def from_scarlet(self, scarlet_model_dir, pixel_scale=HSC_pixel_scale):
from galsim import Image, InterpolatedImage
from galsim.interpolant import Lanczos
with open(scarlet_model_dir, 'rb') as f:
blend, info, mask = dill.load(f)
f.close()
assert ''.join(blend.observations[0].channels) == self.channels
# Crop the mask
new_weights = blend.observations[0].weights
x1, y1 = blend.sources[0].bbox.origin[1:]
x2 = x1 + blend.sources[0].bbox.shape[1:][0]
y2 = y1 + blend.sources[0].bbox.shape[1:][1]
mask = mask.astype(bool)[x1:x2, y1:y2]
mask += np.sum((new_weights[:, x1:x2, y1:y2] == 0),
axis=0).astype(bool)
mockgal_img = blend.sources[0].get_model() * np.repeat(
~mask[np.newaxis, :, :], len(self.channels), axis=0)
gal_image = np.empty_like(self.bkg.images)
for i in range(len(mockgal_img)):
mockgal = InterpolatedImage(Image(mockgal_img[i], dtype=float),
scale=pixel_scale,
x_interpolant=Lanczos(3))
gal_image[i] = mockgal.drawImage(scale=pixel_scale,
nx=self.bkg.images.shape[2],
ny=self.bkg.images.shape[1]).array
# Generate variance map
ra, dec = self.bkg.wcs.wcs_pix2world(self.bkg.images.shape[2] / 2,
self.bkg.images.shape[1] / 2, 0)
info = {
'ra': ra,
'dec': dec,
'scarlet_model': scarlet_model_dir,
'model_type': 'scarlet_lsbg_wvlt_0.5'
}
mock_model = Data(images=gal_image,
variances=None,
masks=None,
channels=self.channels,
wcs=None,
weights=None,
psfs=self.bkg.psfs,
info=info)
# Finished!!!
self.model = mock_model # model only has `images`, `channels`, `psfs`, and `info`!
self.set_mock() # mock has other things, including modified variances.
def resize_mask(self, f, method='iraf', order=5, cval=0.0):
'''Zoom/Resize the mask of Celestial object.
f > 1 means the mask will be resampled (finer)! f < 1 means the mask will be degraded.
Parameters:
f (float): the positive factor of zoom. If 0 < f < 1, the mask will be resized to smaller one.
method (str): interpolation method. Use 'lanczos' or 'spline' or 'iraf'.
order (int): the order Lanczos interpolation (>0).
cval (scalar): value to fill the edges. Default is NaN.
Returns:
shift_image: ndarray.
'''
if method == 'lanczos':
try: # try to import galsim
from galsim import degrees, Angle
from galsim.interpolant import Lanczos
from galsim import Image, InterpolatedImage
from galsim.fitswcs import AstropyWCS
except:
raise ImportError(
'# Import `galsim` failed! Please check if `galsim` is installed!'
)
assert (order > 0) and isinstance(
order,
int), 'order of ' + method + ' must be positive interger.'
galimg = InterpolatedImage(Image(self.mask, dtype=float),
scale=self.pixel_scale,
x_interpolant=Lanczos(order))
ny, nx = self.mask.shape
result = galimg.drawImage(
scale=self.pixel_scale / f, nx=round(nx * f),
ny=round(ny * f)) #, wcs=AstropyWCS(self.wcs))
self.wcs = self._resize_wcs(self.image, self.wcs, f)
self._image = result.array
self.shape = self.mask.shape
self._wcs_header_merge()
self.pixel_scale /= f
return result.array
elif method == 'iraf':
self.save_to_fits('./_temp.fits', 'mask')
if f > 1:
magnify('./_temp.fits', './_resize_temp.fits', f, f)
else:
blkavg('./_temp.fits',
'./_resize_temp.fits',
1 / f,
1 / f,
option='sum')
hdu = fits.open('./_resize_temp.fits')
self.mask = hdu[0].data
self.shape = hdu[0].data.shape
self.header = hdu[0].header
self.wcs = wcs.WCS(self.header)
self.pixel_scale /= f
hdu.close()
imdelete('./*temp.fits')
else:
raise ValueError(
"# Not supported interpolation method. Use 'lanczos' or 'iraf'."
)
def shift_mask(self, dx, dy, method='iraf', order=5, cval=0.0):
'''Shift the mask of Celestial object.
Parameters:
dx, dy (float): shift distance (in pixel) along x (horizontal) and y (vertical).
Note that elements in one row has the same y but different x.
Example: dx = 2 is to shift the image "RIGHT", dy = 3 is to shift the image "UP".
method (str): interpolation method. Use 'lanczos' or 'spline' or 'iraf'
order (int): the order of spline interpolation (within 0-5) or Lanczos interpolation (>0).
cval (scalar): value to fill the edges. Default is NaN.
