def find_point_sources(image,
seeing_pix=4,
threshold_sigma=3,
size_lim=5,
out_file=None,
plot=False):
"""sourceInIm takes a direct image and find all sources above some detection
threshold in it.
Used by find_sources_in_direct_image
Inputs:
image: an array representing the direct image
seeing_pix: seeing size in pixel
threshold_sigma: detection threshold in sigmas away from the standard deviation in background fluctuation
size_lim: the limit of stddev, in pixel, over which we don't accept the fit as a source
out_file: If defined then a .reg file for ds9 is created in the XY format.
"""
threshold = threshold_sigma * np.sqrt(np.var(np.nan_to_num(image)))
#get all cutouts
cutouts = pointFinder(image, seeing_pix, threshold)
#list to collect results
all_results = []
#loop through cutouts, fit gaussian to get position of sources
for i in cutouts:
#make cutouts from indices, then run fit_gaussian_to_cutout
if np.logical_and(
len(np.ravel(image[i])) < 200,
len(np.ravel(image[i])) >
10): #a legit cutout shouldn't be large
if plot:
plt.imshow(image[i], interpolation='none')
res = fit_gaussian_to_cutout(image[i], seeing_pix)
#get x y
x = (i[1].start) + res[0].x_mean.value
y = (i[0].start) + res[0].y_mean.value
x_stddev = res[0].x_stddev.value
y_stddev = res[0].y_stddev.value
#filter out bad fits. stddev should be of some reasonable number
if x_stddev < size_lim and y_stddev < size_lim:
all_results += [(y, x, y_stddev, x_stddev)]
else:
None
#print(i, ' is invalid.' )
#return cutouts
if out_file != None:
f = open(out_file, mode='w')
for i in all_results:
f.write(str(i[1] + 1) + ' ' + str(i[0] + 1) + '\n')
f.close()
return all_results
def putstar_me(gfluxmin, south=True):
if south == True:
fls = sfs
else:
fls = sfn
f = fitsio.read(fls[0])
w0 = starsel_sweep(f, gfluxmin)
fw = f[w0]
#dt = fw.dtype
dt = []
cols = ['BRICKID','OBJID','RA','DEC','DCHISQ','EBV','FLUX_G','FLUX_R','FLUX_Z','MW_TRANSMISSION_G','MW_TRANSMISSION_R','MW_TRANSMISSION_Z',\
'PSFDEPTH_G','PSFDEPTH_R','PSFDEPTH_Z','GAIA_PHOT_G_MEAN_MAG','GAIA_ASTROMETRIC_EXCESS_NOISE','MASKBITS']
for colname in cols:
dt.append((colname, fw.dtype[colname]))
new = np.empty(len(fw), dtype=dt)
for colname in cols:
new[colname][...] = fw[colname][...]
tm = new
#for i in range(1,10):
for i in range(1, len(fls)):
#try:
f = fitsio.read(fls[i])
w = starsel_sweep(f, gfluxmin)
fw = f[w]
new = np.empty(len(fw), dtype=dt)
for colname in cols:
new[colname][...] = fw[colname][...]
tmn = np.concatenate((tm, new), axis=0)
print(i, len(tmn))
tm = tmn
#except:
# print(i)
NS = 'south'
if south != True:
NS = 'north'
s = 0
es = ''
while s == 0:
outf = outdir + 'mysweeps/stars_gfluxg' + str(
gfluxmin) + '_' + NS + es + '.fits'
try:
fitsio.read(outf)
es += 'n'
print(es)
if len(es) > 10:
return 'es too long, probably a bug'
except:
s = 1
#tm = Table(tm,names=fi.dtype.names)
fits = fitsio.FITS(outf, 'rw')
fits.write(tm, names=cols, overwrite=True)
def puttype(type, south=True, ebvfac=1., Rv=3.1):
if south == True:
fls = sfs
else:
fls = sfn
f = fitsio.read(fls[0])
w0 = typesel(f, type, south=south, ebvfac=ebvfac, Rv=Rv)
fw = f[w0]
dt = fw.dtype
new = np.empty(len(fw), dtype=dt)
#cols = ['BRICKID','OBJID','RA','DEC','DCHISQ','EBV','FLUX_G','FLUX_R','FLUX_Z','MW_TRANSMISSION_G','MW_TRANSMISSION_R','MW_TRANSMISSION_Z',\
#'PSFDEPTH_G','PSFDEPTH_R','PSFDEPTH_Z','GAIA_PHOT_G_MEAN_MAG','GAIA_ASTROMETRIC_EXCESS_NOISE','MASKBITS']
#for colname in fw.dtype.names:
cols = fw.dtype.names
for colname in cols:
new[colname][...] = fw[colname][...]
