def estimate_params(img):
img_green = img[..., 1] # supposedly using the green channel is a good idea. alternatively we could use luma.
# this seems to work well enough.
out = pysex.run(img_green, params=[
'X_IMAGE', 'Y_IMAGE', # barycenter
# 'XMIN_IMAGE', 'XMAX_IMAGE', 'YMIN_IMAGE', 'YMAX_IMAGE', # enclosing rectangle
# 'XPEAK_IMAGE', 'YPEAK_IMAGE', # location of maximal intensity
'A_IMAGE', 'B_IMAGE', 'THETA_IMAGE', # ellipse parameters
'PETRO_RADIUS',
# 'KRON_RADIUS', 'PETRO_RADIUS', 'FLUX_RADIUS', 'FWHM_IMAGE', # various radii
], conf_args={ 'DETECT_THRESH': DETECT_THRESH })
# x and y are flipped for some reason.
# theta should be 90 - theta.
# we convert these here so we can plot stuff with matplotlib easily.
try:
ys = out['X_IMAGE'].tonumpy()
xs = out['Y_IMAGE'].tonumpy()
as_ = out['A_IMAGE'].tonumpy()
bs = out['B_IMAGE'].tonumpy()
thetas = 90 - out['THETA_IMAGE'].tonumpy()
# kron_radii = out['KRON_RADIUS'].tonumpy()
petro_radii = out['PETRO_RADIUS'].tonumpy()
# flux_radii = out['FLUX_RADIUS'].tonumpy()
# fwhms = out['FWHM_IMAGE'].tonumpy()
# detect the most salient galaxy
# take in account size and centrality
surface_areas = np.pi * (as_ * bs)
centralities = np.exp(-((xs - 211.5)**2 + (ys - 211.5)**2)/SIGMA2) # 211.5, 211.5 is the center of the image
# salience is proportional to surface area, with a gaussian prior on the distance to the center.
saliences = surface_areas * centralities
most_salient_idx = np.argmax(saliences)
x = xs[most_salient_idx]
y = ys[most_salient_idx]
a = as_[most_salient_idx]
b = bs[most_salient_idx]
theta = thetas[most_salient_idx]
# kron_radius = kron_radii[most_salient_idx]
petro_radius = petro_radii[most_salient_idx]
# flux_radius = flux_radii[most_salient_idx]
# fwhm = fwhms[most_salient_idx]
except TypeError: # sometimes these are empty (no objects found), use defaults in that case
x = 211.5
y = 211.5
a = np.nan # dunno what this has to be, deal with it later
b = np.nan # same
theta = np.nan # same
# kron_radius = np.nan
petro_radius = np.nan
# flux_radius = np.nan
# fwhm = np.nan
# return (x, y, a, b, theta, flux_radius, kron_radius, petro_radius, fwhm)
return (x, y, a, b, theta, petro_radius)
def tweakreg_sextract(files_to_sextract, catfile_name, sexfile_dir=None):
'''
Systematically loop through each file and sextract the sources
and write them in a catalogue
It has to output the catalogue in pixels using the files tweak.sex
and tweak.param
INPUT : files_to_sextract : a python list of file names to source extract
catfile_name : name of the file containing the image and catalogue names
OUTPUT : A source catalogue for each
'''
if sexfile_dir is None:
sexfile_dir = '/'.join(os.path.abspath(__file__).\
split('/')[:-1])+'/tweak_sex/'
catfile = open(catfile_name, "wb")
for iFile in files_to_sextract:
outcat_name = iFile[:-5] + '_sex.cat'
conf_file = sexfile_dir + 'tweak.sex'
filter_name = sexfile_dir + 'gauss_5.0_9x9.conv'
starnnw_name = sexfile_dir + 'default.nnw'
param_file = sexfile_dir + 'tweak.param'
sexCat = sex.run( iFile,
conf_file=conf_file,
param_file=param_file, \
conf_args={'CATALOG_NAME':outcat_name,
'FILTER_NAME':filter_name,
'STARNNW_NAME':starnnw_name})
catfile.write("%s %s\n" % (iFile, outcat_name))
def clusterSizeSex( image, clusterSource):
'''
Get cluster size in arcseconds
'''
try:
sources = sex.run(image)
returnSource = sources[ np.argmin(raSep(sources['X_WORLD'],sources['Y_WORLD'],clusterSource['RA'],clusterSource['DEC'],abs=True))]
radius = returnSource['FLUX_RADIUS1']*fits.open(image)[0].header['CDELT2']*3600/206265*ang_distance(0.2)*1e3
except:
radius =100
return radius
def makecat(self, rerun=True, keepcat=False, verbose=True):
self.cat = pysex.run(
self.filepath,
conf_args={
"DETECT_THRESH": 3.0,
"ANALYSIS_THRESH": 3.0,
"DETECT_MINAREA": 10,
"PIXEL_SCALE": 1.0,
"SEEING_FWHM": 2.0,
"FILTER": "Y",
"VERBOSE_TYPE": "NORMAL" if verbose else "QUIET",
},
params=["X_IMAGE", "Y_IMAGE", "FLUX_AUTO", "FWHM_IMAGE", "FLAGS", "ELONGATION", "NUMBER", "EXT_NUMBER"],
rerun=rerun,
keepcat=keepcat,
catdir="alipy_cats",
)
def component_extractor( image_file, smoothing=20, \
pixel_size_kpc=5, redshift=0.25,\
filtername='gauss_5.0_9x9.conv'):
'''
This is a general script that should extract a given
mass component from the simulations.
