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 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():
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():
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():
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():
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)