tm = new
for i in range(1, len(fls)):
#try:
f = fitsio.read(fls[i])
w = typesel(f, type, south=south, ebvfac=ebvfac, Rv=Rv)
fw = f[w]
new = np.empty(len(fw), dtype=dt)
for colname in cols:
new[colname][...] = fw[colname][...]
tmn = np.concatenate((tm, new), axis=0)
print(i, len(tmn))
tm = tmn
#except:
# print(i)
NS = 'south'
if south != True:
NS = 'north'
s = 0
es = 'ebvfac' + str(ebvfac) + 'Rv' + str(Rv)
outf = outdir + 'mysweeps/' + type + 'dr8_' + NS + es + '.fits'
# while s == 0:
#
# try:
# fitsio.read(outf)
# es += 'n'
# print(es)
# if len(es) > 10:
# return 'es too long, probably a bug'
# except:
# s = 1
#tm = Table(tm,names=fi.dtype.names)
fits = fitsio.FITS(outf, 'rw')
fits.write(tm, names=dt.names, overwrite=True)
def fits(zl,pzl,zs,pzs,filename):
try:
os.remove(filename)
print 'Removing file ',filename
except OSError:
pass
fits=fio.FITS(filename,'rw')
hdr={'extname':'NZ_POSITION','NZDATA':True}
out=np.empty(np.shape(zl),dtype=[('Z_MID','f8')])
out['Z_MID']=zl
fits.write(out,header=hdr)
fits[-1].insert_column('Z_LOW',np.abs(zl-(zl[1]-zl[0])/2.))
fits[-1].insert_column('Z_HIGH',zl+(zl[1]-zl[0])/2.)
if 'notomo' in filename:
fits[-1].insert_column('BIN1',pzl)
else:
for i in range(len(pzl)):
fits[-1].insert_column('BIN'+str(i+1),pzl[i,:])
hdr={'extname':'NZ_SHEAR','NZDATA':True}
out=np.empty(np.shape(zs),dtype=[('Z_MID','f8')])
out['Z_MID']=zs
fits.write(out,header=hdr)
fits[-1].insert_column('Z_LOW',np.abs(zs-(zs[1]-zs[0])/2.))
fits[-1].insert_column('Z_HIGH',zs+(zs[1]-zs[0])/2.)
if 'notomo' in filename:
fits[-1].insert_column('BIN1',pzs)
else:
for i in range(len(pzs)):
fits[-1].insert_column('BIN'+str(i+1),pzs[i,:])
fits.close()
return
def sigma_l2fits(
filename,
nchains,
burnin,
path,
outname,
save=True,
):
"""
Converts c3-h5 dataset to fits for c1 BR and GBR estimator analysis.
ex. c3pp sigma-l2fits chains_v1/chain 5 10 cmb_sigma_l_GBRlike.fits
If "chain_c0001.h5", filename is cut to "chain" and will look in same directory for "chain_c*****.h5".