Returns:
shift_mask: ndarray.
'''
ny, nx = self.mask.shape
if abs(dx) > nx or abs(ny) > ny:
raise ValueError('# Shift distance is beyond the image size.')
if method == 'lanczos':
try: # try to import galsim
from galsim import degrees, Angle
from galsim.interpolant import Lanczos
from galsim import Image, InterpolatedImage
from galsim.fitswcs import AstropyWCS
except:
raise ImportError(
'# Import `galsim` failed! Please check if `galsim` is installed!'
)
# Begin shift
assert (order > 0) and isinstance(
order,
int), 'order of ' + method + ' must be positive interger.'
galimg = InterpolatedImage(Image(self.mask, dtype=float),
scale=self.pixel_scale,
x_interpolant=Lanczos(order))
galimg = galimg.shift(dx=dx * self.pixel_scale,
dy=dy * self.pixel_scale)
result = galimg.drawImage(scale=self.pixel_scale, nx=nx,
ny=ny) #, wcs=AstropyWCS(self.wcs))
self._mask = result.array
# Change the WCS of image
hdr = copy.deepcopy(self.header)
hdr['CRPIX1'] += dx
hdr['CRPIX2'] += dy
self.header = hdr
self.wcs = wcs.WCS(hdr)
self._wcs_header_merge()
return result.array
elif method == 'iraf':
self.save_to_fits('./_temp.fits', 'mask')
imshift('./_temp.fits',
'./_shift_temp.fits',
dx,
dy,
interp_type='poly3',
boundary_type='constant')
hdu = fits.open('./_shift_temp.fits')
self.mask = hdu[0].data
self.shape = hdu[0].data.shape
self.header = hdu[0].header
self.wcs = wcs.WCS(self.header)
hdu.close()
imdelete('./*temp.fits')
else:
raise ValueError(
"# Not supported interpolation method. Use 'lanczos' or 'iraf'."
)
def outlier_rejection(star_list,
pos_list,
mask_list,
ccd_list,
SNR_list=None,
RA_list=None,
DEC_list=None,
shape_std_max=5.,
print_fun=None):
r"""Outlier star rejection method.
Notes
-----
It is based on the measurements from Galsim's HSM adaptive moments.
The method calculates the 2nd order moments from the stars
in the exposure. If there are stars that have an aberrant value in one
of the stats, e1, e2 or R2 we discard the star.
An aberrant value is defined as a value that is more
than ``shape_std_max`` sigmas away from the mean.
It inputs all the lists that will be used as a method and returns
the same lists without the stars that where considered as outliers.
``print_fun`` is a function that prints details about the stars
being removed.
"""
# Define the printing function. Could be printing on a log file.
if print_fun is None:
def print_fun(msg):
print(msg)
# Reject outliers
all_stars = np.concatenate(star_list, axis=2)
all_stars = utils.reg_format(np.copy(all_stars))
all_masks = np.concatenate(mask_list, axis=2)
all_masks = utils.reg_format(np.copy(all_masks))
# hsm thinks 0 means good
badpix_masks = np.rint(np.abs(all_masks - 1))
star_moms = [
hsm.FindAdaptiveMom(Image(star), badpix=Image(bp), strict=False)
for star, bp in zip(all_stars, badpix_masks)
]
star_shapes = np.array([[
moms.observed_shape.g1, moms.observed_shape.g2,
2. * moms.moments_sigma**2,
int(bool(moms.error_message))
] for moms in star_moms])
# Outlier rejection based on e1, e2 and R2
R2_thresh = shape_std_max * np.std(star_shapes[:, 2]) + \
np.mean(star_shapes[:, 2])
R2_bad_stars = (abs(star_shapes[:, 2]) > R2_thresh)
e2_thresh = shape_std_max * np.std(star_shapes[:, 1]) + \
np.mean(star_shapes[:, 1])
e2_bad_stars = (abs(star_shapes[:, 1]) > e2_thresh)
e1_thresh = shape_std_max * np.std(star_shapes[:, 0]) + \
np.mean(star_shapes[:, 0])
e1_bad_stars = (abs(star_shapes[:, 0]) > e1_thresh)
bad_stars = np.logical_or(e1_bad_stars, e2_bad_stars)
bad_stars = np.logical_or(bad_stars, R2_bad_stars)
bad_stars_idx = np.nonzero(bad_stars)[0]
print_fun(bad_stars_idx)
print_fun(bad_stars_idx.shape)
# Create masks
erase_masks = [
np.zeros(star_list[i].shape[2], dtype=bool)
for i in range(len(star_list))
]
if bad_stars_idx.size > 0:
# We have to erase the outliers
# Create the reference ids (to match the global
# array to the list of arrays)
idx_ref = np.zeros((len(all_stars), 3), dtype=int)
glob_id, star_id, ccd_id = 0, 0, 0
for stars in star_list:
star_id = 0
for _star in utils.reg_format(stars):
idx_ref[glob_id, 0] = glob_id
idx_ref[glob_id, 1] = star_id
idx_ref[glob_id, 2] = ccd_id
glob_id += 1
star_id += 1
ccd_id += 1
# select outlier stars
for bad_id in bad_stars_idx:
print_fun('Outlier: Glob_id=%d , star_id=%d , ccd_id=%d' %
(idx_ref[bad_id, 0], idx_ref[bad_id,
1], idx_ref[bad_id, 2]))
erase_masks[idx_ref[bad_id, 2]][idx_ref[bad_id, 1]] = True
for it_star in range(len(star_list)):
mask = ~erase_masks[it_star]
# erase elements and overwrite
star_list[it_star] = star_list[it_star][:, :, mask]
mask_list[it_star] = mask_list[it_star][:, :, mask]
pos_list[it_star] = pos_list[it_star][mask, :]
if SNR_list is not None:
SNR_list[it_star] = SNR_list[it_star][mask]
if RA_list is not None:
RA_list[it_star] = RA_list[it_star][mask]
DEC_list[it_star] = DEC_list[it_star][mask]
return star_list, pos_list, mask_list, ccd_list, SNR_list, RA_list,\
DEC_list, erase_masks
def resize_mask(self, f, method='lanczos', order=5, cval=0.0):
'''Zoom/Resize the mask of Stack object.
