<2.5)\
& ((fs['SOF_CM_MAG_CORRECTED_I'] - fs['SOF_CM_MAG_CORRECTED_Z'])>-1.) & ((fs['SOF_CM_MAG_CORRECTED_I'] - fs['SOF_CM_MAG_CORRECTED_Z'])<2.)
f1 = fitsio.read('/Users/ashleyross/Dropbox/DESY3/Y1mag22_chbpz.fits')
w1 = (f1['mag_auto_i']-f1['mag_auto_z'] +2.*(f1['mag_auto_r']-f1['mag_auto_i'])>1.7) & (f1['mag_auto_i'] >17.5)\
& ((f1['mag_auto_g'] - f1['mag_auto_r'])>-1.) & ((f1['mag_auto_g'] - f1['mag_auto_r'])<3.)\
& ((f1['mag_auto_r'] - f1['mag_auto_i'])>-1.) & ((f1['mag_auto_r'] - f1['mag_auto_i'])<2.5)\
& ((f1['mag_auto_i'] - f1['mag_auto_z'])>-1.) & ((f1['mag_auto_i'] - f1['mag_auto_z'])<2.)
print fma[wa].size * 6., fs[ws].size * facs, f1[w1].size * fac1, f1[
w1].size
fmaw = fma[wa]
fsw = fs[ws]
f1w = f1[w1]
maskf = open(
'/Users/ashleyross/DESY1/mask_Y1redBAO_mean_z_bpz_VFF_4096ring.dat')
npix = 12 * 4096 * 4096
mask = []
for i in range(0, npix):
mask.append(0)
for line in maskf:
pix = int(float(line.split()[0]))
mask[pix] = 1
ng = 0
ng3 = 0
for i in range(0, f1w.size):
ra, dec = f1w[i]['ra'], f1w[i]['dec']
th, phi = radec2thphi(ra, dec)
p = hp.ang2pix(4096, th, phi)
if mask[int(p)] == 1:
ng += 1.
for i in range(0, fsw.size):
ra, dec = fsw[i]['RA'], fsw[i]['DEC']
th, phi = radec2thphi(ra, dec)
p = hp.ang2pix(4096, th, phi)
if mask[int(p)] == 1:
ng3 += 1.
print ng, ng3
ws = (fsw['SOF_CM_MAG_CORRECTED_I'] < 19. + (3 * fsw['DNF_ZMEAN_SOF']))
w1 = (f1w['mag_auto_i'] < 19. + (3. * f1w['mean_z_bpz']))
print fsw[ws].size * facs, f1w[w1].size * fac1, f1w[w1].size
ws = (fsw['SOF_CM_MAG_CORRECTED_I'] < 19. + (3 * fsw['DNF_ZMEAN_SOF'])) & (
fsw['DNF_ZMEAN_SOF'] > 0.6) & (fsw['DNF_ZMEAN_SOF'] < 1.)
w1 = (f1w['mag_auto_i'] < 19. + (3. * f1w['mean_z_bpz'])) & (
f1w['mean_z_bpz'] > 0.6) & (f1w['mean_z_bpz'] < 1.)
print fsw[ws].size * facs, f1w[w1].size * fac1, f1w[w1].size
wa = (fma['DNF_ZMEAN_SOF'] > 0.6) & (fma['DNF_ZMEAN_SOF'] < 1.0)
ws = (fs['DNF_ZMEAN_SOF'] > 0.6) & (fs['DNF_ZMEAN_SOF'] < 1.0)
print fma[wa].size * 6., fs[ws].size * facs
wa = (fma['DNF_ZMEAN_SOF'] > 0.6) & (fma['DNF_ZMEAN_SOF'] < 1.0) & (
fma['SOF_CM_MAG_CORRECTED_I'] - fma['SOF_CM_MAG_CORRECTED_Z'] + 2. *
(fma['SOF_CM_MAG_CORRECTED_R'] - fma['SOF_CM_MAG_CORRECTED_I']) > 1.7)
ws = (fs['DNF_ZMEAN_SOF'] > 0.6) & (fs['DNF_ZMEAN_SOF'] < 1.0) & (
fs['SOF_CM_MAG_CORRECTED_I'] - fs['SOF_CM_MAG_CORRECTED_Z'] + 2. *
(fs['SOF_CM_MAG_CORRECTED_R'] - fs['SOF_CM_MAG_CORRECTED_I']) > 1.7)
print fma[wa].size * 6., fs[ws].size * facs
wa = (fma['DNF_ZMEAN_SOF'] > 0.6) & (fma['DNF_ZMEAN_SOF'] < 1.0) & (fma['SOF_CM_MAG_CORRECTED_I']-fma['SOF_CM_MAG_CORRECTED_Z'] +2.*(fma['SOF_CM_MAG_CORRECTED_R']-fma['SOF_CM_MAG_CORRECTED_I'])>1.7)\
& (fma['MAG_AUTO_I']-1.569*fma['EBV_SFD98'] <19.+(3*fma['DNF_ZMEAN_SOF']))
ws = (fs['DNF_ZMEAN_SOF'] > 0.6) & (fs['DNF_ZMEAN_SOF'] < 1.0) & (fs['SOF_CM_MAG_CORRECTED_I']-fs['SOF_CM_MAG_CORRECTED_Z'] +2.*(fs['SOF_CM_MAG_CORRECTED_R']-fs['SOF_CM_MAG_CORRECTED_I'])>1.7)\
& (fs['SOF_CM_MAG_CORRECTED_I']<19.+(3*fs['DNF_ZMEAN_SOF']))
print fma[wa].size * 6., fs[ws].size * facs
print np.mean(
fma['SOF_CM_MAG_CORRECTED_I'][wa]), np.mean(fma['MAG_AUTO_I'][wa] -
1.569 *
fma['EBV_SFD98'][wa])