See comm_like_tools for further information about BR and GBR post processing
"""
click.echo("{:-^48}".format("Formatting sigma_l data to fits file"))
import h5py
if filename.endswith(".h5"):
filename = filename.rsplit("_", 1)[0]
temp = np.zeros(nchains)
for nc in range(1, nchains + 1):
with h5py.File(
filename + "_c" + str(nc).zfill(4) + ".h5",
"r",
) as f:
groups = list(f.keys())
temp[nc - 1] = len(groups)
nsamples_max = int(max(temp[:]))
click.echo(
f"Largest chain has {nsamples_max} samples, using burnin {burnin}\n")
for nc in range(1, nchains + 1):
fn = filename + "_c" + str(nc).zfill(4) + ".h5"
with h5py.File(
fn,
"r",
) as f:
click.echo(f"Reading {fn}")
groups = list(f.keys())
nsamples = len(groups)
if nc == 1:
dset = np.zeros((
nsamples_max + 1,
1,
len(f[groups[0] + "/" + path]),
len(f[groups[0] + "/" + path][0]),
))
nspec = len(f[groups[0] + "/" + path])
lmax = len(f[groups[0] + "/" + path][0]) - 1
else:
dset = np.append(
dset,
np.zeros((
nsamples_max + 1,
1,
nspec,
lmax + 1,
)),
axis=1,
)
click.echo(
f"Dataset: {path} \n# samples: {nsamples} \n# spectra: {nspec} \nlmax: {lmax}"
)
for i in range(nsamples):
for j in range(nspec):
dset[i + 1, nc - 1,
j, :] = np.asarray(f[groups[i] + "/" + path][j][:])
click.echo("")
# Optimize with jit?
ell = np.arange(lmax + 1)
for nc in range(1, nchains + 1):
for i in range(1, nsamples_max + 1):
for j in range(nspec):
dset[i, nc - 1,
j, :] = dset[i, nc - 1,
j, :] * ell[:] * (ell[:] + 1.0) / 2.0 / np.pi
dset[0, :, :, :] = nsamples - burnin
if save:
click.echo(f"Dumping fits file: {outname}")
dset = np.asarray(dset, dtype="f4")
from astropy.io import fits
head = fits.Header()
head["FUNCNAME"] = ("Gibbs sampled power spectra",
"Full function name")
head["LMAX"] = (lmax, "Maximum multipole moment")
head["NUMSAMP"] = (nsamples_max, "Number of samples")
head["NUMCHAIN"] = (nchains, "Number of independent chains")
head["NUMSPEC"] = (nspec, "Number of power spectra")
fits.writeto(outname, dset, head, overwrite=True)
# FITSIO Saving Deprecated (Use astropy)
if False:
import fitsio
fits = fitsio.FITS(
outname,
mode="rw",
clobber=True,
verbose=True,
)
h_dict = [
{
"name": "FUNCNAME",
"value": "Gibbs sampled power spectra",
"comment": "Full function name",
},
{
"name": "LMAX",
"value": lmax,
"comment": "Maximum multipole moment",
},
{
"name": "NUMSAMP",
"value": nsamples_max,
"comment": "Number of samples",
},
{
"name": "NUMCHAIN",
"value": nchains,
"comment": "Number of independent chains",
},
{
"name": "NUMSPEC",
"value": nspec,
"comment": "Number of power spectra",
},
]
fits.write(
dset[:, :, :, :],
header=h_dict,
clobber=True,
)
fits.close()
return dset
if rank == 0:
dt = [('g1p', 'f8'), ('g1m', 'f8'), ('g1', 'f8'), ('g2p', 'f8'),
('g2m', 'f8'), ('g2', 'f8')]
dplus0 = np.array(pres0, dtype=dt)
dminus0 = np.array(mres0, dtype=dt)
dplus1 = np.array(pres1, dtype=dt)
dminus1 = np.array(mres1, dtype=dt)
dplus2 = np.array(pres2, dtype=dt)
dminus2 = np.array(mres2, dtype=dt)
dplus3 = np.array(pres3, dtype=dt)
dminus3 = np.array(mres3, dtype=dt)
dplus4 = np.array(pres4, dtype=dt)
dminus4 = np.array(mres4, dtype=dt)
with fitsio.FITS('data.fits', 'rw') as fits:
fits.write(dplus0, extname='plus0')
fits.write(dminus0, extname='minus0')
fits.write(dplus1, extname='plus1')
fits.write(dminus1, extname='minus1')
fits.write(dplus2, extname='plus2')
fits.write(dminus2, extname='minus2')
fits.write(dplus3, extname='plus3')
fits.write(dminus3, extname='minus3')
fits.write(dplus4, extname='plus4')
fits.write(dminus4, extname='minus4')
m, msd, c, csd = estimate_m_and_c(pres0, mres0, 0.02, swap12=SWAP12)
print("""\
# of sims: {n_sims}
noise cancel m : {m:f} +/- {msd:f}
noise cancel c : {c:f} +/- {csd:f}""".format(n_sims=len(pres0),