Cautious: don't use ['bicubic', 'nearest', 'cubic', 'bilinear'] methods! They don't conserve the total flux!
Parameters:
f (float): the positive factor of zoom. If 0 < f < 1, the image will be resized to smaller one.
method (str): interpolation method. Use 'lanczos' or 'spline'.
order (int): the order of spline interpolation (within 0-5) or Lanczos interpolation (>0).
cval (scalar): value to fill the edges. Default is NaN.
Returns:
shift_image: ndarray.
'''
if method == 'lanczos':
try: # try to import galsim
from galsim import degrees, Angle
from galsim.interpolant import Lanczos
from galsim import Image, InterpolatedImage
from galsim.fitswcs import AstropyWCS
except:
raise ImportError(
'# Import `galsim` failed! Please check if `galsim` is installed!'
)
assert (order > 0) and isinstance(
order,
int), 'order of ' + method + ' must be positive interger.'
galimg = InterpolatedImage(Image(self.mask, dtype=float),
scale=0.168,
x_interpolant=Lanczos(order))
#galimg = galimg.magnify(f)
ny, nx = self.mask.shape
result = galimg.drawImage(
scale=0.168 / f, nx=round(nx * f),
ny=round(ny * f)) #, wcs=AstropyWCS(self.wcs))
self.wcs = self._resize_wcs(self.mask, self.wcs, f)
self._mask = result.array
self.shape = self.mask.shape
return result.array
elif method == 'spline':
from scipy.ndimage import zoom
assert 0 < order <= 5 and isinstance(
order, int), 'order of ' + method + ' must be within 0-5.'
result = zoom(self.mask,
float(f),
order=order,
mode='constant',
cval=cval)
self._mask = result
self.wcs = self._resize_wcs(self.mask, self.wcs, f)
self.shape = self.mask.shape
return result
elif method in ['bicubic', 'nearest', 'cubic', 'bilinear']:
raise Warning(
"Cautious! Don't use ['bicubic', 'nearest', 'cubic', 'bilinear'] methods! They don't conserve the total flux!"
)
try:
from scipy.misc import imresize
except:
raise ImportError(
'# Import `scipy.misc.imresize` failed! This function may no longer be included in scipy!'
)
result = imresize(self.mask, float(f), interp=method)
self._mask = result.astype(float)
self.wcs = self._resize_wcs(self.mask, self.wcs, f)
self.shape = self.mask.shape
return result.astype(float)
else:
raise ValueError(
"# Not supported interpolation method. Use 'lanczos' or 'spline'."
)
def shift_mask(self, dx, dy, method='lanczos', order=5, cval=0.0):
'''Shift the mask of Stack object.
Parameters:
dx, dy (float): shift distance (in pixel) along x (horizontal) and y (vertical).
Note that elements in one row has the same y but different x.
Example: dx = 2 is to shift the image "RIGHT", dy = 3 is to shift the image "UP".
method (str): interpolation method. Use 'lanczos' or 'spline'.
order (int): the order of spline interpolation (within 0-5) or Lanczos interpolation (>0).
cval (scalar): value to fill the edges. Default is NaN.
Returns:
shift_mask: ndarray.
'''
if not hasattr(self, 'mask'):
raise AttributeError("This `Stack` object doesn't have `mask`!")
ny, nx = self.image.shape
if abs(dx) > nx or abs(ny) > ny:
raise ValueError('# Shift distance is beyond the image size.')
if method == 'lanczos':
try: # try to import galsim
from galsim import degrees, Angle
from galsim.interpolant import Lanczos
from galsim import Image, InterpolatedImage
from galsim.fitswcs import AstropyWCS
except:
raise ImportError(
'# Import `galsim` failed! Please check if `galsim` is installed!'
