def main():
args = parse_args()
print('args = ', args)
# Make the work directory if it does not exist yet.
work = os.path.expanduser(args.work)
print('work dir = ', work)
try:
if not os.path.isdir(work):
os.makedirs(work)
except OSError as e:
print("Ignore OSError from makedirs(work):")
print(e)
pass
if args.use_psfex:
prefix = 'psfex'
else:
prefix = 'piff'
if args.file != '':
print('Read file ', args.file)
with open(args.file) as fin:
exps = [line.strip() for line in fin if line[0] != '#']
print('File included %d exposures' % len(exps))
else:
exps = args.exps
print('Explicit listing of %d exposures' % len(exps))
exps = sorted(exps)
keys = [
'ra', 'dec', 'x', 'y', 'mag', 'obs_e1', 'obs_e2', 'obs_T',
prefix + '_e1', prefix + '_e2', prefix + '_T'
]
data, bands, tilings = read_data(exps,
work,
keys,
limit_bands=args.bands,
prefix=prefix,
use_reserved=args.use_reserved,
frac=args.frac)
e1 = data['obs_e1']
e2 = data['obs_e2']
T = data['obs_T']
p_e1 = data[prefix + '_e1']
p_e2 = data[prefix + '_e2']
p_T = data[prefix + '_T']
de1 = e1 - p_e1
de2 = e2 - p_e2
dT = (T - p_T) / T
psf_whiskers(data['ccd'], data['x'], data['y'], e1, e2, T, de1, de2, dT)
psf_hex(data['ccd'], data['x'], data['y'], e1, e2, T, de1, de2, dT)
def main():
import sys
sys.path.insert(0, '/home/dfa/sobreira/alsina/alpha-beta-gamma/code/src')
import numpy as np
from read_psf_cats import read_data, toList, read_h5
import h5py as h
args = parse_args()
#Reading Metacal
galkeys = ['ra', 'dec', 'T', 'psf_T']
data_galaxies = read_h5(args.metacal_cat, 'catalog/metacal/unsheared', galkeys )
print("Total objects in catalog:", len(data_galaxies))
f = h.File(args.metacal_cat, 'r')
index = f['index']
select = np.array(index['select'])
data_galaxies = data_galaxies[select]
print("Total objects after masking", len(data_galaxies))
Tpsfgal = data_galaxies['psf_T']
Tgal = data_galaxies['T']
prior_gal = np.mean(Tpsfgal/Tgal)
priorgal_max = np.max(Tpsfgal/Tgal)
priorgal_min = np.min(Tpsfgal/Tgal)
print('prior_gal=', prior_gal)
print('prior_gal_min=', priorgal_min)
print('prior_gal_max=', priorgal_max)
#Reading Mike stars catalog
keys = ['ra', 'dec', 'obs_T', 'piff_T', 'mag']
exps = toList(args.exps_file)
data_stars, bands, tilings = read_data(exps, args.piff_cat , keys,
limit_bands=args.bands,
use_reserved=args.use_reserved)
print("Objects", len(data_stars))
data_stars = data_stars[data_stars['mag']<20]
print("Objects with magnitude <20", len(data_stars))
Tpsf = data_stars['piff_T']
Tstars = data_stars['obs_T']
prior = np.mean(Tpsf/Tstars)
prior_max = np.max(Tpsf/Tstars)
prior_min = np.min(Tpsf/Tstars)
print('prior_stars=', prior)
print('prior_stars_min=', prior_min)
print('prior_stars_max=', prior_max)
def main():
import sys
sys.path.insert(0, '/home/dfa/sobreira/alsina/alpha-beta-gamma/code/src')
#sys.path.insert(0, '/global/cscratch1/sd/alsina/alpha-beta-gamma/code/src')
import numpy as np
from read_psf_cats import read_data, toList
from run_rho import do_rho_stats
args = parse_args()
#Make directory where the ouput data will be
outpath = os.path.expanduser(args.outpath)
try:
if not os.path.exists(outpath):
os.makedirs(outpath)
except OSError:
if not os.path.exists(outpath): raise
#STATISTIC USING ONLY RESERVED STARS
keys = ['ra', 'dec','obs_e1', 'obs_e2', 'obs_T',
'piff_e1', 'piff_e2', 'piff_T', 'mag']
exps = toList(args.exps_file)
data_stars, bands, tilings = read_data(exps, args.piff_cat , keys,
limit_bands=args.bands,
use_reserved=args.use_reserved)
print("Objects", len(data_stars))
data_stars = data_stars[data_stars['mag']<20]
print("Objects with magnitude <20", len(data_stars))
meanra = np.mean(data_stars['ra'])
meandec = np.mean(data_stars['dec'])
patchstars = [];
patchstars.append((data_stars['ra']>meanra)&(data_stars['dec']>meandec))
patchstars.append((data_stars['ra']<meanra)&(data_stars['dec']>meandec))
patchstars.append((data_stars['ra']<meanra)&(data_stars['dec']<meandec))
patchstars.append((data_stars['ra']>meanra)&(data_stars['dec']<meandec))
for pat in range(4):
do_rho_stats(data_stars[patchstars[pat]], bands, tilings,
outpath, max_sep=300, sep_units='arcmin', name=
today + 'mod_epiff_magcut_sn'+ '_patch_' +
str(pat + 1) , bandcombo=args.bandcombo,
mod=args.mod, obs=args.obs, shapenoise=args.sn)
def read_somedata2(catalogpath, expolist):
from read_psf_cats import read_data
exps = load_explist(expolist)
exps = sorted(exps)
keys = ['use']
data, bands, tilings = read_data(exps,
catalogpath,
keys,
limit_bands='riz',
prefix='piff',
use_reserved=False,
frac=1.)
file_name = "stars2.fits"
print('Finished reading data')
write_fit(data, file_name)
def run_rhos(args, outpath ):
from read_psf_cats import read_data, toList
#STATISTIC USING ONLY RESERVED STARS
keys = ['ra', 'dec','obs_e1', 'obs_e2', 'obs_T',
'piff_e1', 'piff_e2', 'piff_T', 'mag']
exps = toList(args.exps_file)
data, bands, tilings = read_data(exps, args.piff_cat , keys,
limit_bands=args.bands,
use_reserved=args.use_reserved)
print("Objects", len(data))
data = data[data['mag']<20]
print("Objects with magnitude <20", len(data))
min_sep_list = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0]
for min_sep in min_sep_list:
stats = measure_rho(data, min_sep=min_sep, mod=args.mod, obs=args.obs)
stat_file = os.path.join(outpath, "rho_%f.json"%(min_sep))
write_stats(stat_file,*stats)
def main():
args = parse_args()
print('args = ', args)
