def read_sky_model(self, splinesky=False, slc=None, **kwargs):
'''
Reads the sky model, returning a Tractor Sky object.
'''
fn = self.skyfn
if splinesky:
fn = self.splineskyfn
print('Reading sky model from', fn)
hdr = fitsio.read_header(fn)
skyclass = hdr['SKY']
clazz = get_class_from_name(skyclass)
if getattr(clazz, 'from_fits', None) is not None:
fromfits = getattr(clazz, 'from_fits')
skyobj = fromfits(fn, hdr)
else:
fromfits = getattr(clazz, 'fromFitsHeader')
skyobj = fromfits(hdr, prefix='SKY_')
if slc is not None:
sy, sx = slc
x0, y0 = sx.start, sy.start
skyobj.shift(x0, y0)
skyobj.version = hdr.get('LEGPIPEV', '')
if len(skyobj.version) == 0:
skyobj.version = hdr.get('TRACTORV', '').strip()
if len(skyobj.version) == 0:
skyobj.version = str(os.stat(fn).st_mtime)
skyobj.plver = hdr.get('PLVER', '').strip()
sig1 = hdr.get('SIG1', None)
if sig1 is not None:
skyobj.sig1 = sig1
return skyobj
def read_sky_model(self, splinesky=False, slc=None):
'''
Reads the sky model, returning a Tractor Sky object.
'''
fn = self.skyfn
if splinesky:
fn = self.splineskyfn
print('Reading sky model from', fn)
hdr = fitsio.read_header(fn)
skyclass = hdr['SKY']
clazz = get_class_from_name(skyclass)
if getattr(clazz, 'from_fits', None) is not None:
fromfits = getattr(clazz, 'from_fits')
skyobj = fromfits(fn, hdr)
else:
fromfits = getattr(clazz, 'fromFitsHeader')
skyobj = fromfits(hdr, prefix='SKY_')
if slc is not None:
sy,sx = slc
x0,y0 = sx.start,sy.start
skyobj.shift(x0, y0)
skyobj.version = hdr.get('LEGPIPEV', '')
if len(skyobj.version) == 0:
skyobj.version = hdr.get('TRACTORV', '').strip()
if len(skyobj.version) == 0:
skyobj.version = str(os.stat(fn).st_mtime)
skyobj.plver = hdr.get('PLVER', '').strip()
return skyobj
def readObject(prefix):
k = prefix
objclass = hdr[k]
clazz = get_class_from_name(objclass)
fromfits = getattr(clazz, 'fromFitsHeader')
print('fromFits:', fromfits)
obj = fromfits(hdr, prefix=prefix + '_')
print('Got:', obj)
return obj
def main():
#img = fitsio.read('347736-S5.fits')
img = fitsio.read('392772-N29.fits')
print(img.shape)
img = img.T.copy()
mm = np.median(img)
img -= mm
ps = PlotSequence('sky')
lo,hi = np.percentile(img, [20,80])
ima = dict(vmin=lo, vmax=hi, interpolation='nearest', origin='lower',
cmap='gray')
plt.clf()
plt.imshow(img, **ima)
ps.savefig()
# PAD
padimg = np.zeros((2048, 4096))
padimg[1:-1, 1:-1] = img
img = padimg
from tractor.splinesky import SplineSky
from scipy.ndimage.morphology import binary_dilation
from astrometry.util.util import median_smooth
# # Estimate per-pixel noise via Blanton's 5-pixel MAD
slice1 = (slice(0,-5,10),slice(0,-5,10))
slice2 = (slice(5,None,10),slice(5,None,10))
mad = np.median(np.abs(img[slice1] - img[slice2]).ravel())
sig1 = 1.4826 * mad / np.sqrt(2.)
print('sig1 estimate:', sig1)
mask = np.zeros(img.shape, bool)
mask[binary_dilation(img > 5*sig1, iterations=5)] = True
for mm in [None, mask]:
notmm = None
if mm is not None:
notmm = np.logical_not(mm)
sky = SplineSky.BlantonMethod(img, notmm, 512)
skyfn = 'sky-%s.fits' % (mm is not None and 'mask' or 'nomask')
sky.write_fits(skyfn)
from tractor.utils import get_class_from_name
print('Reading sky model from', skyfn)
hdr = fitsio.read_header(skyfn)
skyclass = hdr['SKY']
clazz = get_class_from_name(skyclass)
if getattr(clazz, 'from_fits'):
fromfits = getattr(clazz, 'from_fits')
skyobj = fromfits(skyfn, hdr)
else:
fromfits = getattr(clazz, 'fromFitsHeader')
skyobj = fromfits(hdr, prefix='SKY_')
sky2 = skyobj
print('sky2', sky2)
sky2.write_fits(skyfn.replace('sky', 'sky2'))
mod = np.zeros_like(img)
sky.addTo(mod)
plt.clf()
plt.imshow(mod, **ima)
plt.title('Blanton method')
ps.savefig()
plt.clf()
plt.imshow(img - mod, **ima)
plt.title('Blanton method (subtracted)')
ps.savefig()
grid = 512
#grid = 256
img = img.astype(np.float32)
med = np.zeros_like(img)
median_smooth(img, mm, grid/2, med)
plt.clf()
plt.imshow(med, **ima)
plt.title('dmedsmooth')
ps.savefig()
plt.clf()
plt.imshow(img - med, **ima)
plt.title('dmedsmooth (subtracted)')
ps.savefig()
sys.exit(0)
med2 = np.zeros_like(img)
mask = np.zeros(img.shape, bool)
