def testSubtractNoBeta(self):
D, P, S_corr, mix_mask = ps.diff(self.si_ref, self.si_new,
beta=False)
self.assertIsInstance(D, np.ndarray)
self.assertIsInstance(P, np.ndarray)
self.assertIsInstance(S_corr, np.ndarray)
self.assertIsInstance(mix_mask, np.ndarray)
def testSubtractNoFitPSF(self):
D, P, S_corr, mix_mask = ps.diff(self.si_ref, self.si_new,
beta=False, iterative=False,
shift=False, fitted_psf=False)
self.assertIsInstance(D, np.ndarray)
self.assertIsInstance(P, np.ndarray)
self.assertIsInstance(S_corr, np.ndarray)
self.assertIsInstance(mix_mask, np.ndarray)
def testSubtractOnlyShift(self):
D, P, S_corr, mix_mask = ps.diff(self.si_ref, self.si_new,
beta=False, iterative=False,
shift=True)
self.assertIsInstance(D, np.ndarray)
self.assertIsInstance(P, np.ndarray)
self.assertIsInstance(S_corr, np.ndarray)
self.assertIsInstance(mix_mask, np.ndarray)
def main(args):
imgsdir = '/home/bruno/Documentos/Data/SNiPTF/imgs'
imgsdir = '/home/bruno/Data/SNiPTF/imgs'
#imgsdir = '/home/bruno/Documentos/Data/LIGO_O2/20171116/ESO202-009'
dest_dir = './test/test_images/test_sub_sniptf'
imgs = glob.glob(imgsdir + '/*sci*.fits')
mask = glob.glob(imgsdir + '/*mask*.fits')
imgs.sort()
mask.sort()
images = [
s.SingleImage(animg, mask=amask) for animg, amask in zip(imgs, mask)
]
#images = [s.SingleImage(animg) for animg in imgs]
for i, animg in enumerate(images[1:]):
#~ t, _ = aa.find_transform(animg.data, images[0].data)
#~ if abs(t.rotation)>5e-4:
#~ k = t.__class__
#~ t = k(np.round(t.params, decimals=5))
#~ reg = aa.apply_transform(t, animg.data, images[0].data)
reg = aa.register(animg.data, images[0].data)
new = s.SingleImage(reg.data, mask=reg.mask)
fits.writeto('/home/bruno/aligned_{}.fits'.format(i),
reg.data,
overwrite=True)
D, P, S_corr, mask = ps.diff(images[0],
new,
align=True,
iterative=False,
shift=True,
beta=True)
D = np.ma.MaskedArray(D.real, mask).filled(np.median(D.real))
fits.writeto(os.path.join(dest_dir, 'Diff_{}.fits'.format(i)),
D.real,
overwrite=True)
fits.writeto(os.path.join(dest_dir, 'P_{}.fits'.format(i)),
P.real,
overwrite=True)
fits.writeto(os.path.join(dest_dir, 'Scorr_{}.fits'.format(i)),
S_corr,
overwrite=True)
new._clean()
for an_img in images:
an_img._clean()
return
def main(imgs_dir, ref_path, new_path, details):
if not os.path.isdir(imgs_dir):
os.makedirs(imgs_dir)
ref_dest = os.path.join(imgs_dir, 'ref.fits')
new_dest = os.path.join(imgs_dir, 'new.fits')
os.rename(ref_path, ref_dest)
os.rename(new_path, new_dest)
ref = si.SingleImage(ref_dest, borders=False) #, crop=((150,150), (150, 150)))
new = si.SingleImage(new_dest, borders=False) #, crop=((150,150), (150, 150)))
#~ ## Adding stars
foo = new.cov_matrix
srcs = new.best_sources
rows = []
for i in range(15):
j = np.random.choice(len(srcs), 1, replace=False)
flux = srcs['flux'][j][0]
xs = srcs['x'][j][0]
ys = srcs['y'][j][0]
position = (ys, xs)
sx, sy = new.stamp_shape
sx += 3
sy += 3
star = extract_array(new.pixeldata.data,
(sx, sy), position,
mode='partial',
fill_value=new._bkg.globalrms)
#print flux
#~ star = flux*new.db.load(j)[0]
x = np.random.choice(new.pixeldata.shape[0]-3*sx, 1)[0] + sx#* np.random.random())
y = np.random.choice(new.pixeldata.shape[1]-3*sy, 1)[0] + sy
# np.int((new.pixeldata.shape[1]-star.shape[1]) * np.random.random())
#~ if new.pixeldata.data[x:x+sx, y:y+sy].shape != star.shape:
#~ import ipdb; ipdb.set_trace()
new.pixeldata.data[x:x+sx, y:y+sy] = star
#~ except:
#~ continue
xc = x+sx/2.
yc = y+sy/2.
app_mag = -2.5*np.log10(flux)+25.