# Make the work directory if it does not exist yet.
work = os.path.expanduser(args.work)
print('work dir = ', work)
try:
if not os.path.isdir(work):
os.makedirs(work)
except OSError as e:
print("Ignore OSError from makedirs(work):")
print(e)
pass
if args.use_psfex:
prefix = 'psfex'
else:
prefix = 'piff'
if args.file != '':
print('Read file ', args.file)
with open(args.file) as fin:
exps = [line.strip() for line in fin if line[0] != '#']
print('File included %d exposures' % len(exps))
else:
exps = args.exps
print('Explicit listing of %d exposures' % len(exps))
exps = sorted(exps)
keys = [
'ra', 'dec', 'x', 'y', 'obs_e1', 'obs_e2', 'obs_T', prefix + '_e1',
prefix + '_e2', prefix + '_T', 'mag'
]
if args.use_reserved:
all_stars = ''
else:
all_stars = '_all'
keys += ['obs_flag', prefix + '_flag']
out_file_name = os.path.join(
work, "psf_%s_%s%s.fits" % (args.tag, args.bands, all_stars))
data = None
if not args.write_data:
try:
data, bands, tilings = read_data_file(out_file_name)
except Exception as e:
print('Caught: ', e)
if data is None:
data, bands, tilings = read_data(exps,
work,
keys,
limit_bands=args.bands,
prefix=prefix,
use_reserved=args.use_reserved,
frac=args.frac)
if args.write_data:
write_data_file(data, out_file_name)
print('all bands = ', bands)
print('all tilings = ', tilings)
for band in bands:
print('n for band %s = ' % band, np.sum(data['band'] == band))
for til in tilings:
print('n for tiling %d = ' % til, np.sum(data['tiling'] == til))
gdata = np.where(data['band'] == 'g')[0]
rdata = np.where(data['band'] == 'r')[0]
idata = np.where(data['band'] == 'i')[0]
zdata = np.where(data['band'] == 'z')[0]
#odddata = np.where(data['tiling']%2 == 1)[0]
#evendata = np.where(data['tiling']%2 == 0)[0]
print('len(gdata) = ', len(gdata))
print('len(rdata) = ', len(rdata))
print('len(idata) = ', len(idata))
print('len(zdata) = ', len(zdata))
#print('len(odddata) = ',len(odddata))
#print('len(evendata) = ',len(evendata))
#bands = ['r', 'i']
do_canonical_stats(data,
bands,
tilings,
work,
max_mag=args.max_mag,
prefix=prefix,
opt=args.opt,
subtract_mean=args.subtract_mean,
do_rho0=args.do_rho0)
def main():
import sys
sys.path.insert(0, '/home/dfa/sobreira/alsina/alpha-beta-gamma/code/src')
#sys.path.insert(0, '/global/cscratch1/sd/alsina/alpha-beta-gamma/code/src')
import numpy as np
from read_psf_cats import read_data, toList, read_metacal
import h5py as h
args = parse_args()
#Make directory where the ouput data will be
outpath = os.path.expanduser(args.outpath)
try:
if not os.path.exists(outpath):
os.makedirs(outpath)
except OSError:
if not os.path.exists(outpath): raise
names = [
'JKR', 'ANGBIN', 'THETA', 'TAU0P', 'TAU0M', 'VAR_TAU0', 'TAU2P',
'TAU2M', 'VAR_TAU2', 'TAU5P', 'TAU5M', 'VAR_TAU5'
]
forms = [
'i4', 'i4', 'f8', 'f8', 'f8', 'f8', 'f8', 'f8', 'f8', 'f8', 'f8', 'f8'
]
dtype = dict(names=names, formats=forms)
nrows = 20
outdata = np.recarray((nrows, ), dtype=dtype)
#Reading Mike stars catalog
keys = [
'ra', 'dec', 'obs_e1', 'obs_e2', 'obs_T', 'piff_e1', 'piff_e2',
'piff_T', 'mag'
]
exps = toList(args.exps_file)
data_sam, bands, tilings = read_data(exps,
args.piff_cat,
keys,
limit_bands=args.bands,
use_reserved=args.use_reserved,
frac=0.01)
data_stars, bands, tilings = read_data(exps,
args.piff_cat,
keys,
limit_bands=args.bands,
use_reserved=args.use_reserved)
print("Objects", len(data_stars))
data_stars = data_stars[data_stars['mag'] < 20]
print("Objects with magnitude <20", len(data_stars))
if (args.tomo):
print('Starting Tomography!')
galkeys = ['ra', 'dec', 'e_1', 'e_2', 'R11', 'R22']
nbins = 4
for bin_c in range(nbins):
print('Starting bin!', bin_c)
data_gal = read_metacal(args.metacal_cat,
galkeys,
zbin=bin_c,
nz_source_file=args.nz_source)
njk = 4
##TODO generate km first an later finnearest,
jkindexes_gals = jk_kmeans(data_sam['ra'], data_sam['dec'],
data_gal['ra'], data_gal['dec'], njk)
for jkidx in range(njk):
print("running jackkniffe region", jkidx)
booljk = [jkindexes_gals != jkidx]
tau0, tau2, tau5 = measure_tau(data_stars,
data_gal[booljk],
mod=args.mod)
jkrarr = np.array([jkidx] * nrows)
angarr = np.arange(nrows)
thetaarr = np.exp(tau0.meanlogr)
tau0parr = tau0.xip
tau2parr = tau2.xip
tau5parr = tau5.xip
tau0marr = tau0.xim
tau2marr = tau2.xim
tau5marr = tau5.xim
vartau0arr = 2 * tau0.varxi
vartau2arr = 2 * tau2.varxi
vartau5arr = 2 * tau5.varxi
array_list = [
jkrarr,
angar,
thetaarr,
tau0parr,
tau0marr,
vartau0arr,
tau2parr,
tau2marr,
vartau2arr,
tau5parr,
tau5marr,
vartau5arr,
]
for array, name in zip(array_list, names):
outdata[name] = array
write_fit(
outdata, names, outpath + 'alltaus_4jk_' + str(bin_c + 1) +
'_' + str(bin_c + 1) + '.fits')
galkeys = ['ra', 'dec', 'e_1', 'e_2', 'R11', 'R22']
data_galaxies = read_metacal(args.metacal_cat, galkeys)
print("Total objects in catalog:", len(data_galaxies))
njk = 4
jkindexes_gals = jk_kmeans(data_sam['ra'], data_sam['dec'],
data_galaxies['ra'], data_galaxies['dec'], njk)
for jkidx in range(njk):
print("running jackkniffe region", jkidx)
booljk = [jkindexes_gals != jkidx]
tau0, tau2, tau5 = measure_tau(data_stars,
data_galaxies[booljk],
mod=args.mod)
jkrarr = np.array([jkidx] * nrows)
angarr = np.arange(nrows)
thetaarr = np.exp(tau0.meanlogr)
tau0parr = tau0.xip
tau2parr = tau2.xip
tau5parr = tau5.xip
tau0marr = tau0.xim
tau2marr = tau2.xim
tau5marr = tau5.xim
vartau0arr = 2 * tau0.varxi
vartau2arr = 2 * tau2.varxi
vartau5arr = 2 * tau5.varxi
array_list = [
jkrarr,
angarr,
thetaarr,
tau0parr,
tau0marr,
vartau0arr,
tau2parr,
tau2marr,
vartau2arr,
tau5parr,
tau5marr,
vartau5arr,
]
for array, name in zip(array_list, names):
outdata[name] = array
write_fit(outdata, names, outpath + args.filename)
def main():
args = parse_args()
print('args = ',args)