mask[binary_dilation(img > 5*sig1, iterations=5)] = True
median_smooth(img, mask, grid/2, med2)
# UN-PAD
img = img[1:-1, 1:-1]
med = med[1:-1, 1:-1]
med2 = med2[1:-1, 1:-1]
mask = mask[1:-1, 1:-1]
sub = img - med
plt.clf()
plt.imshow(sub, **ima)
ps.savefig()
plt.clf()
plt.imshow(img - med2, **ima)
ps.savefig()
plt.clf()
plt.imshow(img * (1-mask), **ima)
ps.savefig()
plt.clf()
plt.imshow(med2, **ima)
ps.savefig()
lo2,hi2 = np.percentile(img, [5,95])
ha = dict(bins=100, range=(lo2,hi2), log=True,
histtype='step')
plt.clf()
n1,b,p = plt.hist(img.ravel(), color='r', **ha)
n2,b,p = plt.hist(sub.ravel(), color='b', **ha)
mx = max(max(n1), max(n2))
plt.ylim(mx*0.1, mx)
ps.savefig()
ha = dict(bins=100, range=(lo2,hi2), histtype='step')
plt.clf()
n1,b,p = plt.hist(img.ravel(), color='r', **ha)
n2,b,p = plt.hist(sub.ravel(), color='b', **ha)
n3,b,p = plt.hist((img - sub).ravel(), color='m', **ha)
#mx = max(max(n1), max(n2))
#plt.ylim(0, mx)
ps.savefig()
def main():
#img = fitsio.read('347736-S5.fits')
img = fitsio.read('392772-N29.fits')
print(img.shape)
img = img.T.copy()
mm = np.median(img)
img -= mm
ps = PlotSequence('sky')
lo, hi = np.percentile(img, [20, 80])
ima = dict(vmin=lo,
vmax=hi,
interpolation='nearest',
origin='lower',
cmap='gray')
plt.clf()
plt.imshow(img, **ima)
ps.savefig()
# PAD
padimg = np.zeros((2048, 4096))
padimg[1:-1, 1:-1] = img
img = padimg
from tractor.splinesky import SplineSky
from scipy.ndimage.morphology import binary_dilation
from astrometry.util.util import median_smooth
# # Estimate per-pixel noise via Blanton's 5-pixel MAD
slice1 = (slice(0, -5, 10), slice(0, -5, 10))
slice2 = (slice(5, None, 10), slice(5, None, 10))
mad = np.median(np.abs(img[slice1] - img[slice2]).ravel())
sig1 = 1.4826 * mad / np.sqrt(2.)
print('sig1 estimate:', sig1)
mask = np.zeros(img.shape, bool)
mask[binary_dilation(img > 5 * sig1, iterations=5)] = True
for mm in [None, mask]:
notmm = None
if mm is not None:
notmm = np.logical_not(mm)
sky = SplineSky.BlantonMethod(img, notmm, 512)
skyfn = 'sky-%s.fits' % (mm is not None and 'mask' or 'nomask')
sky.write_fits(skyfn)
from tractor.utils import get_class_from_name
print('Reading sky model from', skyfn)
hdr = fitsio.read_header(skyfn)
skyclass = hdr['SKY']
clazz = get_class_from_name(skyclass)
if getattr(clazz, 'from_fits'):
fromfits = getattr(clazz, 'from_fits')
skyobj = fromfits(skyfn, hdr)
else:
fromfits = getattr(clazz, 'fromFitsHeader')
skyobj = fromfits(hdr, prefix='SKY_')
sky2 = skyobj
print('sky2', sky2)
sky2.write_fits(skyfn.replace('sky', 'sky2'))
mod = np.zeros_like(img)
sky.addTo(mod)
plt.clf()
plt.imshow(mod, **ima)
plt.title('Blanton method')
ps.savefig()
plt.clf()
plt.imshow(img - mod, **ima)
plt.title('Blanton method (subtracted)')
ps.savefig()
grid = 512
#grid = 256
img = img.astype(np.float32)
med = np.zeros_like(img)
median_smooth(img, mm, grid / 2, med)
plt.clf()
plt.imshow(med, **ima)
plt.title('dmedsmooth')
ps.savefig()
plt.clf()
plt.imshow(img - med, **ima)
plt.title('dmedsmooth (subtracted)')
ps.savefig()
sys.exit(0)
med2 = np.zeros_like(img)
mask = np.zeros(img.shape, bool)
mask[binary_dilation(img > 5 * sig1, iterations=5)] = True
median_smooth(img, mask, grid / 2, med2)
# UN-PAD
img = img[1:-1, 1:-1]
med = med[1:-1, 1:-1]
med2 = med2[1:-1, 1:-1]
mask = mask[1:-1, 1:-1]
sub = img - med
plt.clf()
plt.imshow(sub, **ima)
ps.savefig()
plt.clf()
plt.imshow(img - med2, **ima)
ps.savefig()
plt.clf()
plt.imshow(img * (1 - mask), **ima)
ps.savefig()
plt.clf()
plt.imshow(med2, **ima)
ps.savefig()
lo2, hi2 = np.percentile(img, [5, 95])
ha = dict(bins=100, range=(lo2, hi2), log=True, histtype='step')
plt.clf()
n1, b, p = plt.hist(img.ravel(), color='r', **ha)
n2, b, p = plt.hist(sub.ravel(), color='b', **ha)
mx = max(max(n1), max(n2))
plt.ylim(mx * 0.1, mx)
ps.savefig()
ha = dict(bins=100, range=(lo2, hi2), histtype='step')
plt.clf()
n1, b, p = plt.hist(img.ravel(), color='r', **ha)
n2, b, p = plt.hist(sub.ravel(), color='b', **ha)
n3, b, p = plt.hist((img - sub).ravel(), color='m', **ha)
#mx = max(max(n1), max(n2))
#plt.ylim(0, mx)
ps.savefig()