)
# Begin shift
assert (order > 0) and isinstance(
order,
int), 'order of ' + method + ' must be positive interger.'
galimg = InterpolatedImage(Image(self.mask, dtype=float),
scale=0.168,
x_interpolant=Lanczos(order))
galimg = galimg.shift(dx=dx * 0.168, dy=dy * 0.168)
result = galimg.drawImage(scale=0.168, nx=nx,
ny=ny) #, wcs=AstropyWCS(self.wcs))
self._mask = (result.array > 0.5).astype(float)
# Change the WCS of image
hdr = copy.deepcopy(self.header)
hdr['CRPIX1'] += dx
hdr['CRPIX2'] += dy
self.header = hdr
self.wcs = wcs.WCS(hdr)
return result.array
elif method == 'spline':
from scipy.ndimage.interpolation import shift
assert 0 < order <= 5 and isinstance(
order, int), 'order of ' + method + ' must be within 0-5.'
result = shift(self.mask, [dy, dx],
order=order,
mode='constant',
cval=cval)
self._mask = (result > 0.5).astype(float)
# Change the WCS of image
hdr = copy.deepcopy(self.header)
hdr['CRPIX1'] += dx
hdr['CRPIX2'] += dy
self.header = hdr
self.wcs = wcs.WCS(hdr)
return result
else:
raise ValueError(
"# Not supported interpolation method. Use 'lanczos' or 'spline'."
)
def rotate_mask(self,
angle,
method='lanczos',
order=5,
reshape=False,
cval=0.0):
'''Rotate the mask of Stack object.
Parameters:
angle (float): rotation angle in degress, counterclockwise.
method (str): interpolation method. Use 'lanczos', 'spline', 'cubic',
'bicubic', 'nearest' or 'bilinear'.
order (int): the order of spline interpolation (within 0-5) or Lanczos interpolation (>0).
reshape (bool): if True, the output shape is adapted so that the rorated image
is contained completely in the output array.
cval (scalar): value to fill the edges. Default is NaN.
Returns:
rotate_image: ndarray.
'''
angle = angle % 360
if not hasattr(self, 'mask'):
raise AttributeError("This `Stack` object doesn't have `mask`!")
else:
if method == 'lanczos':
try:
from galsim import degrees, Angle
from galsim.interpolant import Lanczos
from galsim import Image, InterpolatedImage
from galsim.fitswcs import AstropyWCS
except:
raise ImportError(
'# Import `galsim` failed! Please check if `galsim` is installed!'
)
# Begin rotation
assert (order > 0) and isinstance(
order,
int), 'order of ' + method + ' must be positive interger.'
galimg = InterpolatedImage(Image(self.mask, dtype=float),
scale=0.168,
x_interpolant=Lanczos(order))
galimg = galimg.rotate(Angle(angle, unit=degrees))
ny, nx = self.image.shape
result = galimg.drawImage(scale=0.168, nx=nx,
ny=ny) #, wcs=AstropyWCS(self.wcs))
self._mask = (result.array > 0.5).astype(float)
self.wcs = self._rotate_wcs(self.mask, self.wcs, angle)
return result.array
elif method == 'spline':
from scipy.ndimage.interpolation import rotate as rt
assert 0 < order <= 5 and isinstance(
order, int), 'order of ' + method + ' must be within 0-5.'
result = rt(self.mask,
-angle,
order=order,
mode='constant',
cval=cval,
reshape=reshape)
self._mask = (result > 0.5).astype(float)
self.wcs = self._rotate_wcs(self.mask, self.wcs, angle)
return result
elif method in ['bicubic', 'nearest', 'cubic', 'bilinear']:
raise Warning(
"Cautious! Don't use ['bicubic', 'nearest', 'cubic', 'bilinear'] methods! They don't conserve the total flux!"
)
try:
from scipy.misc import imrotate
except:
raise ImportError(
'# Import `scipy.misc.imrotate` failed! This function may no longer be included in scipy!'
)
result = imrotate(self.mask, -angle, interp=method)
self._mask = (result > 0.5).astype(float)
self.wcs = self._rotate_wcs(self.mask, self.wcs, angle)
return result
else:
raise ValueError(
"# Not supported interpolation method. Use 'lanczos', 'spline', 'cubic', \
'bicubic', 'nearest', 'bilinear'.")
def resize_mask(self, f, method='cubic', order=5, cval=0.0):
'''
Zoom/Resize the mask of Celestial object.
f > 1 means the mask will be resampled (finer)! f < 1 means the mask will be degraded.