# Make the work directory if it does not exist yet.
work = os.path.expanduser(args.work)
print('work dir = ',work)
try:
if not os.path.isdir(work):
os.makedirs(work)
except OSError as e:
print("Ignore OSError from makedirs(work):")
print(e)
pass
if args.use_psfex:
prefix='psfex'
else:
prefix='piff'
if True:
if args.file != '':
print('Read file ',args.file)
with open(args.file) as fin:
exps = [ line.strip() for line in fin if line[0] != '#' ]
print('File included %d exposures'%len(exps))
else:
exps = args.exps
print('Explicit listing of %d exposures'%len(exps))
exps = sorted(exps)
keys = ['ra', 'dec', 'x', 'y', 'mag', 'obs_e1', 'obs_e2', 'obs_T',
prefix+'_e1', prefix+'_e2', prefix+'_T']
data, bands, tilings = read_data(exps, work, keys, limit_bands=args.bands, prefix=prefix,
use_reserved=args.use_reserved, frac=args.frac)
e1 = data['obs_e1']
e2 = data['obs_e2']
T = data['obs_T']
p_e1 = data[prefix+'_e1']
p_e2 = data[prefix+'_e2']
p_T = data[prefix+'_T']
de1 = e1-p_e1
de2 = e2-p_e2
dT = (T-p_T)/T
#psf_resid(data['mag'], de1, de2, dT)
psf_whiskers(data['ccd'], data['x'], data['y'], e1, e2, T, de1, de2, dT)
if False:
ngmix_data = get_ngmix_epoch_data()
gal_whiskers(*ngmix_data, filename='ngmix_whiskers.eps', title='ngmix')
ngmix_data2 = get_ngmix_epoch_data(use_gold=False)
evscol(*ngmix_data2, filename='ngmix_evscol.eps', title='ngmix')
ccd31 = ccd==31
ccd, x, y, e1, e2, s, w = ngmix_data
gal_whiskers(ccd[ccd31], x[ccd31], y[ccd31], e1[ccd31], e2[ccd31], s[ccd31], w[ccd31],
filename='ngmix_ccd31.eps', scale=5, auto_size=True, title='ccd31')
if False:
im3shape_data = get_im3shape_epoch_data()
gal_whiskers(*im3shape_data, filename='im3shape_whiskers.eps', title='im3shape')
im3shape_data2 = get_im3shape_epoch_data(use_gold=False)
evscol(*im3shape_data2, filename='im3shape_evscol.eps', title='im3shape')
def main():
matplotlib.use('Agg') # needs to be done before import pyplot
args = parse_args()
work = os.path.expanduser(args.work)
print('work dir = ', work)
outpath = os.path.expanduser(args.outpath)
try:
if not os.path.exists(outpath):
os.makedirs(outpath)
except OSError:
if not os.path.exists(outpath): raise
if args.file != '':
print('Read file ', args.file)
with open(args.file) as fin:
exps = [line.strip() for line in fin if line[0] != '#']
else:
exps = args.exps
exps = sorted(exps)
if args.use_psfex:
prefix = 'psfex'
else:
prefix = 'piff'
keys = [
'ra', 'dec', 'x', 'y', 'mag', 'obs_e1', 'obs_e2', 'obs_T', 'obs_flag',
prefix + '_e1', prefix + '_e2', prefix + '_T', prefix + '_flag'
]
data, bands, tilings = read_data(exps,
work,
keys,
limit_bands=args.bands,
prefix=prefix,
use_reserved=args.use_reserved,
frac=args.frac)
for bands in ['r', 'i', 'z', 'riz']:
this_data = data[np.in1d(data['band'], list(bands))]
if len(this_data) == 0:
print('No files with bands ', bands)
continue
used = this_data[prefix + '_flag'] & ~RESERVED == 0
#airmass = this_data['airmass']
#sky = this_data['sky']
#sigsky = this_data['sigsky']
#fwhm = this_data['fwhm']
#med_airmass = np.median(airmass)
#med_sky = np.median(sky)
#med_sigsky = np.median(sigsky)
#med_fwhm = np.median(fwhm)
#print('airmass: ',min(airmass),med_airmass,max(airmass))
#print('sky: ',min(sky),med_sky,max(sky))
#print('sigsky: ',min(sigsky),med_sigsky,max(sigsky))
#print('fwhm: ',min(fwhm),med_fwhm,max(fwhm))
ra = this_data['ra']
dec = this_data['dec']
x = this_data['x']
y = this_data['y']
m = this_data['mag']
print('full mag range = ', np.min(m), np.max(m))
print('used mag range = ', np.min(m[used]), np.max(m[used]))
e1 = this_data['obs_e1']
print('mean e1 = ', np.mean(e1))
e2 = this_data['obs_e2']
print('mean e2 = ', np.mean(e2))
T = this_data['obs_T']
print('mean s = ', np.mean(T))
pe1 = this_data[prefix + '_e1']
print('mean pe1 = ', np.mean(pe1))
pe2 = this_data[prefix + '_e2']
print('mean pe2 = ', np.mean(pe2))
pT = this_data[prefix + '_T']
print('mean pT = ', np.mean(pT))
print('min mag = ', np.min(m))
print('max mag = ', np.max(m))
print('mean T (used) = ', np.mean(T[used]))
print('mean e1 (used) = ', np.mean(e1[used]))
print('mean e2 (used) = ', np.mean(e2[used]))
print('mean pT (used) = ', np.mean(pT[used]))
print('mean pe1 (used) = ', np.mean(pe1[used]))
print('mean pe2 (used) = ', np.mean(pe2[used]))
de1 = e1 - pe1
de2 = e2 - pe2
dT = T - pT
print('mean dT (used) = ', np.mean(dT[used]))
print('mean de1 (used) = ', np.mean(de1[used]))
print('mean de2 (used) = ', np.mean(de2[used]))
if args.use_psfex:
min_mused = 0
else:
min_mused = np.min(m[used])
print('min_mused = ', min_mused)
bin_by_mag(m, dT, de1, de2, min_mused, bands, outpath)
bin_by_mag(m[used], dT[used], de1[used], de2[used], min_mused,
bands + '_used', outpath)
make_hist(dT, T, de1, de2, bands, outpath)
make_hist(dT[used], T[used], de1[used], de2[used], bands + '_used',
outpath)
def main():
matplotlib.use('Agg') # needs to be done before import pyplot
args = parse_args()
work = os.path.expanduser(args.work)
print('work dir = ',work)
if args.file != '':
print('Read file ',args.file)
with open(args.file) as fin:
exps = [ line.strip() for line in fin if line[0] != '#' ]
else:
exps = args.exps
exps = sorted(exps)
if args.use_psfex:
prefix = 'psfex'
else:
prefix = 'piff'
keys = ['ra', 'dec', 'x', 'y', 'mag', 'obs_e1', 'obs_e2', 'obs_T', 'obs_flag',
prefix+'_e1', prefix+'_e2', prefix+'_T', prefix+'_flag']
data, bands, tilings = read_data(exps, work, keys, limit_bands=args.bands, prefix=prefix,
use_reserved=args.use_reserved, frac=args.frac)