rows.append([yc, xc, app_mag, flux])
newcat = Table(rows=rows, names=['x', 'y', 'app_mag', 'flux'])
fits.writeto(filename=new_dest, header=fits.getheader(new_dest),
data=new.pixeldata.data, overwrite=True)
new._clean()
new = si.SingleImage(new_dest, borders=False) #, crop=((150,150), (150, 150)))
newcat.write(os.path.join(imgs_dir, 'transient.list'),
format='ascii.fast_no_header',
overwrite=True)
fits.writeto(filename=os.path.join(imgs_dir, 'interp_ref.fits'), data=ref.interped, overwrite=True)
fits.writeto(filename=os.path.join(imgs_dir, 'interp_new.fits'), data=new.interped, overwrite=True)
try:
print 'Images to be subtracted: {} {}'.format(ref_dest, new_dest)
import time
t0 = time.time()
D, P, S, mask = ps.diff(ref, new, align=False,
iterative=False, shift=False, beta=True)
dt_z = time.time() - t0
new._clean()
ref._clean()
mea, med, std = sigma_clipped_stats(D.real)
D = np.ma.MaskedArray(D.real, mask).filled(mea)
fits.writeto(os.path.join(imgs_dir,'diff.fits'), D, overwrite=True)
#~ utils.encapsule_R(D, path=os.path.join(imgs_dir, 'diff.fits'))
utils.encapsule_R(P, path=os.path.join(imgs_dir, 'psf_d.fits'))
utils.encapsule_R(S, path=os.path.join(imgs_dir, 's_diff.fits'))
scorrdetected = utils.find_S_local_maxima(S, threshold=3.5)
print 'S_corr found thath {} transients were above 3.5 sigmas'.format(len(scorrdetected))
ascii.write(table=np.asarray(scorrdetected),
output=os.path.join(imgs_dir, 's_corr_detected.csv'),
names=['X_IMAGE', 'Y_IMAGE', 'SIGNIFICANCE'],
format='csv')
S = np.ascontiguousarray(S)
#~ s_bkg = sep.Background(S)
mean, median, std = sigma_clipped_stats(S)
sdetected = sep.extract(S-median, 3.5*std,
filter_kernel=None)
print 'S_corr with sep found thath {} transients were above 3.5 sigmas'.format(len(sdetected))
ascii.write(table=sdetected,
output=os.path.join(imgs_dir, 'sdetected.csv'),
format='csv')
## With OIS
t0 = time.time()
ois_d = ois.optimal_system(fits.getdata(new_dest), fits.getdata(ref_dest))[0]
dt_o = time.time() - t0
utils.encapsule_R(ois_d, path=os.path.join(imgs_dir, 'diff_ois.fits'))
## With HOTPANTS
t0 = time.time()
os.system('hotpants -v 0 -inim {} -tmplim {} -outim {} -r 15 -tu 40000 -tl -100 -il -100 -iu 40000'.format(new_dest, ref_dest,
os.path.join(imgs_dir, 'diff_hot.fits')))
dt_h = time.time() - t0
return [newcat.to_pandas(), [dt_z, dt_o, dt_h]]
except:
raise
def main(args):
imgsdir = '/home/bruno/Data/LIGO_O2/Jan04/newstacks/PGC073926'
dest_dir = './test/test_images/test_propersubtract'
imgs = glob.glob(imgsdir + '/pgc073926_17*.fits')
#mask = glob.glob(imgsdir+'/*mask*.fits')
imgs.sort()
#mask.sort()
#images = [s.SingleImage(animg, mask=amask) for animg, amask in zip(imgs, mask)]
images = []
for animg in imgs:
print(animg)
try:
o = s.SingleImage(animg,
borders=True,
crop=((150, 150), (150, 150)))
images.append(o)
except:
pass
#~ hdu = fits.open(animg)
#~ data = hdu[0].data
#~ images = [s.SingleImage(animg, borders=True, crop=((250,250), (250, 250)))
#~ for animg in imgs]
fits.writeto(os.path.join(dest_dir, 'InterpedRef.fits'),
images[0].interped,
overwrite=True)
for i, animg in enumerate(images[1:]):
#~ ## Erasing stars
srcs = animg.best_sources
f60 = np.percentile(srcs['cflux'], q=60)
jj = np.random.choice(len(srcs), len(srcs) / 3, replace=False)
mea, med, std = sigma_clipped_stats(animg.data)
sx, sy = animg.stamp_shape
st = animg._bkg.globalrms
for aj in jj:
star = srcs[aj]
x = star['x']
y = star['y']
if x < sx or animg.data.shape[0] - x < sx:
continue
if y < sy or animg.data.shape[1] - y < sy:
continue
if star['cflux'] < f60:
continue
print(x, y)
noise = np.random.normal(loc=med, scale=st, size=animg.stamp_shape)
animg.data.data[np.int(x - sx / 2.):np.int(x + sx / 2.),
np.int(y - sy / 2.):np.int(y + sy / 2.)] = noise
## Adding stars
#~ foo = animg.cov_matrix
#~ srcs = animg.best_sources
#~ jj = np.random.choice(len(srcs), 12, replace=False)
#~ for aj in jj:
#~ star = animg.db.load(aj)[0]
#~ x, y = np.random.choice(np.min(animg.data.shape)-np.max(star.shape),
#~ 2, replace=True)
#~ print star.shape
#~ animg.data.data[x:x+star.shape[0], y:y+star.shape[1]] = star
#~ xc, yc = x+star.shape[0]/2., y+star.shape[1]/2.
#~ print xc, yc
fits.writeto(os.path.join(dest_dir, 'InterpedNew_{}.fits'.format(i)),
animg.interped,
overwrite=True)
try:
D, P, S_corr, mask = ps.diff(animg,
images[0],
align=True,
iterative=False,
shift=False,
beta=True)
mea, med, std = sigma_clipped_stats(D.real, mask)
D = np.ma.MaskedArray(D.real, mask).filled(mea)
fits.writeto(os.path.join(dest_dir, 'Diff_{}.fits'.format(i)),
D,
overwrite=True)
fits.writeto(os.path.join(dest_dir, 'P_{}.fits'.format(i)),
P.real,
overwrite=True)
fits.writeto(os.path.join(dest_dir, 'Scorr_{}.fits'.format(i)),
S_corr,
overwrite=True)
except:
print('subtraction failed')
print('ref: ', images[0])
print('new: ', animg)
for an_img in images:
an_img._clean()
return