Parameters:
f (float): the positive factor of zoom. If 0 < f < 1, the mask will be resized to smaller one.
method (str): interpolation method. Use 'lanczos', 'cubic', 'quintic' or 'iraf'.
First three methods require ``GalSim`` installed.
Other methods are now consistent with "iraf" results.
order (int): the order Lanczos interpolation (>0).
cval (float): value to fill the edges. Default is 0.
Returns:
resize_mask (ndarray): resized image. The "mask" attribute of ``Celestial`` class will also be changed accordingly.
'''
if not hasattr(self, 'mask'):
raise ValueError("This object doesn't have mask yet!")
if method == 'lanczos' or method == 'cubic' or method == 'quintic':
try: # try to import galsim
from galsim import degrees, Angle
from galsim.interpolant import Lanczos
from galsim import Image, InterpolatedImage
from galsim.fitswcs import AstropyWCS
except:
raise ImportError(
'# Import `galsim` failed! Please check if `galsim` is installed!'
)
assert (order > 0) and isinstance(
order,
int), 'order of ' + method + ' must be positive interger.'
if method == 'lanczos':
galimg = InterpolatedImage(Image(self.mask, dtype=float),
scale=self.pixel_scale,
x_interpolant=Lanczos(order))
else:
galimg = InterpolatedImage(Image(self.mask, dtype=float),
scale=self.pixel_scale,
x_interpolant=method)
ny, nx = self.mask.shape
if f > 1:
result = galimg.drawImage(scale=self.pixel_scale / f,
nx=int((nx - 1) * f + 1),
ny=int((ny - 1) * f + 1))
self.header = self._resize_header_wcs(self.mask, f)
self.header['CRPIX1'] += (1 - f * 1)
self.header['CRPIX2'] += (1 - f * 1)
self._mask = result.array
self.shape = self.mask.shape
self.header['NAXIS1'] = result.array.shape[1]
self.header['NAXIS2'] = result.array.shape[0]
self.pixel_scale /= f
self.wcs = wcs.WCS(self.header)
#### Cautious! The following block could be wrong! ####
## Probably you'll need extra shift of image
dshift = 2 * (1 - f * 1) % 0.5
self.shift_mask(dshift, dshift, method='spline')
# We don't want to shift wcs.
self.header['CRPIX1'] -= dshift
self.header['CRPIX2'] -= dshift
self.wcs = wcs.WCS(self.header)
#### Cautious! The above block could be wrong! ####
else:
from math import ceil
b = round(1 / f)
nxout = ceil(nx / b)
nyout = ceil(ny / b)
result = galimg.drawImage(scale=self.pixel_scale * b,
nx=nxout,
ny=nyout)
self.header = self._resize_header_wcs(self.mask, f)
self.header['CRPIX1'] += 0.5 - 1 / b / 2
self.header['CRPIX2'] += 0.5 - 1 / b / 2
self._mask = result.array
self.shape = self.image.shape
self.header['NAXIS1'] = result.array.shape[1]
self.header['NAXIS2'] = result.array.shape[0]
self.pixel_scale *= b
self.wcs = wcs.WCS(self.header)
#### Cautious! The following block could be wrong! ####
## Probably you'll need extra shift of image
dshift = 0.5 - 1 / b / 2
self.shift_image(-dshift, -dshift, method='spline')
# We don't want to shift wcs.
self.header['CRPIX1'] -= dshift
self.header['CRPIX2'] -= dshift
self.wcs = wcs.WCS(self.header)
#### Cautious! The above block could be wrong! ####
return self.mask
elif method == 'iraf':
self.save_to_fits('./_temp.fits', 'mask')
if f > 1:
magnify('./_temp.fits', './_resize_temp.fits', f, f)
else:
blkavg('./_temp.fits',
'./_resize_temp.fits',
round(1 / f),
round(1 / f),
option='sum')
hdu = fits.open('./_resize_temp.fits')
self.mask = hdu[0].data
self.shape = hdu[0].data.shape
self.header = hdu[0].header
self.wcs = wcs.WCS(self.header)
self.pixel_scale /= f
hdu.close()
imdelete('./*temp.fits')
return self.mask
else:
raise ValueError(
"# Not supported interpolation method. Use 'lanczos', 'spline' or 'iraf'."
)
def shift_mask(self, dx, dy, method='spline', order=5, cval=0.0):
'''Shift the mask of Celestial object.
Parameters:
dx (float): shift distance (in pixel) along x (horizontal).
Note that elements in one row has the same y but different x.
Example: dx = 2 is to shift the mask "RIGHT" (as seen in DS9), dy = 3 is to shift the image "UP".
dy (float): shift distance (in pixel) along y (vertical).
Note that elements in one row has the same y but different x.
Example: dx = 2 is to shift the mask "RIGHT" (as seen in DS9), dy = 3 is to shift the image "UP".
method (str): interpolation method. Use 'spline', lanczos' or 'iraf'.