use_bands = band_combinations(args.bands)
for bands in use_bands:
this_data = data[np.in1d(data['band'], list(bands))]
if len(this_data) == 0:
print('No files with bands ',bands)
continue
used = this_data[prefix+'_flag'] & ~RESERVED == 0
#airmass = this_data['airmass']
#sky = this_data['sky']
#sigsky = this_data['sigsky']
#fwhm = this_data['fwhm']
#med_airmass = np.median(airmass)
#med_sky = np.median(sky)
#med_sigsky = np.median(sigsky)
#med_fwhm = np.median(fwhm)
#print('airmass: ',min(airmass),med_airmass,max(airmass))
#print('sky: ',min(sky),med_sky,max(sky))
#print('sigsky: ',min(sigsky),med_sigsky,max(sigsky))
#print('fwhm: ',min(fwhm),med_fwhm,max(fwhm))
ra = this_data['ra']
dec = this_data['dec']
x = this_data['x']
y = this_data['y']
m = this_data['mag']
print('full mag range = ',np.min(m),np.max(m))
print('used mag range = ',np.min(m[used]),np.max(m[used]))
e1 = this_data['obs_e1']
print('mean e1 = ',np.mean(e1))
e2 = this_data['obs_e2']
print('mean e2 = ',np.mean(e2))
T = this_data['obs_T']
print('mean s = ',np.mean(T))
pe1 = this_data[prefix+'_e1']
print('mean pe1 = ',np.mean(pe1))
pe2 = this_data[prefix+'_e2']
print('mean pe2 = ',np.mean(pe2))
pT = this_data[prefix+'_T']
print('mean pT = ',np.mean(pT))
print('min mag = ',np.min(m))
print('max mag = ',np.max(m))
print('mean T (used) = ',np.mean(T[used]))
print('mean e1 (used) = ',np.mean(e1[used]))
print('mean e2 (used) = ',np.mean(e2[used]))
print('mean pT (used) = ',np.mean(pT[used]))
print('mean pe1 (used) = ',np.mean(pe1[used]))
print('mean pe2 (used) = ',np.mean(pe2[used]))
de1 = e1 - pe1
de2 = e2 - pe2
dT = T - pT
print('mean dT (used) = ',np.mean(dT[used]))
print('mean de1 (used) = ',np.mean(de1[used]))
print('mean de2 (used) = ',np.mean(de2[used]))
if args.use_psfex:
min_mused = 0
else:
min_mused = np.min(m[used])
print('min_mused = ',min_mused)
bin_by_mag(m, dT, de1, de2, min_mused, bands)
bin_by_mag(m[used], dT[used], de1[used], de2[used], min_mused, bands+'_used')
make_hist(dT, T, de1, de2, bands)
make_hist(dT[used], T[used], de1[used], de2[used], bands+'_used')
def main():
import sys
sys.path.insert(0, '/home/dfa/sobreira/alsina/alpha-beta-gamma/code/src')
#sys.path.insert(0, '/global/cscratch1/sd/alsina/alpha-beta-gamma/code/src')
import numpy as np
from read_psf_cats import read_data, toList, read_metacal
from astropy.io import fits
import gc
args = parse_args()
#Make directory where the ouput data will be
outpath = os.path.expanduser(args.outpath)
try:
if not os.path.exists(outpath):
os.makedirs(outpath)
except OSError:
if not os.path.exists(outpath): raise
##Format of the fit file output
names = ['BIN1', 'BIN2', 'ANGBIN', 'VALUE', 'ANG']
forms = ['i4', 'i4', 'i4', 'f8', 'f8']
dtype = dict(names=names, formats=forms)
nrows = 20
outdata = np.recarray((nrows, ), dtype=dtype)
namesout = ['TAU0P', 'TAU2P', 'TAU5P', 'TAU0M', 'TAU2M', 'TAU5M']
#Reading Mike stars catalog
keys = [
'ra', 'dec', 'obs_e1', 'obs_e2', 'obs_T', 'piff_e1', 'piff_e2',
'piff_T', 'mag'
]
exps = toList(args.exps_file)
data_stars, bands, tilings = read_data(exps,
args.piff_cat,
keys,
limit_bands=args.bands,
use_reserved=args.use_reserved)
print("Objects", len(data_stars))
data_stars = data_stars[data_stars['mag'] < 20]
print("Objects with magnitude <20", len(data_stars))
del bands, tilings, exps, keys
gc.collect()
if (args.tomo):
print('Starting Tomography!')
galkeys = ['ra', 'dec', 'e_1', 'e_2', 'R11', 'R22']
nbins = 4
for bin_c in range(nbins):
print('Starting bin!', bin_c)
data_gal = read_metacal(args.metacal_cat,
galkeys,
zbin=bin_c,
nz_source_file=args.nz_source)
tau0, tau2, tau5 = measure_tau(data_stars, data_gal, mod=args.mod)
tau0parr = tau0.xip
tau2parr = tau2.xip
tau5parr = tau5.xip
tau0marr = tau0.xim
tau2marr = tau2.xim
tau5marr = tau5.xim
vartau0arr = 2 * tau0.varxi
vartau2arr = 2 * tau2.varxi
vartau5arr = 2 * tau5.varxi
taus = [tau0parr, tau2parr, tau5parr, tau0marr, tau2marr, tau5marr]
vares = [
vartau0arr, vartau2arr, vartau5arr, vartau0arr, vartau2arr,
vartau5arr
]
for i, nam in enumerate(namesout):
covmat = np.diag(vares[i])
hdu = fits.PrimaryHDU()
hdul = fits.HDUList([hdu])
covmathdu = fits.ImageHDU(covmat, name='COVMAT')
hdul.insert(1, covmathdu)
zangarray = np.exp(tau0.meanlogr)
valuearray = np.array(taus[i])
bin1array = np.array([bin_c] * nrows)
bin2array = np.array([bin_c] * nrows)
angbinarray = np.arange(nrows)
array_list = [
bin1array, bin2array, angbinarray, valuearray, zangarray
]
for array, name in zip(array_list, names):
outdata[name] = array
corrhdu = fits.BinTableHDU(outdata, name=nam)
hdul.insert(2, corrhdu)
hdul.writeto(outpath + nam + '_bin_' + str(bin_c) + '.fits',
overwrite=True)
else:
galkeys = ['ra', 'dec', 'e_1', 'e_2', 'R11', 'R22']
data_galaxies = read_metacal(args.metacal_cat, galkeys)
print("Total objects in catalog:", len(data_galaxies))
tau0, tau2, tau5 = measure_tau(data_stars, data_galaxies, mod=args.mod)
tau0marr = tau0.xim
tau2marr = tau2.xim
tau5marr = tau5.xim
tau0parr = tau0.xip
tau2parr = tau2.xip
tau5parr = tau5.xip
vartau0arr = 2 * tau0.varxi
vartau2arr = 2 * tau2.varxi
vartau5arr = 2 * tau5.varxi
taus = [tau0parr, tau2parr, tau5parr, tau0marr, tau2marr, tau5marr]
vares = [
vartau0arr, vartau2arr, vartau5arr, vartau0arr, vartau2arr,
vartau5arr
]
for i, nam in enumerate(namesout):
covmat = np.diag(vares[i])
hdu = fits.PrimaryHDU()
hdul = fits.HDUList([hdu])
covmathdu = fits.ImageHDU(covmat, name='COVMAT')
hdul.insert(1, covmathdu)