'''
image = fits.open(image_file)[0].data
if smoothing is not None:
smoothed_image = gauss_filter(image, smoothing)
else:
smoothed_image = image
pixel_size_degrees = pixel_size_kpc / (lensing.ang_distance(redshift) *
1e3) * 180. / np.pi
#Create a random string to component find
file_name = 'component_finding_' + str(np.random.random_integers(
0, 100000)) + '.fits'
fits.writeto(file_name, smoothed_image, clobber=True)
conf_path = '/Users/DavidHarvey/Library/Code/IDL/rrg/code/shape/sex_files/'
hot_conf = conf_path + 'wavdetectpy.sex'
filtername = conf_path + '/' + filtername
conf_args = {
'WEIGHT_TYPE': 'NONE',
'FILTER_NAME': filtername,
'FILTER': 'YES'
}
sources = sex.run(file_name, conf_file=hot_conf, conf_args=conf_args)
os.system('rm -fr ' + file_name)
ra = sources['X_IMAGE'] * pixel_size_degrees
dec = sources['Y_IMAGE'] * pixel_size_degrees
sources = append_rec(sources, 'RA', ra, usemask=False, asrecarray=True)
sources = append_rec(sources, 'DEC', dec)
return sources
def makecat(self, rerun=True, keepcat=False, verbose=True):
self.cat = pysex.run(self.filepath,
conf_args={
'DETECT_THRESH': 3.0,
'ANALYSIS_THRESH': 3.0,
'DETECT_MINAREA': 10,
'PIXEL_SCALE': 1.0,
'SEEING_FWHM': 2.0,
"FILTER": "Y",
'VERBOSE_TYPE':
'NORMAL' if verbose else 'QUIET'
},
params=[
'X_IMAGE', 'Y_IMAGE', 'FLUX_AUTO',
'FWHM_IMAGE', 'FLAGS', 'ELONGATION', 'NUMBER',
"EXT_NUMBER"
],
rerun=rerun,
keepcat=keepcat,
catdir="alipy_cats")
def ref_sex(refimage, sexfile_dir=None):
'''
PURPOSE : TO SEXTRACT THE COORDINATES OF THE OBJECTS IN
FOR THE REFERENCE CATALOGUE
'''
if sexfile_dir is None:
sexfile_dir = '/'.join(os.path.abspath(__file__).\
split('/')[:-1])+'/tweak_sex/'
conf_file = sexfile_dir + 'tweak_wcs.sex'
filter_name = sexfile_dir + 'gauss_5.0_9x9.conv'
starnnw_name = sexfile_dir + 'default.nnw'
param_file = sexfile_dir + 'tweak_wcs.param'
sexCat = sex.run( refimage,
conf_file=conf_file,
param_file=param_file, \
conf_args={'CATALOG_NAME':'reference.cat',
'FILTER_NAME':filter_name,
'STARNNW_NAME':starnnw_name})
def result(fits):
cat = pysex.run(fits, params=['X_IMAGE', 'Y_IMAGE', 'FLUX_APER'],
conf_args={'PHOT_APERTURES':5})
print cat['FLUX_APER']
image = afwImage.MaskedImageF(fits)
binsize = 128
nx = int(image.getWidth()/binsize) + 1
ny = int(image.getHeight()/binsize) + 1
bctrl = afwMath.BackgroundControl(nx, ny)
bkgd = afwMath.makeBackground(image, bctrl)
statsImage = afwMath.cast_BackgroundMI(bkgd).getStatsImage()
image -= bkgd.getImageF(afwMath.Interpolate.NATURAL_SPLINE)
return bkgd
return
def makecat(self, rerun=True, keepcat=False, verbose=True):
self.cat = pysex.run(self.filepath, conf_args={'DETECT_THRESH':3.0, 'ANALYSIS_THRESH':3.0, 'DETECT_MINAREA':10,
'PIXEL_SCALE':1.0, 'SEEING_FWHM':2.0, "FILTER":"Y", 'VERBOSE_TYPE':'NORMAL' if verbose else 'QUIET'},
params=['X_IMAGE', 'Y_IMAGE', 'FLUX_AUTO', 'FWHM_IMAGE', 'FLAGS', 'ELONGATION', 'NUMBER', "EXT_NUMBER"],
rerun=rerun, keepcat=keepcat, catdir="alipy_cats")