If using 'iraf', default interpolation is 'poly3. 'Lanczos' requires ``GalSim`` installed.
order (int): the order of Spline or Lanczos interpolation (>0).
cval (float): value to fill the edges. Default is 0.
Returns:
shift_mask (ndarray): shifted mask. The "mask" attribute of ``Celestial`` class will also be changed accordingly.
'''
ny, nx = self.mask.shape
if abs(dx) > nx or abs(ny) > ny:
raise ValueError('# Shift distance is beyond the image size.')
if method == 'lanczos' or method == 'cubic' or method == 'quintic':
try: # try to import galsim
from galsim import degrees, Angle
from galsim.interpolant import Lanczos
from galsim import Image, InterpolatedImage
from galsim.fitswcs import AstropyWCS
except:
raise ImportError(
'# Import ``galsim`` failed! Please check if ``galsim`` is installed!'
)
# Begin shift
assert (order > 0) and isinstance(
order,
int), 'order of ' + method + ' must be positive interger.'
if method == 'lanczos':
galimg = InterpolatedImage(Image(self.image, dtype=float),
scale=self.pixel_scale,
x_interpolant=Lanczos(order))
else:
galimg = InterpolatedImage(Image(self.image, dtype=float),
scale=self.pixel_scale,
x_interpolant=method)
galimg = galimg.shift(dx=dx * self.pixel_scale,
dy=dy * self.pixel_scale)
result = galimg.drawImage(scale=self.pixel_scale, nx=nx,
ny=ny) #, wcs=AstropyWCS(self.wcs))
self._mask = result.array
# Change the WCS of image
hdr = copy.deepcopy(self.header)
hdr['CRPIX1'] += dx
hdr['CRPIX2'] += dy
self.header = hdr
self.wcs = wcs.WCS(self.header)
return result.array
elif method == 'iraf':
self.save_to_fits('./_temp.fits', 'mask')
imshift('./_temp.fits',
'./_shift_temp.fits',
dx,
dy,
interp_type='poly3',
boundary_type='constant')
hdu = fits.open('./_shift_temp.fits')
self.mask = hdu[0].data
self.shape = hdu[0].data.shape
self.header = hdu[0].header
self.wcs = wcs.WCS(self.header)
hdu.close()
imdelete('./*temp.fits')
return self.mask
elif method == 'spline':
from scipy.ndimage.interpolation import shift
assert 0 < order <= 5 and isinstance(
order, int), 'order of ' + method + ' must be within 0-5.'
result = shift(self.mask, [dy, dx],
order=order,
mode='constant',
cval=cval)
self._mask = result
# Change the WCS of image
hdr = copy.deepcopy(self.header)
hdr['CRPIX1'] += dx
hdr['CRPIX2'] += dy
self.header = hdr
self.wcs = wcs.WCS(self.header)
return result
else:
raise ValueError(
"# Not supported interpolation method. Use 'lanczos' or 'iraf'."
)
def resize_image(self, f, method='cubic', order=3, cval=0.0):
'''
Zoom/Resize the image of Celestial object.
f > 1 means the image will be resampled (finer)! f < 1 means the image will be degraded.
Parameters:
f (float): the positive factor of zoom. If 0 < f < 1, the image will be resized to smaller one.
method (str): interpolation method. Use 'spline', 'iraf', or 'lanczos', 'cubic', 'quintic'.
We recommend using 'spline' or 'iraf. The last three methods require ``GalSim`` installed.
Other methods are now consistent with "iraf" results.
order (int): the order Lanczos interpolation (>0).
cval (float): value to fill the edges. Default is 0.
Returns:
resize_image (ndarray): resized image. The "image" attribute of ``Celestial`` class will also be changed accordingly.
'''
if method == 'lanczos' or method == 'cubic' or method == 'quintic':
try: # try to import galsim
from galsim import degrees, Angle
from galsim.interpolant import Lanczos
from galsim import Image, InterpolatedImage
from galsim.fitswcs import AstropyWCS
except:
raise ImportError(
'# Import `galsim` failed! Please check if `galsim` is installed!'
)
assert (order > 0) and isinstance(
order,
int), 'order of ' + method + ' must be positive interger.'