angarray = np.exp(tau0.meanlogr)
valuearray = np.array(taus[i])
bin1array = np.array([-999] * nrows)
bin2array = np.array([-999] * nrows)
angbinarray = np.arange(nrows)
array_list = [
bin1array, bin2array, angbinarray, valuearray, angarray
]
for array, name in zip(array_list, names):
outdata[name] = array
corrhdu = fits.BinTableHDU(outdata, name=nam)
hdul.insert(2, corrhdu)
hdul.writeto(outpath + nam + '.fits', overwrite=True)
def main():
matplotlib.use('Agg') # needs to be done before import pyplot
args = parse_args()
work = os.path.expanduser(args.work)
print('work dir = ', work)
if args.file != '':
print('Read file ', args.file)
with open(args.file) as fin:
exps = [line.strip() for line in fin if line[0] != '#']
else:
exps = args.exps
exps = sorted(exps)
if args.use_psfex:
prefix = 'psfex'
else:
prefix = 'piff'
keys = [
'ra', 'dec', 'x', 'y', 'mag', 'obs_e1', 'obs_e2', 'obs_T', 'obs_flag',
prefix + '_e1', prefix + '_e2', prefix + '_T', prefix + '_flag'
]
if not args.use_dat:
data, bands, tilings = read_data(exps,
work,
keys,
limit_bands=args.bands,
prefix=prefix,
use_reserved=args.use_reserved,
frac=args.frac)
use_bands = band_combinations(args.bands)
for bands in use_bands:
if not args.use_dat:
this_data = data[np.in1d(data['band'], list(bands))]
if len(this_data) == 0:
print('No files with bands ', bands)
continue
print('bands = ', bands)
print('unique flags = ', np.unique(this_data[prefix + '_flag']))
if args.use_reserved:
RESERVED = 65
used = this_data[prefix + '_flag'] & ~RESERVED == 0
else:
used = this_data[prefix + '_flag'] == 0
print('used = ', used)
print('unique used = ', np.unique(used))
ra = this_data['ra']
dec = this_data['dec']
x = this_data['x']
y = this_data['y']
m = this_data['mag']
print('full mag range = ', np.min(m), np.max(m))
print('used mag range = ', np.min(m[used]), np.max(m[used]))
e1 = this_data['obs_e1']
print('mean e1 = ', np.mean(e1))
e2 = this_data['obs_e2']
print('mean e2 = ', np.mean(e2))
T = this_data['obs_T']
print('mean s = ', np.mean(T))
pe1 = this_data[prefix + '_e1']
print('mean pe1 = ', np.mean(pe1))
pe2 = this_data[prefix + '_e2']
print('mean pe2 = ', np.mean(pe2))
pT = this_data[prefix + '_T']
print('mean pT = ', np.mean(pT))
print('min mag = ', np.min(m))
print('max mag = ', np.max(m))
print('mean T (used) = ', np.mean(T[used]))
print('mean e1 (used) = ', np.mean(e1[used]))
print('mean e2 (used) = ', np.mean(e2[used]))
print('mean pT (used) = ', np.mean(pT[used]))
print('mean pe1 (used) = ', np.mean(pe1[used]))
print('mean pe2 (used) = ', np.mean(pe2[used]))
de1 = e1 - pe1
de2 = e2 - pe2
dT = T - pT
print('mean dT (used) = ', np.mean(dT[used]))
print('mean de1 (used) = ', np.mean(de1[used]))
print('mean de2 (used) = ', np.mean(de2[used]))
if args.use_psfex:
min_mused = 0
elif args.use_dat:
# Save these by hand. TODO: put this in output file?
d = {'r': 15.0976, 'i': 15.2468, 'z': 14.8781, 'riz': 14.8781}
if not bands in d: continue
min_mused = d[bands]
else:
min_mused = np.min(m[used])
print('min_mused = ', min_mused)
if args.use_dat:
infile1 = 'dpsf_mag_' + bands + '.dat'
bin_data1 = read_bins(infile1)
infile2 = 'dpsf_mag_' + bands + '_used.dat'
bin_data2 = read_bins(infile2)
else:
bin_data1 = bin_by_mag(m, dT, de1, de2, min_mused)
outfile1 = 'dpsf_mag_' + bands + '.dat'
write_bins(bin_data1, outfile1)
bin_data2 = bin_by_mag(m[used], dT[used], de1[used], de2[used],
min_mused)
outfile2 = 'dpsf_mag_' + bands + '_used.dat'
write_bins(bin_data2, outfile2)
pdffile1 = 'dpsf_mag_' + bands + '.pdf'
plot_bins(bin_data1, pdffile1)
pdffile2 = 'dpsf_mag_' + bands + '_used.pdf'
plot_bins(bin_data2, pdffile2, min_mused)
def main():
args = parse_args()
print('args = ',args)
# Make the work directory if it does not exist yet.
work = os.path.expanduser(args.work)
print('work dir = ',work)
try:
if not os.path.isdir(work):
os.makedirs(work)
except OSError as e:
print("Ignore OSError from makedirs(work):")
print(e)
pass
if args.use_psfex:
prefix='psfex'
else:
prefix='piff'
if args.file != '':
print('Read file ',args.file)
with open(args.file) as fin:
exps = [ line.strip() for line in fin if line[0] != '#' ]
print('File included %d exposures'%len(exps))
else:
exps = args.exps
print('Explicit listing of %d exposures'%len(exps))
exps = sorted(exps)
keys = ['ra', 'dec', 'x', 'y', 'obs_e1', 'obs_e2', 'obs_T',
prefix+'_e1', prefix+'_e2', prefix+'_T', 'mag']
data, bands, tilings = read_data(exps, work, keys, limit_bands=args.bands, prefix=prefix,
use_reserved=args.use_reserved, frac=args.frac)
print('all bands = ',bands)
#print('all tilings = ',tilings)
#out_file_name = os.path.join(work, "psf_%s.fits"%args.tag)
#write_data(data, out_file_name)
for band in bands:
print('n for band %s = '%band, np.sum(data['band'] == band))
#for til in tilings:
#print('n for tiling %d = '%til, np.sum(data['tiling'] == til))
gdata = np.where(data['band'] == 'g')[0]
rdata = np.where(data['band'] == 'r')[0]
idata = np.where(data['band'] == 'i')[0]
zdata = np.where(data['band'] == 'z')[0]
#odddata = np.where(data['tiling']%2 == 1)[0]
#evendata = np.where(data['tiling']%2 == 0)[0]
print('len(gdata) = ',len(gdata))
print('len(rdata) = ',len(rdata))
print('len(idata) = ',len(idata))
print('len(zdata) = ',len(zdata))
#print('len(odddata) = ',len(odddata))
#print('len(evendata) = ',len(evendata))
#bands = ['r', 'i']
do_canonical_stats(data, bands, tilings, work, alt_tt=False, prefix=prefix, lucas=args.lucas)
def main():
args = parse_args()
print('args = ',args)
# Make the work directory if it does not exist yet.
work = os.path.expanduser(args.work)
print('work dir = ',work)
try:
if not os.path.isdir(work):
os.makedirs(work)
except OSError as e:
print("Ignore OSError from makedirs(work):")
print(e)
pass
if args.use_psfex:
prefix='psfex'
else:
prefix='piff'
if True:
if args.file != '':
print('Read file ',args.file)
with open(args.file) as fin:
exps = [ line.strip() for line in fin if line[0] != '#' ]
print('File included %d exposures'%len(exps))
else:
exps = args.exps
print('Explicit listing of %d exposures'%len(exps))
exps = sorted(exps)
keys = ['ra', 'dec', 'x', 'y', 'mag', 'obs_e1', 'obs_e2', 'obs_T',
prefix+'_e1', prefix+'_e2', prefix+'_T']
data, bands, tilings = read_data(exps, work, keys, limit_bands=args.bands, prefix=prefix,
use_reserved=args.use_reserved, frac=args.frac)
e1 = data['obs_e1']
e2 = data['obs_e2']
T = data['obs_T']
p_e1 = data[prefix+'_e1']
p_e2 = data[prefix+'_e2']
p_T = data[prefix+'_T']
de1 = e1-p_e1
de2 = e2-p_e2
dT = (T-p_T)/T
#psf_resid(data['mag'], de1, de2, dT)
psf_whiskers(data['ccd'], data['x'], data['y'], e1, e2, T, de1, de2, dT)
if False:
ngmix_data = get_ngmix_epoch_data()
gal_whiskers(*ngmix_data, filename='ngmix_whiskers.eps', title='ngmix')
ngmix_data2 = get_ngmix_epoch_data(use_gold=False)
evscol(*ngmix_data2, filename='ngmix_evscol.eps', title='ngmix')
ccd31 = ccd==31
ccd, x, y, e1, e2, s, w = ngmix_data
gal_whiskers(ccd[ccd31], x[ccd31], y[ccd31], e1[ccd31], e2[ccd31], s[ccd31], w[ccd31],
filename='ngmix_ccd31.eps', scale=5, auto_size=True, title='ccd31')
if False:
im3shape_data = get_im3shape_epoch_data()
gal_whiskers(*im3shape_data, filename='im3shape_whiskers.eps', title='im3shape')
im3shape_data2 = get_im3shape_epoch_data(use_gold=False)
evscol(*im3shape_data2, filename='im3shape_evscol.eps', title='im3shape')
def main():
import sys
sys.path.insert(0, '/home/dfa/sobreira/alsina/alpha-beta-gamma/code/src')
#sys.path.insert(0, '/global/cscratch1/sd/alsina/alpha-beta-gamma/code/src')
import numpy as np
from read_psf_cats import read_data, toList
args = parse_args()
#Make directory where the ouput data will be
outpath = os.path.expanduser(args.outpath)
try:
if not os.path.exists(outpath):
os.makedirs(outpath)
except OSError:
if not os.path.exists(outpath): raise
#STATISTIC USING ONLY RESERVED STARS
keys = [
'ra', 'dec', 'obs_e1', 'obs_e2', 'obs_T', 'piff_e1', 'piff_e2',
'piff_T', 'mag'
]
exps = toList(args.exps_file)
data_sam, bands, tilings = read_data(exps,
args.piff_cat,
keys,
limit_bands=args.bands,
use_reserved=args.use_reserved,
frac=0.01)
data, bands, tilings = read_data(exps,
args.piff_cat,
keys,
limit_bands=args.bands,
use_reserved=args.use_reserved)
print("Objects", len(data))
data = data[data['mag'] < 20]
print("Objects with magnitude <20", len(data))
names = [
'JKR', 'ANGBIN', 'THETA', 'RHO0P', 'RHO0M', 'VAR_RHO0', 'RHO1P',
'RHO1M', 'VAR_RHO1', 'RHO2P', 'RHO2M', 'VAR_RHO2', 'RHO3P', 'RHO3M',
'VAR_RHO3', 'RHO4P', 'RHO4M', 'VAR_RHO4', 'RHO5P', 'RHO5M', 'VAR_RHO5'
]
forms = [
'i4', 'i4', 'f8', 'f8', 'f8', 'f8', 'f8', 'f8', 'f8', 'f8', 'f8', 'f8',
'f8', 'f8', 'f8', 'f8', 'f8', 'f8', 'f8', 'f8', 'f8'
]
dtype = dict(names=names, formats=forms)
nrows = 20
outdata = np.recarray((nrows, ), dtype=dtype)
njk = 4
jkindexes = jk_kmeans(data_sam['ra'],
data_sam['dec'],
data['ra'],
data['dec'],
njk,
plot=True)
#print (jkindexes)
for jkidx in range(njk):
print("running jackkniffe region", jkidx)
rho0, rho1, rho2, rho3, rho4, rho5 = measure_rho(
data[jkindexes != jkidx], mod=args.mod, obs=args.obs)
jkrarr = np.array([jkidx] * nrows)
angarr = np.arange(nrows)
thetaarr = np.exp(rho0.meanlogr)
rho0marr = rho0.xim
rho1marr = rho1.xim
rho2marr = rho2.xim
rho3marr = rho3.xim
rho4marr = rho4.xim
rho5marr = rho5.xim
rho0parr = rho0.xip
rho1parr = rho1.xip
rho2parr = rho2.xip
rho3parr = rho3.xip
rho4parr = rho4.xip
rho5parr = rho5.xip
varrho0arr = 2 * rho0.varxi
varrho1arr = 2 * rho1.varxi
varrho2arr = 2 * rho2.varxi
varrho3arr = 2 * rho3.varxi
varrho4arr = 2 * rho4.varxi
varrho5arr = 2 * rho5.varxi
array_list = [
jkrarr, angarr, thetaarr, rho0parr, rho0marr, varrho0arr, rho1parr,
rho1marr, varrho1arr, rho2parr, rho2marr, varrho2arr, rho3parr,
rho3marr, varrho3arr, rho4parr, rho4marr, varrho4arr, rho5parr,
rho5marr, varrho5arr
]
for array, name in zip(array_list, names):
outdata[name] = array
write_fit(outdata, names, outpath + args.filename)
def main():
import sys
sys.path.insert(0, '/home/dfa/sobreira/alsina/alpha-beta-gamma/code/src')
#sys.path.insert(0, '/global/cscratch1/sd/alsina/alpha-beta-gamma/code/src')
import numpy as np
from read_psf_cats import read_data, toList, read_h5
from run_rho import do_tau_stats
import h5py as h
args = parse_args()
#Make directory where the ouput data will be
outpath = os.path.expanduser(args.outpath)
try:
if not os.path.exists(outpath):
os.makedirs(outpath)
except OSError:
if not os.path.exists(outpath): raise
#Reading metacal catalog
#galkeys = ['ra']
#blabla = read_h5(args.metacal_cat, 'catalog/metacal/sheared_1m', galkeys )
#Reading Mike stars catalog
keys = [
'ra', 'dec', 'obs_e1', 'obs_e2', 'obs_T', 'piff_e1', 'piff_e2',
'piff_T', 'mag'
]
exps = toList(args.exps_file)
data_stars, bands, tilings = read_data(exps,
args.piff_cat,
keys,
limit_bands=args.bands,
use_reserved=args.use_reserved)
print("Objects", len(data_stars))
data_stars = data_stars[data_stars['mag'] < 20]
print("Objects with magnitude <20", len(data_stars))
meanra = np.mean(data_stars['ra'])
meandec = np.mean(data_stars['dec'])
if (args.tomo):
#Make directory where the ouput data will be
ipath = os.path.join(args.outpath, 'tomo_taus')
outpath = os.path.expanduser(ipath)
try:
if not os.path.exists(outpath):
os.makedirs(outpath)
except OSError:
if not os.path.exists(outpath): raise
print('Starting Tomography!')