if method == 'lanczos':
galimg = InterpolatedImage(Image(self.image, dtype=float),
scale=self.pixel_scale,
x_interpolant=Lanczos(order))
else:
galimg = InterpolatedImage(Image(self.image, dtype=float),
scale=self.pixel_scale,
x_interpolant=method)
ny, nx = self.image.shape
if f > 1:
result = galimg.drawImage(scale=self.pixel_scale / f,
nx=int((nx - 1) * f + 1),
ny=int((ny - 1) * f + 1))
self.header = self._resize_header_wcs(f)
self.header['CRPIX1'] += (1 - f * 1)
self.header['CRPIX2'] += (1 - f * 1)
self._image = result.array
self.shape = self.image.shape
self.header['NAXIS1'] = result.array.shape[1]
self.header['NAXIS2'] = result.array.shape[0]
self.pixel_scale /= f
self.wcs = wcs.WCS(self.header)
#### Cautious! The following block could be wrong! ####
## Probably you'll need extra shift of image
dshift = 2 * (1 - f * 1) % 0.5
self.shift_image(dshift, dshift, method='spline')
# We don't want to shift wcs.
self.header['CRPIX1'] -= dshift
self.header['CRPIX2'] -= dshift
self.wcs = wcs.WCS(self.header)
#### Cautious! The above block could be wrong! ####
else:
from math import ceil
b = round(1 / f)
nxout = ceil(nx / b)
nyout = ceil(ny / b)
result = galimg.drawImage(scale=self.pixel_scale * b,
nx=nxout,
ny=nyout)
self.header = self._resize_header_wcs(f)
self.header['CRPIX1'] += 0.5 - 1 / b / 2
self.header['CRPIX2'] += 0.5 - 1 / b / 2
self._image = result.array
self.shape = self.image.shape
self.header['NAXIS1'] = result.array.shape[1]
self.header['NAXIS2'] = result.array.shape[0]
self.pixel_scale *= b
self.wcs = wcs.WCS(self.header)
#### Cautious! The following block could be wrong! ####
## Probably you'll need extra shift of image
dshift = 0.5 - 1 / b / 2
self.shift_image(-dshift, -dshift, method='spline')
# We don't want to shift wcs.
self.header['CRPIX1'] -= dshift
self.header['CRPIX2'] -= dshift
self.wcs = wcs.WCS(self.header)
#### Cautious! The above block could be wrong! ####
return self.image
elif method == 'iraf':
self.save_to_fits('./_temp.fits', 'image')
if f > 1:
magnify('./_temp.fits', './_resize_temp.fits', f, f)
else:
blkavg('./_temp.fits',
'./_resize_temp.fits',
round(1 / f),
round(1 / f),
option='sum')
try:
hdu = fits.open('./_resize_temp.fits')
except Exception as e:
raise ValueError(
'Interpolation using IRAF filed with error "{}". \n Please try another interpolation method!'
.format(e))
self.image = hdu[0].data
self.shape = hdu[0].data.shape
self.header = hdu[0].header
#### Remove redundant PC keywords ###
for i in self.header['PC*'].keys():
del self.header[i]
#####################################
self.wcs = wcs.WCS(self.header)
self.pixel_scale /= f
hdu.close()
imdelete('./*temp.fits')
return self.image
elif method == 'spline':
ny, nx = self.image.shape
if f > 1:
from scipy import ndimage
assert 0 < order <= 5 and isinstance(
order, int), 'order of ' + method + ' must be within 0-5.'
nx_zoomed = (nx - 1) * f + 1
f_eff = nx_zoomed / nx
result = ndimage.zoom(self.image, f_eff, order=order)
result *= 1 / (f_eff**2
) # Multiplying by this factor to conserve flux
self.header = self._resize_header_wcs(f)
#self.header['CRPIX1'] += (1 - f * 1)
#self.header['CRPIX2'] += (1 - f * 1)
self._image = result
self.shape = self.image.shape
self.header['NAXIS1'] = result.shape[1]
self.header['NAXIS2'] = result.shape[0]
self.pixel_scale /= f
self.wcs = wcs.WCS(self.header)
#### Cautious! The following block could be wrong! ####
## Probably you'll need extra shift of image
dshift = 2 * (1 - f * 1) % 0.5
self.shift_image(dshift, dshift, method='spline')
# We don't want to shift wcs.
self.header['CRPIX1'] -= dshift
self.header['CRPIX2'] -= dshift
self.wcs = wcs.WCS(self.header)
#### Cautious! The above block could be wrong! ####
else:
b = round(1 / f)
ny_bin = int(ny / b)
nx_bin = int(nx / b)
shape = (ny_bin, b, nx_bin, b)
x_crop = int(nx_bin * b)
y_crop = int(ny_bin * b)
result = self.image[0:y_crop,
0:x_crop].reshape(shape).sum(3).sum(1)
self.header = self._resize_header_wcs(f)
self.header['CRPIX1'] += 0.5 - 1 / b / 2
self.header['CRPIX2'] += 0.5 - 1 / b / 2
self._image = result
self.shape = self.image.shape
self.header['NAXIS1'] = result.shape[1]
self.header['NAXIS2'] = result.shape[0]
self.pixel_scale *= b
self.wcs = wcs.WCS(self.header)
else:
raise ValueError(
"# Not supported interpolation method. Use 'lanczos', 'spline' or 'iraf'."
)
def gen_mock_lsbg(self,
galaxy,
zp=HSC_zeropoint,
pixel_scale=HSC_pixel_scale,
verbose=True):
'''
Generate mock low surface brightness galaxies.