galkeys = ['ra', 'dec', 'e_1', 'e_2', 'R11', 'R22']
data_gal = read_h5(args.metacal_cat, 'catalog/metacal/unsheared',
galkeys)
print("Total objects in catalog:", len(data_gal))
dgamma = 2 * 0.01
f = h.File(args.metacal_cat, 'r')
index = f['index']
select = np.array(index['select'])
select_1p = np.array(index['select_1p'])
select_1m = np.array(index['select_1m'])
select_2p = np.array(index['select_2p'])
select_2m = np.array(index['select_2m'])
n = h.File(args.nz_source, 'r')
zbin_array = np.array(n['nofz/zbin'])
nbins = 4
for bin_c in range(nbins):
print('Starting bin!', bin_c)
ind = np.where(zbin_array == bin_c)[0]
ind_1p = np.where(np.array(n['nofz/zbin_1p']) == bin_c)
ind_1m = np.where(np.array(n['nofz/zbin_1m']) == bin_c)
ind_2p = np.where(np.array(n['nofz/zbin_2p']) == bin_c)
ind_2m = np.where(np.array(n['nofz/zbin_2m']) == bin_c)
R11s = (data_gal['e_1'][select_1p][ind_1p].mean() -
data_gal['e_1'][select_1m][ind_1m].mean()) / dgamma
R22s = (data_gal['e_2'][select_2p][ind_2p].mean() -
data_gal['e_2'][select_2m][ind_2m].mean()) / dgamma
Rs = [R11s, R22s]
patchstars = []
patchgal = []
patchstars.append((data_stars['ra'] > meanra)
& (data_stars['dec'] > meandec))
patchstars.append((data_stars['ra'] < meanra)
& (data_stars['dec'] > meandec))
patchstars.append((data_stars['ra'] < meanra)
& (data_stars['dec'] < meandec))
patchstars.append((data_stars['ra'] > meanra)
& (data_stars['dec'] < meandec))
patchgal.append((data_gal[select][ind]['ra'] > meanra)
& (data_gal[select][ind]['dec'] > meandec))
patchgal.append((data_gal[select][ind]['ra'] < meanra)
& (data_gal[select][ind]['dec'] > meandec))
patchgal.append((data_gal[select][ind]['ra'] < meanra)
& (data_gal[select][ind]['dec'] < meandec))
patchgal.append((data_gal[select][ind]['ra'] > meanra)
& (data_gal[select][ind]['dec'] < meandec))
for pat in range(4):
patchstarbool = patchstars[pat]
data_starsaux = data_stars[patchstarbool]
patchgalbool = patchgal[pat]
do_tau_stats(data_gal[select][ind][patchgalbool],
Rs,
data_starsaux,
bands,
tilings,
outpath,
max_sep=300,
sep_units='arcmin',
name=today + 'mod_bin_' + str(bin_c + 1) + '_' +
str(bin_c + 1) + '_patch_' + str(pat + 1),
bandcombo=args.bandcombo,
mod=args.mod,
shapenoise=args.sn)
galkeys = ['ra', 'dec', 'e_1', 'e_2', 'R11', 'R22']
data_gal = read_h5(args.metacal_cat, 'catalog/metacal/unsheared', galkeys)
print("Total objects in catalog:", len(data_gal))
dgamma = 2 * 0.01
f = h.File(args.metacal_cat, 'r')
index = f['index']
select = np.array(index['select'])
select_1p = np.array(index['select_1p'])
select_1m = np.array(index['select_1m'])
select_2p = np.array(index['select_2p']) #added by Lucas:
select_2m = np.array(index['select_2m']) #added by Lucas
R11s = (data_gal['e_1'][select_1p].mean() -
data_gal['e_1'][select_1m].mean()) / dgamma
R22s = (data_gal['e_2'][select_2p].mean() -
data_gal['e_2'][select_2m].mean()) / dgamma
#added by Lucas: modified to to select_2p and 2m
Rs = [R11s, R22s]
print("Total objects after masking", len(data_gal))
print("R11s=", R11s)
print("R22s=", R22s)
patchstars = []
patchgal = []
patchstars.append((data_stars['ra'] > meanra)
& (data_stars['dec'] > meandec))
patchstars.append((data_stars['ra'] < meanra)
& (data_stars['dec'] > meandec))
patchstars.append((data_stars['ra'] < meanra)
& (data_stars['dec'] < meandec))
patchstars.append((data_stars['ra'] > meanra)
& (data_stars['dec'] < meandec))
patchgal.append((data_gal[select]['ra'] > meanra)
& (data_gal[select]['dec'] > meandec))
patchgal.append((data_gal[select]['ra'] < meanra)
& (data_gal[select]['dec'] > meandec))
patchgal.append((data_gal[select]['ra'] < meanra)
& (data_gal[select]['dec'] < meandec))
patchgal.append((data_gal[select]['ra'] > meanra)
& (data_gal[select]['dec'] < meandec))
for pat in range(4):
patchstarbool = patchstars[pat]
data_starsaux = data_stars[patchstarbool]
patchgalbool = patchgal[pat]
do_tau_stats(data_gal[select][patchgalbool],
Rs,
data_starsaux,
bands,
tilings,
outpath,
max_sep=300,
sep_units='arcmin',
name=today + 'mod_bin_' + 'patch_' + str(pat + 1),
bandcombo=args.bandcombo,
mod=args.mod,
shapenoise=args.sn)
def main():
args = parse_args()
print('args = ',args)
# Make the work directory if it does not exist yet.
work = os.path.expanduser(args.work)
print('work dir = ',work)
try:
if not os.path.isdir(work):
os.makedirs(work)
except OSError as e:
print("Ignore OSError from makedirs(work):")
print(e)
pass
if args.use_psfex:
prefix='psfex'
else:
prefix='piff'
if args.file != '':
print('Read file ',args.file)
with open(args.file) as fin:
exps = [ line.strip() for line in fin if line[0] != '#' ]
print('File included %d exposures'%len(exps))
else:
exps = args.exps
print('Explicit listing of %d exposures'%len(exps))
exps = sorted(exps)
keys = ['ra', 'dec', 'x', 'y', 'obs_e1', 'obs_e2', 'obs_T',
prefix+'_e1', prefix+'_e2', prefix+'_T', 'mag']
out_file_name = os.path.join(work, "psf_%s_%s.fits"%(args.tag, args.bands))
if not args.write_data:
try:
data, bands, tilings = read_data_file(out_file_name)
except Exception as e:
print('Caught: ',e)
data = None
if data is None:
data, bands, tilings = read_data(exps, work, keys,
limit_bands=args.bands, prefix=prefix,
use_reserved=args.use_reserved, frac=args.frac)
if args.write_data:
write_data_file(data, out_file_name)
print('all bands = ',bands)
print('all tilings = ',tilings)
for band in bands:
print('n for band %s = '%band, np.sum(data['band'] == band))
for til in tilings:
print('n for tiling %d = '%til, np.sum(data['tiling'] == til))
gdata = np.where(data['band'] == 'g')[0]
rdata = np.where(data['band'] == 'r')[0]
idata = np.where(data['band'] == 'i')[0]
zdata = np.where(data['band'] == 'z')[0]
#odddata = np.where(data['tiling']%2 == 1)[0]
#evendata = np.where(data['tiling']%2 == 0)[0]
print('len(gdata) = ',len(gdata))
print('len(rdata) = ',len(rdata))
print('len(idata) = ',len(idata))
print('len(zdata) = ',len(zdata))
#print('len(odddata) = ',len(odddata))
#print('len(evendata) = ',len(evendata))
#bands = ['r', 'i']
do_canonical_stats(data, bands, tilings, work,
max_mag=args.max_mag, prefix=prefix, lucas=args.lucas,
subtract_mean=args.subtract_mean, do_rho0=args.do_rho0)
def get_fields(bands, expsfile, catpath):
with open(expsfile) as fin:
exps = [line.strip() for line in fin if line[0] != '#']
exps = sorted(exps)
keys = [
'ra', 'dec', 'x', 'y', 'mag', 'obs_e1', 'obs_e2', 'obs_T', 'obs_flag',
prefix + '_e1', prefix + '_e2', prefix + '_T', prefix + '_flag'
]
prefix = 'piff'
work = os.path.expanduser(catpath)
print('work dir = ', work)
data, bands, tilings = read_data(exps,
work,
keys,
limit_bands=bands,
prefix=prefix,
use_reserved=args.use_reserved,
frac=args.frac)
bands = ['riz']
this_data = data[np.in1d(data['band'], list(bands))]
if len(this_data) == 0:
print('No files with bands ', bands)
raise Exception('No files with bands ', bands)
used = this_data[prefix + '_flag'] & ~RESERVED == 0
ra = this_data['ra']
dec = this_data['dec']
x = this_data['x']
y = this_data['y']
m = this_data['mag']
print('full mag range = ', np.min(m), np.max(m))
print('used mag range = ', np.min(m[used]), np.max(m[used]))
e1 = this_data['obs_e1']
print('mean e1 = ', np.mean(e1))
e2 = this_data['obs_e2']
print('mean e2 = ', np.mean(e2))
T = this_data['obs_T']
print('mean s = ', np.mean(T))
pe1 = this_data[prefix + '_e1']
print('mean pe1 = ', np.mean(pe1))
pe2 = this_data[prefix + '_e2']
print('mean pe2 = ', np.mean(pe2))
pT = this_data[prefix + '_T']
print('mean pT = ', np.mean(pT))
print('min mag = ', np.min(m))
print('max mag = ', np.max(m))
print('mean T (used) = ', np.mean(T[used]))
print('mean e1 (used) = ', np.mean(e1[used]))
print('mean e2 (used) = ', np.mean(e2[used]))
print('mean pT (used) = ', np.mean(pT[used]))
print('mean pe1 (used) = ', np.mean(pe1[used]))
print('mean pe2 (used) = ', np.mean(pe2[used]))
de1 = e1 - pe1
de2 = e2 - pe2
dT = T - pT
print('mean dT (used) = ', np.mean(dT[used]))
print('mean de1 (used) = ', np.mean(de1[used]))
print('mean de2 (used) = ', np.mean(de2[used]))
if args.use_psfex:
min_mused = 0
else:
min_mused = np.min(m[used])
print('min_mused = ', min_mused)
return m[used], dT[used], de1[used], de2[used], min_mused
def main():
import sys
sys.path.insert(0, '/home/dfa/sobreira/alsina/alpha-beta-gamma/code/src')
#sys.path.insert(0, '/global/cscratch1/sd/alsina/alpha-beta-gamma/code/src')
import numpy as np
from read_psf_cats import read_data, toList
from astropy.io import fits
args = parse_args()
#Make directory where the ouput data will be
outpath = os.path.expanduser(args.outpath)
try:
if not os.path.exists(outpath):
os.makedirs(outpath)
except OSError:
if not os.path.exists(outpath): raise
#STATISTIC USING ONLY RESERVED STARS
keys = [
'ra', 'dec', 'obs_e1', 'obs_e2', 'obs_T', 'piff_e1', 'piff_e2',
'piff_T', 'mag'
]
exps = toList(args.exps_file)
data, bands, tilings = read_data(exps,
args.piff_cat,
keys,
limit_bands=args.bands,
use_reserved=args.use_reserved)
print("Objects", len(data))
data = data[data['mag'] < 20]
print("Objects with magnitude <20", len(data))
names = ['BIN1', 'BIN2', 'ANGBIN', 'VALUE', 'ANG']
forms = ['i4', 'i4', 'i4', 'f8', 'f8']
dtype = dict(names=names, formats=forms)
nrows = 20
outdata = np.recarray((nrows, ), dtype=dtype)
namesout = [
'RHO0P', 'RHO1P', 'RHO2P', 'RHO3P', 'RHO4P', 'RHO5P', 'RHO0M', 'RHO1M',
'RHO2M', 'RHO3M', 'RHO4M', 'RHO5M'
]
rho0, rho1, rho2, rho3, rho4, rho5 = measure_rho(data,
mod=args.mod,
obs=args.obs)
angarr = np.arange(nrows)
thetaarr = np.exp(rho0.meanlogr)
rho0parr = rho0.xip
rho1parr = rho1.xip
rho2parr = rho2.xip
rho3parr = rho3.xip
rho4parr = rho4.xip
rho5parr = rho5.xip
rho0marr = rho0.xim
rho1marr = rho1.xim
rho2marr = rho2.xim
rho3marr = rho3.xim
rho4marr = rho4.xim
rho5marr = rho5.xim
varrho0arr = 2 * rho0.varxi
varrho1arr = 2 * rho1.varxi
varrho2arr = 2 * rho2.varxi
varrho3arr = 2 * rho3.varxi
varrho4arr = 2 * rho4.varxi
varrho5arr = 2 * rho5.varxi
rhos = [
rho0parr, rho1parr, rho2parr, rho3parr, rho4parr, rho5parr, rho0marr,
rho1marr, rho2marr, rho3marr, rho4marr, rho5marr
]
vares = [
varrho0arr, varrho1arr, varrho2arr, varrho3arr, varrho4arr, varrho5arr,
varrho0arr, varrho1arr, varrho2arr, varrho3arr, varrho4arr, varrho5arr
]
for i, nam in enumerate(namesout):
covmat = np.diag(vares[i])
hdu = fits.PrimaryHDU()
hdul = fits.HDUList([hdu])
covmathdu = fits.ImageHDU(covmat, name='COVMAT')
hdul.insert(1, covmathdu)
angarray = np.exp(rho0.meanlogr)
valuearray = np.array(rhos[i])
bin1array = np.array([-999] * nrows)
bin2array = np.array([-999] * nrows)
angbinarray = np.arange(nrows)
array_list = [bin1array, bin2array, angbinarray, valuearray, angarray]
for array, name in zip(array_list, names):
outdata[name] = array
corrhdu = fits.BinTableHDU(outdata, name=nam)
hdul.insert(2, corrhdu)
hdul.writeto(outpath + nam + '.fits', overwrite=True)