'''
import galsim
from galsim import Angle, Image, InterpolatedImage, degrees
from galsim.fitswcs import AstropyWCS
from galsim.interpolant import Lanczos
big_fft_params = galsim.GSParams(maximum_fft_size=20000)
if not isinstance(galaxy['comp'], list):
galaxy['comp'] = list(galaxy['comp'])
if len(galaxy['comp']) == 1:
galaxy['flux_fraction'] = [1.0]
# print some information
if verbose:
print('# Generating mock galaxy.')
print(' - Total components: ', len(galaxy['comp']))
print(' - Types: ',
[c['model'].__name__ for c in galaxy['comp']])
print(' - Flux fraction: ', galaxy['flux_fraction'])
# Empty canvas
field = np.empty_like(self.bkg.images[0])
model_images = np.empty_like(self.bkg.images)
# Calculate RA, DEC of the mock galaxy
y_cen = self.bkg.images.shape[2] / 2
x_cen = self.bkg.images.shape[1] / 2
galaxy['ra'], galaxy['dec'] = self.bkg.wcs.wcs_pix2world(
x_cen, y_cen, 0)
# Calculate flux based on i-band mag and SED
i_band_loc = np.argwhere(np.array(list(self.channels)) == 'i')[0][
0] # location of i-band in `channels`
seds = np.array([c['sed'] for c in galaxy['comp']])
# Normalize SED w.r.t i-band
seds /= seds[:, i_band_loc][:, np.newaxis]
tot_sed = np.sum(seds *
np.array(galaxy['flux_fraction'])[:, np.newaxis],
axis=0)
for i, band in enumerate(self.channels):
galaxy[f'{band}mag'] = -2.5 * np.log10(tot_sed[i]) + galaxy['imag']
if verbose:
print(f' - Magnitude in {self.channels}: ',
[round(galaxy[f'{band}mag'], 1) for band in self.channels])
#### Star generating mock galaxy in each band ####
for i, band in enumerate(self.channels): # griz
# Random number seed
# This random number seed should be fixed across bands!!!
rng = galsim.BaseDeviate(23333)
# Sky background level
sky_SB = 29 # mag/arcsec^2
sky_level = 10**((zp - sky_SB) / 2.5) # counts / arcsec^2
# Define the PSF
interp_psf = InterpolatedImage(Image(self.bkg.psfs[i] /
self.bkg.psfs[i].sum(),
dtype=float),
scale=pixel_scale,
x_interpolant=Lanczos(3))
# Total flux for all components
tot_flux = 10**((zp - galaxy[f'{band}mag']) / 2.5)
gal_list = []
for k, comp in enumerate(galaxy['comp']):
# Define the galaxy
gal = comp['model'](**comp['model_params'],
gsparams=big_fft_params)
gal_shape = galsim.Shear(
**comp['shear_params']) # Shear the galaxy
gal = gal.shear(gal_shape)
if 'shift' in comp.keys(): # Shift the center
gal = gal.shift(comp['shift'])
# Add star forming knots
if 'n_knots' in comp.keys() and comp['n_knots'] > 0:
if not 'knots_frac' in comp.keys():
raise KeyError(
'`knots_frac` must be provided to generate star forming knots!'
)
else:
if 'knots_sed' in comp.keys():
knot_frac = comp['knots_frac'] * \
(comp['knots_sed'] /
np.sum(comp['knots_sed']))[i]
else:
knot_frac = comp['knots_frac'] * 0.25 # flat SED
knots = galsim.RandomKnots(
comp['n_knots'],
half_light_radius=comp['model_params']
['half_light_radius'],
flux=knot_frac,
rng=rng)
gal = galsim.Add([gal, knots])
gal = gal.withFlux(tot_flux *
galaxy['flux_fraction'][k]) # Get Flux
gal_list.append(gal)
# Adding all components together
gal = galsim.Add(gal_list)
# Convolve galaxy with PSF
final = galsim.Convolve([gal, interp_psf])
# Draw the image with a particular pixel scale.
gal_image = final.drawImage(scale=pixel_scale,
nx=field.shape[1],
ny=field.shape[0])
# Add noise
sky_sigma = hsc_sky[f'{band}'] / 3.631 * \
10**((zp - 22.5) / 2.5) * pixel_scale**2
noise = galsim.GaussianNoise(rng, sigma=sky_sigma)
# gal_image.addNoise(noise)
# Generate mock image
model_img = gal_image.array
model_images[i] = model_img
# Generate variance map
mock_model = Data(images=model_images,
variances=None,
masks=None,
channels=self.channels,
wcs=None,
weights=None,
psfs=self.bkg.psfs,
info=galaxy)
# Finished!!!
self.model = mock_model # model only has `images`, `channels`, `psfs`, and `info`!
self.set_mock() # mock has other things, including modified variances.